Health Insurance

Health Insurance & Health
Jamaica: 2009
2. Per cent of Each Sex
70.00

Male
65.00

Female

70-74

Seeking Medical Care

60.00

60-64

50-54
55.00

40-44

30-34
50.00 R Sq Quadratic =0.751

20-24

45.00

10-14
8.00 10.00 12.00 14.00 16.00 18.00 20.00

Health Insurance
12 10

0-4
10 12

8

2

6

4

0

0

2

4

6

Per cent

Paul Andrew Bourne

8

Health Insurance & Health
 

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Health Insurance & Health
         

Paul Andrew Bourne
Director  Socio‐Medical Research Institute   

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©Paul A. Bourne, 2011  First Published in Jamaica, 2011 by  Paul Andrew Bourne  66 Long Wall Drive  Stony Hill,  Kingston 9,  St. Andrew      National Library of Jamaica Cataloguing Data      Health Insurance & Health 

 
Includes index    ISBN    Bourne, Paul Andrew      All rights reserved. Published, 2011    Cover designed by Paul Andrew Bourne 

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Preface
The population of Jamaica was estimates to be 2,698,810 people (end of year, 2009), with about 49.3% males (sex ratio = 97.1) and 11% in the older age adulthood category (60+ years old). There are two features about the Jamaican population that must be noted here 1) the feminization at older ages and 2) a high rate of growth at older ages (80+ years) compared to other age cohorts. There is evidence that showed that there is a strong statistical correlation between people ‘seeking medical care’ and ‘health insurance coverage’ in Jamaica. However, the relationship between the two aforementioned variables is curvilinear one as people will seek more medical care with the ownership of more health insurance coverage, and this will fall after more than 18% of Jamaicans purchasing health insurance coverage. Despite the fact that there is direct association between health insurance and health care seeking behaviour, in 2007, only 21.2% of Jamaicans were holders of health insurance coverage (572,148 Jamaicans). With only 21 out of every 100 Jamaica being holders of health insurance in 2007, this speaks to the high cost of individual health coverage and it justifies the public health care utilization in this country and the switching from the public health care to the private health care utilization with increased income and wealth (socioeconomic status). This volume comprehensively examines health insurance and health among Jamaicans, using survey data for 2002 and 2007. Health Insurance and Health is but the commencement of those phenomena, and I hope that this will foster more discussion in the future as well as guide research. Paul Andrew Bourne Director Socio-Medical Research Institute March 2011
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Acknowledgement
The writing of a book is a time consuming and a tedious process, which is assisted by many people. A book is not a singulate effort and this must be recognized by the author(s), editor(s) and/or publisher(s). Like many other authors, I am indebted to many people who contributed in different ways to the completion of this book. These individuals are 1) Mrs. Evadney Bourne, 2) Kimani Bourne, 3) Kerron Bourne, 4) Paul Andrew Bourne, Jnr, who stayed up with me on countless nights, and longer on Saturdays and Sundays. Ms. Neva South-Bourne, whose tireless efforts and endless patience in proofreading some of the chapters as well as Mrs. Cindi Scholefield. I am also indebted to the Derek Gordon Databank, University of the West Indies, Mona (Jamaica) that made the dataset available from which many of the chapters emerged. The majority of the chapters are published works in different journals, and I am grateful for their permission to use the materials in this book (North American Journal of Medical Sciences, Health, Current Research Journal in Social Sciences, International Journal of Collaborative Research on Internal Medicine and Public Health, HealthMed Journal, and Journal of Clinical and Diagnostic Research; Journal of Applied Sciences Research). Finally, I would like to thank all my co-authored who wrote different articles with me. Any errors of omission or commission in this book should not be ascribed to anyone or organizations as these are of the author.

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Table of Contents
 

Preface Acknowledgement Chapter 1 

iv v 1 

Health  insurance  coverage  in  Jamaica:  Multivariate  analyses  using  two  cross‐sectional  survey  data for 2002 and 2007    Chapter 2  31 

Disparities  in  self‐rated  health,  health  care  utilization,  illness,  chronic  illness  and  other  socioeconomic characteristics of the Insured and Uninsured    Chapter 3  Self‐reported health and medical care‐seeking behaviour of uninsured Jamaicans  Chapter 4  87  63 

Variations  in  health,  illness  and  health  care‐seeking  behaviour  of  those  in  the  upper  social  hierarchies in a Caribbean society  Chapter 5  Health of children less than 5 years old in an Upper Middle Income Country: Parents’ views  Chapter 6  Health Inequality in Jamaica, 1988‐2007  Chapter 7  Social determinants of self‐reported health across the Life Course  Chapter 8  Sociomedical Public Health in Jamaica  Chapter 9  226  194  172  137  113 

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Modelling social determinants of self‐evaluated health of poor older people in a middle‐income  developing nation  Chapter 10  Self‐rated health of the educated and uneducated classes in Jamaica  Chapter 11  278  252 

Retesting and refining theories on the association between illness, chronic illness and poverty:  Are there other disparities?  Chapter 12  304 

Variations  in  social  determinants  of  health  using  an  adolescence  population:  By  different  measurements, dichotomization and non‐dichotomization of health  Chapter 13  Childhood Health in Jamaica: changing patterns in health conditions of children 0‐14 years    Chapter 14  The uninsured ill in a developing nation    Chapter 15    Determinants of self‐rated private health insurance coverage in Jamaica  Chapter 16  359  331 

391 

415 

Difference  in  social  determinants  of  health  between  men  in  the  poor  and  the  wealthy  social  strata in a Caribbean nation             
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Health Insurance & Health
 

viii   

Chapter 1
Health insurance coverage in Jamaica: Multivariate analyses using two crosssectional survey data for 2002 and 2007

Paul Andrew Bourne
Health insurance is established as an indicator of health care-seeking behaviour. Despite this reality, no study existed in Jamaica that examines those factors that determine private health insurance coverage. This study bridges the gap in the literature as it seeks to determine correlates of private health insurance coverage. The aim of this study is to understand those who possess Health insurance coverage in Jamaica so as to aid public health policy formulation. This study used two secondary cross-sectional data from the Jamaica Survey of Living Conditions (JSLC). The JSLC was commissioned by the PIOJ and the Statistical Institute of Jamaica (STATIN) in 1988. The surveys were taken from a national cross-sectional survey of 25 018 respondents (for 2002) and 6,782 people (for 2007) from the 14 parishes across Jamaica. The JSLC is a selfadministered questionnaire where respondents are asked to recall detailed information on particular activities. The questionnaire was modelled from the World Bank’s Living Standards Measurement Study (LSMS) household survey. There are some modifications to the LSMS, as JSLC is more focused on policy impacts. The surveys used stratified random probability sampling technique to draw the original sample of respondents. Descriptive statistics were used to provide background information on the sample, and logistic regression was to determine predictors of private health insurance coverage. Health insurance coverage can be predicted by socio-demographic factors (such as area of residence; education, marital status, social support, social class, gender, age), and economic (consumption and income). The findings revealed some similarities and dissimilarities between data for 2002 and 2007. Area of residence, consumption, educational level, marital status, income and social support were determinants over the two periods. Asset ownership was a factor in 2002 but not in 2007. For 2007, age, gender and social class were factors and not for 2002. A dissimilarity in this study was with social support. It was found that in 2002, social support was negatively correlated with Health insurance coverage and this shifts to a positive correlate in 2007. In 2002, age and gender were not associated with Health insurance coverage but these became significant predictors in 2007. Interestingly, poor health status is not correlated with private health insurance coverage. More health insurance coverage is owned by urban than by other town or rural residents. Health insurance coverage is more structured for employed people who are in the private or public sectors more within urban and other towns than rural areas indicating that rural residents, who are faced high poverty and self-employment, will be more likely in continuing their choice in home remedy or nontraditional medicine in order to address their ill-health. Health which is strongly correlated with income means that poor individuals, families, societies, nations, will be less healthy and will need assistance in the form of health insurance to be able to reduce mortality.
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Introduction Health is more than the absence of diseases (WHO, 1948); as the absence of diseases is an antithesis (negative definition) of health and does not capture the positive aspects to this phenomenon. In the preamble to its Constitution in 1946, the WHO noted that health includes social, psychological and physical wellbeing; indicating that any measurement of health must include non-epidemiologic factors and that this must recognize the positive ingredients in the construction of health. One scholar coined the terms ‘Biopsychosocial model’ to explain the different facets that must be understood, evaluated and treated in addressing the care of unhealthy patients (Engel, 1960). Engel’s ‘Biopsychosocial model’ was employed to mean that health includes biological, social, psychology and other determinants. While one scholar opined that this definition of health as forwarded by the WHO as well as by extension Engel was too broad and elusive, and creates a difficulty to measure (Bok, 2004), the WHO’s conceptual definition of health recognizes the importance of social and behavioural factors in determining health status. They cannot be omitted in medical care treatment nor should we seek a measurement in order to operationalizing health as this will not be in keeping with the construct of the comprehensive phenomenon. Caldwell (1993) wrote that the behavioural and lifestyle practices are a major determinant in health (see also, Bourne, 2009), and that this in explaining mortality is not new. Caldwell’s perspective does not only highlight the role that people play in their own quality of life; but that their actions (or inactions) hold a crucible part of their health status. Smoking, alcohol consumption, physical inactivity, wreckless driving, unhealthy diets and other choices are all decisions people take in life that will either negatively or positively influence their health status, and later will become a public health challenge. The tendency of people to become involved in
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particular lifestyle practices account for pre-mature mortality for many of them. Material deprivation, psychosocial stressors, high levels of risky behaviour, unhealthy living conditions, social exclusion, perceived lack of control, limited access to good-quality health care, constrained choices and physical inactivity account for higher levels of dysfunctions. According to the WHO (2005), 60% of all death are owing to chronic illness, and that 80% of chronic dysfunctions occur in low-to-middle income countries, which speaks to the growing lifestyle practices (or lack). Material deprivation and psychosocial stressors increased the risk of diseases for poor people and people in general which is embedded in the statistics of the WHO publication. According to the WHO (2005, p. 66), 95% of Jamaicans with chronic dysfunctions experienced financial difficulties owing to their illness “…and [that] a high proportion of people admitting such difficulties avoided some medical treatment as a result (p. 66). It was also noted that in India diabetic patients spent significantly more of their annual salary on medical care. The statistics from the WHO (2005) showed that 25% of the poor’s annual income is spent on private care compared to 4% of people with higher incomes. People are aware that illnesses are inevitable, owing to the high cost of medical care in order to access health care services they will then use health insurance coverage. Health care costs can be so high that people become poor; and the recurring nature of some ailments can deplete people’s income and wealth to the point of poverty. It is this reality that accounts for health insurance coverage. Health insurance coverage is a by-product for people because it is demanded for lower treatment costing when illnesses occur. Therefore, health insurance coverage not only lowers treatment cost of illnesses but also lowers the psychosocial stressor on income, and the family’s wellbeing.

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Morrison (2000) titled an article ‘Diabetes and hypertension: Twin Trouble’ in which he established that diabetes mellitus and hypertension have now become two problems for Jamaicans and in the wider Caribbean. This situation was equally collaborated by Callender (2000) at the 6th International Diabetes and Hypertension Conference, which was held in Jamaica in March 2000. The researcher found that there was a positive association between diabetic and hypertensive patients - 50% of individuals with diabetes had a history of hypertension (Callender, 2000, p. 67). Those diseases are not only lifestyle causing, they can be expensive to treat especially if they are severe. Hence, health insurance coverage is sought in keeping with the probability of illness. Health insurance is therefore a health care-seeking behaviour and it can be used to indicate people’s perception of a futuristic likelihood of illness. It can estimate people’s fear of their inability to afford medical costs, their preparation for not wanting to deplete income, lower wealth and the lack of it can account for some premature mortality. From the findings of a crosssectional study conducted by Powell et al. (2007) of some 1,338 Jamaicans, 19.0% of respondents perceived that their economic wellbeing to be ‘very bad’. In addition, when they asked, “Does your salary and the total of your family’s salary allow you to satisfactorily cover your needs?” 57.4% of them felt that this “does not cover” their expenses (Powell et al., 2007, p. 29). In addition, out of a maximum score of 10, those in the lower class scored 5.9 for how do they ‘feel about the state of their health’ compared to a score of 6.6 for those in the upper class and a score of 6.7 for the middle class. This again goes to the rationale of demanding health insurance coverage for the poor people. Bourne (2009) found that there is no significant statistical relationship between health insurance and health care seeking behaviour or health

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insurance and good health of Jamaicans, suggesting that it is not inaffordability of health care that drives health insurance coverage; but something else. An extensive review of health literature in Jamaica found no study that has examined determinants of health insurance coverage. Health insurance in Jamaica was a private good up to 2007, and so it could only be had by those who were employed. Hence using data up to 2007 would be examining Health insurance coverage of employed Jamaicans. The aim of this study is to have an understanding of those who possess Health insurance coverage in Jamaica, so as to aid public health policy formulation. In keeping with the aim, this study sought to determine correlates of Health insurance coverage in Jamaica, using cross-sectional data for 2002 and 2007. Methods This study used two secondary cross-sectional data from the Jamaica Survey of Living Conditions (JSLC). The JSLC was commissioned by the Planning Institute of Jamaica (PIOJ) and the Statistical Institute of Jamaica (STATIN) in 1988. These two organizations are responsible for planning, data collection and policy guideline for Jamaica, and have been conducting the JSLC annually since 1989. The two cross-sectional surveys used for this study were conducted in 2002 and 2007 (World Bank, 2002; PIOJ & STATIN, 2003; PIOJ & STATIN, 2008). The surveys were taken from a national cross-sectional survey of 25 018 respondents (for 2002) and 6,782 people (for 2007) from the 14 parishes across Jamaica. The surveys used stratified random probability sampling technique to drawn the original sample of respondents. The non-response rate for the 2002 survey was 29.7% and 26.2% for the 2007 survey. The sample was weighted to reflect the population (World Bank, 2002; PIOJ & STATIN, 2003; PIOJ & STATIN, 2008).
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The JSLC is a self-administered questionnaire where respondents are asked to recall detailed information on particular activities. The questionnaire was modelled from the World Bank’s Living Standards Measurement Study (LSMS) household survey. There are some

modifications to the LSMS, as JSLC is more focused on policy impacts (World Bank, 2002). The questionnaire covers demographic variables, health, immunization of children 0–59 months, education, daily expenses, non-food consumption expenditure, housing conditions, inventory of durable goods and social assistance. Interviewers are trained to collect the data from household members. The survey is conducted between April and July annually. Descriptive statistics such as mean, standard deviation (SD), frequency and percentage were used to analyze the socio-demographic characteristics of the sample. Chi-square was used to examine the association between non-metric variables, and an Analysis of Variance (ANOVA) was used to test the relationships between metric and non-dichotomous categorical variables. Logistic regression examined the relationship between the dependent variable and some predisposed independent (explanatory) variables, because the dependent variable was a binary one (self-reported health status: 1 if reported good health status and 0 if poor health). The results were presented using unstandardized B-coefficients, Wald statistics, Odds ratio and confidence interval (95% CI). The predictive power of the model was tested using the Omnibus Test of Model and Hosmer & Lemeshow (2000) to examine goodness of fit. The correlation matrix was examined in order to ascertain whether autocorrelation (or multicollinearity) existed between variables. Based on Cohen & Holliday (1982) correlation can be low (weak) - from 0 to 0.39; moderate – 0.4-0.69, and strong – 0.7-1.0 (see also, Cohen & Cohen, 2003; Cohen, 1988). This was used to exclude (or allow) a variable in the model. In
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addition, variables were excluded from the model if they had in excess of 20% of the cases missing. Odds Ratio (OR) was used to interpret each significant variable. Multivariate regression framework (Asnani et al., 2008; Hambleton et al., 2005) was utilized to assess the relative importance of various demographic, socio-economic characteristics, physical environment and psychological characteristics, in determining the health status of Jamaicans; and this has also been employed outside of Jamaica (Cohen & Holliday, 1982; James, 2001; Ross et al., 1990). This approach allowed for the analysis of a number of variables simultaneously; and is used to examine health insurance coverage. Secondly, the dependent variable is a binary dichotomous one and this statistic technique has been utilized in the past to do similar studies. Having identified the determinants of health status from previous studies, using logistic regression techniques, final models were built for Jamaicans as well as for each of the geographical sub-regions (rural, peri-urban and urban areas) and sex of respondents using only those predictors. Models The current study will employ multivariate analyses in the study of health and medical care seeking behaviour of Jamaicans. The use of this approach is better than bivariate analyses as many variables can be tested simultaneously for their impact (if any) on a dependent variable. HIt=f(Ht, Ai, Gi, HHi, ARi, lnC, ∑Di, EDi, MRi, Si, HTi, lnY, CRi, MCt, SSi, Ti , CIi, Pi, Eni, HSB, εi ) (1) Where HIi is health insurance coverage of person i, Ht (ie self-rated current health status in time t) is a function of age of respondents, Ai ; sex of individual i, Gi; household head of individual i, HHi; area of residence, ARi; house tenure of individual i, HTi; logged
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consumption per person per household member, lnC; summation of durable goods and asset owned, ∑Di; Education level of individual i, EDi; marital status of person i, MRi; social class of person i, Si;; logged income, lnY; crowding of individual i, CRi; medical expenditure of individual i in time period t, MCt; social support of individual i, SSi; social assistance (ie welfare) individual i, Ti; crime index, CIi; physical environment of individual i, Eni, health care seeking behaviour and an error term (ie. residual error). The final models that were derived from the general Equation (1) that can be used to predict Health insurance coverage of Jamaicans are Equation (2) and Equation (3): HIt(Jamaicans, (2) HIt(Jamaicans, (3) Measures An explanation of some of the variables in the model is provided here. Self-reported is a dummy variable, where 1 (good health) = not reporting an ailment or dysfunction or illness in the last 4 weeks, which was the survey period; 0 (poor health) if there were no self-reported ailments, injuries or illnesses (Bourne & Rhule, 2009). While self-reported ill-health is not an ideal indicator of actual health conditions because people may underreport, it is still an accurate proxy of ill-health and mortality (Idler & Kasl, 1991; Idler & Benyamini, 1997; Bourne & Rhule, 2009). Social supports (or networks) denote different social networks with which the individual is involved (1 = membership of and/or visits to civic organizations or having friends who visit ones home or with whom one is able to network, 0 = otherwise). Psychological conditions are
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2007) 2002)

=f(ARi,

lnC,

EDi,

MRi,

lnY,

SSi,

∑Di,

HSB,

εi)

=f(ARi,

lnC,

EDi, MRi,

lnY,

SSi,

Ai,

Gi,

S i,

HSB,

εi )

the psychological state of an individual, and this is subdivided into positive and negative affective psychological conditions (Diener, 2000; Harris & Lightsey, 2005). Positive affective psychological condition is the number of responses with regard to being hopeful, optimistic about the future and life generally. Negative affective psychological condition is number of responses from a person on having lost a breadwinner and/or family member, having lost property, being made redundant or failing to meet household and other obligations. Health status is a binary measure (1=good to excellent health; 0= otherwise) which is determined from “Generally, how do you feel about your health”? Answers for this question are in a Likert scale matter ranging from excellent to poor. Health care-seeking behaviour is derived from the question: Have you visited a health care practitioner, pharmacist or healer in the past four 4 weeks, with an option of yes or no. For the purpose of the regression was coded as 1=yes, 0=otherwise. Crowding is the total number of individuals in the household divided by the number of rooms (excluding kitchen, verandah and bathroom). Age is a continuous variable in years. Results Demographic characteristic and bivariate analyses In 2002 the sample was 25,018 respondents: 12,332 males (49.3%) and 12,675 females (50.7%). In 2007 the sample was 6,782 respondents with there being marginally more females (51.3%) than males (48.7%; Table 1.1). The findings in Table 1.1 revealed that urbanization was taken place in 2002, there were 13.4% of respondents living in urban zones and this shifted to 29.5% in 2007. The percentage of Jamaicans dwelling in rural areas declined from 61% in 2002 to 49.0% in 2007. In 2002, 12.5% of respondents indicated that they had an illness in the 4-week survey period and this increased by 2.4% in 2007. Sixty-four percent of respondents reported having visited a health care facility (including a healer), and this increased to 66% in 2007. The social
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class categorization of Jamaicans remained relatively the same over the studied period; and the percentage of respondents who had health insurance coverage increased from 11.0% in 2002 to 20.2% in 2007. The mean number of visits made to health care institutions (including healers) declined from 1.7 days (SD=1.4 days) to 1.4 days (SD=1.1 days). On the other hand, crowding increased by 135% in 2007 over 2002; and medical care expenditure also increased by 29.1% over the period (Table 1.1). Based on Table 1.2, the mean annual income of respondents in 2002 was Ja $331,488.32 (SD = JA $304,040.77) and this increased by 108.6% in 2007: Ja $691,560.45 (SD = Ja $128,742.65). On disaggregating income by area of residence, it was revealed that there was significant statistical difference between income of respondents and their area of residents. On average, urban respondents received 1.6 times more income than rural residents in 2007 and this was similar in 2002 (approximately 1.5 times more). The disparity in income between urban and other town respondents was lower (in 2007 – 1.1 times more and this was the same in 2002) than that between urban and rural dwellers. A cross-tabulation between health status and self-reported illness revealed a significant statistical correlations - χ2(df = 2) = 1,289.23, p < 0.001 (Table 1.3). Table 1.3 revealed that an individual who reported poor health status was 9.3 times more likely to have an illness than those stating a dysfunction. On the other hand, an individual who reported good health status was 2.0 more likely not to report an illness than those reporting at least one ailment. Based on Table 1.3, more males (85.4%) reported good health status than females (79.2%) - (χ2(df = 2) = 44.666, p < 0.001) - and the converse was true for poor health status, with 5.5% of females compared to 4.2% of males. Based on Table 1.4, there was a change in pattern of 5-leading recurring illnesses in Jamaica. In 2002, hypertension was the leading cause of self-reported dysfunctions (21.6%) followed by cold (19.9%); unspecified ailments (18.1%); diabetes mellitus (11.6%) and asthma (9.6%). However in 2007, the leading prevalence of self-reported ailments shifted to unspecified
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ailments (23.4%) followed by hypertension (20.6%); cold (14.9%), diabetes mellitus (12.3%) and 9.5% asthma cases. Furthermore, a significant statistical relationship was found between diagnosed recurring illness was gender in both years: In 2002 (χ2(df = 1) = 125.469, p < 0.001, n = 3,063) and in 2007 (2 χ2(df = 1) = 40.916, p < 0.001, n= 999; Table 1.4). Table 1.4 showed that diabetes mellitus and hypertension were significant more among for females than males and that arthritis, unspecified illnesses, asthma diarrhoea and cold were more prevalent among males than females. Table 1.5 showed that there was a significant statistical correlation between medical careseeking behaviours and gender: In 2002 (χ2(df = 1) = 9.006, p = 0.003) and in 2007, (χ2(df = 1) = 3.004, p < 0.048). In 2002 data revealed that more females sought medical care (66%) than males (60.7%); and this was the case in 2007: 67.6% for females and 62.3% for males (Table 1.5). In 2007, there was a significant statistical correlation between health care-seeking behaviour of Jamaicans and health insurance coverage (χ2(df = 1) = 16.712, p < 0.001). The association was a very weak one (r = 0.128). However, the findings revealed that 76.2% (n = 189) of people with private health insurance visited a health care practitioner compared to 62.0% (n = 468) those who do not have health insurance coverage. Multivariate analyses In 2007, health insurance coverage in was correlated with logged consumption (OR = 1.90, 95% CI = 1.12 - 3.23); logged income (OR = 1.71, 95% CI = 1.02 - 2.87); durable goods (OR = 1.09, 95% CI = 1.02 - 1.17); marital status (married: OR = 3.91, 95% CI = 2.47 - 6.20); area of residence (urban areas: OR = 2.24, 95% CI = 1.23 - 4.09); education (secondary: OR = 2.97,

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95% CI = 1.46 - 6.00; tertiary: OR = 18.76, 95% CI = 8.12 - 43.43); and social support (OR = 0.54, 95% CI = 0.36 - 0.80; Table 1.7). For 2002, health insurance coverage model was a predictive model (χ2 (df = 24) = 451.35, p < 0.001; Hosmer and Lemeshow goodness of fit χ2=5.91, P = 0.66), with 92.4% of the data being correctly classified (41.1% - correct classification of cases of self-rated Health insurance coverage and 98.4% of cases of self-rated no private health insurance coverage; Table 1.7). The model (Table 1.7) can explain 44.7% of the variability in Health insurance coverage of Jamaicans (for 2002). Health insurance coverage in Jamaica for 2007 can be determined by 10 variables. These were logged consumption (OR = 1.00, 95% CI = 1.00 - 1.00); logged income (OR = 1.00, 95% CI = 1.00 - 1.00); marital status (married: OR = 1.84, 95% CI = 1.52 - 2.22); area of residence (urban areas: OR = 1.30, 95% CI = 1.08 - 1.57); education (secondary or tertiary: OR = 1.45, 95% CI = 1.09 - 1.92); and social support (OR = 1.33, 95% CI = 1.04 - 1.70); age (OR = 1.01, 95% CI = 1.01 - 1.02); social class (upper class: OR = 1.61, 95% CI = 1.08 - 1.57) and by gender (male: OR = 0.81, 95% CI = 0.69 - 0.95). For 2007, the factors that determine health insurance coverage in Jamaica is a predictive model (χ2 (df = 20) =590.07, p < 0.001; Hosmer and Lemeshow goodness of fit χ2=7.25, P = 0.51), with 79.4% of the data being correctly classified (40.4% - correct classification of cases of self-rated Health insurance coverage and 96.4% of cases of self-rated no private health insurance coverage). For 2007, the model can explain 49.1% of the variability in private health insurance coverage. Discussion

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There are some sociodemographic determinants of Health insurance coverage in Jamaica that have remained predictors. These include area of residence, consumption, education, marital status, income and social support. Durable goods were a predictor of health insurance coverage in 2002; however, this is ceased to be the case in 2007. Over time, health insurance coverage was determined by some additional factors such as age, gender, and social class. Of the 6 predictors of Health insurance coverage in Jamaica that continued to be factors in both periods, there is dissimilarity. Social support which was a negative determinant in 2002 reversed to a positive one in 2007. It is expected that those with more social support would be less likely to purchase health insurance coverage as there is a higher probability that they can be assisted in times of medical needs by the social networks with which they are apart. The church, civic associations and societies, family, friends and associates are more likely to extend a helping hand in time of medical need, and this account for the unwillingness of people to purchase private health insurance because this socio-economic support is present. In 2007, the findings revealed that Health insurance coverage was positively correlated to social support which invalidates the aforementioned perspective. The inflation rate in Jamaica rose by 194% in 2007 over 2006, which indicates that net disposable individual and household income would have fallen substantially and that each individual would have seen an erosion of his purchasing power coupled with higher cost of living. The direct correlation between social support and Health insurance coverage can be explained by social institutions encouraging its members to purchase insurance to offset the increased costs. They probably may be less likely to offer the same level of assistance to all its members like the previous period when costings were lower. The economic cost will create a challenge for those social networks to spread their limited financial resources over a wider cross-section of people with diverse needs. This then is a part of
13   

the explanation why Health insurance coverage was the highest in Jamaica in 2007 (21.2%) over the 2 decades; and in 2007, medical care-seeking behaviour was 66% which fell by 5.7% over 2006. The current study revealed that married people were more likely to purchased private health insurance than those who were never married and that there is no significant difference in purchase of health insurance between those who were divorced, separated or widowed and those who were never married. In 2002, the findings showed that married people were 4 times more buy Health insurance coverage compared to those who were never married and that this ratio fell to 2 times more in 2007. This lower of disparity in ownership of Health insurance coverage between the married and never married cohorts in Jamaica is an indication of people’s willingness to subsidize medical care cost with private health insurance coverage; the lowering of their disposable income owing to increased cost of living; increased awareness of seeking medical care and the high cost of doing so; and the changing typology of diseases which require continuous monitoring by health care practitioners and how this is likely to erode income and wealth, and that this would be best mitigated against through the provision of health insurance. An another interest finding that is embedded in the disparity of more married than unmarried people owning private health insurance is the explanation for why married people have a greater health status than unmarried people. Health insurance coverage is an indicator of health care-seeking behaviour, which goes to the core of married people’s willing to address health concerns owing to their recognition of the family (ie children and spouse) depending on them for care, protection and financial support. According to Moore et al. (1997, 29), people who reside with a spouse have a different base of support that those in other social arrangements (See also Smith & Waitzman 1994; Lillard & Panis 1996). Cohen & Wills (1985) found that
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perceived support from one’s spouse increased wellbeing (see also Smith & Waitzman 1994), while Ganster et al. (1986) reported that support from supervisors, family members and friends was related to low health complaints. Koo, Rie & Park (2004) findings revealed that being married was a ‘good’ cause for an increase in psychological and subjective wellbeing in old age. Smith & Waitzman(1994) offered the explanation that wives found dissuade their husband from particular risky behaviours such as the use of alcohol and drugs, and would ensure that they maintain a strict medical regimen coupled with proper eating habit (see also Ross et al., 1990; Gore, 1973). In an effort to contextualize the psychosocial and biomedical health status of particular marital status, one demography cited that the death of a spouse meant a closure to daily communicate and shared activities, which sometimes translate into depression that affect the wellbeing more of the elderly who would have had investment must in a partner (Delbés & Gaymu 2002, p. 905). Embedded in Smith and Waitzman finding is the positive effecting of marriage on men’s health status. This speaks to culture of men’s unwillingness to seek medical care, and the role of the spouse in reducing this practice. The current study found that men were 19.2% less likely that women own health insurance, indicating once again their unwillingness to seek medical care. Health literature has established that women are more likely to seek medical care than men (Stekelenburg et al., 2009; PIOJ & STATIN, 2001) and that this was concurred by the current study. Interestingly, in 2002, for every 156 females that sought medical care there were 100 males; but in 2007, the ratio widens to 160 females for every 100 males. Although females sought more health care services than males, statistics revealed that the latter group spent more days in illness (mean = 10.3 days) than females (mean number of days suffered from illness = 9.3 days) (PIOJ & STATIN, 2008).
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Poor health status which is an indicator of health conditions means that females were more likely to seek medical care to address those concerns compared to males who were suffering from the different illnesses. Of the 3 specified chronic illnesses (arthritis, diabetes mellitus, and hypertension) females are influenced by the more severe types, and thus explain the greater probability of them seeking medical care and buying health insurance coverage than males. This research found that in 2002, females were 2.1 times more likely to report having hypertension and 1.5 times more likely to claim that they have diabetes mellitus than males. In 2007, the disparity in self-reported hypertension fell to 1.7 times and increased to 2 times for diabetes mellitus. For arthritis, the disparity was narrowly greater for males than females. In 2002, for every 120 males that reported arthritis there were 100 females and this was 111 males for every 100 females in 2007. Men are not only unwilling culturally to display emotions, fear, weakness and illness, they are equally reserved about speaking of their health conditions. Such a position is embedded in the culture, which states that boys should ‘suppress reaction to pain’ and to speak of illness to lower ones maleness (Chevannes, 2001, p. 37). Chevannes’s work explains the current findings as well to provide in-depth information on statistics published in the Jamaica Survey of Living Conditions (JSLC). The JSLC (2000) reported that men were 0.7 times less likely to self-report sicknesses, injuries and/or ailments compared to their female counterparts. In a number of societies, traditional females seek health-care more than males, which allow for a better monitoring and diagnostic assessment of their health conditions as against men. Higher income means the individual, family, society and nation has more to it disposable to cover non-consumption items such as health insurance. Easterlin argued that “those with higher income will be better able to fulfill their aspiration and, and other things being equal, on
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an average, feel better off” (Easterlin, 2001a, p. 472), indicating a bivariate relationship between subjective well-being and income. Stutzer & Frey (2003) found that the association between subjective wellbeing and income to be a non-linear one. According to Stutzer & Frey (2003) “In the data set for Germany, for example, the simple correlation is 0.11 based on 12, 979 observations” (p. 9). The current study concur with Easterlin that greater income can purchase other goods, which accounts for the positive correlation between income and private health insurance coverage. This is also in keeping with Brown et al.’s study (2008) which had income as a predictor of health care-seeking behaviour. The current research went further than Brown et al (2008) and Easterlin (2001) studies as it found that those who consume more on food and nonfood items are more likely to own Health insurance coverage than those who consume less. Hence, it is expected that wealthy will be significantly more likely to own Health insurance coverage than the poor. In Jamaica, statistics from the Planning Institute of Jamaica and Statistical Institute of Jamaica (2007) revealed that poverty is substantially a rural phenomenon and that the more of the wealthy live in urban area, then more urban dwellers having Health insurance coverage is reinforcing the literature that more money provide access to a wider spread of goods and services outside of basic necessities. The current research has provided more interest information in the literature as wide gap that existed in 2002 between the wealthy and the poor in regards to ownership of private health insurance, narrowed in 2007. Another interesting finding of this study is the positive significant correlation between health insurance coverage and educational attainment. In 2002, those with tertiary level education were 19 times more likely to own health insurance coverage in Jamaica and this narrowed substantially to 1.4 times more than those with primary and below education. The
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narrowing of the gap of those who owned health insurance coverage between the tertiary and the primary level education can due to knowledge of ill-health, lowered income, the role of the media in information the populace about the role of health insurance coverage in reducing medical cost on seeking health care. Interestingly private health insurance companies in Jamaica have expanded health insurance schemes to Credit Unions, and so this is giving greater access of this product to the poor who are mostly members of the Union. The positive significant correlation of age and health insurance coverage in Jamaica can be accounted for by the biological changes and the high cost of medical care due to this futuristic probability. Organism aged naturally, which explains biological ageing. Ageing is synonymous with reduced functional limitations (or increased health conditions), suggesting that the older people become they will be more willing to purchase Health insurance coverage due to the future cost of medical care and the high likeliness of illness because of health conditions. Gompertz’s law in Gavriolov & Gavrilova (2001) showed that there is fundamental quantitative theory of ageing and mortality of certain species (the examples here are as follows – humans, human lice, rat mice, fruit flies, and flour beetles (see also, Gavriolov & Gavrilova, 1991). Gompertz’s law went further to establish that human mortality increase twofold with every 8 years of an adult life, which means that ageing increases in geometric progression. This phenomenon means that human mortality increases with age of the human adult, but that this becomes less progress in advance ageing. Thus, biological ageing is a process where the human cells degenerate with years (i.e. the cells die with increasing in age), which is well established in evolutionary biology (Medawar 1946; Carnes and Olshansky, 1993; Carnes et al., 1999; Charlesworth, 1994). A study on the elderly in the Caribbean Food and Nutrition Institute’s magazine Cajanus found that 70% of individuals who were patients within different typologies of health services in
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Jamaica were senior citizens (Caribbean Food and Nutrition Institute1999; Anthony 1999), and this emphasize the need of elderly to purchase health insurance in order to cover the cost of health care. A study conducted by Costa, using secondary data drawn from the records of the Union Army (UA) pension programme that covered some 85% of all UA, showed there is an association between chronic conditions and functional limitation – which include difficulty walking, bending, blindness in at least one eye and deafness (Costa 2002). Again this is reiterating the need to seek medical care owing to ageing, and justifying the positive correlation between age and health insurance coverage in this study. Interestingly health insurance is among the greatest predictor of health care-seeking behaviour in the United States (Call & Ziegenfuss, 2007), and this is not the case in Jamaica as only 21 out of every 100 Jamaicans possessed health insurance coverage in 2007. However of those who claimed to have private health insurance coverage, 8 out of 10 visited health care facilities, suggesting that those with this facility would be a great predictor of health care-seeking behaviour. It should be noted that Jamaica does not have a national health insurance coverage which is opened to the general populace. Instead (in 2007), the government introduced a national health insurance coverage in which people with particular ailments can access services and medication at particular public institutions free and a national health insurance scheme which caters to the elderly Jamaicans (ages 60 years and older). Conclusion The socioeconomic determinants of Health insurance coverage in Jamaica have expanded in 2007 over 2002. Area of residence, consumption, income, educational attainment, marital status and social support have remained factors in 2007 over 2002; but age, gender and social class are currently new sociodemographic variables that explain private health insurance in Jamaica.
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Furthermore, females seeking more medical care in Jamaica has been fundamentally linked to culture and this is undoubtedly so; but this study has found that the typology of their health conditions is another pivotal rationale for this disparity. The reported health conditions with which males reported more of than females are illnesses that can be substantially over the counter with non-traditional medicine, and so further goes to the reason for their low access of traditional health care services. In Jamaica, the employment typology in area of residents is different and contributes to the disparity in private health insurance coverage. Employment in rural area is substantially selfemployment (ie farming) and this type of employment is not designed around private health insurance coverage. Health insurance coverage is more structured for employed people who are in the private or public sectors more within urban and other towns than rural areas indicating that rural residents, who are faced high poverty and self-employment, will be more likely in continuing their choice in home remedy or non-traditional medicine in order to address their illhealth. Health which is strongly correlated with income means that poor individuals, families, societies, nations, will be less healthy and will need assistance in the form of health insurance to be able to reduce mortality. In concluding, the information with which this provided can be used by public health services in formulating programmes that can be address the concerns of males and rural poor.

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References Anthony, B.J. (1999). Nutritional assessment of the elderly. Cajanus 32, 201-216. Asnani, M.R., Reid, M.E., Ali, S.B., Lipps, G., Williams-Green, P. (2008). Quality of life in patients with sickle cell disease in Jamaica: rural-urban differences. Journal of Rural and Remote Health 8, 890-899. Bok, S. (2004). Rethinking the WHO definition of health: Working Paper Series, 14. Retrieved on May 26, 2007, from http://www.golbalhealth.harvard.edu/hcpds/wpweb/Bokwp14073.pdf Bourne, P.A. (2009). Socio-demographic determinants of Health care-seeking behaviour, selfreported illness and Self-evaluated Health status in Jamaica. International Journal of Collaborative Research on Internal Medicine & Public Health 1(4),101-130. Bourne, P.A., & Rhule, J. (2009). Good Health Status of Rural Women in the Reproductive Ages. International Journal of Collaborative Research on Internal Medicine & Public Health 1(5):132-155. Brown, P.H., De Brauw, A., & Theoharides, C. (2008). Health-Seeking Behavior and Hospital Choice in China’s New Cooperative Medical System. Social Science Electronic Publishing. Caldwell, J.C. (1993). Health transition: The cultural, social and behavioural determinants of health in the Third World. Soc Sci Med 36,125-135. Call, K.T., Ziegenfuss, J. (2007). Health insurance coverage and Access to Care Among Rural and Urban Minnesotans. Rural Minnesota Journal 2:11-35. Callender, J. (2000). Lifestyle management in the hypertensive diabetic. Cajanus 33, 67-70. Caribbean Food and Nutrition Institute. 1999. Health of the elderly. Cajanus 32, 217-240. Carnes, B.A., & Olshansky S.J. (1993). Evolutionary perspectives on human senescence. Population. Development Review 19, 793-806. Carnes, B.A., Olshansky, S.J., Gavrilov, L.A., Gavrilova, N.S. & Grahn D. (1999). Human longevity: Nature vs. nurture - fact or fiction. Persp Biol. Med. 42: 422-441. Charlesworth, B. (1994). Evolution in Age-structured Populations (2nd ed). Cambridge: Cambridge University Press. Chevannes, B. (2001). Learning to be a man: Culture, socialization and gender identity in five Caribbean communities. Kingston, Jamaica: University of the West Indies Press. Cohen, L., Holliday, M. (1982). Statistics for Social Sciences. London: Harper & Row. Cohen, S., & Wills T.A. 1985. Stress, social support, and the buffering hypothesis. Psychological bulletin 98,31-357. Costa, D.L. (2002). Chronic diseases rates and declines in functional limitation. Demography 39, 119-138. Costa, D.L. (2000). Understanding the twentieth-century decline in chronic conditions among older men. Demography 37,53-72. Delbés, C., & Gaymu, J. (2002). The shock of widowed on the eve of old age: Male and female experience. Demography 3, 885-914. Diener, E. (2000). Subjective well-being: The science of happiness and a proposal for a national index. American Psychological Association 55, 34-43. Easterlin, R.A. (2001). Income and happiness: Towards a unified theory. Economic Journal 111, 465-484
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Engel, G.L. (1977). The need for a new medical model: A challenge for biomedicine. Science 196, 129-136. Engel, G. L. (1960). A unified concept of health and disease. Perspectives in Biology and Medicine 3, 459-485. Engel, G.L. (1977). The care of the patient: art or science? Johns Hopkins Medical Journal 140, 222-232. Engel, G.L. (1978). The biopsychosocial model and the education of health professionals. Annals of the New York Academy of Sciences 310, 169-181. Engel, G.L. (1980). The clinical application of the biopsychosocial model. American Journal of Psychiatry 137,535-544. Ganster, D.C., Fusilier, M.R., & Mayers B.T. (1986). The social support and health relationship: Is there a gender difference? Journal of Occupational Psychology 59, 145-153. Gavrilov, L. A., Gavrilova N.S. (2001). The reliability theory of aging and longevity. Journal of theor. Biology 213, 527-545. Gavrilov, L.A., & Gavrilova, N.S. (1991). The biology of ¸life Span: A Quantitative Approach. New York: Harwood Academic Publisher. Gore, W. R. (1973). Sex, Marital Status, and mortality. American Journal of Sociology 79, 4567. Hambleton, I.R., Clarke, K., Broome, H.L., Fraser, H.S., Brathwaite, F., Hennis, A.J. (2005). Historical and current predictors of self-reported health status among elderly persons in Barbados. Rev Pan Salud Public 17(5-6), 342-352. Harris, P.R., Lightsey, O.R., Jr. (2005). Constructive thinking as a mediator of the relationship between extraversion, neuroticism, and subjective wellbeing. European Journal of Personality 19, 409-426. Hosmer, D., Lemeshow, S. (2000). Applied Logistic Regression (2nd ed). John Wiley & Sons Inc., New York. Idler, E.L., Benyamini, Y. (1997). Self-reported health and mortality: a review of twenty-seven community studies. J Health Soc Behav 38, 21-37. Idler, E.L., Kasl, S. (1991). Health perceptions and survival: Do global evaluations of health status really predict mortality. Journal of Gerontology 46(2), S55-S65. James, C. (2001). Factors associated with health status of older Americans. Age and Ageing 30(6), 495-501. Koo, J., Rie, J., & Park, K. (2004). Age and gender differences in affect and subjective wellbeing. Geriatrics and Gerontology International 4, S268-S270. Lillard, L., Panis A., & Constantin, W.A. (1996). Marital status and mortality: The role of health. Demography 33:313-327. Medawar, P. B. (1946). Old age and natural death. Mod. Q 2,30-49. [Reprinted in the Uniqueness of the Individual (Medawar, P. B., ed. 1958), pp. 17-43. New York: Basic Books. Moore, E.G., Rosenberg, M. W., & McGuinness, D. (1997). Growing old in Canada: Demographic and geographic perspectives. Ontario, Canada: Nelson. Morrison, E. (2000). Diabetes and Hypertension: Twin Trouble. Cajanus 33, 61-63. Planning Institute of Jamaica, (PIOJ), & Statistical Institute of Jamaica (STATIN). (2008). Jamaica Survey of Living Conditions, 2007. Kingston: PIOJ, STATIN.
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Planning Institute of Jamaica, (PIOJ), & Statistical Institute of Jamaica (STATIN). 2003. Jamaica Survey of Living Conditions, 2002. Kingston: PIOJ, STATIN. Planning Institute of Jamaica, (PIOJ), & Statistical Institute of Jamaica (STATIN). 2001.Jamaica Survey of Living Conditions, 2000. Kingston: PIOJ, STATIN. Planning Institute of Jamaica, (PIOJ), & Statistical Institute of Jamaica (STATIN). 2000.Jamaica Survey of Living Conditions, 1999. Kingston: PIOJ, STATIN. Powell, L.A., Bourne P., & Waller L. (2007). Probing Jamaica’s Political culture, volume 1: Main trends in the July-August 2006 Leadership and Governance Survey. Kingston, Jamaica: Centre for Leadership and Governance. Ross, C. E., Mirowsky, J., & Goldsteen, K. (1990). The impact of the family on health. Journal of Marriage and the Family 52, 1059-1078. Smith, Ken R., and Waitzman, Norman J. 1994. Double jeopardy: Interaction effects of martial and poverty status on the risk of mortality. Demography 31:487-507. Stekelenburg, J., Jager, B., Kolk, P., Westen, E., Kwaak, A., &Wolffers, I. (2009). Health care seeking behaviour and utilization of traditional healers in Kalaboo, Zambia. Health Policy 71,67-81. Stutzer, A., & Frey, B.S. (2003). Reported subjective wellbeing: A challenge for economic theory and economic policy. Retrieved on August 31, 2007, from http://www.crema-research.ch/papers/2003-07.pdf World Bank, Development Research Group, Poverty and human resources. (2002). Jamaica Survey of Living Conditions (LSLC) 1988-2000: Basic Information. http://www.siteresources.worldbank.org/INTLSMS/Resources/.../binfo2000.pdf. (accessed August 14, 2009). World Health Organization, (W.H.O). (2005). Preventing Chronic Diseases a vital investment. Geneva: WHO.

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Table 1.1. Demographic characteristic of samples: 2002 and 2007 Variable Number Gender Male Female Area of residence Urban Other Rural Illness Yes No Visits health care facilities Yes No Social class Poor Middle Upper Private Health Insurance Coverage Yes No Health status Good Fair Poor 12,332 12,675 3,357 6,401 15,260 3,010 21,103 1,966 1,113 9,931 4,984 10,099 2,671 21,546 2002 Percent 49.3 50.7 13.4 25.6 61.0 12.5 87.5 63.9 36.1 39.7 19.9 40.0 11.0 89.0 Number 3,303 3,479 2,002 1,458 3,322 980 5,609 658 347 2,697 1,351 2,734 1,314 5,203 5,397 848 320 2007 Percent 48.7 51.3 29.5 21.5 49.0 14.9 85.1 65.5 34.5 39.4 19.9 40.3 20.2 79.8 82.2 12.9 4.9

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Table 1.2. Income, Crowding, Age, by Area of residence: 2002 and 2007 Characteristic Year Category Mean Income Ja$ 2002† Urban $440,451.50 Other towns $385,625.70 Rural $284,810.20 Total $331,488.32 2007†† Urban $865,674.20 Other towns $771,300.50 Rural $551,633.70 Total $691,560.45 Crowding 2002 Urban 2.0 persons Other towns 2.0 persons Rural 2.0 persons Total 2 persons 2007 Urban 4.3 persons Other towns 4.6 persons Rural 5.0 persons Total 4.7 persons Age 2002 28.2 yrs 2007 29.9 yrs No of visits to health care facilities Medical expenditure
†Ja $40.97 = US $1.00 ††Ja $80.47 = US $1.00

SD $521,519.38 $276,644.12 $231,540.04 $304,040.77 $673,512.10 $597,582.65 $389,765.68 $128,742.65 1.4 persons 1.4 persons 1.4 persons 1.4 persons 2.4 persons 2.3 persons 2.5 persons 2.5 persons 22.0 yrs 21.8 yrs 1.4 days 1.1 days $2,946.02 $4,711.15

p-value < 0.001

< 0.001

> 0.05

< 0.001

2002 2007 2002† 2007††

1.7 days 1.4 days $1,144.14 $1,477.07

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Table 1.3. Health status by self-reported illness, and gender: 2007 Characteristic Self-reported dysfunction1 Category 0 ≥1 Total Male Female Total Good 89.1 42.8 5381 85.4 79.2 5397 Health status (%) Fair Poor 8.7 2.2 36.8 20.4 845 319 10.4 15.3 848 4.2 5.5 320 Total 5569 976 6545 3195 3370 6565

Gender2
1 2

2 2

χ (df = 2) = 1,289.23, p < 0.001, c=0.405

χ (df = 2) = 44.666, p < 0.001, c=0.082

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Table 1.4. Self-reported diagnosed recurring illness by gender and years (2002, 2007) Yea r Sex Col d Male Femal e Total 200 7 Male Femal e Total Self-reported diagnosed recurring illness (%) Diarrhoe a Asthm a 11.4 8.3 294 11.7 8.0 95 Diabete s 9.3 13.2 356 7.7 15.4 123 Hypertensio Arthriti n s 12.9 27.6 661 14.4 24.8 206 7.6 6.3 209 6.0 5.4 56 Othe r 20.1 16.6 553 25.4 22.1 234 No 12. 5 7.7 297 14. 9 8.2 109 Tota l

200 2

22.9 3.1 17.8 2.4 610 83

125 2 181 1 306 3 402 597 999

17.2 2.7 13.4 2.7 149 27

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Table 1.5. Medical Care-Seeking Behaviour by Gender, 2002, 2007 2002 Medical Care-Seeking Behaviour Yes1 No2 Total 1 2 χ (df = 1) = 9.006, p = 0.003, n = 3,079 2 2 χ (df = 1) = 3.004, p = 0.048, n= 1,005 Male 60.7 39.3 1266 Female 66.0 34.0 1813 Male 62.3 37.7 406 Female 67.6 32.4 599 2007

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Table 1.6. Health insurance coverage by Area of Residence, 2007 Area of Residence Health Insurance No coverage 72.0 Private Coverage Public Coverage 19.2 8.7 77.9 15.1 7.0 1401 85.5 7.1 7.4 3177 79.8 12.4 7.7 6517 Urban Other towns Rural Total

Total 1939 χ2(df = 4) = 184.347, p < 0.001, n = 6,517

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Table 1.7. Logistic Regression: Predictors of Private Health Coverage in Jamaica
Characteristic Age Log consumption Log income Log medical expenditure Household head Medical care seeking behaviour Sex Male Marital status Separated, divorced or widowed Married †Never married Area of residence Urban Other towns †Rural Education Secondary Tertiary †Primary or below House tenure: owned Social class Middle Upper †Lower Social support Health status Good health Durable goods index (excluding land) Physical environment Crime index Asset ownership (ie land or property) Psychological condition Negative affective conditions Log crowding Social welfare Time spent in health care facilities Public Private Illness Injury N Chi2 Nagelkerke R2 LR *P< 0.05, **P< 0.01, ***P< 0.001 OR 1.00 1.90 1.71 0.99 4.61 0.88 0.88 1.38 3.91 1.00 2.24 1.19 1.00 2.97 18.8 1.00 1.76 0.88 1.88 1.00 0.54 0.93 1.09 0.78 1.01 0.79 0.96 1.33 0.96 1.43 4.01 0.68 2002 95% CI 0.98-1.02 1.12-3.23* 1.02-2.87* 0.81-1.21 0.21-99.16 0.42-1.83 0.60-1.30 0.49-3.88 2.47-6.20*** 1.23-4.09** 0.75-1.89 1.46-6.00** 8.11-43.43*** 0.16-19.4 0.32-2.41 0.68-5.24* 0.36-0.80** 0.56-1.53 1.01-1.17* 0.48-1.27 0.99-1.03 0.51-1.22 0.91-1.02 0.88-2.02 0.79-1.20 0.02-85.3 0.44-36.43 0.36-1.75 25,007 451.3 0.45 776.4 0.96 1.61 1.00 1.33 1.05 0.63-1.46 1.04-2.49* 1.04-1.70* 0.84-1.31 OR 1.01 1.00 1.00 1.00 1.03 1.65 0.81 1.19 1.84 1.00 1.30 1.11 1.00 1.45 1.00 2007 95% CI 1.01-1.02*** 1.00-1.00* 1.00-1.00*** 1.00-1.00 0.86-1.23 1.07-2.41* 0.69-0.95* 0.87-1.64 1.52-2.22*** 1.08-1.57* 0.90-1.36 1.09-1.92*

1.07 0.79 1.00 1.00 1.14 1.12

0.98-1.16 0.52-1.20 1.00-1.00 1.00-1.00 0.90-1.43 0.57-2.20 6,565 590.1 0.49 4,126.8

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Chapter 2
Disparities in self-rated health, health care utilization, illness, chronic illness and other socioeconomic characteristics of the Insured and Uninsured

Paul A. Bourne
This study examines self-rated health status, health care utilization, income distribution, and health insurance status of Jamaicans, and the disparity by the insured and uninsured. It also models self-rated health status, health care utilization, income distribution, and how these differ between the insured and uninsured. Cross-sectional data from the 2007 Jamaica Survey of Living Conditions (JSLC), conducted by the Planning Institute of Jamaica (PIOJ) and the Statistical Institute of Jamaica (STATIN), were used to analyse the information for this study. The JSLC is a modification of the World Bank’s Living Standard Household Survey, with a sample of 6,783 respondents. Analytic models, using multiple logistic and linear regressions, were used to determine factors which explain self-rated health status, health care utilization, and income distribution. Disparities in self-rated health status, health care utilization, and income distribution were examined by the insured and uninsured. Majority (61.1%) of those who reported being diagnosed with a chronic condition were 60+ years old (diabetes mellitus, 59.3%; hypertension, 60.2%; arthritis, 67.9%) and 2.4% were children. The mean age of those with chronic illness was 62.3 years (SD = 16.2), and this was 61.5 years (SD = 16.5) for the uninsured and 63.8 years (SD = 15.8) for those with insurance coverage. Only 20.2% of respondents had health insurance coverage (private, 12.4%; NI Gold, public, 5.3%; other public, 2.4%). Most of the chronically ill were uninsured (67%). More people with chronic illnesses who had health insurance coverage were elderly, (65.9%), compared to uninsured chronically ill elderly (58.4%). Majority of health insurance was owned by those in the upper class, (65%), and 19%, by those in the lower socioeconomic strata. Insured respondents were 1.5 times (Odds ratio, OR, 95% CI = 1.06 – 2.15) more likely to rate their health as moderate-to-very good compared to the uninsured, and they were 1.9 times (95% CI = 1.31-2.64) to seek more medical care, 1.6 times (95% CI = 1.022.42) more likely to report having chronic illness, and more likely to have greater income (β = 0.094) than the uninsured. Illness is a strong predictor of why Jamaicans seek medical care (R2 = 71.2% of 71.9%), and health insurance coverage accounted for less than one-half percent of the variance in health care utilization. However, health care utilization is a strong predictor of selfreported illness, but it was weaker than illness explaining health care utilization (61.1% of 66.5%). Public health insurance was mostly had by those with chronic illnesses (76%) compared to 44% private health coverage and 38% had no coverage (χ2 = 42.62, P < 0.0001). With the health status of the insured being 1.5 times more than the uninsured, their health care utilization being 1.9 times more than the uninsured and illness being a strong predictor of health care seeking, any reduction in the health care budget in developing nations denotes that vulnerable
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groups (such as elderly, children and the poor) will seek less care, and this will further increase the mortality among those cohorts.

Introduction This study examines self-rated health status, health care utilization, income distribution, and health insurance status of Jamaicans, and the disparity between the insured and uninsured. It also models self-rated health status, health care utilization, income distribution, and how these differ between the insured and uninsured. The current findings revealed that 20.2% of Jamaicans had health insurance coverage (i.e. 2,140,316 Jamaicans are uninsured, using end of year population for 2007), suggesting that a large percent of the population are having to use out of pocket payment or government’s assistance to pay their medical bills. The health of individuals within a society goes beyond the individual to the socioeconomic development, standard of living, production and productivity of the nation. Individuals’ health is therefore the crux of human’s development, survivability and explains the rationale as to why people seek medical care on the onset of ill-health. In seeking to preserve life, people demand and utilize health care services. Western societies are structured that people meet health care utilization with a combination of approaches. These approaches can be any combination of out of pocket payment, health insurance coverage, government assistance and families’ aid. In Latin America and the Caribbean, health care is substantially an out of pocket expenditure aided by health insurance policy and government’s health care policy. Within the context of the realities in those nations, the health of the populace is primarily based on the choices, decisions, responsibility and burden on the individual. Survival in developing nations
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are distinct from Developed Western Nations as Latin America and Caribbean peoples’ willingness, frequency, and demand for health care as well as health choices are based on affordability. Affordability of health care is assisted by health insurance coverage; as the provisions of care offered by the governmental policies mean that the public health care system will be required to meet the needs of many people. Those people will be mostly children, elderly and other vulnerable groups. The public health care system in many societies often time involve long queues, long waiting times, frustrated patients and poor people who are dependent on the service. In order to circumvent the public health care system, people purchase health insurance policies as a means of reducing futuristic health care cost as well as an avoidance of the utilization of public health care. Uninsurance in any society means a dependency on the public health care system, premature mortality and oftentimes public humiliation. The insured on the other hand are able to circumvent many of the experiences of the poor, elderly, children and other vulnerable cohorts who rely on public health care system. Insurance in developing nations, and in particular Jamaica, is private system between the individual and a private insurance company. Because of the nature of health insurance and insurance, people buy into a pool which is usually accommodated through employment. Such a reality excludes retired elderly, unemployed, unemployable, and children of those cohorts. In seeking to understand health care non-utilization and high mortality in developing nations, insurance coverage (or lack of) becomes crucial in any health discourse. There is high proportion of uninsured in the United States and this is equally the reality in many developing nations, particularly in Jamaica [1-6]. According to the World Health Organization (WHO), 80% of chronic illnesses were in low and middle income countries, and
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60% of global mortality is caused by chronic illnesses [7]. It can be extrapolated from the WHO’s findings that uninsurance is critical in answering some of the health disparities within and among groups and the sexes in the society. The realities of the health inequalities between the poor and the wealthy and the sexes in a society and those in the lower income strata having more illnesses and in particular chronic conditions [7-12] is embedded in financial deprivation. The WHO stated that “In reality, low and middle income countries are at the centre of both old and new public health challenges” [7]. The high risk of death in low income countries is owing to food insecurity, low water quality, low sanitation coupled with in access to financial resources [11, 13]. Poverty makes it insurmountable for poor people to respond to illness unless health care services are free. Hence, the people who are poor will suffer even more so from chronic diseases. The WHO captures this aptly “...People who are already poor are the most likely to suffer financially from chronic diseases, which often deepens poverty and damage long term economic prospects” [7]. This goes back to the inverse correlation between poverty and higher level education, poverty and non-access to financial resources, and now poverty and illness. According to the WHO [7], “In Jamaica 59% of people with chronic diseases experienced financial difficulties because of their illnesses...” and emphasize the importance of health insurance coverage and the public health care system for vulnerable groups. Previous studies showed that health insurance coverage is associated with health care utilization [1-6], and this provides some understanding of health care demand (or the lack of) in developing countries. Studies have been conducted on the general health of the insured and/or uninsured, health care utilization and other health related issues [1-6] have used a piecemeal approach, which means that there is a gap in the literature that could provides more insight into
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the insured and uninsured. While the current body of health literature provide pertinent information on health and health care utilization and how these differ based on the insured and uninsured, health choices are complex and requires more than piecemeal inquiry.

Materials and methods Data methods This study is based on data from the 2007 Jamaica Survey of Living Conditions (JSLC), conducted by the Planning Institute of Jamaica (PIOJ) and the Statistical Institute of Jamaica (STATIN). The JSLC is an annual and nationally representative cross-sectional survey that collects information on consumption, education, health status, health conditions, health care utilization, health insurance coverage, non-food consumption expenditure, housing conditions, inventory of durable goods, social assistance, demographic characteristics and other issues [14]. The information is from the civilian and non-institutionalized population of Jamaica. It is a modification of the World Bank’s Living Standards Measurement Study (LSMS) household survey [15]. Overall, the response rate for the 2007 JSLC was 73.8%. Over 1994 households of individuals nationwide are included in the entire database of all ages [16]. A total of 620 households were interviewed from urban areas, 439 from other towns and 935 from rural areas. This sample represents 6,783 non-institutionalized civilians living in Jamaica at the time of the survey. The JSLC used complex sampling design, and it is also weighted to reflect the population of Jamaica. Statistical analysis
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Statistical analyses were performed using the Statistical Packages for the Social Sciences v 16.0 (SPSS Inc; Chicago, IL, USA) for Windows. Descriptive statistics such as mean, standard deviation (SD), frequency and percentage were used to analyze the socio-demographic characteristics of the sample. Chi-square was used to examine the association between nonmetric variables, and an Analysis of Variance (ANOVA) was used to test the equality of means among non-dichotomous categorical variables. Means and frequency distribution were considered significant at P < 0.05 using chi-square, independent sample t-test, and analysis of variance f test, multiple logistic and linear regressions. Analytic Models Cross-sectional analyses of the 2007 JSLC were performed to compare within and between subpopulations and frequencies. Logistic regression examined the relationship between the dichotomous binary dependent variable and some predisposed independent (explanatory) variables. A pvalue < 0.05 was selected to established statistical significance. Analytic models, using multiple logistic and linear regressions, were used to ascertain factors which are associated with (1) self-rated health status, (2) health care utilization, (3) selfreported illness, (4) self-reported diagnosed chronic illness, and income. For the regressions, design or dummy variables were for all categorical variables (using the reference group listed last). Overall model fit was determined using log likelihood ratio statistic, odds ration and rsquared. Stepwise regressions were used to determine the contribution of each significant variable. All confidence interval (CIs) for odds rations (ORs) were calculated at 95%. Results
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Demographic characteristic of sample The sample was 6,783 respondents (48.7% males and 51.3% females). Children constituted 31.3%; other aged adults, 31.3%; young adults, 25.9%; and elderly, 11.9%. The elderly comprised 7.7% young-old, 3.2% old-old and 1.0% oldest-old. Majority of the sample had no formal education (61.8%); primary, 25.5%; secondary, 10.8% and tertiary, 2.0%. Two-thirds of the sample sought health in the last 4-weeks; 69.2% were never married; 23.3% married; 1.7% divorced; 0.9% separated and 4.9% were widowed respondents. Almost 15% reported an illness in the last 4-weeks (43.3% had chronic conditions, 30.4% had acute conditions and 26.3% did not specify the condition). Of those who reported an illness in the last 4- weeks, 87.9% provided information on the typology of conditions: cold, 16.7%; diarrhea, 3.0%; asthma, 10.7%; diabetes mellitus, 13.8%; hypertension, 23.1%; arthritis, 6.3%; and specified conditions, 26.3%. Marginal more people were in the upper class (40.3%) compared to the lower socioeconomic strata (39.8%). Only 20.2% of respondents had health insurance coverage (private, 12.4%; NI Gold, public, 5.3%; other public, 2.4%). Majority of health insurance was owned by those in the upper class (65%) and 19% by those in the lower socioeconomic strata. Bivariate analyses Sixty-one percent of those with chronic conditions were elderly compared to 16.6% of those with other conditions (including acute ailments). Only 39% of those with chronic conditions were non-elderly compared to 83.4% of those with other conditions – (χ2 = 187.32, P < 0.0001). Thirty-three percent of those with chronic illnesses had health insurance coverage compared to 17.8% of those with acute and other conditions - (χ2 = 26.65, P < 0.0001).
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Furthermore examination of self-reported health conditions by health insurance status revealed that diabetics recorded the greatest percent of health insurance coverage (43.9%) compared to hypertensive, (28.2%); arthritic (25.5%); acute conditions’ patients (17.0%) and other health conditions respondents (18.8%). Sixty-seven percent of respondents who reported being diagnosed with chronic conditions sought medical care in the last 4-weeks compared to 60.4% of those with acute and other conditions (χ2 = 4.12, P < 0.042). Those with primary or below education were more likely to have chronic illnesses (45.0%) compared to secondary level (6.1%) and tertiary level graduants (11.1%) - (χ2 = 23.50, P < 0.0001). There was no

statistical association between typology of illness and social class - (χ2 = 0.63, P = 0.730): upper class, 44.6%; middle class, 41.1% and lower class, 43.0%. This study found significant statistical association between health insurance status and (1) educational level (χ2 = 45.06, P < 0.0001), (2) social class (χ2 = 441.50, P < 0.0001), and (3) age cohort (χ2 = 83.13, P < 0.0001). Forty-two percent of those with at most primary level education had health insurance coverage compared to 16.3% of secondary level and 42.2% of tertiary level respondents. Thirty-three percent of upper class respondents had health insurance coverage compared to 16.7% of those in the middle class and 9.4% of those in the lower socioeconomic strata. Almost 33% of the oldest-old had health insurance coverage compared to 15.1% of children; 18.4% of young adults; 23.6% of other aged- adults; 28.6% of young-old and 24.9% of old-old. A significant statistical association was found between health insurance status and area of residence (χ2 = 138.80, P < 0.0001). Twenty-eight percent of urban dwellers had health insurance coverage compared to 22.1% of semi-urban respondents and 14.5% of rural residents. Furthermore, similarly a significant relationship existed between health care seeking behaviour and health insurance status (χ2 = 33.61, P < 0.0001). Fourteen percent of those with health
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insurance sought medical care in the last 4-weeks compared to 9.0% of those who did not have health insurance coverage. Likewise a statistical association was found between health insurance status and typology of illness (χ2 = 26.65, P < 0.0001). Fifty-eight percent of those with insurance coverage had chronic illnesses compared to 38.3% of those without health insurance. Concurringly, 42% of those with insurance coverage had acute or other conditions compared to 62% of those who did not have health insurance coverage. Further examination revealed that other public health insurance was mostly had by those with chronic illnesses (76%) compared to NI Gold (public, 65%) and 44% private health coverage (χ2 = 42.62, P < 0.0001). Private health coverage was most had by those with non-chronic illnesses (56%) compared to 35% with NI Gold (public) and 25% other public coverage. No significant statistical difference was found between the average medical expenditure of those who had insurance coverage and non-insured (t = 0.365, P = 0.715) – mean average medical expenditure of those without health insurance was USD 10.68 (SD = 33.94) and insured respondents’ mean average medical expenditure was USD 9.93 (SD = 18.07) - (Ja. $80.47 = US $1.00 at the time of the survey). There was no significant statistical relationship between health care utilization (publicprivate health care visits) and health conditions (acute or chronic illnesses) – χ2 = 0.001, P = 0.975. 49.2% of those who had chronic illnesses used public health care facilities compared to 49.3% of those with acute conditions. There is a statistical difference between the mean age of respondents with non-chronic and chronic illnesses (t = - 23.1, P < 0.0001). The mean age of some with chronic illnesses was 62.3 years (SD = 16.2) compared to 29.3 years (SD = 26.1) for those with non-chronic illnesses. Furthermore, the mean age of insured respondents with chronic illnesses was 63.8 years (SD =
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15.8) compared to 32.5 years for those with non-chronic conditions. Concurringly, uninsured chronically ill respondents’ mean age was 61.5 years (SD = 16.5) compared to 28.6 years (SD = 25.9) for those with non-chronic illnesses. Table 2.1 examines information on crowding index, total annual food expenditure, annual non-food expenditure, income, age, time in household, length of marriage, length of illness and number of visits made to medical practitioner by health insurance status. Self-rated health status, health care seeking behaviour, illness, educational level, social class, area of residence, and health conditions, health care utilization by health insurance status are presented in Table 2.2. Table 2.3 presents information on age cohort of respondents by diagnosed health conditions. A significant statistical association was found between the two variables χ2 = 436.8, P < 0.0001. Table 2.4 examines illness by age of respondents controlled for by health insurance status. There existed a significant statistical relationship between illness and age of respondents, but none between the uninsured and insured, P = 0.410. Table 2.5 presents information on the age cohort by diagnosed health conditions, and diagnosed health conditions controlled by health status. There is a statistical difference between the mean age of respondents and the typology of self-reported illnesses (F = 99.9, P < 0.0001). Those with cold, 19.2 years (SD = 23.9); diarrhoea, 30.3 years (SD = 31.4); asthma, 22.9 years (SD = 22.1); diabetes mellitus, 60.9 years (SD = 16.0); hypertension, 62.5 years (SD = 16.8); arthritis, 64.3 years (SD = 14.5), and other conditions, 38.3 years (SD = 25.3). Analytic Models
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Nine variables account for (Table 2.6), 32.8% of the variance in moderate-to-very good selfrated health status of Jamaicans The variables are medical expenditure, health insurance status, area of residence, household head, age, crowding index, total food expenditure, health care utilization and illness. Self-reported illnesses accounted for 62.2% of the explained variability of moderate-to-very good health status. Table 2.7 shows information on the explanatory factors of self-reported illnesses. Seven factors accounted for 66.5% of the variability in self-reported illnesses. Ninety-two percent of the variability in self-reported illnesses was accounted for by health care utilization (health care seeking behaviour). Three variables emerged as statistically significant correlates of health care utilization. They accounted for 71.9% of the variance in health care utilization. Most of the variability can be explained by self-reported illnesses (71.2%, Table 2.8). Self-reported diagnosed chronic illnesses can be explained by 5 variables (gender, marital status, health insurance status, age and length of illness), and they accounted for 27.7% of the variance in self-reported diagnosed chronic illness (Table 2.9). Sixty-two percent of the variability in income can be explained by crowding index, social class, household head, health insurance status, self-rated health status, health care utilization, area of residence and marital status). Most of the variability in income can be explained by social class (Table 2.10). Table 2.11 presents information on the explanatory variables which account for health insurance coverage. Six variables emerged as significant determinants of health insurance coverage (age, income, chronic illness, health care utilization, marital status and upper
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socioeconomic class). The explanatory variables accounted for 19.4% of the variability in health insurance coverage. Income was the most significant determinant of health insurance coverage (explained 43% of the explained variance, 19.4%).

Discussion The current study revealed that 15 out of every 100 Jamaicans reported having an illness in the last 4-weeks, and 57% of those with an illness had chronic conditions. Sixty-one out of every 100 of those with chronic illnesses were 60+ years; 67% of the chronically ill sought medical care when compared to 66% of the population. Most of the chronically ill respondents were uninsured (67%). The chronically ill had mostly primary level education, and there was no statistical association between typology of illness and social class. Almost 2 in every 100 chronically ill Jamaicans were children (less than 19 years), and most of them were uninsured. Nine percent more of the chronically ill who the other aged adult cohort did not have health insurance coverage. Insured respondents were 1.5 times more likely to rate their health as moderate-to-very good compared to the uninsured, and they were 1.9 times more likely to seek more medical care, 1.6 times more likely to report having chronic illnesses, and more likely to have greater income than the uninsured. Illness is a strong predictor of why Jamaicans seek medical care (R2 = 71.2% of 71.9%), and health insurance coverage accounted for less than onehalf percent of the variance in health care utilization. However, health care utilization is a strong predictor of self-reported illness, but it was weaker than illness explaining health care utilization (61.1% of 66.5%). Public health insurance was most common among those with chronic illnesses (76%) compared to 44% private health coverage and 38% had no coverage. Those in the upper

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income strata’s income was significant more than those in the middle and lower socioeconomic group, but chronic illnesses were statistically the same among the social classes. Health disparities in a nation are explained by socioeconomic determinants as well as health insurance status. Previous research showed that health care utilization and health disparities are enveloped in unequal access to insurance coverage and social differences [2, 4, 17-19]. The present paper revealed that health insurance coverage is mostly had by those in the upper class, with less than 20 in every 100 insured being in the lower socioeconomic class. Although this study found that those in the lower class does not have more chronic illness than those in the wealthy class, 86 out of every 100 uninsured respondents indicated that their health status was poor. Health insurance coverage provides valuable economic relief for chronically ill respondents as this allows them to access needed health care. Like Hafner-Eaton’s research [2], this paper found that health insurance status was the third most powerful predictor of health care utilization. Forty-nine to every 100 chronically ill persons use the public health care facilities. This mean that health insurance coverage appeases the health care burden of its holder, but the insured in Jamaica are mostly wealthy, older, chronically ill, married, and seek more medical care than the uninsured. The uninsured ill are therefore less likely to demand health care, and this economic burden of health care is either going to be the responsibility of the state, the individual or the family. The difficulty here is that the uninsured are more likely to be in the lower-tomiddle class, of working age or children, experienced more acute illness, 38 out of every 100 chronically ill are in the lower class, these provide a comprehensive understanding of the insured and uninsured that will allow for explanations in health disparities between the socioeconomic strata and sexes. With 43 out of every 100 people in the lower socioeconomic strata self-reported
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being diagnosed with chronic illness, health insurance coverage, public health system and other policy intervention aid in their health, and health care utilization. Among the material deprivation of the poor is uninsurance. Those in the wealthy socioeconomic group in Jamaica were 3.5 times more likely to be holder of health insurance coverage than those in the lower socioeconomic strata. And Gertler and Sturm [3] identified that health insurance cause a switching from public health to the private health system, which indicates that a reduction in public health expenditure and health insurance will significantly influence the health of the poor. This research showed that only 19% of those with health insurance were in the lower class. Therefore issue of uninsurance creates futuristic challenges for the poor in regard to their health and health care utilization. As on the onset of illness, those in the lower income strata without health insurance must first think about their illness and weight this against the cost of losing current income in order to provide for their families as well as parents of ill children must also do the same. The public health care system will relieve the burden of the poor, and while those with health insurance are more likely to utilize health care, this is a futuristic product in enhancing a decision to utilize health care. But outside of those issues, their choices (or lack), the cost of public health care, national insurance scheme and general price index in the society further lowers their quality of life. Although the poor may be dissatisfied with the public health care system (waiting time, crowding, discriminatory practices by medical practitioners), better health for them without health coverage is through this very system. It can be extrapolated therefore from the present data that there are unmet health needs among some people in the lower socioeconomic strata. As those who do not have health insurance, want to avoid the public health care system owing to dissatisfaction or inafffordability, and will only seek health care when their symptoms are severe and sometimes
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the complications from the delay make it difficult to be addressed on their visits. Among unmet health needs of the poor will be medication. Even if they attend the public health care system and are treated, the system does not have all the medications which is an indication that they are expected to buy some. The challenge of the poor is to forego purchasing medication for food, and this means their conditions would not have been rectified by the health care visitation. By their very nature, the socioeconomic realities of the poor such as lower access to education, proper nutrition, good physical milieu, poor sanitation and lower health coverage, cripple their future health status, this accounts for high premature mortality and hinders health care utilization. It is this lower health care utilization which accounts for their increase risk of mortality as the other deprivations such as proper sanitation and nutrition exposes them to disease causing pathogens which means that their inability to afford health insurance increased their reliance on the public health care system. The present findings showed that the uninsured are mostly poor and within the context of Lasser et al.’s work [20] that they receive worse access to care, are less satisfied with the care they receive and medical services than the insured in the US, this is an indication of further resistant of the poor from willingly demanding health care as this rehashes their dissatisfaction and humiliation. Despite the dissatisfaction and humiliation, their choices are substantially the public health care system, abstinence from care, risk of death, and the burden of private health care. Apart of the rationales why those in the lower socioeconomic strata have fewer health coverage than those in the wealthy income group are (1) inafffordability, (2) type of employment (mostly part time, seasonal, low paid and uninsured position) which makes it too difficult for them to be holders of health insurance and this retards the switch from public-to-private health care utilization. Recently a study conducted by Bourne and Eldemire-Shearer [21] found that 74% of those in the poorest income quintile utilized public
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hospitals compared to 58% of those in the second poor quintile and 31% of those in the wealthiest 20%. Then, if public health becomes privatized or become increasingly more expensive for recipients, the socioeconomically disadvantaged population (poor, elderly, children and other vulnerable groups) will become increasingly exposed to more agents that are likely to result in their deaths, increased utilization of home remedy as well as the widening of the health outcome inequalities among the socioeconomic strata. Illness and particularly chronic condition can easily result in poverty, before mortality sets in. With the World Health Organization (WHO) opined that 80% of chronic illnesses were in low and middle income countries and that 60% of global mortality is caused by chronic illness [7], leveling insurance coverage can reduce burden of care for those in the lower socioeconomic strata. The importance of health insurance to health care utilization, health status, productivity, production, socioeconomic development, life expectancy, poverty reduction strategy and health intervention must include increase health insurance coverage of citizenry within a nation. The economic cost of uninsured people in a society can be measured by the lost of production, payment of sick time, mortality, lowered life expectancy and cost of care for children, orphanage and elderly who become the responsibility of the state from the death of the poor. Therefore the opportunity cost of reduced public health care budget is the economic cost of the aforementioned issues, and goes to the explanation of premature mortality in a society. Particularly the chronically ill, they benefit from health insurance coverage not because of the reduced cost of health care, but the increased health care utilization that result from health coverage. From the findings of Hafner-Eaton’s work [2], the chronically ill in the United States were 1.5 times more likely to seek medical care and while this is about the same for Jamaicans, health insurance is responsible to their health care utilization and not the condition or illness.
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According to Andrulis [22], “Any truly successful, long-term solution to the health problems of the nation will require attention at many points, especially for low-income populations who have suffered from chronic underservice if not outright neglect” Embedded in Andrulis’s work is the linkage between poverty, poor health care service delivery, differences in health outcomes among the socioeconomic groups, higher mortality among particular social class, deep-seated barriers in health care delivery and the perpetuation of those and how they can increase health differences among the socioeconomic strata. The relationship between poverty and illness is well established in the literature [7, 8, 23] as poverty means deprivation from proper nutrition, safe drinking water, and those issues contribute to lower health, production, productivity, and more illness in the future. Free public health care or lower public health care cost does not mean equal opportunity to access, eliminate the barriers to equal opportunity, neither does it increase health and wellness for the poor and remove lower health disparities among the socioeconomic groups. However, lower-income, increase price indices, removal of government subsidy from public health care, increased uninsurance, lower health care utilization, increase poverty, premature mortality and lower life expectancy of the population and particular subpopulations. Increases in diseases (acute and chronic) are owing to lifestyle practices of people. Lifestyle practices are voluntary lifestyle choices and practices [24]. The poor are less educated, more likely to be unemployed, undernourished, deprived from financial resources, and their voluntary actions will be about survival and not diet, nutrition, exercise and other healthy lifestyle choice. Lifestyle choices such as diet, proper nutrition, and sanitation, safe drinking water are costly, which oftentimes occurs because of poverty, some people can afford to make these choices. It follows therefore that those in the lower socioeconomic strata’s voluntary action will be unhealthy choices which are cheaper. Poverty therefore handicaps its people, and
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predetermines unhealthy lifestyle choices, which further accounts for greater mortality, lower life expectancy, health insurance coverage and private health care utilization. Conclusion Poverty is among the social determinants of health, health care utilization, and health insurance coverage in a society. While the current study does not support the literature that chronic illnesses were greater among those in the lower socioeconomic strata, they were less likely to have health insurance coverage compared to the upper class. Poverty denotes socioeconomic deprivation of resources which appears in a society, and goes to the crux of health disparities among the socioeconomic groups and sexes. Health care utilization is associated with health insurance coverage as well as government’s assistance, and this embodies the challenges of those in the vulnerable groups. Within the current global realities, many governments are seeking to reduce their public financing of health care which would further shift the burden of health care to the individual, and this will even increase premature mortality among those in the lower socioeconomic strata. Governments in developing nations continue to invest in improving public health measures such as safe drinking water, sanitation, mass immunization) and the training of medical personnel, building clinics and hospitals and there is definite a need to include health insurance coverage to their public health measure as this will increase access to health care utilization. Any increase in health care utilization will be able to improve health outcome, reduce health disparities between the socioeconomic groups and the sexes that will see improvements in the quality of life of the poor.

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In summary, with the health status of the insured being 1.5 times more than the uninsured, their health care utilization being 1.9 times more than the uninsured and illness being a strong predictor of health care seeking, any reduction in the health care budget in developing nations denotes that vulnerable groups (such as elderly, children and poor) will seek less care, and this will further increase the mortality among those cohorts.

Conflict of interest
The authors have no conflict of interest to report.

Disclaimer
The researchers would like to note that while this study used secondary data from the Jamaica Survey of Living Conditions, none of the errors in this paper should be ascribed to the Planning Institute of Jamaica or the Statistical Institute of Jamaica, but to the researchers.

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References 1. Miles S, Parker K. Men, women, and health insurance. The New England Journal of Medicine 1997; 336:218-221. 2. Hafner-Eaton C. Physical utilization disparities between the uninsured and insured: Comparisons of the chronically ill, acutely ill, and well nonelderly populations. JAMA 1993; 269:787-792. 3. Gertler P, Sturm R. Private health insurance and public expenditures in Jamaica. Journal of Econometrics 1997; 77:237-257. 4. Doty MM, Holmgren AL. Unequal access: insurance instability among low-income workers and minority. Issue Brief (Common Fund) 729:1-6. 5. Bourne PA. Self-reported health and medical care-seeking behaviour of uninsured Jamaicans. North Am J Med Sci 2010; 2: 71-80. 6. Bourne P. Health insurance coverage in Jamaica: Multivariate analyses using two crosssection survey data for 2002 and 2007. Int J of Collaborative Research on Internal Medicine and Public Health 1:195-213. 7. World Health Organization, WHO. Preventing Chronic Diseases a vital investment. Geneva: WHO; 2005. 8. Van Agt HME, Stronks K, Mackenbach JP. Chronic illness and poverty in the Netherlands. Eur J of Public Health 2000; 10:197-200. 9. Fox J ed. Health inequalities in European Countries. Aldershot: Gower Publishing Company Limited; 1989. 10. Illsley R, Svenson PG, ed. Health inequalities in Europe. Soc Sci Med 1990; 31(special issue):223-420. 11. Sen A. Poverty: An ordinal approach to measurement. Econometricia 1979; 44, 219231. 12. Casas JA, Dachs JN, Bambas A. Health disparity in Latin America and the Caribbean: The role of social and economic determinants. In: Pan American Health Organisation. Equity and health: Views from the Pan American Sanitary Bureau, Occasional Publication No. 8. Washington DC; 2001: pp. 22-49. 13. Marmot M .The influence of Income on Health: Views of an Epidemiologist. Does money really matter? Or is it a marker for something else? Health Affairs 2002; 21:3146. 14. Planning Institute of Jamaica, (PIOJ), Statistical Institute of Jamaica, (STATIN). Jamaica Survey of Living Conditions, 1989-2007. Kingston: PIOJ, STATIN; 1989-2008. 15. World Bank, Development Research Group, Poverty and Human Resources. Jamaica Survey of Living Conditions, 1988-2000. Basic information. Washington: The World Bank; 2002. (September 2, 2009, at http://siteresources.worldbank.org/INTLSMS/Resources/33589861181743055198/3877319-1190214215722/binfo2000.pdf). 16. Statistical Institute Of Jamaica. Jamaica Survey of Living Conditions, 2007 [Computer file]. Kingston, Jamaica: Statistical Institute Of Jamaica [producer], 2007. Kingston, Jamaica: Planning Institute of Jamaica and Derek Gordon Databank, University of the West Indies [distributors]; 2008.
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17. Hayward RA, Shapiro MF, et al. Inequalities in health services among insured Americans. Do working-age adults have less access to medical care than the elderly. N Engl J Med 1988;318:1507-12. 18. Fiscella K, Williams DR. Health disparities based on socioeconomic inequities: implications for urban health care. Acad Med 2004;79:1139-47. 19. LaVeist TA, Carroll T. Race of physician and satisfaction with care among AfricanAmerican patients. J Natl Med Assoc 2002; 94:937-43. 20. Lasser KE, Himmelstein DU, Woolhandler S. Access to care, health status, and health disparities in the United States and Canada: Results of a Cross-National PopulationBased Survey. Am J Public Health 200696:1300-1307. 21. Bourne PA, Eldemire-Shearer D. Public hospital health care utilization in Jamaica. Australian J of Basic and Applied Scie 2009; 3:3067-3080. 22. Andrulis DP. Access to care is the centerpiece in the elimination of socioeconomic disparities in health. Ann Intern Med 1998; 129:412-416. 23. Foster AD. Poverty and illness in low-income rural areas. The American Economic Review 1994; 84:216-220. 24. Barnekow-Bergkvist M, Hedberg GE, Janlert U, Jansson E. Health status and health behaviour in men and women at the age of 34 years. European J of Public Health 1998; 8:179-182.

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Table 2.1. Crowding, expenditure, income, age, and other characteristics by health insurance status Health insurance status P Characteristics Non-insured Insured mean ± SD mean ± SD Crowding index 4.9 ± 2.6 4.1±2.1 t = 10.32, < 0.0001 Total annual food expenditure1 3476.09±2129.97 3948.12±2257.97 t = - 6.81, < 0.0001 Annual non-food expenditure1 3772.91±3332.50 6339.40±5597.60 t = - 21.33, < 0.0001 1 Income 7703.62±5620.94 12374.89±9713.00 t = - 22.75, < 0.0001 Age (in year) 28.7±21.4 35.0 ±22.7 t = - 9.40, < 0.0001 Time in household (in years) 11.7±1.6 11.8±1.3 t = - 1.62, 0.104 Length of marriage 16.9±14.3 18.3±13.8 t = - 1.55, 0.122 Length of illness 14.7±51.1 14.1±36.2 t = - 0.217, 0.828 No. of visits to medical practitioner 1.4±1.0 1.5±1.2 t = - 0.659, 0.511
1

Expenditures and income are quoted in USD (Ja. $80.47 = US $1.00 at the time of the survey)

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Table 2.2. Health, health care seeking behaviour, illness and particular demographic characteristics by health insurance status Health insurance status P Characteristic Coverage No coverage Private n (%) Public, NI Gold n (%) Other Public n (%) n (%) χ2 = 42.62, P < 0.0001 Health conditions Acute and other 53 (56.4) 24 (34.8) 13 (24.5) 415 (61.7) Chronic 41 (43.6) 45 (65.2) 40 (75.5) 258 (38.3) χ2 = 70.09, P < 0.0001 Health care seeking behaviour No 724 (89.3) 283 (81.3) 118 (75.2) 4735 (91.0) Yes 87 (10.7) 63 (18.2) 39 (24.8) 468 (9.0) χ2 = 67.14, P < 0.0001 Illness No 699 (86.2) 272 (78.6) 101 (64.3) 4453 (85.8) Yes 112 (13.8) 74 (21.4) 56 (35.7) 736 (14.2) χ2 = 78.10, P < 0.0001 Education level Primary and below 684 (84.4) 318 (92.2) 144 (91.7) 4536 (87.4) Secondary 80 (9.9) 23 (6.7) 9 (5.7) 577 (11.1) Tertiary 46 (5.7) 4 (1.2) 4 (2.5) 74 (1.4) χ2 = 596.08, P < 0.0001 Social class Lower 78 (9.6) 135 (39.0) 31 (19.7) 2345 (45.1) Middle 111 (13.7) 80 (23.1) 27 (17.2) 1085 (20.9) Upper 622 (76.7) 131 (37.9) 99 (63.1) 1773 (34.1) χ2 = 190.29, P < 0.0001 Area of residence Urban 373 (46.0) 106 (30.6) 63 (40.1) 1397 (26.8) Semi-urban 212 (26.1) 66 (19.1) 32 (20.4) 1091 (21.0) Rural 226 (27.9) 174 (50.3) 62 (39.5) 2715 (52.2) χ2 = 67.14, P < 0.0001 Self-rated health status Poor 699 (86.2) 272 (78.6) 101 (64.3) 4453 (85.8) Moderate-to-excellent 112 (13.8) 74 (21.4) 56 (35.7) 736 (14.2) χ2 = 30.06, P < 0.0001 Health care utilization Private 65 (79.3) 29 (47.5) 18 (46.2) 215 (46.8) Public 17 (20.7) 32 (52.5) 21 (53.8) 244 (53.2)
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Table 2.3. Age cohort by diagnosed illness
Diagnosed illness Acute condition Cold Age cohort n (%) Children n (%) n (%) n (%) n (%) n (%) n (%) n (%) Diarrhoea Asthma Diabetes mellitus Chronic condition Hypertension Arthritis Other Total

97 (65.1)

13 (48.1)

51 (53.7)

3 (2.4)

0 (0.0)

0 (0.0)

54 (23.1)

218 (24.5)

Young adults

14 (94)

2 (7.4)

16 (16.8)

3 (2.4)

6 (2.9)

1 (1.8)

43 (18.4)

85 (9.6)

Other-aged adults

22 (14.8)

6 (22.2)

18 (18.9)

44 (35.8)

76 (36.9)

17 (30.4)

85 (36.3)

268 (30.1)

Young old

8 (5.4)

2 (7.4)

7 (7.4)

49 (39.8)

61 (29.6)

22 (39.3)

32 (13.7)

181 (20.3)

Old Elderly

8 (5.4)

3 (11.1)

2 (2.1)

19 (15.4)

49 (23.8)

14 (25.0)

13 (5.6)

108 (12.1)

Oldest Elderly Total

0 (0.0) 149

1 (3.7) 27

1 (1.1) 95

5 (4.1) 123

14 (6.8) 206

2 (3.6) 56

7 (3.0) 234

30 (3.4) 890

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Table 2.4. Illness by age of respondents controlled for health insurance status Age of respondents Characteristic Uninsured Insured Mean ± SD Mean ± SD Illness Acute condition Cold 18.8 ± 23.5 21.0 ± 26.3 Diarrhoea 28.4 ± 30.3 31.8 ± 13.5 Asthma 21.0 ± 21.7 29.4 ± 22.9 Chronic condition Diabetes mellitus 58.7 ± 16.1 63.8 ± 15.4 Hypertension 62.1 ± 17.3 63.6 ± 15.7 Arthritis 64.0 ± 13.3 65.0 ± 18.7 Other condition 38.1 ± 25.0 39.2 ± 26.8 F statistic 73.1, P < 0.0001 23.3, P < 0.0001

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Table 2.5. Age cohort by diagnosed health condition, and health insurance status Diagnosed health condition Acute Chronic n (%) Age cohort Children Young adults Other aged-adults Young-old Old-old Oldest-old Total n (%) Diagnosed health condition Acute Chronic Uninsured n (%) n (%) Acute Chronic Insured n (%) n (%)

Characteristic

215 (42.6) 3 (0.8) 75 (14.9) 10 (2.6) 131 (25.9) 137 (2.6) 49 (9.7) 132 (34.3) 26 (5.1) 82 (21.3) 9 (1.8) 21 (5.5) 505 385 2 χ = 317.5, P < 0.0001

183 (44.1) 1 (0.4) 32 (35.6) 2 (1.6) 58 (14.0) 6 (2.3) 17 (18.9) 4 (3.2) 110 (26.5) 100 (38.6) 21 (23.3) 37 (29.4) 37 (8.9) 82 (31.7) 12 (13.3) 50 (39.7) 20 (4.8) 55 (21.2) 6 (6.7) 27 (21.4) 7 (1.7) 15 (5.8) 2(2.2) 6 (4.8) 415 259 90 126 2 2 χ = 234.5, P < 0.0001 χ = 73.6, P < 0.0001

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Table 2.6. Logistic regression: Explanatory variables of self-rated moderate-to-very good health
Explanatory variable Average medical expenditure Health insurance coverage (1= insured) Urban Other †Rural Household head Age Crowding index Total food expenditure Health care seeking (1=yes) Illness Model fit χ2 = 574.37, P < 0.0001 -2LL = 1477.76 Nagelkerke R2 = 0.328 †Reference group ***P < 0.0001, **P < 0.01, *P < 0.05 Coefficient Std. error Odds ratio 95.0% C.I. R2 0.003 0.005 0.007 0.006

0.000 0.410 0.496 0.462

0.000 0.181 0.180 0.197

1.00* 1.51* 1.64** 1.59* 1.00 1.46* 0.96*** 0.86*** 1.00*** 0.51** 0.24***

1.00 -1.00 1.06 - 2.15 1.15 - 2.34 1.08 - 2.34

0.376 -0.046 -0.156 0.000 -0.671 -1.418

0.154 0.004 0.035 0.000 0.211 0.212

1.08 - 1.97 0.95 - 0.96 0.80 - 0.92 1.00 - 1.00 0.34 - 0.77 0.16 - 0.37

0.004 0.081 0.010 0.003 0.005 0.204

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Table 2.7. Logistic regression: Explanatory variables of self-reported illness
Explanatory variable Average medical expenditure Male Married Age Coefficient Std Error Odds ratio 95.0% C.I.

R2
0.001 0.003 0.002 0.037 0.002 0.009 0.611

0.000 -0.467 0.527 0.031 0.000 -1.429

0.000 0.137 0.146 0.004 0.000 0.213 0.262

1.00* 0.63** 1.69*** 1.03*** 1.00** 0.24*** 342.11***

1.00 - 1.00 0.48 - 0.82 1.27 - 2.25 1.02 - 1.04 1.00 -1.00 0.16 -0.36 204.71 -571.72

Total food expenditure
Self-rated moderate-to-excellent health

5.835 Health care seeking (1=yes) Model fit χ2 = 2197.09, P < 0.0001 -2LL = 1730.41 Hosmer and Lemeshow goodness of fit χ2 = 4.53, P = 0.81 Nagelkerke R2 = 0.665 †Reference group ***P < 0.0001, **P < 0.01, *P < 0.05

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Table 2.8. Logistic regression: Explanatory variables of health care seeking behaviour
Explanatory variable Coefficient Std error Odds ratio 95.0% C.I. R2

Health insurance coverage (1= insured)
Self-reported illness Self-rated moderate-to-excellent health

0.620 5.913 -0.680

0.179 0.252 0.198

1.86** 369.92*** 0.51**

1.31 - 2.64 225.74 - 606.17 0.34 - 0.75

0.003 0.712 0.004

Model fit χ2 = 1997.86, P < 0.0001 -2LL = 1115.93 Hosmer and Lemeshow goodness of fit χ2 = 1.49, P = 0.48 Nagelkerke R2 = 0.719 †Reference group ***P < 0.0001, **P < 0.01, *P < 0.05

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Table 2.9. Logistic regression: Explanatory variables of self-reported diagnosed chronic illness
Explanatory variable Male Married †Never married Coefficient -1.037 0.425 Std error 0.205 0.199 Odds ratio 0.36*** 1.53* 1.00 1.58* 1.05*** 1.13* 95.0% C.I. 0.24 - 0.53 1.04 - 2.26

R2 0.048 0.012

Health insurance coverage (1= insured)
Age Logged Length of illness

0.454 0.047 0.125

0.220 0.005 0.059

1.02 - 2.42 1.04 - 1.06 1.01 - 1.27

0.008 0.201 0.008

Model fit χ2 = 136.32, P < 0.0001 -2LL = 673.09 Hosmer and Lemeshow goodness of fit χ2 = 15.96, P = 0.04 Nagelkerke R2 = 0.277 †Reference group ***P < 0.0001, **P < 0.01, *P < 0.05

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Table 2.10. Multiple regression: Explanatory variables of income
Unstandardized Coefficients B 11.630 0.206 1.265 0.692 Std. Error 0.061 0.008 0.052 0.047 Standardized Coefficients Beta 0.625*** 0.649*** 0.347*** 95% CI 11.511 - 11.750 0.190 - 0.221 1.162 - 1.368 0.599 - 0.784

Explanatory variable Constant Crowding index Upper class Middle Class †Lower class Household head

R2

0.195 0.320 0.133

-0.181 0.137 0.165 0.109 0.145 0.130 0.075

0.038 0.042 0.040 0.039 0.046 0.049 0.038

-0.108*** 0.075** 0.094*** 0.063** 0.079** 0.063** 0.044*

-0.256 - -0.106 0.054 - 0.220 0.088 - 0.243 0.033 - 0.185 0.055 - 0.235 0.033 - 0.226 0.000 - 0.150

0.012 0.007 0.006 0.003 0.002 0.003 0.001

Health insurance coverage (1= insured)
Self-rated good health status

Health care seeking (1=yes)
Urban Other town †Rural area Married †Never married

F = 144.15, P < 0.0001 R2 = 0.682 †Reference group ***P < 0.0001, **P < 0.01, *P < 0.05

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Table 2.11. Logistic regression: Explanatory variables of health insurance status (1= insured)
Explanatory variable Age Income Chronic condition Health care seeking (1=yes) Married †Never married Upper class †Lower class Coefficient 0.014 0.000 0.563 0.463 0.647 Std. error 0.006 0.000 0.210 0.211 0.192 Odds ratio 1.01* 1.00*** 1.7** 1.59* 1.91** 95.0% C.I. 1.00 - 1.03 1.00 - 1.00 1.16 - 2.65 1.05 - 2.40 1.31 - 2.79 R2 0.040 0.082 0.013 0.010 0.024

0.841

0.227

3.46***

1.49 - 3.62

0.025

Model fit χ2 = 95.7, P < 0.0001 -2LL = 686.09 Hosmer and Lemeshow goodness of fit χ2 = 5.08, P =0.75 Nagelkerke R2 = 0.194 †Reference group ***P < 0.0001, **P < 0.01, *P < 0.05

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Chapter 3
Self-reported health and medical care-seeking behaviour of uninsured Jamaicans

Paul A. Bourne
On examination of the literature in Latin America and the Caribbean, and in particular Jamaica, no study could be found that investigated the health and health care-seeking behaviour of uninsured people. This study bridges the gap in the literature, by evaluating uninsured Jamaicans’ medical care-seeking behaviour and good health status. The study extracted a sample of 5,203 uninsured respondents 15 years and older from a national probability cross-sectional survey of 6,782 Jamaicans. Descriptive statistics were used to provide background information on the sample; cross-tabulations evaluated bivariate analyses, and logistic regression was used to model health and medical care-seeking behaviour. Good health of uninsured Jamaicans is correlated -reported biological condition (OR =0.114, 95% CI = 0.090 -0 .145) followed by age (OR =0.952, 95% CI = 0.946- 0.959); gender (OR = 1.501, 95% CI = 1.221–1.845); consumption (OR = 1.000, 95% CI = 1.000–1.000); social class (upper class OR = 0.563, 95% CI = 0.357–0.888); education (secondary and above OR = 0.622, 95%CI = 0.402–0.963), and area of residence (other towns OR = 1.351, 95% CI = 1.026–1.778). Medical care-seeking behaviour is associated with age (OR = 1.020, 95% CI = 1.006 – 1.033); poor health status (OR = 2.303, 95% CI = 1.533–3.461), and marital status (married OR = 0.518, 95% CI = 0.325– 0.824). The findings are far reaching and provide an understanding of the uninsured, and the information can be used to aid public health intervention and education programmes.

Introduction
Poverty is among the reasons for some people in developing nations not seeking medical care; and it also explains premature death owing to low health care utilization. The World Health Organization (WHO) [1] opined that 80% of chronic illnesses were in low and middle income countries, suggesting that poverty interfaces with illness and creates other socio-economic challenges. Poverty does not only impact on illness, it causes premature deaths, lower quality of
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life, lower life and healthy life expectancy, low development and other social ills such as crime, high pregnancy rates, and social degradation of the community. According to Bourne & Beckford [2], there is a positive correlation between poverty and unemployment; poverty and illness; and crime and unemployment. Sen [3] encapsulated this well when he put forward the idea that low levels of unemployment in the economy are associated with higher levels of capabilities. The WHO [1] opined that 60% of global mortality is caused by chronic illness, and within the context that four-fifths of chronic dysfunctions are in low-to-middle income countries, health insurance coverage reduces the burden of out-of-pocket medical expenditure for the individual and the family. Jamaica is among those countries classified as developing nations. Hence, the challenges which were stated earlier also influence the quality of life of some people within the society. In 1988, Jamaica’s unemployment rate was 18.9% and 2 decades later (2007), it fell by 67.2% (to 6.2%) which indicates close to full-employment. [4] This significant reduction in unemployment rates cannot be the only indicator used to evaluate the socio-economic status of Jamaica, or for a hasty conclusion to be drawn that the quality of life of Jamaicans is better in 2007 compared to 1988. In 1988 the inflation rate in Jamaica was 8.8% and this increased by over 90%, suggesting that the economic cost of living for Jamaicans was substantially higher than twenty years earlier. It is important to note that the inflation rate in 2007 (16.8%) increased by 194.7% over 2006. A national representative probability sample cross-sectional survey of 1,338 Jamaicans which was conducted in 2007 revealed that 68.7% of respondents claimed that their current economic situation was at most the same compared to 12 months ago, and of this figure 25% mentioned that it was worse. [5] Furthermore, 62% of the sample indicated that their salaries were not able
64   

to satisfactorily cover their basic needs, and 71.9% claimed that they were concerned about the likelihood of being unemployed in the next 12 months. Those realities, then, explain why in 2007, the number of Jamaicans seeking medical care fell to 66% over 70% in the previous year; while the self-reported figures rose to an unprecedented 15.5%. In Jamaica, rural poverty is twice (15.3%) that of urban poverty (6.2%). [4] This may create the impression that urban poverty is low and does not demand an examination. Poverty is poverty and whether it occurs in rural, peri-urban and urban areas; its effect is the same. Hence, when poverty is coupled with unemployment, chronic illnesses will require health care for either preventive or curative measures which must lead to a financial commitment that can erode their resources or that of their families. [5] In 2007, statistics on health in Jamaica showed that 50.8% of people in the poorest income quintile (i.e. below the poverty line) indicated that they were unable to afford to seek medical care, compared to 36.7% of those just above the poverty line and 7.1% of those in the wealthiest income quintile. [4] It is private health insurance and social security that facilitate access to medical care for the poor and do assist in reducing the financial commitment of individuals and families for those with chronic or recurring illnesses. Twentyone of every 100 Jamaican in 2007 has health insurance coverage, suggesting that the majority of people pay for medical care out of their pockets. Many studies have examined the insured and health care demand of the general populace [6-10] but on reviewing the literature no study was found in Latin America and the Caribbean, in particular Jamaica, that has investigated the uninsured in regards to their medical care-seeking behaviour and health status. According to Call & Ziegenfuss, [7] health insurance is a significant predictor of access to medical care services, and people who do not have access to health
65   

insurance have less possibilities of accessing health care services. This was contradicted by Bourne [11] who found that health insurance is not significant when correlated with the medical care-seeking behaviour of Jamaicans or a predictor of the good health of Jamaicans [11] or female Jamaicans. [12] Call & Ziegenfuss [7] added that rural residents are more restricted from access to health insurance coverage than urban citizens, suggesting that medical care-seeking behaviour would be lower for rural than urban residents. While Call & Ziegenfuss’ perspectives provide us with basic information about the insured, it is inadequate for this cohort of people based on the findings of Bourne [11], and Bourne & Rhule [12]. For 2007, statistics revealed that 21.2% of Jamaicans had health insurance coverage and 66% sought medical care, indicating that most of the people who utilized medical care services did not use health coverage. Within the context of the global economic downturn, increased job redundancies and prices of commodities, the uninsured will be asked to pay more for medical care. Apart from the increased odds of not utilizing health care services, little is known about the uninsured in Latin American and the Caribbean, and in particular Jamaica. This study will bridge the gap in the literature, by evaluating their health status, medical care-seeking behaviour, and the medical conditions of uninsured Jamaicans in order to establish whether there are differences in the three geographical regions, and to use the information for public health intervention and policy formulation. The researcher used data from the 2007 Jamaica Survey of Living Conditions to evaluate medical care-seeking behaviour, medical conditions, purchased medication, and the health status of uninsured Jamaicans as well as building two models for good health status and health care-seeking behaviour of this uninsured group.

Methods and materials
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Data The current study extracted a sample of 5,203 respondents 15 years of age and over from a national probability cross-sectional survey (Jamaica Survey of Living Conditions, JSLC) of 6,782 Jamaicans [13-15]. The cross-sectional survey was conducted between May and August 2007 from the 14 parishes across Jamaica and included 6,782 people of all ages [16]. The JSLC used stratified random probability sampling technique to draw the original sample of respondents, with a non-response rate of 26.2%. The sample was weighted to reflect the population. [13-15] Study instrument The JSLC used an administered questionnaire where respondents were asked to recall detailed information on particular activities. The questionnaire was modelled on the World Bank’s Living Standards Measurement Study (LSMS) household survey. There are some modifications to the LSMS, as the JSLC is more focused on policy impacts. The questionnaire covers demographic variables, health, and other issues. Interviewers were trained to collect the data from household members. Data on 5, 203 individuals who indicated not having health insurance coverage was used in data analysis. Statistical methods Descriptive statistics such as mean, standard deviation, frequency and percentage were used to analyze the socio-demographic characteristics of the sample. Chi-square analyses were used to examine the association between non-metric variables for area of residence, and gender of respondents. Logistic regression analyses examined 1) the relationship between good health
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status and some socio-demographic, economic and biological variables; as well as 2) a correlation between medical care-seeking behaviour and some socio-demographic, economic and biological variables. The statistical package SPSS for Windows version 16.0 (SPSS Inc;

Chicago, IL, USA) was used to analyze the data. A p-value less than 5% was used to indicate statistical significance. The correlation matrix was examined in order to ascertain if autocorrelation and/or multicollinearity existed between variables. Based on Cohen and Holliday [17] correlation can be low (weak) - from 0 to 0.39; moderate – 0.4-0.69, and strong – 0.7-1.0. The approach in addressing collinearity (r > 0.6) was to independently enter variables in the model to determine which one should be retained during the final model construction. The method of retaining or excluding a variable from the model was based on the variables’ contribution to the predictive power of the model and its goodness of fit. [18-24] Wald statistics were used to determine the magnitude (or contribution) of each statistically significant variable in comparison with the others, and the Odds Ratio (OR) for the interpreting of each significant variable. Models The current study will employ multivariate analyses in the study of the health status (Equation [1]) and medical care seeking behaviour of Jamaicans (Equation [2]). The use of this approach is better than bivariate analyses as many variables can be tested simultaneously for their impact (if any) on a dependent variable. Ht=f(Ai, Gi, HHi, ARi, lnC, EDi, MRi, Si, ∑MCt, SRIi, εi) 1

Where Ht (i.e. self-rated good current health status in time t) is a function of age of respondents Ai; sex of individual i, Gi; household head of individual i, HHi; area of
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residence, ARi; logged consumption per person per household member, lnC; Education level of individual i, EDi; marital status of person i, MRi; social class of person i, Si; summation of medical expenditure of individual i in time period t, MCt; self-reported illness, SRIi, and an error term (i.e. residual error). MCSBi=f(PHt ,Ai, Gi, HHi, ARi, lnC, EDi, MRi, Si, CRi, εi) 2

Where MCSBi is medical care-seeking behaviour of individual i is a function of PHt (ie self-rated poor current health status in time t of individual i); age of respondents Ai; sex of individual i, Gi; household head of individual i, HHi; area of residence, ARi; logged consumption per person per household member, lnC; education level of individual i, EDi; marital status of person i, MRi; social class of person i, Si; logged consumption per person per household member i, lnC; crowding of person i, CRi; and an error term (i.e. residual error). From Equation (1) was derived Equation (3) and Equation (4): Ht=f(Ai, lnC, SRIi, Si, EDi, ARi, Gi, εi) MCSBi=f(PHt ,Ai, MRi, εi) Measures An explanation of some of the variables in the model is provided here. Self-reported illness status is a dummy variable, where 1 = reporting an ailment or dysfunction or illness in the last 4 weeks, which was the survey period; 0 if there were no self-reported ailments, injuries or illnesses. [11, 12, 25] While self-reported ill-health is not an ideal indicator of actual health conditions because people may under-report, it is still an accurate proxy of ill-health and mortality. [26, 27] Health status is a binary measure where 1=good to excellent health; 0= otherwise which is determined from “Generally, how do you feel about your health”? Answers
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3 4

for this question were on a Likert scale matter ranging from excellent to poor. Age group was classified as children (ages less than 15 years); young adults (ages 15 through 30 years); other aged adults (ages 30 through 59 years); young-old (ages 60 through 74 years); old-old (ages 75 through 84 years) and oldest-old (ages 85+ years). Medical care-seeking behaviour was taken from the question ‘Has a health care practitioner, healer , or pharmacist been visited in the last 4 weeks?’ with there being two options Yes or No. Medical care-seeking behaviour therefore was coded as a binary measure where 1=Yes and 0= otherwise.

Results
Socio-demographic characteristics of sample The sample was 5,203 uninsured respondents (49.2% males and 50.8% females). Of the sample, 32.9% were children; 26.9% young adults; 30.0% other aged adults; 10.8% elderly. The

majority of those sampled had good health status (82.9%); 73% were never married; 62.0% visited medical care-seeking behaviour; 60.3% had at most no formal education; 52.2% lived in rural areas; 21.0% in semi-urban areas and 26.8% in urban areas. Fifty-nine percent of the sample purchased the prescribed medication, and 14.2% reported an illness. Of those who reported ailments, 89.5% revealed that they were diagnosed by health care practitioners. Approximately 17% indicated cold; 3.5% diarrhoea; 9.8% asthma; 19.7% hypertension; 5.5% arthritis; 25.3% and unspecified dysfunctions. Forty-five percent of the sample were poor (23.1% below the poverty line), 20.9% in the middle class, and 34.1% were classified as wealthy (14.8% in the wealthiest group). A significant statistical correlation was found between medical care-seeking behaviour and health status (χ2 (df = 2) =36.199, P < 0.001, n=752). Seventy-six percent (N= 160) of those
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who reported poor health status sought medical care compared to 68.0% (n = 174) of those who reported fair health status and 50.6% (n= 170) of those who indicated good health status. Table 3.1 revealed that significantly more rural residents were poor (58.7%) compared to 34.9% of semi-urban and 26.5% of urban dwellers. Only 21.2% of rural respondents were in the upper class which was significantly lower than those in semi-urban areas (42.6%) and the percentage is even greater in urban zones (52.5%). A cross-tabulation between health status and area of residence revealed a statistical correlation (P<0.001). Further examination showed that substantially more rural respondents indicated poor health status (6.3%) than semi-urban (3.3%) and urban (3.9%) (see Table 3.1). Significantly more rural dwellers reported being diagnosed with a recurring illness (15.9%) than semi-urban (11.8%) and urban respondents (12.7%). No significant statistical correlation was found between medical care-seeing behaviour and area of residence (P= 0.375). Seventeen percent of females reported a recurring illness which was significantly more than the 12% for males (Table 3.2). Of the diagnosed recurring illness, approximately twice as many females reported diabetes mellitus (11.3%) and hypertension (24.6%) than males (6.1%) and 12.6% respectively. While more males indicated cold (18.1%); diarrhoea (3.6%); asthma (11.3%); arthritis (6.5%); and unspecified (27.5%) compared to females – cold (15.6%); diarrhoea (3.4%); asthma (8.8%); arthritis (4.7%), and 23.7% unspecified ailments. A cross-tabulation between health status and self-reported illness found that there was a significant statistical correlation (χ2 (df = 2) = 989.552, P < 0.001). The association was a moderately strong one (contingency coefficient = 0.401). Further examination of the results revealed that 89.4% (n=3,964) of those who reported no illness had good health status, and only 43.7% of respondents with an ailment indicated poor health status. Approximately 22% of
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individuals with at least one dysfunction had poor health status compared to 2.3% of those who did not have an illness (Table 3.3). A significant statistical correlation existed between self-reported illness and age cohort (χ2 (df = 5) = 407.365, P < 0.001, n = 5,189). The findings revealed that 12.4% children reported at least one illness compared to 5.5% of young adults and following this age cohort self-reported illness increased to 14.7% for other aged adults; 33.3% of young old; 49.7% of old-old and 51.2% of oldest-old. Multivariate Analysis Table 3.4 examines variables that seek to explain the good health status of insured Jamaicans. Good health statuses of uninsured Jamaicans are correlated with socio-demographic, economic and biological factors. The correlates of good health status of uninsured Jamaicans are statistically significant (χ2 (df = 15) =993.114, P < 0.001; -2 Log likelihood = 2554.359; Nagelkerke R2 =0.390; Hosmer and Lemeshow goodness of fit χ2=11.159), and 84.6% of the data were correctly classified: 94.9% of cases in good health status were correctly classified and 46.6% were cases with poor health status. Table 3.5 presents information on variables that determine (or not) the medical careseeking behaviour of uninsured Jamaicans. The correlates that explain medical care-seeking behaviour of uninsured respondents are statistically significant χ2 (df = 14) = 47.79, P < 0.001; -2 Log likelihood = 648.32; Nagelkerke R2 =0.117; Hosmer and Lemeshow goodness of fit χ2=4.480), and 67.5% of the data were correctly classified: 88.1% of data correctly classified medical care-seeking behaviour and 30.0% of data otherwise.

Discussion
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Caribbean societies, in particular Jamaica, have seen an increase in illnesses such as HIV/AIDS, malignant neoplasm, diabetes mellitus, hypertension, ischaemic heart disease, and arthritis [2833] which require continued treatment. Although this is a reality, only 21.2% of Jamaicans had health insurance coverage in 2007, indicating that the majority of people are without health insurance coverage and many people will not be able to afford medical care. The current study found that approximately one-half of Jamaicans who do not have health insurance were poor compared to 34.1% of the wealthy and 20.9% of those in the middle class. Substantially more Jamaicans below the poverty line (23.1%) did not have health insurance compared to 14.8% of those in the wealthiest 20%. In addition, 33% were children compared to 11% who were older than 60 years. Although there is a preponderance of Jamaicans who are poor and uninsured, this research found that there was no statistical difference between medical care-seeking behaviour and social class; medical care-seeking behaviour and sex; and health care-seeking behaviour and area of residence. Embedded in this finding is the dominance of a non-medical care-seeking behaviour culture in Jamaica, and it is so fundamental that education, social class and income are not able to retard the practice. This is captured in an analysis of the study that had 44 out of every 100 respondents indicating that they were ill enough to seek medical care compared to 34 out of every 100 in the population; and 18 out of every 100 stated they preferred home remedies compared to 30 in 100 in the populace. Sixty-six out of every 100 Jamaicans sought medical care, comprising the poorest 20%to-wealthiest 20% in 2007. The current study revealed that 45 out of every 100 poor people were not covered by health insurance compared to 17 out of 50 for the wealthy and 21 out of 100 for the middle class. Concomitantly, 33 out of every 100 children (less than 15 years) and 60 out of every 100 Jamaicans who had no formal education were not covered by health insurance. The
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rationale which accounts for the fact that there is no significant difference in medical careseeking behaviour among the social classes is embedded in the removal of user fees in the health care system; and how this has narrowed the health care-seeking behaviour gap between the poor and the wealthy. In 2007, the government of Jamaica introduced national health insurance coverage for those who suffer from particular illnesses, as well as for those who are older than 60 years. This social security coverage commissioned by the Jamaican government eliminates health insurance for ‘unwell’ patients, suggesting that health is conceptualized as diseases, which is not in keeping with an operationalization of health offered by the WHO. [34] According to the WHO, health does not only mean the absence of disease, but it must include social, psychological and physical wellbeing. The health insurance coverage offered by the government explains the low uninsured group among the Jamaican elderly. Hence, this means that most of those who possess health insurance would have private coverage; the high ‘unwell’ Jamaicans therefore justify the high non-insured group in the nation. This paper examines the uninsured or the ‘unwell’. This analysis has found that good health status can be determined by age, consumption, self-reported illness, social class, education, area of residence and gender of respondents, which concurs with other studies. [35-39] While this study is the first of its type in Jamaica, its findings are similar to other multivariate studies that have examined the health status of people. Using data for elderly Barbadians, Hambleton et al.’s work [35] found that dysfunction accounted for the most explanatory power in health status, which is confirmed by this analysis. The model that was developed for the good health status of uninsured Jamaicans was based on the 7 aforementioned variables with a coefficient of determination of the current study being 39.0% (Nagelkerke R2 =0.390). This predictive model seems weak; but Hambleton et al’s work on
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elderly Barbadians had a coefficient of determination of 38.2%, indicating that the analysis of this paper is relatively good in keeping with a non-Jamaican study of a similar nature. In spite of the similarities, there are some notable differences with other studies. Eightthree out of every 100 uninsured Jamaicans reported at least good health status; 20 out of every 100 were hypertensive; 9 out of 100 diabetic and 6 out of 100 arthritic compared to the percentage of respondents in the population with particular health conditions: hypertension, 22 out of every 100; diabetes mellitus, 12 out of every 100; and, arthritis, 9 out of every 100. It is interesting to note that Jamaicans have a preference for private health care utilization [15] but this is not the case for the uninsured. In 2007, 52 out of every 100 Jamaican visited private health care services compared to 6 out of every 100 of the uninsured. The percentage of uninsured who visited public health care facilities (34 out of every 100) was also lower than in the general populace (41 out of every 100). The analysis of this study went further than that of other identified studies as it found that uninsured Jamaicans who resided in rural areas reported a greater percentage of illnesses, followed by urban, than other town residents. Marmot [35] opined that income influences health as it provides access to more resources, medical services, and lower infant mortality. The analysis of this work concurs with Marmot [35] and PAHO et al. [9] as consumption (which can proxy income) is positively correlated with good health status. With this reality, there seems to be a paradox, as those in the wealthy classes had lower good health status than those in the poor classes. Income undoubtedly provides access to more resources, better physical conditions and opens the way to better quality of water and food; it also offers individuals, societies or nations the highest quality medical services which cannot be accessed by the poor. [35] There is another
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side to this discourse in that the lifestyle practices of the wealthy help to erode the advantages of income. According to Bourne, McGrowder & Holder-Nevins, [41] health behaviour which is a function of one’s culture suggests that the wealthy will continue their involvement in parties and other social arrangements which will involve the use of alcoholic beverages, smoking and other risky lifestyle practices that reduce the advantage of income. While income can buy access to better medical services, this paper highlights that it cannot buy good health. It is clear from the current study that wealthy uninsured Jamaicans are using their income the wrong way in regards to its negative impact on health. Insufficient money is associated with more illness; however, this study has revealed that there is no statistical difference between the wealthy and the poor seeking medical care. Although the wealthy substantially used private health care facilities and the poor utilized public health facilities, [15] embedded in this analysis therefore is the fact that the quality of primary level care in Jamaica is of a high standard. While there is no difference between the wealthy uninsured and the poor uninsured seeking medical care, the study revealed that those with poor health status were 2.3 times more likely to seek health care services than those in good health. The analysis of this work showed that 22 out of every 100 uninsured Jamaicans who indicated at least one health condition reported poor health status. Hence this study highlights the fact that there is a disparity between respondents’ conceptualization of health status and that of illness, as 44% of uninsured ill respondents indicated that they had good health status. The JSLC report revealed that the prevalence of recurrent (chronic) diseases is highest among individuals 65 years and over. [41] According to PIOJ & STATIN [42] individuals 60-64 years were 1.5 times more likely to report an injury than children less than five years old, and the figure was even higher for those 64 years and older (2.5 times more). It should be noted here that
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this increase in self-reported cases of injuries/ailments does not represent an increase in the incidence of cases as the JSLC for 2004 said that the proportion of recurring/chronic cases fell from 49.2% in 2002 to 38.2% in 2004 [43]. Eldemire [44] found that 34.8% of new cases of diabetes and 39.6% of hypertension were associated with senior citizens (i.e. ages 60 and over). Bourne, McGrowder, & Crawford [39] found that the poor health status of people 60 to 64 years was 33.2% and this increased to 36.1% for elderly 65 to 69 years, 49.4% for elderly 70 to 74 years and 51.7% for those 75 years and older, emphasizing the positive correlation between increased ailments and ageing of the Jamaican elderly. An analysis of the current study revealed that there is no significant difference among the populations across the 3 geographical areas in Jamaica in regards to health care-seeking behaviour, suggesting that the uninsured medical care-seeking behaviour is the same in the 3 geographical areas. This research concurs with the finding of a study by Call & Ziegenfuss [7] meaning that the uninsured in Jamaica are not atypical as they are in keeping with those in Minnesota, United States. Further, no significant correlation was found among urban, semiurban, rural and educational levels of uninsured Jamaicans which were similar to that of Call & Ziegenfuss. Many studies have shown that married people (or those in unions) had greater health status than those who were never married. [45-51] The current work disagreed with those findings as it found that there was no significant statistical correlation between good health status of married uninsured people, and those who were never married, or separated, divorced or widowed. Analysis of this research revealed that those who were married were 48.2% less likely to seek medical care than those who were never married. The answer to this lies in the lifestyle practices of these people. Smith & Waitzman [49] offered the explanation that wives were able
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to dissuade their husband from particular risky behaviours such as the use of alcohol and drugs, and would ensure that they maintain a strict medical regimen coupled with proper eating habits. [50,51] Koo, Rie & Park’s findings [48] revealed that being married was a ‘good’ cause for an increase in psychological and subjective wellbeing in old age. This study is the first of its kind in the Caribbean, in particular Jamaica, which models the health care-seeking behaviour of uninsured respondents, and so there is nothing to compare it with. The coefficient of determination for this model was 11.9%, which means that although it is low its validation will need further research.

Limitation of study
A single cross-sectional study cannot be used to examine causality, as well as a snap shot survey cannot effectively capture the continuous matter of the variables. The severity of illness was excluded from the medical care-seeking behaviour model because of missing cases and this could have been critical to the study.

Conclusion
The findings of this research are far reaching and provide an understanding of the uninsured, and the information can be used to aid public health intervention and education programmes.

Conflict of interest
There is no conflict of interest to report.

References
1. World Health Organization, (WHO). Preventing Chronic Diseases a vital investment. Geneva: WHO;2005. 2. Bourne PA, Beckford O. Illness and Unemployment in Jamaica. Paper presented at the Caribbean Studies Association, CSA, 34th Annual Conference Hilton, Kingston, Jamaica,
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June 1-4, 2009. 3. Sen A. Poverty: An ordinal approach to measurement. Econometrica 1979;44: 219-231. 4. Statistical Institute of Jamaica. Demographic statistics, 2007. Kingston: STATIN;2008.
5. Powell LA, Bourne P, Waller L. Probing Jamaica’s Political Culture: Main trends in the July‐August  2006  Leadership  and  Governance  Survey,  Volume  1.  Kingston:  Centre  for  Leadership  and  Governance,  Department  of  Government,  the  University  of  the  West  Indies,  Mona,  Jamaica;2007 

6. International Labour Organization, (ILO). Health Insurance in Developing Countries: The Social Security Approach. Geneva: ILO;1990. 7. Call KT, Ziegenfuss J. Health insurance coverage and Access to Care Among Rural and Urban Minnesotans. Rural Minnesota J 2007;2, 11-35. 8. Pan American Health Organization, World Health Organization. Trade in Health Services: Global, regional, and country perspectives. Washington D.C: PAHA & WHO;2002. 9. Pan American Health Organization, The Inter-American Development Bank, The World Bank. Investment in Health: Social and Economic Returns. PAHO: Washington D.C.: PAHO;2001. 10. World Bank. The demand for health care in Latin America. Lessons from the Dominican Republic and El Salvador. Washington D.C: The World Bank;1993. 11. Bourne PA. Socio-demographic Correlates of Health care-seeking behaviour, selfreported illness and Self-evaluated Health status in Jamaica. Int J of Collaborative Research on Internal Medicine & Public Health 2009;1:101-130. 12. Bourne PA, Rhule J. Good Health Status of Rural Women in the Reproductive Ages. International Journal of Collaborative Research on Internal Medicine & Public Health 2009;1:132-155. 13. World Bank, Development Research Group, Poverty and human resources. Jamaica Survey of Living Conditions (LSLC) 1988-2000: Basic Information. Washington DC; 2002. (Accessed November 4, 2009, at http://www.siteresources.worldbank.org/INTLSMS/Resources/.../binfo2000.pdf)
14. PIOJ, STATIN. Jamaica Survey of Living Conditions, 2002. Kingston: PIOJ & STATIN;2003.  15. PIOJ,  STATIN.    Jamaica  Survey  of  Living  Conditions,  2007.  Kingston,  Jamaica:    PIOJ  &  STATIN;2008. 

16. Statistical Institute Of Jamaica, Jamaica Survey of Living Conditions. [Computer file]. Kingston, Jamaica: Statistical Institute Of Jamaica [producer], 2007. Kingston, Jamaica: Planning Institute of Jamaica and Derek Gordon Databank, University of the West Indies [distributors];2007. 17. Cohen L, Holliday M. Statistics for Social Sciences. London: Harper & Row;1982. 18. Hair JF, Black B, Babin BJ, Anderson RE, Tatham RL. Multivariate data analysis, 6th ed. New Jersey: Prentice Hall;2005. 19. Mamingi N. Theoretical and empirical exercises in econometrics. Kingston: University of the West Indies Press;2005. 20. Zar JH. Biostatistical analysis, 4th ed. New Jersey: Prentice Hall;1999. 21. Hamilton JD. Time series analysis. New Jersey: Princeton University Press;1994. 22. Kleinbaum DG, Kupper LL, Muller KE. Applied regression analysis and other multivariable methods. Boston: PWS-Kent Publishing;1988. 23. Cohen J, Cohen P. Applied regression/correlation analysis for the behavioral sciences, 2nd ed. New Jersey: Lawrence Erlbaum Associates;1983.
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24. Koutsoyiannis A. Theory of econometrics, 2nd ed. New York: MacMillan Publishing;1977. 25. Finnas F, Nyqvist F, Saarela J. Some methodological remarks on self-rated health. The Open Public Health J 2008;1: 32-39. 26. Idler EL, Benjamin Y. Self-rated health and mortality: A Review of Twenty-seven Community Studies. J of Health and Social Behavior 1997; 38: 21-37. 27. Idler EL, Kasl S. Self-ratings of health: Do they also predict change in functional ability? Journal of Gerontology 1995; 50B : S344-S353.
28. Planning  Institute  of  Jamaica,  (PIOJ).  Economic  and  Social  Survey  Jamaica,  2007.  Kingston:  PIOJ;2008. 

29. Statistical Institute of Jamaica, (STATIN). Demographic statistics, 2007. Kingston; 2008. 30. Pan American Health Organization, (PAHO). Health in the Americas 2007, Volume 11 – Countries. Washington D.C.: PAHO;2007. 31. Bain B. HIV/AIDS – The rude awakening/stemming the tide. In O. Morgan, O, (Ed.), Health Issues in the Caribbean. Kingston: Ian Randle;2005: pp. 62-76. 32. Morgan, O (ed). Health Issues in the Caribbean. Kingston: Ian Randle;2005. 33. Jamaica, Ministry of Health. Epidemiological profile of selected health conditions and services in Jamaica, 1990-2002. Kingston: Health Promotion and Protection Division, Ministry of Health;2005. 34. World Health Organization, (WHO). Preamble to the Constitution of the World Health Organization as adopted by the International Health Conference, New York, June 19-22, 1946; signed on July 22, 1946 by the representatives of 61 States (Official Records of the World Health Organization, no. 2, p. 100) and entered into force on April 7, 1948. “Constitution of the World Health Organization, 1948.” In Basic Documents, 15th ed. Geneva, Switzerland: WHO; 1948. 35. Hambleton IR, Clarke K, Broome HL, Fraser HS, Brathwaite F, Hennis AJ. Historical and current Correlates of self-reported health status among elderly persons in Barbados. Revista Panamericana de Salud Pứblica 2005;17: 342-352. 36. Grossman M. The demand for health - a theoretical and empirical investigation. New York: National Bureau of Economic Research;1972. 37. Smith JP, Kington R. Demographic and Economic Correlates of Health in Old Age. Demography 1997;34:159-70. 38. Bourne PA. Good health status of older and oldest elderly in Jamaica: Are there differences between rural and urban areas? The Open Geriatric Medicine J 2009;2:18-27. 39. Bourne PA, McGrowder DA, Crawford TV. Decomposing mortality rates and examining health status of the elderly in Jamaica. The Open Geriatric Medicine J 2009;2: 34-44. 40. Marmot M. The influence of income on health: Views of an Epidemiologist. Does money really matter? Or is it a marker for something else? Health Affairs 2002;21:31-46. 41. Bourne PA, McGrowder DA, Holder-Nevins D. Public health behaviour-change intervention model for Jamaicans: Charting the Way forward in Public Health. Asian J of Medical Sciences (in print).
42. PIOJ, STATIN. Jamaica Survey of Living Conditions, 2000. Kingston: PIOJ & STATIN; 2001.  43. PIOJ, STATIN. Jamaica Survey of Living Conditions, 2004. Kingston: PIOJ & STATIN;2005. 

44. Eldemire D. A situational analysis of the Jamaican elderly, 1992. Kingston: Planning Institute of Jamaica;1995. 45. Bourne PA, McGrowder DA. Rural health in Jamaica: examining and refining the
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predictive correlates of good health status of rural residents. J of Rural and Remote Health 2009;9 : 1116. 46. Bourne PA. Medical Sociology: Modelling Well-being for elderly People in Jamaica. West Indian Med J 2008;57: 596-04. 47. Bourne PA. Health Correlates: Using Secondary Data to Model Correlates of Wellbeing of Jamaicans. West Indian Med J 2008;57: 476-81. 48. Koo J, Rie J, Park K. Age and gender differences in affect and subjective wellbeing. Geriatrics and Gerontology Int 2004;4:S268-S270. 49. Smith KR, Waitzman NJ. Double jeopardy: Interaction effects of martial and poverty status on the risk of mortality. Demography 1994;31,487-507. 50. Ross CE, Mirowsky J, Goldsteen K. The impact of the family on health. J of Marriage and the Family 1990;52: 1059-1078. 51. Gore WR. Sex, marital status, and mortality. Am J of Sociology 1973;79:45-67.

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Table 3.1: Socio-demographic characteristics of sample Area of residence Variable Urban Semi-urban n (%) n (%) Sex Male 662 (47.4) 544 (49.9) Female 735 (52.6) 547 (50.1) Social class Poor 370 (26.5) 381 (34.9) Middle 294 (21.0) 245 (22.5) Upper 733 (52.5) 465 (42.6) Age group Children 418 (29.9) 334 (30.6) Young adults 411 (29.4) 306 928.0) Other aged adults 416 (29.8) 344 (31.5) Young old 93 (6.7) 72 (6.6) Old-old 48 (3.4) 27 (2.5) Oldest-old 11 (0.8) 8 (0.7) Health status Good 1137 (81.7) 956 (87.6) Fair 201 (14.4) 99 (9.1) Poor 54 (3.9) 36 (3.3) Education No formal 841 (60.4) 687 (63.1) Basic 174 (12.5) 118 (10.8) Primary/preparatory 168 (12.1) 158 (14.5) Secondary/High 166 (11.9) 111 (10.2) Tertiary 43 (3.1) 14 (1.3) Marital status Married 177 (18.3) 132 (17.5) Never married 721 (74.5) 562 (74.6) Divorced 18 (1.9) 17 (2.3) Separated 5 (0.5) 8 (1.1) Widowed 47 (4.9) 34 (4.5) Self-reported illness Yes 176 (12.7) 128 (11.8) No 1215 (87.30 958 (88.2) Medical care-seeking behaviour Yes 120 (66.3) 78 (59.5) No 61 (33.7) 53 (40.5) 1.4 days (SD 1.4 days Number of visits to medical = 0.7) (SD= 1.3) facilities

P Rural n (%) 0.284 1354 (49.9) 1361 (50.1) < 0.001 1594 (58.7) 546 (20.1) 575 (21.2) 0.002 961 (35.4) 646 (23.8) 803 (29.6) 199 (7.3) 82 (3.0) 24 (0.9) < 0.001 2202 (81.6) 329 (12.2) 169 (6.3) < 0.001 1599 (59.1) 362 (13.4) 429 (15.8) 300 (11.1) 17 (0.6) 0.012 382 (21.9) 1245 (71.4) 15 (0.9) 20 (1.1) 82 (4.7) 0.001 432 (15.9) 2280 (84.1) 0.375 270 (60.9) 173 (39.1) 1.4 days (SD = 1.0) 0.846

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Table 3.2: Sociodemographic characteristic by Sex Variable Male Self-reported illness Yes No Diagnosed Self-reported illness Cold Diarrhoea Asthma Diabetes mellitus Hypertension Arthritis Other (unspecified) No Medical care-seeking behaviour Yes No Purchase medication Prescribed medicine Partial prescription Prescribed/over the counter Over counter Prescribed/did not buy None prescribed required Number of visits to medical facilities Mean (SD)

Sex Female 298 (11.7) 2256 (88.3) 56 (18.1) 11 (3.6) 35 (11.3) 19 (6.1) 39 (12.6) 20 (6.5) 85 (27.5) 44 (14.2) 182 (58.5) 129 (41.5) 170 (56.9) 3 (1.0) 9 (3.0) 20 (6.7) 9 (3.0) 88 (29.4) 1.3 days (0.7) 438 (16.6) 2197 (83.4)

P < 0.001 < 0.001 69 (15.6) 15 (3.4) 39 (8.8) 50 (11.3) 109 (24.6) 21 (4.7) 105 (23.7) 35 (7.9) 0.101 286 (64.4) 158 (35.6) 0.251 259 (60.1) 13 (3.0) 15 (3.5) 25 (5.8) 17 (3.9) 102 (23.7) 1.4 days (1.1)

0.252

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Table 3.3. Health status by Self-reported dysfunction Self-reported Dysfunction Health Status Good Fair Poor Total
χ2 (df = 2) =989.552, P < 0.001

No ailment n (%) 3964 (89.4) 372 (8.4) 100 (2.3) 4436

At least one ailment n (%) 320 (43.7) 255 (34.8) 158 (21.6) 733

Total n (%) 4284 (82.9) 627 (12.1) 258 (5.0) 5169

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Table 3.4. Jamaicans

Ordinary Logistic Regression: Correlates of Good Health Status of Uninsured
Wald statistic 191.667 11.692 323.527 0.314 6.107 1.154 1.277 0.000 4.525 0.870 4.593 14.872 0.741 0.040 3.003 Odds ratio 0.95 1.00 0.11 1.09 0.56 1.00 1.15 0.81 1.00

Variable Age Logged consumption per capita Self reported illness Middle class Upper class †Lower class Married Divorced/separated/widowed †Never married Primary schooling Secondary and above †No formal education Urban area Other town †Rural area Man Household head Cost of public medical care Cost of private medical care

Coefficient -0.049 0.000 -2.168 0.086 -0.575 0.138 -0.217 19.089 -0.475 -0.115 0.301 0.406 0.097 0.000 0.000

Std Error 0.004 0.000 0.121 0.154 0.233 0.129 0.192 40192.970 0.223 0.124 0.140 0.105 0.113 0.000 0.000

95.0% C.I. 0.95 -0.96*** 1.00 - 1.00** 0.09 -0.15*** 0.81 - 1.47 0.36 - 0.89* 0.89 -1.48 0.55 - 1.17 0.00 -0.00 0.40 - 0.96* 0.70 -1.14 1.03 -1.78* 1.22 -1.85*** 0.88 -1.37 1.00 - 1.00 1.00 -1.00

0.62 1.00 0.89 1.35 1.00 1.50 1.10 1.00 1.00

χ2 (df = 15) =993.114, P < 0.001 -2 Log likelihood = 2554.359 Nagelkerke R2 =0.390 Hosmer and Lemeshow goodness of fit χ2=11.159, P = 0.693 Overall correct classification = 84.6% Correct classification of cases of good health status = 94.9% Correct classification of cases of poor health status = 46.6% †Reference group *P < 0.05, **P < 0.01, ***P < 0.001

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Table 3.5. Ordinary Logistic Regression: Correlates of Medical Care-Seeking Behaviour of Uninsured Jamaicans
Variable Man Age Middle class Upper class †Lower Poor health Urban area Other town †Rural Crowding Per capita consumption Secondary and above †No formal education Married Divorced, separated/widowed †Never married Head household Coefficient -0.282 0.019 0.544 0.683 0.834 0.070 -0.243 0.111 0.000 0.431 -0.659 -0.453 -0.210 Std. Error 0.205 0.007 0.284 0.427 0.208 0.248 0.260 0.067 0.000 0.571 0.237 0.332 0.218 Wald statistic 1.894 8.213 3.675 2.558 16.139 0.079 0.877 2.749 0.017 0.569 7.720 1.864 0.933 Odds ratio 0.76 1.02 1.72 1.98 1.00 2.30 1.07 0.78 1.00 1.12 1.00 1.54 1.00 0.52 0.62 1.00 0.81 95% C.I. 0.51 - 1.13 1.01 - 1.03** 0.99 - 3.00 0.86 - 4.57 1.53 - 3.46*** 0.66 - 1.75 0.47 - 1.31 0.98 - 1.27 1.00 - 1.00 0.50 - 4.71 0.33 -0 .82** 0.33 - 1.22 0.53 - 1.24

χ2 (df = 14) = 47.79, P < 0.001 -2 Log likelihood = 648.32 Nagelkerke R2 =0.117 Hosmer and Lemeshow goodness of fit χ2=4.480, P = 0.811 Overall correct classification = 67.5% Correct classification of cases of medical care-seeking behaviour = 88.1% Correct classification of cases of no medical care-seeking behaviour = 30.0% †Reference group *P < 0.05, **P < 0.01, ***P < 0.001

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Chapter 4
Variations in health, illness and health care-seeking behaviour of those in the upper social hierarchies in a Caribbean society

Paul Andrew Bourne Little research exists in the Caribbean, and in particular Jamaica, on the upper class, and no study emerged from a search of the literature examining health, illness, and health care-seeking behaviour of this group. To provide pertinent information on the upper class in regards to their general health status, illnesses, typology of illnesses, health care seeking behaviours and factors which determine their (1) moderate-to-very good health status, (2) illness, and (3) health care seeking behaviour in order to make available to policy specialists and public health practitioners information on this group, to be used as a guide in their decision making policies. A sample of 2,734 respondents from the wealthiest 20% and second wealthy social hierarchies was extracted from a cross-sectional survey of 6,783 respondents. An administered questionnaire was used to collect the data, which were stored and analyzed using SPSS for Windows 16.0 (SPSS Inc; Chicago, IL, USA). The questionnaire was modelled on the World Bank’s Living Standards Measurement Study (LSMS) household survey. The majority of the sample stated at least good health status (83.3%), with 0.5% indicating very poor health status, and 15.3% who indicated an illness in the last 4-week period. Four variables emerged as statistically correlated with moderate-to-very good health status of those in the upper class (i.e. second wealthy and wealthiest 20%) - Model fit χ2 = 57.54, P < 0.0001. The model explained 33.2% of the variance in moderate-to-very good health status, and that the model is a good fit for the data. Three variables emerged as statistically correlated with self-reported illness - Model fit χ2 = 1087.7, P < 0.0001. The significant variables (i.e. health care-seeking behaviour, good health status, and marital status) accounted for 72.4% of the variability in self-reported illness. Three variables emerged as statistically significant correlates of health care-seekers - Model fit χ2 = 995.45, P < 0.0001. The statistically significant correlates (i.e. good health status, self-reported illness, marital status) accounted for 76.4% of the variance in health care-seeking behaviour of the upper class. Rural residents continue to have lower moderate-to-very good health status when compared to the general population, and the second wealthy and the wealthiest 20% in Jamaica. Although only 4 percent of the upper social hierarchy utilizes the public health care system, there is still a demand for public health services for this group, and it must be taken into account as a part of the general planning for the health care system of the country.

Introduction
Studies have long established health disparities between the poor and the wealthy classes, and this is no different in Latin America and the Caribbean [1-17]. According to the World Health
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Organization [7], 80% of chronic illnesses were in low and middle income countries, which illustrate the dichotomy between illness and material deprivation. The dichotomy between illness and poverty is not only limited to low-to-middle income nations, as a study in the Netherlands found that those who were chronically ill were more likely to be poor [15], and this was also found in other European nations [16,17]. The association between insufficient money and health is not limited to illness, but the WHO [7] opined that 60% of global mortality is caused by chronic illness, which raised another issue, the relationship between poverty and premature mortality. Marmot [8] postulated that money makes a difference in health, infant mortality and general morality. The association between income and health expands beyond the direct relationship between income and access to good physical and social milieu, good nutrition and access to high quality health care services, to the indirect association between income and health through access to education, employment, material resources and occupational class. Clearly there are inequalities in health between those in the upper class and those in the lower class [18, 19], but limited studies existed on the wealthy and the wealthiest 20% in nations. In keeping with public health aims, many studies have been carried out on the poor; poverty and illness; poverty and productivity; chronic illness, capabilities and poverty, but what about the second wealthy and the wealthiest 20% in regard to their health, illness, health care-seeking behaviour and factors which influence health, illness and health care-seeking behaviour? Public health is about improvements in the health conditions of all members of a society and not just a particular group. Embedded in the mandate of public health is the access to information which will guide policy formulation, intervention and health education programmes,
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and so information is equally needed on the affluent groups. Limited information, if any, exists in the Caribbean on the health of the second wealthy and wealthiest 20% classes. While general statistics indicate that the upper class has a greater health status and more access to material resources than the poor class, the former group constitutes a percentage of the population and must be studied like the poor class. The current study revealed that the prevalence rate of the upper class utilizing public health care facilities (i.e. hospitals and health centres) was 4%, suggesting that this group must be planned for, as they utilize and demand public health care resources like other social classes. Concurringly, this research showed that 3% of those in the wealthy social class had chronic illnesses, and that 1% had diabetes mellitus, which denotes that public health must make available resources for this group. Within the context that the upper social class utilizes public health care resources, it is surprising that no studies exist in Jamaica that have examined health, illness, and the health care seeking-behaviour of this social group. The current study aims to provide pertinent information on the upper class in regards to their general health status, illness, typology of illness, health care seeking behaviours and factors which determine their (1) moderate-to-very good health status, (2) illness, and (3) health care seeking behaviour, in order to make available to policy specialists and public health practitioners information on this group, which will serve as a guide for their decision-making policies.

Methods and materials
Sample A sample of 2,734 respondents from the wealthiest 20% and second wealthy social hierarchy was extracted from a cross-sectional survey of 6,783 respondents: 50.5% in the wealthiest 20% and 49.5% in the second wealthy group. The survey was carried out jointly by the Planning
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Institute of Jamaica and the Statistical Institute of Jamaica [20]. The method of selection of the sample from each survey was based solely on rural residence. The survey (Jamaica Survey of Living Conditions) was begun in 1989, collecting data from Jamaicans in order to assess government policies. Each year since 1989, the JSLC has added a new module in order to examine that phenomenon which is critical within the nation. In 2002, the foci were on 1) social safety net and 2) crime and victimization; while for 2007, there was no focus. The current sample was extracted from the 2007 dataset. The survey was drawn using stratified random sampling. This design was a two-stage stratified random sampling design where there was a Primary Sampling Unit (PSU) and a selection of dwellings from the primary units. The PSU is an Enumeration District (ED), which is composed of a minimum of 100 residences in rural areas and 150 in urban areas. An ED is an independent geographical unit that shares a common boundary. This means that the country was grouped into strata of equal size based on dwellings (EDs). Based on the PSUs, a listing of all the dwellings was made, and this became the sampling frame from which a Master Sample of dwellings was compiled, which in turn provided the sampling frame for the labour force. One third of the Labour Force Survey (i.e., LFS) was selected for the JSLC [20]. The sample was weighted to reflect the general population of the nation. The JSLC 2007 [20] was conducted in May and August of that year. An administered questionnaire was used to collect the data, which were stored and analyzed using SPSS for Windows 16.0 (SPSS Inc; Chicago, IL, USA). The questionnaire was modelled on the World Bank’s Living Standards Measurement Study (LSMS) household survey. There are some modifications to the LSMS, as the JSLC is more focused on policy impacts. The questionnaire covered areas such as socio-demographic variables, for example education, daily expenses (for
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the past 7-day period), food and other consumption expenditures, inventory of durable goods, health variables, crime and victimization, social safety net, and anthropometry. The questionnaire contains standardized items such as socio-demographic variables, excluding crime and victimization, which were added in 2002 and later removed from the instrument, with the exception of a few new modules each year. The non-response rate for the survey for 2007 was 27.7%. The non-response includes refusals and cases rejected in data cleaning. Measures Self-rated health status: is measured using people’s self-rating of their overall health status [21], which ranges from excellent to poor. The question that was asked in the survey was “How is your health in general?” And the options were very good; good; fair; poor and very poor. For the purpose of the model in this study, self-rated health was coded as a binary variable (1= good, 0 = Otherwise) [21-28]. The binary good health status was used as the dependent variable. Self-reported illness (or self-reported dysfunction): The question was asked: “Is this a diagnosed recurring illness?” The answering options are: Yes, Influenza; Yes, Diarrhoea; Yes, Respiratory diseases; Yes, Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No. A binary variable was later created from this construct (1=no 0=otherwise) in order to be applied in the logistic regression. Age is a continuous variable which is the number of years alive since birth (using last birthday). Age groups were classified as children, young adults, other adults, young-old (or young-elderly), old-old, and oldest-old: children – 0 to 14 years; young adults – 15 to 30 years; other adults – 31 to 59 years; young-old – 60 to 74 years; old-old - 75 – 84 years and oldest-old – 85+ years.

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Medical care-seeking behaviour was taken from the question ‘Has a health care practitioner or pharmacist been visited in the last 4 weeks?’ with there being two options: Yes or No. Medical care-seeking behaviour therefore was coded as a binary measure where 1= Yes and 0 = otherwise. Crowding is the total number of individuals in the household divided by the number of rooms (excluding kitchen, verandah and bathroom). Sex: This is a binary variable where 1= male and 0 = otherwise. Social supports (or networks) denote different social networks with which the individual is involved (1 = membership of and/or visits to civic organizations, or having friends who visit one’s home or with whom one is able to network, 0 = otherwise). Statistical Analysis Descriptive statistics such as mean, standard deviation (SD), frequency and percentage were used to analyze the socio-demographic characteristics of the sample. Chi-square was used to examine the association between non-metric variables, and t-test and an Analysis of Variance (ANOVA) were used to test the relationships between metric and/or dichotomous and non-dichotomous categorical variables. Box-plots were used to examine what was happening among age, selfreported illness, and social hierarchy as well as age, typology of illness and social hierarchy (i.e. poorest 20% and wealthiest 20%). Multiple logistic regression techniques were conducted to identify parameters and their estimates. Stepwise logistic regression technique was used to determine the contribution of each significant determinant to the model. A p-value less than 0.05 (two-tailed) was selected to indicate statistical significance (i.e. 95% confidence interval).
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Results
Table 4.1 presents information on the socio-demographic characteristics of the sample. One percent of the sample reported an injury. Of those who reported an injury, 67.9% stipulated the injury experienced in the last 4weeks. Domestic accidents and incidents accounted for 47.3% of the injuries experienced. Fifteen percent of the sample indicated an illness in the last 4 weeks. Of those who reported an illness, 89.1% stipulated the typology of the health condition. When the respondents were asked if they had purchased the prescribed medication, 67.7% said yes. Of those who did not purchase the medication, 9.5% claimed they were unable to afford it; 39.7% said they were not ill enough; 27.6% remarked that they used a home remedy; 5.2% indicated that they did not have the time and 18.1% stated other. Seventy-one percent of the sample sought medical care in the last 4weeks, 32.5% had health insurance coverage (i.e. 23.7% private). The majority of the sample stated at least good health status (83.3%), with 0.5% indicating very poor health status. Of the sample, only 10.6% indicated where the medical visit took place in the last 4weeks. Of those who responded (n=288), 27.4% indicated a public hospital, 61.8% said a private health care centre and 12.5% remarked that it was a public health care centre. Twentynine percent of those who responded to typology of medical facility used in the last 4weeks had chronic conditions and attended a public facility. The prevalence rate of the upper class utilizing public health care facilities (i.e. hospitals and health centres) was 4% (3% had a chronic illness; of the 3%, 1% had diabetes mellitus). There was no significant statistical association between marital status and social hierarchy (i.e. second wealthy or wealthiest 20%) – χ2 = 8.518, P = 0.744.
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Table 4.2 shows information on particular variables and social hierarchy. A significant statistical relationship existed between area of residence and social hierarchy. Those in the wealthiest 20% were more likely to be urban dwellers (48.6%) than those in the second wealthy social group (36.9%) - χ2 = 57.002, P < 0.0001. Rural dwellers were more likely to be wealthy (59.1%) compared to semi-urban residents (50.1%) and urban respondents (42.1%). Concurringly, urban settlers were more likely to be in the wealthiest 20% (57.9%) compared to semi-urban (49.9%) and rural respondents (40.9%) – P < 0.0001. There was a significant statistical association between educational level and social hierarchy (χ2 = 30.53, P < 0.0001). Those in the wealthiest 20% were more likely to be educated at the tertiary level (5.3%), as compared to those in the second wealthy social group (1.9%). Likewise there was a statistical relationship between health insurance coverage and social hierarchy (χ2 = 113.27, P < 0.0001). Forty-two percent of those in the wealthiest 20% had health insurance coverage compared to 22.6% of those in the second wealthy social group. There were significant statistical differences between those in the wealthy and the wealthiest 20% (1) age ( t = - 4.745, P < 0.001) – mean age of the wealthy 30.14 ± 21.1, and the wealthiest 20% 33.9 ± 20.4; (2) crowding (t = 15.991, P < 0.0001 – mean household crowding for those in the wealthy group was 4.2 ± 2.2 compared to 3.0 ± 1.6 for those in the wealthiest 20%, and (3) total expenditure (t = - 16.219, P < 0.0001) – mean total expenditure for those in the wealthy group was USD 9,713.00 ± USD 5,327.88 and those in the wealthiest 20% was USD 14,915.29 ± USD 10,550.99. Furthermore, there was a significant statistical difference between mean duration of illness of those in the second wealthy social group (23.8 days ± 96) and those
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in the wealthiest 20% (9.9 days ± 18.7) – t = 1.985, P = 0.048; but none between duration of marriage and social hierarchy (wealthy, 16.7 years ± 14.6; wealthiest 20%, 17.3 ± 13.6) – t = 0.593, P = 0.553. Multivariate analyses

Table 4.3 shows information on particular variables that are correlated (or not) with self-reported moderate-to-very good health status of the sample. Four variables emerged as statistically correlated with moderate-to-very good health status of those in the upper class (i.e. second wealthy and wealthiest 20%) - Model fit χ2 = 57.54, P < 0.0001. The model explained 33.2% of the variance in moderate-to-very good health status, and the model is a good fit for the data (Hosmer and Lemeshow goodness of fit χ2 = 2.87, P = 0.94, -2LL = 194.22). Eighty-one percent of the data were correctly classified: 94.9% of those who had indicated moderate-to-very good health status and 33.3% of those that were classified into poor and very poor health status. Table 4.4 presents information on variables that either correlated or did not correlate with self-reported illness of the sample. Three variables emerged as statistically correlated with selfreported illness - Model fit χ2 = 1087.7, P < 0.0001. The significant variables (i.e. health careseeking behaviour, good health status, and marital status) accounted for 72.4% of the variability in self-reported illness. The model is a good fit for the data (Hosmer and Lemeshow goodness of fit χ2 = 8.11, P = 0.42, -2LL = 649.69). Ninety-five percent of the data were correctly classified: 72.2% of those who were classified as having an illness and 99.6% of those who did not report an illness. Table 4.5 displays variables that seek to explain the variability in self-reported health care-seeking behaviour of the sample. Three variables emerged as statistically significant
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correlates of health care-seekers - Model fit χ2 = 995.45, P < 0.0001. The statistically significant correlates (i.e. good health status, self-reported illness, marital status) accounted for 76.4% of the variance in health care-seeking behaviour of the upper class. The model was a good fit for the data - Hosmer and Lemeshow goodness of fit χ2 = 3.64, P = 0.90. Ninety-five percent of the data were correctly classified: 96.2% of those who had selected seeking medical care in the last 4 weeks and 95.3% of those who did not seek medical care.

Discussion
The present work revealed that 88 out of every 100 respondents in the upper class in Jamaica indicated that their health status was at least good, with only 5 in every 1,000 experiencing very poor health statuses. One in every 100 had an injury and 15 per 100 had an illness in the last 4week period. The prevalence rate of self-reported diagnosed acute health conditions was 36 per 1,000 and 96 per 1,000 for chronic conditions. Twenty-four per 1,000 had diabetes mellitus; 28 out of every 1,000 had hypertension and 7 per 1,000 reported having been diagnosed with arthritis. Seventy-one percent sought medical care; there was no significant statistical association between (1) self-reported injury and being second wealthy or in the wealthiest 20% as well as (2) between self-reported illness and social hierarchy (i.e. second wealthy or wealthiest 20%). The mean length of time experiencing the current illness (in days) was greater for those in the second wealthy class, as compared to those in the wealthiest 20%. Although only 1% of the sample reported an injury in the study, 47.3% of the injuries were owing to domestic accidents and domestic incidents, and 21.1% were due to motor vehicle accidents. Four percent of the sample utilized public health care facilities for their last medical visit, and 11.8% of the sample were elderly (ages 60 years and beyond), 24.6% children (ages less than 15 years); 49.6% of those in the wealthiest 20% dwelled in urban areas compared to 36.9% of those in the second wealthy
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social group. Those in the wealthiest 20%, according to average total expenditure, were 1.5 times more than those in the second wealthy class and they were 2.9 times more educated at the tertiary level. Concurringly, rural upper class respondents had the lowest moderate-to-very good health status; those with good health status were 48% less likely to seek medical care; those with illnesses were 449 times more likely to seek medical care, and married upper class respondents were 45% less likely to seek health care, while married wealthy residents were 2.3 times more likely to report an illness. Marmot [8] asked the question “Does money matter for health? If so, why?” and opined that it does in terms of access to good nutrition, material resources, lower infant mortality, health care choices, and a good physical environment compared to those in the lower socioeconomic group. Clearly there are differences in health outcomes between the social hierarchies [1-17], but does money matter for health between the second wealthy and the wealthiest 20%? The current study found that money does not matter for health between the wealthy and the wealthiest 20%. Money does not matter for the general health status of the wealthy and the wealthiest 20%, but also for self-reported injuries and illnesses (i.e. both acute and chronic conditions). Embedded in this finding is the reality that there is a basic amount of money necessary, and any more than that will not improve the health of the individual. This work showed that those in the wealthiest 20% on average spent almost 2 times more than those in the second wealthy class, and are about 3 times more educated at the tertiary level, but this does not produce additional improvements in health for the wealthiest 20%. The present paper found that a large health disparity occurred between upper class respondents and geographic area of residents, which concurs with the findings of Vila et al.’s work. Vila et al.’s research [9] used self-reported health status (i.e. fair-to-poor health status) and
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found that lower socioeconomic class residents of Milwakee had the greatest fair-to-poor health status with those in the upper class indicated the least fair-to-poor health status. Concurringly, they also found that upper socioeconomic group had the greatest health in the city, which was different in this research. In this study, upper socioeconomic group who resided in semi-urban areas were the healthiest, and had lower total annual expenditure than those upper class respondents who lived in urban areas. The huge health disparity was found between the upper class rural and semi-urban dwellers, suggesting that lifestyle practices in semi-urban geographic areas was greatest and was remarkably different from that of upper class rural respondents. However, the health disparity is among those who dwell in particular geographical areas, and those who have health insurance coverage, and not between the wealthy and the wealthiest 20%. Rural upper class Jamaicans had the least moderate-to-very good health status. This health disparity is substantial as upper class semi-urban residents were 4.8 times more likely to report moderate-to-very good health status, and those who dwelled in urban areas were 4.3 times more likely to report moderate-to-very good health status compared to those in the rural areas. Such inequality in health emphasized that the lifestyle of rural residents is such that money does not equate their health status with those of their other wealthy urban and semi-urban peers. This is embedded in the present work as there is no significant statistical correlation between selfreported illness and area of residence, or area of residence and health care seeking behaviour of the upper class. It follows that it is not money and illness that separate the rural from the other affluent respondents, but this must be therefore embedded in the cultural differences between people. Another finding which emerged from the current research is the fact that married upper class respondents reported more illness than those who were never married, yet the former group sought less medical attention than the latter group. Although married upper class respondents
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reported more illness, there was no statistical correlation between marital status and moderate-tovery good health status. A plethora of studies have examined the health status of married and non-married respondents and the verdict is that the former group’s health status is greater [2935], which means that money removes this health disparity. According to Moore et al. [35], people who reside with a spouse have a different base of support which aids in better health choices and justifies greater health status, as against those without social support from a marital union. This was also found in earlier studies by Smith and Waitzman [31] and Lillard and Panis [34]. Cohen and Wills [36] found that perceived support from one’s spouse increased well-being, while Ganster et al. [37] reported that support from supervisors, family members and friends was related to low health complaints. Another study found that being married was a ‘good’ cause for an increase in psychological and subjective well-being in old age [38]. Smith and Waitzman [31] offered the explanation that wives were likely to dissuade their husbands from particular risky behaviours such as the use of alcohol and drugs, and would ensure that they maintained a strict medical regimen coupled with proper eating habits. On the contrary, this paper revealed that married affluent Jamaicans were more likely to report illness, as compared to never-married wealthy respondents, but that this does not translate into better health status for one group over the other. Using the relationship of the absence of illness to health of the wealthy-to-wealthiest 20% of Jamaicans, this should denote that the wealthiest should be healthier than the second wealthy. Clearly, there is a cognitive disparity between the image of health and illness. Illness is well established to be a narrow approach to the conceptualization of health [39-46], and this is what emerged as the case for the upper class. According to the WHO [39], health is social, psychological and physical wellbeing and not merely the absence of illness. Clearly upper class
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respondents subscribe to this conceptualization as experiencing illness was correlated with low moderate-to-very good health status, but illness was not a factor which determines the moderateto-very good health status of those in the upper class. Ferrer and Palmer’s work [14] revealed marginal health variabilities between those people in the second wealthy and the wealthiest 20%, and using self-reported to measure health status, this study found no statistical association between self-reported health and the two social hierarchies. The present work goes further than Ferrer and Palmer’s research that used health status and investigated general illness and particular health conditions and those in the second wealthy and the wealthiest 20%. Ferrer and Palmer’s research did not examine illness or particular typology of illness. Statistics revealed that 15.5% of Jamaicans reported an illness in the last 4weeks in 2007 [47] compared to 15.3% of those in the upper class. Seemingly there is no difference between self-reported illness in the population and those in the upper class, but further examination of the diagnosed health conditions revealed some differences between the population and the subpopulation. For the population, the prevalence rates for people with asthma were 87 per 1,000; diabetes mellitus, 120 per 1,000; hypertension, 224 per 1,000 and arthritis, 88 per 1,000 [47] compared to those in the upper class, being asthma, 12 per 1,000; diabetes mellitus, 24 per 1,000; hypertension, 28 per 1,000 and arthritis, 7 per 1,000. The findings of this study highlight that those in the affluent social hierarchy have a lower prevalence of chronic illness than people in the general population of Jamaica, which concurs with the literature that those in the lower socioeconomic group were more likely to experience more chronic illness than the affluent. Although those in the wealthy-to-wealthiest 20% group in Jamaica had a lower prevalence of chronic health conditions compared to the general population, they had a prevalence rate of 37 per 1,000 for other health conditions.
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The other conditions constitute ailments such as prostate and breast cancers, ischemic heart disease, malignant neoplasm of the trachea, bronchus and other heart diseases. Statistics on the mortality of males 5 years and older revealed that cerebro-vascular diseases, diabetes mellitus, ischemic heart diseases, malignant neoplasm of the prostate, hypertensive disease, chronic lower respiratory infections, other heart diseases and malignant neoplasm of the trachea and HIV were among the 10 leading causes of death [48]. For females 5 years and older it was about the same as the 10 leading causes of death for males, except for malignant neoplasm of the prostate and malignant neoplasm of the trachea, these being replaced by malignant neoplasm of the breast and pneumonia. Although the upper class clearly has lower prevalence rates of particular chronic illnesses, compared to the general population, and more than those in the poorest 20% [47], diabetes mellitus, hypertension and other health conditions are high among them and may explain the levels of mortality among those therein. Chronic illnesses are linked to lifestyle causes, and though they have lower rates of chronic illness than people in the lower socioeconomic group, the reality among the upper class is that their lifestyle explains their particular morbidity and mortality. A study by Wilks et al. [49] found that 64.3% of Jamaicans were currently using alcohol (i.e. liquor, wine, beer or stout, and mixed alcoholic coolers), 13.5% used marijuana, 14.5% smoked cigarettes, and the rates were even greater for males than females. Concurringly, 71% of those in the upper class consumed alcohol (i.e. 84.3% of males and 48.7% of females); 9.8% smoked cigarettes (i.e. 12.4% of males and 6.7% of females); 10.4% smoked marijuana (i.e. 16.9% of males and 2.2% of females) and 10.5% used illegal drugs (17.1% of males and 2.7% of females) [49]. Furthermore, the percentage of upper class males who consumed alcohol was more than for those males in the lower (76.1%) and the middle
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class (79.4%) [49]. Unhealthy lifestyle practices are therefore responsible for the composition of illnesses which are experienced by the upper class and account for many of their ailments. Furthermore, it is clear from the findings that among the upper socioeconomic class there are no vulnerable groups, but what is equally evident is that socioeconomic status accounted for a major role in determining the health status of upper class Jamaica as was found for all socioeconomic classess in Blanc et al.’s work [11].

Conclusion
While poverty is associated with illness and illness is more related to poverty and lower health status for the poor than for those in the upper class, the same is not true of the relationship between the wealthy and the wealthiest 20% in Jamaica. It follows that money and wealth, beyond a certain amount, does not add any further improvements to good health status. Income and wealth beyond that which is accessible to the second wealthy in Jamaica do not provide those beyond that with any greater health status. However, what emerged from the current work is that the health disparity between the rural areas’ affluent people and others is vast, suggesting that there are some underlying cultural conditions which exist among the rich of different geographical areas, and which do not disappear because the individual is wealthy. Another pertinent finding is that the wealthy spent more days in illness compared to the wealthiest 20%, but this does not translate into lower moderate-to-very good health status. A part of the justification for this non-health disparity is owing to their conceptualization of health compared to the image of illness. There are affluent Jamaicans who utilize the public health care system, and many of them have diabetes mellitus. Within the context of the utilization of the public health care system by
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the wealthy, although the percentage is very small, the current finding are important to public health policy makers in understanding the service utilization of this group and their health, and illness profile. In summary, money and wealth beyond that which is accessible by the second wealthy in Jamaica will show no further disparity in moderate-to-very good health status. The paper highlighted the fact that health insurance coverage is not a good measure of health care-seeking behaviour and illness is not a good proxy for the health status of the upper class. However, the health disparity which existed for the general society among the different areas of residents is the same for the upper class. Rural residents continue to have lower moderate-to-very good health status than the general population, and the second wealthy and the wealthiest 20% in Jamaica. Although only 4 percent of the upper social hierarchy utilizes the public health care system, there is still a demand for public health services for this group, and it must be taken into account as a part of the general planning for the health care system of the country. Conflict of interest The author has no conflict to interest to report

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Table 4.1. Demographic characteristics of sample Characteristics Social hierarchy Second wealthy Wealthiest 20% Sex Male Female Area of residence Urban Semi-urban Rural Injury Yes No Self-reported typology of injury Motor vehicle accident Domestic accident Industrial accident Domestic incident Other (unspecified events) Self-reported illness Yes No Self-reported diagnosed illness Acute conditions Influenza Diarrhoea Respiratory Chronic condition Diabetes mellitus Hypertension Arthritis Other Educational level Primary or below Secondary Tertiary Length of time married median (inn years) Number of visits to medical practitioners in last 4-weeks mean (SD) Length of illness median (in days)

Frequency 1352 1382 1356 1378 1184 706 844 28 2622 4 7 5 2 1 405 2237 56 8 34 66 76 19 102 2311 241 95

% 49.5 50.5 49.6 50.4 43.3 25.8 30.9 1.1 98.9 21.1 36.8 26.3 10.5 5.3 15.3 84.7 15.5 2.2 9.4 18.3 21.1 5.3 28.3 87.3 9.1 3.6 12 (Range = 1, 71) 1.4 (1.1) 5 (Range = 0,200)

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Table 4.2. Particular variables by social hierarchy Area of residence Urban Semi-urban Rural Sex Male Female Self-reported diagnosed health condition Acute conditions Influenza Diarrhoea Asthma Chronic conditions Diabetes mellitus Hypertension Arthritis Other (unspecified) Health care-seeking behaviour Yes No Self-reported illness Yes No Self-reported health status Very good Good Fair Poor Social hierarchy Wealthy Wealthiest 20% n (%) n (%) 499 (36.9) 685 (49.6) 354 (26.2) 352 (25.5) 499 (36.9) 345 (25.0) 667 (49.3) 685 (50.7) 32 (17.9) 3 (1.7) 12 (6.7) 33 (18.4) 38 (21.2) 8 (4.5) 53 (29.0) 141 (68.4) 65 (31.6) 200 (15.3) 1105 (84.7) 567 (43.2) 536 (40.8) 157 (12.0) 42 (3.2) 689 (49.9) 693 (50.1) 24 (13.2) 5 (2.7) 22 (12.2) 33 (18.1) 38 (18.1) 11 (6.0) 49 (26.9) 155 (73.5) 56 (26.5) 205 (15.3) 1132 (84.7) 531 (40.0) 565 (42.5) 185 (13.9) 45 (3.4)
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P χ2 = 57.002, P < 0.0001

χ2 = 0.074, P = 0.407 χ2 = 5.190, P = 0.520

χ2 = 1.272, P = 0.154 χ2 = 0.000, P = 0.520 χ2 = 8.815, P = 0.066

Very poor

11 (0.8)

3 (0.2)

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Table 4.3. Logistic regression: Moderate-to-very good health status by particular variables Coefficient -0.051 -0.351 -19.926 -0.353 -0.383 Std. Error 0.013 0.387 13414.774 0.433 0.549 Wald 15.260 0.822 0.000 0.666 0.487 P 0.000 0.365 0.999 0.415 0.485 Odds ratio 0.95 0.70 0.00 0.70 0.68 1.00 0.997 0.000 1.474 1.584 0.408 0.000 0.439 0.511 5.976 4.712 11.258 9.622 0.015 0.030 0.001 0.002 0.940 0.650 2.71 1.00 4.37 4.88 1.00 1.03 1.00 1.22, 6.02 1.00, 1.00 1.85, 10.34 1.79, 13.26 0.46, 2.30 1.00, 1.00 95% CI 0.93, 0.98 0.33, 1.50 0.000, 0.30, 1.64 0.23, 2.00

Age Male Self-reported illness Married Divorced, separated or widowed †Never married Health insurance Medical expenditure Urban area Other town †Rural area

Head of household 0.031 0.410 0.006 Per capita consumption 0.000 0.000 0.206 2 Model fit χ = 57.54, P < 0.0001 Hosmer and Lemeshow goodness of fit χ2 = 2.87, P = 0.94 -2LL = 194.22 Nagelkerke R2 =0.332 †Reference group

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Table 4.4. Logistic regression: Self-reported illness by particular variables Std. Error 0.008 0.233 0.260 0.421 0.244 0.257 0.294 0.243 0.000 0.248 0.417 Wald statistic 2.769 3.188 9.960 0.113 1.132 1.832 0.551 2.810 0.595 56.921 212.549 Odds ratio 1.01 0.66 2.27 0.87 1.00 0.77 0.71 0.80 1.00 1.50 1.00 0.15 437.11 95.0% C.I. 1.0, 1.03 0.42, 1.04 1.37, 3.79 0.38, 1.98 0.48, 1.24 0.43, 1.17 0.45, 1.43 0.93, 2.42 1.00, 1.00 0.10, 0.25 193.02, 989.89

Variable Age Male Married Divorced, separated or wid †Never married Health insurance Urban area Other town †Rural area Head of household Per capita consumption Good health status Health care-seekers Model fit χ2 = 1087.7, P < 0.0001

Coefficient 0.013 -0.415 0.821 -0.141 -0.259 -0.347 -0.219 0.408 0.000 -1.872 6.080

P 0.096 0.074 0.002 0.737 0.287 0.176 0.458 0.094 0.440 0.000 0.000

Hosmer and Lemeshow goodness of fit χ2 = 8.11, P = 0.62 -2LL = 649.69 Nagelkerke R2 =0.724 †Reference group

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Table 4.5. Logistic regression: Self-reported health seeking behaviour by particular variable Coefficient Age Male Married Divorced, separated or wid † Never married Health insurance Urban area Other town †Rural area Head of household Per capita consumption Self-reported illness Good health status Model fit χ2 = 995.45, P < 0.0001 0.014 -0.109 -0.601 -0.291 0.463 0.134 -0.034 -0.069 0.000 6.108 -0.658 Std. Error 0.008 0.260 0.295 0.445 0.269 0.287 0.328 0.270 0.000 0.417 0.266 Wald statistic 3.080 0.175 4.151 0.429 2.954 0.218 0.011 0.066 0.042 214.598 6.147 P 0.079 0.676 0.042 0.513 0.086 0.640 0.918 0.797 0.837 0.000 0.013 Odds ratio 1.02 0.90 0.55 0.75 1.00 1.59 1.14 0.97 1.00 0.93 1.00 449.37 0.52 95.0% C.I. 1.00, 1.03 0.54, 1.49 0.31, 0.98 0.31, 1.79 0.94, 2.69 0.65, 2.01 0.51, 1.84 0.55, 1.58 1.00, 1.00 198.47, 1017.42 0.31, 0.87

Hosmer and Lemeshow goodness of fit χ2 = 3.64, P = 0.90 -2LL = 446.41 Nagelkerke R2 =0.764 †Reference group

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Chapter 5
Health of children less than 5 years old in an Upper Middle Income Country: Parents’ views

Paul Andrew Bourne

Health literature in the Caribbean, and in particular Jamaica, has continued to use objective indices such as mortality and morbidity to examine children’s health. The current study uses subjective indices such as parent-reported health conditions and health status to evaluate the health of children instead of traditional objective indices. The study seeks 1) to examine the health and health care-seeking behaviour of the sample from the parents’ viewpoints; and 2) to compute the mean age of the sample with a particular illness and describe whether there is an epidemiological shift in these conditions. Two nationally representative cross-sectional surveys were used for this study (2002 and 2007). The sample for the current study is 3,062 respondents aged less than 5 years. For 2002, the study extracted a sample of 2,448 under 5 year olds from the national survey of 25,018 respondents, and 614 under 5 year olds were extracted from the 2007 survey of 6,728 respondents. Parents-reported information were used to measure issues on children under 5 years old. In 2007, 43.4% of the sample had very good health status; 46.7% good health status; 2.5% poor health and 0.3% very poor health status. Almost 15% of children had illnesses in 2002, and 6% more had illnesses in 2007 over 2002. In 2002, the percentage of the sample with particular chronic illnesses was: diabetes mellitus (0.6%); hypertension (0.3%) and arthritis (0.3%). However, none was recorded in 2007. The mean age of children less than 5 years old with acute health conditions (i.e. diarrhoea, respiratory diseases and influenza) increased over 2002. In 2007, 43.4% of children less than 5 years old had very good health status; 46.7% good health status; 7.1% fair health status; 2.5% poor and 0.3% very poor health status. The association between health status and parent-reported illness was - χ2 (df = 4) = 57.494, P < 0.001 – with the relationship being a weak one, correlation coefficient = 0.297. A cross-tabulation between health status and parent-reported diagnosed illness found that a significant statistical correlation existed between the two variables - χ2 (df = 16) = 26.621, P < 0.05, cc = 0.422, - with the association being a moderate one, correlation coefficient = 0.422. A cross tabulation between health status and health care-seeking behaviour found a significant statistical association between the two variables - χ2 (df = 4) = 10.513, P < 0.033 - with the correlation being a weak one – correlation coefficient = 0.281.Rural children had the least health status. The health disparity that existed between rural and urban less than 5 year olds showed that this will not be removed simply because of the abolition of health care utilization fees.
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Introduction
In many contemporary nations, objective indices such as life expectancy, mortality and diagnosed morbidity are still being widely used to measure the health of people, a society and/or a nation [1-6]. The World Health Organisation (WHO) in the Preamble to its Constitution in the 1940s wrote that health is more important than disease, as it expands to the social, psychological and physical wellbeing of an individual [7]; and lately that during the 21st century the emphasis must be on healthy life expectancy [8,9]. In keeping with its opined emphasis, the WHO formulated a mathematical approach that diminished life expectancy by the length and severity of time spent in illness as the new thrust in measuring and examining health. Although healthy life expectancy removes time spent in illness and severity of dysfunctions, it fundamentally rests on mortality. The WHO therefore, instead of moving forward, has given some scholars, who are inclined to use objective indices in measuring health, a guilty feeling about continuing this practice. The Caribbean, and in particular Jamaica, continues to use mortality and morbidity to measure the health of children or infants [1-6]. The use of mortality, morbidity and life expectancy is the practice of Caribbean scholars, and is widely used in Jamaica by the: Ministry of Health (MOHJ) [10]; Statistical Institute of Jamaica (STATIN) [11]; Planning Institute of Jamaica (PIOJ) [12]; PIOJ and STATIN [13] as well as the Pan American Health Organization (PAHO) [14] in measuring health. In spite of the conceptual definition opined by the WHO in the Preamble to its Constitution in 1946, the health of children who are less than 5 years old in Jamaica is still measured primarily by using mortality and morbidity statistics. Recently a book entitled ‘Health Issues in the Caribbean’ [15] had a section on Child Health; however the articles were on 1) nutrition and child health development [16] and 2) school achievement and
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behaviour in Jamaican children [17], indicating the void in health literature regarding health conditions. An extensive review of health literature in the Caribbean region found no study that has used national survey data to examine the health status of children less than 5 years of age. The current study fills this gap in the literature by examining the health status of children less than 5 years of age using cross-sectional survey data which are based on the views of patients. The objectives of this study are 1) to examine the health and health care-seeking behaviour of the sample; and 2) to evaluate the mean age of the sample with a particular illness and to describe whether there is an epidemiological shift in these conditions.

Materials and methods
Sample The current study used two secondary nationally representative cross-sectional surveys (for 2002 and 2007) to carry out this work. The sub-samples are children less than 5 years old, and the only criterion for selection was being less than 5 years old. The sample in the current study is 3,062 respondents of ages less than 5 years. For 2002, a sub-sample of 2,448 less than-5 year olds was extracted from the national survey of 25,018 respondents in 2002, and information on 614 less than-5 year olds was extracted from the 2007 survey. The survey (Jamaica Survey of Living Conditions) began in 1989 to collect data from Jamaicans in order to assess government policies. Since 1989, the JSLC has added a new module each year in order to examine that phenomenon, which is critical within the nation [18, 19]. In 2002, the focus was on 1) social safety nets, and 2) crime and victimization, while for 2007, there was no focus. Methods
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Stratified random sampling technique was used to draw the sample for the JSLC. This design was a two-stage stratified random sampling design where there was a Primary Sampling Unit (PSU) and a selection of dwellings from the primary units. The PSU is an Enumeration District (ED), which comprises a minimum of 100 residences in rural areas and 150 in urban areas. An ED is an independent geographical unit that shares a common boundary. This means that the country was grouped into strata of equal size based on dwellings (EDs). Based on the PSUs, a listing of all the dwellings was made, and this became the sampling frame from which a Master Sample of dwellings was compiled, which in turn provided the sampling frame for the labour force. One third of the Labour Force Survey (i.e. LFS) was selected for the JSLC [18, 19]. The sample was weighted to reflect the population of the nation [18-20]. The JSLC 2007 was conducted in May and August of that year; while the JSLC 2002 was administered between July and October of that year. The researchers chose this survey based on the fact that it is the latest survey on the national population, and that that it has data on the selfreported health status of Jamaicans. An administered questionnaire was used to collect the data from parents on children less than 5 years old, and the data were stored, retrieved and analyzed using SPSS for Windows 16.0 (SPSS Inc; Chicago, IL, USA). The questionnaire was modelled on the World Bank’s Living Standards Measurement Study (LSMS) household survey. There are some modifications to the LSMS, as the JSLC is more focused on policy impacts. The questionnaire covered areas of socio-demographic variables – such as education; daily expenses (for the past 7 days); food and other consumption expenditures; inventory of durable goods; health variables; crime and victimization; social safety net and anthropometry. The non-response rates for the 2002 and 2007 surveys were 26.2% and 27.7% respectively. The non-response includes refusals and cases rejected in data cleaning.
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Measures Social class: This variable was measured based on the income quintiles: The upper classes were those in the wealthy quintiles (quintiles 4 and 5); the middle class was quintile 3 and the poor were the lower quintiles (quintiles 1 and 2). Age is a continuous variable in years. Health conditions (i.e. parent-reported illness or parent-reported dysfunction): The question was asked: “Is this a diagnosed recurring illness?” The answering options are: Yes, Cold; Yes, Diarrhoea; Yes, Asthma; Yes, Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No. Self-rated health status: “How is your health in general?” And the options were: Very Good; Good; Fair; Poor and Very Poor. Medical care-seeking behaviour was taken from the question ‘Has a health care practitioner, healer or pharmacist been visited in the last 4 weeks?’ with there being two options: Yes or No. Parent-reported illness status. The question is ‘Have you had any illness other than due to injury (for example a cold, diarrhoea, asthma, hypertension, diabetes or any other illness) in the past four weeks? Here the options were Yes or No. Statistical analysis Descriptive statistics, such as mean, standard deviation (SD), frequency and percentage were used to analyze the socio-demographic characteristics of the sample. Chi-square was used to examine the association between non-metric variables, and Analysis of Variance (ANOVA) was used to test the relationships between metric and non-dichotomous categorical variables, whereas an independent sample t-test was used to examine the statistical correlation between a metric
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variable and a dichotomous categorical variable. The level of significance used in this research was 5% (i.e. 95% confidence interval).

Results
Demographic characteristic of sample In 2002, the sex ratio was 98.8 males (less than 5 years old) to 100 females (less than 5 years old), which shifted to 116.2 less than-5 year old males to 100 less than-5 year old females. The sample over the 6 year period (2002 to 2007) revealed internal migrations to urban zones (Table 5.1): In 2002, 59.6% of respondents resided with their parents and/or guardians in rural areas, which declined to 5.07%. The percentage of children less than 5 years of age whose parents were in the poorest 20% fell to 25.4% in 2007 over 29.6% in 2002. In 2007 over 2002, 1.7 times less children less than 5 years of age were taken to public hospitals, compared to 1.2 times less taken to private hospitals (Table 5.1). Approximately 6% more children less than 5 years were ill in 2007 over 2002. Based on Table 5.1, less than-5 year olds with particular chronic illnesses had: diabetes mellitus (0.6%); hypertension (0.3%) and arthritis (0.3%). However, none was recorded in 2007. There were some occasions on which the response rates were less than 50%: In 2002, health care-seeking behaviour was 14.3%; parent-reported diagnosed health conditions, 14.2%; and visits to health care institutions, 8.9% (Table 5.1). For 2007, the response rate for health care-seeking behaviour was 20.2%; parent-reported diagnosed health conditions, 20.2%, and less than 11% for cost of medical care. Health conditions

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Based on Table 5.1, the percentage of less than-5 year olds with particular acute conditions saw a decline in colds and asthmatic cases, as well as chronic conditions. Figure 5.1 revealed that in 2007 the mean age of children less than 5 years old with acute health conditions (i.e. diarrhoea, respiratory diseases and influenza) increased over 2002. On the other hand, the mean age of those with unspecified illnesses declined from 1.76 years (SD = 1.36 years) to 1.64 years (SD = 1.36 years). Concomitantly, the greatest mean age of the sample was 2.71 years (SD = 1.21 years) for asthmatics in 2007 and 2.59 years (1.24 years) in 2002. It should be noted here that the mean age of a child less than 5 years of age in 2002 with diabetes mellitus was 1.50 years (2.12 years). Health status In 2002, the JSLC did not collect data on the general health status of Jamaicans, although this was done in 2007. Therefore, no figures were available for health status for 2002. In 2007, 43.4% of children less than 5 years old had very good health status; 46.7% good health status; 7.1% fair health status; 2.5% poor and 0.3% very poor health status. The response rate for the health status question was 96.9%. Ninety-seven percent of the sample was used to examine the association between health status and parent-reported illness - χ2 (df = 4) = 57.494, P < 0.001 – with the relationship being a weak one, correlation coefficient = 0.297. Table 5.2 revealed that 24.2% of children less than 5 years of age who reported an illness had very good health status, compared to 2 times more of those who did not report an illness. One percent of parents indicated that their children (of less than 5 years) who had no illness had poor health status, compared to 5.6 times more of those with illness who had poor health status.
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Health conditions, health status and medical care-seeking behaviour A cross-tabulation between health status and parent-reported diagnosed illness found that a significant statistical correlation existed between the two variables - χ2 (df = 16) = 26.621, P < 0.05, cc = 0.422, - with the association being a moderate one, correlation coefficient = 0.422 (Table 5.3). Based on Table 5.3, children less than 5 years old with asthma were less likely to report very good health status (5.9%), compared to those with colds (30.5%); diarrhoea (22.2%); and unspecified health conditions (22.7%). When health status by parent-reported illness (in %) was examined by gender, a significant statistical relationship was found, P < 0.001: males - χ2 (df = 4) = 25.932, P < 0.05, cc = 0.320, and females - χ2 (df = 4) = 39.675, P < 0.05, cc = 0.356. The health statuses of males less than 5 years old in the very good and good categories were greater than those of females (Figure 5.2). However, the females had greater health statuses in fair and poor health status than males, with more males reporting very poor health status than females. Based on Figure 5.3, even after controlling health status and parent-reported illness (in %) by area of residence, a significant statistical association was found: urban - χ2 (df = 3) = 10.358, P < 0.05, cc = 0.238; semi-urban - χ2 (df = 3) = 9.887, P = 0.021, cc = 0.273, and rural χ2 (df = 3) = 45.978, P < 0.001, cc = 0.365. Concomitantly, children less than 5 years of age were the least likely to have very good health status (19.4%) compared to rural (25.8%) and semiurban children (25.9%). Furthermore, the respondents who resided in urban areas were 2.1 times more likely to have parent-reported very poor health status, compared to rural respondents. In examining health status and reported illness (in %) by social classes, significant statistical relationships were found, P < 0.05: poor-to-poorest classes - χ2 (df = 4) = 52.374, P =
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0.021, cc = 0.393; middle class - χ2 (df = 3) = 8.821, P = 0.032, cc = 0.259, and wealthy class - χ2 (df = 3) = 10.691, P = 0.02, cc = 0.234. Based on Figure 5.4, middle class children who are less than 5 years old had the greatest very good health status (37%) compared to the wealthy class (26.8%) and the poor-to-poorest classes (16.1%). Fourteen percent of poor-to-poorest class children who are less than 5 years old had at most poor health status compared to 0% of the middle class and 4.9% of the wealthy class, while 1.8% of poor-to-poorest classes less than 5 years of age had very poor health status. When health status and parent-reported illness was examined by age, sex, social class, and area of residence, the correlation was a weak one – correlation coefficient = 0.295, P < 0.001, n=583. A cross tabulation between health status and health care-seeking behaviour found a significant statistical association between the two variables - χ2 (df = 4) = 10.513, P < 0.033 with the correlation being a weak one – correlation coefficient = 0.281. A child less than 5 years old was 2.44 times more likely to be taken for medical care if he/she had at most poor health status. On the other hand, a child who had very good health status was 1.97 times more likely not to be taken to health care practitioners (Figure 5.5). In 2007, an examination of the health care-seeking behaviour and parent-reported illness of the sample revealed no statistical correlation - χ2 (df = 1) = 0.430, P = 0.618. Sixty-two percent of the sample, who was ill, was taken to health care practitioners, while 38.5% were not. On the other hand, more were taken for medical care than in 2007 in the 4-week period of the survey. No statistical correlation was noted for the aforementioned variables in 2002 - χ2 (df = 1) = 1.188, P = 0.276. Of those who reported ill, 63.7% were taken to health care practitioners.

Discussion
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Infant mortality has been declining since the 1970s, and this has further decreased since 2004 [14]; this, as the literature shows, is not a good measure of health. The current study found that, using general health status, children less than 5 years of age in Jamaica had good health. The findings revealed that 90 out of every 100 less than-5 year olds had at least good health status, with 44 out of every 100 having very good health status. In spite of the good health status of less than-5 year olds in Jamaica in 2007, 20.8% of them had an illness in the 4-week period of the survey, which is a 5.9% increase over 2002. It is interesting to note the shift in this study away from specific chronic illnesses. In 2002, 30 out of every 1,000 less than-5 year olds in Jamaica were diagnosed with hypertension and arthritis (i.e. parent-reported), with 60 out of 1,000 having been parent-reported with diabetes mellitus. None such cases were found in 2007, suggesting that in the case of the children who had those particular chronic illnesses, their parents had either migrated with them or they had died. Concomitantly, the country is seeing a reduction in children less than 5 years old with colds; however, marginal increases were seen in diarrhoea, asthma and unspecified health conditions over the last 6 years. Although there were increased reported cases of illness over the studied period, in 2007, 62 out of every 100 ill children were taken to medical practitioners, and this fell from 64 in every 100 in 2002. One of the arguments put forward by some people is that what retards or abates health care-seeking behaviour is medical cost. With the abolition of health care user fees for children since 2007, the culture must be playing a role in parents and/or guardians not taking children who are ill to medical care facilities for treatment. Medical cost cannot be divorced from the expenditure that must be incurred in taking the child to the health care facility. In 2007, 25 out of every 100 children less than 5 years of age had parents and/or guardians who were less than the poverty line. Although this has declined by
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4.2% since 2002, it nevertheless means that there are children whose parents are incapacitated by other factors. Marmot [21] opined that the financial inability of the poor is what accounts for their lowered health status, compared to other social classes. The current study concurs with the findings of Marmot, as it was revealed that children less than 5 years of age from poor households had the least health status. This means that poverty is not merely eroding the health status of poor Jamaicans, but that equally it is decreasing the health status of poor children. Rural poverty in Jamaica is at least twice as great as urban poverty, and approximately 4 times more than semi-urban [13], which provides another explanation for the poor health status of children less than 5 years of age. The current study found that 3.2% of those children dwelling in urban zones recorded at most poor health status, compared to 13.6% of rural children, suggesting that the health status of the latter group is 4.3 times worse than the former. This means that poverty in rural zones is exponential, eroding the quality of life of children who are less than 5 years old. Poverty in semi-urban areas was 4% which is 2.5 times less than that for the nation; and those less than 5 years of age recorded the greatest health status, supporting Marmot’s perspective that poverty erodes the health status of a people. Hence, the decline in health care-seeking behaviour for this sample is embedded in the financial constraints of parents and/or guardians as well as their geographical challenges. The terrain in rural zones in Jamaica is such that medical care facilities are not easily accessible to residents compared to urban dwellers. With this terrain constraint comes the additional financial burden of attending medical care facilities at a location which is not in close proximity to the home of rural residents, and this accounts for the vast health disparity between rural and urban children. As a result of the above, the removal of health care utilization fees for children less than 18 years of age does not correspond to an increased utilization of medical care services, or lowered numbers of unhealthy
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children less than 5 years of age. If rural parents are plagued with financial and location challenges, their children will not have been immunized or properly fed, and their nutritional deficiency would explain the health disparity that exists between them and urban children who have easier access to health care facilities. The removal of health care utilization fees is not synonymous with an increased utilization of medical care for children less than 5 years old, as 46.5% of the sample attended public hospitals for treatment in 2002, and after the abolition of user fees in April 2007 utilization fell by 1.7 times compared to 2002. In order to understand stand why there is a switch from health care utilization to mere survival, we can examine the inflation rate. In 2007, the inflation rate was 16.8% which is a 133% increase over 2002 (i.e. 7.2%), which translates into a 24.7% increase in the prices of food and non-alcoholic beverages, and a 3.4% increase in health care costs [22]. Here the choice is between basic necessities and health care utilization, which further erodes health care utilization in spite of the removal of user fees for children. Health status uses the individual self-rating of a person’s overall health status [23], which ranges from excellent to poor. Health status therefore captures more of people’s health than diagnosed illness, life expectancy, or mortality. However, how good a measure is it? Empirical studies show that self-reported health is an indicator of general health. Schwarz & Strack [24] cited that a person’s judgments are prone to systematic and non-systematic biases, suggesting that it may not be a good measure of health. Diener, [25] however, argued that the subjective index seemed to contain substantial amounts of valid variance, indicating that subjective measures provide some validity in assessing health, a position with which Smith concurred [26]. Smith [26] argued that subjective indices do have good construct validity and that they are a
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respectably powerful predictor of mortality risks [27], disability and morbidity [27], though these properties vary somewhat with national or cultural contexts. Studies have examined self-reported health and mortality, and have found a significant correlation between a subjective and an objective measure [27-29]: life expectancy [30]; and disability [28]. Bourne [30] found that the correlation between life expectancy and self-reported health status was a strong one (correlation coefficient, R = 0.731); and that self-rated health accounted for 53% of the variance in life expectancy. Hence, the issue of the validity of subjective and objective indices is good, with Smith [26] opining that the construct validity between the two is a good one. The current research found that parent-reported illness and the health status of children less than 5 years of age are significantly correlated. However, the statistical association was a weak one (correlation coefficient = 0.297), suggesting that only 8% of the variance in health status can be explained by parent-reported children’s illnesses. This is a critical finding which reinforces the position that self-reported illnesses (or health conditions) only constitute a small proportion of people’s health. Therefore, using illness to measure the health status of children who are less than 5 years of age is not a good measure of their health, as illness only accounts for 8% of health status. However, based on Bourne‘s work [30], health status is equally as good a measure of health as life expectancy. One of the positives for the using of health status instead of life expectancy is its coverage in assessing more of people’s general health status by using mortality or even morbidity data.

Conclusion
In summary, the general health status of children who are less than 5 years old is good; however, social and public health programmes are needed to improve the health status of the
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rural population, which will translate into increased health status for their children. The health disparity that existed between rural and urban children less than 5 years of age showed that this will not be removed simply because of the abolition of health care utilization fees. In keeping with this reality, public health specialists need to take health care to residents in order to further improve the health status of children who are less than 5 years old.

Conflict of interest
The author has no conflict of interest to report.

Disclaimer
The researcher would like to note that while this study used secondary data from the Jamaica Survey of Living Conditions, 2007, none of the errors that are within this paper should be ascribed to the Planning Institute of Jamaica or the Statistical Institute of Jamaica as they are not there, but owing to the researcher.

References
1. Lindo, J. (2006) Jamaican perinatal mortality survey, 2003. Jamaica Ministry of Health. Kingston, pp. 1-40. 2. McCarthy, J.E., and Evans-Gilbert, T. (2009) Descriptive epidemiology of mortality and morbidity of health-indicator diseases in hospitalized children from western Jamaica. American Journal of Tropical Medicine and Hygiene, 80,596-600. 3. Domenach, H., and Guengant, J. (1984) Infant mortality and fertility in the Caribbean basin. Cah Orstom (Sci Hum), 20,265-72. 4. Rodriquez, F.V., Lopez, N.B., and Choonara, I. (2002) Child health in Cuba. Arch Dis Child, 93,991-3. 5. McCaw-Binns, A., Holder, Y., Spence, K., Gordon-Strachan, G., Nam, V., and Ashley, D. (2002) Multi-source method for determining mortality in Jamaica: 1996 and 1998. Department of Community Health and Psychiatry, University of the West Indies. International Biostatistics Information Services. Division of Health Promotion and Protection, Ministry of Health, Jamaica. Statistical Institute of Jamaica, Kingston
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6. McCaw-Binns, A.M., Fox, K., Foster-Williams, K., Ashley, D.E., and Irons, B. (1996) Registration of births, stillbirths and infant deaths in Jamaica. International Journal of Epidemiology, 25,807-813. 7. World Health Organization, (WHO). (1948) Preamble to the Constitution of the World Health Organization as adopted by the International Health Conference, New York, June 19-22, 1946; signed on July 22, 1946 by the representatives of 61 States (Official Records of the World Health Organization, no. 2, p. 100) and entered into force on April 7, 1948. “Constitution of the World Health Organization, 1948.” In Basic Documents, 15th ed. WHO, Geneva. 8. World Health Organization, (WHO). (2004) Healthy life expectancy 2002: 2004 World Health Report. WHO, Geneva. 9. WHO. (2000) WHO Issues New Healthy Life Expectancy Rankings: Japan Number One in New ‘Healthy Life’ System. WHO; 2000, Washington D.C. & Geneva. 10. Jamaica Ministry of Health, (MOHJ). (1992-2007) Annual report 1991-2006. MOHJ, Kingston. 11. Statistical Institute of Jamaica, (STATIN). (1981-2009) Demographic statistics, 19802008. STATIN, Kingston. 12. Planning Institute of Jamaica, (PIOJ). (1981-2009) Economic and Social Survey, 19802008. PIOJ, Kingston. 13. PIOJ, and STATIN. (1989-2009) Jamaica Survey of Living Conditions, 1988-2008. PIOJ and STATIN, Kingston. 14. Pan American Health Organization, (PAHO). (2007) Health in the Americas, 2007, volume II Countries. PAHO, Washington DC. 15. Morgan, W. (ed). (2005) Health issues in the Caribbean. Ian Randle, Kingston. 16. Walker, S. Nutrition and child health development. In Morgan, W. (ed). Health issues in the Caribbean. Ian Randle, Kingston, pp. 15-25. 17. Samms-Vaugh, M., Jackson, M., and Ashley, D. (2005) School achievement and behaviour in Jamaican children. In Morgan, W, (ed). Health issues in the Caribbean. Ian Randle, Kingston, pp. 26-37. 18. Statistical Institute Of Jamaica. (2008) Jamaica Survey of Living Conditions, 2007 [Computer file]. Kingston, Jamaica: Statistical Institute Of Jamaica [producer], 2007. Kingston, Jamaica: Planning Institute of Jamaica and Derek Gordon Databank, University of the West Indies [distributors]. 19. Statistical Institute Of Jamaica. (2003) Jamaica Survey of Living Conditions, 2002 [Computer file]. Kingston, Jamaica: Statistical Institute Of Jamaica [producer], 2002.
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Kingston, Jamaica: Planning Institute of Jamaica and Derek Gordon Databank, University of the West Indies [distributors]. 20. World Bank, Development Research Group, (2002). Poverty and human resources. Jamaica Survey of Living Conditions (LSLC) 1988-2000: Basic Information. 21. Marmot, M (2002) The influence of income on health: Views of an Epidemiologist. Does money really matter? Or is it a marker for something else? Health Affair, 21,31-46. 22. Bourne, P.A (2009) Impact of poverty, not seeking medical care, unemployment, inflation, self-reported illness, health insurance on mortality in Jamaica. North American Journal of Medical Sciences, 1, 99-109. 23. Kahneman, D., and Riis, J. (2005) Living, and thinking about it, two perspectives. In Huppert, F.A., Kaverne, B. and N. Baylis, The Science of Well-being, Oxford University Press. 24. Schwarz, N., and Strack, F. (1999) Reports of subjective well-being: judgmental processes and their methodological implications. In Kahneman, D., Diener, E., Schwarz, N, (eds). Well-being: The Foundations of Hedonic Psychology. Russell Sage Foundation: New York, pp. 61-84. 25. Diener, E. (1984) Subjective well-being. Psychological Bulletin, 95,542–75. 26. Smith, J. (1994) Measuring health and economic status of older adults in developing countries. Gerontologist, 34, 491-6. 27. Idler, E.L., and Benjamin, Y. (1997) Self-rated health and mortality: A Review of Twenty-seven Community Studies. Journal of Health and Social Behavior, 38, 21-37. 28. Idler, E.L., and Kasl, S. (1995) Self-ratings of health: Do they also predict change in functional ability? Journal of Gerontology 50B, S344-S353. 29. Schechter, S., Beatty, P., and Willis, G.B. (1998) Asking survey respondents about health status: Judgment and response issues. In Schwarz, N., Park, D., Knauper, B., and S. Sudman, S (ed.). Cognition, Aging, and Self-Reports. Ann Arbor. Taylor and Francis, Michigan. 30. Bourne, P.A. (2009) The validity of using self-reported illness to measure objective health. North American Journal of Medical Sciences, 1,232-238.

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Table 5.1. Socio-demographic characteristic of sample, 2002 and 2007 2002 2007 Variable n % n % Sex Male 1216 49.7 330 53.7 Female 1231 50.3 284 46.7 Income quintile Poorest 20% 725 29.6 156 25.4 Poor 554 22.6 140 22.8 Middle 474 19.4 126 20.5 Wealthy 402 16.4 117 19.1 Wealthiest 20% 293 12.0 75 12.2 Self-reported illness Yes 345 14.9 125 20.8 No 1969 85.0 475 79.2 Visits to health care facilities (hospitals) Private, yes 17 7.8 5 6.7 Public, yes 100 46.3 20 26.7 Area of residence Rural 1460 59.6 311 50.7 Semi-urban 682 27.9 125 20.4 Urban 306 12.5 178 29.0 Health (or, medical) care-seeking behaviour Yes 221 63.3 76 61.3 No 128 36.7 48 38.7 Health insurance coverage Yes, private 211 9.0 66 11.1 Yes, public * * 33 5.5 No 2123 91.0 496 83.4 Self-reported diagnosed health conditions Acute Cold 185 53.3 60 48.4 Diarrhoea 20 5.8 9 7.3 Asthma 46 13.3 17 13.7 Chronic Diabetes mellitus 2 0.6 0 0 Hypertension 1 0.3 0 0 Arthritis 1 0.3 0 0 54 15.6 22 17.7 Other (unspecified) 38 11.0 16 12.9 Not diagnosed 1.53 (SD = 0.927) 1.43 (SD = 0.989) Number of visits to health care institutions Duration of illness Mean (SD) 8.51 days (6.952 days) 8.07 days (7.058 days) Cost of medical care Public facilities Median (Range)in USD 2.36 (157.26)1 0.00 (64.62)2 1 Private facilities Median (Range)in USD 13.76 (117.95) 10.56 (49.71)2 1 USD1.00 = Ja. $50.87 2 USD1.00 = Ja. $80.47 *In 2002, all health insurance coverage was private and this was change in 2005 to include some public option

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Table 5.2. Health status by self-reported illness Self-reported illness Health status Very good Good Fair Poor Very poor Total Yes n (%) 30 (24.2) 61 (49.2) 23 (18.5) 9 (7.3) 1 (0.1) 124 No n (%) 227 (48.3) 217 (46.2) 19 (4.0) 6 (1.3) 1 (0.2) 470

χ2 (df = 4) = 57.494, P < 0.001, cc = 0.297, n = 594

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Table 5.3. Health status by self-reported diagnosed illness Self-reported diagnosed illness Health status Very good Cold 18 (30.5) Diarrhoea 2 (22.2) Asthma 1 (5.9) Unspecified 5 (22.7) No 5 (31.3)

Good

31 (52.5)

5 (55.6)

4 (23.5)

11 (50.0)

8 (50.0)

Fair

7 (11.9)

2 (22.2)

8 (47.1)

3 (13.6)

3 (18.8)

Poor

2 (3.4)

0 (0.0)

4 (23.5)

3 (13.6)

0 (0.0)

Very good Total

1 (1.7) 59

0 (0.0) 9

0 (0.0) 17

0 (0.0) 22

0 (0.0) 16

χ2 (df = 16) = 26.621, P < 0.05, cc = 0.422,

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Figure 5.1. Mean age of health conditions of children less than 5 years old, 2002 and 2007

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Figure 5.2. Health status by Parent-reported illness (in %) examined by gender

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Figure 5.3. Health status by parent-reported illness (in %) examined by area of residence

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Figure 5.4. Health status by parent-reported illness (in %) examined by social classes

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Figure 5.5. Health status by health care-seeking behaviour

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Chapter 6
Health Inequality in Jamaica, 1988-2007

Paul A. Bourne

In Jamaica, mortality for men is not only greater than that of women as indicated by the life expectancy but of the five leading causes of death (malignant neoplasms; cerebrovascular disease; heart disease; diabetes mellitus and homicides), the rates for men were greater in four of the five categories (malignant neoplasms; cerebrovascular; heart disease and homicides). Despite these realities, men seek less medical care than women while staying longer in hospitals for curative care. Hence, this study examines medical seeking behaviour, self-reported ill-health, and sex differential in medical seeking health in nation. The current research used secondary data. The data were extracted from the Jamaica Survey of Living Conditions (JSLC) on medical care seeking behaviour, self-reported illness (or ill-health) and the sex composition of those who reported ill-health. The JSLC was born out of the World Bank’s Living Standard Survey. Data were also taken from the Ministry of Health’s Annual Report, which provided statistics on actual percentage of Jamaicans who visited public hospitals. The current study used 19 years of published data extracted from the JSLC (1988-2007). Scatter diagrams and best fitted lines were used to examine correlations and trends. Over a 2-decade period, 1988 to 2007, only a small percentage of Jamaicans reported ill-health (between 9 to 19 %) and 15.5% in 2007, which is an increase of 3.3% over the previous year. Despite this low figure, increasingly more men sought medical care over the study period (41.1%) compared to women (29%). Nevertheless, health care seeking behaviour is still sex bias – 68.1% of women and 62.8% of men who reported health conditions. For men, more of medical care seeking behaviour is explained by ill-health (rsquared=35.4%) than women (r-squared 8.8%). This study is one of the first to examine and provide some explanation on sex differentials in health care behaviour and self-reported illness/injury in Jamaica. We found that while more men who report ill-health have been seeking medical care, the gap between the sexes in regard health seeking behaviour has been narrowing.

Introduction Globally, in 1950-1955, life expectancy for women was 47.9 years compared to 45.2 years for men. One-half of a century later, the disparity increased to 4.2 years (68.1 years for women and 63.9 years for men). For the Caribbean, in the same aforementioned period, life expectancy was 53.5 years and 50.8 years for women and men respectively; and 50 years later, the disparity
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increased to 5.5 years (70.9 years for women and 65.4 years for men) which was greater than that for the world. Life expectancy which is an indicator of mortality and morbidity are also a measure for health status; and speaks to the quality of labour for the society. Although there are some morbidity that are not life threatening, health literature showed that healthy life is not equivalent to lived years. The World Health Organization being aware of this disparity developed the DALE (ie disability adjusted life expectancy) to discount life expectancy for the time lost due to illness. Based on this information, statistics revealed that developing countries lost 9 years of life expectancy owing to unhealthy years (or illness); and this is still within the cultural context of men’s unwillingness to seeking medical care. While this provides a general framework for the rationale of the disparity in life expectancy of the sexes, it does not afford us a comprehensive understanding of health inequality. There has always been a health differential between the sexes in Jamaica [1] Dating back to 1880, which was the first time that life expectancy data was recorded for men and women in the island, women outlived men. Statistics for Jamaica showed that for the period 1880 and 1882, women lived approximately 3 years more than men and 122 years later (20022004), they outlived them by 6 years, which is an additional 3 years. Globally, women live longer than men by 8 years which is 2 years more than that of the life expectancy sex differential in Jamaica. Women are not only living longer than their male counterparts, but they are enjoying greater quality of life[2] A study of 3,009 older people done in 2007 in Jamaica[3] revealed that elderly women had a higher quality of life (3.3 ± 2.2) than men (2.8 ± 1.8; p value = 0.001), which concurred with the earlier work done by the WHO in 1998. But, studies that have examined well-being have shown that men experienced a greater economic wellbeing than

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women[4], despite not having a higher subjective wellbeing. What is explaining this health differential between the sexes? Life expectancy which is calculated using mortality data indicate that men are experiencing particular pathogen causing diseases which are accounting for the greater increase in mortality and lower life expectancy than women. An epidemiological profile of selected health conditions and services in Jamaica for 1990-2002 was conducted by the Health Promotion and Protection Division, Ministry of Health in 2005 which indicated that malignant neoplasm was the leading cause of death in Jamaica. It was 39% greater for men than women. The second leading cause of death, cerebrovascular disease, was 14% higher for men than women; heart diseases rate was 71.2 per 100,000 for men and 66.1 per 100,000 for women, and diabetes mellitus was greater for women than men. The statistics revealed that mortality caused by diabetes mellitus was 64% higher for women than men. Jamaica is not unique in regard to i) women outliving men, ii) particular morality is greater for men than women, and ii) some of the leading causes and death are sex specific[2] The issue of higher mortality differential between the sexes at older ages begins with boys suffering more illnesses and injuries than girls[5] The World Health Organization (WHO) offered a potent finding that age-and sex differential in mortality dates back to 1955[2] This indicates that higher mortality in the world’s population tend to favour men, and justifies the longer life that they live compared to men. In demography, life expectancy is used to measure health. But this approach fails to capture health as one can be alive but enjoy optimum health – living with varying levels of morbidity. There is an argument that morbidity is accounted for in mortality, and this so.
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However, some dysfunctions are not death causing, and so quality of life (health) will be lower with these health conditions. It is owing to this reality that the World Health Organization (WHO) introduced what is known as healthy life expectancy which discounts life expectancy by morbidity. Healthy Life Expectancy One of the drawbacks to the use of life expectancy is its absence to capture ‘hale’ years of life. Traditionally when life expectancy is measured, it uses mortality data to predetermine the number of years of life yet to be lived by an individual, assuming that he/she subscribes to the same mortality patterns of the group. The emphasis of this approach is on length of life and not on the quality of those years lived. Hence changes in life expectancy are primary due to mortality movements, and imply changes in external conditions of the socio-biological environment. These changes include the components of public health, the physical milieu, and technological/medical advancement. With all the aforementioned conditions that have improved over the last century, increased life expectancy in the world is not surprising to scholars. One way of evaluating population ageing in the world or in any geopolitical space is ‘life expectancy’. Today, it should come as no surprise to people that many developing nations have been experiencing increased gains in additional years of life for members with its population in comparison to 20th century. Associated with ageing are high probability of increased dysfunctions and the unavoidable degeneration of the body. This explains why it is germane to analyze healthy life expectancy and not merely life expectancy. Healthy life expectancy is defined as the number of years that an individual is expected to live in ‘good’ health. Technological advancement is
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able to prolong life, but it is not able to remove morbidity and its deterioration in quality of lived years of the individual. Thus, while life expectancy in the Caribbean is increasing and that this is in keeping with the rest of the world, there is a simultaneous increase in chronic diseases and resurgence of infectious disease. This reality highlights the disparity between quantity of years lived and the quality of those lived years because of sociopsychological conditions- such as loneliness, bereavement, social support (or the lack of), low self-esteem, and low selfactualization and so on. In evaluating health or wellbeing, we must seek to examine more than just the number of years that an individual is likely to survive as we should be concerned about the quality of those years. Even though, life expectancy is an indicator of health, the new focus is on healthy life expectancy. Based on the Healthy People 2010, the new thrust is on increasing quality of years of life. In attempting to capture ‘quality of years lived’, in 1999, the WHO introduced an approach that allows us to evaluate this, by the ‘disability adjusted life expectancy’ (DALE)[6] DALE does not only use length of years to indicate health and wellbeing status of an individual or a nation, but incorporate the number of years lived without disabilities. DALE is a modification of the traditional ‘life expectancy’ approach in assessing health. It uses the number of years lived as its principal component. This is referred to as ‘full health’. In addition, the number of years of ill-health is weighted based on severity as another component in the equation. This is then subtracted from the expected overall life expectancy to give what is referred to as years of hale life. Embedded in this approach is the adjustment of years lived in ‘ill-health’.

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Having arrived at ‘healthy life expectancy’, the WHO has found that poorer countries lost more from their ‘traditional life expectancy’ than developed nations. The reasons forwarded by the WHO are the plethora of dysfunctions and the devastating effects of some tropical diseases like malaria that tend to strike children and young adults. The institution found that these accounted for a 14 percent reduction in life expectancy for poorer countries and 9 percent for more developed nations. [6] This is in keeping with a more holistic approach to the measure of health and wellbeing with which this study seeks to capture. By using the biopsychosocial model in the evaluation of wellbeing of aged Jamaicans, we will begin to understand factors that are likely to influence the quality of lived years of the elderly, and not be satisfied with the increased length of life of the populace. Looking at the life expectancy data for Jamaica, the figure is 74.1 years for both sexes[6] but by using healthy life expectancy it is 65.1 years[6] Here life expectancy has been increasing at a faster rate than ‘healthy life expectancy’. Therefore, Jamaicans are expected to spend some 9 years of their life in ‘poor health’. In summary, the use of life expectancy to measure health is inadequate and so morbidity must be taken into consideration. When life expectancy is discounted by morbidity, it provides an account of the healthy life expectancy of an individual. Hence, the use of life expectancy to indicate health for men and women is equally insufficient in health analysis. It is evident from statistics on life expectancy and particular diseases causing mortality that men are experiencing a lower health status, and what accounts for this reality? Within the context of the aforementioned issues, and the fact that medical health care seeking has increased from 54.6% in 1989 to 66.0% in 2007 and that there is a decline of 5.7% over 2006 (Table 6.1), is this offering some explanation the sex differential in health status? Although less Jamaicans are seeking medical care of those who reported illnesses, 27.1% more Jamaicans reported dysfunctions (Table 6.1),
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suggesting that there is greater health differential between the sexes. Hence, for this study, medical seeking behaviour, self-reported ill-health, and sex differential in medical seeking health care and self-reported ill-health will be examine to provide a better understanding of the healthy life expectancy of the sexes in Jamaica. Materials and Method The current research used secondary data. The data constitute statistics from the Planning Institute of Jamaica and the Statistical Institute of Jamaica (in Jamaica Survey of Living Conditions, JSLC) and Ministry of Health Jamaica (MOH). The data were extracted from the JSLC on medical care seeking behaviour, self-reported illness (or ill-health) and the sex composition of those who reported ill-health. The Ministry of Health’s Annual Report provided data on actual percentage of Jamaicans who visited public hospitals, which was contrasted by the JSLC’s self-reported visits to public hospitals in order to further examine the sex differentials on subjective ill-health. This study used 19 years of published data extracted from the JSLC (1988-2007). The JSLC was born out of the World Bank’s Living Standard Survey. The JSLC began in 1988 when the Planning Institute of Jamaica (PIOJ) in collaboration with the Statistical Institute of Jamaica (STATIN) adopted with some modifications of the World Bank's Living Standards Measurement Study (LSMS) household surveys. The JSLC has its focus on policy implications of government programmes, and so each year a different module is included, evaluating a particular programme. The JSLC is a self-administered questionnaire where respondents are asked to recall detailed information on particular activities. The questionnaire covers demographic variables, health, immunization of children 0 to 59 months, education, daily expenses, non-food consumption
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expenditure, housing conditions, inventory of durable goods, and social assistance. Interviewers are trained to collect the data, which is in preparation of the household members. The survey is usually conducted between April and July annually. Furthermore, the instrument is posted on the World Bank’s site to provide information on the typologies of question and the (http://www.worldbank.org/html/prdph/lsms/country/jm/docs/JAM04.pdf). Ministry of Health is the body which is constituted by statutes to regulate all health institutions in the country. The Ministry of Health (MOH) collects statistics on health, health services, health utilization, health related matters, and carry out health mandate of the government. MOH has decentralized its operations. The island is sub-divided in four regions (South-East; North-East; Western, and Southern), which emerged owing to the passage of the National Health Service Act of 1997. Each region operates as a semi-autonomous regional body under the general directs of the central Ministry of Health, which is subject to the directions of the Minister of Health. The central Ministry of Health collates all the data sent it by the four health authorities in country. Therefore, data revealed in the Annual Reported of the Ministry of Health, Jamaica, reflect actual accounts of the health matters in the country. Scatter diagrams and best fitted lines were used to examine correlations between different variables, and percentages were also utilized to evaluate events over two decade (1988-2007). Measure Sex is the biological composition of being men or women. Sex differential is the disparity between self-reported ill-health of male or female.

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Medical Care Seeking Behaviour denotes the proportion of self-reported cases of visits for seeking medical care of those who indicated ill-health. Self-reported Illness is the percentage of people who have reported cases of dysfunctions (illhealth or health conditions) as indicated by a respondent in a 4-week reference period. Poverty is measured using the poverty line. The poverty line estimate is particular attainable consumption expenditure in excess of a minimum necessary level of expenditure on a representative bundle of necessary goods and services valued at germane prices. (JSLC 2008) Results Some scholars may want to believe that the use of subjective data on health (self-reported illhealth) cannot be used to proxy health as it is not a good estimate of actual health status. In order to remove this myth, the researcher will examine the actual figures provided by the Ministry of Health on visits to public health care facilities and those garnered by the Jamaica Survey of Living Conditions (JSLC). The JSLC is an annual probability sampled survey which collects data from Jamaicans based on their recollection of events (self-reported). Based on Table 6.4, self-reported health as indicated by the JSLC is a good proxy of visits. The data revealed that in 1997, the difference between Jamaicans recall of events and those actually happened as recorded by the Ministry of Health was marginally different (1%). Some 7 years later (2004), the difference between same phenomena was 6.1% suggesting that subjective assessment of health is a good proxy for actual health. It is within this context, that the researcher will examine self-reported health data from JSLC to understanding health differential between the sexes in Jamaica.
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During the periods of the greatest double digits inflation in history of Jamaica (early 1990s) (Table 6.2) in particular inflationary rates that were in excess of 25% (1990-1995), Jamaicans reported the lowest percentages in ill-health (health conditions). Moreover, in 1991 when inflation was at it peaks, the prevalence of poverty stood at its highest (44.6%), and the data showed that self-reported illnesses were 13.7%. This figure was the fifth highest selfreported ill-health in an 18 year period (1989-2007). In the unprecedented inflation of 1991 (80.2%), less men sought medical care (12.0%) over 1990 (16.35) compared to 15.0% in 1991 and 20.3% in 1990. In 1990, it was the first time in the history the of nation that inflation rose to in excess of 20% and self-reported illness reached its maximum of 18.3%, and medical care seeking behaviour was at its lowest (38.6%). In addition, in 1990, both sexes sought the most medical care (Table 6.3). Two years later (1992), inflation rate fell by 49.9% (to 40.2%) over 1991 which explains the rationale for the 24.0% decrease in prevalence of poverty; self-reported ill-health declined by 22.6%, ownership of health insurance increased so to were people seeking medical care and the private health care utilization. The irony here is that 17.5% less men reported accessing medical care for their ill-health and 24.7% less women. This indicates that more of those people who did not report ill-health visited private health care facilities for medical care. In 1993, inflation declined further by 25.1%; poverty saw a reduction of 28.0%; self-reported health conditions increased by 13.2%; health insurance coverage increased by 12.2%; number of people seeking medical care increased by 1.8%. In that same period, the number of women who sought care was 3.8 times more (19.5%) than men (5.1%). Hence, high inflation was reducing visits for medical care and another matter which emerged from the data during that period, that those who attending public hospitals began reducing their visits while private hospital users, increased utilization (Table
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6.2). There is a paradox post-2005 as inflation increased by an unprecedented 194.7% in 2007 over 2006 and this explains a corresponding decline in the number of persons who sought medical care (by 5.7%). Nevertheless, the number of men who visited health care facilities increased in the period by 21.2% and the number of women was 1.24 times more than men. The data show that in the last 17 years, women place more emphasis on their health than men. Between 1988 and 2007, it was only on one occasion that men have indicated having sought more medical care than women (in 1997) (Table 6.3). The difference between men seeking medical care and that of women was 0.7%. If health seeking behaviour is a proxy for preventative care, then it would appear that they were more health conscious. This is the not the case as in the same period, then spent more days receiving care (mean of 11 days) compared to 10 for women. Hence, this increased in health seeking behaviour was owing to curative and preventative care. Nevertheless, over the studied period, severity of care for both sexes has been reality the same. Using mean number of days men received care for illness/injury, the difference is minute, suggesting that severity of illness between the sexes in Jamaica is the same. Another interesting finding that emerged from the data is the narrowing of the gap between public health care utilization and private health care utilization in the nations, suggesting that costing of living is accounting for more visits to public care facilities. Embedded in those findings is the affordability in people’s decision to seek medical care. This indicates that there are some other conditions that are interfacing with men’s and women’s decision to visit health care facilities for care outside of prices (inflation). Results: Bivariate Analyses

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Percentage of People Seeking Medical Care by Percentage of People reporting Illness On examination of Figure 6.1, it was revealed that a negative correlation exists between number of people who sought medical care and percentage of people who reported ill-health. This indicates that as more people report health conditions, less of them are likely to seek medical care. Furthermore, 16.3% of the variability in people seeking medical care can be explained by illness, suggesting that ill-health is not a good reason for Jamaicans visiting health care practitioners. On further investigation of people seeking medical care and self-reported illness/injury, data (Tables 6,2 & 6.3) revealed that on the occasion when the highest percentage of illnesses were reported, the least number of person sought care for those conditions. This irony was equally the case for men (16.3%) as well as women (20.3%) (Table 6.3). Percentage of People Seeking Medical Care by Prevalence of Poverty On examination of a scatter diagram; it was observed that there is a negative correlation between the percentage of people seeking medical care and prevalence of poverty. The best fit line revealed that 57.6% of why people seek health care in Jamaica is determined by poverty (Figure 6.2). Hence, people are highly likely to visit health care facilities in periods of low poverty and vice versa. This indicates that medical care is not simply about ill-health, it is equally determined by affordability, suggesting that people will switch to home care in periods of increased poverty. Irrespective of this knowledge, is there is sex disparity in regard to seeking medical care and reporting illness?

Percentage of Men Seeking Medical Care by Percentage of Men reporting Illness
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Generally 16.3% of why Jamaicans visit health care facilities in search of care is owing to their health conditions. However, for men, 35.4% of why they sought medical care was due to illhealth as 35.4% of the variability in men seeking medical care can be explained by medical care. On decomposing the data, when the least percentage of men sought medical care assistance (37.9%), the most percentage of them reported illness (16.3%) (Table 6.3). Furthermore, when the lowest percentage of men reported ill-health (health conditions/injuries) (7.4%), this was in 60% of those seeking more medical care. However, in 1999 and 2004, low self-reported illness was correlated with relatively high health seeking behaviour. Percentage of Women Seeking Medical Care by Percentage of Women reporting Illness Health (medical) care seeking behaviour of women is lowly correlated with self-reported illness (injury) (Figure 6.3). The scatter plot revealed that generally, the more women reported health conditions the less likely they are to seeking medical care. Some 8.8% of the variability in medial care seeking behaviour of this cohort can be explained by a change in self-reported health conditions. Self-reported illness of women accounted for 54% less of the explanatory reason for seeking medical care compared to that of the both sexes (16.3%), suggesting that women’s health care behaviour is driven by other factors than ill-health. There are some similarities between health care seeking behaviour and self-reported illness of both sexes as when women reported the least percentage of health care seeking behaviour, this was corresponding to the most reported health conditions (Table 6.3). Furthermore, when the least percentage of ill-health was reported, this earmarked 59th percentage of the highest seeking medical care behaviour of women. These were also the case for men. Deconstruction the Self-Reported Health Status of Jamaicans by Sex, 1989-2006
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Over the last 2 decades (1988-to-2007), a small proportion of Jamaicans have reported illness (or dysfunction) (Table 6.5). This has been has high as 168 per 1,000 (in 1989) to a low of 88 per 1,000 (in 1997), and the figure was 155 per 1,000 in 2006 (Table 6.5). On deconstruction the population self-reported health status, it was revealed that women continue to report more health conditions than men. In 1989, there 123 women (or women) who reported health conditions to 100 men (or men), and in 2004, the ratio was as high as 153 women per 100 men. This indicates that 53% more women reported health conditions than men in the latter year and there was an increase of 30% more women reporting dysfunctions over the 2 decades. Over the studied period, in 1992, the disparity in self-reported health conditions between men and women was very close of which there were 114 women to 100 men as it relates to self-reported health conditions. On the other hand, over the last decade (1997-to-2006), the disparity was 136 or 153 women per 100 men, and in the last 2 years the value has been relatively stable (136 or 137 women per 100 men). Percentage of People Seeking Medical Care by Percentage with Health Insurance Health Insurance is one indicator of people’s intent to access care. On examination of the data (Table 6.2), only a small percentage of Jamaicans in 2007 had health insurance (21.1%). This meant that more people who will become ill would need to meet their medical expenses out of savings, current income and assistance from social support agent(s). Table 6.2 revealed in 8.6% of Jamaica had health insurance coverage during the period when the inflation rate was at its peak (80%) and when it fell to 40.2%, health insurance coverage increased by only 0.4%. Further investigation of health seeking behaviour and health insurance coverage showed that the ownership of health insurance was positively related to health seeking behaviour. A bivariate
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correlation between the two aforementioned factors revealed that 56.1% of the variability in people seeking medical care was as a result of ownership of health insurance (Figure 6.5).

Ownership of Health Insurance and Prevalence of Poverty

Poverty does not only mean ones inability to purchase consumption items, but also nonconsumption items such as health insurance. On examining a scatter diagram with a best fit line to establish any correlation between the two aforementioned variables, it was observed that a moderately strong correlation existed (R-squared = 0.597) – Figure 6.5. This means that 60% of the variability in ownership of health insurance can be accounted for by prevalence of poverty, suggesting that poor is less likely to have health insurance coverage. Discussion In the conclusion of the health chapter in one of the JSLC’s reports [8] it reads “Sex differentials with respect to self-reported illness and health seeking behaviours need to be investigated.” This is the rationale for this study, to provide an assessment of differences in subjective health and medical seeking behaviour of men and women in Jamaica. Globally, regionally and in particular Jamaica, women seek more health care than men [1,
2, 4-7]

This is not alarming as it commences from at childhood. In 1998, one health organization

wrote that girls are less likely to be injured and have broken bones compared to boys [2], which continue during the life span. So when the mortality rates show a higher rate for men than women [2, 6], this is just a continuation of early socialization. Health, therefore, is sex bias. One of the rationales for the emphasis on health care by women is reason for male’s abstinence, the
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culture. Within many cultures, men are not to display any form of weakness which includes illhealth. This culturalization has embedded in boys and avoidance of speak of illness/injury and the image of ill-health is negative and is primarily feministic in nature. This is not limited to Jamaica or African descendent societies as it is equally the case in European geopolitical zones such as Norway [9] Many cultures view (image) of health is the absence of diseases and this is sometimes linked to cure of the gods or moral rationale, suggesting that ill-health is a weakened biological state. Men who are culturalized to be strong and macho must now balance ill-health within a plural culture. The 21 st century has seen the exponential increase in life expectancy of men compared to women in nineteenth and earlier centuries, but what about high mortality for this group. There has always been feminization of life expectancy in Jamaica since 1880 (Table 6.1) and the disparity in life expectancy has double from 1880-1882 to 2002-2004 from 3 years to 6 years respectively. Life expectancy which is an indicator of health does not only speak of longer life, there are also some cultural changes that account for this increased life span, the social milieu. Despite the advancement in medical technology, men continue to outnumber women in particular mortality rates. These include heart disease and neoplasm to name a few non-

communicable diseases. Heart disease and neoplasms are caused through either lifestyle behaviour or heredity, and the former explains more of heart disease than the latter. Globally, the fact that women outlived men by 8 years and in Jamaica by 6 years, lifestyle behaviour undoubtedly is explaining the higher morbidity in heart diseases of men. Life style behaviour is expressed in health seeking behaviour, the purchase of health
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insurance, preventative care and not curative care. Jamaica women continue to seek more care than men, and this concurs with the finding so other studies [2, 3, 10, 11] Women do not take their health for granted as society labels them with the nurturing role of children as well as ascribe them softer tasks. This means that health and ill-health are interpreted and viewed from within the perspective of personal experiences, and expectations. It is through the socialization process which is carried out by mothers (women) that ill-health and health will be defined which accounts for ones expectation and some percentage of how the world is viewed and interpreted by people. In a qualitative study that was done in Nairobi slums, the authors found a strong correlation between severity of illness and health seeking behaviour of children[12] These children do not seek care of themselves, but are taken for medical care by their mothers. Another study on street children (ages 5 to 13 years), who take themselves, like the Nairobi study attended health care institutions for care dependent on i) severity of illness and ii) if it stops their economic livelihood[13] Eight percentage of the sampled population of the latter study (in Pakistan) were boys (men). This speaks to the image of health as viewed by men, and when care is sought by them. Ill-health, therefore, based on the image of health seen through the lens of men is weak, breaches machoism and borders on the fringes of feminism. Within the homophobis world, despite the gradual reduction of the degree in some societies, men (or boys) do not want to be labeled weak, homosexual or effeminate. Hence, there is dialectic here as men want to live which means that they must address ill-health and at the same time they must appear to be macho. Men are less likely to both report ill-healths as well as seek medical care because of its image and social labels that they may ascribe to them by society. Women also play a part in this process as they grown their boys to be strong, ‘tough’, and that they should not show weakness. Ill-health
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is a weakness (or negative health), and so women on seeing men visiting health practitioners especially if this is frequent construe this as weak, but his is not ascribe to a female for doing the same thing. Medical care seeking behaviour is, therefore, construed as indicating ill-health (curative care) and not preventative care for men. Chevannes[14] wanting to explain how men are as they are, opined that early socialization played a critical role in shaping men’s masculinity, image of self and interpretation of the world around them. The image of health as viewed as far back as prehistoric society is that of sickness, a curse, a plague, a weakness and a state of biological incapacitation. Men who are culturalized to be strong cannot afford to be seen as weak or incapacitate by their peers or the opposite sex as the society removes the acclaim of greater, power and prestige from any such male. This means that men must now report and display less signs of ill-health (weakness), and the only time that illness must be shown is in times of severity which is close to death. Jamaican men displaying low medical care seeking behaviour as cultural underpinnings, and so does their unhealthy lifestyle practices. Unhealthy lifestyle is undoubtedly explaining high mortality of men than women. This dates back to prehistoric society, when men must hunters, heroes, warriors and fierce to defend themselves, their tribes and women. Such events meant that they would take more risk than women, and this has continued during the centuries. Although vast amount of information are available on health and health treatment, men continue to indulge in risky behaviour which accounts for their high morbidity and mortality in some conditions. The literature speaks to 80% of injuries and between 30-40% of cases with

cardiovascular conditions and diabetes mellitus could have been prevented by lifestyle practices
[5]

This explains much of the health conditions and increased in reported ill-health and medical
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care seeking behaviour. What is the role of education in health differential in the sexes? Education which is well established has directly correlated with better health [15-22] does not remove early culturalization by family, peer groups and religious affiliations. The general education level of the Jamaican populace has been improving since the last 3-decade, but this does mean the remove of the sex bias health image or stigma of weakness associated with illness. In 1989, 54.6% of Jamaicans sought care for ill-health and in 2007 that figure has increased by 9.9% (to 60%). In the same period that rate of increase for women was 29.0% compared to 41.1% for men. Nevertheless, in 2007, for every 100 men that sought care for ill-health, 108 women sought medical care. Although, we cannot divorce health from the social milieu, more men are seeking medical care for illness and this accounts for the faded difference between the mean numbers of days spent for care in both sexes. The 21 st century has aided men in their recognition for the need to seek medical care for ill-health, in spite of traditional cosmologies [23,
24]

In contemporary societies, illness for men is not tied to health conditions such as neoplasm, heart disease, hypertension, mellitus diabetes and stroke, but is synonymous to sexuality which is a legacy of their socialization[14, 23-29] A medical doctor ascribes to the 21st century, sex roles that are tied to sex (biological category). This means that being male is linked to being the stronger sex, fertile, and sexual prowess. Society has not removed from its men that sex stereotype, and so the image of health for them is substantially tied to sexuality. Men, therefore, do not see themselves as ill, unless they are impotent. Culturally, because impotency and infertility are a curse, men will not openly speak about those matters or/and other heath conditions. Again, male means strength, sexual potency, and these are all at the other end of the pendulum of ill-health. This explains the reason for the lower purchase of individual health
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insurance as this symbolizes weakness or preparation of some negative conditions. In spite of this reality, over the last one-half of a decade, there has been an increase in health insurance coverage and health seeking behaviour of both sexes. As of 2007, 2.1% more women had health insurance coverage than men (20.1%), which was more than the national average of 21.1%. Again this speaks to the differences in image of health held sexes and how their decision is based on this view. Health insurance is a component of lifestyle practices justify the advantages that women enjoy compared in men concerning health status. This is also reinforced in the fact (in 2007) that for every 133 women who indicated that they were unable to afford to seek medical care 100 men [1], showing that men are naturally, owing to their culturalization, unwilling to seek medical care and this is evident in their lifestyle practices, purchase of health insurance, reporting ill-health and visits to health care institution for preventative and curative care [1, 5] According to one scholars income buys health [30], which has some merit. The merit to this argument is linked to the fact that income affords one the ability and option to purchase better foods, medical care, a particularly good physical environment that are all positively correlated with good health[3, 15-18] There is a negative side to affluence and income, as it afford particular lifestyle that retard good health. Income affords one the lifestyle to purchase cigar, tobacco, speedy cars, and in the process remove the disadvantage of low income or poverty. In a study done by a group Caribbean scholars of 1,338 Jamaicans (ages 15 to 99 years), they found that greatest subjective psychosocial wellbeing was had by the middle class followed by the wealth and lastly by the poor [31] Embedded in the income and health debate, is the difficulty of the poor in seeking medical care (curative and preventative care). This study has shown that there is a moderately strong correlation between seeking medical care and prevalence of poverty, suggesting that poor
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men are even less likely to seek care than those in the middle to upper class. When poverty is coalescing with the cultural biases and image of health, men are likely to suffer more as they must balance ill-health which is a weakness with in affordability. The issue of affordability is seen in the percentage of those in the poorest quintile with health insurance in 2007 (6.6%) compared to 12% in quintile 2, 18% in quintile 3, 22.7% in quintile 4 and 43.4% of those in the wealthiest quintile. Embedded in this disparity is the poor’s inability to plan for the eventuality of ill-health coupled with deplorable reality of the physical environment. This physical environment is such to account for ill-health situation the poor will become even more ill. Concluding Comments In summary, illness is still seen and interpreted by Jamaicans as punishment and negative health, and this explains their low self-reported health conditions and health care seeking behaviour. Men who are product of the society must abide within the image of its dictates, which justifies their unwillingness to seek medical care, report illness, purchase health insurance coverage and create an image of weakness. In spite of this reality, men have become more involved that women in seeking medical care over the last 17 years. This means that the society is becoming increasingly more cognizant that ill-health is more than negative health or is simply equivalent to weakness, female or less macho men. Although men are substantially driven by health conditions to seek medical care than women, they are becoming more involved in health care treatment.
[32]

, and when poor nutrition is added to this

Recommendation Further efforts are needed to eliminate more of the barriers of the negative image of health and
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the use of medical services for ill-health in Jamaica. Medical practitioners, health care workers, social workers and researchers must integrate the image of men in their treatment, and create an atmosphere which is conducive to health care for men. A single prevalence study is needed to ascertain the influence of each of the identified variables in this study and others in order to understand the role of poverty, health insurance, ill-health, on the health seeking behaviour of Jamaicans, the media, education as well as confounding variables such as sex, age, religiosity, area of residence and subjective social class. In addition, a study is necessary to ascertain whether the increased in self-reported health is owing to unemployment, and how much of illhealth is accounted for by psychological conditions. References 1. Planning Institute of Jamaica (PIOJ), and the Statistical Institute of Jamaica (STATIN), 1990-2008. Jamaica Survey of Living Conditions, 1989-2007. Kingston: PIOJ, STATIN. 2. World Health Organization, (WHO), 1998. The World Health Report 1998: Life in the 21st Century, A vision for all. Geneva: WHO. 3. Bourne, PA., 2008. Medical Sociology: Modelling Well-being for elderly People in Jamaica. West Indian Med J, 57(6):596-04 4. Rudkin, L., 1993. Sex differences in economic wellbeing among the elderly of Java. Demography, 30:209-226.
5. The  Health  Promotion  and  Protection  Division,  Ministry  of  Health  Jamaica  (MOH),  2005.  Epidemiology Profile of Selected Health Conditions and Services in Jamaica, 1990‐2002.  MOH. 

6. WHO, 2000. WHO Issues New Healthy Life Expectancy Rankings: Japan Number One in New ‘Healthy Life’ System. WHO. 7. Statistical Institute of Jamaica, (STATIN), 2007. Demographic Statistics, 2006. STATIN. 8. WHO, 2003. Healthy life expectancy. Washington DC: WHO. 9. Planning Institute of Jamaica (PIOJ), and the Statistical Institute of Jamaica (STATIN). 2000. Jamaica Survey of Living Conditions, 1999. PIOJ, STATIN. 10. Kaasa, K. 1998. Loneliness in old age: Psychosocial and health predictors. Norwegian Journal of Epidemiology, 8:195-201.
11. Hutchinson, G., Simeon, DT., Bain, BC., Wyatt, GE., Tucker, MB., and E LeFranc, 2004. Social and  health determinants of wellbeing and life satisfaction in Jamaica. International Journal of Social  Psychiatry, 50 (1):43‐53.  12. Hambleton, IR., Clarke, K., Broome, Hl., Fraser, HS., Brathwaite, F., and AJ. Hennis, 2005.  Historical and current predictors of self‐reported health status among elderly persons in  Barbados. Revista Panamericana de  salud Pύblic, 17(5‐6):342‐353.  158   

13. Taff, N., and G. Chepngeno, 2005. Determinants of health care seeking for childhood illness in Nairobi slums. Tropical Medicine and International Health, 10:240-245. 14. Ali, M., and A. de Muynck, 2002. Illness incidence and health seeking behaviour among street children in Rawlpindi and Islamabad, Pakistan – a qualitative study. Child Care, Health & Development, 31:525-532. 15. Chevannes, B., 2001. Learning to be a man: Culture, socialization and sex identity in five Caribbean communities. The University of the West Indies Press. 16. Bourne, P., 2007. Using the biopsychosocial model to evaluate the wellbeing of the Jamaican elderly. West Indian Medical J, 56: (suppl 3), 39-40. 17. Bourne, PA., 2008. Health Determinants: Using Secondary Data to Model Predictors of Well-being of Jamaicans. West Indian Medical J, 57(5):476-481.
18. Longest  BB,  Jr,  2002.  Health  Policymaking  in  the  United  States,  3rd  ed.  Health  Administration Press.  19. Brannon, L., and J.   Feist,  2007. Health psychology.  An introduction to behavior and health 6th  ed. Thomson Wadsworth.    20. Grossman,  M.,  1972.  The  demand  for  health‐  a  theoretical  and  empirical  investigation.  National Bureau of Economic Research.  21. Smith, JP., and  R.  Kington,  1997.   Demographic and economic correlates of health in old age.   Demography; 34:159‐170.  

22. Ross, CE., and J. Mirowsky, 1999. Refining the association between education and health: The effects of quantity, credential, and selectivity. Demography; 36:445-460. 23. Freedman, VA., and LG. Martin, 1999. The role of education in explaining and forecasting trends in functional limitations among older Americans. Demography, 36:461-473.
24. Meryn, S., 2004. Sex Quo Vadis: 21 the first female century: The Journal of Men’s health & sex,  1: 3‐5.  25. Spector, RE., 2004. Cultural diversity in health and illness, 6th ed. New Jersey. 

26. Barrow, Christine. 1998. Caribbean Sex Ideologies: Introduction and Overview. In Caribbean Portraits: essays on Sex Ideologies and Identities, Ed., Christine, B, Ian Randle Publishers, pp: xi-xxxviii. 27. Chevannes, B., 1999. What we sow and what we reap – problems in the cultivation of male identity in Jamaica. Grace, Kennedy Foundation. 28. Brown, J., and B. Chevannes,1998. Why man stay so – ties the Heifer and loose the bull: an examination of sex socialization in the Caribbean. University of the West Indies. 29. Bailey, W., (ed), 1998. Sex and the family in the Caribbean. Institute of Social and Economic Research.
30. Marmot, M., 2003.The influence of Income on Health:  Views of an Epidemiologist: Does money  really matter? Or is it a maker for something else?  Health Affairs, 21:31‐46. 

31. Powell, LA., Bourne, P., and L. Waller, 2007. Probing Jamaica’s Political Culture: Main Trends in the July-August 2006 Leadership and Governance Survey, volume 1. Centre for Leadership and Governance, Department of Government, the University of the West Indies. 32. Pacione, M., 2006. Urban environmental quality and human wellbeing –a social geographical perspective. Landscape and Urban Planning, 65:19-30.

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160   

Table 6.1: Life Expectancy at Birth of Jamaicans by Sex: 1880-2004 Average Expected Years of Life at Birth Period: 1880-1882 1890-1892 1910-1912 1920-1922 1945-1947 1950-1952 1959-1961 1969-1970 1979-1981 1989-1991 1999-2001 2002-2004 Man 37.02 36.74 39.04 35.89 51.25 55.73 62.65 66.70 69.03 69.97 70.94 71.26 Woman 39.80 38.30 41.41 38.20 54.58 58.89 66.63 70.20 72.37 72.64 75.58 77.07

Sources: Demographic Statistics (1972-2006) in Bourne, P. Determinants of well-being of the Jamaican Elderly. Unpublished thesis, The University of the West Indies, Mona Campus; 2007.

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Table 6.2: Inflation, Public-Private Health Care Service Utilization, Incidence of Poverty, Illness and Prevalence of Population with Health Insurance (in per cent), 1988-2007 Year Inflation Mean Public Utilization Private Utilization Prevalence of poverty Illness Health Seeking Medical Care Days of Illness NI 54.6 38.6 47.7 50.9 51.8 51.4 58.9 54.9 59.6 60.8 68.4 60.7 63.5 64.1 NI 65.1 NI 70.0 66.0 NI 11.4 10.1 10.2 10.8 10.4 10.4 10.7 10.0 9.9 11.0 11.0 9.0 10.0 10.0 NI 10.0 NI 9.8 9.9

Insurance Coverage NI 16.8 18.3 13.7 10.6 12.0 12.9 9.8 10.7 9.7 8.8 10.1 14.2 13.4 12.6 NI 11.4 NI 12.2 15.5 NI 8.2 9.0 8.6 9.0 10.1 8.8 9.7 9.8 12.6 12.1 12.1 14.0 13.9 13.5 NI 19.2 NI 18.4 21.2

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

8.8 17.2 29.8 80.2 40.2 30.1 26.8 25.6 15.8 9.2 7.9 6.8 6.1 8.8 7.2 13.8 13.7 12.6 5.7 16.8

NI 42.0 39.4 35.6 28.5 30.9 28.8 27.2 31.8 32.1 37.9 37.9 40.8 38.7 57.8 NI 46.3 NI 41.3 40.5

NI 54.0 60.6 57.7 63.4 63.8 66.7 66.4 63.6 58.8 57.3 57.1 53.6 54.8 42.7 NI 46.4 NI 52.8 51.9

NI 30.5 28.4 44.6 33.9 24.4 22.8 27.5 26.1 19.9 15.9 16.9 18.9 16.9 19.7 NI 16.9 NI 14.3 9.9

Source: Bank of Jamaica, Statistical Digest, Jamaica Survey of Living Conditions, Economic and Social Survey of Jamaica, various issues Note: Inflation is measured point-to-point at the end of each year (December to December), based on Consumer Price Index (CPI) NI – No Information Available

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Table 6.3: Seeking Medical Care, Self-reported illness, and Sex composition of those who report illness and Seek Medical Care in Jamaica (in percentage), 1988-2007 Seeking Medical Care Men NI 44.7 37.9 48.5 49.0 48.0 49.0 59.0 50.5 60.0 57.8 64.2 57.4 56.3 62.1 NI 64.2 NI 71.7 62.8 Seeking Medical Care Women NI 52.8 39.2 47.4 52.5 54.7 53.4 58.9 58.5 59.3 62.8 71.1 63.2 68.2 65.3 NI 65.7 NI 68.8 68.1 Reporting Reporting IllnessIllnessMen Women NI 15.0 16.3 12.1 9.9 10.4 11.6 8.3 9.7 8.5 7.4 8.1 12.4 10.8 10.4 NI 8.9 NI 10.3 13.1 NI 18.5 20.3 15.0 11.3 13.5 14.3 11.3 11.8 10.9 10.1 12.2 16.8 15.9 14.6 NI 13.6 NI 14.1 17.8 Mean Mean Days Days Of Of Illness Illness Men Women NI NI 10.6 11.1 10.2 10.2 10.0 10.3 10.7 10.9 10.7 10.1 10.3 10.4 10.6 10.7 10.0 11.0 11.0 10.0 11.0 11.0 11.0 11.0 9.0 9.0 9 10 10.0 10.0 NI NI 11.0 10.0 NI NI 9.7 10.0 10.6 9.3

Year 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Seeking Medical Care NI 54.6 38.6 47.7 50.9 51.8 51.4 58.9 54.9 59.6 60.8 68.4 60.7 63.5 64.1 NI 65.1 NI 70.0 66.0

Health Insurance Coverage NI 8.2 9.0 8.6 9.0 10.1 8.8 9.7 9.8 12.6 12.1 12.1 14.0 13.9 13.5 NI 19.2 NI 18.4 21.2

Source: Jamaica Survey of Living Conditions, various issues NI - No Information was available

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Table 6.4: Public Health Care Visits (using the JSLC, data) and Actual Health Care Visits (using Ministry of Health Jamaica, data), 1997 and 2004 Public Health Care Visits in Jamaica Year 1997 Actual Visits, MOH1 % 33.1 Self-reported Visits, JSLC % 32.1

2004

52.9*

46.8

Source: Ministry of Health Jamaica and the Jamaica Survey of Living Conditions (JSLC)
1

The Percentages of Actual visits were computed by author

*Preliminary data were used to calculate this percentage

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Table 6.5: Self-reported Health Status per 1,000 by Population, Men and Women; Sex-Ratio of Self-reported Health Status, and Female to Male Ratio of Self-reported Health Status, 1989-2006 Year Self-reported Health Status per 1,000 Male-to-Female Female-to-Male ratio ratio of Selfof Self-reported Health reported Health Status Population Men Women Status

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

168 183 137 106 120 129 98 107 97 88 101 142 134 126 114 122 155

150 163 121 99 104 116 83 97 85 74 81 124 108 104 89 103 131

185 203 150 113 135 143 113 118 109 101 122 168 159 146 136 141 178

81 80 81 88 77 81 73 82 78 73 66 74 68 71 65 73 74

123 125 124 114 130 123 136 122 128 136 151 135 147 140 153 137 136

Computed by Paul Andrew Bourne from Jamaica Survey of Living Conditions from various years

165   

70.00

60.00

Seeking Medical Care

50.00

40.00

R Sq Linear = 0.163

30.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00

Illness/Injury

Figure 6.1: Percentage of Men Seeking Medical Care by Percentage of Men reporting Illness

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70.00

60.00

Seeking Medical Care

50.00

40.00

R Sq Linear = 0.576

30.00 10.00 20.00 30.00 40.00

Prevalence of Poverty

Figure 6.2: Percentage of People Seeking Medical Care by Prevalence of Poverty

167   

70.00

Health Care Seeking Behaviour of Men

60.00

50.00

40.00

R Sq Linear = 0.354

7.50

10.00

12.50

15.00

17.50

Self-reported Health of Men

Figure 6.3: Percentage of Men Seeking Medical Care by Percentage of Men reporting Illness

168   

Health Care Seeking Behaviour of Women

70.00

60.00

50.00

40.00

R Sq Linear = 0.088

10.00

12.00

14.00

16.00

18.00

20.00

22.00

Self-reported Health of Women

Figure 6.4: Percentage of Women Seeking Medical Care by Percentage of Women reporting Illness

169   

70.00

60.00

Seeking Medical Care

50.00

40.00

R Sq Linear = 0.561

30.00 9.00 12.00 15.00 18.00 21.00

Health Insurance

Figure 6.5: Percentage of people Seeking Medical Care by Percentage with Health Insurance

170   

21.00

18.00

Health Insurance

15.00

12.00

R Sq Linear = 0.597 9.00

10.00

20.00

30.00

40.00

Prevalence of Poverty

Figure 6.6: Ownership of Health Insurance and Prevalence of Poverty

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Chapter 7
Social determinants of self-reported health across the Life Course

Paul Andrew Bourne

The socio-psychological and economic factors produced inequalities in health and need to be considered in health development. In spite of this, extensive review of health Caribbean revealed that no study has examined health status over the life course of Jamaicans. With the value of research to public health, this study is timely and will add value to understanding the elderly, middle age and young adults in Jamaica. The aim of this study is to develop models that can be used to examine (or evaluate) social determinants of health of Jamaicans across the life course, elderly, middle age and young adults. The current study used dataset of 2002 Jamaica Survey of Living Conditions (JSLC). It is a cross-sectional survey which used stratified random probability sampling technique to collect data from respondents. Logistic regression analyses were used to model the social determinants of health status of Jamaicans across the life course. Eleven variables emerged as statistically significant predictors of current good health Status of Jamaicans (p<0.05). The factors are retirement income (95%CI=0.49-0.96), logged medical expenditure (95% CI =0.91-0.99), marital status (Separated or widowed or divorced: 95%CI=0.31-0.46; married: 95%CI=0.50-0.67; Never married), health insurance (95%CI=0.029-0.046), area of residence (other towns:, 95%CI=1.05-1.46; rural area:), education (secondary: 95%CI=1.17-1.58; tertiary: 95%CI=1.47-2.82; primary or below: OR=1.00), social support (95%CI=0.75-0.96), gender (95%CI=1.281-1.706), psychological affective conditions (negative affective: 95%CI=0.939-0.98; positive affective: 95%CI:1.05-1.11), number of males in household (95%CI:1.07-1.24), number of children in household (95%CI=1.12-1.27) and previous health status. There are disparities in the social determinants of health across the life course, which emerged from the current findings. The findings are far reaching and can be used to aid policy formulation and how social determinants of health are viewed in the future.

INTRODUCTION

Health is a multidimensional construct which goes beyond dysfunctions (illnesses, ailment or injuries) [1-14]. Although World Health Organization (WHO) began this broaden conceptual framework in the late 1940s [1], Engel [3] was the first to develop the biopsychosocial model that
172   

can be used to examine and treat health of mentally ill patient. Engel’s biopsychosocial model was both in keeping with WHO’s perspective of health and again a conceptual model of health. Both WHO and Engel’s works were considered by some scholar as too broad and as such difficult to measure [15]; although this perspective has some merit, scholars have ventured into using different proxy to evaluate the ideal conceptual definition forwarded by WHO for some time now. Psychologists have argued that the use of diseases to proxy health is unidirectional (or negative) [2], and that the inclusion of social, economic and psychological conditions in health is broader and more in keeping with the WHO’s definition of health than diseases. Diener was the first psychologist to forward the use of happiness to proxy health (or wellbeing) of an individual [16, 17]. Instead of debating along the traditional cosmology health, Diener took the discussion into subjective wellbeing. He opined that happiness is a good proxy for subjective wellbeing of a person, and embedded therein is wider scope for health than diseases. Unlike classical economists who developed Gross Domestic Product per capita (GDP) to examine standard of living (or objective wellbeing) of people as well this being an indicator of health status along with other indicators such as life expectancy, Diener and others believe that people are the best judges of their state. This is no longer a debate, as some economists have used happiness as a proxy of health and wellbeing [18-20]; and they argued that it is a good measurement tool of the concept. Theoretical Framework Whether the proxy of health (or wellbeing) is happiness, self-reported health status, selfrated health conditions, life satisfaction or ill-being, it was not until in the 1970s that econometric analyses were employed to the study of health. Grossman [9] used econometric to capture factors that simultaneously determine health stock of a population. Grossman’s work transformed the
173   

conceptual framework outlined by WHO and Engel to a theoretical framework for the study of health. Using data for the world, Grossman established an econometric model that captures determinants of health. The model read (Model 1): Ht = ƒ (Ht-1, Go, Bt, MCt, ED) ……………………………………………….. Model (1) where Ht – current health in time period t, stock of health (Ht-1) in previous period, Bt
–

smoking and excessive drinking, and good personal health behaviours (including exercise – Go), MCt,- use of medical care, education of each family member (ED), and all sources of household income (including current income). Grossman’s model was good at the time; however, one of the drawbacks to this model was the fact that some crucible factors were omitted by the aforementioned model. Based on that limitation, using literature, Smith and Kington [10] refined, expanded and modified Grossman’s work as it omitted important variables such as price of other inputs and family background or genetic endowment which are crucible to health status. They refined Grossman’s work to include socioeconomic variables as well as some other factors [Model (2)]. Ht = H* (Ht-1, Pmc, Po, ED, Et, Rt, At, Go) ………………………..…………… Model (2) Model (2) expresses current health status Ht as a function of stock of health (Ht-1), price of medical care Pmc, the price of other inputs Po, education of each family member (ED), all sources of household income (Et), family background or genetic endowments (Go), retirement related income (Rt ), asset income (At). It is Grossman’s work that accounts for economists like Veenhoven’s [20] and Easterlin’s [19] works that used econometric analysis to model factors that determine subjective wellbeing.
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Like Veenhoven [20], Easterlin [19] and Smith and Kington [10], Hambleton et al. [6] used the same theoretical framework developed by Grossman to examine determinants of health of elderly (ages 65+ years) in Barbados. Hambleton et al.’s work refined the work of Grossman and added some different factors such as geriatric depression index; past and current nutrition; crowding; number of children living outside of household; and living alone. Unlike Grossman’s study, he found that current disease conditions accounted for 67.2% of the explained variation in health status of elderly Barbadians, with life style risks factors accounting for 14.2%, and social factors 18.6%. One of the additions to Grossman’s work based on Hambleton et al.’s study was actual proportion of each factor on health status and life style risk factors. A study published in 2004, using life satisfaction and psychological wellbeing to proxy wellbeing of 2,580 Jamaicans, Hutchinson et al. [21] employed the principles in econometric analysis to examine social and health factors of Jamaicans. Other studies conducted by Bourne on different groups and sub-groups of the Jamaican population have equally used the principles of econometric analysis to determine factors that explain health, quality of life or wellbeing [5, 8, 22, 23]. Despite the contribution of Hutchinson et al’s and Bourne’s works to the understanding of wellbeing, there is a gap in the literature on a theoretical framework explains good health status of the life course of Jamaicans. The current study will model predictors of good health status of Jamaicans as well as good health status of young adults, middle age adults and elderly in order to provide a better understanding of the factors that influence each cohort. METHODS Participants and questionnaire

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The current research used a nationally cross-sectional survey of 25,018 respondents from the 14 parishes in Jamaica. The survey used stratified random probability sampling technique to draw the 25,018 respondents. The non-response rate for the survey was 29.7% with 20.5% who did not respond to particular questions, 9.0% did not participated in the survey and another 0.2% was rejected due to data cleaning. The study used secondary cross-sectional data from the Jamaica Survey of Living Conditions (JSLC). The JSLC was commissioned by the Planning Institute of Jamaica (PIOJ) and the Statistical Institute of Jamaica (STATIN). These two organizations are responsible for planning, data collection and policy guideline for Jamaica. The JSLC is a self-administered questionnaire where respondents are asked to recall detailed information on particular activities. The questionnaire covers demographic variables, health, immunization of children 0 to 59 months, education, daily expenses, non-food consumption expenditure, housing conditions, inventory of durable goods, and social assistance. Interviewers are trained to collect the data from household members. The survey is conducted between April and July annually. Model The multivariate model used in this study is a modification of those of Grossman and Smith & Kington which captures the multi-dimensional concept of health, and health status. The present study further refine the two aforementioned works and in the process adds some new factors such as psychological conditions, crowding, house tenure, number of people per household and a deconstruction of the numbers by particular characteristics i.e. males, females and children (ages ≤ 14 years). Another fundamental difference of the current research and those of Grossman, and Smith and Kington is that it is area specific as it is focused on Jamaican residents.
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The proposed model that this research seeks to evaluate is displayed below [Model (3)]:
Ht = f(Ht-1,Pmc, EDi, Rt, At, Qt, HHt, Ci, Eni, MSi, HIi, HTi, SSi, LLi,Xi, CRi, Di, Oi, Σ(NPi,PPi), Mi,Ni, FSi, Ai, Wi, εi)….. Model (3)

The current health status of a Jamaica, Ht, is a function of 23 explanation variables, where Ht is current health status of person i, if good or above (i.e. no reported health conditions four week leading up to the survey period), 0 if poor (i.e. reported at least one health condition); Ht-1 is
stock of

health for previous period; lnPmc is logged cost of medical care of person i; EDi is

educational level of person i, 1 if secondary, 1 if tertiary and the reference group is primary and below; Rt is retirement income of person i, 1 if receiving private and/or government pension, 0 if otherwise; HIi is health insurance coverage of person i, 1 if have a health insurance policy, 0 if otherwise; HTi is house tenure of person i, 1 if rent, 0 if squatted; Xi is gender of person i, 1 if female, 0 if male; CRi is crowding in the household of person i; Σ(NPi,PPi) NPi is the summation of all negative affective psychological conditions and PPi is the summation of all positive affective psychological conditions; Mi is number of male in household of person i and Fi is number of female in household of person i; Ai is the age of the person i and Ni is number of children in household of person i; LLi is living arrangement where 1= living with family members or relative, and 0=otherwise and social standing (or social class), Wi. Statistical analysis Statistical analyses were performed using Statistical Packages for the Social Sciences (SPSS) for Windows, Version 16.0 (SPSS Inc; Chicago, IL, USA). A single hypothesis was tested, which was ‘health status of rural resident is a function of demographic, social, psychological and economic variables.’ The enter method in logistic regression was used to test the hypothesis in order to determine those factors that influence health status of rural residents if the dependent
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variable is a binary one; and linear multiple regression in the event the dependent variable was a normally distributed metric variable . The final model was established based on those variables that are statistically significant (ie. p < 0.05) – ie 95% confidence interval (CI), and all other variables were removed from the final model (p>0.05). Continuing, categorical variables were coded using the ‘dummy coding’ scheme. The predictive power of the model was tested using Omnibus Test of Model and Hosmer and Lemeshow [24] was used to examine goodness of fit of the model. The correlation matrix was examined in order to ascertain whether autocorrelation (or multi-collinearity) existed between variables. Cohen and Holliday [25] stated that correlation can be low/weak (0 to 0.39); moderate (0.4-0.69), or strong (0.7-1.0). This was used in this study to exclude (or allow) a variable in the model. Where collinearity existed (r > 0.7), variables were entered independently into the model to determine those that should be retained during the final construction of the model. To derive accurate tests of statistical significance, we used SUDDAN statistical software (Research Triangle Institute, Research Triangle Park, NC), and this was adjusted for the survey’s complex sampling design. Finally, Wald statistics was used to determine the magnitude (or contribution) of each statistically significant variables in comparison with the others, and the odds ratio (OR) for the interpreting each significant variables. Results: Modelling Current Good Health Status of Jamaicans, Elderly, Middle Age and Young adults Predictors of current Good Health Status of Jamaicans. Using logistic regression analyses, eleven variables emerged as statistically significant predictors of current good health status of Jamaicans (p<0.05, Model 4). The factors are retirement income, logged medical expenditure, marital status,
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health insurance, area of residence, education, social support, gender, psychological affective conditions, number of males in household, number of children in household and previous health status (Table 7.1). Ht = f(Ht-1, Rt, Pmc, EDi, MSi, HIi, SSi,ARi, Xi, Σ(NPi,PPi), Mi,Ni, εi)...……..... Model (4) The model [ie Model (4)] had statistically significant predictive power (χ2 (27) =1860.639, p < 0.001; Hosmer and Lemeshow goodness of fit χ2=4.703, p = 0.789) and overall correctly classified 85.7% of the sample (correct classified 98.3% of cases of good health status and correctly classified 33.9% of cases of dysfunctions). There was a moderately strong statistical correlation between age, marital status, education, retirement income, per capita income quintiles, property ownership, and so these were omitted from the initial model (ie model 3). Based on that fact, three age groups were classified (young adults – ages 15 to 29 years; middle age adults – ages 30 to 59 years; and elderly – ages 60+ years) and the initial model was once again tested. There were some modifications of the initial model in keeping with the age group. For young adults the initial model was amended by excluding retirement income, property ownership, divorced, separated or widowed, number of children in household, and house tenure. The exclusion was based on the fact that more than 15% of cases missing in some categories and a high correlation between variables. Predictors of current Good Health Status of elderly Jamaicans. From the logistic regression analyses that were used on the data, eight variables were found to be statistically significant in predicting good health Status of elderly Jamaicans (P < 0.5) (Model 5). These factors were education, marital status, health insurance, area of residence, gender, psychological conditions,

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number of males in household, number of children in household and previous health status (Table 7.2). Ht = f(Ht-1, EDi, MSi, HIi, ,ARi, Xi, Σ(PPi), Mi,Ni, εi)...……………… …..... Model (5) The model had statistically significant predictive power (model χ2 (27) =595.026, P < 0.001; Hosmer and Lemeshow goodness of fit χ2=5.736, p = 0.677) and overall correctly classified 75.5% of the sample (correctly classified 94.6% of cases of good or beyond health status and correct classified 44.7% of cases of dysfunctions).

Predictors of current Good Health Status of middle age Jamaicans. Using logistic regression, six variables emerged as statistical significant predictors of current good health status of middle age Jamaican (p < 0.05) (Model 6). These factors are logged medical expenditure, physical

environment, health insurance, gender of respondents, psychological condition, number of children in household and previous health status (Table 7.3) Ht = f(Ht-1, Pmc, Eni, HIi, Xi, Σ(NPi),Ni, εi)..........................………..... Model (6) Based on table 7.3, the model had statistically significant predictive power (model χ2 (27) =547.543, p < 0.001; Hosmer and Lemeshow goodness of fit χ2=4.318, p = 0.827) and overall correctly classified 87.2% of the sample (correctly classified 98.3% of cases of good or beyond health status and correct classified 28.2% of cases of dysfunctions).

Predictors of current Good Health Status of young adult in Jamaica. Using logistic regression, two variables emerged as statistically significant predictors of current good health status of young adults in Jamaica (p<0.05) (Model 7). These are health insurance coverage, psychological condition, social class and previous health status (Table 7.4).
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Ht = f(Ht-1, Wi, HIi, Σ(NPi), εi)................................…………….....Model (7) From Table 7.3, the model had statistically significant predictive power (model χ2 (19) =453.733, p < 0.001; Hosmer and Lemeshow goodness of fit χ2=5.185, p = 0.738) and overall correctly classified 92.6% of the sample (correctly classified 99.0% of cases of good or beyond health status and correct classified 28.2% of cases of dysfunctions). Limitations to the Models Good Health Status of Jamaicans [ie Model (4)], elderly [ie Model (5)], middle age adults [ie Model (6)], and young adults [ie Model (7) are derivatives of Model (3). Good Health Status[ie Model (4) – Model (7)] cannot be distinguished and tested over different time periods, person differential, and these are important components of good health.

Ht = f(Ht-1, Rt, Pmc, EDi, MSi, HIi, SSi,ARi, Xi, Σ(NPi,PPi), Mi,Ni, εi)...………………………..... Model (4) Ht = f(Ht-1, EDi, MSi, HIi, ,ARi, Xi, Σ(PPi), Mi,Ni, εi)...………………………………………..... Model (5) Ht = f(Ht-1, Pmc, Eni, HIi, Xi, Σ(NPi),Ni, εi)....................................……………………………..... Model (6) Ht = f(Ht-1, Wi, HIi, Σ(NPi), εi).......................................................……………………….…….......Model (7) Ht = f(Ht-1,Pmc, EDi, Rt, At, Qt, HHt, Ci, Eni, MSi, HIi, HTi, SSi, LLi,Xi, CRi, Di, Oi, Σ(NPi,PPi), Mi,Ni, FSi, Ai, Wi,εi)……………………………………………………………………….. Model (3)

The current work is a major departure from Grossman’s theoretical model as he assumed that factors affecting good health Status over the life course are the same, this study disagreed with this fundamental assumption. This study revealed that predictors of good health status are not necessarily the same across the life course, and differently from that of the general populace. Despite those critical findings, healthy time gained can increase good health status directly and
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indirectly but this cannot be examined by using a single cross-sectional study. Health does not remain constant over any specified period, and to assume that this is captured in age is to assume that good or bad health change over year (s). Health stock changes over short time intervals, and so must be incorporated within any health model. People are different even across the same ethnicity, nationality, next of kin and socialization. This was not accounted for in the Grossman’s or the current work, as this is one of the assumptions. Neither Grossman’s study nor the current research recognized the importance of differences in individuals owing to culture, socialization and genetic composition. Each individual’s is different even if that person’s valuation for good health Status is the same as someone else who share similar characteristics. Hence, a variable P representing the individual should be introduced to this model in a parameter α (p). Secondly, the individual’s good (or bad) health is different throughout the course of the year and so time is an important factor. Thus, the researcher is proposing the inclusion of a time dependent parameter in the model. Therefore, the general proposition for further studies is that the function should incorporate α (p, t) a parameter depending on the individual and time. An unresolved assumption of this work which continues from Grossman’s model is that people choose health stock so that desired health is equal to actual health. The current data cannot test this difference in the aforementioned health status and so the researcher recommends that future study to account for this disparity so we can identify factors of actual health and difference between the two models. Discussions

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This study has modelled current good status of Jamaicans. Defining health into two categories (ie good – not reported an acute or illness; or poor – reported illness or ailment), this study has found that using logistic regression health status can be modeled for Jamaicans. The findings revealed that the probability of predicting good health status of Jamaicans was 0.789, using eleven factors; and that approximately 86% of the data was correctly classified in this study. Continuing, in Model (4) approximately 98% of those who had reported good health status were correctly classified, suggesting that using logistic regression to examine good health status of the Jamaican population with the eleven factors that emerged is both a good predictive model and a good evaluate or current good health status of the Jamaican population. This is not the first study to examine current good health status or quality of life in the Caribbean or even Jamaica [6, 2123, 26], but that none of those works have established a general and sub-models of good health over the life course. In Hambleton et al’s work, the scholars identified the factors (ie historical, current, life style, diseases) and how much of health they explain (R2=38.2%). However, they did not examine the goodness of fit of the model or the correctness of fit of the data. Bourne’s works [12,13] were similar to that of Hambleton et al’s study, as his study identified more factors (psychological conditions; physical environment, number of children or males or females in household and social support) and had a greater explanatory power (adjusted r square = 0.459) but again the goodness of fit and correctness of fit of the data were omitted. Again this was the case in Hutchinson et al.’s research. Like previous studies in the Caribbean that have examined health status [6, 21-23, 26], those conducted by the WHO and other scholars [27-32] did not explore whether social
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determinants of health vary across the life course. Because this was not done, we have assumed that the social determinants are the same across the life. However, a study by Bourne and Eldemire-Shearer [33] introduced into the health literature that social determinants differ across social strata for men. Such a work brought into focus that there are disparities in the social determinants of health across particular social characteristic and so researchers should not arbitrarily assume that they are the same across the life course. While Bourne and EldemireShearer’s work [33] was only among men across different social strata in Jamaica (poor and wealthy), the current study shows that there are also differences in social and psychological determinants of health across the life course. The current study has concluded that the factors identified to determine good health status for elderly, had the lowest goodness of fit (approximately 68%) while having the greatest explanatory power (R2= 35%). The findings also revealed low explanatory powers for young adults (R2=22.6%) and middle age adults (R2=23%), with latter having a greater goodness of fit for the data as this is owing to having more variables to determine good health. Such a finding highlights that we know more about the social determinants for the elderly than across other age cohorts (middle-aged and young adults). And that using survey data for a population to ascertain the social determinants of health is more about those for the elderly than across the life course of a population. Another important finding is of the eleven factors that emerge to explain good health status of Jamaicans, when age cohorts were examine it was found that young adults had the least number of predictors (ie health insurance, social class and negative affective psychological conditions). This suggests that young adult’s social background and health insurance are
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important factors that determine their good health status and less of other determinants that affect the elderly and middle age adults. It should be noted that young adult is the only age cohort with which social standing is a determinant of good health. Even though the good health status model that emerged from this study is good, the low explanatory power indicates that young adults are unique and further study is needed on this group in order to better understand those factors that account for their good health. Furthermore, this work revealed that as people age, the social determinants of health of the population are more in keeping with those of the elderly than at younger ages. Hence, the social determinants identified by Grossman [9], Smith and Kington [10] and purported by Abel-Smith [11] as well as the WHO [27] and affiliated researchers [28-32] are more for the elderly population than the population across the life course. Conclusions There are disparities in the social determinants of health across the life course, which emerged from the current findings. The findings are far reaching and can be used to aid policy formulation and how we examine social determinants of health. Another issue which must be researched is whether there are disparities in social determinants of health based on the conceptualization and measurement of health status (using self-reported health, and health conditions). Disclosures The author reports no conflict of interest with this work.

Disclaimer
The researcher would like to note that while this study used secondary data from the Jamaica Survey of Living Conditions (JSLC), none of the errors in this paper should be ascribed to the Planning Institute of Jamaica (PIOJ) and/or the Statistical Institute of Jamaica (STATIN), but to the researcher.
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Acknowledgement
The author thanks the Data Bank in Sir Arthur Lewis Institute of Social and Economic Studies, the University of the West Indies, Mona, Jamaica for making the dataset (2002 JSLC) available for use in this study, and the National Family Planning Board for commissioning the survey.

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Table 7.1: Good Health Status of Jamaicans by Some Explanatory Variables
Wald statistic Variable Middle Quintile Two Wealthiest Quintiles Poorest-to-poor Quintiles* Retirement Income Household Head Logged Medical Expenditure Average Income Average Consumption Environment Separated or Divorced or Widowed Married Never married* Health Insurance Other Towns Urban Area Rural Area* House Tenure - Rent House Tenure - Owned House Tenure- Squatted* Secondary Education Tertiary Education Primary and below* Social Support Living Arrangement Crowding Land ownership Gender Negative Affective Positive Affective Number of males in household Number of females in household Number of children in household Constant -0.17 -0.06 -0.01 -0.07 0.39 -0.04 0.07 0.14 0.06 0.17 1.89 0.07 0.13 0.04 0.07 0.07 0.01 0.01 0.04 0.04 0.03 0.65 6.33 0.20 0.08 0.90 28.67 14.96 26.26 13.36 2.36 29.16 8.31 0.012 0.659 0.772 0.342 0.000 0.000 0.000 0.000 0.124 0.000 0.004 0.85 0.95 0.99 0.93 1.48 0.96 1.08 1.15 1.06 1.19 6.59 0.75 0.73 0.91 0.81 1.28 0.94 1.05 1.07 0.98 1.12 0.96 1.22 1.07 1.08 1.71 0.98 1.11 1.24 1.14 1.27 0.31 0.71 0.08 0.17 15.81 18.09 0.000 0.000 1.36 2.03 1.17 1.45 1.58 2.82 -1.08 -0.42 0.88 0.55 1.48 0.58 0.224 0.447 0.34 0.66 0.06 0.23 1.93 1.93 -3.31 0.21 -0.01 0.12 0.08 0.13 776.64 6.64 0.00 0.000 0.010 0.952 0.04 1.24 0.99 0.03 1.05 0.78 0.05 1.46 1.27 Coefficient -0.03 -0.11 -0.38 0.17 -0.05 0.00 0.00 0.01 -0.97 -0.55 Std Error. 0.10 0.10 0.17 0.29 0.02 0.00 0.00 0.07 0.10 0.08 0.09 1.26 4.88 0.37 5.10 1.56 0.16 0.02 87.36 53.05 P 0.764 0.261 0.027 0.543 0.024 0.212 0.689 0.891 0.000 0.000 CI (95%) Odds Ratio 0.97 0.90 0.68 1.19 0.95 1.00 1.00 1.01 0.38 0.58 Lower 0.81 0.74 0.49 0.68 0.91 1.00 1.00 0.88 0.31 0.50 Upper 1.17 1.09 0.96 2.08 0.99 1.00 1.00 1.16 0.46 0.67

χ2 (27) =1860.639, p < 0.001; n = 8,274 -2 Log likelihood = 6331.085 Hosmer and Lemeshow goodness of fit χ2=4.703, p = 0.789. Nagelkerke R2 =0.320 Overall correct classification = 85.7% (N=7,089) Correct classification of cases of good or beyond health status =98.3% (N=6,539) Correct classification of cases of dysfunctions =33.9% (N=550); *Reference group

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Table 7.2: Good Health Status of Elderly Jamaicans by Some Explanatory Variables
Coefficient Middle Quintile Two Wealthiest Quintiles Poorest-to-poor quintiles Retirement Income Household Head Logged Medical Expenditure Average Income Environment Separated or Divorced or Widowed Married Never married* Health Insurance Other Towns Urban Rural areas* House tenure - rented House tenure - owned House tenure – squatted* Secondary Education Tertiary Education Primary or below* Social support Living arrangement Crowding Landownership Gender Negative Affective Positive Affective Number of male Number of females Number of children Constant -0.08 0.26 -0.05 0.17 0.47 -0.03 0.07 0.18 0.05 0.22 -1.32 0.11 0.18 0.09 0.13 0.12 0.02 0.02 0.07 0.07 0.06 1.44 0.47 2.11 0.29 1.72 14.67 1.97 9.26 6.75 0.49 12.09 0.83 0.495 0.146 0.593 0.190 0.000 0.160 0.002 0.009 0.485 0.001 0.362 0.93 1.30 0.95 1.19 1.60 0.97 1.07 1.19 1.05 1.24 0.27 0.75 0.91 0.80 0.92 1.26 0.94 1.03 1.04 0.91 1.10 1.15 1.84 1.14 1.54 2.04 1.01 1.12 1.36 1.21 1.40 -0.46 0.81 0.11 0.35 16.06 5.45 0.000 0.020 0.63 2.26 0.51 1.14 0.79 4.47 -20.37 1.22 40192.9 1.24 0.00 0.96 1.000 0.327 0.00 3.38 0.00 0.30 -3.35 0.33 0.40 0.22 0.14 0.21 241.88 5.32 3.48 0.000 0.021 0.062 0.04 1.39 1.49 0.02 1.05 0.98 0.05 1.83 2.27 -0.10 0.12 -0.22 0.89 -0.06 0.00 -0.16 -0.49 -0.33 Std Error 0.15 0.17 0.22 0.65 0.04 0.00 0.12 0.15 0.15 Wald statistic 0.47 0.47 1.00 1.86 2.16 0.93 1.80 11.00 4.82 P 0.495 0.491 0.317 0.172 0.142 0.335 0.180 0.001 0.028 Odds Ratio 0.90 1.12 0.81 2.44 0.95 1.00 0.86 0.61 0.72 CI (95%) Lower 0.67 0.81 0.53 0.68 0.88 1.00 0.68 0.46 0.54 Upper 1.22 1.56 1.23 8.76 1.02 1.00 1.08 0.82 0.97

38.60

χ2 (27) =595.026, p < 0.001; n = 2,002 -2 Log likelihood = 2,104.66 Hosmer and Lemeshow goodness of fit χ2=5.736, p = 0.677. Nagelkerke R2 =0.347 Overall correct classification = 75.5% (N=1.492) Correct classification of cases of good or beyond health status =94.6% (N=1,131) Correct classification of cases of dysfunctions =44.7% (N=361); *Reference group

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Table 7.3: Good Health Status of Middle Age Jamaicans by Some Explanatory Variables
Coefficient Middle Quintile Two Wealthiest Quintiles Poorest-to-poor Quintiles* Retirement Income Household Head Logged Medical Expenditure Average Income Environment Separated or Divorced or Widowed Married Never married* Health Insurance Other Towns Urban Rural areas* House tenure - rented House tenure - owned House tenure – squatted* Secondary education Tertiary education Primary or below* Social support Living Arrangement Crowding Landownership Gender Negative Affective Positive Affective Number of males in house Number of female in house Number of children in house Constant 0.03 -0.29 -0.57 0.50 -0.09 0.00 0.31 Std Error 0.15 0.15 0.36 0.45 0.04 0.00 0.12 Wald statistic 0.04 3.67 2.44 1.24 6.44 0.53 7.41 P 0.834 0.055 0.119 0.265 0.011 0.465 0.006 Odds Ratio 1.03 0.75 0.57 1.66 0.91 1.00 1.37 CI (95%) Lower 0.76 0.56 0.28 0.68 0.85 1.00 1.09 Upper 1.40 1.01 1.16 4.01 0.98 1.00 1.71

-0.20 -0.18 -3.04 0.11 -0.01 17.94 -1.33 0.19 0.34 -0.08 -0.19 -0.05 -0.13 0.51 -0.08 0.05 0.03 0.08 0.10 3.29

0.23 0.11 0.17 0.12 0.19 20029.78 1.12 0.13 0.23 0.10 0.21 0.06 0.11 0.11 0.02 0.02 0.06 0.06 0.04 1.25

0.77 2.68 320.76 0.75 0.00 0.00 1.43 2.11 2.23 0.57 0.87 0.65 1.47 21.41 24.66 4.51 0.23 2.09 5.47 6.89

0.380 0.102 0.000 0.387 0.963 0.999 0.232 0.146 0.135 0.450 0.351 0.419 0.226 0.000 0.000 0.034 0.630 0.149 0.019 0.009

0.82 0.84 0.05 1.11 0.99

0.53 0.68 0.03 0.87 0.68 0.00 0.03 0.94 0.90 0.76 0.55 0.85 0.71 1.34 0.90 1.00 0.92 0.97 1.02

1.28 1.04 0.07 1.42 1.44

0.26 1.20 1.41 0.93 0.83 0.95 0.88 1.66 0.92 1.05 1.03 1.08 1.11 26.77

2.35 1.55 2.21 1.13 1.24 1.07 1.08 2.06 0.95 1.10 1.14 1.21 1.21

χ2 (27) =547.543, p < 0.001; n = 3,799 -2 Log likelihood = 2,776.972 Hosmer and Lemeshow goodness of fit χ2=4.318, p = 0.827. Nagelkerke R2 =0.230 Overall correct classification = 87.2% (N=3,313) Correct classification of cases of good or beyond health status =98.3% (N=3,143) Correct classification of cases of dysfunctions =28.2% (N=170); *Reference group

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Table 7.4: Good Health Status of Young Adults Jamaicans by Some Explanatory Variables
Coefficient Middle Quintile Two Wealthiest Quintiles Poorest-to-poor quintiles* Household Head Logged Medical Expenditure Average Income Environment Health Insurance Other Towns Urban Rural area* Secondary education Tertiary education Primary and below* Social support Crowding Gender Negative Affective Positive Affective Number of males in house Number of females in house Married Never married* Constant -0.06 -0.39 -0.14 0.04 0.19 -0.04 0.07 0.13 0.06 0.08 2.75 0.41 0.47 0.13 0.06 0.15 0.02 0.03 0.07 0.06 0.22 0.67 0.02 0.70 1.22 0.65 1.60 4.22 6.81 3.67 0.87 0.13 16.62 0.886 0.405 0.269 0.420 0.206 0.040 0.009 0.055 0.351 0.717 0.000 0.94 0.68 0.87 1.05 1.20 0.96 1.07 1.13 1.06 1.09 15.57 0.43 0.27 0.68 0.94 0.90 0.93 1.02 1.00 0.94 0.70 2.09 1.69 1.12 1.16 1.60 1.00 1.13 1.29 1.20 1.68 -0.06 -0.59 -0.25 0.01 0.00 -0.03 -3.73 0.23 -0.05 Std Error 0.19 0.18 0.39 0.04 0.00 0.13 0.21 0.15 0.18 Wald statistic 0.10 11.10 0.41 0.09 3.29 0.04 321.51 2.42 0.07 P 0.747 0.001 0.520 0.760 0.070 0.840 0.000 0.120 0.788 Odds Ratio 0.94 0.55 0.78 1.01 1.00 0.97 0.02 1.26 0.95 CI (95%) Lower 0.65 0.39 0.36 0.93 1.00 0.75 0.02 0.94 0.68 Upper 1.37 0.78 1.68 1.10 1.00 1.26 0.04 1.69 1.34

χ2 (19) =453.733, p < 0.001; n = 4,174 -2 Log likelihood = 2,091.88 Hosmer and Lemeshow goodness of fit χ2=5.185, p = 0.738. Nagelkerke R2 =0.226 Overall correct classification = 92.6% (N=3,864) Correct classification of cases of good or beyond health status =99.0% (N=3,757) Correct classification of cases of dysfunctions =28.2% (N=107); *Reference group

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Chapter 8
Sociomedical Public Health in Jamaica

Paul Andrew Bourne
An extensive review of health and health care-seeking behaviour studies revealed that studies that have examined health care-seeking behaviour and/or health status have used a piecemeal approach by either investigating health or health care-seeking behaviour. The current research seeks to examine (1) demographic characteristics of health care-seekers; (2) sociomedical characteristics of health status; (3) factors that account for health status; (4) factors that explain health care-seeking behaviour and (5) characteristics of those who reported having been diagnosed with particular health conditions. The current study used a sample of 6,783 respondents. The survey was drawn using stratified random sampling. An administered questionnaire was used to collect the data, which were stored and analyzed using SPSS for Windows 16.0 (SPSS Inc; Chicago, IL, USA). Logistic regressions were used to established (1) health status and (2) health care-seeking behaviour model. Two-thirds of the variability in health status was accounted for medical factors such as self-reported illness and length of illness compared to one-third by social conditions. Four variables emerged as statistically significant correlates of self-reported health care-seeking behaviour: self-reported illness, (OR = 358.31, 95% CI = 233.31, 550.30); health status, (OR = 0.46, 95% CI = 0.31, 0.67); health insurance coverage, (OR = 1.74, 95% CI = 1.26, 2.40); age, (OR = 1.01, 95% CI = 1.00, 1.01); and per capita consumption, (OR = 1.00, 95% CI = 1.00, 1.00). The problems which must be addressed by public health policy makers are how to address the high percent of Jamaicans who are current diagnosed with chronic illness (i.e. 43 %) as well the fact that even children are now diagnosed with diabetes mellitus, and use the social conditions to improve health and quality of life of Jamaica.

Introduction

The discipline of public health unlike medicine relies on individuals’ perceptions, beliefs, customs, idiosyncrasies, culture and practices in order to improve health and quality of life and
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not merely an understanding the aetiology of diseases. Public health is, therefore, left with the arduous task of comprehending the human experiences and practices, and using them to enhance, modify and change peoples’ unhealthy behavioural lifestyle. Although Albert
[1]

opined that

public health can improve health and quality of life of older people, this also extends to all peoples. In 2005, the Pan-American Journal of Public Health had an exclusive issue which examined health, well-being, ageing, and proposed a framework for public health action.
[2]

Public health can only enhance health and quality of life if it understands the people it serves, and this denotes that its programmes will only be effective if they are supported by sociomedical research (including epidemiologic inquiry) on national and sub-national populations. While peoples’ behaviours share some general similarities across geopolitical boundaries, a case can also be made equally about the dissimilarities, inequalities and socio-economic differences in and among people within the same nation. Those similarities and differences are responsible for the thrust to study and document information on particular phenomena in order to effectively implement public health programmes that will address the weaknesses, inequalities, deficiencies and challenges of people. It is for this reason why much information have been collected and documented on chronic diseases, mortality, disability and health care cost these pose a challenge to the healthy life expectancy of humans. The present body of knowledge on mortality, morbidities and disability in the world [3-10], and in particular the Caribbean, owes much too continuous biomedical research. But by simply understanding the aetiology of diseases does not mean that technology and medicine can eradicate the presence of diseases in humans, without an understanding of the social aspects of the targeted group. Peoples’ beliefs, customs, perception and biases pose a challenge to public
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[3-5]

as

health from attaining its mandate because beliefs guide practices.[11] Within the context that humans’ perspective is important in science and public health, without an understanding of their image on things, it will be impossible for medicine and the natural sciences to effectively address medical conditions that are deemed public health problems. Population health and population health in transition is each a function of social, environment, psychological and biomedical conditions, and not only disease composition and history. It is for this very rationale why public health must rely on sociomedical research and good quality data.
[12-14]

Hence, this is a justification for researchers’ continuous mode of

investigation of phenomena in order to understand issues experienced by humans. The Caribbean is no different from the rest of the world in this regards, and this provide some explanation why Caribbean scholars, the Planning Institute of Jamaica (PIOJ) and the Statistical Institute of Jamaica (STATIN) continue to embark on social research which include health, lifestyle practices and data quality
[13-20]

in order to aid public health practitioners to effectively

understand phenomena and address changes in peoples’ behaviour. Pappaioanou et al. [12] forwarded a perspective that the capacity of evidence-based public health must be strengthened in developing countries in order to identified priority health problems, respond to public health crises, implement effect strategies and evaluate cost effective interventions. This therefore justifies PIOJ and STATIN, Wilks et al and Bourne’s continuous examination of self-reported health, lifestyle of people of Caribbean people in order to set the platform of public health programmes. Books have been dedicated to ‘Health issues in the Caribbean,’ ‘Equity and Health’, and ‘Investment in Health’ in Latin American and the Caribbean
[21-24]

, but none of those text or other studies in the region, and in particular Jamaica,
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have examined in a single research factors that explain health status and health care seeking behaviour as well as health conditions and the disparities by socioeconomic conditions. An extensive review of health and health care-seeking behaviour revealed that studies that have examined health care-seeking behaviour or health status have used a piecemeal approach by either investigating health, health care-seeking behaviour [25-36] or health conditions. The current research bridge the gap by examining (1) demographic characteristics of health care-seekers; (2) sociomedical characteristics of health status; (3) factors that account for health status; (4) health conditions; (5) factors that explain health care-seeking behaviour and (6) characteristics of those who reported having been diagnosed with particular health conditions.

Materials and methods Method
The current study used a sample of 6,783 respondents. The sample was drawn from a large nationally representative cross-sectional survey of 6,783 Jamaicans.
[37]

The survey was drawn

using stratified random sampling. This design was a two-stage stratified random sampling design where there was a Primary Sampling Unit (PSU) and a selection of dwellings from the primary units. The PSU is an Enumeration District (ED), which constitutes of a minimum of 100 dwellings in rural areas and 150 in urban areas. An ED is an independent geographic unit that shares a common boundary. This means that the country was grouped into a strata of equal size based on dwellings (EDs). Pursuant to the PSUs, a listing of all the dwellings was made, and this became the sampling frame from which a Master Sample of dwellings was compiled, which in turn provided the sampling frame for the labour force. One third of the 2007 Labour Force Survey (i.e. LFS) was selected for the survey.
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This study used JSLC 2007 which was conducted by the Statistical Institute of Jamaica (STATIN) and the Planning Institute of Jamaica (PIOJ) between May and August 2007. The researchers chose this survey based on the fact that it is the latest survey on the national population and that it has data on self-rated health status of Jamaicans. An administered questionnaire was used to collect the data, which were stored and analyzed using SPSS for Windows 16.0 (SPSS Inc; Chicago, IL, USA). The questionnaire was modelled from the World Bank’s Living Standards Measurement Study (LSMS) household survey. There are some

modifications to the LSMS, as JSLC is more focused on policy impacts. The questionnaire covered areas such as socio-demographic, economic and health variables. The non-response rate for the survey was 26.2%. Descriptive statistics such as mean, standard deviation (SD), frequency and percentage were used to analyze the socio-demographic characteristics of the sample. Chi-square was used to examine the association between non-metric variables, and an Analysis of Variance (ANOVA) and independent sample t-test were used to examine the relationships between metric and non-dichotomous categorical variables. Logistic regression examined the relationship between the dependent variable and some predisposed independent (explanatory) variables, because the dependent variable was a binary one (self-reported health status: 1 if reported good health status and 0 if poor health). The results were presented using unstandardized B-coefficients, Wald statistics, Odds ratio and confidence interval (95% CI). The predictive power of the model was tested using the Omnibus Test of Model and Hosmer & Lemeshow [38] were used to examine the goodness of fit of the model. The correlation matrix was examined in order to ascertain whether autocorrelation
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(or multicollinearity) existed between variables. Based on Cohen & Holliday [39] correlation can be low (weak) - from 0 to 0.39; moderate – 0.4-0.69, and strong – 0.7-1.0. This was used to exclude (or allow) a variable in the model. The correlation matrix was examined in order to ascertain if autocorrelation or collinearity existed between variables. Where collinearity existed (r > 0.7), variables were entered independently into the model to help determine which one must retained during the final model construction (the decision was based on the variable’s contribution to the predictive power of the model and the goodness of fit) [40]. Wald statistics were used to determine the magnitude (or contribution) of each statistically significant variable in comparison with the others, and the Odds Ratio (OR) for the interpreting of each significant variable. Multivariate regression framework [35,41] was utilized to assess the relative importance of various demographic, socio-economic characteristics, physical environment and psychological characteristics, in determining the health status of Jamaicans; and this has also been employed outside of Jamaica. simultaneously.
[33,34,36]

This approach allowed for the analysis of a number of variables

Secondly, the dependent variable is a binary dichotomous one and this

statistical technique has been utilized in the past to do similar studies. Having identified the determinants of health status from previous studies, using logistic regression techniques; final models were built for Jamaicans as well as for each of the geographical sub-regions (rural, periurban and urban areas) and sex of respondents using only those predictors that independently predict the outcome. A p-value of 0.05 was used for all tests of significance.

Measure
Age is a continuous variable which is the number of years alive since birth (using last birthday)
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Age group is a non-binary measure: children (ages less than 15 years); young adults (ages 15 to 30 years); other-aged adults (ages 31 to 59 years); young elderly (ages 60 to 74 years); old elderly (ages 75 to 84 years) and oldest elderly (ages 85 years and older). Self-reported illness (or self-reported dysfunction): The question was asked: “Have you had an illness such as influenza, asthma et cetera in the past 4-week?” Health conditions (i.e. parent-reported illness or parent-reported dysfunction): The question was asked: “Is this a diagnosed recurring illness?” The answering options are: Yes, Influenza; Yes, Diarrhoea; Yes, Asthma; Yes, Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No. Self-rated health status: “How is your health in general?” And the options were: Very Good; Good; Fair; Poor and Very Poor. Medical care-seeking behaviour was taken from the question ‘Has a health care practitioner, healer or pharmacist been visited in the last 4 weeks?’ with there being two options: Yes or No. Self-rated health status: “How is your health in general?” And the options were very good; good; fair; poor and very poor. For this study the construct was categorized into 3 groups – (i) good; (ii) fair, and (iii) poor. A binary variable was later created from this variable (1=good and fair 0=otherwise). Social hierarchy: This variable was measured based on income quintile: The upper classes were those in the wealthy quintiles (quintiles 4 and 5); middle class was quintile 3 and poor those in lower quintiles (quintiles 1 and 2).

Results
200   

Table 8.1 presents information on the demographic characteristic of the sample by area of residence. The sample was 6 782 respondents: 48.7% males and 51.3% females. Based on Table 8.1, 34.2% of urban residents were in the wealthiest 20% compared to 24.1% of those in the periurban and 10.4% in rural areas. On the other hand, poverty was substantially a rural phenomenon (29.8%) compared to peri-urban (11.5%) and urban areas (9.3%). There is a significant statistical association between medication purchased by respondents and area of residence. Twenty percent of respondents who attended public health-care facilities purchased medication, while 81.4% of those who visited private health-care facilities purchased medication. Twenty-five percent (25.2%) of those in rural area who attended public health-care facilities purchased medication compared to 13.6% of those in peri-urban and 14.2% of those in urban areas. Almost ninety-one percent (90.5%) of those in urban area who visited private health-care facilities purchased medication compared to 86.8% of peri-urban and 74.7% of rural residents. Rural residents reported the most illness (16.6%) compared to urban (13.4%) and peri-urban respondents (12.9%). Eighty percent (82.2%) of the respondents indicated at least good health status (with 37.0% said excellent health status) compared to 0.8% who claimed very poor health status. One percent (1.1%) of the sample was injured in the 4-week period of the survey, while 14.9% was reported an illness and 43.2% indicated a chronic illness (i.e. Diabetes mellitus, 13.8%; hypertension, 23.1%; and arthritis, 6.2%) compared to 30.4% reported acute illness (influenza, 16.7%; diarrhoea, 3.0%; and asthma, 10.7%). Almost 66% (i.e. 65.5%) of the sample visited a health care practitioner (i.e. doctor, nurse, healer, pharmacist) in the 4-week period of the survey; 29.6% was heads of households; married, 23.3%; never married, 69.2%; divorced, 1.7%; separated, 0.9%; widowed, 4.9%; and the median number of person per room was 4 (range = 1,
201   

17). The median annual income was USD 7 050.66 (range = USD 261.56, USD 6 523.66) and median per capita consumption was USD 1 523.88 (range = USD 179.57, USD 20 325.55). Table 8.2 highlights information on sociomedical characteristics of sample by sex of respondents. Males were more likely to be married (24.3%) than females (22.4%), and the latter was more likely to be widowed (7.3%) than the former (2.3%). Females reported more illnesses (17.5%) than males (12.1%), and they were more likely to have hypertension and diabetes mellitus than males. However, there was no significant statistical relationship between health care utilization and sex of respondents: males, 62.3% and females, 67.6% (χ2 = 3.004, P < 0.083). There was no significant statistical relationship between those who purchased medication and their sex. Twenty percent (20.2%) of females who visited public health care facilities purchased medication compared to 19.7% of males (χ2 = 0.023, P = 0.879). Eight-one percent of females who attended private health-care facilities purchased medication compared to 81.9% of males (χ2 = 0.100, P = 0.752). Males were more likely to be household heads (32.7%) than females (26.7%) - χ2 = 29.207, P < 0.0001. When the significant statistical association between marital status and social standing was disaggregated by sex, this was explained by females (χ2 = 54.48, P = 0.0001) and not males (χ2 = 24.77, P = 0.074). Almost 49% (48.8%) of divorced females were in the wealthiest 20% compared to 27.0% of those who are married, 21.8% of widowed; 20.0% of separated as well as those who were never married respondents. Table 8.3 shows sociomedical characteristics of sample by marital status. Divorced respondents were most likely to be in the wealthiest 20% (44.2%) compared to separated respondents (31.7%); married, 28.3%; never married, 21.8% and widowed respondents, 21.4%
202   

(χ2 = 67.45, P < 0.0001). Forty percent of the widowed respondents indicated an illness compared to those who are separated, 29.3%; divorced, 28.6%; married, 24.6% and never married, 10.8%. A significant statistical association was found between area of residents and those who attend public hospitals (χ2 = 7.94, P < 0.019), private hospitals (χ2 = 30.30, P < 0.0001), and private health care centres (χ2 = 10.19, P < 0.006), while no between area of residents and public health care centres (χ2 = 4.23, P < 0.13). Rural residents were most likely to visit public hospitals (37.2%) compared to urban (27.7%) and peri-urban residents (25.6%). With respect to private hospital utilization, rural residents recorded the least visits (2.3%) than peri-urban (6.8%) and urban residents (15.0%). Similarly, rural dwellers recorded the least utilization of private health care centres (46.2%) than urban (52.7%) and peri-urban residents (63.2%). Rural dwellers recorded the longest time spent in illness (56.6 days ± 169.3) compared to urban dwellers (9.6 days ± 17.9) and peri-urban residents (53.3 days ± 154.4) – F-statistic = 9.58, P < 0.0001. There is a significant statistical association between area of residence and educational levels (χ2=78.02, P < 0.0001). Sixty-eight percent of those with tertiary level education dwelled in urban areas compared to 16% of those in peri-urban and 20.6% in rural areas. No significant statistical association was found between social class and self-reported illness (χ2=3.28, P < 0.512) as well as between self-reported diagnosed health conditions and social class (χ2=28.6, P < 0.236). Figure 8.1 highlights information on self-reported diagnosed illness by marital status of respondents disaggregated by sex of respondents. A significant statistical association was found between self-reported diagnosed illness by marital status even when the data was disaggregated
203   

by sex (male – χ2 = 52.43, P < 0.001; females - χ2 = 56.2, P < 0.0001), but the relationship was strong for males (contingency coefficient = 0.425) than females (contingency coefficient = 0.339). A significant statistical relationship existed between self-reported diagnosed health conditions and age group (χ2 = 436.8, P < 0.0001). Younger people were more likely to have acute conditions and older people are likely to have chronic conditions (Figure 8.2). Despite this fact 1.4% of Jamaica children have diabetes mellitus. Table 8.5 examines factors that are correlated with self-evaluated health status of Jamaicans. Of the 13 variables that were tested in the model, 9 emerged as statistically correlated with health status and that the model was a good fit for the data (Hosmer and Lemeshow goodness of fit χ2=18.49 (8), P = 0.78; -2LL = 3321.07). The model (i.e. 9 significant correlates of self-evaluated health status) accounted for 40.3% of the variability in self-reported health status: 84.8% of the data were correctly classified, 95.3% of those in good or excellent selfevaluated health status and 47.5% of those in fair to poor health status. Two-thirds of the variability in health status was accounted for medical factors such as self-reported illness and length of illness compared to one-third by social factors (i.e. age, sex, per capita consumption, health care-seeking behaviour, area of residence, marital status and social class). Of the social factors, consumption accounted for less than 1% of the variance in self-evaluated health status (i.e. 0.5%) and social class accounted for 0.1%. Table 8.6 presents information on the self-reported health care-seeking behaviour of respondents by explanatory variables. Four variables accounted for 71.1% of the variability in self-reported health care-seeking behaviour. Using logistic regression analyses, 4 variables emerged as statistically significant correlates of self-reported health care-seeking behaviour: self204   

reported illness, (OR = 358.31, 95% CI = 233.31, 550.30); health status, (OR = 0.46, 95% CI = 0.31, 0.67); health insurance coverage, (OR = 1.74, 95% CI = 1.26, 2.40); age, (OR = 1.01, 95% CI = 1.00, 1.01); and per capita consumption, (OR = 1.00, 95% CI = 1.00, 1.00). From the correlation matrix, there is a moderate statistical correlation between self-reported health illness and self-evaluated health status (r = 0.64).

Discussion
The current study revealed that 29.8% of rural residents were in the poorest 20% (i.e. poorest income quintile) in Jamaica compared to 11.5% of peri-urban and 9.3% of urban residents. Rural poverty lies between 2.5 to 3.3 times more than peri-urban and urban poverty, and 1.3 times more people report illness in those areas than in peri-urban or urban areas. Rural residents are not only rural and report more illness than other residents; they are 1.9 times less likely to have health insurance coverage than urban residents and 1.5 times less likely than peri-urban dwellers. They are also more likely to utilize public hospitals and spent more time nursing in illness than other residents, and also had the least consumption per person. However, their self-evaluated health status was the same as urban dwellers but less than that for peri-urban settlers, and there was no significant statistical correlation among health care-seekers based on their area of residences. Concurrently, males were more likely to record greater moderate-to-excellent health status than females; more likely to be married; less likely to be widowed; less likely to report an illness; less likely to have diabetes mellitus and hypertension; more likely to have asthma, arthritis; unspecified conditions and influenza than females. Irrespective of the more female than males reporting having been diagnosed with chronic conditions, there was no significant correlation between health care seeking behaviour and sex of respondents. The findings continued as those in the wealthiest 20% were more likely to be divorced people; but those who
205   

were classified as divorced, separated and widowed were less likely to be healthier than those who were never married. Those who were never married reported the lowest percent of having had an illness in the 4-week period of the survey. Two-thirds of the variability was accounted for medical factors such as self-reported illness and length of illness compared to one-third by social factors (i.e. age, sex, per capita consumption, health care-seeking behaviour, area of residence, marital status and social class). Four variables accounted for 71.1% of the variability in self-reported health care-seeking behaviour, and self-reported illness accounted for 70% of the explanatory power. People who reported moderate-to-excellent health status were 55% less likely to seek health care and those who reported an illness were 358.3 times more likely to seek health care. Less than one-half percent of the variance in health care-seeking behaviour can be explained by health insurance coverage, and that an individual who indicated that he/she is ill is 81% less likely to stated moderate-to-excellent health status. Public health is influenced by both the continuous revelations in research as well as science of people’s behaviour in order to effectively plan behaviour modifications. The behaviour change required for developing countries must be tailored within the context of the research findings [42], and cannot be left to the dictates of studies on developed nations. Apart of the justification for studies on a particular geo-political boundary are based on inequalities, economic and health disparities among and between people within a nation, and this is particularly in reference to Latin America and the Caribbean.[43-45] With public health taking must of its cue from both medical and social sciences, there is obviously a rationale for the social determinants in the study of public health. The some time ago embarked a thrust of examining social determinants in understanding health, health conditions and health treatment. In recent years the World Health Organization (WHO) has increasingly drawn attention to the importance
206   

of the relationship between health and social conditions in determining the health of individuals and populations [46]. The social determinants (non-biological factors), produce inequalities in health and need to be considered in health development. Addressing social determinants and health policy now includes the basis for political action both nationally and internationally.[47-51] The findings of the present work highlights and concur with the literature about the dominant of the biomedical conditions in health. The findings revealed that two-thirds of variability in health status can be accounted for by self-reported illness and length of illness. Although this fact speaks to the dominance of biomedical conditions, it does also recognize the importance of social determinants in health. A study by Hambleton et al.
[36]

on elderly

Barbadians found that as much as 88% of the variability in self-reported health status could be explained by current diseases. While the current work has a lower percent of explanation model which is due to the sample that include young people, it highlights a rationale for the ease of use of the biomedical conditions and in the process sideline the need for the social determinants in health, health utilisation and health treatment. Clearly illnesses are fundamental in the health discourse, and it is also critical in the understanding health care-seeking behaviour of people. In this research, a respondent who is ill is 358.3 times more likely to seek care. This highlights not only the dominance of illness to health care-seeking, but the image of health that is held by Jamaica and how this influence outcome. This is supported by the finding that revealed that people who self-reported their health status to be moderate-to-good were 54% less likely to seek health care. Embedded in such a finding is structure of the health care delivery in Jamaica, which dates back to 130ce to 200ce in Ancient Rome, when health and health care was in keeping with traditional biomedical model that views the exposure to specific pathogen as the cause of diseases in organisms. Within this image of health was people’s perception of what
207   

constituted a need to demand health care services which were illness and this fashioned the health care industry at the time. Clearly the image of health and health care delivery in Jamaica is framed around the aetiology of diseases and the not the multidimensional approach to the image of health which is in keeping with the broad definition offered by the WHO in 1948. [52] The overemphasis on illness, disability and severity of illness in framing people’s willing to seek medical care is not atypical to Jamaica as this was found in other societies.[26-31, 53] Money is well established as being positively correlated with health status.
[54]

Money

does matter in access to resources, opportunities, choices and quality of care. The current findings found that people whose consumption expenditure are higher have a greater health status, which concurs with the literature that money does matter for health. Money does not only matter for health, it also is important for health seeking behaviour. Despite the positive of money, those who are most likely to be in the wealthiest 20% had lower health status. This paper found that divorced, separated and widowed Jamaicans were more likely to have a lower health status than those who were never married and this was also the case for the upper class with reference to the lower class. Although the finding does indicate that divorced and separated respondents were wealthier than other marital statuses, this is a negative for their health status. Also embedded in this finding is the fact that significant statistical association between social standing and marital status was among females. This denotes that wealthiest females were most likely to be divorced which offers an explanation that money can buy health, psychological comfort, happiness and these would have been the case for these females in the study. Divorced therefore provides females with more economic resources, but this does not compensate for the lost of the spouse, and further removes the benefits of the economic gains from health. In addition divorced females recorded the highest percent of diabetes mellitus among all
208   

respondents followed by separated women. Hypertension was substantially more among separate males and widowed females, suggesting that separation from spouse becomes a disbenefit for Jamaica and therefore account for the unhealthy life style practices which were not identified in never married and/or married respondents. There are obvious benefits from having money as this was evident in rural residents having the least money, the most self-reported illness, and the highest public hospital utilisation. Despite the income inequalities and economic disparities between rural and other residents in Jamaica, the former residents are able to experience a self-reported health status which is the same of those in the affluent urban areas. This means that there are some basic standard of living enjoyed by rural Jamaica which cushioned the wide income inequalities that exist between them and urban dwellers. Apart of what creates the cushion for rural residents is the quality of primary health care facilities offered to them by public hospitals in the country. With most rural residents utilizing public hospitals, public health offerings have played a critical role in removing some of the health inequalities that could have been owing to income inequalities. Another factor which mitigates the negatives of income inequalities among the different area of residents is the communal settings in rural areas, and how this aids in providing socio-economic support among residents. Poverty in rural areas is therefore shared by the wider community as people seek to assist others in need, vulnerable, less fortunate and economic challenged in life. It is this communal culture that sees sharing of food, finances and social institutions that helps to retard the negative of poverty from rural residents. The poor are classified as in the lower socioeconomic status. It is empirically well established in research that they are less likely to be healthy than those in the higher socioeconomic groups
[55, 56]

, which is not the case in Jamaica. They have a greater self209 

 

evaluated health status than those in the higher socioeconomic groups. Concurrent this research does not concur with the literature that poverty is more common among the chronically ill [57] or that the poor reported having more illness than the higher socioeconomic class. This was also highlighted in the fact that rural residents were substantially more likely to poor, but shared the same health status as those in urban areas. However what emerged from the current findings is that peri-urban residents had a greater health status than other residents, and this could be due to the fact that more of them were in the never married group who had the lowest rate of illness as well as chronic illnesses. Residents in peri-urban area has greater income than those who dwelled in rural areas but less than those in urban areas which indicates that some money is important in health, but that is not responsible for greater health. It can be extrapolated from this finding that peri-urban residents are more involved in healthier lifestyle choices than residents in other geographic areas, which is accounting for their health more than money and higher formal education. This study uncovers a paradox between subjective health and objective health. The present work found that males reported less illness, had greater self-evaluated health status, but using statistics on life expectancy in Jamaica females outlive males between 4 to 7 years.
[58, 59]

In 1880-1882, Jamaica females outlive males by 2.9 years and in 2002-2004, this was increased to 5.8 years.[20] For 2007, statistics published by the WHO revealed that this difference was 5 year. [59] This questions the validity of subjective health data in the evaluation of health, and begs the question “How valid is subjective health data in Jamaica?” A study by Bourne
[17]

found a

strong statistical correlation between life expectancy at birth for the Jamaicans and self-reported illness (r = - 0.731); and this association was weaker females (r = - 0.683) than males (r = 0.796). Hence, there is validity in the use of subjective index to measure health. This suggests
210   

that the afore-mentioned disparity in subjective and objective indexes to measure health is not a paradox, but an issue which needs further examination. The inverse relationship between health and age is long established in research literature
[1, 34, 35]

as well as the shift from acute to chronic conditions in old ages. [60, 61] Morrison [60] in an

article entitled ‘Diabetes and hypertension: Twin Trouble’ forwarded that diabetes mellitus and hypertension have now become two problems for Jamaicans and in the wider Caribbean. Callender
[61]

concurred with Morrison

[60]

that there is a positive association between diabetic

and hypertensive patients (i.e. 50% of individuals with diabetes had a history of hypertension), and that this is a public health problem in the Caribbean. This study narrows the chronic conditions to older people, but also noted that 1.4% of children in Jamaica had diabetes mellitus, 3.5% of young adults and 16.4% of other adults. If Callender’s are true then in a short while onehalf of those afore-mentioned individuals will have dual chronic conditions. A recently conducted study by Wilks et al.
[13]

provide some historical background to chronic illnesses in

Jamaica as they found that 31% of Jamaicans indicated that their parent and/or grand parents had diabetes mellitus; 47% said that hypertension, 17.1% strokes and 15.7% said their parents and/or grandparents had cancer. Diabetes mellitus and hypertension therefore continue to be silent killers in Jamaica, and their history dates back to former generations. Public health practitioners need to urgent begin a campaign of lifestyle practices geared towards children as there is evidence to support healthy lifestyle practices among all groups, and in particular children, who are frequently, omitted from healthy lifestyle programmes. The current study highlighted that there are many inequalities (i.e. systematic, avoidable and important difference) in health status among Jamaicans and that these need to be rectified in order to attain the resolution of the World Health Assembly (WHA48.8).[62] Jamaica now has a
211   

primary health care system which is free to all, but this has still not met equity (i.e. unnecessary and avoidable differences which are considered to be unfair and unjust) in health care throughout the society. Free health care for all in Jamaica have not addressed issues such as exposure to unhealthy, stressful living and working conditions; natural selection or related social mobility; transient health advantage; gender discrimination; socioeconomic discrimination; inequitable deployment of resources around the nation; and the organization of some health services around the country. Inequalities and inequities in Latin America and the Caribbean have been empirically researched by Pan American Health Organization (PAHO), and further readings can be had by examining two of its publications
[23, 24]

as well as Whitehead. [63] It is clear from the

current findings that merely making primary health care free for all will not reduce many of the public health challenges in a nation and among its people. So while Jamaica has done the former, there are obvious signs that reaching the poor with health care does not address many other health inequalities and inequities. Using statistics for 90 countries, the WHO [59] revealed that in many of these nations there are health disparities and inequities between and among people which is concurred by Global Forum for Health Research [64] and this study. This reinforces the need for public health practitioners not to rely on national averages and information which originates from within the health sector but on sociomedical determinants on groups and subgroups within the population.

Conclusion
Although biomedical conditions accounted for more of health than social determinants, the current study highlighted the value of the social determinants in the health discourse. The social issues in this research brought to the fray the fact that separation from one spouse influenced health status, healthy behaviour and health conditions. It does not cease there as the image of
212   

health is substantially driven by illness which account for seeking (or not seeking) medical care. In addition to the afore-mentioned issues, there is a clear public health challenge that exists in Jamaica which is how to address the unhealthy lifestyle practices of people who have been separated from their spouses as well as the fact that particular health conditions appear to be associated with particular social characteristics. Another public challenge is how to change the image of health in Jamaica from illness to wellness or wellbeing. This public health challenge must commence with the restructuring of the health care system and its delivery which is primary driven by the biomedical factors instead of holistic health. Increasing attention must be placed on this reorganization as if the health care is fashioned more around curative care, then people will use this image of health care to frame their concept of health and health demands. Some of the disparities that emerged from the current work from the literature highlights the fact that public health in Jamaica cannot rely on the research findings in other geo-political boundaries to craft policies and intervention programmes as the will be ineffective in addressing its mandate owing to the sociodemographic differences of Jamaicans. Public health therefore must rely on research findings within it geo-political area while understanding what obtains in other areas in order to embark on intervention programmes that will improve health and quality of life of people. Within this context, one of the problems which must be addressed by public health policy makers is how to address the high percent of Jamaicans who are current diagnosed with chronic illness (i.e. 43 %) as well the fact that even children are now diagnosed with diabetes mellitus suggesting that public health must embark on programmes that address living longer and healthier with (and without) chronic illnesses. In sum, the inequalities and/or inequities which emerged in this study are social issues which explain medical conditions and it is this merger of medicine and sociology that is needed
213   

to effectively improve the health and quality of life of people. Concurrently, policy makers need to change the concept of health of Jamaicans and this can be enhanced by (1) leisure and exercise facilities in communities as well as in health care facilities; (2) reduce the inequalities in working and living conditions of the vulnerable and disadvantaged groups; (3) address the healthdamaging behaviour of some social groups; (4) administrative reform of professionals in regards to the dissemination of information to lay people; (5) examine, monitor and evaluate the implication of health policies on the socioeconomic groups within the society; (6) pollution control caps; (7) assist in food hygiene, nutrition, sanitation and health education moreso in times of economic hardships; and (8) commence a databank that collects data on the cultural and behavioural practices of people in order to effectively formulate health policies. In addition to the afore-mentioned issues, while the current study is not a representation of the Caribbean, based on the Pan American Health Organization research on Latin America and the Caribbean investment in health and health care modernization have not reduced the inequalities and inequities in nations among different social groups within those nation
[23, 24]

, which is what

emerged from the current work. Clearly, health inequalities and inequities in Latin America and the Caribbean are very much the same, and any public health intervention programmes that do not address this reality will be ineffective in aiding health and quality of life of its people. Health protection therefore must be embedded in science of human behaviour (i.e. social determinants) as well as an understanding of the pathogenesis of diseases (i.e. sociomedical public health).

Conflict of interest
The author has no conflict of interest to report

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Figure 8.1. Self-reported diagnosed illness by marital status for sex

219   

Figure 8.2. Self-reported diagnosed illness by age group

220   

Table 8.1. Demographic characteristic by area of residence Area of residence Characteristic Urban Semi-urban n (%) n (%) Social standing Poorest 20% Poor Middle Wealthy Wealthiest 20% Self-reported Injury Yes No Sex Male Female Marital status Married Never married Divorced Separated Widowed Self-evaluated illness Yes No Self-reported diagnosed illness Influenza Diarrhoea Asthma Diabetes mellitus Hypertension Arthritis Other Health insurance coverage Yes No Health care-seeking behaviour Yes No Consumption per capita (in USD) 186 (9.3) 243 (12.1) 389 (19.4) 499 (24.9) 685 (34.2) 16 (0.8) 1933 (99.2) 943 (47.1) 1059 (52.9) 326 (23.3) 972 (69.3) 32 (3.2) 9 (0.6) 63 (4.5) 261 (13.4) 1690 (86.6) 25 (10.9) 4 (1.9) 33 (14.4) 32 (14.0) 47 (20.5) 16 (7.0) 72 (31.4) 542 (28.0) 1397 (72.0) 190 (71.2) 77 (28.8) 2632.57±2040.89 167 (11.5) 273 (18.7) 312 (21.4) 354 (24.3) 352 (24.1) 16 (1.1) 1408 (98.9) 706 (48.4) 752 (51.6) 217 (21.6) 702 (70.0) 23 (2.3) 12 (1.2) 49 (4.9) 183 (12.9) 1231 (87.1) 44 (26.0) 4 (2.4) 11 (6.5) 27 (16.0) 41 (24.3) 10 (5.9) 32 (18.9) 310 (22.1) 1091 (77.9) 119 (63.6) 68 (36.4) 2223.76±1753.22

P Rural n (%) 990 (29.8) 838 (25.2) 650 (19.6) 499 (15.0) 345 (10.4) 41 (1.3) 3186 (98.7) 1954 (49.8) 1668 (50.2) 513 (24.1) 1462 (68.7) 22 (1.0) 20 (0.9) 112 (5.3) 536 (16.6) 2688 (83.4) 80 (16.3) 19 (3.9) 51 (10.4) 64 (13.0) 118 (24.0) 30 (6.1) 130 (26.4) 462 (14.5) 2715 (85.5) 349 (63.3) 202 (36.7) 1499.18±1095.70 χ2 = 881.51 P < 0.0001

χ2 = 2.25 P = 0.325 χ2 = 3.67 P = 0.16 χ2 = 15.46 P = 0.05

χ2 = 15.43 P < 0.0001 χ2 = 29.59 P = 0.003

χ2 = 138.80 P < 0.0001 χ2 = 5.21 P = 0.07 F =344.31, 0.0001 P <

221   

Table 8.2. Sociomedical characteristic by sex of respondents Sex Male Female Characteristic Consumption per capita (in USD)1 2018.20±1712.76 1962.30±1592.01 1 Total Expenditure (on food) (in USD) 3488.32±2187.43 3616.80±2201.34 No. of days in public health care 6.6 ±6.2 6.0±4.7 facilities No. of days in private health care 5±0 1±0 facilities Medical expenditure - public 3.67±17.51 8.36±67.69 (in USD)1 Medical expenditure – private 14.05±21.12 14.15±30.58 (in USD)1 Self-reported diagnosed illness Influenza 69 (20.2) 80 (14.6) Diarrhoea 11 (3.2) 16 (2.9) Asthma 47 (13.7) 48 (8.8) Diabetes mellitus 31 (9.1) 92 (16.8) Hypertension 58 (17.0) 148 (27.0) Arthritis 24 (7.0) 32 (5.8) Other 102 (29.8) 132 (24.1) Social standing Poorest 20% 671 (20.3) 672 (19.3) Poor 640 (19.4) 714 (20.5) Middle 636 (19.3) 715 (20.6) Wealthy 667 (20.2) 685 (19.7) Wealthiest 20% 689 (20.9) 693 (19.9) Self-reported Injury Yes 41 (1.3) 32 (0.9) No 3169 (98.7) 3358 (99.1) Marital status Married 522 (24.3) 534 (22.4) Never married 1528 (71.1) 1608 (67.4) Divorced 34 (1.6) 43 (1.8) Separated 16 (0.7) 25 (1.0) Widowed 50 (2.3) 174 (7.3) Self-evaluated illness Yes 388 (12.1) 592 (17.5) No 2820 (87.9) 2789 (82.5) Health care-seeking behaviour Yes 253 (62.3) 405 (67.6) No 153 (37.7) 194 (32.4) USD 1.00 = Ja $80.47 at the time of the survey

P t =1.39, P = 0.16 t = -2.41, P = 0.016 t = 0.35, P = 0.73

t = -1.02, P = 0.31 t = -0.044, P = 0.97 χ2 = 30.25, P < 0.0001

χ2 = 4.35, P = 0.361

χ2 = 1.68, P = 0.196 χ2 = 61.94, P < 0.0001

χ2 = 38.12, P < 0.0001 χ2 = 3.004, P < 0.083

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Table 8.3. Sociomedical characteristic by marital status of respondents Marital status Characteristic Married Never married n (%) n (%) Social standing Poorest 20% 153 (14.5) 564 (18.0) Poor 181 (17.1) 928 (20.0) Middle 185 (17.5) 633 (20.2) Wealthy 238 (22.5) 626 (20.0) Wealthiest 20% 299 (28.3) 685 (21.8) Self-reported Injury Yes 16 (1.5) 37 (1.2) No 1040 (98.5) 3091 (98.8) Self-evaluated illness Yes 259 (24.6) 338 (10.8) No 795 (75.4) 2789 (89.2) Self-reported diagnosed illness Influenza 18 (7.4) 28 (9.2) Diarrhoea 2 (0.8) 7 (2.3) Asthma 10 (4.1) 31 (10.2) Diabetes mellitus 48 (19.7) 39 (12.8) Hypertension 91 (37.3) 69 (22.6) Arthritis 24 (9.8) 22 (7.2) Other 51 (20.9) 109 (35.7) Health insurance coverage Yes 357 (34.1) 552 (17.9) No 691 (65.9) 2528 (82.1) Health care-seeking behaviour Yes 173 (65.3) 239 (68.1) No 92 (34.7) 112 (31.9) Head of household Yes 564 (53.4) 1163 (37.1) No 492 (46.6) 1973 (62.9)

P Divorced n (%) 4 (5.2) 6 (7.8) 18 (23.4) 15 (19.5) 34 (44.2) 0 (0.0) 77 (100.0) 22 (28.6) 55 (71.4) 1 (4.8) 1 (4.8) 2 (9.5) 10 (47.6) 3 (14.3) 1 (4.8) 3 (14.3) 27 (35.1) 50 (64.9) 15 (68.2) 7 (31.8) 57 (74.0) 20 (26.0) Separated 9 (22.0) 3 (7.3) 10 (24.4) 6 (14.6) 13 (31.7) 1 (2.4) 40 (97.6) 12 (29.3) 29 (70.7) 1 (8.3) 1 (8.3) 0 (0.0) 4 (33.3) 5 (41.7) 1 (8.3) 0 (0.0) 8 (19.5) 33 (80.5) 8 (61.5) 5 (38.5) 27 (65.9) 14 (34.1) Widowed χ2 = 67.45, P < 0.0001 43 (19.2) 36 (16.1) 58 (25.9) 39 (17.4) 48 (21.4) 3 (1.3) 220 (98.7) 90 (40.4) 133 (59.6) 4 (4.5) 3 (3.4) 1 (1.1) 19 (21.6) 37 (42.0) 8 (9.1) 16 (18.2) 60 (26.9) 163 (73.1) 90 (40.4) 133 (59.6) 181 (80.8) 43 (19.2) 223 

χ2 = 2.16, P = 0.71 χ2 = 233.86, P < 0.0001 χ2 = 75.36, P < 0.0001

χ2 = 127.20, P < 0.0001 χ2 = 233.86, P < 0.0001 χ2 = 258.12, P < 0.0001

 

Table 8.5. Stepwise logistic regression: Self-evaluated health status by explanatory variables 95.0% C.I. Std. Odds Explanatory variables Coefficient Error ratio Lower Upper P Illness (1= yes) Age Per capita consumption Health behaviour Sex (1= male) Upper class †Lower class Peri-urban †Rural Length of illness Divorced, widowed separated or care-seeking -1.648 -0.045 0.000 -0.720 0.348 -0.345 0.340 -0.003 0.152 0.003 0.000 0.178 0.091 0.164 0.114 0.001 0.000 0.000 0.000 0.000 0.000 0.035 0.003 0.004 0.192 0.956 1.000 0.487 1.417 0.708 1.000 1.405 1.000 0.997 0.143 0.951 1.000 0.343 1.184 0.513 1.125 0.995 0.259 0.961 1.000 0.690 1.695 0.977 1.756 0.999

R2 change 0.266 0.114 0.005 0.004 0.006 0.001 0.002 0.003 0.002

-0.355

0.153

0.021

0.701 1.000

0.519

0.947

†Never married
Hosmer and Lemeshow goodness of fit χ =18.49 (8), P = 0.78 Nagelkerke R2 =0.403 -2LL = 3321.07 †Reference group
2

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Table 8.6. Stepwise logistic regression: Self-reported health care-seeking behaviour by explanatory variables 95.0% C.I. Explanatory variable Std. Coefficient Error 0.191 0.163 0.219 0.003 0.000 P 0.000 0.001 0.000 0.037 0.021 Odds ratio 0.455 1.741 358.313 1.005 1.000 Lower 0.313 1.263 233.307 1.000 1.000 Upper 0.662 2.398 550.297 1.010 1.000 0.005 0.004 0.700 0.001 0.001 Health status (1=moderate-toexcellent) -0.787 Health insurance Self-reported illness Age Per capita consumption 0.554 5.881 0.005 0.000 R2 change

Hosmer and Lemeshow goodness of fit χ2=7.12 (8), P = 0.52 Nagelkerke R2 =0.711 -2LL = 1525.53 †Reference group

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Chapter 9
Modelling social determinants of self-evaluated health of poor older people in a middle-income developing nation

Paul A Bourne
Over the last 2 decades (1988-2007), poverty in Jamaica has fallen by 67.5%, and this is within the context of a 194.7% increase in inflation for 2007 over 2006. It does not abate there, as Jamaicans are reporting more health conditions in a 4-week period (15.5% in 2007) and at the same time this corresponds to a decline in the percentage of people seeking medical care. Older people’s health status is of increasing concern, given the high rates of prostate cancer, genitourinary disorders, hypertension, diabetes mellitus and the presence of risk factors such as smoking. Yet, there is a dearth of studies on the health status of older people in the two poor quintiles. This study examined (1) the health status of those elderly Jamaicans who were in the two poor quintiles, and (2) factors that are associated with their health status. A sample of 1,149 elderly respondents, with an average age of 72.6 years (SD=8.7 years) were extracted from a total survey of 25,018 Jamaicans. The initial survey sample was selected from a stratified probability sampling frame of Jamaicans. An administered questionnaire was used to collect the data. Descriptive statistics were used to examine background information on the sample, and stepwise logistic regression was used to ascertain the factors which are associated with health status. The health status of older poor people was influenced by 6 factors, and those factors accounted for 26.6% of the variability in health status: Health insurance coverage (OR=13.90; 95% CI: 7.98-24.19), age of respondents (OR=7.98; 95% CI: 1.02-1.06), and secondary level education (OR=1.82; 95% CI: 1.35-2.45). Males are less likely to report good health status than females (OR=0.56; 95% CI: 0.42-0.75). Older people in Jamaica do not purchase health insurance coverage as a preventative measure but as a curative measure. Health insurance coverage in this study does not indicate good health but is a proxy of poor health status. The demand of the health services in Jamaica in the future must be geared towards a particular age cohort and certain health conditions, and not only to the general population, as the social determinants which give rise to inequities are not the same, even among the same age cohort.

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1. INTRODUCTION

Factors determining the poor health status of the elderly in Jamaica can be viewed from the perspective of a socio-medical dichotomy. Such factors include poverty (resulting in one’s inability to access loans, quality education and health care), lifestyle (e.g. smoking, sedentary habits, sexual and dietary practices and physical inactivity), resulting in prostate cancer, genitourinary disorders, hypertension, diabetes mellitus and premature death. In 2005, the World Health Organization began a thrust in examining the social determinants of health, and despite that reality there is a lack of literature in this regard on the elderly poor people in Jamaica. These parameters were explored in the current research by using a sample of 1,149 elderly poor Jamaicans. The findings of this paper reveal that the cost of medical care is positively correlated with health conditions, and that economic constraints account for the decline in the elderly seeking medical care. Older people in Jamaica do not purchase health insurance coverage as a preventative measure but as a curative measure. Health insurance coverage in this study does not indicate good health, but on the contrary, it is a proxy of poor health status. It is also noted that income is positively correlated with a higher standard of living and life expectancy. In support of this claim, studies have shown that life expectancy in many developing countries [1], in particular the Caribbean (Barbados, Guadeloupe, Jamaica, Martinique, Trinidad and Tobago) has exceeded 70 years, and they are now experiencing between 8-10% of their population living to 60+ years old. Life expectancy, which is a good indicator of the health status of a populace, is higher in countries with high GDP per capita. This means that income is able to purchase better quality products [2], and indirectly affects the length of years lived by people. GDP per capita is
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used as an objective valuation of standard of living [3-12]. While a country’s GDP per capita may be low, life expectancy is high because health care is free for the population. Despite this fact, material living standards undoubtedly affect the health status and wellbeing of people, as well as the level of females’ educational attainment [6] and the nutrition intake of the poor. On the other hand, when there is economic growth, the society has more to spend on nutrition, health care, better physical milieu, better quality food, safer sanitation and education. Good health is, therefore, linked to economic growth, something which is established in a plethora of studies by economists. Developing countries (a term synonymous with poverty) do not only constitute low levels of democracy, civil unrest, corruption [13], high mortality and crude birth rates, but one must also include nutritional deficiency [14]. The WHO in 1998 put forward the position that 20% of the population in developing countries do not have access to enough food to meet their basic needs and provide vital nutrients for survival. In the Caribbean, and in particular Jamaica, poverty is typical, and many of the ills that affect other developing nations outside of this region are the same. The poor in this society are facing insurmountable challenges in buying the necessary health care. In 2007, between 51 and 53% of those in the poor quintiles in Jamaica sought medical care, compared to 61-68 % of those in the middle-to-wealthiest quintiles. When those who had reported that they were ill were asked why they had not sought medical care, 51% of those in the poorest quintile indicated that they ‘could not afford it’, with 36.7% of those in the poor quintile giving the same response, and the percentage declines as the wealth of the person increases to the wealthiest quintile (7.7% of those in the wealthiest quintile).

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Over the last 2 decades (1988-2007), poverty in Jamaica has fallen by 67.5% and this is in the context of a 194.7% increase in inflation for 2007 over 2006. Jamaicans are reporting more health status in a 4-week period (15.5% in 2007) and at the same time this is associated with a decline in the percentage of people seeking medical care. Older people’s health status is of increasing concern, given the high rates of prostate cancer, genitourinary disorders, hypertension, diabetes mellitus and the presence of risk factors such as smoking in earlier life. Yet, there is a dearth of studies on the health status of older people in the two poor quintiles. Works which have examined the social determinants of health have used data for the population [2,3], but none emerged from a literature research using data for poor old people. This study examined (1) the health status of those elderly Jamaicans who were in the two poor quintiles, and (2) factors that are associated with their health status.

2. MATERIALS AND METHODS
2.1 Sample A sample of 1,149 elderly respondents was extracted from a larger survey of 25,018 Jamaicans. The sample was based on being 60+ years old, and being classified in the two poorest income categorizations. The initial survey sample (n = 25, 018) was across the 14 parishes, and was conducted between June and October 2002. The sample (n=25,018 or 6,976 households out of a planned 9,656 households) was drawn using a stratified random sampling technique. This design was a two-stage stratified random sampling design, where there was a Primary Sampling Unit (PSU) and a selection of dwellings from the primary units. The PSU is an Enumeration District (ED), which constitutes of a minimum of 100 dwellings in rural areas and 150 in urban zones.
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An ED is an independent geographic unit that shares a common boundary. This means that the country was grouped into strata of equal size based on dwellings (EDs). Based on the PSUs, a listing of all the dwellings was made, and this became the sampling frame from which a Master Sample of dwellings was compiled, and which provided the frame for the labour force. The survey adopted was the same design as that of the labour force, and it was weighted to represent the population of the country. The survey was a joint collaboration between the Planning Institute of Jamaica and the Statistical Institute of Jamaica. The data were collected by a comprehensive administered questionnaire, which was primarily completed by heads of households for all household members. The questionnaire was adapted from the World Bank’s Living Standards

Measurement Study (LSMS) household surveys, and was modified by the Statistical Institute of Jamaica with a narrower focus, to reflect policy impacts as well. The instrument assessed: (i) the general health of all household members; (ii) social welfare; (iii) housing quality; (iv) household expenditure and consumption; (v) poverty and coping strategies, (vi) crime and victimization, (vii) education, (viii) physical environment, (ix) anthropometrics measurement and immunization data for all children 0-59 months old, (x) stock of durable goods, and (xi) demographic questions. Data were stored and retrieved in SPSS for Windows, version 16.0 (SPSS Inc; Chicago, IL, USA). The current study is explanatory in nature. Descriptive statistics were presented to provide background information on the sampled population. Following the provision of the aforementioned demographic characteristics of the sub-sample, chi-square analyses were used to test the statistical association between some variables, t-test statistics and analysis of variance
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(i.e. ANOVA) were also used to examine the association between a metric dependent variable and either a dichotomous variable or non-dichotomous variable respectively. Logistic regression was used to examine the statistical association between a single dichotomous dependent variable and a number of metric or other variables (Empirical Model). The logistic regression was used because in order to test the association between a single dichotomous dependent variable and a number of explanatory factors simultaneously, it was the best available technique. A p-value < 0.05 (two-tailed) was selected to indicate statistical significance in this study. Where collinearity existed (r > 0.7), variables were entered independently into the model to determine those that should be retained during the final model construction. To derive accurate tests of statistical significance, SUDDAN statistical software was used (Research Triangle Institute, Research Triangle Park, NC), and this was adjusted for the survey’s complex sampling design. 2.2 Measure Social determinants. These denote the conditions under which people are born, grow, live, work and age, including the health system. Crowding. This is the total number of persons living in a room with a particular household. , where is each person in the household and r is the number of rooms excluding kitchen, bathroom and verandah. Age: This is a continuous variable in years, ranging from 15 to 99 years. Old/Aged/Elderly. An individual who has celebrated his/her 60th birthday or beyond. Negative Affective Psychological Condition: Number of responses from a person on having lost a breadwinner and/or family member, loss of property, having been made redundant, failure to meet household and other obligations.
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Private Health Insurance Coverage (or Health Insurance Coverage) proxy Health-Seeking Behaviour, is a dummy variable which speaks to 1 for self-reported ownership of private health insurance coverage, and 0 for not reporting ownership of private health insurance coverage. Gender: Gender is a social construct which speaks to the roles that males and females perform in a society. This variable is a dummy variable, 1 if male and 0 if otherwise.

Health conditions: The report of having had an ailment, injury or illness in the last four weeks, which was the survey period. This variable is a binary measure, where 1=self-reported health status or illnesses, and 0=otherwise (not reporting an illness, injured or dysfunctions). Poverty: In this study, the definition of poverty is the same as that used to estimate poverty in Jamaica. It is established from the basis of a poverty line. In order to compute the per capita poverty line in each geographical area (Kingston Metropolitan Area, Other Towns and Rural Areas), the cost of living for a basket of goods is divided by an average family of five. The basket of goods is established by the Ministry of Health based on the normal nutrients of the average family. Based on a per capita approach, there are five per capita income quintiles, with the poorest being below the poverty line (quintile 1) and the wealthiest being in quintile 5. Elderly, Aged or Old persons. Using the same definition offered by the United Nations in the Report of the World Assembly on Ageing, July 26-August 6, 1982 in Vienna, that the elderly are persons who are 60+ years old. Older-poor (elderly-poor, aged-poor). All aged persons below and just above the poverty line (quintiles 1 & 2) in Jamaica.

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3. RESULTS
3.1 Demographic characteristics of sample Consistent with the demographic characteristics of the ageing population, the sample was 1,149 of which there were 45% males (N=517) compared to 55% females (N=632). The mean age of the sample was 72.6 years (SD=8.7 years). Most of the sample were married (40%, N=452), 50.5% (N=580) of the sample were in the poorest 20% of per capita income quintile, 95% (N=1,087) were not receiving retirement income; those who were heads of households (98.3%, N=1,129), those who had at most primary education (65.2%, N=700) and those who did not have health insurance coverage (86.0%, N=973) (Table 9.1 ). Thirty-seven percent (37.2%) of the sample indicated having had an illness in the last 4week period. Approximately 64% of the respondents indicated that they sought health care for their health conditions. When the respondents were asked if they had visited a health practitioner for any other reason during the last 12 months, 57.1% reported yes and 30.3% reported going for ‘regular checkups’. Of those who indicated yes, 37.2% visited public health care institutions, and 18.7% went to private clinics, compared to 5.7% who claimed that they attended both health care facilities. The typologies of illness included colds (1.4%), diabetes mellitus (5.7%), hypertension (42.9%) and arthritis (31.4%), while 18.6% did not specify their health condition(s). Only 2% of the respondents had health insurance coverage; 61% purchased the prescribed medication; and 81.8% of those who indicated having not bought their medication reported that they could not afford it.

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The median number of days for how long an illness lasted was 7 days, with a median medical expenditure of US $7.85 (US $1.00 = Ja. $50.97). 3.2 Bivariate Correlation of Health Status and Age Cohort

Of the 1,149 sample respondents for this study, 98.8% (N=1,135) were used for the statistical correlation between health status and gender. Of the 1,135 respondents, there were 688 youngold, 327 old-old and 120 oldest-old poor Jamaicans. There was a correlation between the two above-mentioned variables – χ2 (df=2) = 22.863, p-value < 0.001. On an average, 46% of the aged-poor (N=523) reported that they had at least one illness/injury in the survey period. The most health status was reported by the oldest-old poor (59.2%, N=71), 52.9% (N=173) and the least by the young-old (40.6%, N=279). Embedded in these findings is that for every 1 youngold poor who indicated that he/she had an illness/injury, there are 1.5 oldest-old and 1.3 old-old poor.

3.3 Multivariate Analysis

The results of the multiple logistic regression model (in Table 9.2), were statistically significant [Model χ2 (df=18) = 229.47; -2Log likelihood = 1130.37; p-value < 0.001]. Table 9.2 showed that 26.6% of the variances in the health status of older people in Jamaica were accounted for by the independent variables used in the multiple logistic regressions. The mold revealed that there were 6 statistically significant factors that determined health conditions. These predictors are age (OR=1.04, 95% CI=1.02-1.06), health insurance coverage (OR=13.90, 95% CI=7.98-24.19), physical environment (OR=1.42, 95% CI=1.06-1.89), cost of medical care (OR=1.00, 95%
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CI=1.00-1.00), secondary level education (OR=1.82, 95% CI=1.35-2.45) with reference to primary and below education, and gender of respondents (OR=0.56, 95% CI=0.42-0.75). Controlling for the effect of other variables, the average likelihood of reporting illness/injury in a 4-week reference period declined by 17 times for those who had dysfunctions. The model had statistically significant predictor power (Model χ2 (df=18) = 229.47; Homer and Lemeshow goodness of fit χ2= 3.739, P=0.880), and correctly classified 70% of the sample (correctly classified 55.4% of those with dysfunctions and 82.3% of those without dysfunctions) (Table 9.2). The logistic regression model can be written as: Log (probability of dysfunctions/probability of not reporting dysfunctions) = -4.185 + 0.039 (Age) + 2.632 (Health Insurance coverage, 1= yes, 0=no) + 0.348 (Physical Environment, 1=yes, 0=no) + 0.000 (Cost of Medical Care) + 0.598 (Secondary level education=1, 0=primary and below) – 0.581 (Sex).

4. DISCUSSION

People are living longer [15], which means that on average the elderly are living 15-20 years after retirement. Demographic ageing at the micro and macro levels implies a demand for certain services such as geriatric care. In addition to preventative care, there will be a need for particular equipment and products (i.e. wheelchairs, walkers etc.). Then there are future preparations for pension and labour force changes, along with the social and economic costs associated with ageing, as well as the policy based research to better plan for the reality of these age groups. The World Health Organization (WHO), in explaining the ‘problems’ that are likely to occur because of population ageing, argues that the 21st Century will not be easy for policy makers as it is pivotal in the preparation process to postpone ailments and disabilities, and the challenge of
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providing a particular standard of health for the populace [16]. What constitutes population ageing? Some demographers have put forward the benchmark of 8-10% as an indicator of population ageing [17]. Within the construct of Gavrilov and Heuveline’s perspective, the Jamaican population began experiencing this significant population ageing as of 1975 (using 60+ years for ageing) or 2001 (if ageing is 65+ years). The issue of population ageing will double come 2050, irrespective of the chronological definition of ageing, but what about the elderly poor health conditions? Let us examine the disparity between long life and quality of lived years. Ali, Christian & Chung [18] who are medical doctors, cite the case of a 74 year-old man who had epilepsy, and presented the findings in the West Indian Medical Journal. They write that “Elderly patients are frequently afflicted with paroxysmal impairments of consciousness, because they frequently have chronic medical disorders such as diabetes mellitus and hypertension, and can also be on many medications….Many elderly patients may have more than one cause for this symptom” [18]. The case presented by the medical doctors emphasizes the point we have been arguing that long life does not imply quality of lived years. Although the case study cited here does not constitute a general perspective on all the elderly, other quantitative studies have concurred with Ali, Christian and Chung’s general findings. Scientists agree that biological ageing means degeneration of the human body, and such a reality means that longer life will not mean quality years. Population ageing is going to be a socioeconomic, psychological and political challenge today, tomorrow and in the future of developing countries and nations like Jamaica. This reinforces the position postulated by the WHO that healthy life expectancy [19] is where we
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ought to be going, as the new thrust is not living longer but how many of those years are lived without dysfunctions. Within the context of healthy life expectancy, studies that will be used to guide policy are those that incorporate many determinants, and not only biological conditions [20-25]. But none of those studies examined poor old people. Hambleton [20] and Bourne [2325] are Caribbean scholars who have researched social determinants using the population of the poor, and this gap to date in the literature needs to be addressed, as the elderly constitute a vulnerable group, and the poor elderly group is even more vulnerable. Any policy which seeks to reduce poverty must take into account the poor elderly. ‘Ageing in poverty’ implies that persons remain in their local environments with the ability to live in their own home - wherever that might be - for as long as confidently and comfortably possible. It inherently includes not having to move from one's current residence in order to secure the necessary support services in response to changing needs. The ageing of Caribbean populations has been accompanied by a shift to chronic non-communicable diseases as major causes of morbidity. While overall national trends have been reported, examination of local patterns of morbidity are increasingly important, as they have implications for the services to be provided, the mix of human resources, and the maintenance of health and functional status that facilitate ageing in place. Research has shown that crowding is strongly correlated with the wellbeing of the elderly (ages 60+ years) [23]; however this phenomenon, which is synonymous with poverty, does not influence the health status of poor elderly Jamaicans. Embedded in this finding is the fact that older people, in particular those in poor quintiles, interpret people around not as a negative force but as good social networking and interaction. What, then, influences their health conditions?
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Poverty speaks to a particular environment; Pacione [26] showed that one’s physical environment affects one’s quality of life, and other scholars have agreed with this finding. The current study concurs with Pacione and others, in that the physical milieu is positively correlated with health conditions. Although Michael Pacione’s work was on the general population, Bourne’s works [23, 24] examined the elderly population (ages 60+ years) and found a negative association between physical environment and wellbeing, and this study concurred with that of the aforementioned researcher on the correlation between physical environment and health conditions. In this study, an important finding is to refine the correlation. Health insurance coverage is among the many indicators of the health-seeking behaviour of a populace. For the poor elderly, it is the most significant predictor of health conditions. The correlation is a strong positive one, indicating that health insurance coverage is a good proxy for more ill-health than good health. The current research found that those elderly poor who owned health insurance were 14 times more likely to report dysfunctions (or injuries) than those who did not. Health insurance is, therefore, a cost reducer for those who are aware that they are ill, and it is not in demand as a preventative measure. Arising from this fact is the role played by the costs of medical and curative care. Health is influenced by more than disease-causing pathogens. [27] The cost of medical care is positively correlated with health conditions, suggesting that the more dysfunctions (or injuries) that the elderly poor report, the more they are likely to spend on medical care. The elderly poor are prevented from seeking preventative care as against curative care. The latest data published by the Planning Institute of Jamaica and the Statistical Institute of Jamaica[28] showed that 37.3% of elderly people are at least poor, with 20.6% falling
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in the poorest quintile. This further explains the rationale for the reduction in the demand for medical care within the context of a precipitous increase in inflation in 2007 over 2006 (194%). With the steady rise in the cost of health care, as well as the increase in general food and nonalcoholic beverage prices in Jamaica, coupled with the fact that illness in older age requires care, the elderly poor are facing increasingly difficult times. The severity of the economic situation has seen a dramatic increase in the number of Jamaicans not seeking medical care for illness/injury. Although there is a decline in the general population seeking medical care (66%), more of the elderly do seek health care (72.3%) and this is owing to recurrent chronic illness which was shown to affect 74.2% of them28. Illnesses/injuries are precipitously affecting the elderly, and the data showed that self-reported illness for the elderly was 2.3 times more (36.6%) than in the general population (15.5%) [28]. In 2007, the elderly poor who constitute 38% of the poor-to-poorest in the population are mostly household heads (67.3%) and often unemployed, and within this context they must provide for their own health needs and those of their family, despite the harsh economic challenges and increased cost of health care. In 2002, 12.9% of Jamaicans were unable to afford medical care, and approximately 4 years later, the figure had risen by 162.8% to 33.9% in 2007. This is within the context of a 26.3% decline in poverty for the same period. Generally poverty has been falling over the last 2 decades in Jamaica, and inflation has fluctuated, justifying the increased amount spent on food and beverages [28], and the corresponding reduction in health care expenditure. In Jamaica remittances, which subsidize income for many households, have fallen by 7.7% and the reduction is 33% for those in the poor-to-the-poorest income quintiles. If the cost of medical care is positively correlated with the health status of the elderly poor, then can it be said that the poor elderly have more ill-health within the context of biological ageing and lowered access to
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employment income? Marmot [2] opined that there is a direct association between income and poor health, and this further helps us to understand the embedded health challenge of the elderly poor, as they must meet the increasing costs of medical care, cost of living, lower income, illnesses and severity of health conditions. On examining the health statistics for 2007 [28], the indication was that 50.8% of those in the poorest income quintile were unable to afford to seek medical care, and the figure was 36.7% of those in the poor quintile. In order to understand the severity of the situation regarding the aged-poor people in Jamaica, let us analyze the aforementioned within the context of the aged-poor. The official statistical publication for Jamaica for 2007 [28] showed that 20.6% percent of the elderly people are in the poorest quintile and 17.7% in the poor quintile which means that a little over half of the aged-poorest in Jamaica (10.4%) were unable to afford medical care, and 6.5% of the aged-poor had financial difficulty affording medical care expenditure. One of the choices that must be made by the aged-poor in Jamaica is a switch from the formal medical care service to utilizing home remedies and overthe-counter medications, instead of visiting their personal physicians or health care facilities. Since 1988 when the Jamaican authorities began collecting data on self-reported health conditions, men have been reporting less health status than women [28]. The reporting of less illness does not mean that men are healthier than women, as the same statistical report [28] shows that women seek more medical care than men. Morbidity data for the sexes in Jamaica is typical, as in Mexico City, Havana and Santiago-Chile at least 60% of females compared to 50% of males aged 60+ years old reported fair-to-poor health [29]. Continuing, Buenos Aires, Montevideo and Bridgetown-Barbados had twice the figures of the aforementioned geo-political zones [29]. This is in keeping with women’s protective role of self, and their willingness to have a regard for their future health status accounts for a higher health status and not a lower one,
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although they report more dysfunctions than men. If life expectancy were to be used to proxy good health status, females are healthier than men given that they outlive them by 6 years in Jamaica and 8 years in the world. Furthermore, in 2000-2005, life expectancy for men was 69.5 years and 74.7 years for women, and come 2045-2050 they both would have gained an additional 2 and one-quarter years more to their life span. The equal and constant rate of change in the life expectancy of both sexes in Jamaica highlights the fact that men do not enjoy better overall health status than their female counterparts. More years of life for both sexes means that the life course opens itself to coronary heart disease, stroke and diabetes mellitus, and so morbidity must be examined in this discourse. Studies done by the Ministry of Health reveal that of the five leading causes of mortality in Jamaica, which are malignant neoplasm, heart disease, diabetes mellitus, homicide and cerebrovascular diseases [30], more men die from more of the aforementioned conditions than women. Malignant neoplasms are 39% greater for men than women; cerebrovascular diseases are 14% higher for females than males; heart disease was 71.2 per 100, 000 for men and 66.1 per 100,000 for women; and diabetes mellitus was 64% more for females than males [30]. The greater vulnerability of men to particular mortality than women is typical across Latin America and the Caribbean [29], pointing to gender bias (that is feminization) in visits to health care facilities, which are embedded in the life expectancy rates and visits to health care institutions. The matter of reporting less health status, once again, does not imply a healthier person, as health is not on a continuum, with ill-health on one extreme and good health on the other. Health is more in keeping with cyclical flow, and changes over the life course with time, experiences and socio-physical environmental conditions. Hence, asking about ill-health is not a good proxy for health status, as in 2007 a group of Caribbean scholars conducted a national representative
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prevalence survey of some 1,338 Jamaicans, and found that those who indicated themselves to be of the lower class had the least self-reported health status [13]. The discipline of gerontology – scientific inquiry into the biological, psychological, and social aspects of ageing - has shown that ageing is not necessarily without increased health conditions; it is natural for aged people to complain and die more of dysfunctions than other age cohorts [31, 32] and that is directly related to their basal metabolic rate [33] and the nature of the life course of the aged [34]. Here functional ageing is an explanation for the image of ageing, and it can be measured by normal physical changes, diminished short-term memory, reduced skin elasticity and a decline in aerobic capacity. It is well established in the research literature that age is directly correlated with health status for the elderly, and in this study the finding concurs with the literature. The current research shows that age is the second most significant predictor of health status for the elderly poor, and explains why the disparity in poor health in Latin and America and the Caribbean is higher for older persons than younger people [29]. Population ageing is synonymous with more disability and more non-communicable diseases such as malignant neoplasms, hypertension, diabetes, and heart diseases than younger ages. Donald Bogue [35] noted that health problems increase with ageing, and that one’s health issues intensify with ageing. Therefore, an unhealthy lifestyle – tobacco consumption, physical inactivity, unprotected sex, and unhealthy diet - over the life course will affect the elderly in latter life, and the declining health of the elderly poor is the same within the sub-categories of the elderly – young-old, old-old and oldest old. Issues of the elderly cannot be discussed without an examination of area of residence. This study found no correlation between the aged-poor’s health status and area of residence.
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Using data since 1989 (from various issues of the Jamaica Survey of Living Conditions), population ageing is biased by gender as well as by specific area of residence. Over the last decade (1997-2007), the number of elderly Jamaicans living in rural areas has declined from 54.3% to 46.6% (a rate of 14.1%). For the same period, the rate of increase of the aged populace in the Kingston Metropolitan Area (100% cities) was 19.5%, down from 27.2% (in 1997) while the increase in the aged population over the same period in Other Towns was 12.9% over 18.5% in 1997. Regarding the prevalence of poverty for the region (2007), rural poverty was 3.8 times more than that in Other Towns, and 2.5 times more than that in the Kingston Metropolitan Area. Despite the compounding economic challenges of poverty coupled with ageing, the poor-elderly in Jamaica do not experience a difference in their health status owing to area of residence. Here the health issues of the aged poor are independent of their area of residence, suggesting that in the population the poor are age-residence insensitive. This contradicts research literature on the health status of the elderly which has shown a correlation between the aged and their areas of residence [23,24,48], indicating that the physical characteristics of the aged poor are the same in different areas of residence, and therefore do not account for any poor health, disability, functional inability or psychological conditions. Like the WHO [36], the researcher believes that although ageing is a biological phenomenon, it cannot be due only to biological conditions, as ageing relates to bio-psychosocial [20, 25, 37-49] and environmental conditions [23-26], since people – biological organisms – must operate in a socio-physical milieu throughout their life span, and this demands an expansion of biological conditions in the ageing discourse. The very nature of gerontology must coalesce biopsychosocial and environmental conditions in assessing ageing and the health of the aged, which are in keeping with the WHO’s Constitution of 1948, and this has also been
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established in many Caribbean scholarships [20,23-25,42-49]. Within the context of the abovementioned challenges for elderly people, when this is coupled with poverty which affects 10.2% of elderly Jamaicans (N=29,794) in 2007, it intensifies the challenges experienced by elderly people. With the increased cost of food and non-alcoholic beverages, fuel and household supplies, housing and household operational expenses, the health status of the older-poor will continue to deteriorate, as they will not be able to afford health care services. The decline in medical care-seeking behaviour of Jamaicans speaks to the challenges of older people and the rise in instances of switching to alternative medicine. This is further intensified by poverty; and rural poverty, which is more severe than that found in urban areas [50], will further compound the challenges of the health status of the aged populace. Older people who are poor must operate within the same biopsychosocial and physical environment during their lifetimes as other persons. Even among the WHO commissioned studies [51-53], as well as other studies on the social determinants of health [2,3, 20-25], the population of the poor elderly were not examined. Likewise in the Caribbean, scholars have examined the social determinants of the population or the elderly population, with poverty being an independent variable [20, 23-25]. Any policy that seeks to address the health status of the elderly poor must take into consideration, or concentrate and/or rely on, not only the population in general, but the cohort of the elderly in particular. The experiences and demands of the elderly are not the same as the general population, and the current study shows that social determinants of health are somewhat different for the general elderly population and the poor elderly cohort. The WHO [51] opined that the social determinants of health for the most part account for the health inequities between and within nations, which substantiates the differences that emerged between the elderly in other studies
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[20, 23-24] and the current study of the poor elderly. These findings are far-reaching, and can be used to guide policy and research. The elderly-poor in Jamaica are experiencing ‘health poverty’ which cannot be alleviated by unresearched policies or research policies on the general population, but by the elderly cohorts in particular.

5. Conclusion
In summary, the number of elderly persons who reported health conditions in Jamaica is 3 times more than that for the nation (i.e. 12.6%), suggesting that health care expenditure for Jamaicans is substantially used to address health care needs for the aged population. With the number of elderly come 2025 estimated to be 14.5% over 10.9% for 2007, health care expenditure will be primarily absorbed in caring for this age cohort. Public health practitioners must begin programmes to deal with this pending reality. Ageing is a process which denotes that the high number of health conditions affecting the elderly would have started earlier, based on some of the decisions that they undertook (or did not) leading up to their current age. Hence, there is a need to have a public health campaign geared towards the promotion of healthy lifestyle practices for ages close to sixty years, in conjunction with one for children and for the working-age population. The programme should target check-ups, preventative care, signs of the onset of particular health conditions, and the distinction between ill health and good health care practices. The demand of the health services in Jamaica in the future must be geared towards a particular age cohort and certain health conditions, and not only to the general population, as the social determinants which give rise to inequities are not the same even among the same age cohort.

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6. Disclosure
The author reports no conflict of interest for this study.

7. Disclaimer
The researcher would like to note that while this study used secondary data from the Jamaica Survey of Living Conditions, none of the errors in this paper should be ascribed to the Planning Institute of Jamaica or the Statistical Institute of Jamaica, but to the researcher.

8. Acknowledgement
The dataset for this study was made available from the databank of SALISES (Sir Arthur Lewis Economic Institute), Faculty of Social Sciences, the University of the West Indies, Mona, Jamaica and for this the researcher is indebted and greater appreciate this gesture.

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20. Hambleton IR, Clarke K, Broome HL, Fraser HS, Brathwaite F, Hennis AJ. (2005). Historical and current predictors of self-reported health status among elderly persons in Barbados. Rev Pan Salud Public, 17:342-352. 21. Smith JP, Kington R. (1997). Demographic and economic correlates of health in old age. Demography, 34(1):159-170. 22. Grossman M. (1972). The demand for health- a theoretical and empirical investigation. New York: National Bureau of Economic Research. 23. Bourne, P. (2007). Determinants of well-being of the Jamaican Elderly. Unpublished thesis, The University of the West Indies, Mona Campus. 24. Bourne, P. (2007). Using the biopsychosocial model to evaluate the wellbeing of the Jamaican elderly. West Indian Medical J, 56(suppl 3), 39-40. 25. Bourne PA. (2008). Health Determinants: Using Secondary Data to Model Predictors of Well-being of Jamaicans. West Indian Medical J, 57,476-481. 26. Pacione M. (2003). Urban environmental quality and human wellbeing –a social geographical perspective. Landscape and Urban Planning, 65,19-30. 27. Abel-Smith B. (1994). An introduction to health: Policy, Planning and Financing. Harlow: Pearson Education. 28. Planning Institute of Jamaica, (PIOJ), Statistical Institute of Jamaica (STATIN). (19892008). Jamaica Survey of Living Conditions, 1988-2007. Kingston: PIOJ, STATIN. 29. United Nations, ECLAC. (2003). Older Person In Latin America and the Caribbean: Situation and Policies. Regional Intergovernmental Conference on Ageing: Towards a Regional Strategy for the Implementation in Latin America and the Caribbean of the Madrid International Plan of Action on Ageing. Santiago, Chile; UN, ECLAC. 30. The Health Promotion and Protection Division, Jamaican Ministry of Health (MOH). (2005). Epidemiology Profile of Selected Health status and Services in Jamaica, 19902002. Kingston; MOH. 31. Wu D, Cypser R, Yashin AI, Jonson TE. (2008). The U-Shaped Response of Initial Mortality in Caenorhabditis elegans to Mild Heat Shock: Does It Explain Recent Trends it Human Mortality. The Journal of gerontology: Biological Sciences, 63,660-668. 32. Raynaud-Simon A, Kuhn M, Moulis J. (2008). Tolerance and Efficacy of a New Enteral Formula Specifically Designed for Elderly Persons: An Experimental Study in the Aged Rat. The Journal of gerontology: Biological Sciences, 63,669-677. 33. Ruggiero C, Metter EJ, Melenovsky V, Cherubini A, Najjar SS, Ble A, Senin U, Longo DL, Ferrucci L. (2008). High Basal Metabolic Rate Is a Risk Factor For Mortality: The Baltimore Longitudinal Study of Aging. The Journal of gerontology: Biological Sciences 63(7):668-706. 34. WHO. (2001). Life course perspectives on coronary heart disease, stroke and diabetes: Key issues and implications for policy and research. Summary Report of A Meeting of Experts 2-4 may 2001. Geneva: WHO. 35. Bogue DJ. (1999). Essays in human ecology, 4. The ecological impact of population aging. Chicago: Social Development Center. 36. WHO. (2002). Active Ageing: A Policy Framework. Geneva: WHO.
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37. Engel G. (1960). A unified concept of health and disease. Perspectives in Biology and Medicine, 3,459-485. 38. Engel G. (1977). The care of the patient: art or science? Johns Hopkins Medical Journal, 140,222-232. 39. Engel G. (1977). The need for a new medical model: A challenge for biomedicine. Science, 196,129-136. 40. Engel G. (1978). The biopsychosocial model and the education of health professionals. Annals of the New York Academy of Sciences, 310, 169-18. 41. Engel G. (1980). The clinical application of the biopsychosocial model. American Journal of Psychiatry, 137,535-544. 42. Eldemire D. (1995). A situational analysis of the Jamaican elderly, 1992. Kingston: The Planning Institute of Jamaica. 43. Eldemire D. (1997). The Jamaican elderly: A socioeconomic perspective & policy implications. Social and Economic Studies, 46, 175-193. 44. Eldemire-Shearer D. (2003). Organization of Long-term Care Services for Seniors. Workshop Proceedings, Ageing Well: A Life Course Perspective, the University of the West Indies, Mona and WHO/PAHO Collaborating Centre on Ageing and Health. 45. Eldemire D. (1996). Older women: A situational analysis, Jamaica 1996. New York: United Nations Division for the Advancement of Women. 46. Eldemire D. (1994). The elderly and the family: The Jamaican experience. Bulletin of Eastern Caribbean Affairs, 19,31-46. 47. Eldemire D. (1987). The elderly – A Jamaican perspective. In: Grell, Gerald A. C. (ed). 1987. The elderly in the Caribbean: Proceedings of continuing medical education symposium. Kingston, Jamaica: University Printery. 48. Eldemire D. (2008). Ageing-The Response: Yesterday, Today and Tomorrow. Inaugural Professorial Lecture, UWI, Mona, Jamaica, January 31,2008. 49. Kalache A. (2003). Active Ageing: WHO Perspective. Workshop Proceedings, Ageing Well: A Life Course Perspective, the University of the West Indies, Mona and WHO/PAHO Collaborating Centre on Ageing and Health. 50. Henry-Lee A. (2001). The Dynamics of Poverty in Jamaica, 1989-1999. Social and Economic Studies, 199-228.
51. WHO.  (2008).  The  Social  Determinants  of  Health.  http://www.who.int/social_determinants/en/ (accessed April 28, 2009).  Available  at 

52. Kelly MP, Morgan A, Bonnefoy J, Butt J, Bergman V. (2007).The social determinants of health: Developing an evidence base for political action. Final Report to World Health Organization Commission on the Social Determinants of Health from Measurement and Evidence Knowledge Network. Available from http://www.who.int/social_determinants/resources/mekn_final_report_102007.pdf (accessed April 29, 2009) 53. Solar O, Irwin, A. (2005). Towards a Conceptual Framework for Analysis and Action on the Social Determinants of Health. 2005, Geneva: Commission on Social Determinants of Health.

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Table 9.1: Socio-demographic characteristics of sample Description Gender Male Female Marital status Married Never married Divorced Separated Widowed Per capita Income quintile Poorest Poor Retirement Income No Yes Household head No Yes Health Insurance coverage No Yes Educational Level Primary and below Secondary Tertiary Age Total Medical Care Expenditure Per capita consumption US $1.00 = JA$50.97 N 517 632 452 357 10 22 290 580 569 1087 57 20 1129 973 158 700 363 10 72.63 years (SD=8.7 years) $1,067.64 (SD=$2,000.00) $30,998.07 (SD=$9,833.00) Percent 45.0 55.0 40.0 31.6 0.9 1.9 25.6 50.5 49.5 95.0 5.0 1.7 98.3 86.0 14.0 65.2 33.8 0.9

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Table 9.2: Logistic Regression: Socio-demographic correlates of health status of poor older people in Jamaica, N=1,033 OR 95.0% C.I. Variable Age 1.04 1.02 - 1.06*** Retirement income 0.75 0.38 - 1.49 Per capita consumption 1.00 1.00 - 1.02 Separated, divorced or widowed Married Never married (reference group) Health insurance Environment Household head Cost of medical care Secondary Tertiary Primary and below (reference group) Semi-urban Urban areas Rural areas (reference group) Sex Living arrangement Crowding Crime index Positive affective
Model Chi-square (df =18) = 229.47, p-value < 0.0001 -2Log likelihood = 1130.37; Nagelkerke R-square = 0.266 Hosmer and Lemeshow test P = 0.880 *P < 0.05, **P < 0.01, ***P < 0.001

1.07 1.11 1.00 13.90 1.42 3.34 1.00 1.82 0.43 1.00 0.78 0.86 1.00 0.56 1.20 0.89 1.00 0.96

0.74 - 1.55 0.77 - 1.58 7.98 - 24.19*** 1.06 - 1.89* 0.37 - 30.01 1.00 - 1.05** 1.35 - 2.45*** 0.07 - 2.63 0.51 - 1.19 0.50 - 1.49 0.42 - .75*** 0.77 - 1.88 0.78 - 1.02 0.98 - 1.03 0.90 - 1.01

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Chapter 10
Self-rated health of the educated and uneducated classes in Jamaica

Paul Andrew Bourne
Education provides choices, opportunities, access to resources and it is associated with an increased likelihood of higher income. Does this holds true in developing nations like Jamaica, and does the educated class experience greater self-rated health status than the uneducated classes? The current study will identify the socio-demographic correlates of self-rated health status of Jamaicans, examine the effects of these variables, explore self-rated health status and self-reported diagnosed recurring illness among the educated and uneducated classes, compute mean income among the different educational types, and determine whether a significant statistical correlation exists between the different educational cohorts. The current study utilised the data set of Jamaica Survey Living Conditions which is a cross-sectional survey. It is a national probability survey, and data were collected across the 14 parishes of the island. Stratified random sampling techniques were used to draw the sample. Self-rated health statuses of respondents are correlated with age, income, crowding, sex, marital status, area of residence, and self-reported illness (es) – χ2= 1,568.4, P < 0.001. Respondents with tertiary level educations were most likely to be classified in the wealthiest 20% (53.4%) and there was no significant statistical difference between their health status and the lower educated classes. There is a need for a public health care campaign that is specifically geared towards the educated classes as their educational achievement is not translating itself into better health care-seeking behaviour and health status than the uneducated classes.

Introduction
Health is imperative for socio-economic and political development of people, a society and a nation. It is within this context that a study of health is critical as it relates to the wider society. Traditionally, the concept of health is measured using life expectancy, mortality, and diagnosed
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illness. In the social sciences, researchers have used self-rated health status [1-9], and selfreported illness [10-17] to measure health. Apart from those terminologies, other synonyms such as self-assessed health, self-reported health, perceived health, self assessment of health, global health status, and health status have all been used to speak about health. It follows from the aforementioned perspective that all those terms imply the same measurement of health or health status. Self-rated health status is among the subjective indexes used to measure health, and some scholars argue that they are not a good assessment of health when it comes to life expectancy, per capita income, or mortality [18-20]. The subjective/objective indexes of measuring health emerged as scholars sought to ensure that the measurement of health was a reliable and valid one. Some scholars opined that the self-assessment of one’s health status was more comprehensive than objective assessment [3, 5, 21] as it included one’s health and general life satisfaction. Studies have shown that subjective indexes are a good measurement for mortality [2, 22-24] and life expectancy [25]. Concurringly, a recently conducted study by Bourne [25] found that self-assessed illness was not a good measure of mortality; however, it was was very useful when it came to the subject of life expectancy in Jamaica. The subjective indexes in measuring health open themselves up to systematic and unsystematic biases [26]. People’s perception can be biased as they may inflate or deflate their status in an interview or on a self-administered instrument (i.e., questionnaire). Another aspect of bias in subjective evaluation of health is the matter of recall. It is well established in research literature that as people age, their mental faculties decline [27-32], suggesting that some people will have difficulties recalling experiences which happened in the past. Within the context of the
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time recollection, bias can occur in subjective indexes. Kahneman [33] devised a procedure of integrating and reducing the subjective biases when he found that instantaneous subjective evaluations are more reliable than assessments of recollection of experiences. Contrary to Kahmeman’s work, Bourne’s [25] results show that self-assessed health for a 4-week period is a good measure of life expectancy (objective index). In spite of the fact that subjective indexes are a good measure of objective health, the former still contains biases, which Diener [34] opines still have valid variance. It is well established in health research that there is a correlation between or among different socio-demographic, psychological and economic variables [4, 6-17, 20] and self-rated health status. The correlates include education, marital status, area of residence, education, income, psychological conditions (i.e., positive and negative psychological affective conditions), and other variables. Freedman & Martin [35], using data from 1984 and 1993’s panel survey of Income and Program Participation, noted that there was an association between educational level and physical functioning of people over 65 years. Another study by Koo, Rie & Park [36], using multivariate regression, concluded that education was a predictor of increased subjective wellbeing (t [2523] = 7.83, P<0.001], which means that education was more than associated with health. Concomitantly, another research found that the number of years of school (i.e., the Quantity Theory) was a crucial predictor of health status of an individual [37] which indicates that tertiary level graduates are more likely to be healthier than non-tertiary level educated people. While education provides choices, opportunities, access to resources and is associated with increased likelihood of achieving a higher income, does it hold true in developing nations
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like Jamaica that the educated class has greater self-rated health status than the uneducated classes? A paucity of information (research literature) exists in Jamaica on the educated and uneducated classes and their self-rated health status, self-reported illness(es), the areas in which the educated and uneducated classes reside, health care-seeking behaviour among the different educational classes and the self-rated health status of Jamaicans and its correlates. The current study is important, as it uses a statistical technique which accommodates all items in self-rated health status categories as opposed to dichotomising self-rated health. Dichotomising self-rated health status in good and poor health means that some of the original information will be lost; and this explains why some researchers argue for the maintenance of the Likert nature of the measuring tool over dichotomisation [38-40]. Secondly, the study is significant as it included more variables: (1) educational levels and area of residence, (2) educational levels and health care-seeking behaviour, (3) health insurance coverage and educational levels, (4) self-reported illness(es) and educational levels, (5) social standing and educational levels. The objectives of the current study therefore are to (1) identify the sociodemographic and economic correlates of self-rated health status of Jamaicans, (2) examine the effects of these variables, (3) explore self-rated health status and self-reported diagnosed recurring illness among the educated and uneducated classes, (4) calculate the mean age of respondents in the different educational categories, (5) compute mean income among the different educational types, and (6) determine whether a significant statistical correlation exists between the different educational cohorts.

Materials and methods
Data
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A joint survey on the living conditions of Jamaicans was conducted between May and August of 2007 by the Planning Institute of Jamaica (PIOJ) and the Statistical Institute of Jamaica (STATIN) [41]. The survey is called the Jamaica Survey of Living Conditions (JSLC) which began in 1988 and is now conducted annually. The JSLC is a modification of the World Bank’s Living Standards Measurement Study (LSMS) which is a household survey [42]. The current study used the JSLC’s data set for 2007 in order to carry out the analyses of the data [43]. It had a sample size of 6,783 respondents, with a non-response rate of 26.2%. The JSLC is a cross-sectional survey which used stratified random sampling techniques to draw the sample. It is a national probability survey, and data was collected across the 14 parishes of the island. The design for the JSLC was a two-stage stratified random sampling design where there was a Primary Sampling Unit (PSU) and a selection of dwellings from the primary units. The PSU is an Enumeration District (ED), which constitutes a minimum of 100 residences in rural areas and 150 in urban areas. An ED is an independent geographic unit that shares a common boundary. This means that the country was grouped into strata of equal size based on dwellings (EDs). Based on the PSUs, a listing of all the dwellings was made, and this became the sampling frame from which a Master Sample of dwellings was compiled. This, in turn, provided the sampling frame for the labour force. One third of the Labour Force Survey (i.e. LFS) was selected for the JSLC. The sample was weighted to reflect the population of the nation. Instrument A self-administered instrument (i.e., questionnaire) was used to collect the data from respondents. The questionnaire covers socio-demographic variables such as education, age, and
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consumption, as well as other variables like social security, self-rated health status, self-reported health conditions, medical care, inventory of durable goods, living arrangements, immunisation of children 0–59 months, and other issues. Many survey teams were sent to each parish according to the sample size. The teams consisted of trained supervisors and field workers from the Statistical Institute of Jamaica. Statistical Analyses The Statistical Packages for the Social Sciences – SPSS-PC for Windows version 16.0 (SPSS Inc; Chicago, IL, USA) – was used to store, retrieve and analyze the data. Descriptive statistics such as median, mean, percentages, and standard deviation were used to provide background information on the sample. Cross tabulations were used to examine non-metric dependent and independent variables. Analysis of variance was used to evaluate a metric and a nondichotomous variable. Ordinal logistic regression was used to determine socio-demographic, economic and biological correlates of health status of Jamaicans, and identify whether the educated have a greater self-rated health status than uneducated respondents. A 95% confidence interval was used to examine whether a variable is statistically significant or not. There was no selection criterion used for the current study. On the other hand, for the model, the selection criteria were based on 1) the literature; 2) low correlations, and 3) nonresponse rate. The correlation matrix was examined in order to ascertain if autocorrelation and/or multicollinearity existed between variables. Based on Cohen and Holliday [44] and Cohen and Cohen [45], low (weak) correlation ranges from 0.0 to 0.39, moderate – 0.4-0.69, and strong – 0.7-1.0. This was used to exclude (or allow) a variable in the model. Any correlation that had at least a moderate value was excluded from the model in order to reduce multicollinearity and/or
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autocorrelation between or among the independent variables [46-51]. Another approach in addressing and/or reducing autocorrelation was to include in the model all variables that were identified from the literature review with the exception of those where the percentage of missing cases were in excess of 30%. The current study used the ordinal nature of the dependent variable (self-rated health status or self-rated health) which denotes that none of the original data will be lost as is the case in dichotomising self-rated health. Ordered regression model is written as:

, s = 1, …k,

(1)

Where x is the vector of covariates with coefficient to be estimated, k is the number of cut-points for the dependent variable, and αs, αl stand for the intercepts in the regression models. Anderson [52] opined that ø1=1 and øk, and that other constraints are possible. In the current study, the researcher set ø1=1 and 0= ø1< ø2 < …< øk =1 to correspond to the levels from very good to very poor, and other levels of health are relative to “very good”. Based on Anderson’s arguments, the monotone increase of ‘ø’s are dealt with by varying the sign for β. Within this context, a positive estimation of coefficient denotes that those with this characteristic would be negatively associated with good health status and those without would positively associated with good health status (or self-rated health status). Simply put, positive estimation of coefficients means poor health and negative estimation of coefficients denotes better self-reported health status. Measurement of variables Dependent variable
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Self-rated health status (i.e., self-rated health) was derived from the question, “Generally, how is your health?” with the options being very good, good, fair (or moderate), poor, or very poor. The ordinal nature of this variable was used as was the case in the literature [38-40]. Independent variables Information on self-reported illness was derived from the question, “Have you had any illnesses other than injury?” The examples given include cold, diarrhoea, asthma attack, hypertension, arthritis, diabetes mellitus or other illness. A further question about illness asked, “(Have you been ill) In the past four weeks?” The options were yes and no. This variable was re-coded as binary value, 1 = yes and 0 = otherwise. Information about self-reported diagnosed recurring illness was derived from the question, “Is this a diagnosed recurring illness?” The options were: (1) yes, cold; (2) yes, diarrhoea; (3) yes, asthma; (4) yes, diabetes mellitus; (5) yes, hypertension; (6) yes, arthritis; (7) yes, other; (8) no. Information on medical care-seeking behaviour was taken from the question, “Has a health care practitioner, healer, or pharmacist been visited in the last 4 weeks?” The options were yes or no. Medical care-seeking behaviour therefore was coded as a binary measure where 1 = yes and 0 = otherwise. The term crowding refers to the average number of person(s) per room excluding the kitchen, bathroom, and veranda (i.e., total number of people in household divided by the total number of rooms excluding kitchen, bathroom and veranda). Total annual expenditure was used to measure income.

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Income quintile was used to measure social standing. The income quintiles ranged from poorest 20% to wealthiest 20%.

Results
Demographic characteristic of sample and bivariate analyses The sample was 6,783 respondents: 48.7% males and 51.3% females. Eighty-two percent of respondents rated their health status as at least good compared to 4.9% who rated it as poor. Fifteen percent of respondents reported some form of illness within the last 4 weeks. Of those who recorded an ailment, 89% reported that the dysfunction was a diagnosed recurring one. The most frequently recurring illness was unspecified conditions (23.4%) followed by hypertension (20.6%), cold (14.9%), diabetes mellitus (12.3%), and others (Table 10.1). The median age of the sample was 29.9 years (range = 99 years). The median annual income was US $7,050.66 (rate in 2007: 1US$ = Ja$80.47; range = US $4,406.20), and median crowding was 4.0 persons per room (range = 16 persons). A cross-tabulation between educational level and area of residence revealed a significant statistical correlation – χ2(df = 40 = 78.02, P < 0.001 (Table 10.2). Based on Table 10.2, 0.8% of rural respondents had tertiary level education and 5.4 times more urban residents had tertiary level education compared to rural respondents. No significant statistical correlation existed between educational level and sex of respondents – χ2 (df = 2) = 5.61, P > 0.05 (Table 10.3). Similarly, no significant statistical association was found between purchased prescribed medication and educational levels of respondents - χ2 (df = 10) = 11.9, P > 0.05.

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A significant statistical difference was found between mean age of respondents who are at different educational levels – F statistic [2, 6589] = 214.64, P < 0.001. The mean age of respondents with primary level of education and below was 32.0 years (SD = 22.6, 95% CI = 31.4-32.6) compared to 14.6 years (SD = 1.7, 95% CI = 14.5-14.8) for those with secondary education level and 26.4 years (SD = 10.6, 95% CI = 24.6-28.2) for those with tertiary education level. A cross-tabulation between self-reported illness and educational level revealed a significant statistical association - χ2 (df = 2) = 61.33, P < 0.001. Respondents with primary education level and below recorded the greatest percent of people with illness(es) (16.2%) followed in descending order by tertiary level (9.2%) and secondary level respondents (5.4%). The statistical correlation was a weak one – correlation coefficient = 0.10. A significant statistical correlation existed between self-reported diagnosed recurring illness and educational level – χ2 (df = 14) = 42.56, P < 0.001 (Table 10.4). Respondents with secondary level education (37.5%) had the highest percent of unspecified health conditions followed in descending order by tertiary (33.3%) and primary level respondents (22.7%). Hypertension was substantially a phenomenon occurring among those with primary education level and below: 21.6%, compared to 8.3% of tertiary level individuals. Similarly, diabetes mellitus (12.8%) was more prevalent among primary level respondents compared to 5.0% of secondary level respondents. On the other hand, asthma was the greatest among tertiary level respondents (33.3%) compared to secondary level (22.5%) and primary level respondents (8.7%). Respondents with tertiary level education were most likely to be classified in the wealthiest 20% (53.4%) compared to those with secondary education who were more likely to be
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in the middle class and those with primary level education were either in the poorest 20% (20.3%) or in the wealthiest 20% (20.3) (Table 10.4) – χ2 (df = 8) = 124.53, P < 0.001. Of the 20.2% of respondents who had health insurance coverage, tertiary level people were more likely to have private coverage (35.9%) followed by primary or below (12.0%) and secondary level individuals (11.6%) – χ2 (df = 4) = 76.95, P < 0.001 (Table 10.4). Concurringly, a significant statistical difference existed between the mean age among the different educational levels in which respondents were categorised (Table 10.4) – F statistic [2, 6589] = 214.6, P < 0.001: mean age for those with at most primary level education was 32.0 years (SD = 22.6) compared to a mean age of 26.4 years (SD = 10.6) for those with tertiary level education. When educational level of respondents was disaggregated into no formal, basic, and primary to tertiary, the mean age of respondents with no formal education was 42.7 years (SD = 18.0), 2.7 years (SD = 1.9) for basic school level respondents, and 9.0 years (SD = 2.2) for those who have primary level education – F statistic [4,6587] = 2207.9, P < 0.001 Multivariate analysis Self-rated health statuses of respondents are correlated with (1) age, (2) income, (3) crowding, (4) sex, (5) marital status, (6) area of residence, and (7) self-reported illness(es) – χ2= 1,568.4, P < 0.001; and that the data is a good fit for the model – LL = 9,218.0. The 7 socio-demographic and economic correlates accounted for 33% of the variability in self-rated health status (Table 10.5). Based on the Table 10.5, the older the respondents get, the more likely they are to rate their health status as poor and this was the same for crowding and for those who report an illness (health condition). Urban residents are more likely to report poor self-rated health status than rural residents. However, there was no statistical difference between self-rated health status for rural and semi-urban residents. Married people are more likely to report better self-rated health
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status than widowed people, people with more income are more likely to report better health status, and males are more likely than females to report better health status. However, no significant statistical difference was found between self-rated health status among the educated and uneducated cohorts.

Discussion
The current study concurs with the literature in that self-reported illness has the most influence on self-rated health status of people [8]. In a study of elderly Barbadians (ages 60+ years), Hambleton et al. [8] found that current illness accounted for 87.7% of the variance in self-rated health status. In another study on married people in Jamaica, Bourne and Francis [53] found that 73% of self-reported illnesses explains the variability in self-reported health status. Embedded in the current finding is whether self-rated health is examined on elderly or married people. Current self-reported illnesses accounted for a critical proportion of self-rated health and can be used to measure health. Within this context, self-reported illness is a good measure of self-rated health, and this has been established by other studies [10-17, 25]. A recently conducted research found that self-reported illness accounted for 54% (r-square) of the variance in life expectancy of Jamaicans [25], and this increased to 63% for males. Subjective indexes such as self-rated health and self-reported illness can be used to measure health, but the latter is a better measure and this must be taken into consideration in the interpretation of findings using this measurement. The challenges noted by some researchers in using self-rated health are: (1) bias and (2) the dichotomisation of the measure. While bias is synonymous with subjective assessment or evaluation of any construct, the validity of using the measure is high. Diener [34] noted in 1984 that there are still some valid variances, which was validated in a recent study by Bourne [25]. Health literature has long established that subjective indexes such as self-rated health, happiness,
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and life satisfaction are good measures of health as they are more comprehensive (including social activities and relationships, psychological conditions, emotions, spirituality, life satisfaction) while still incorporating the objective component [3, 21, 34]. This is justified by studies that found strong statistical correlations between subjective health and objective indexes such as life expectancy [25] and mortality [2, 22-24]. It should be noted here that subjective indexes (e.g., self-reported illness) and mortality are lowly correlated in Jamaica [25], which suggests that health literature among regions has revealed different findings. This denotes that the wholesale use of what is obtained in one nation cannot be applied to another without understanding socio-demographic characteristics. However, Jamaica, like other nations, can use subjective indexes to assess health status of its people and by extension its entire population. The issue of the dichotomisation of self-rated health, because some of the original values will be lost, is now resolved by this study as self-rated health was dichotomised and findings were similar to those who had dichotomised the dependent variable (i.e., self-rated health status). What are the similarities and dissimilarities between the two statistical approaches in operationalising subjective health? Studies in the Caribbean found that age, marital status, crowding, sex of respondents, area of residence, income and illnesses were statistically correlated with subjective health [8, 1017, 53], which is validated by the current study. Even some non-Caribbean studies have found the aforementioned variables to be statistically associated with subjective health [7, 9], indicating that dichotomising self-rated health status does not fundamentally change most of the sociodemographic, economic, and biological variables.

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Examining data on married people by way of dichotomising self-rated health status, Bourne [25] found that men had a greater self-reported health status than women, and in the current study (non-dichotomisation of self-rated health status), males had a higher health status than females. On the other hand, in Bourne’s work [25], he found in descending order selfreported illnesses, age, income and sex to be the only factors of self-reported good health while in the non-dichotomised study more variables accounted for health status. Nevertheless, ranking of the correlates were similar in both studies as in the current. The factors in descending order were self-reported illness, age, crowding, income, sex and the others, indicating the closeness of the statistical approaches. Married people are a component of the general populace and they have socio-demographic and economic experiences which differ from some unmarried people. The literature showed that income is strongly correlated with self-rated health. However, in Jamaica this is clearly not the case. In Jamaica, income plays a secondary role to illness and age and when self-rated health is non-dichotomised, it becomes an even weaker variable. Although income affords one particular choices (or lack thereof), the educated class in Jamaica received more income than uneducated classes, yet the former class is not healthier than the latter. This finding is contrary to the literature that showed the association between higher education and health [7-9]. Education influences social standing and income, but it does not directly influence good health status in Jamaica. Concurringly, the current work found that education is positively correlated with more health insurance coverage. However, health insurance coverage is not significantly associated with better health status. Embedded here is the fact that health insurance coverage in Jamaica is not an indicator of health care-seeking behaviour but a product that is purchased for the eventuality of the onset of illness, as it will lower out-of-pocket medical care expenditure.
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Education provides its recipients with knowledge, access to knowledge, access to income and other empowerment, but it does not mean that the educated classes are more concerned about their health, and this can be measured using health care-seeking behaviour and knowledge about the illnesses that are affecting the individual. The current paper found that 25 out of every 100 educated Jamaicans are aware of their health condition(s), and this is greater than that for uneducated classes. Jamaicans with the least level of education were most cognizant of their ailments and sought medical care just as much as did educated Jamaicans. Education, therefore, does not denote empowerment to seek medical care, which is embedded in the culture, in particular for men. Education is still unable to break the bondages of the perceptions of society which purport that health is weakness, and that to display weakness as a man removes his masculinity. This continues to shackle Jamaicans, particularly men, who still subscribe to the traditional notion that illness is correlated to weakness and that men should not display weakness. It is this cultural perspective that bars many men from visiting health care facilities, except in cases of severe illness or if they are married [25]. Hence, mortality being greater for men is not surprising [54] as many men will die prematurely because of the fact that they are reluctant to visit health care institutions. This reluctance to seek medical care is not limited to males. In 1988, when Jamaica began collecting data on the living conditions of its people, females sought more medical care than males, but the disparity ranged between -2 to 6%. In 2007, 68% of females sought medical care compared to 63% of males, which means that higher education, which is substantially a female phenomenon in Jamaica, is not fundamentally improving the health status of females or even males. Educated Jamaicans are more likely to live in urban areas and those with primary education levels or below are more likely to live in semi-urban zones. The current findings found
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that semi-urban respondents were more likely to have better health status, although they are more likely to have at most primary level education. In 2007, statistics revealed that 15.3% of Jamaicans in rural areas were below the poverty line compared to 4% of semi-urban and 6.2% of urban Jamaicans [41], indicating that poverty is more synonymous with rural areas, yet there is no significant statistical difference between the self-rated health status of rural and urban Jamaicans. Income makes a difference in health, as those with more means can access more and greater resources including health care, but clearly income beyond a certain amount is retarding the health status of Jamaicans. This study cannot stipulate a baseline income that people should receive in order to prevent a decline in health status. However, there is clearly a state of contentment among the poor and very poor who were equally as healthy as the wealthy. The health disparity between them and the educated showed no significant statistical difference and this emphasises that wealth does not automatically transfer itself into health. Another issue which is evident in the data is the variability in the measurement of health among the social classes, as the poorest 20% reported less illness than the wealthiest 20% [41], yet the former group still dwells in slums, inner-city neighbourhoods, and violent communities, and they have lower levels of education. Despite Diener’s findings [34] that the variance is minimal, Bourne’s work showed a strong association between subjective health (i.e., self-reported illness) and life expectancy – a correlation coefficient between 50 and 60% for a single variable is strong. However, this highlights that there are still some challenges embedded in the use of self-rated health status.

Conclusion
While the dichotomisation of self-rated health status loses some of the original data, when selfrated health is non-dichotomised, socio-demographic and biological variables accounted for 33%
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of the explanation of the variance and this was 44% using dichotomisation for married Jamaica, suggesting dichotomisation of health status still holds some validity. Another critical finding that emerged from the current work is that education is not improving the health status of Jamaicans. However, it is correlated with better social standing and higher income. Income is significantly associated with better health status and it played a secondary role to self-reported illness and age of respondents. Education is associated with more health insurance coverage, but that health insurance coverage cannot be used to measure health care-seeking behaviour or measure better health status of Jamaicans. In summary, there is a need for a public health care campaign that is specifically geared towards the educated classes as their educational achievement is not translating itself into better health care-seeking behaviour and health status than the uneducated which suggests that societal pressures are barring Jamaicans from better health status choices.

Conflict of interest
The author has no conflict of interest to report.

Acknowledgement Disclaimer
The researcher would like to note that while this study used secondary data from the Jamaica Survey of Living Conditions, 2007, none of the errors that are within this paper should be ascribed to the Planning Institute of Jamaica or the Statistical Institute of Jamaica, but rather to the researcher.

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Table 10.1. Demographic characteristic of sample, n=6,783 Characteristic Sex Male Female Marital status Married Never married Divorced Separated Widowed Social standing Poorest 20% Poor Middle Wealthy Wealthiest 20% Area of residence Urban Semi-urban Rural Self-reported illness Yes No Self-reported diagnosed recurring illness Cold Diarrhoea Asthma Diabetes mellitus Hypertension Arthritis Unspecified Not reported as diagnosed Health care-seeking behaviour Yes No Self-rated health status Very good Good Moderate Poor Very poor

n 3303 3479 1056 3136 77 41 224 1343 1354 1351 1352 1382 2002 1458 3322 980 5609 149 27 95 123 206 56 234 109 658 347 2430 2967 848 270 50

% 48.7 51.3 23.3 69.2 1.7 0.9 4.9 19.8 20.0 19.9 19.9 20.4 29.5 21.5 49.0 14.9 85.1 14.9 2.7 9.5 12.3 20.6 5.6 23.4 10.9 65.5 34.5 37.0 45.2 12.9 4.1 0.8
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Table 10.2. Educational level by area of residence, n = 6,592 Characteristic Area of residence Total Educational level Urban Semi-urban Rural % % % % Primary and below 84.8 89.0 88.0 87.3 Secondary 10.9 9.6 11.2 10.8 Tertiary 4.3 1.5 0.8 2.0 Total 1952 1421 3219 6592 Chi-square (df = 4) = 78.02, P < 0.001, cc = 0.11

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Table 10.3. Education level by sex of respondents, n = 6,592 Characteristic Male % Educational level Primary and below Secondary Tertiary Total Chi-square (df = 2) = 5.61, P > 0.05 87.9 10.5 1.6 3207 Sex Female % 86.6 11.0 2.4 3385 Total % 87.3 10.8 2.0 6592

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Table 10.4. Self-reported diagnosed recurring illness and social standing by educational level Educational Level Total Primary or Secondary Tertiary Characteristic below % % % % Self-reported diagnosed recurring illness1 Cold 15.0 17.5 0.0 14.9 Diarrhoea 2.9 0.0 0.0 2.7 Asthma 8.7 22.5 33.3 9.5 Diabetes mellitus 12.8 5.0 0.0 12.3 Hypertension 21.6 0.0 8.3 20.6 Arthritis 5.9 0.0 0.0 5.6 Unspecified condition 22.7 37.5 33.3 23.4 Not diagnosed 10.5 17.5 25.0 10.9 Total 947 40 12 999 2 Social standing (income quintile) Poorest 20% 20.3 19.7 3.8 19.9 Poor 20.0 21.7 7.6 20.0 Middle 19.4 24.5 16.0 19.9 Wealthy 19.9 20.3 19.1 19.9 Wealthiest 20% 20.3 13.7 53.4 20.2 Total 5752 709 131 6592 Health Insurance coverage3 No 79.8 83.7 57.8 79.8 Private 12.0 11.6 35.9 12.5 Public 8.1 4.6 6.3 7.7 Total 5682 689 128 6499 Age4 Mean (SD) in years 32.0 (22.6) 14.6 (1.7) 26.4 (10.6) 30.0 (21.8) Health care-seeking behaviour5 Yes 65.7 60.0 66.7 65.5 No 34.3 40.0 33.3 34.5 Total 953 40 12 1005 8,381.88 9,580.20 14,071.67 8,623.84 Income6 Mean (SD) in US$7 (6,641.28) (7,712.81) (9,31.10) (6,874.54) 1 Chi-square (df = 14) = 42.56, P < 0.001, cc=0.20 2 Chi-square (df = 8) = 124.53, P < 0.001, cc=0.14 3 Chi-square (df = 4) = 76.95, P < 0.001, cc=0.11 4 F statistic [2,6589] = 214.6, P < 0.001 5 Chi-square (df = 2) = 0.6, P > 0.05 6 F statistic [2,6589] = 52.4, P < 0.001 7 Rate in 2007:1US$= Ja$80.47

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Table 10.5. Ordinal logistic regression: Socio-demographic and biological differentials of selfrated health status of Jamaicans Std. 95% CI Characteristic Estimate Error Wald Upper Lower P
Excellent self-rated health Good self-rated health (ø1) Fair self-rated health (ø2) Poor self-rated (ø3) Very poor (ø4) Age Income Crowding Primary or below Secondary Tertiary (=0) Sex (female=0) Married Never married Divorced Separated Widowed (=0) Poorest 20% Poor Middle Wealthy Wealthiest 20% (=0) Urban Semi-urban Rural (=0) Private insurance Public insurance Public insurance – other No insurance coverage (=0) Illness 0.0 0.540 3.504 5.935 8.659 0.045 -3.79E-007 0.083 -0.187 0.042 -0.221 -0.554 -0.352 -0.469 -0.109 0.203 0.013 0.028 -0.238 0.217 0.008 -0.175 0.026 0.387 0.0 0.345 0.625 0.985 1.425 0.008 0.000 0.025 0.252 0.267 0.077 0.200 0.192 0.319 0.369 0.163 0.140 0.126 0.122 0.090 0.085 0.110 0.149 0.209 2.456 31.465 36.327 36.909 34.055 10.636 11.130 0.553 0.025 8.290 7.704 3.342 2.171 0.087 1.554 0.009 0.048 3.782 5.789 0.008 2.542 0.032 3.433 0.117 0.000 0.000 0.000 0.000 0.001 0.001 0.457 0.874 0.004 0.006 0.068 0.141 0.768 0.213 0.925 0.826 0.052 0.016 0.927 0.111 0.859 0.064 -0.135 2.279 4.005 5.865 0.030 -6.06E-007 0.034 -0.681 -0.481 -0.372 -0.945 -0.729 -1.094 -0.832 -0.116 -0.262 -0.219 -0.477 0.040 -0.159 -0.389 -0.265 -0.022 1.216 4.728 7.865 11.452 0.060 -1.51E-007 0.132 0.307 0.566 -0.071 -0.163 0.025 0.155 0.615 0.523 0.288 0.274 0.002 0.395 0.174 0.040 0.318 0.796

2.377

0.401

35.152

0.000

1.591

3.163

Nagelkerke r-square = 0.33 Chi-square = 1,568.4, P < 0.001 LL = 9,218.0 n=4,433

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Chapter 11
Retesting and refining theories on the association between illness, chronic illness and poverty: Are there other disparities?

Paul Andrew Bourne
Poverty is well established as being associated with illness and chronic illness. Studies which have examined this phenomenon have done so using objective indices such as life expectancy, infant mortality and general morality. This study (1) examined subjective indices such as selfreported illness and self-reported health, (2) re-tested the theories that chronic illnesses are more likely to be greater in number among the poor and that illnesses are positively correlated with poverty, and (3) evaluated other social characteristics that account for the poverty-illness theory. The current study used a secondary cross-sectional dataset from the Jamaica Survey of Living Conditions (JSLC). The JSLC used an administered questionnaire where respondents were asked to recall detailed information on particular activities. The questionnaire was modelled on the World Bank’s Living Standards Measurement Study (LSMS) household survey. The crosssectional survey was conducted between May and August 2002 in the 14 parishes across Jamaica and included 25,018 people of all ages. The statistical package SPSS 16.0 was used for the analysis. A p-value less than 5% (2-tailed) was used to indicate statistical significance. Those in the two wealthy social hierarchies were 18% less likely to report chronic illnesses compared to those in the two poor social hierarchies. Males were 69% less likely to report chronic illness compared to females as well as 56% less likely to indicate an illness. When the chronic illnesses were disaggregated by sex of respondents, the prevalence rate of females with hypertension was 2.2 times more than hypertensive males; 3.2 times more than male arthritic patients, and 3.0 times more than male diabetics. Forty-five percent of those with chronic illnesses were married. While poverty has declined in Jamaica since the 1990s, the health disparity between the poor and the upper social hierarchy continues to this day. The information provided in this research has far-reaching implications, and may be used to guide policies, frame interventions and provide a focus for future research in Jamaica.

Introduction
Empirically there are many studies which have found and established a statistical association between poverty and illness [1-8]. Some research has shown that those in the lower socioeconomic status are less healthy than those in the wealthy socioeconomic groups [9, 10]. A
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study by Van Agt et al. [8] found that poverty was greater among chronically ill people than the non-chronically ill, and the WHO [4] concurred with Van Agt et al. [8] when it opined that 80% of chronic illnesses were in low and middle income countries. Poverty is not only associated with illness and ill-health, but also higher rates of mortality. According to the WHO [4], 60% of global mortality is caused by chronic illness, and this should be understood within the context that four-fifths of chronic dysfunctions are in low-to-middle income countries. The rationales given for the poverty and illness theory are (1) money (insufficient financial resources); (2) medical expenditure; and (3) other types of socio-political incapacity [3, 8, 11]. Sen [11] encapsulated this well when he opined that high levels of unemployment in the economy are associated with higher levels of capabilities, pointing to money and other incapacities of those who are likely to be unemployed in the society. The poor are therefore more likely to be unemployed, to be ill, to suffer from more chronic illnesses, to have insufficient money, low levels of educational attainment, to experience a greater percentage of infant and other mortality and to live in an inadequate physical environment, compared to those in the wealthy social hierarchies. Using objective indices such as infant mortality and life expectancy to measure the health of a population, studies in Latin America and the Caribbean concur with the aforementioned research. Cass et al. [12] found that infant mortality in Peru for those in the poorest quintile (i.e. poorest 20%) was almost 5 times more than that for those in the wealthiest quintile (i.e. wealthiest 20%). Another study revealed that out-of-pocket medical expenditure accounts for some people becoming poor and that a greater percentage of these people do not have health insurance coverage [2]. One study highlighted the fact that life expectancy between the poorest 20% and the wealthiest 20% was 6.3 years and this was 14.3 years for disability-free life
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expectancy [13]. The relationship between poverty and illness is longstanding, and the Director of the Pan American Health Organization in 2001 wrote that it is still evident in contemporary societies [14]. He however went further to state that poverty affects mental as well as physical health, and concurs with the literature that those in the lower socioeconomic status have greater levels of illnesses (i.e. psychopathology). It has been clearly understood and well-established for centuries that poverty is associated with illness, and that it affects those individuals by constricting their capacity, which further affects their health. The poor have less access to money and other resources than the wealthy, and are also deprived of a good health outcome in the future. A study by Mayer et al. [15] provided evidence that there is a strong relationship between health and future economic growth, suggesting that current poverty contracts future health and economic prosperity. Mayer et al.’s work provides pertinent insight into the retardation of poverty, but also gives an understanding of how poverty affects health, production, productivity and how it poses a present and future problem for public health policy makers. How is this of concern to public health policy makers in Jamaica? A recent study conducted by Bourne [16] found that (1) moderate and direct correlation between the prevalence of poverty (in %) and unemployment (R2 = 0.48); (2) direct association existed between not seeking medical care (in %) and prevalence of poverty (in %) – R2 = 0.58; (3) a strong statistical relationship between prevalence of poverty and mortality – R2 = 0.51; and (4) a non-linear relationship between not seeking medical care and illness. From Bourne’s findings, the challenges for public health specialists as well as policy makers are a reality in Jamaica, as in other nations. If poverty is associated with unemployment and not seeking medical
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care, and not seeking medical care is related to illness, it appears to be a non-issue to re-test the established theory of poverty and illness and poverty and chronic illness in Jamaica, but this is not the case as there is self-reported illness may not give the same result as diagnosed illnesses. None of the aforementioned studies that have examined poverty and illness have used self-reported data to test the poverty and illness, and poverty and chronic illness phenomena. The aims of the current study are to investigate (1) poverty and self-reported illness, (2) poverty and self-reported chronic illness, and (3) other socio-demographic characteristics, in order to provide an understanding of existing disparities as well as to concur with, or refute, current theories.

Methods
Study population The current study used a secondary cross-sectional dataset from the Jamaica Survey of Living Conditions (JSLC). The JSLC was provided by the Planning Institute of Jamaica (PIOJ) and the Statistical Institute of Jamaica (STATIN) for analysis [17-19]. These two organizations are responsible for planning, data collection and formulating policy guidelines for Jamaica. The cross-sectional survey was conducted between May and August 2002 in the 14 parishes across Jamaica and included 25,018 people of all ages [20]. The JSLC used stratified random probability sampling technique to draw the original sample of respondents, with a non-response rate of 26.2%. The sample was weighted to reflect the population. Study instrument The JSLC used an administered questionnaire where respondents were asked to recall detailed information on particular activities. The questionnaire was modelled on the World Bank’s Living
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Standards Measurement Study (LSMS) household survey. The questionnaire covered demographic variables, health, education, daily expenses, non-food consumption expenditure and other variables. Interviewers were trained to collect the data from household members. Statistical methods Descriptive statistics were used to provide socio-demographic characteristics of the sample. Chisquare analyses were used to examine the association between non-metric variables. Analysis of variance was used to test the statistical significance of a metric and non-dichotomous variable. Logistic regression analyses examined 1) the relationship between good health status and some socio-demographic, economic and biological variables; as well as 2) a correlation between medical care-seeking behaviour and some socio-demographic, economic and biological variables. The statistical package SPSS 16.0 was used for the analysis. A p-value less than 5% (2-tailed) was used to indicate statistical significance. The correlation matrix was examined in order to ascertain if autocorrelation and/or multicollinearity existed between variables. Based on Cohen and Holliday [21] correlation can be low (weak) - from 0 to 0.39; moderate – 0.4-0.69, and strong – 0.7-1.0. Any variable that had at least moderate (r > 0.6) was re-examined in order to address multicollinearity and/or autocorrelation between or among the independent variables [22-28]. Another approach in addressing collinearity (r > 0.6) was to independently enter variables in the model to determine which one should be retained during the final model construction. The method of retaining or excluding a variable from the model was based on the variables’ contribution to the predictive power of the model and its goodness of fit. Wald statistics were used to determine the magnitude

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(or contribution) of each statistically significant variable in comparison with the others, and the Odds Ratio (OR) for the interpreting of each significant variable. Measures Self-reported illness status is a dummy variable, where 1 = reporting an ailment or dysfunction or illness in the last 4 weeks, which was the survey period; 0 if there were no self-reported ailments, injuries or illnesses [29-31]. While self-reported ill-health is not an ideal indicator of actual health conditions because people may under-report, it is still an accurate proxy of ill-health and mortality [32, 33]. Health status is a binary measure where 1=good to excellent health; 0= otherwise which is determined from “Generally, how do you feel about your health?” Answers for this question are on a Likert scale, ranging from excellent to poor. Medical care-seeking behaviour was taken from the question “Has a health care practitioner, healer, or pharmacist been visited in the last 4 weeks?” with there being two options: Yes or No. Medical care-seeking behaviour therefore was coded as a binary measure where 1=Yes and 0= otherwise. Crowding is the total number of individuals in the household divided by the number of rooms (excluding kitchen, verandah and bathroom). Sex: This is a binary variable where 1= male and 0 = otherwise. Age is a continuous variable which is the number of years alive since birth (using last birthday).

where ki represents the frequency with which an individual

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witnessed or experienced a crime, where i denotes 0, 1 and 2, in which 0 indicates not witnessing or experiencing a crime, 1 means witnessing 1 to 2, and 2 symbolizes seeing 3 or more crimes. Tj denotes the degree of the different typologies of crime witnessed or experienced by an individual (where j = 1…4, which 1 = valuables stolen, 2 = attacked with or without a weapon, 3 = threatened with a gun, and 4 = sexually assaulted or raped. The summation of the frequency of crime by the degree of the incident ranges from 0 to a maximum of 51.

Result
The sample was 25,018 respondents: males, 49.3%; rural residents, 61%; semi-urban residents, 25.6%; married, 16.2%; never married, 67.3%; divorced, 0.8%; separated, 1.2%; widowed, 5.6%; self-reported illness, 12.5%; self-reported injury, 1.2%; health care seekers in the last 4-week period, 63.9%; level of education primary or below, 20.9; secondary level education, 73.1%, and the mean age of the sample was 28.8 years (SD = 22.0 years). The mean number of people per room was 2.0 (SD = 1.4), and the mean number of crimes experienced (including family members) was 2.1 (SD = 8.0). Table 11.1 presents information on demographic characteristics of the sample by area of residence for 2002. There was a significant statistical association between social hierarchy and area of residence – χ2 = 1739.98, P < 0.0001. Poverty (i.e. poorest 20%) was substantially a rural phenomenon (74.9%) compared to semi-urban poverty (17.2%) and urban poverty (7.9%) - χ2 = 1739.98, P < 0.0001. Almost 14% of rural residents reported having an illness in the last 4 weeks compared to semi-urban residents (10.9%) and urban residents (10.9%) - χ2 = 36.861, P < 0.0001. However, for 2002, no significant statistical relationship existed between self-reported diagnosed health conditions and area of residents - χ2 = 12.62, P = 0.397.

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The mean age of the sample was 28.8 years (± 22.0 years), with there being a statistical difference between the mean ages of respondents based on their area of residence – F-statistic [2, 24991] = 7.28, P < 0.0001: the mean age of rural residents was 29.1 years (± 22.6 years); that of semi-urban residents was 27.9 years (± 21.0) and the mean age of urban dwellers was 29.1 years (± 21.0 years). Concurringly, the mean number of visits to health care practitioners in the last 4week period was 1.7 (± 1.4). There was a significant statistical difference between the mean number of visits to health care practitioners and area of residence (F-statistic = 5.48, P = 0.004: the mean number of visits by rural residents was 1.6 (± 1.2) compared and 2.0 (± 2.5) for urban dwellers, but non between rural and semi-urban dwellers (1.6 ± 1.2). However, there was no significant difference between mean medical expenditure and area of residence (mean public health care expenditure was USD 9.05 ± USD 25.65 – F-statistic [2, 1126] = 0.577, P = 0.562; and mean private health care expenditure was USD 24.40 ± USD 37.13 – F-statistic [2,935] = 0.577, P = 0.220). There was a significant statistical difference between crime and victimization and area of residence - F-statistic [2, 24958] =28.604, P < 0.0001. The mean number of crimes and incidents of victimization experienced by people in rural residents was 1.8 ± 7.7 compared to semi-urban residents, 2.3 ± 8.0; and urban dwellers, 2.9 ± 9.3. Table 11.2 examines visits to health care facilities, health insurance coverage, educational level and crime by social hierarchy. When self-reported illness and social hierarchy was disaggregated by area of residence, the significant statistical relationship was explained by rural areas (χ2 = 30.92, P < 0.0001) and not semi-urban (χ2 = 8.84, P = 0.065) and urban areas (χ2 = 1.74, P = 0.789).

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Table 11.3 presents information on self-reported injury, normally go if ill/injured, why didn’t seek care for current illness, length of illness and number of visits to health practitioner by social hierarchy. A statistical relationship existed between each of the variables (P < 0.0001). A statistical difference existed between the mean length of the illness among the social hierarchy – F statistic = 2.536, P = 0.038. This difference was accounted for by the poorest 20% and the wealthy (P = 0.049) and the poorest 20% and the wealthiest 20% (P = 0.049). Likewise the statistical difference between the mean number of visits made to medical practitioner(s) and social hierarchy were accounted for by the poorest 20% and wealthy (P = 0.011) and the poorest 20% and wealthiest 20%. The prevalence of chronic illness was 104 out of every 10,000 respondents. On disaggregating the overall prevalence of chronic illness into the different typology of conditions it was found that 5 out of every 10,000 respondents had diabetes mellitus; 50 out of every 10,000 had hypertension; 28 per 10,000 had arthritis; and other chronic illnesses (unspecified) accounted for 21 per 10,000. Chronic illness was more a female phenomenon than for males- χ2 = 6.56, P = 0.013. The prevalence rate of females with chronic illness was 144 per 10,000 compared to 62 per 10,000 for males. Furthermore, the prevalence rates of those with particular chronic illnesses by sex was as follows: diabetes mellitus 2 per 10,000 for males and 7 per 10,000 for females; hypertension 32 per 10,000 for males and 69 per 10,000 for females; arthritis 13 per 10,000 for males and 42 per 10,000 for females and other chronic conditions, 15 per 10,000 for males and 27 per 10,000 for females. Seventy-two percent of those who indicated that they had a chronic illness sought medical care in the last 4-week period, compared to 78.9% not suffering from a chronic illness who sought medical attention - χ2 = 0.030, P = 0.562. Likewise no statistical association existed
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between health insurance coverage and chronic illness - χ2 = 0.048, P = 0.649. Concurringly, there was a significant statistical association between marital status and individuals with chronic illness - χ2 = 12.708, P = 0.013. Of those who indicated that they had chronic illness, 44.9% were married; 29.1% were never married; 0.4% divorced; 1.2% separated and 24.4% widowed.

Multivariate analyses Table 11.4 provides information on particular variables and their correlation (or not) with selfreported illness. Of the 17 variables identified from the literature and available for this study, 5 emerged as being statistically significant correlates of self-reported illness of Jamaicans (i.e. social hierarchy, medical expenditure, sex, age and income) - Model χ2 (17) =56.45, P < 0.001. The statistically significant correlates accounted for 14.8% of the variability in self-reported illness.

Table 11.5 examines social hierarchy and sex and their influence (or not) on self-reported chronic illness. One sex emerged as being a statistically significant correlate of self-reported chronic illness in Jamaica - Model χ2 (3) =6.42, P < 0.001.

Discussion
The current study revealed that 13 out of every 100 Jamaicans reported an illness in the 4-week surveyed period. Concurringly, those in the two wealthy social hierarchies were 18% less likely to report chronic illnesses compared to those in the two poor social hierarchies, and the former group was 64% less likely to report an illness compared to the latter group. Males were 69% less likely to report chronic illness compared to females, as well as 56% less likely to indicate an illness. The prevalence rate of those with chronic illness was 104 per 10,000 respondents –
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diabetes, 5 per 10,000; hypertension, 50 per 10,000; arthritis, 28 per 10,000 and other chronic conditions, 21 per 10,000. When the chronic illnesses were disaggregated by sex of respondents, the prevalence rate of females with hypertension was 2.2 times more than hypertensive males; 3.2 times more than male arthritic patients, and 3.0 times more than male diabetics. Poverty was substantially a rural phenomenon (75%), and almost 14% of rural residents indicated an illness compared to semi-urban (11%) and urban dwellers (11%). The disparity did not cease there as rural residents had the least percentage of people with tertiary level education, and the least per capita consumption, which was 57.4% of consumption per capita of urban residents and 69.0% of that consumption per capita of semi-urban people. On the contrary, those in the poorest 20% self-reported fewer injuries (owing to work and care accidents, poisoning, and burns) than those in the wealthiest 20%. For centuries, using objective indices such as life expectancy, infant mortality and general mortality, it has been well established that poverty is associated with illness, and those with more chronic illnesses are more likely to be poor. The current study, using self-reported illnesses, has concurred with the literature that the poor report more illnesses and are more likely to have more chronic illness than those in the upper class. This study, however, found that there is no significant statistical correlation between self-reported illness or chronic illness of those in the poor social hierarchies and those in the middle class. The current research does not concur with the literature that married people are healthier than other marital cohorts [34-38] as the findings showed no statistical association between marital status and self-reported illness. However, the findings revealed that almost 45% of those with chronic illnesses were married compared to those who were never married, widowed, separated or divorced. Lillard and Panis [39] contradicted many of the traditional findings, for instance that
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married people are healthier and report less health conditions than non-married people. They found that healthier men are less likely to be married; and secondly, that healthier married men enter into unions later in life and that they do postpone remarriage. Conversely, Lillard and Panis [39] revealed that it is unhealthy men who enter marriage at an early age, which suggests that these men do so because of health reasons [39]. This then would support the current research of married people indicating more chronic illnesses than non-married people. Concurringly, married people do not report more illnesses, but do report more chronic illnesses than non-married people in this study. An interesting finding that emerged from this study is the low statistical relationship between self-reported illness and self-reported injury (i.e. contingency coefficient = 0.11). Furthermore 4.4% of those who indicated that they were ill had an injury in the last 4 weeks, and of those who had an injury, 46.2% claimed they were ill. This denotes that few people considered illness and injury and vice versa. Illnesses therefore is in keeping with acute and chronic health conditions, and less so with injuries caused by accidents, burns, poisoning and other such events. Marmot [3] asked the question “Does money matter for health? If so, why?” It is the lack of money (i.e. insufficient money) that accounts for the inability of the poor to access (1) higher level education; (2) greater and better, or the best, health care treatment; (3) a better physical milieu; (4) lower levels of infant mortality; (5) better material conditions; (6) clean water and nutrition; and (7) social position. It follows that poverty incapacitates the individual and this extends into the future if he/she is not assisted by external sources. Does money really make a difference in Jamaica? The answer is a resounding yes. Those in the poorest 20% spent on
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average almost 3 times less than those in the wealthiest 20%, and the second poor spent 2 times less than those in the wealthiest 20% on medical expenditure. Concurringly, 76 out of every 100 of those in the poorest 20% normally utilize public health facilities (including hospitals) compared to 28 out of every 100 of those in the wealthiest 20%. Poverty therefore retards people’s health care choices, expenditure on medication, and by extension healthy life expectancy. The current study found that 35 out of every 100 respondents in the poorest 20% indicated that the reason why they have not visited a health care practitioner was owing to insufficient funds, compared to 9 out of every 100 of those in the wealthiest 20%. Furthermore, findings from the present research showed that people who spend more on medical expenditure are 39% less likely to report an illness, suggesting that the poor are more likely to be living with their health conditions without seeking medical care, compared to the wealthy. This matter of insufficient financial resources hampers the healthy life expectancy of the poor, as well as explaining the greater infant and general mortality among them than those in the upper class. According to Grossman [40], Smith and Kington [41], there is a positive statistical association between income and health, and income and demand for health, which further unfolds the complexity of poverty and health. Corbett [42] argued that Edwin Chadwick, in the 1840s, believed “that the primary cause of pauperism and misery was not poverty or rampant capitalism, but filth.” This study is not arguing that the main cause of pauperism is ill-health, but it does substantiate an association between poverty and illness and poverty and chronic illness. This finding is contrary to the belief of Edwin Chadwick; insufficient money does account for some amount of illness, and illness can lead to poverty and future constraints on capabilities, limiting opportunities for the creation of a better life for themselves.

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If those in the poorest 20% group experienced illnesses and visited medical practitioners more than those in the upper class, it follows that poverty explains (1) most of the prevalence of illness, (2) the severity of the illness, and (3) more chronic illnesses. Money therefore does matter in health, and offers an explanation of how chronic illness can result in poverty, and how pauperism leads to increased morbidity and premature mortality. An understanding of poverty in Jamaica as well as a comprehensive knowledge of the relationship between poverty and illness as well as the other health inequalities, will aid physicians in understanding the reasons for the disproportionately greater number of poor visiting them and having particular chronic illnesses. Health is also a social phenomenon, and so physicians need training in the roles of social determinants and their influence on health, as these are outside of the clinical laboratory, but provide an understanding of those on the social margins of the health care system. Given that illness is influenced by exposure to pathogens, the socio-physical milieu of the poor, coupled with their incapacitation because of money, provides some insights into their plight. It is critical to understand this group and where they live, as Kiefer said, and to see poverty “not as a simple economic condition, but as a state of demoralization, where people lack all or most of the minimum ingredients we accept as the basis of a decent life” [43] and we can also add the justifications of their encounter with illness and particular health conditions such as tuberculosis, HIV/AIDS, diarrhoea, respiratory tract infections, arthritis and malaria. Another issue is nutritional deficiency, as some people hold the belief that so long as they have something to eat, or a ‘full tummy’, it is enough to prevent illness. The image of a ‘full tummy’ is embedded in those in the lower socioeconomic class and not the upper class. It follows therefore that households in lower socioeconomic group find it difficult to address material, food and opportunity deprivation within the context of a social setting to pay special
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attention to the nutritional value in food intake. Households in low-income groups are substantially found in rural areas in Jamaica where a ‘full tummy’ is important and not the nutritional intake of the food groups. According to Foster [44] “…a better-off individual who is generally healthy may be more readily able to identify when he or she is ill than a poor individual with low caloric intake.” Within Foster’s perspective lies the underlying fact that reported illnesses among those in the lower socioeconomic group may be understated figures, as their image of ill-health is hampered by nutritional deficiency. Diet and nutrition are important ingredients in good health [45], but do residents of low-income rural areas as well as low-income urban areas know that a deficient intake of calcium, iron, magnesium, zinc, folate, vitamin A, vitamin B6 and vitamin C is responsible for some of their illnesses? And another aspect to this discussion is their image of health, illness and the role that these play in influencing the collected survey data on health, health conditions and health outcome from those in the lower socioeconomic group.

Conclusion
For centuries researchers have been using objective indices such as life expectancy, infant mortality and the general mortality of a population or sub-population to measure health, and these have been used to establish a statistical association with poverty. Other scholars and institutions have found a significant statistical relationship between diagnosed illness and poverty, but this research has established that self-reported illness and self-reported diagnosed health conditions can be used instead of the objective indices of the past. While those people in poor social hierarchies were more likely to report more illnesses and self-reported chronic

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illnesses than those in the wealthy group, there is no difference between those in the poor group and the middle class. Those with chronic illnesses are not only more likely to be poor, they are married, females, rural residents, less educated at the tertiary level, more likely to visit public hospitals, most likely to have hypertension, and there is less probability that they will utilize health care facilities than the upper class. In summary, subjective indices such as self-reported illness or selfreported diagnosed health conditions can be used to measure health as the traditional infant mortality, general mortality and life expectancy. Poverty indeed still continues to influence illhealth, and those with chronic illnesses are more likely to be poor than in the upper class, but other demographic characteristics provide more information on the poor and those with chronic illnesses. In summary, much investment has been made in health and this clearly has not reduced the inequalities and disparities between and among the different social groups in Jamaica. It means that merely mobilizing greater domestic resources for health will not address the inequalities, as using national health aggregates do not provide a detailed understanding of the disparities between and among groups. While poverty has declined in Jamaica since the 1990s, the health disparity between the poor and the upper social hierarchy has continued to this day. The information provided in this research has far-reaching implications, and can be used to guide policies, frame interventions and provide a focus for future research in Jamaica.

The way forward
Subjective indices such as self-reported illness and self-reported chronic illness can be used to measure ill-health and replace infant and general mortality in the study of health. The use of national statistics does not provide a comprehensive understanding of the health disparity and
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inequalities between and among the social groups in a society. In order to address some of the health inequalities and disparities in society, programmes are needed that will address issues in rural areas, gender, income inequalities, and the health disparities between public and private health care services offered to the public. Another area which must be addressed is that of the nutritional deficiencies between and among the social hierarchies and area of residences. A national dietary survey is needed in order to provide evidence for policy intervention as well as the role of identified social problems and their influence on mental health. Concurringly, future research is needed to examine the harmful effects of mental health on the accumulation of people’s negative life events, and their effects on the crime problem in the Caribbean. Another issue which must be investigated is the quality of care offered to the poor from the perspective of the individual (i.e. a survey research). This would provide pertinent information as to whether those people who are poor perceived themselves to be receiving the worst health, and to devise a method that will objectively assess, service and deliver to the social group in order to address this, if it is contributing to the lower health outcomes. Researchers need to treat poverty as an illness and not a cause of illness, which would allow for a new shift in the study of poverty, and this thereby could provide more answers to health practitioners and policy makers.

Conflict of interest
The author has no conflict of interest to report.

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18. PIOJ, STATIN. Jamaica Survey of Living Conditions, 2002. Kingston: PIOJ & STATIN; 2003.  295   

19. PIOJ,  STATIN.    Jamaica  Survey  of  Living  Conditions,  2007.  Kingston,  Jamaica:    PIOJ  &  STATIN;  2008. 

20. Statistical Institute Of Jamaica, Jamaica Survey of Living Conditions. [Computer file]. Kingston, Jamaica: Statistical Institute Of Jamaica [producer], 2007. Kingston, Jamaica: Planning Institute of Jamaica and Derek Gordon Databank, University of the West Indies [distributors]; 2002. 21. Cohen L, Holliday M. Statistics for Social Sciences. London: Harper & Row; 1982. 22. Hair JF, Black B, Babin BJ, Anderson RE, Tatham RL. Multivariate data analysis, 6th ed. New Jersey: Prentice Hall; 2005. 23. Mamingi N. Theoretical and empirical exercises in econometrics. Kingston: University of the West Indies Press; 2005. 24. Zar JH. Biostatistical analysis, 4th ed. New Jersey: Prentice Hall; 1999. 25. Hamilton JD. Time series analysis. New Jersey: Princeton University Press; 1994. 26. Kleinbaum DG, Kupper LL, Muller KE. Applied regression analysis and other multivariable methods. Boston: PWS-Kent Publishing; 1988. 27. Cohen J, Cohen P. Applied regression/correlation analysis for the behavioral sciences, 2nd ed. New Jersey: Lawrence Erlbaum Associates; 1983. 28. Koutsoyiannis A. Theory of econometrics, 2nd ed. New York: MacMillan Publishing; 1977. 29. Bourne PA. Socio-demographic Correlates of Health care-seeking behaviour, selfreported illness and Self-evaluated Health status in Jamaica. International Journal of Collaborative Research on Internal Medicine & Public Health 2009; 1(4), 101-130. 30. Bourne PA, Rhule J. Good Health Status of Rural Women in the Reproductive Ages. International Journal of Collaborative Research on Internal Medicine & Public Health 2009; 1(5):132-155. 31. Finnas F, Nyqvist F, Saarela J. Some methodological remarks on self-rated health. The Open Public Health Journal 2008; 1: 32-39. 32. Idler EL, Benjamin Y. Self-rated health and mortality: A Review of Twenty-seven Community Studies. Journal of Health and Social Behavior 1997; 38: 21-37. 33. Idler EL, Kasl S. Self-ratings of health: Do they also predict change in functional ability? Journal of Gerontology 1995; 50B (6): S344-S353. 34. Bourne PA. Medical Sociology: Modelling Well-being for elderly People in Jamaica. West Indian Medical Journal 2008; 57: 596-04. 35. Bourne PA. Health determinants: Using secondary data to model predictors of well-being of Jamaicans. West Indian Medical Journal 2008;57: 476-480 36. Smith KR, Waitzman NJ. Double jeopardy: Interaction effects of martial and poverty status on the risk of mortality. Demography 1994; 31:487-507. 37. Ross CE, Mirowsky J. 1999. Refining the association between education and health: The effects of quantity, credential, and selectivity. Demography 1999; 36:445-460. 38. Gore WR. Sex, marital status, and mortality. American Journal of Sociology 1973; 79:4567. 39. Lillard LA, Panis CWA. 1996. Marital status and mortality: The role of health. Demography 1996; 33:313-327. 40. Grossman M. The demand for health - a theoretical and empirical investigation. New York: National Bureau of Economic Research, 1972.
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41. Smith JP, Kington R. Demographic and Economic Correlates of Health in Old Age. Demography 1997; 34:159-70. 42. Corbett S. Health and Social Justice in the Age of Chadwick Britain 1800–1854. Public Health Promotion Int 1999; 14 (4): 381-382. 43. Kiefer CW. Health work with the poor: A practical guide. New Brunswick, NJ: Rutgers University Press; 2000: p. 78. 44. Foster AD. Poverty and illness in low-income rural areas. The Am Economic Review 1994;84(2):216-220. 45. Khetarpal A, Kochar G. Health and well-being of rural women. The Internet Journal of Nutrition and Wellness 2007;3(1)

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Table 11.1: Demographic characteristic of sample, 2002 Characteristic Sex Male Female Marital status Married Never married Divorced Separated Widowed Self-reported diagnosed illness Acute conditions Influenza Diarrhoea Respiratory Chronic conditions Diabetes mellitus Hypertension Arthritis Other Health care-seeking behaviour Yes No Self-reported illness Yes No Health insurance Yes No Social hierarchy Poorest 20% Poor Middle Wealthy Wealthiest 20% Per capita consumption mean ± SD (in USD) Urban n (%) 7727(50.7) 7524(49.3) 2460(25.5) 6436(66.6) 56(0.6) 104(1.1) 610(6.3) 1(0.5) 4(2.1) 6(3.1) 10(5.2) 82(42.9) 48(25.1) 40(20.9) 1302(63.8) 740(36.2) 1987(13.5) 12713(86.5) 1036(7.0) 13714(93.0) 3724(24.4) 3574(23.4) 3169(20.8) 2774(18.2) 2017(13.2) 1181±1340 2002 Area of residence Semi-urban Urban n (%) n (%) 3062(47.9) 3337(52.1) 1115(26.9) 2758(66.5) 41(1.0) 49(1.2) 187(4.5) 0(0.0) 5(8.9) 2(3.6) 1(1.8) 29(51.8) 13(23.2) 6(10.7) 436(63.4) 252(36.6) 669(10.9) 5488(89.1) 1023(16.5) 5178(83.5) 858(13.4) 968(15.1) 1217(19.0) 1427(22.3) 1929(30.1) 1771±1605 1543(46.0) 1814(54.0) < 0.0001 475(21.0) 1619(71.6) 26(1.2) 32(1.4) 108(4.8) 0.397 0(0.0) 0(0.0) 1(3.1) 1(3.1) 15(46.9) 8(25.0) 7(21.9) 0.816 228(65.3) 121(34.7) < 0.0001 354(10.9) 2902(89.1) < 0.0001 612(18.7) 2654(81.3) < 0.0001 393(11.7) 414(12.3) 598(17.8) 822(24.5) 1130(33.7) 2129±2434 P

< 0.0001

†USD 1.00 = Ja. $ 80.47 at the time of the survey) (2007) ††USD 1.00 = Ja. $50.97 (in 2002)

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Table 11.2. Particular variable by social hierarchy, 2002 Characteristic Poorest 20% Poor Social hierarchy Middle Wealthy P Wealthiest 20% 0.002 < 0.0001

Sex Male 2454(49.3) 2345(47.3) 2440(49.0) 2482(49.4) 2611(51.4) Female 2520(50.7) 2609(52.7) 2542(51.0) 2540(50.6) 2464(48.6) Marital status Married 569(21.1) 656(22.3) 742(23.3) 860(25.4) 1223(31.7) Never married 1926(71.3) 2094(71.2) 2229(69.9) 2303(67.9) 2261(58.7) Divorced 14(0.5) 5(0.2) 16(0.5) 26(0.8) 62(1.6) Separated 30(1.1) 21(0.7) 30(0.9) 31(0.9) 73(1.9) Widowed 162(6.0) 164(5.6) 173(5.4) 172(5.1) 234(6.1) Visits to health care institutions (for last visit) Public hospitals 166(49.3) 135(38.5) 164(42.7) 175(42.1) 137(30.6) Private hospitals 14(4.2) 29(8.3) 19(5.0) 40(9.7) 52(11.7) Public health care centre 107(31.7) 102(29.1) 75(19.6) 64(15.5) 34(7.6) Private health care centre 76(22.6) 120(34.1) 137(35.6) 176(42.2) 258(57.2) Health insurance ownership Yes 84(1.7) 172(3.6) 270(5.6) 655(13.5) 1490(30.7) No 4745(98.3) 4651(96.4) 4574(94.4) 4204(86.5) 3370(69.3) Educational level Primary and below 609(24.6) 588(22.0) 628(22.7) 604(20.1) 568(16.5) Secondary 1837(74.3) 2048(76.5) 2114(75.3) 2249(75.0) 2292(66.4) Tertiary 25(1.0) 41(1.5) 57(2.0) 146(4.9) 591(17.1) Crime and victimization index mean ± SD 2.4±10.2 1.5±4.9 2.0±7.2 2.2±8.5 2.4±8.2 Age mean ± SD 25.5±22.7 26.8±22.2 28.3±21.9 29.6±21.3 33.8±20.9 Crowding mean ± SD 3.0±1.8 2.3±1.3 2.0±1.2 1.6±0.9 1.2±0.8 Total medical expenditure mean ± SD (in 15.22±28.91 21.67±37.99 22.54±42.87 33.11±70.35 45.53±79.52 USD)†

< 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001

< 0.0001 < 0.0001 < 0.0001
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†USD 1.00 = Jamaican $50.97

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Table 11.3. Self-reported injury, normally go if ill/injured, why didn’t seek care for current illness, length of illness and number of visits to health practitioner by social hierarchy, 2002 Characteristic Self-reported injury No Yes Poorest 20% Poor Social hierarchy Middle Wealthy P Wealthiest 20% < 0.0001 4811(99.1) 4815(99.1) 4801(98.9) 4806(98.7) 4797(98.2) 46(0.9) 43(0.9) 54(1.1) 61(1.3) 87(1.8) < 0.0001

Normal go it ill/injury Public hospital 2252(46.4) 2004(41.3) 1786(36.8) 1449(29.7) 1049(21.5) Public health centre 1474(30.3) 1124(23.2) 854(17.6) 605(12.4) 315(6.5) Private hospital 1123(23.1) 1713(35.3) 2202(45.4) 2799(57.4) 3498(71.6) Pharmacy 2(0.0) 0(0.0) 1(0.0) 3(0.1) 3(0.1) Other 7(0.1) 8(0.2) 12(0.2) 17(0.3) 10(0.4) Why didn’t seek care for current illness Could not afford it 72(35.1) 61(26.3) 47(21.3) 23(11.2) 19(8.6) Was not ill enough 59(28.8) 92(39.7) 111(50.2) 105(51.2) 97(43.9) Use home remedy 50(24.4) 43(18.5) 35(15.8) 47(22.9) 61(27.6) Did not have the time 2(1.0) 2(0.9) 10(4.5) 6(2.9) 14(6.3) Other (unspecified) 22(10.7) 34(14.7) 18(8.1) 24(11.7) 30(13.6) Length of illness (in 11.5±10.4 10.8±10.0 10.4±10.9 9.8±9.7 9.9±9.7 days) mean ± SD 6.1±8.8 5.5±8.6 4.9±7.7 4.6±6.3 4.8±7.7 Number of visits to health practitioner mean ± SD

< 0.0001

0.038 0.007

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Model χ2 =56.45, P < 0.001 -2 Log likelihood = 368.58 Nagelkerke R2 =0.148

Table 11.4. Logistic regression: Self-reported illness by particular variables Std. Odds Variable Wald Coefficient error ratio P statistic Injury -0.20 0.32 0.40 0.53 0.82 Health care-seeking 0.57 0.43 1.81 0.18 1.78 Middle -0.80 0.51 2.49 0.12 0.45 Two Wealthy quintiles -1.03 0.51 4.02 0.04 0.36 †Two poor quintiles 1.00 Logged medical -0.49 0.14 12.00 0.00 0.61 expenditure Durable goods 0.01 0.07 0.01 0.91 1.01 Separated, divorced or 0.27 0.64 0.18 0.67 1.31 widowed Married 0.08 0.42 0.03 0.86 1.08 †Never married 1.00 Physical environment -0.43 0.33 1.74 0.19 0.65 Semi-urban -0.01 0.37 0.00 0.99 0.99 Urban 0.96 0.77 1.58 0.21 2.62 †Rural 1.00 Secondary -0.33 0.44 0.55 0.46 0.72 Tertiary -0.90 0.87 1.07 0.30 0.41 †Primary or below 1.00 Sex 0.81 0.32 6.54 0.01 0.44 Crowding -0.15 0.16 0.88 0.35 0.86 Age 0.03 0.01 5.51 0.02 1.03 Total expenditure 0.00 0.00 3.54 0.06 1.00

95.0% C.I. Lower 0.44 0.77 0.17 0.13 0.47 0.88 0.38 0.47 0.34 0.48 0.59 0.31 0.08 0.24 0.63 1.01 1.00 Upper 1.52 4.09 1.21 0.98 0.81 1.16 4.57 2.47 1.23 2.07 11.72 1.71 2.23 0.83 1.18 1.05 1.00

Hosmer and Lemeshow goodness of fit χ2= 6.53, P = 0.59
Overall correct classification =97.1% Correct classification of cases of self-rated illness =100.0% Correct classification of cases of not self-rated ill =54.9% †Reference group

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Table 11.5. Logistic regression: Self-reported chronic illness by some variable Variable Middle Two wealthy quintiles †Two poor quintiles
Model χ2 =6.42, P < 0.001 -2 Log likelihood = 368.58 Nagelkerke R2 =0.06

Coefficient -0.34 -0.33 -1.16

Std. error 0.66 0.58 0.49

Wald statistic 0.26 0.31 5.75

P 0.61 0.58 0.02

Odds ratio 0.72 0.72 1.00 0.31

95.0% C.I. Lower 0.20 0.23 0.12 Upper 2.62 2.26 0.81

Sex

Hosmer and Lemeshow goodness of fit χ2= 1.34, P = 0.854
Overall correct classification =93.2% Correct classification of cases of self-rated illness =100.0% Correct classification of cases of not self-rated ill =49.9% †Reference group

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Chapter 12
Variations in social determinants of health using an adolescence population: By different measurements, dichotomization and non-dichotomization of health

Paul A. Bourne

On examining health literature, no study emerged that evaluated whether the social determinants vary across measurement, dichotomization, non-dichotomization and aged cohorts. With the absence of research on the aforementioned areas, it can be extrapolated that social determinants of health are constant across measurement, dichotomization and non-dichotomization, and this assumption is embedded in health planning. This paper seeks to elucidate (1) whether social determinants of health vary across measurement of health status (ie self-rated health status or self-reported antithesis of disease) or the cut-off (dichotomization) and/or the non-cut-off of health status (non-dichotomization), (2) examine the similarities between social determinants found in the literature and that of using an adolescence population, (3) whether particular demographic characteristic as well as illness and health status vary by area of residence of respondents, (4) the health status of the adolescence population, (5) typology of health conditions that they experience, and (6) evaluate the antithesis of illness (disease) and self-rated health. The current study extracted a sample of 1, 394 respondents aged 10 to 19 years old from the 2007 Jamaica Survey of Living Conditions (JSLC). The present subsample represents 20.6% of the 2007 national cross-sectional sample (n = 6,783). Multivariate logistic and ordinal logistic regression analyses were used to examine the association between many independent variables and a single dependent variable. In this study, health was measured using (1) self-rated health status or (2) the antithesis of illness (not reporting a health condition). The dichotomization of each denotes the use of two groups, and non-dichotomization means that self-rated health status was used in its Likert scale form (i.e. very good; good; moderate; poor and very poor). Antithesis of illness is a better measure than self-reported health status in determining social determinants because of its explanatory power (53%) compared to those that used the self-rated health status (at most 38%). There were noticeable variations in social determinants of health among the dichotomized, non-dichotomized health and antithesis of illness. Social determinants of health vary across the measurement and dichotomization and non-dichotomization of health status. The findings provide insights into the social determinants and health, and recommend that we guard against a choiced approach without examining the studied population in question.

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Introduction
Adolescents aged 10 to 19 years are among the most studied groups in regard health issues in the Caribbean, particularly sexuality and reproductive health matters [1-4]. Apart of the rationales for the high frequency of studies on those in the adolescence years are owing to the prevalence of HIV/AIDS, unwanted pregnancy, inconsistent condom usage, mortality arising from the HIV/AIDS virus, and other risky sexual behaviour. With one half of those who are infected with the HIV/AIDS virus being under 25 years old [1], this provides a justification for the importance of researching this aged cohort. Statistics revealed that the HIV virus is the 3rd leading cause of mortality among Jamaicans aged 10-19 years old (3.4 per 100,000, for 1999 to 2002) [5], and again this provides a validation for the prevalence of studies on this cohort. Outside of the Caribbean, sexuality and reproductive health matters among adolescents are well studied [6-11], suggesting that those issues are national, regional and international. While sexuality and reproductive health matters are critical to the health status of people [1], reproductive health problems as well as sexuality form a part of the general health status. Health is more that the ‘antithesis of diseases’ [12] or reproductive health problems as it extends to social, psychological or physical wellbeing and not merely the antithesis of diseases [13]. Bourne opined that despite the broadened definition of health as offered by the WHO [14], illness is still widely studied in the Caribbean, particularly among medical researchers and/or scholars. A search of the West Indian Medical Journal for the last one half decade (2005-2010), a Caribbean scholarly journal, revealed that the majority of the studies have been on different variations of illness, and antithesis of diseases instead of the broadened construct of health.

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Outside of the West Indian Medical Journal, few Caribbean studies have sought to examine the health status of adolescents [15-18] but even fewer published research were found that examine quality of life of those in the adolescence years [19]. Even though quality of life is a good measure of general health status, international studies exploring quality of life and selfrated health status among the adolescence years are many [20-25] compared to those in Jamaica. A comprehensive review of the literature on health status, particularly among the adolescence population, revealed that none has used a national survey data to examine social determinants of health across different measurement and dichotomization of health (the recoding of the measure into two groups) to assess whether there is variability in determinants as well as explore the health of this cohort. Even among studies which have examined social determinants of health, particularly among the population [26-34], few have used the elderly population [35-37] and only men in the poor and the wealthy social strata [37, 38], but none emerged in a literature research that have used the adolescent population (ages 10-19 years). On examining health literature, no study emerged that evaluated whether the social determinants of health vary across measurement, dichotomization and non-dichotomization of health (using the measure in its Likert scale form), and age cohort. With the absence of research on the aforementioned areas, it can be extrapolated that social determinants of health are constant across measurement, dichotomization and nondichotomization, and this assumption is embedded in health planning. The absence of such information means that critical validity to the discourse and use of social determinants would have been lost, as social determinants of health are used in the planning of health policies, future research and in explaining health disparities.

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Statistics revealed that one in every five Jamaican is aged 10-19 years old [39], which means this is a substantial population and because of its influence of future labour supply it is of great value. Although Pan American Health Organization (PAHO) [5] stated that adolescents enjoy good health, and only about 2% of morality in 2003, which was equally the case for adolescents in the Americas, this information does not indicate distancing examination from their health status. The current work, therefore, will bridge the gap in the literature by evaluating social determinants of health among those in the adolescence years across varying measurement of health. Using data for 2007 Jamaica Survey of Living Conditions (2007 JSLC), this paper seeks to elucidate (1) whether social determinants of health vary across measurement of health status (ie self-rated health status or self-reported antithesis of disease) or the cut-off (dichotomization) and/or the non-cut-off of health status (non-dichotomization), (2) are there similarities between social determinants found in the literature and that of using an adolescence population, (3) whether particular demographic characteristic as well as illness and health status vary by area of residence of respondents, (4) what is the health status of the adolescence population, (5) typology of health conditions that they experience, and (6) evaluate the antithesis of illness (disease) and self-rated health.

Methods and measure
Data The current study extracted a sample of 1, 394 respondents aged 10 to 19 years old from the 2007 Jamaica Survey of Living Conditions (JSLC). The inclusion/exclusion criterion for this study is aged 10 to 19 years old. The present subsample represents 20.6% of the 2007 national cross-sectional sample (n = 6,783). The JSLC is an annual and nationally representative crosssectional survey that collects information on consumption, education, health status, health
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conditions, health care utilization, health insurance coverage, non-food consumption expenditure, housing conditions, inventory of durable goods, social assistance, demographic characteristics and other issues [40]. The information is from the civilian and noninstitutionalized population of Jamaica. It is a modification of the World Bank’s Living Standards Measurement Study (LSMS) household survey [41]. An administered questionnaire was used to collect the data. The survey was drawn using stratified random sampling. This design was a two-stage stratified random sampling design where there was a Primary Sampling Unit (PSU) and a selection of dwellings from the primary units. The PSU is an Enumeration District (ED), which constitutes a minimum of 100 residences in rural areas and 150 in urban areas. An ED is an independent geographic unit that shares a common boundary. The country was grouped into strata of equal size based on dwellings (EDs). Based on the PSUs, a listing of all the dwellings was made, and this became the sampling frame from which a Master Sample of dwellings was compiled, which in turn provided the sampling frame for the labour force. One third of the Labour Force Survey (LFS) was selected for the JSLC. Overall, the response rate for the 2007 JSLC was 73.8%. Over 1994 households of individuals nationwide are included in the entire database of all ages [40]. A total of 620 households were interviewed from urban areas, 439 from other towns and 935 from rural areas. This sample represents 6,783 non-institutionalized civilians living in Jamaica at the time of the survey. The JSLC used complex sampling design, and it is also weighted to reflect the population of Jamaica. This study utilized the data set of the 2007 JSLC to conduct our work [42]. Measure
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Age is a continuous variable which is the number of years alive since birth (using last birthday) Adolescence population is described as the population aged 10 to 19 years old [23] Self-reported illness (or self-reported dysfunction): The question was asked: “Is this a diagnosed recurring illness?” The answering options are: Yes, Cold; Yes, Diarrhoea; Yes, Asthma; Yes, Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No. For the antithesis of disease (illness) a binary variable was created, where 1= not reported a health condition (no to each illness) and 0 = otherwise (absence of reporting an illness). The use of two groups for selfreported illness denotes that this variable was dichotomized into good health (from not reported a health condition) and poor health (i.e. having reported an illness or health condition). Thus, the seven health conditions were treated as dichotomous variables, coded as was previous stated. Self-rated health status: This was taken from the question “How is your health in general?” The options were very good; good; fair; poor and very poor. For purpose of this study, the variable was either dichotomized or non-dichotomized. The dichotomization of self-rated health status denotes the use of two groups. There were four dichotomization of self-rated health status – (1) very poor-to-poor health status and otherwise; (2) good and otherwise; (3) good-to-very good health status and otherwise and (4) moderate-to-very good self reported health status and otherwise. The dichotomized variables were measured as follow: 1= very poor-to-poor health, 0 = otherwise 1= good, 0 = otherwise 1 =good-to-very good, 0 = otherwise 1= moderate-to-very good, 0 = otherwise

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The non-dichotomization of self-rated health status means that the measure remained in its Likert scale form (i.e. very good; good; moderate; poor and very poor health status). Social class (hierarchy): This variable was measured based on income quintile: The upper classes were those in the wealthy quintiles (quintiles 4 and 5); middle class was quintile 3 and poor those in lower quintiles (quintiles 1 and 2). Family income is measure using total expenditure of the household as reported by the head. Statistical analysis Statistical analyses were performed using the Statistical Packages for the Social Sciences v 16.0 (SPSS Inc; Chicago, IL, USA) for Windows. Descriptive statistics such as mean, standard deviation (SD), frequency and percentage were used to analyze the socio-demographic characteristics of the sample. Chi-square was used to examine the association between nonmetric variables, and analysis of variance for metric and non-dichotomous nominal variables. Logistic regression was used to evaluate a dichotomous dependent variable (self-rated health status and antithesis of illness) and some metric and/or non-metric independent variables. However, ordinal logistic regression was used to examine a Likert scale variable (self-rated health status) and some metric and/or non-metric independent variables. A pvalue of < 5% (twotailed) was used to establish statistical significance. Each model begins with variables identified in the literature (Models 1-5), will be tested using the current data and the significant variables highlighted using an asterisk (Tables 12.3 and 12.4).

Models

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The use of multivariate analysis to study health status and subjective wellbeing (i.e. self-reported health) is well established in the literature [36-38]. Previous works have examined the

dichotomization of health status in order to establish whether a particular measurement of health status is different from others [43-45]. The current study will employ multivariate analyses to examine health by different dichotomization and statistical tools to determine if the social determinants remain the same. The use of this approach is better than bivariate analyses as many variables can be tested simultaneously for their impact (if any) on a dependent variable. Scholars like Grossman [33], Smith & Kingston [34], Hambleton et al. [37], Bourne [46], Kashdan [47], Yi & Vaupel [48], and the World Health Organization pilot work a 100question quality of life survey (WHOQOL) [49] have used subjective measures to evaluate health. Diener [50,51] has used and argued that self-reported health status can be effectively applied to evaluate health status instead of objective health status measurement, and Bourne [46] found that self-reported health may be used instead of objective health. Embedded in the works of those researchers is the similarity of self-reported health status and self-reported dysfunction in assessing health. Thus, in this work we will use self-reported health status and the antithesis of illness to measure health, and dichotomize self-reported health status as follows (1) good health = 1, 0 = otherwise; (2) good-to-excellent health=1, 0 = otherwise; (3) moderate-to-excellent health=1, 0 = otherwise; and (4) very poor-to-poor health= 1, 0 = otherwise. Another measure was that health was evaluated by all the 5-item scale (from very poor to excellent health status), using ordinal logistic regression. The current study will examine the social determinants of self-rated health of Jamaican adolescents and whether the social determinants vary by measurement and dichotomization and/or non-dichotomization of health. Five hypotheses (models) were tested in order to

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determine any variability in social determinants based on the measurement of health status. Model (1) is the antithesis of disease, non-dichotomization of self-reported health (antithesis of disease); Model (2) is the non-dichotomization of self-rated health status (ie using the 5-item Likert scale as a continuous variable), and Models (3-6) are the different dichotomized self-rated health status (ie. 3= very poor-to-poor; 4=good, 5=moderate-to-very good 6=good-to-very good). All the models were tested with the same set of social determinants of health, with the only variability being the measurement of health status (self-rated health status), cut-off of health (dichotomization) and/or non-dichotomization of self-rated health status.

HA=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, εi)

(1)

where HA (i.e. self-rated antithesis of diseases) is a function of age of respondents, Ai; sex of individual i, Gi; area of residence, ARi; current self-reported illness of individual i, It; logged duration of time that individual i was unable to carry out normal activities (or length of illness), lnDi; Education level of individual i, EDi; union status of person i, USi; social class of person i, Si; health insurance coverage of person i, HIi; logged family income, lnY; crowding of individual i, CRi; logged medical expenditure of individual i in time period t, lnMCt; social assistance of individual i, SAi; and an error term (ie. residual error). Note that length of illness was removed from the model as it had 93.5% of the cases were missing as well as union status which had 58.2%.

HND=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, εi)

(2)

Where HND denotes the non-dichotomization of self-rated health status.

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Note that length of illness was removed from the model as it had 93.5% of the cases were missing as well as union status which had 58.2%.

HD1=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, εi)

(3)

Where HD1 is very poor-to-poor self-rated dichotomized health status. Note that length of illness was removed from the model as it had 93.5% of the cases were missing as well as union status which had 58.2%. HD2=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, εi) Where HD2 is good self-rated dichotomized health status. Note that length of illness was removed from the model as it had 93.5% of the cases were missing as well as union status which had 58.2%. (4)

HD1-4=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, εi)

(5)

Where HD3 is very moderate-to-very good self-rated dichotomized health status. Note that length of illness was removed from the model as it had 93.5% of the cases were missing as well as union status which had 58.2%.

HD1-4=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, εi)

(6)

Where HD4 is good-to-excellent self-rated dichotomized health status. Note that length of illness was removed from the model as it had 93.5% of the cases were missing as well as union status which had 58.2%.

Results

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Demographic characteristics of studied population Table 12.1 presents information on demographic characteristic of the sampled population. Of the population (n = 1,394), 43.9% has primary or below primary level education, 53.1% secondary level and 3.0% had tertiary level education. Table 12.2 presents information on the particular demographic characteristic as well as health status and self-reported illness of respondents by area of residence. Table 12.3 depicts information of variables which explain the antithesis of illness among the adolescence population. Table 12.4 shows the different dichotomizations of self-rated health status and nondichotomized self-rated health status, and the various social determinants which explain each. Table 12.5 examines associations between self-rated health status and antithesis of illness (or disease).

Limitations of study
This study was extracted from a cross-sectional survey dataset (Jamaica Survey of Living Conditions, 2007). Using a nationally representative cross-sectional survey dataset, this research extracted 1394 adolescent Jamaicans which denote that the work can be used to generalize about the adolescent population in Jamaica at the time in question (2007). However, it cannot be used to make predictions, forecast, and establish trends or causality about the studied population.

Discussion

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The current work showed that while the majority of Jamaican adolescents have at least self-rated good health status (92 out of every 100); some indicated at most moderate self-rated health status. Even though only 1.4% of the sample mentioned that they have very poor-to-poor health status, 6.5% indicated that they experienced a health condition in the last 30 days. Of those who reported a health condition, 5.3% were diagnosed with chronic illness (diabetes mellitus, 3.9%; hypertension, 1.3%). Although 2.4 times more adolescent in rural areas are in the lower class compared with those in urban areas, rural adolescents have a greater good health status compared to their urban counterparts, but this was the reverse for rural and periurban adolescents. Another important finding was that there is no statistical association between health conditions and area of residence, but urban and periurban adolescents were more likely to have health insurance coverage compared to those in rural areas. In Jamaica, the adolescence population’s health status is comparable to those in the United States [23], suggesting that inspite of the socioeconomic disparities between the two nations and among its peoples, the self-reported health status among adolescent Jamaicans is good. The high health status of those in the adolescence population in Jamaica speaks good of the inter dynamics within the countries, but does not imply that they are the same across the two nations or can it be interpreted that the quality of life of Jamaicans is the same as those in the United States. Simply put, the adolescence population in Jamaica is experiencing a good health status although HIV/AIDS, unwanted pregnancies, and inconsistent condom usage are high in this cohort [1-5]. While the aforementioned results about good health status of Jamaican adolescents concurs with PAHO’s work in 2003 [5] and others [17], which has continued into 2007, the current paper provides more information on health matters of adolescents aged 10-19 years than

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that offered by PAHO. An adolescent in Jamaica who seeks medical attention is 100% less likely to report an illness, and those who indicated at least good self-rated health status was 13 times more likely not to report an illness. Continuing, adolescents in the upper class are 15 times more likely to report very poor-to-poor health status compared to those in the lower class. And that those who indicated very poor-to-poor health status are more likely to seek medical care (10 times), live in crowded household and less likely to spend more on consumption and nonconsumption items. On the other hand, those who stated that their health status was at least moderate were less likely to live in crowded household, spent more on consumption and nonconsumption items. Using a 2007 national probability dataset for the adolescence population in Jamaica, we can add value to the existing literature on health status as well as the social determinants of health. Grossman introduced the use of econometric analysis in the examination of health in the 1970s to establish determinants of self-rated health [33], which has spiraled a revolution in this regard since that time. Using data for the world’s population, he identified particular social determinants of health that was later expanded upon by Smith and Kington [34]. Since the earlier pioneers’ work on social determinants of health [33, 34], the WHO joined the discourse in 2000s [27] as well as Marmot [26], Kelly et al. [28]; Marmot and Wilkinson [29]; Solar and Irwin [30]; Graham [31]; Pettigrew et al. [32], Bourne [35], Bourne [36], Hambleton et al. [37] and Bourne and Shearer [38], but none of them evaluated whether there was variability in the determinants of health depending on the measurement and/or dichotomization of health. The variability in social determinants of health was established by Bourne and Shearer [38] in a study between men in the poor and the wealthy social strata in a Caribbean nation, but the literature at large has not recognized the variances in social determinants based on the

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dichotomization and non-dichotomization self-rated health status, and measurement of heath (using antithesis of illness and self-rated health status). Such a gap in the literature cannot be allowed to persist as it assumes that social determinants are consistent over the measurement of health. Bourne [43] like Manor et al. [44] and Finnas et al. [45] have dichotomized self-reported health status and cautioned future scholars about how the dichotomization can be best done. According to Bourne [43] “The current study found that dichotomi[z]ing poor health status is acceptable assuming that poor health excludes moderate health status, and that it should remain as is and ordinal logistic be used instead of binary logistic regression” [43, p.310], and others warned against the large dichotomization of self-rated health status [44,45]. Because self-rated health status is a Likert scale variable, ranging from very poor to very good health status, many researchers arbitrarily dichotomized it, but the cut-off is not that simple as was noted by Bourne [43], Manor et al. [44] and Finnas et al. [45]. From data on Jamaicans, Bourne’s work revealed that the cut-off in the dichotomization of self-rated health status should be best done without moderate health when dichotomizing for poor health status [43]. All the scholars agreed that narrowed cut-offs are preferable in the dichotomization of self-rated health status, but only a few variables were used (marital status, age, social class, area of residence and self-reported illness) [43-45]. Bourne postulated that “By categorising an ordinal measure (i.e., self-reported health) into a dichotomous one, this means that some of the original data will be lost in the process.” [43, p.295]. Using many more variables, the present work highlighted that some social determinants of health are lost as a result of the dichotomization process. Simply put, the social determinants of health are not consistent across the dichotomization process which concurs with the literature.

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While we concur with other scholars that by dichotomizing self-rated health status some social determinants are lost in the process [43-45], we will not argue with those who opined that self-rated health status should remain a Likert scale measure [52, 53]. The evidence is in that more social determinants in the non-dichotomized self-rated health do not give a greater explanatory power; instead this model had the least explanation. This indicates that more is not necessarily better, and such information must be taken into account in a decision to cut-off at a particular point. The fact that more social determinants of health emerged when health was nondichotomized coupled with a lower explanatory power compared with when it is dichotomized as very poor-to-poor health means that using self-rated health as a Likert scale valve is not preferable to dichotomizing it. A narrower dichotomization of self-rated health status, particularly very poor-to-poor health, as well as moderate-to-very good health status yielded greater explanations than non-dichotomizing health status. This study used both the antithesis of illness and self-rated health status to measure, and evaluates the social determinants of health, and assess whether antithesis of illness is still a better measure of health than self-rated health status. A comparison of the social determinants based on the measurement of health revealed that for the Jamaican adolescence population, antithesis of illness is a better measure than self-reported health status in determining social determinants because of its explanatory power (53%) compared to those that used the self-rated health status (explanatory power at most 38%). On the other hand, the antithesis of illness had fewer social determinants compared with those in self-rated health status, suggesting that more social determinants of health should not be preferred to fewer because the latter measure had a greatest explanation. Like dichotomizing self-rated health status, variation also exists among

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dichotomization of health and antithesis of illness. Thus, it appears that the antithesis of illness may provide a better measure for the social determinants of health than self-rated health status. Diener [50, 51] had postulated that self-reported health status can be effectively applied to evaluate health status instead of objective health status measurement (morbidity, life expectancy, mortality), and Bourne [46] found a strong statistical association between selfreported illness and particular objective measure of health (life expectancy, r = -0.731); but a weak relationship between self-reported illness and mortality. Using a nationally representative sample 6,782 Jamaicans, one researcher warned against using self-reported illness as a measure of health as he found that men were over-reporting their illness [54], and this means they were over-rating their antithesis of illness. Those studies highlight the challenges in using subjective measures in evaluating health as they are not consistent like the objective ones such as mortality, life expectancy, and diagnosed morbidity. Nevertheless, on examining the antithesis of illness and self-rated health status, it was revealed that 2.9% of those who indicated very good health status had an illness compared to 20% of those who reported an illness who had very good health status. From the current work again it emerged that there is disparity between self-reported illness (or antithesis of illness) and self-rated health status, indicating why caution is required in using either one or the other. Other disparities between antithesis of illness and self-rated health status highlighted that antithesis of illness is a better measure of health than self-rated health status. Clearly despite the efforts of the WHO in broadening the conceptualization of health away from the antithesis of illness, the Jamaican adolescence population has not moved to this new frontier. As when they were asked to report on the antithesis of illness, they gave lower values than indicated for selfrated health status. Because antithesis of illness captures health more than self-rated health
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status, this justifies why the former had a greater explanation when the social determinants of health were examined than that of self-rated health status. But, where were their differences in the variables used in one measure compared with the others? In fact, all the variables used in this study were social determinants that were identified in the literature [26-38], and many of them were not significant for the adolescence population of this research. It can be extrapolated from the current work that social determinants of health for a population are not the same for a sub-population, in particular adolescence population. Thus, when the WHO [27] and affiliated scholars [26, 28-32] forwarded social determinants of health, prior to that some scholars like Grossman [33] and Smith and Kington [34] had already social determinants of health of a population. However, none of them stipulated that there are disparities and variations in these based on the dichotomization, non-dichotomization, subpopulation, and measurement of health (ie self-rated health or antithesis of illness). Using a cross-sectional survey (2003 US National Survey of Children's Health) of some 102,353 children aged 0 to 17 years, Victorino and Gauthier [55] established that there were some variations in social determinants of health based on particular health outcomes. The health outcomes used by Victorino and Gauthier are presence of asthma, headaches/migraine, ear infections, respiratory allergy, food/digestive allergy, or skin allergy, which are health conditions. Another research using the 2003 US National Survey of Children's Health (NSCH) investigated the association of eight social risk factors on child obesity, socioemotional health, dental health, and global health status [56]. From a research in England, Currie et al. [57] found disparity in income gradient associated with subjectively assessed general health status, and no evidence of an income gradient associated with chronic conditions except for asthma, mental illness, and skin conditions.
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This paper concurs with the literature that there are variations in some social determinants of health status across measurement, dichotomization and non-dichotomization of health. However, the present work went further than the current literature and found that particular dichotomization of health had stronger explanatory power, and disparity in determinants. As such, the variations in social determinants of health vary across the dichotomization and measurement of health as this paper showed that more social factors do not translate into greater explanatory power; and that stronger explanation does not denotes more social determinants. And the social determinants of health had the greatest explanatory power used antithesis of illness to measure health.

Conclusion
In summary, the general health status of the adolescence population in Jamaica is good, but 7 in every 100 have reported an illness of which some had chronic conditions (diabetes mellitus, 3.9% and hypertension, 1.3%), and those who classified as being in the wealthy class were more likely to report very poor-to-poor health status compared with those in the lower class. Another important finding was that rural adolescents had a greater health status than urban adolescents, but periurban adolescents had the greatest health status. Outside of the aforementioned good health news, the social determinants of self-rated health status vary across the measurement of and dichotomization and non-dichotomization of health and the population used. This work provides invaluable insights into how social determinants should be examined, modify the general social determinants of health offered by the World Health Organization and some associated scholars. By varying the measurement, dichotomization and non-dichotomization of health, this work provide some justification as to

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whether a particular dichotomization of health is better or non-dichotomization is preferable to dichotomization. This researcher will not join the group of scholars who are purporting for the nondichotomization of self-rated health status, but we recognized that discourse offers some information. However, we will chide researchers against arbitrarily using a particular dichotomization, non-dichotomization and measurement without understanding peoples’ perception of health to which they seek to examine, and evaluate these. Thereby, despite the international standardized definition of a phenomenon, people may a different view as to this issue. Disclosures The author reports no conflict of interest with this work.

Disclaimer
The researcher would like to note that while this study used secondary data from the 2007 Jamaica Survey of Living Conditions (JSLC), none of the errors in this paper should be ascribed to the Planning Institute of Jamaica and/or the Statistical Institute of Jamaica, but to the researcher.

Acknowledgement
The author thank the Data Bank in Sir Arthur Lewis Institute of Social and Economic Studies, the University of the West Indies, Mona, Jamaica for making the dataset (2007 Jamaica Survey of Living Conditions, JSLC) available for use in this study.

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Table 12.1: Demographic characteristic of studied population, n = 1394 Characteristic n Sex Male Female Union status Married Common-law Visiting Single Social assistance Yes No Area of residence Urban Periurban Rural Population Income Quintile Poorest 20% Second poor Middle income Second wealthy Wealthiest 20% Self-reported illness Yes No Self-reported diagnosed illness Influenza Diarrhoea Respiratory illness (ie asthma) Diabetes mellitus Hypertension Other conditions (unspecified) Health care-seeking behaviour Yes No Self-rated health status Very good Good Moderate Poor Very poor Health insurance coverage No Yes Age, mean (Standard deviation, SD) Length of illness, median (range)

Percent 672 722 1 14 73 494 232 1108 394 287 713 320 328 287 263 196 89 1251 22 1 16 3 1 33 50 43 631 601 84 18 2 1123 194 48.2 51.8 0.2 2.4 12.5 84.8 17.3 82.7 28.3 20.6 51.1 23.0 23.5 20.6 18.9 14.1 6.6 93.4 28.9 1.3 21.1 3.9 1.3 43.4 53.8 46.2 47.2 45.0 6.3 1.3 0.1 85.3 14.7 14.2 years (SD = 2.8 years) 5 days ( 0 – 36 days)

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Table 12.2: Particular demographic variables by area of residence, n = 1,394 Characteristic Area of residence Urban Periurban n (%) n (%) Self-reported illness Yes 27 (7.1) 15 (5.4) No 352 (92.9) 264 (94.6) Self-rated health status Very good 162 (42.7) 141 (50.4) Good 172 (45.4) 132 (47.1) Moderate 38 (10.0) 7 (2.5) Poor 7 (1.8) 0 (0.0) Very poor 0 (0.0) 0 (0.0) Social class Lower 101 (25.6) 108 (37.6) Middle 88 (22.3) 58 (20.2) Upper 205 (52.0) 121 (42.2) Educational level Primary or below 138 (36.6) 136 (48.6) Secondary 213 (56.5) 136 (48.6) Tertiary 26 (6.9) 8 (2.9) Sex Male 213 (54.1) 148 (51.6) Female 181 (45.9) 139 (48.4) Health insurance coverage Yes 73 (19.4) 37 (13.6) No 303 (80.6) 235 (86.4) Length of illness, mean ± SD 6.0 ± 5.7 days 7.8 ± 9.0 days

P, χ2 Rural n (%) 47 (6.9) 635 (93.1) 328 (48.4) 297 (43.9) 39 (5.8) 11 (1.6) 2 (0.3) 172.64, < 0.0001 439 (61.6) 141 (19.8) 133 (18.7) 37.79, < 0.0001 312 (46.1) 359 (53.0) 6 (0.9) 1.20, 0.548 361 (50.6) 352 (49.4) 9.36, 0.009 84 (12.6) 585 (87.4) 6.4 ± 6.5 days F = 0.42, 0.857 0.628, 0.931 24.82, 0.002

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Table 12.3: Logistic regression: Variables of antithesis of illness among adolescence population, n = 1,280 Characteristic OR CI (95%) Age 1.1 1.0 - 1.3 Health care-seeking (1=yes) 0.0 0.0 - 0.01* Health insurance coverage (1=yes) 1.0 0.4 - 2.5 Primary education (reference group) 1.0 Secondary 1.8 0.9 - 3.7 Tertiary 1.9 0.3 - 15.1 lnMedical 0.8 0.1 - 5.0 Male 1.4 0.7 - 2.6 Social assistance from government 1.6 0.6 - 4.4 Logged family income 0.8 0.3 - 1.8 Rural area (reference group) Urban 1.6 0.7 - 3.8 Periurban 1.2 0.5 - 2.9 Poor-to-Very poor health status (reference group) 1.0 Moderate-to-Very good health status 0.3 0.03 - 2.1 Good-to-Very good health status 12.6 6.0 - 26.3* Lower class (reference group) Middle class 1.6 0.5 - 5.2 Upper 0.8 0.2 - 3.1 Crowding 0.9 0.8 - 1.1 Model χ2, P 287.08, < 0.0001 -2 LL 327.56 R2 0.53 Hosmer and Lemeshow χ2 = 4.40, P = 0.82 OR denotes odds ratio, CI (95%) means 95% confidence interval and *P < 0.05

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Table 12.4: Logistic and Ordinal Logistic regression: Factors explaining self-reported health status of adolescents, n = 1,280 Self-rated health status Very poor-to-poor Good Moderate-to-very Good-to-ver Characteristic good good OR CI (95%) OR CI (95%) OR CI (95%) OR CI (95
Self-reported illness (1=yes) Age Health care-seeking (1=yes) Health insurance coverage (1=yes) Primary education (reference group) Secondary Tertiary Logged Medical expenditure Social assistance from government Lower class (reference group) Middle class Upper Rural area (reference group) Urban Periurban Male Logged family income Crowding Model χ2, P -2 LL R2 Hosmer and Lemeshow OR denotes odds ratio; *P < 0.05 2.0 1.0 10.0 0.3 1.0 0.7 0.0 1.6 0.2 1.0 0.6 14.9 1.0 1.6 0.0 0.9 0.1 1.6 0.3 – 15.6 0.9 – 1.2 1.0 – 96.5* 0.04 – 2.8 0.3 – 1.9 0 – 0.0 0.7 – 3.6 0.03 – 1.7 0.1 – 2.9 1.9 – 118.3 * 0.1 0.9 0.7 1.1 1.0 0.9 0.4 0.6 1.2 1.0 2.1 0.7 1.0 0.6 3.3 1.5 1.3 0.9 0.05 – 0.2* 0.9 – 1.1 0.3 – 1.9 0.6 – 2.2 0.6 – 1.5 0.1 – 1.0 0.4 – 1.2 0.6 – 2.2 0.9 – 4.5 0.3 – 1.4 0.5 1.0 0.1 3.0 1.0 1.4 5E+007 0.1 – 4.4 0.8 – 1.2 0.01 – 0.5* 0.4 – 25.5 0.5 – 3.8 0.0 -

0.4 – 3.0 0.0 - 0.0 0.3 – 2.3 0.04 – 0.4* 1.3 – 2.0* 59.66, < 0.0001 146.38 0.38 χ2 = 4.6, P = 0.82

0.4 – 1.0* 1.3 – 8.2* 1.0 – 2.4 0.9 – 2.0* 0.8 – 1.0* 113.11, < 0.0001 588.76 0.20 χ2 = 4.61, P = 0.80

4.8 0.6 – 38.5 1.0 1.8 0.3 – 9.6 0.1 0.01 – 0.5* 1.0 0.9 0.3 – 2.7 2E+0007 1.1 0.4– 3.0 8.2 2.8 – 23.8* 0.6 0.5 – 0.8* 30.37, < 0.0001 175.67 0.31 χ2 = 4.36, P = 0.94

0.1 0.05 – 0.9 0.9 – 0.7 0.3 – 1.2 0.6 – 1.0 1.0 0.6 – 0.4 0.2 – 0.7 0.4 – 1.2 0.6 – 1.0 2.2 1.0 – 0.7 0.3 – 1.0 0.6 0.4 – 3.3 1.53– 1.4 0.9 – 2.0 1.2 – 0.9 0.8 – 0 113.11, <0.0 58 χ2 = 4.61, P =

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Table 12.5: Self-rated health status and antithesis of illness, n = 1,330 Self-rated health status Characteristic Very good Good Moderate Poor n (%) n (%) n (%) n (%) Antithesis of illness No 18 (2.9) 38 (6.4) 26 (31.3) 7 (38.9) Yes 611 (97.1) 560 (93.6) 57 (68.7) 11 (61.1) χ2 = 125.58, P < 0.0001 Good health (Antithesis of illness) Characteristic No n (%) Self-rated health status Very good 18 (20.0) Good 38 (42.7) Moderate 26 (29.2) Poor 7 (7.9) Very poor 0 (0.0) χ2 = 125.58, P < 0.0001

Very poor n (%) 0 (0.0) 2 (100.0) Yes n (%) 611 (49.2) 560 (45.1) 57 (4.6) 11 (0.9) 2 (0.2)

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Chapter 13
Childhood Health in Jamaica: changing patterns in health conditions of children 0-14 years

Paul Andrew Bourne
The new thrust by WHO is healthy life expectancy. Therefore, health must be more than morbidity. It is within this framework that a study on childhood health in Jamaica is of vital importance. This study 1) expands the health literature in Jamaica and by extension the Caribbean, 2) will aid public health practitioners with research findings upon which they are able to further improve the quality of life of children, 3) investigates the age at with children in Jamaica become influenced by particular chronic diseases and 4) assesses the subjective wellbeing of children. The current study extracted a sample of 8,373 and 2,104 children 0-14 years from two surveys collected jointly by the Planning Institute of Jamaica and the Statistics Institute of Jamaica for 2002 and 2007 respectively. A self-administered questionnaire was used to collect the data. Ninety-one percent of children in Jamaica, for 2007, reported good health. The number of children who had diarrhoea fell by 84.2% in 2007 over 2002, and a similar reduction was observed for those with asthma (42.1% in 2002 and 19.7% in 2007). Another critical finding was that 1.2% of children, in 2007, had diabetes mellitus compared to none in 2002. Public health now has an epidemiological profile of health conditions of children and the demographic shifts which are occurring and this can be used for effective management and planning of the new health reality of the Jamaican child.

INTRODUCTION One of the measures of child health and the health status of the general populace is infant mortality or mortality, which is well studied in Jamaica and the wider Caribbean [1-11]. The simple rationale for the use of mortality in evaluating health status is owing to its ease in which it can be used to precisely measure its outcome unlike other indicators such as quality of life, subjective wellbeing, happiness or life satisfaction [12-22]. Another reason for the use of infant
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mortality in the measurement of health is because of the strong inverse significant correlation between it and/or general mortality and life expectancy [23,24]. There is no denial therefore that infant mortality and/or mortality in general play a critical role in determining health outcomes. Although life expectancy emerged from mortality, the former only speak to length of life and not the quality of those lived years. An individual can live for 40 years or even 100 years, of which all those years were lived in severe morbidity. It is owing to aforementioned rationale why the World Health Organization (WHO) developed a mathematical technique which discount the life expectancy by the years spent in disability or morbidity [25]. The WHO therefore emphasized healthy life expectancy and not life expectancy. Health therefore must be more than morbidity as it expands to quality of life. Within the broadest definition of health conceptualized by the WHO in the 1940s [26], is social, psychological and physical wellbeing and not the mere absence of diseases suggesting that health is more than living to the quality of those lived years. Health has been expanded to mean much more than the absence of diseases to include measures of healthy life expectancy, happiness, utility, personal preference, and self-reported quality of life [12-22]. Simply put, wellbeing is subjectively what is ‘good’ for each person [26]. It is sometimes connected with good health. Crisp [26] offered an explanation for this, when he said that “When discussing the notion of what makes life good for the individual living that life, it is preferable to use the term ‘wellbeing’ instead of ‘happiness”, which explains the rationale for this project utilizing the term wellbeing and not good health. The issue of wellbeing is embodied in three theories – (1) Hedonism, (2) Desire, and (3) Objective List. Using ‘evaluative hedonism’, wellbeing constitutes the greatest balance of
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pleasure over pain [26, 27]. With this theorizing, wellbeing is just personal pleasantness, which represents that more pleasantries an individual receives, he/she will be better off. The very construct of this methodology is the primary reason for a criticism of its approach (i.e. ‘experience machine’), which gave rise to other theories. Crisp [26] using the work of Thomas Carlyle described the hedonistic structure of utilitarianism as the ‘philosophy of swine’, because this concept assumes that all pleasure is on par. He summarized this adequately by saying that “… whether they [are] the lowest animal pleasures of sex or the highest of aesthetic appreciation” [26]. The desire approach, on the other hand, is on a continuum of experienced desires. This is popularized by welfare economics. As economists see wellbeing as constituting satisfaction of preference or desires [26, 27], which makes for the ranking of preferences and its assessment by way of money. People are made better off, if their current desires are fulfilled. Despite this theory’s strengths, it has a fundamental shortcoming, the issue of addiction. This forwarded by the possible addictive nature of consuming ‘hard drugs’ because of the summative pleasure it gives to the recipient. Objective list theory: This approach in measuring wellbeing list items not merely because of pleasurable experiences nor on ‘desire-satisfaction’ but that every good thing should be included such as knowledge and-or friendship. It is a concept influenced by Aristotle, and “developed by Thomas Hurka as perfectionism” [26]. According to this approach, the

constituent of wellbeing is an environment of perfecting human nature. What goes on an ‘objective list’ is based on reflective judgement or intuition of a person. A criticism of this technique is elitism. Since an assumption of this approach is that, certain things are good for
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people. Crisp [26] provided an excellent rationale for this limitation, when he said that “…even if those people will not enjoy them, and do not even want them”. In Arthaud-day et al work [28], applying structural modeling, subjective well was found to constitute “(1) cognitive evaluations of one's life (i.e., life satisfaction or happiness); (2) positive affect; and (3) negative affect.” Subjective wellbeing therefore is the individual’s own viewpoint. If an individual feels his/her life is going well, then we need to accept this as the person’s reality. One of drawbacks to this measurement is, it is not summative, and it lacks generalizability. In keeping therefore with the broad definition of health forwarded by the WHO, any study of health must go beyond mortality. A comprehensive search of health literature in the Caribbean in particular found no research that 1) using national cross-sectional survey(s) examined health status of children, 2) investigated the changing pattern of morbidity which affect children ages 0-14 years, 3) investigated whether health status (ie. subjective wellbeing) and self-reported morbidities (ie health conditions) are correlated, and if they are good measure for each other, 4) investigated whether from among the health conditions, chronic diseases and the time they begin to affect children as well as the 5) demographic characteristics of health conditions affecting children. The current study will examine the aforementioned issues as health literature in the region on child health must expand beyond infant mortality. The objectives of the study are to 1) expand the health literature in Jamaica and by extension the Caribbean, 2) understand the status of child health outside of mortality, 3) aid public health practitioners with research upon which they are able to further improve the quality of life of children by adding quality to their lived years, 4) investigate the age at with children in Jamaica become influenced
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by chronic disease, it typology and 5) evaluate the subjective wellbeing of children as is done for the general populace and elderly [30-37]. The current study used two cross-sectional surveys which were conducted jointly by the Planning Institute of Jamaica and the Statistical Institute of Jamaica (for 2002 and 2007) that collect data on Jamaicans. A subsample of 8,373 and 2,104 children 0-14 years was extracted from a sample of 25,018 and 6,783 respondents for 2002 and 2007 respectively. The survey was a national probability sample of Jamaica, and it was weighted to reflect the populace and subpopulations. The response rate for each survey was in excess of 72%. Descriptive statistics, such as mean, standard deviation (SD), frequency and percentage were used to analyze the sociodemographic characteristics of the sample. Chi-square was used to examine the association between non-metric variables, and Analysis of Variance (ANOVA) was used to test the relationships between metric and non-dichotomous categorical variables whereas independent sample t-test was used to examine a statistical correlation between a metric variable and a dichotomous categorical variable. The level of significance used in this research was 5% (ie 95% confidence interval).

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METHODS AND MATERIALS The current study extracted a sample of 8,373 and 2,104 children 0-14 years from two surveys collected jointly by the Planning Institute of Jamaica and the Statistics Institute of Jamaica for 2002 and 2007 respectively.[38,39] The method of selecting the sample from each survey was solely based on an individual being less than or equal to 14 years. The survey (Jamaica Survey of Living Condition) began in 1989 to collect data from Jamaicans in order to assess policies of the government. Since 1989, yearly the JSLC adds a new module in order to examine that phenomenon which is critical within the nation. In 2002, the foci were on 1) social safety net and 2) crime and victimization; and for 2007, there was no focus. The sample for the earlier survey was 25,018 respondents and for the latter, it was 6,783 respondents. The survey was drawn using stratified random sampling. This design was a two-stage stratified random sampling design where there was a Primary Sampling Unit (PSU) and a selection of dwellings from the primary units. The PSU is an Enumeration District (ED), which constitutes a minimum of 100 residence in rural areas and 150 in urban areas. An ED is an independent geographic unit that shares a common boundary. This means that the country was grouped into strata of equal size based on dwellings (EDs). Based on the PSUs, a listing of all the dwellings was made, and this became the sampling frame from which a Master Sample of dwelling was compiled, which in turn provided the sampling frame for the labour force. One third of the Labour Force Survey (ie LFS) was selected for the JSLC. [40, 41] The sample was weighted to reflect the population of the nation. The JSLC 2007 [40] was conducted May and August of that year; while the JSLC 2002 was administered between July and October of that year. The researchers chose this survey based
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on the fact that it is the latest survey on the national population and that that it has data on selfreported health status of Jamaicans. A self-administered questionnaire was used to collect the data, which were stored and analyzed using SPSS for Windows 16.0 (SPSS Inc; Chicago, IL, USA). The questionnaire was modelled from the World Bank’s Living Standards Measurement Study (LSMS) household survey. There are some modifications to the LSMS, as JSLC is more focused on policy impacts. The questionnaire covered areas such as socio-demographic variables – such as education; daily expenses (for past 7-day; food and other consumption expenditure; inventory of durable goods; health variables; crime and victimization; social safety net and anthropometry. The non-response rate for the survey for 2007 was 26.2% and 27.7%. The nonresponse includes refusals and rejected cases in data cleaning. Measures Social class: This variable was measured based on the income quintiles: The upper classes were those in the wealthy quintiles (quintiles 4 and 5); middle class was quintile 3 and poor those in lower quintiles (quintiles 1 and 2). Health care-seeking behaviour. This is a dichotomous variable which came from the question “Has a doctor, nurse, pharmacist, midwife, healer or any other health practitioner been visited?” with the option (yes or no). Age is a continuous variable in years. Child. A person who has celebrated less than or equal to 14 years.

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Health conditions (ie. self-reported illness or self-reported dysfunction): The question was asked: “Is this a diagnosed recurring illness?” The answering options are: Yes, Cold; Yes, Diarrhoea; Yes, Asthma; Yes, Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No. Self-rated health status: “How is your health in general?” And the options were very good; good; fair; poor and very poor. Statistical Analysis Descriptive statistics, such as mean, standard deviation (SD), frequency and percentage were used to analyze the socio-demographic characteristics of the sample. Chi-square was used to examine the association between non-metric variables, and Analysis of Variance (ANOVA) was used to test the relationships between metric and non-dichotomous categorical variables whereas independent sample t-test was used to examine a statistical correlation between a metric variable and a dichotomous categorical variable. The level of significance used in this research was 5% (ie 95% confidence interval). RESULT For this study there were two samples (8,373 from 2002 data survey and 2,104 from the 2007 survey). In 2002, the sample was 50.7% males and 49.3% females compared to 51.3% males and 48.7% females for 2007. The mean age for the sample in 2002 was 7.2 years (SD = 4.2 years) and 7.3 years (SD = 4.3 years) for 2007. The proportion of the sample in particular social class (using population income quintile) was relative the same across the two years. The number of days recorded as suffering from illness fell by 2 days in 2007 over 2002 (median number of days experiencing ill-health). In 2002, 9.4% of the sample reported an illness/injury in the 4-week period of the survey and this increased by 34.0% (to 12.6%). The percent of the sample that
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visited health care practitioners marginally increase from 56.7%, in 2002, to 58.6% in 2007. Concurrently, 9.3% of sample was covered by health insurance (ie total private in 2002) and this increased by 62.4% and a part of this was accounted for by a 5.1% having public health insurance coverage. In 2002, 62.6% of the sample dwelled in rural areas, 25.1% in semi-urban areas and 12.3% in urban areas compared to a shift which was noticed in 2007 as 53.2% resided in rural areas and 20.2% in semi-urban areas with 26.6% lived in urban zones (Table 13.1). The general health status of children in Jamaica, for 2007, was good (91.3%) compared to 6.7% fair and 2.0% poor. Interestingly, in the current study, a shift in health condition was noticed in 2007 over 2002. The number of children who had diarrhoea fell by 84.2% in 2007 over 2002, and a similar reduction was observed for those with asthma (42.1% in 2002 and 19.7% in 2007). Another critical finding was that 1.2% of children, in 2007, had diabetes mellitus compared to none in 2002. On the contrary, 37.5% of children, in 2007, had cold which increased from none in 2002 (Table 13.1). A cross-tabulation between health conditions and sex of respondents, revealed that no significant statistical correlation existed between the two variables and that this was for both years: For 2002 - χ2 (df = 2) = 0.232, p> 0.05; and for 2007 - χ2 (df = 5) = 8.915, p> 0.5 (Table 13.2). In spite of the aforementioned, the new diabetic cases were accounted for by females (for 2007). In 2002, no significant statistical relationship existed between diagnosed health conditions and area of residents (χ2 (df = 4) = 1.301, p > 0.05). On the other hand, a statistical correlation was observed for 2007 between the aforementioned variables. Furthermore, more
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children in semi-urban areas had cold than those who dwelled in other areas. On the contrary, diabetic cases were found in urban areas and none in other geographical zones. The findings revealed also that more rural children had asthma and more urban children had unspecified health conditions (Table 13.3). Table 13.4 revealed that no significant association was found between diagnosed health condition and social class (ie population income quintile). However, the diabetic cases were spread among the lower class (poorest 20%, 1.9%; and poor, 1.8%) and the upper class (wealthy, 2.0%). The examination of diagnosed health conditions by mean age of respondents revealed that a significant relationship existed between the two aforementioned variables in 2007, F statistic = 4.875, p < 0.001; but none in 2002 - F statistic = 3.334, p > 0.05. In 2007, the mean age of a child with diabetes mellitus was 12.33 years (SD = 2.1 yrs), 95% CI = 7.16 – 17.5 (Table 13.5). However the mean age a child with diarrhoea lower than a child and other health conditions. The first time in the history of the Jamaica Survey of Living Conditions (JSLC) that health status and self-reported health condition was collected together was in 2007. Hence, the current study will cross-tabulate both in order to determine whether a significant correlation exist between them and what is the strength of a relationship if one does exist. Based on Table 13.6 a weak significant statistical association exist between health status and self-reported health condition - χ2 (df = 2) = 174.512, p < 0.0001, cc= 0.282. On further examination of the findings, it was observed that no child was classified has having very good health status. Ninety-four percent of sample who had no health condition reported good health compared to 70% of those who had at least one health condition. Of those who had at least one health
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condition, 9.4% of them reported poor health status compared to 1% who had no health condition (Table 13.6). Using independent sample t-test, in 2002, the current study found that there was a significant difference between the mean age of those who sought and not seek medical care – t3.425 , p < 0.001. The mean age of those who do not seek medical care higher, 6.2 years (SD = 4.1), compared to those who seek care, 5.2 years (SD = 4.2 years). However, there was no difference in 2007: seek care – mean age 5.2 years (SD = 4.1 years) and not seek care – mean age 5.8 years (SD = 4.2 years). On examination as to whether a significant statistical correlation existed between health care-seeking behaviour and sex of respondents, none was found in each year – p > 0.05 (Table 13.7). DISCUSSION It is established in epidemiology that diseases in childhood do influence poor health in adulthood [42], suggesting the value of child health to health status over the life course. Another importance to the study of health status is its contribution to all typology of development as human capital is critical to socio-economic and political systems. In Jamaica, the Statistical Institute of Jamaica [42] estimated that for 2007, there was 28.3% of the nation’s population was less than 14 years. Simply put, there are 45 children for every 100 working age (ages 15-64 years) Jamaican; and to omitted the health status of this cohort is to substantially neglect a critical sector of the population. The current study found that 2 in every 100 children had poor health status; and that weak significant statistical correlation existed between health status and self-reported health conditions. This therefore concurs and contradicts another study that found
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statistical association between health conditions and health status [36]. Hambleton et al. [36], examining data for elderly Barbadians, found that self-reported health conditions accounted for most of the variability in health status (ie. current diseases accounted for 33.5% out of R2 = 38.3%). This takes the study in the direct of current diseases (ie health conditions) of children in Jamaica. This study revealed 34% increase in cases of self-reported diseases in Jamaican children. Only 13 in 100 children in Jamaica, in 2007, had a least one health condition. These conditions include cold, diarrhoea, asthma, diabetes mellitus and other unspecified diseases. In 2007, 20 in every 100 children had asthma, 5 out of every 100 diarrhoea cases, 38 in every 100 had cold and 21 in every 100 unspecified conditions. Of the different typology of chronic dysfunctions, 12 in every 1,000 reported diabetes mellitus and no cases were found of hypertension and arthritis. Given the breadth of the unspecified category, this could include cancers, HIV/AIDS and other communicable or non-communicable diseases. In spite of this uncertainty, what emerged from the current research is the change in pattern of health conditions of children between 2002 and 2007. A study conducted by Walker [43] found that growth retardation in children influence blood pressure, obesity, and other chronic health conditions, and that some 5-6% of children in Trinidad and Tobago, and Jamaica are classified in this group. Walker also found that these children are more likely to experience more episodes of diarrhaea, fever and other morbidities. This research revealed that number of cases of asthma, diarrhoea and unspecified conditions fell accompanied with a corresponding rise in cold and diabetes mellitus. Interestingly to note is that the 1.2% of child population that were diagnosed diabetic patients represents 2.3%
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of the female population. The diabetic cases were not only females, but urban residents. Of those with diabetes, 1.9% was in the poorest 20%, 1.8% poor and 2.0% of the wealthy social class. Continuing, the mean age of female diabetic children was 12.3 years; and this indicates the year age in which diabetes mellitus begin to affect females in Jamaica. The aforementioned finding explains the disproportionate number of females to males in the general population that have diabetes -14% females to 7.7% males [40]. Although no cases of hypertension was reported in this study, it is established that diabetes mellitus is correlated with hypertension. Diabetes Mellitus is not the only challenge faced by patients, but McCarthy [44] argues that between 30 to 60% of diabetics also suffer from depression, which is a psychiatric illness. Diabetes mellitus does influence the health status of children and follows them across the life course. It affects lifestyle choice, functional capacity, and like McCarthy said the psychological state of people. This health condition also affects other disease. Morrison [45] opined that diabetes mellitus and hypertension have now become two problems for Jamaicans and in the wider Caribbean. This situation was equally collaborated by Callender [46] who found that there was a positive association between diabetic and hypertensive patients - 50% of individuals with diabetes had a history of hypertension [46]. Children with diabetes mellitus therefore are highly likely to develop hypertension in the future, and so children in Jamaica in the future will have twin chronic conditions. This envelope further shifts in health conditions of children in Jamaica; Morrison alluded to a transitory shift from infectious communicable diseases to chronic noncommunicable diseases as a rationale for the longevity of the Anglophone Caribbean populace and this does not mitigates against lowered healthy life expectancy of the sexes in particular females who live 6 years more than males [34,42].

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Diabetes mellitus and any other typology of chronic diseases do more than affect healthy life expectancy; they are directly correlated with mortality. Statistics from the Statistical Institute of Jamaica [42] is the leading cause of deaths in female Jamaicans. The reality of changing pattern of health conditions from communicable to non-communicable and the fact that this is accounted with urban poor and wealthy, indicate that public health policies are needed to address this currently and in the future. Another important fact that embedded in the current study is the early age in which females are having chronic disease, and this indicates the length of time with which they will life with this non-curable disease or likeliness of mortality. A study on morbidity and mortality patterns in the Caribbean established that the transition in morbidity is not atypical to Jamaica [47], and that the leading cause of mortality in region is similar to developed nations. WHO [48] opined that 80% of chronic illnesses were in low and middle income countries, indicating the preponderance of chronic illness in regions such as the Caribbean as well as the fact that chronic illnesses are also a part of the landscape of industrialized nations. With the changing pattern of morbidity of children in Jamaica, this will support modifications in lifestyle behaviour which must begin from children to the populace. Although there is no statistical difference between the 3 area of residents and health conditions, the fact that the chronic dysfunctions were found in urban areas denote that public health policies must begin in earnest in those places. There is another situation that must be explored here and that is response of health services, and the management of care for those who are affected by chronic illnesses. It should be noted that 57 out of every 100 children were taken for medical care which speaks to the high proportion of children despite being ill who were not taken to traditional medical facilities. A part of the rationale for this non-medical care seeking behaviour of children is adults’ definition of health and the cultural perspective of health.
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Generally, health in Jamaica is defined as the absence of illness which although is negative and narrow in scope speaks to people’s perspective on the matter. Interestingly in this discourse is not only the narrowed definition of health, but that severity in health conditions is substantially what drives medical care-seeking and not on the onset of illness or preventative care. This goes to the crux of why only 57 out of every 100 children who are ill would be taken to health care practitioners as their families are less likely to taken then for conditions such as the cold, but also provide an explanation for the low medical care seeking behaviour for the general populace. Statistics revealed that for the last 2 decades (1988-2007), there were 4 times (years) in which males sought more medical care than females – 1991 (48.5% males to 47.4% females); 1995(59.0% males to 58.9% females): 1997 (60.0% males to 59.3% females) and 2006 (71.7% males to 68.8% females) [30, 41, 40], which speaks to some embedded culturalization for this health care-seeking disparity in nation. While this is not atypical to Jamaica [49-51], that fact that the current study revealed that there was no significant statistical difference between male and female children being taken for medical care, the disparity that exist in the general populace begin in young adulthood. This is the period in which identify formulation begins in adolescents and when males begin to imitate the practices of adult men. The adolescent male therefore will seek less medical care because his adult counter believes that this is weak, feminine and reduces his machoism. One anthropologist in seeking to explain the practices of Caribbean men used social learning theory to examine the lifestyle practices of boys [52]. Chevannes [52] argued that the young imitate the roles of society members through role modeling of what constitute acceptable and good roles which is supported by reinforcement. The young male is a subset of the society,
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and if men are less likely to seek health care because of a cultural perspective that they form of ill-health which goes to the crux of their manhood and possibly seeks to threaten it, young males as soon as they are somewhat responsible for their choices will do more of the same as their mentors. This gender role of sexes and health disparity which results after childhood is not limited to Jamaica or the Caribbean but a study carried out by Ali and de Muynck [53] found that street children in Pakistan had a similar gender stereotype about health, health care and medical care seeking-behaviour. Using a descriptive cross-sectional study carried out during September and October 2000 of 40 school-aged street children (8-14 years), they found boys were reluctant to seek medical care except when there is severity of ill-health, it threatens their economic livelihood or there is a perceived reduction in functional capacity. The reason being that mild ailment is not severe enough to barr them from physical functioning and within the context of the general population that men ought to be tough, this means that they are okay; and so some morbidity are not for-hospital, which was so the case in Nairobi slums [54]. This again justifies why some children in Jamaica are not taken to health practitioners as there is a perception that some illness requires home remedy. Statistics revealed that 56.0% of children (ages 0-4) who were not taken for medical treatment despite having an illness was because home remedies were used, figure was 32.8% for those 5-9 years and 25.6% for those 10-19 years [40]. Inaffordability accounted for 33%, 32.5% and 35.9% of those ages 0-4 years, 5-9 years and 10-19 years respectively who were not brought to health care practitioner even though they were ill. CONCLUSION The general health status of children in Jamaica is good; but this mitigate against the relatively
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low age with which females are reported to have had diabetes mellitus and the changing pattern of health conditions which have occurred since the 2002. Public health now has an epidemiological profile of health conditions of children and the demographic shifts which are occurring and this can be used for effective management and planning of the new health reality of the Jamaican child. With the removal of health care user fees for children ages 0-18 years from the health care landscape of Jamaica (since May 28, 2007), the transition to chronic cases in this cohort means that health care expenditure in the future will rise as we seek to care for those patients over there life course. It is critical that future research examine the composition of unspecified health conditions as this constitutes a significant percentage of diseases in 2007 unlike 2002. Conflict of interest There is no conflict of interest to report References 1. McCaw-Binns A, Holder Y, Spence K, Gordon-Strachan G, Nam V, Ashley D. Multi-source method for determining mortality in Jamaica: 1996 and 1998: Department of Community Health and Psychiatry, University of the West Indies. International Biostatistics Information Services. Division of Health Promotion and Protection, Ministry of Health, Jamaica. Statistical Institute of Jamaica; 2002. 2. McCaw-Binns AM, Fox K, Foster-Williams K, Ashley DE, Irons B. Registration of births, stillbirths and infant deaths in Jamaica. Int J of Epid. 1996; 25:807-813. 3. Lindo J. Jamaican perinatal mortality survey, 2003. Kingston: Jamaica Ministry of Health 2006:1-40. 4. Fox K, et al. Assessing the level of births and birth registration in Jamaica. Kingston: Jamaica Ministry of Health. 2006:1-29. 4. Desai P, Hanna B, Melville B, Wint B. Infant mortality rates in three parishes of western Jamaica, 1980. West Indian Med J. 1983;32:83-87.
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5. Domenach H, Guengant J. Infant mortality and fertility in the Caribbean basin. Cah Orstom (Sci Hum). 1984;20:265-72. 6. Hagley KE. Nutrition and health in the developing world: the Caribbean experience. Proceedings of the Nutrition Society. 1993;52:183-187. 7. Rodriquez FV, Lopez NB, Choonara I. Child health in Cuba. Arch Dis Child 2002;93:991-3. 8. McCarthy JE, Evans-Gilbert T. Descriptive epidemiology of mortality and morbidity of health-indicator diseases in hospitalized children from western Jamaica. Am J Trop Med Hyg. 2009;80:596-600. 9. Trotman H, Lord C. Predictors of survival in very low birth weight infants at the University Hospital of the West Indies, Jamaica. Trop Doct. 2008;38:183-5. 10. Olugbuyi O, Samms-Vaughan M, Trotman H. Mortality of very-low-birth-weight infants in Jamaica. Trop Doc. 2006;36:169-71. 11. Stutzer A, Frey BS. Reported subjective well-being: A challenge for economic theory and economic policy. Working paper No. 07. Center for Research in Economics, Management and the Arts; 2003:1-48. 12. Veenhoven R. Happiness in nations, subjective appreciation of in 56 nations 1946-1992. Rotterdam, Netherlands: Erasmus University;1993. 13. Siahpush M, Spittal M, Singh GK. Happiness and life satisfaction prospectively predict selfrated health, physical health, and the presence of limiting, long-term health conditions. Am J Health Promot. 2008;23:18-26. 14. Smith DM, Langa KM, Kabeto MU, Ubel PA. Health, Wealth, and Happiness Financial Resources Buffer Subjective Well-Being After the Onset of a Disability. Am Psychological Society. 2005; 16: 663-666. 15. Selim S. Life Satisfaction and Happiness in Turkey. Social Indicators Research. 2008;88:531-562. 16. Schwarz N, Strack F. Reports of subjective well-being: judgmental processes and their methodological implications. In: Kahneman D, Diener E, Schwarz N, (eds) Well-being: The Foundations of Hedonic Psychology. Russell Sage Foundation: New York, 1999: p. 61-84. 17. Michalos AC, Zumbo BD, Hubley A. Health and the Quality of Life. Social Indicators Research. 2002; 51:245-286. 18. Diener E, Seligma, MEP. Very happy people. Psychological Science. 2002;13:81–84.

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19. Diener E, Suh M, Lucas E, Smith H. Subjective well-being: Three decades of progress. Psychological Bulletin. 1999;125:276-302. 20. Diener E. Subjective well-being. Psychological Bulletin. 1984;95:542–75. 21. Diener E. Subjective well-being: the science of happiness and a proposal for a national index. American Psychologist. 2000;55:34–43. 22. Vaupel JW. How change in age-specific mortality affects life expectancy. Population Studies. 1986;40:147-157. 23. Preston SH, Heuveline P, Guillot M. 2001. Demography: Measuring and Modeling Population Processes. Oxford: Blackwell; 2001. 24. World Health Organization, (WHO). Healthy life expectancy 2002: 2004 World Health Report. Geneva: WHO; 2004. 25. World Health Organization, (WHO). Preamble to the Constitution of the World Health Organization as adopted by the International Health Conference, New York, June 19-22, 1946; signed on July 22, 1946 by the representatives of 61 States (Official Records of the World Health Organization, no. 2, p. 100) and entered into force on April 7, 1948. “Constitution of the World Health Organization, 1948.” In Basic Documents, 15th ed. Geneva, Switzerland: WHO, 1948. 26. Crisp R. Well-Being. In: Zalta E, editor. The Stanford Encyclopedia of Philosophy. California: Center for the Study of Language and Information; 2008. 27. Whang KM. Well-being Syndrome in Korea: A view from the Perspective of Biblical Counseling. Evangelical Review of Theology. 2006;30:152-161. 28. Arthaud-day ML, Rode JC, Mooney CH, Near JP. 2005. The Subjective Well-being Construct: A Test of its Convergent, Discriminant, and Factorial Validity. Social Indicators Research. 2005;74:445-476. 29. Bourne PA. Socio-demographic determinants of Health care-seeking behaviour, self-reported illness and Self-evaluated Health status in Jamaica. Int J of Collaborative Research on Internal Medicine & Public Health. 2009; 1:101-130. 30. Bourne PA, McGrowder DA, Crawford TV. Decomposing Mortality Rates and Examining Health Status of the Elderly in Jamaica. The Open Geriatric Medicine J. 2009; 2:34-44. 31. Bourne PA. Growing old in Jamaica: Population Ageing and Senior Citizens’ Wellbeing. Kingston: Department of Community Health and Psychiatry, Faculty of Medical Sciences, the University of the West Indies, Mona, WI:2009. 32. Bourne PA. A theoretical framework of good health status of Jamaicans: using econometric analysis to model good health status over the life course. North Am J of Medical Sci. 2009; 1: 86-95.

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33. Bourne PA. Medical Sociology: Modelling Well-being for elderly People in Jamaica. West Indian Med J. 2008; 57:596-04. 34. Bourne PA. Health Determinants: Using Secondary Data to Model Predictors of Wellbeing of Jamaicans. West Indian Med J 2008; 57:476-81. 35. Hutchinson G, Simeon DT, Bain BC, Wyatt GE, Tucker MB, LeFranc E. 2004. Social and Health determinants of well-being and life satisfaction in Jamaica. Int J of Soci Psychiatry. 50:43-53. 36. Hambleton IR, Clarke K, Broome HL, Fraser HS, Brathwaite F, Hennis AJ. 2005. Historical and current predictors of self-reported health status among elderly persons in Barbados. Rev Pan Salud Public. 17: 342-352. 37. Statistical Institute Of Jamaica. Jamaica Survey of Living Conditions, 2007 [Computer file]. Kingston, Jamaica: Statistical Institute Of Jamaica [producer], 2007. Kingston, Jamaica: Planning Institute of Jamaica and Derek Gordon Databank, University of the West Indies [distributors], 2008. 38. Statistical Institute Of Jamaica. Jamaica Survey of Living Conditions, 2002 [Computer file]. Kingston, Jamaica: Statistical Institute Of Jamaica [producer], 2002. Kingston, Jamaica: Planning Institute of Jamaica and Derek Gordon Databank, University of the West Indies [distributors], 2003. 39. Planning Institute of Jamaica, (PIOJ), Statistical Institute of Jamaica, (STATIN). Jamaica Survey of Living Conditions, 2007. Kingston: PIOJ, STATIN, 2008. 40. Planning Institute of Jamaica, (PIOJ), Statistical Institute of Jamaica, (STATIN). Jamaica Survey of Living Conditions, 2002. Kingston: PIOJ, STATIN, 2003. 41. Kuh D, Ben-Shlomo Y, editors. A life course approach to chronic disease epidemiology. New York: Oxford University Press; 1997. 42. Statistical Institute of Jamaica, (STATIN). Demographic statistics, 2007. Kingston, STATIN; 2008. 43. Walker S. Nutrition and child health development. In: Morgan W, editor. Health issues in the Caribbean. Kingston: Ian Randle; 2005: p. 15-25. 44. McCarthy FM. Diagnosing and treating psychological problems in patients with diabetes and hypertension. Cajanus 2000;33:77-83.
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45. Morrison E. Diabetes and hypertension: Twin trouble. Cajanus. 2000;33:61-63. 46. Callender J. Lifestyle management in the hypertensive diabetic. Cajanus. 2000;33:67-70. 47. Ivey MA, Legall G, Boisson EV, Hinds A. Mortality trends and potential years of life lost in the English and Dutch-speaking Caribbean, 1985-2000. West Indian Med J. 2008;57:122-131. 48. World Health Organization, (WHO). Preventing Chronic Diseases a vital investment. Geneva: WHO; 2005. 49. Stekelenburg J, Jager B, Kolk P, Westen E, Kwaak A, & Wolffers I. Health care seeking behaviour and utilization of traditional healers in Kalabo, Zambia. Health Policy. 2005;71: 6781. 50. Sudha G, Nirupa C, Rajasakthivel M, Sivasusbramanian S, Sundaram V, Bhatt S, Subramaniam K, Thiruvalluvan E, Matthew R, Renu G & Santha T. Factors influencing the careseeking behavior of chest symptomatic: a community-based study involving rural and urban population in Tamil Nadu, South India. Tropical Medicine & Int Health. 2003;8:336-341. 51. Akande TM, Owoyemi JO. Healthcare-seeking behaviour in Anyigba, North-Central, Nigeria. Research J of Med Sci. 2009;3:47-51. 52. Chevannes B. Learning to be a man: Culture, socialization and gender identity in five Caribbean communities. Kingston: University of the West Indies Press; 2001. 53. Ali M, de Muynck A. 2005. Illness incidence and health seeking behaviour among street children in Pawalpindi and Islamabad, Pakistan – qualitative study. Child: Care, Health and Development. 2005;31:525-32. 54. Taff N, Chepngeno G. 2005. Determinants of health care seeking for children illnesses in Nairobi slums. Tropical Medicine and Int Health. 2005;10:240-45.

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Table 13.1. Sociodemographic characteristic of sample
Variable Sex Male Female Health care-seeking behaviour Yes No Health insurance coverage Yes No Area of residence Rural Semi-urban Urban Self-reported illness Yes No Diagnosed Health conditions Cold Diarrhoea Asthma Diabetes mellitus (ie diabetes) Hypertension Arthritis Other Not Population Income quintile Poorest 20% Poor Middle Wealthy Wealthiest 20% Age Mean (SD) Length of illness Median Number of visits to health practitioner(s) median Crowding mean (SD) 2002 N= 8373 50.7 49.3 56.7 43.3 9.3 90.7 62.6 25.1 12.3 9.4 90.6 31.6 42.1 26.3 26.0 22.9 20.3 18.0 12.8 7.2 yrs (4.2 yrs) 7 days 1.0 2.5 persons (1.5 persons) 2007 N=2104 51.3 48.7 58.6 41.4 15.1 84.9 53.2 20.2 26.6 12.6 87.4 37.5 5.0 19.7 1.2 20.8 17.0 26.0 22.6 19.5 18.9 13.0 7.3 yrs (4.3 yrs) 5.0 days 1.0 5.5 persons (2.3 persons)

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Table 13.2. Diagnosed health conditions by Sex, 2002 and 2007
Variable 20021

20072

Diagnosed Health conditions Cold Diarrhoea Asthma Diabetes Hypertension Arthritis Other No
1 2 2 2

Male 27.3 45.5 27.3 -

Female

Male 35.7

Female 39.2 6.9 17.7 2.3 22.3 11.5

37.5 37.5

3.1 21.7 0.0 -

25.0 -

19.4 20.2

χ (df = 2) = 0.232, p> 0.05 χ (df = 5) = 8.915, p> 0.5

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Table 13.3. Diagnosed health conditions by area of residence
Variable 20021 20072

Diagnosed Health conditions Cold Diarrhoea Asthma Diabetes Hypertension Arthritis Other No
1 2 2 2

Rural

Semi-urban

Urban

Rural

Semi-urban

Urban

33.3 41.7 25.0 -

40.0 40.0 20.0 -

0.0 50.0 50.0 -

27.0 25.4 20.6 27.0

56.5 2.2 15.2 13.0 13.0

36.0 8.0 18.7 2.3 23.3 12.0

χ (df = 4) = 1.301, p > 0.05 χ (df = 10) = 25.079, p = 0.005, cc = 0.297

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Table 13.4. Diagnosed health conditions by Population income quintile
Variable 20021 20072

Diagnosed Health conditions Cold Diarrhoea Asthma Diabetes Hypertension Arthritis Other No
1 2 2 2

Poorest 20%

Poor

Middle

Wealthy

Wealthiest 20%

Poorest 20%

Poor

Middle

Wealthy

Wealthiest 20%

75.0 0.0 25.0 -

-

16.7 66.7 1.0

14.3 57.1 28.6 -

50.0 0.0 50.0 -

35.8 3.8 22.6 1.9 28.3 7.5

37.5 12.5 17.9 1.8 19.6 10.7

44.3 4.9 18.0 0.0 16.4 16.4

36.7 2.0 14.3 2.0 20.4 24.5

30.0 0.0 27.5 0.0 20.0 22.5

χ (df = 6) = 8.105, p > 0.05 χ (df = 20) = 25.079, p > 0.05

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Table 13.5. Mean Age of respondent who has a particular health condition
Variable 20021 20072

Diagnosed Health conditions Cold Diarrhoea Asthma Diabetes Hypertension Arthritis Other No
1 2

Mean age (SD) 1.5 yrs (1.5yrs) 5.0 yrs (3.0 yrs) 5.4 yrs (3.8 yrs) -

95% CI - 0.09 -3.09 2.51-7.49 0.62 – 10.18 -

Mean age (SD) 4.4 yrs (4.0 yrs) 3.5 yrs (2.8 yrs) 6.5 yrs (3.5 yrs) 12.33 yrs (2.1 yrs) 6.0 yrs (4.5 yrs) 5.8 yrs (4.3)

95% CI 3.55 – 5.15 1.93 – 5.15 5.51 – 7.47 7.16 – 17.5 4.82 – 7.26 4.46 – 7.20

F statistic = 3.334, p > 0.05 F statistic = 4.875, p < 0.001

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Table 13.6. Health status by self-reported illness
Variable

20021

20072 Self-reported illness None (in %) 94.3 4.7 1.0 At least one (in %) 70.2 20.4 9.4

Self-reported illness None Health status Very good Good Fair Poor
1 2 2

At least one (in %) -

(in %) -

In 2002, health status data were not collected. This took place the first time in 2007 χ (df = 2) = 174.512, p < 0.0001, cc= 0.282

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Table 13.7. Health (or medical) care-seeking behaviour by sex
Variable 20021 Sex 20072 Sex

Health care-seeking behaviour Sought care Did not seek care
1 2 2 2

Male 42.2 57.8

Female 44.5 55.5

Male 40.8 59.2

Female 42.0 58.0

χ (df = 1) = 0.419, p > 0.05 χ (df = 1) = 0.040, p > 0.05

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Chapter 14
The uninsured ill in a developing nation

Paul Andrew Bourne
Empirical studies have used a piecemeal approach to the examination of health, health careseeking, uninsured people and the health status of those who are chronically ill, but no study emerged in an extensive literature search, on the developing nations, and in particular Latin America and the Caribbean, that has investigated health and health care-seeking behaviour among uninsured ill people in a single research. The current study aims to narrow this divide by investigating health, self-reported diagnosed health conditions, and health care-seeking behaviour among uninsured ill Jamaicans, and to model factors which account for their moderate-to-very good health status as well as health care-seeking behaviour. The current study utilises cross-sectional survey data on Jamaicans which was collected in 2007. The survey is a modification of the World Bank’s Living Standard Household Survey. This work extracted a sample of 736 respondents who indicated that they were ill and uninsured from a sample of 6,783 respondents. Logistic regression analyses examined 1) the relationship between moderateto-very good health status and some socio-demographic, economic and biological variables; as well as 2) a correlation between medical care-seeking behaviour and some socio-demographic, economic and biological variables. Sixty out of every 100 uninsured ill Jamaicans were females; 43 out of every 100 were poor; 59 out of every 100 uninsured ill persons dwelled in rural areas; 1 of every 2 utilised public health care facilities, two-thirds had chronic health conditions, and 22 out of every 100 reported at least poor health. Moderate-to-very good health status was correlated with age (OR = 0.97, 95% CI = 0.95-0.98); male (OR = 0.60, 95% CI = 0.37-0.97); middle class (OR = 0.45, 95% CI = 0.21-0.95); logged income (OR = 2.87, 95% CI = 1.50-5.49); area of residence (Other Town – OR = 2.33, 95^% CI = 1.19-4.54; Urban – OR = 2.01, 95% CI = 1.11-3.62), and health care-seeking behaviour (OR = 0.45, 95% CI = 0.27-0.74). Sixty-one of every 100 uninsured respondents with ill health sought medical care. Medical care-seeking behaviour was significantly related to chronic illness (OR = 2.25, 95%CI = 1.31-3.88); age (OR = 1.03, 95%CI = 1.01-1.04); crowding (OR = 1.12, 1.01-1.24); income (OR = 1.00, 95% CI = 1.00-1.00); and married people (OR = 0.48, 95% CI = 0.28-0.82). Uninsured ill Jamaicans who resided in rural areas had the lowest moderate-to-very good health status, but there was no difference in health care-seeking behaviour based on the geographical location of residence. Despite the fact that there is health insurance coverage available for those who are chronically ill and elderly in Jamaica, there are still many such people who are without health insurance coverage. The task of public health specialists and policy makers is to fashion public education and interventions that will address many of the realities which emerged in this research.

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Introduction
In all cultures, people desire good health and long life. Ill-health, therefore, is a challenge to the aim of healthy life expectancy, and is the rationale for investments in health options such as exercise, diet, nutrition, science and technology, medical consultation and/or health care utilisation. All living organisms will experience ill-health as well as good health over their life courses; and when ill-health threatens the quality and length of life, it becomes the justification for humans’ willingness to rectify, address and possibly postpone illnesses. Ill-health (i.e. illness, sickness or ailment) threatens existence, productivity, development, the individual and the wider society, and because of that humans demand the best health care options. Demand for health care must be paid for by (1) a combination of health insurance coverage and out-of-pocket payment, (2) the state, (3) out-of-pocket payments or (4) relatives, associates and/or family members. Illhealth can be a burden to the individual, family, community and the nation, and it is a probability against which people and the society seek to protect themselves. All illnesses require some typology of treatment, and while this does not necessarily have to be a traditional medical practitioner, curing illness means that the individual must forego consuming something in order to restore his/her good health. Some illnesses such as the common cold may not require a trained medical practitioner to cure, but often the individual will be required to spend money on over-the-counter medications, use a home remedy or utilise non-traditional healers in the quest to restore his/her former healthy state. There are other illnesses such as diabetes mellitus, heart disease, kidney problems, hypertension, HIV/AIDS, sexually transmitted infections, and other chronic and non-

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communicable diseases, which require the attention of traditional medical experts to address their cure. The traditional medical practitioners require payment in the form of cash and/or health insurance coverage. Because individuals desire to restore their health, they are expected to provide payment for health care, which for particular health conditions can be exorbitantly high. It is this reality which may result in premature mortality if the state does not provide health care coverage for those who are economically challenged and/or vulnerable. The World Health Organization (WHO) [1] opined that 80% of chronic illnesses were in low and middle income countries, suggesting that illness interfaces with poverty. The WHO continued that 60% of global mortality was caused by chronic illness, and this should be understood within the context that four-fifths of chronic dysfunctions are in low-to-middle income countries [1]. It also postulated that “In reality, low and middle income countries are at the centre of both old and new public health challenges” [1]. Embedded in the realities outlined by the WHO are the incapacity of the poor, the association between poverty and illness, between poverty and premature mortality, poverty and human suffering, and poverty and future retardation of economic growth, and the fact that health insurance provides some cushion against this, for the individual and for society. Other studies have equally found that there is a significant statistical relationship between poverty and illness [2-4] and poverty and chronic illness, [5] which means that illness can make the vulnerable less likely to survive and the wealthy become poor. The high risk of mortality in developing countries is owing to food insecurity, low water quality and low sanitation coupled with inadequate access to material resources. Poverty makes it an insurmountable hurdle for poor people to effectively address illness unless health care
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services are free. Hence, those in the lower socioeconomic class will be expected to have poorer health, as they are crippled by their material deprivation and low health options. The WHO captures this aptly “... People who are already poor are the most likely to suffer financially from chronic diseases, which often deepen poverty and damage long term economic prospects”. [1] Among the challenges for people living in poverty is access to health insurance coverage. Such a possibility means that the burden of health care is an out-of-pocket payment that cannot be provided by the poor, and this will eliminate life in the process. Cass et al. [6] found that infant mortality in Peru for those in the poorest quintile (i.e. poorest 20%) was almost 5 times more than for those in the wealthiest quintile (i.e. wealthiest 20%). This indicates the extent of the health challenge of the poor, and the role that the lack of health insurance and income play in the demise of individuals and even their children. Another research paper revealed that life expectancy between the poorest 20% and the wealthiest 20% was 6.3 years, and this figure rose to 14.3 years for disability-free life expectancy, [7] suggesting that access and lack of access to resources explain health and healthy life expectancy in and among the social classes in a society. Grossman [8] found a positive correlation between income and health status, indicating that money makes a difference in health, health care-seeking behaviour, physical milieu and health care coverage. Smith and Kington, [9] on the other hand, went further than Grossman when they postulated that money buys health. This viewpoint is somewhat deceptive, as money provides access to good physical milieu, the best health care options, nutrition, dietary choices and health information which are not readily available to the poor, but it does not buy health. Health is not a commodity for sale, and so it cannot be purchased, but money allows for access to better health choices and by
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extension can change health outcomes. Those issues could be the intent of Smith and Kington, when they say that money buys health, and they further exemplify the challenges if an individual does not have access to it. Material deprivation is such that the poor will be far from concerned with health insurance coverage, proper diet and nutrition, health care choices, but more with survivability. This denotes that they will be living on the margins of survivability and the decision to purchase health insurance will be the opportunity cost of food, clothing, shelter, minimal education and health options. Within the context of material and widespread health deprivation for those in the lower socioeconomic strata, the state must play a role in aiding improvements in the healthy life expectancy of those therein. It is through this avenue that public health must act in order to fulfill the aim of the state in improving the quality of life of all residents in the nation. Public health uses information from within and outside the society to improve the health and quality of people’s lives, and this requires continuous research findings. According to the WHO, “In Jamaica 59% of people with chronic diseases experience financial difficulties because of their illness...” Hence, poverty and illness, poverty and chronic illness, and poverty and low access to material resources are well established in research literature, but a dearth of information existed in Latin America and the Caribbean, and in particular Jamaica, on the sick and uninsured. Can we assume that they are all poor people, and use this to plan for them in a developing nation? An extensive review of the literature in developing nations, and in particular Latin America and the Caribbean, did not produce a single study that has examined health, and health care-seeking behaviour among uninsured ill people. The current study aims to narrow this divide by investigating health, self-reported diagnosed health conditions and health care-seeking
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behaviour, at the same time examining who are the unhealthy and uninsured, and modelling factors which account for the moderate-to-very good health status of uninsured ill Jamaicans, in order to provide public health specialists with pertinent information that can be used to address some of the challenges within the society.

Methods and material
Data The current study utilised the latest cross-sectional survey data in Jamaica to examine health, self-reported diagnosed health conditions and health care-seeking behaviour, and to model factors which account for the moderate-to-very good health status of unhealthy and uninsured Jamaicans. The Jamaica Survey of Living Conditions (JSLC) began collecting data from Jamaicans in 1988 and the latest dataset available is for 2007. The JSLC is a modification of the World Bank’s Living Standard Household Survey [10, 11]. This work extracted a sample of 736 respondents who indicated that they were ill and not insured, from a sample of 6,783 respondents [12]. The cross-sectional survey was conducted between May and August 2002 in the 14 parishes across Jamaica, and included 6,783 respondents of all ages. The JSLC used a stratified random probability sampling technique to draw the original sample of respondents, with a non-response rate of 26.2%. The sample was weighted to reflect the population. The design was a two-stage stratified random sampling design where there was a Primary Sampling Unit (PSU) and a selection of dwellings from the primary units. The PSU is an Enumeration District (ED), which constitutes of a minimum of 100 dwellings in rural areas and 150 in urban areas. An ED is an independent geographical unit that shares a common boundary.
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This means that the country was grouped into strata of equal size based on dwellings (EDs). Pursuant to the PSUs, a listing of all the dwellings was made, and this became the sampling frame from which a Master Sample of dwellings was compiled, which in turn provided the sampling frame for the labour force. One third of the 2007 Labour Force Survey (i.e. LFS) was selected for the survey. Study instrument The JSLC used an administered questionnaire where respondents were asked to recall detailed information on particular activities. The questionnaire was modelled on the World Bank’s Living Standards Measurement Study (LSMS) household survey. The questionnaire covered demographic variables, health, education, daily expenses, non-food consumption expenditure, and other variables. Interviewers were trained to collect the data from household members. Statistical methods Descriptive statistics were used to provide socio-demographic characteristics of the sample. Chisquare analyses were used to examine the association between non-metric variables. Analysis of variance was used to test the statistical significance of a metric and non-dichotomous variable. Logistic regression analyses examined 1) the relationship between good health status and some socio-demographic, economic and biological variables; as well as 2) a correlation between medical care-seeking behaviour and some socio-demographic, economic and biological variables. The statistical package SPSS 16.0 was used for the analysis. A p-value less than 5% (2-tailed) was used to indicate statistical significance.

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The correlation matrix was examined in order to ascertain if autocorrelation and/or multicollinearity existed between variables. Based on Cohen and Holliday [13] correlation can be low (weak) - from 0 to 0.39; moderate – 0.4-0.69, and strong – 0.7-1.0. Any variable that had at least moderate (r > 0.6) was re-examined in order to address multicollinearity and/or autocorrelation between or among the independent variables [14-16]. Another approach in addressing collinearity (r > 0.6) was to independently enter variables in the model to determine which one should be retained during the final model construction. The method for retaining or excluding a variable from the model was based on its contribution to the predictive power of the model and its goodness of fit [17]. Wald statistics were used to determine the magnitude (or contribution) of each statistically significant variable in comparison with the others, and the Odds Ratio (OR) for the interpreting of each significant variable. Measurement Health status is a binary measure where 1= moderate-to-very good health; 0= otherwise which is determined from “Generally, how do you feel about your health”? Answers to this question were analyzed on a Likert scale ranging from excellent to poor. Medical care-seeking behaviour was taken from the question ‘Has a health care practitioner, healer, or pharmacist been visited in the last 4 weeks?’ with there being two options: Yes or No. Medical care-seeking behaviour therefore was coded as a binary measure where 1=Yes and 0= otherwise. Crowding is the total number of individuals in the household divided by the number of rooms (excluding kitchen, verandah and bathroom). Sex: This is a binary variable where 1= male and 0= otherwise. Age is a continuous variable which is the number of years alive since birth (using last birthday). Age group is a non-binary measure: children (aged less than 15 years); young adults (ages 15 to 30 years); other-aged adults (ages 31 to 59 years); young elderly (ages 60 to 74 years); old elderly
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(ages 75 to 84 years) and oldest elderly (ages 85 years and older). Social hierarchy: This variable was measured based on income quintile: The upper classes were those in the wealthy quintiles (quintiles 4 and 5); middle class was quintile 3 and the poor were those in the lower quintiles (quintiles 1 and 2). Chronic illnesses: These are ailments or diseases that are prolonged, not likely to be resolved spontaneously, and are infrequently cured. Inequity denotes differences that are unnecessary and avoidable, but are also thought to be unfair and unjust, and these are adjudged based on the context of the customs operating in the society in general. Equity in health means (1) equal access to care for equal needs, (2) equal access to utilisation for equal needs, and (3) equal quality of care for all in the society. Inequalities in health mean patterns of socioeconomic disparities in health outcome which are systematic, avoidable and important within a country. Model The multivariate model used in this study is in keeping with wanting to capture the multidimensional concept of health and the health care-seeking behaviour of uninsured ill people. Utilising logistic regression on secondary cross-sectional data, the present study modelled moderate-to-very good health status and the health care-seeking behaviour of uninsured ill Jamaicans. Using a p-value of less than 0.05 to indicate statistical significance, each model reflects only those variables that are statistically significant.
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Health Model

Hit = f(Ait, Xi, SSit, lnYit, ARit, HSBit, εit) ………………………………. [1]
Health Care-seeking Behaviour Model

Hit = f(Ait, CIit, Hit, lnYit, CRit, MSit, εit) ………………………………. [2]
Where Hti is current moderate-to-very good health status of uninsured ill person i in time period t; Ai is age (in years) of person i in time period t; Xi is gender of person i; SSit is social class of person i in time period t; lnYit is logged income of person i in time period t; ARit is area of residence in time period time t; HSBit is health care-seeking behaviour in time period t; CRi is crowding in the household of person i in time period t; CIit is chronic illness of person i in time period t; MSit is marital status of person i in time period t; εit is residual error of person i - in time period t.

Results
Table 14.1 presents information on the demographic characteristics of the sample. The sample was 736 respondents (i.e. 10.85% of the initial survey) who indicated that they were both sick and uninsured, and of which 40.5% were males. Concurringly, of the sample 95.4% had at most primary level education and 0.8% had tertiary level education. Children constituted 28.7% of the sample; young adults, 10.2%; other adults, 31.3%; young-old, 16.4%; old-old, 10.5%; and oldest-old, 3.0%. The median age was 42.0 years (range = 0 – 99 years). The median total annual expenditure was USD 5,689.89 (range = USD 261.56 – 32,780.78; US$ 1.00 = J$ 80.47 - at the time of the survey). The number of visits made to medical practitioner(s) was 1.4 ± 1.0), while
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the amount of time spent in private care facilities was 3.0 ± 2.8 compared to 5.2 ± 5.0 for public care facilities). The mean cost of public medical care was USD 4.44 ± USD 16.14 compared to USD 13.64 ± USD 28.22 for private medical expenditure. Of those who utilised public health care facilities, 22.9% of them purchased the prescribed medication compared to 78.8% who visited private health care facilities. Table 14.2 highlights information on health care-seeking behaviour, health care utilisation, self-reported illness and area of residence by social hierarchy. Based on Table 14.2, there were significant statistical associations between (1) health care-seeking behaviour and social hierarchy; (2) public health care centre utilisation and social hierarchy, and (3) private health care centre utilisation and social hierarchy. Table 14.3 highlights information on monthly food expenditure, per capita consumption, length of illness, number of visits made to health practitioners, medical expenditure and selfreported diagnosed illness by area of residence. Based on Table 14.3, there were significant statistical associations between (1) monthly food expenditure and area of residence and (2) per capita consumption and area of residence – P < 0.05. However, there were no significant statistical relationships between the other variables and area of residence – P > 0.05. There was a statistical association between health care-seeking behaviour and age group of respondents – χ2 = 11.1, P = 0.048. As uninsured ill people become older, they are more likely to seek medical care: Children, 54.8%; old-adults, 54.8%; other-age adults, 64.0; young-old, 63.3%; old-old, 73.3%; and oldest old, 66.7%. There was a statistical relationship between having chronic illness and being the household head – χ2 = 63.3, P < 0.0001. Almost 55% of those with chronic illnesses were

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household heads, compared to 22.4% who did not have chronic illness but were household heads. A significant statistical association existed between sex and having chronic illness - χ2 = 4.7, P < 0.031. More females had chronic illness (69.8%) than males (61.7%). There was a significant statistical association between health status and typology of illnesses (i.e. acute and chronic conditions) - χ2 = 62.3, P < 0.0001. Thirty-seven percent of those with chronic illnesses reported at least poor health status compared to 12.2% of those with acute conditions. On the other hand, 61.1% of those with acute conditions reported at least good health status compared to 31.3% of those with chronic conditions. A statistical difference was found between the mean income of those in the different social hierarchies – F statistic = 277.50, P < 0.0001. The mean income for those in the poorest 20% was USD 666.07 ± 175.40 followed by the second poor, USD 1,090.68 ± 132.14; middle class, USD 1,489.69 ± 169.07; second wealthy, USD 2,131.55 ± 254.49 and the wealthiest 20%, USD 4,201.39 ± 235.26. Multivariate analysis Table 14.5 shows variables which are correlated (or not) with the moderate-to-very good health status of uninsured ill respondents. Seven variables emerged as significantly associated with moderate-to-very good health status – Model χ2 = 83.70, P < 0.001, -2 Log likelihood = 482.9 – and they accounted for 23% of the variability in health status. The model is a good fit for the data - Hosmer and Lemeshow goodness of fit χ2= 3.72, P = 0.88. Table 14.6 presents information on variables and self-reported health care seeking behaviour of uninsured respondents. Six variables emerged as significant statistical correlates of self-reported health care-seeking behaviour - Model χ2 = 47.9, P < 0.001, -2 Log likelihood =
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486.1. The model is a good fit for the data - Hosmer and Lemeshow goodness of fit χ2= 8.11, P = 0.62.

Discussion
The current research used a sample of respondents who indicated both experiencing ill-health and having no health insurance coverage. Of the sample of respondents (i.e. n = 736), 60 out of every 100 were females, 43 out of every 100 were poor, 35 out of every 100 were in the upper social class, 59 out of every 100 dwelled in rural areas, 3 out of every 100 had been injured during the last 4 weeks, 61 out of every 100 sought medical care, 50 out of every 100 utilised public health care, two-thirds reported being diagnosed with a chronic illness, 31 out of every 100 were elderly, and 29 out of every 100 were children. Those in the lower socioeconomic class were more likely to dwell in rural areas. Those in the poorest 20% were more likely to use public health centres, and the wealthiest 20% were more likely to utilise private health care centres. Fifty-four percent of those in the poorest 20% sought medical care in the last 4 weeks compared to 72% of those in the wealthiest 20%. Concurringly, of the sample, 78.4% indicated at least fair health status. Moderate-to-very good health status was explained by age, sex, social class, income, area of residence and health care-seeking behaviour. Rural residents had the least moderate-to-very good health status among uninsured ill Jamaicans. People who dwelled in Other Towns were 2.3 times more likely to indicate moderate-to-very good health compared to those in rural areas, and those in urban areas were 2.0 times more likely to claim moderate-tovery good health status. Those who indicated having a chronic illness were 37% less likely to report moderate-to-very good health. In addition, the present sample represents 70% of those who indicated having an illness in Jamaica for 2007.

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Statistics from the Planning Institute of Jamaica and the Statistical Institute of Jamaica [10] showed that 15.5% of Jamaicans reported ill-health in 2007. Within the context of the current findings and that of PIOJ and STATIN, it computes that 71% of those who were experiencing illness were without health insurance coverage. Given that 50% of those who claimed to be experiencing ill-health utilised the public health care system and the fact that twothirds of the illnesses were chronic conditions (3 females for every 2 males were uninsured and ill, and 6 out of every 10 uninsured ill people were of the dependent age cohort - less than 15 years or 60+ years), the public health care sector in Jamaica needs to recognize the impending challenges of uninsured unhealthy people. Van Agt et al. [5] found that the chronically ill were more likely to be poor, a statement with which this study concurs. In this paper, 43.2% of the chronically ill were poor (25.2% of poorest 20%) compared to 35.2% of the upper class (15.3% of the wealthiest 20%). This study went further than Van Agt et al.’s work, as the chronically ill were more likely to be elderly (42.5% of the chronically ill were 60+ years), to seek more medical care, were more likely to utilise public health facilities, more likely to live in rural areas (59.1%), more likely to be household heads (54.8%) and more likely to be females (63%). Clearly the poor are highly vulnerable to chronic illness [1, 5] and material deprivation [4], which accounts for more of them not having health insurance coverage while suffering from ill-health. Hence, those who are uninsured and ill must interface with chronic health conditions as well as income deprivation. Income is well established in the health literature as being associated with health [4, 8, 9], and this explains the fact that those in the lower socioeconomic class have poorer health than those in the upper class [18, 19]. This paper found that uninsured ill people with more income are 2.9 times more likely to report moderate-to-very good health status, and they are also more
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likely to seek medical care. The challenge for those in the lower class is more than lower health status; it is also being deprived of the health care that they need. Statistics revealed that poverty in Jamaica is substantially a rural phenomenon (prevalence of poverty in rural areas, 15.3%; semi-urban poverty, 4.0%; urban poverty 6.2%) [10]. This study highlights that those who are ill and uninsured are likely to dwell in rural zones, explaining how financial deprivation accounts for lower ownership of health insurance coverage, the worst health being found among those in rural areas compared to city dwellers. Using per capita consumption to measure income in this study, it was revealed that urban residents had 1.7 times more income than rural residents, and that semi-urban residents had 1.3 times more income than rural dwellers, suggesting that the health disparities between the geographical dwellers is explained by this income inequity. It is therefore this access to more income that accommodates the greater health status of the urban and semi-urban respondents, compared to the rural dwellers, and it highlights a real need to correct income inequality among the socioeconomic groups in the nation. A study by Stronks et al. [20] found an interrelationship between income, health and employment status, which further argues for greater health for urban and semi-urban dwellers, as rural residents are more likely to be seasonally employed, self-employed or have low-income employment. While income is related to better health status, which is also the case among uninsured ill people, concurring with the literature on a population [8, 9, 20], the great health disparity between the different social classes is more related to income than place of residence. Such a finding provides clarification for a study done by Vila et al. [21] which stated that great health disparities in the city of Milwaukee were associated with area of residence by different social hierarchy. Income has a greater influence on better health than area of residence, and it even correlates with health care-seeking behaviour among the uninsured ill, unlike area of residence.
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Money matters in the health of uninsured Jamaicans as well as the general populace, as it offers a better explanation for peoples’ choices, accounting for the greater health of those who are able to choose, than their place of residence. Lack of access to money, therefore, in any geographical locality, explains health and material deprivation. Hence, it is not the fact of being in a rural area that accounts for poor health, but material and other deprivations are greater in rural areas, a factor which provides an understanding for the massive health disparity between them and city residents. Poverty is associated with premature mortality, and the current research provides some explanation for this established fact. This paper is on uninsured ill Jamaicans, and the findings highlighted that 54% of those in the poorest 20% visited a health care practitioner, 58% of the poor compared to 65% of the second wealthy and 72% of the wealthiest 20%. While the affluent class has access to material and other resources to address health concerns, the poor are not as privileged as the upper class. This research found that 70.1% of those in the poorest 20% had at least one chronic health condition, the second poor, 61.2%; the second wealthy, 72.7% and the wealthiest 20%, 68.7%, which means that non-utilisation of medical care is likely to lead to complications and possible premature mortality. The WHO had stated that 60% of global mortality is caused by chronic illness, but clearly poverty, non-treatment of chronic illnesses and cultural practices are all a part of the rationale for mortality, and not merely the condition. Although those who suffer from chronic conditions in Jamaica are able to access public health insurance which can reduce out-of-pocket payments for treatment and medication, clearly the culture prevents some people from accessing this facility. This work showed that a large percentage of uninsured ill people dwelled in rural areas, where poverty was 2.5 times more than urban poverty and 3.8% more than semi-urban poverty, arguing for the role of the culture in
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preventing them from accessing assistance from the state. With this preponderance of unwillingness on the part of poor and rural residents to access health insurance, accompanied by their low demand for health services compared to the wealthy, the inference is that many of them will seek health services based only on severity of illness. Chronic illnesses are such that nonmedical practitioners should not interpret when conditions are serious and warrant health care assistance. It is this culture underpinning that accounts for the premature mortality and not the poverty or illness, as those with chronic health conditions in Jamaica are able to access public health care despite their reluctance to access public health insurance coverage. With not having health insurance coverage, poverty and illness are likely to become a burden to individuals and family, and when those social agents are unable to assist with the costing of medical treatment, it will then become the responsibility of the state. This paper did not examine nutrition and health, but a study by Khetarpal and Kochar [22] found a statistical relationship between nutrition and health in rural women, which offers some explanation for the great health disparities in geographical areas of residence. Another study by Foster [23] on low-income rural areas concurs with Khetarpal and Kochar [22] that nutrition accounts for health or ill-health, as the body requires particular nutrients. It can be extrapolated from the aforementioned studies, to that of the current one, that great disparities in health status among the different geographical areas in Jamaica can be explained by the nutritional intake (or lack of intake) based on where people dwell in this nation. There is a question which must be addressed in order to provide some explanation for the seemingly low nutritional intake of rural uninsured residents: Are rural residents less likely to intake the required nutrients compared to residents in other geographical areas in Jamaica? The answer is clearly yes as more of the uninsured ill Jamaicans are poor, and this means that they will be less
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concerned about the required nutrient intake than food consumption and mere survivability. Poverty is therefore more a factor in insurance, illness, lower health status and health careseeking behaviour than the geographical area of residence, but what about the general health status of the uninsured ill, and is it lower than that of the population of Jamaica? Almost 78 out of every 100 uninsured ill Jamaicans claimed to have at least good health status. A study by Bourne [24] found that 82 in every 100 Jamaicans reported at least good health status, which is greater than that for the uninsured ill people. Furthermore, 3.3 times more Jamaicans indicated very good health compared to the uninsured ill Jamaicans. The health disparities were not only between the good and very good health status of Jamaicans and uninsured ill Jamaicans, but were also evident for poor health status. Comparatively, 4.4 times more uninsured ill Jamaicans claimed at least poor health as compared to the general population (i.e. 4.9%), and 3 times more uninsured chronically ill Jamaicans reported at least poor health status compared to those with acute health conditions. The current study concurs with (1) Reed and Tu’s work [25] that uninsured chronically ill people in America reported lower health status (or worse health) and (2) Bourne and McGrowder [26] which stated that 25.3% of chronically ill Jamaicans reported at least poor health. Reed and Tu went on to state that the majority of uninsured people with chronic illnesses delay health care utilisation owing to cost, which explains an aspect of this study, that although 43.2% of the uninsured ill people were living in poverty (i.e. poorest 20% and second poor income quintile), 39% did not seek medical care. Faced with poverty, no health insurance coverage and chronic illness, uninsured ill Jamaicans are highly likely to face all kinds of life challenges such as material deprivation, dietary and nutritional deficiencies, high risk of health complications, high out-of-pocket medical bills, disruptions in family life, future vulnerabilities and premature mortality. When this burden
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becomes untenable for the individual, family and wider community, it will then become the responsibility of the state [27]. This justifies the need to expand public health insurance to protect the poor, the chronically ill and the vulnerable in a society [28], as chronic illness can erode the economic livelihood of an individual and therefore delay needed health care [29]. One study stated that uninsured households are one illness away from financial catastrophe [30], indicating that if a household was already in poverty this will become the burden of the state or may lead to premature mortality, as the individual will be unable to access needed health care owing to his/her inability to afford medical care. This implies that poverty encapsulates powerlessness, physical weakness, illness, chronic illness, premature mortality, lack of productive assets, emotional distress, constricted freedom and future impoverishment due to the aforementioned conditions, if they are not addressed by policy makers. While impoverishment in urban areas is highly visible in the form of squalor, dilapidated edifices, zinc fencing, improper sanitation, squatting and violence, rural poverty is less easily identifiable and may be overlooked by the naked eye. Clearly, using health disparities between area of residence and the socioeconomic strata, rural poverty in Jamaica is showing signs of depleting the human capital more than urban poverty. According to Harpham and Reichenheim [31], on the disaggregating of rural and urban health indicators, the latter ‘appear’ to have better health status. This study dispels the notion of ‘appearance’ and goes to the reality of the health differential using self-reported health among urban, semi-urban and rural uninsured ill Jamaicans. The discipline of public health cannot only use external findings to carry out its mandate, or divorce itself from the realities which emerge from the current study; poverty is destroying the human capabilities and resilience of the Jamaican people and more so in the case of rural uninsured ill people. Because poverty is strongly associated with illness, and illness can
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result in poverty [32-34], those who are presently uninsured, ill and poor are highly vulnerable to ill-health and premature mortality, which argues for an immediate health campaign to address the challenges among the socioeconomic strata and area of residence, as these were not alleviated with the introduction of the National Health Fund – NHF [35]. The NHF is a statutory company which was established by the NHF Act (2003) with a Chairman and Board of Management appointed by the Minister of Health. It was established in 2003 to provide direct assistance to patients with chronic conditions, to purchase drugs and fund support to private and public companies for approved projects [35]. The NHF is a social health insurance which is geared towards alleviating out-of-pocket payments for medication for those who suffer from chronic illnesses. Fourteen chronic illnesses are covered by the NHF, with respect to pharmaceutical benefits in direct assistance to ill individuals. The chronic health conditions that are covered by the NHF are hypertension, diabetes mellitus, breast cancer, prostate cancer, glaucoma, arthritis, asthma, high cholesterol, rheumatic heart disease, major depression, epilepsy, psychosis, ischemia and vascular diseases. The NHF became operational in August 2003, and has undoubtedly aided many chronically ill, non-poor and poor Jamaicans. With all the investment, the NHF has not failed to have a major coverage of chronically ill respondents using the Fund. The individuals are mostly rural residents, poor, under 60 years of age, and female. Such a reality speaks to the administrative and operational failure of the NHF to improve the lives of its intended population owing to the centralization of its operations in Kingston, which is an urban area in Jamaica. The verdict is in, that merely instituting an agency to carry out a particular task (which is to distribute benefits evenly across the socioeconomic strata, area of residence and sex) will not provide solutions to the inequalities and inequities in health between the particular groups in Jamaica. This study concurs with one in Finland [36]
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showing that the poor are more vulnerable to illnesses, and research conducted in the United Kingdom [37] found that those in the lower socioeconomic stratum were more likely to die prematurely than those in the upper income groups. Embedded in those findings is the fact that any equitable distribution of NHF benefits to those in the different socioeconomic strata will show further unfairness and injustices in the health outcomes which already exist, owing to income inequalities.

Conclusion
Two-thirds of uninsured ill Jamaicans are chronically ill. The uninsured ill are mostly within the dependent age cohort (children and elderly), they are female and are rural respondents who are generally poor people. With one half of the uninsured ill respondents utilising the public health care system, and only 2 in every 10 of them purchasing medications, there are serious future challenges for public health in Jamaica. There is an inverse relationship between the health status of uninsured ill Jamaicans and those in socioeconomic strata. The findings of this study highlight the likely challenge of the state in assisting uninsured ill Jamaicans. Despite the fact that health insurance coverage is freely accessible to those who are chronically ill in Jamaica, there are still many such people who are without health insurance coverage, and some are not even seeking medical care. Another reality which emerged from this paper is that although health care utilisation is free in Jamaica for children 18 years and younger, 45 out of every 100 of those uninsured and ill did not seek medical care, emphasizing people’s interpretation of illnesses that require medical attention, and how this retards health care demand. The task of public health specialists and policy makers, therefore, is to fashion public education and intervention programmes that will address many of the realities which emerged in this research. The great health disparity between the lower socioeconomic strata and those in the upper strata, as well as
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those who reside in rural areas, cannot be left to resolve itself, as clearly it has not happened in the past and the situation cannot be allowed to continue indefinitely in the future. The Way Forward The variations in health status and health care-seeking behaviour within and between the socioeconomic strata who are uninsured ill people, clearly present information that reveals public health concerns, and highlights many challenges which are still unresolved in Jamaica. The current study did not examine the emotional distress and mortality patterns of uninsured ill respondents, and this should be the subject of some future study, as it would provide needed information about these individuals. Despite the investments in health, the health sector and poverty alleviation programmes in Latin America and the Caribbean, there is still a need to study the heterogeneity in health outcome between the socioeconomic strata and area of residence, as health disparity between and within countries is still great and not in keeping with health inequality eradication in the region. Another unresolved issue stemming from the present research is how much of the cognitive dimension explains the health differential between the socioeconomic strata and the area of residence. In order to understand how to address policy intervention and health education programmes for people in Jamaica, studies need to examine the breadth and scope of cognitive dimensions in explaining health inequalities. This will allow public health technocrats to understand why 70.3% of those who were ill in Jamaica in 2007 did not have health insurance, and some of the chronically ill people, despite having access to public health insurance, did not possess such insurance, and did not seek medical care. A critical issue which needs to be addressed in the future is the structure of the National Health Fund (the NHF is accessible to, and provides public health insurance coverage for, those experiencing chronic illnesses). Barrett and Lalta [32] wrote that “The National Health Fund dealt with these issues by
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treating the non-poor and the poor as part of the same target beneficiary. Survey data and health officials indicate that the poor suffer as much from chronic diseases as the rich, but are less likely to seek treatment, or are only able to pay for part of their prescription drugs by reducing out-ofpocket payment …” This study is 4 years after the operational establishment of the NHF, and new findings are coming in, which show that the NHF cannot treat different socioeconomic strata in the same way, neither can it deal equitably with those who reside in different geographical areas. The health disparities will not be addressed by merely offering equal

benefits to all within the context of the current findings, as these will only perpetuate health inequalities and inequities. The NHF therefore needs to be restructured in order to provide definitions based on socioeconomic class and area of residence, so as to effectively alleviate some of the challenges which emerged from this research.

Conflict of interest
The author has no conflict of interest to report.

Disclaimer
The researcher would like to note that while this study used secondary data from the Jamaica Survey of Living Conditions, 2007, none of the errors that are within this paper should be ascribed to the Planning Institute of Jamaica or the Statistical Institute of Jamaica as they are not theirs, but are instead owing to the researcher.

References
1. World Health Organization, WHO. Preventing Chronic Diseases a vital investment. Geneva: WHO; 2005: p. 9 2. WHO. Dying for change - Poor peoples experience of health and ill health. Retrieved on 29th October from http://www.who.int/hdp/publications/en/index.html. 3. Wagstaff A Poverty, equity, and health: Some research findings. In: Equity and health: Views from Pan American Sanitary Bureau. Pan American Health Organization, Occasional publication No. 8, Washington DC, US; 2001: pp.56-60.

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4. Marmot M. The influence of Income on Health: Views of an Epidemiologist. Does money really matter? Or is it a marker for something else? Health Affairs. 2002; 21: 3146. 5. Van Agt HME, Stronks K, Mackenbach JP. Chronic illness and poverty in the Netherlands. Eur J of Public Health 2000; 10:197-200. 6. Casas JA, Dachs JN, Bambas A. Health disparity in Latin America and the Caribbean: The role of social and economic determinants. In: Pan American Health Organisation. Equity and health: Views from the Pan American Sanitary Bureau, Occasional Publication No. 8. Washington DC; 2001: pp. 22-49. 7. Pate E, Collado C, Solis JA. Health equity and maternal mortality. In: Equity and health: Views from Pan American Sanitary Bureau. Pan American Health Organization, Occasional publication No. 8, Washington DC, US, 2001: pp.85-98. 8. Grossman M. The demand for health - a theoretical and empirical investigation. New York: National Bureau of Economic Research, 1972.
9. Smith JP, Kington R.  Demographic and Economic Correlates of Health in Old Age.  Demography  1997; 34:159‐70. 

10. Planning Institute of Jamaica (PIOJ), Statistical Institute of Jamaica (STATIN). Jamaica Survey of Living Conditions, 1988-2007. Kingston: PIOJ & STATIN; 1989-2008. 11. World Bank, Development Research Group, Poverty and human resources. Jamaica Survey of Living Conditions (LSLC) 1988-2000: Basic Information. Washington DC; 2002. Retrieved on August 14, 2009, from, http://www.siteresources.worldbank.org/INTLSMS/Resources/.../binfo2000.pdf 12. Statistical Institute Of Jamaica. Jamaica Survey of Living Conditions, 2007 [Computer file]. Kingston, Jamaica: Statistical Institute of Jamaica [producer], 2007. Kingston, Jamaica: Planning Institute of Jamaica and Derek Gordon Databank, University of the West Indies [distributors], 2008. 13. Cohen L, Holliday M. Statistics for Social Sciences. London: Harper & Row; 1982. 14. Hair JF, Black B, Babin BJ, Anderson RE, Tatham RL. Multivariate data analysis, 6th ed. New Jersey: Prentice Hall; 2005. 15. Mamingi N. Theoretical and empirical exercises in econometrics. Kingston: University of the West Indies Press; 2005. 16. Cohen J, Cohen P. Applied regression/correlation analysis for the behavioral sciences, 2nd ed. New Jersey: Lawrence Erlbaum Associates; 1983. 17. Cohen J, Cohen P. Applied regression/correlation analysis for the behavioral sciences, 2nd ed. New Jersey: Lawrence Erlbaum Associates; 1983. 18. Fox J, ed: Health inequalities in European Countries. Aldershot: Gower Publishing Company Limited; 1989. 19. Illsley R, Svensson PG, eds: Health inequities in Europe. Soc Sci Med 1990; 31(special issue):223-420. 20. Stronks K, Van De Mheen H, Van Den Bos J, MacKenbach JP. The interrelationship between income, health and employment status. Int J of Epidemiol 26:592-600. 21. Vila PM, Swain GR, Baumgardner DJ, Halsmer SE, Remington PL, Cisler RA. Health disparities in Milwaukee by socioeconomic status. Wisconsin Med J 2007; 106:366-372. 22. Khetarpal A, Kochar GK. Health and well-being of rural women. The Internet Journal of Nutrition and Wellness 2007; 3.
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23. Foster AD. Poverty and illness in Low-Income Rural Areas. The Am Economic Review 1994; 84:216-220. 24. Bourne PA. Dichotomising poor self-reported health status: Using secondary crosssectional survey data for Jamaica. North American J of Med Sci. 2009; 1(6): 295-302. 25. Reed MC, Tu HT. Triple jeopardy: low income, chronically ill and uninsured in America. Issue Brief Cent Stud Health Syst Change 2002 ;( 49):1-4. 26. Bourne PA, McGrowder DA. Health status of patients with self-reported chronic diseases in Jamaica. North American J of Med Sci. 2009; 1(7): 356-364. 27. Becker G. The uninsured and the politics of containment in U.S. health care. Med Anthropol. 2007; 26(4):293-8. 28. Tu HT, Reed MC. Options for expanding health insurance for people with chronic conditions. Issue Brie Cent Stud Health Syst Change 2002; 50:1-4. 29. Tu HT, Cohen GR. Financial and health burdens of chronic conditions grow. Track Rep 2009; 24:1-6. 30. Cook K, Dranove D, Sfekas A. Does major illness cause financial catastrophe? Health Serv Res 2009. [Epub]. 31. Harpam T, Reichenmeim M. Urbanisation and health. In: Lankinen KS, Bergstrom S, Makela PH, Peltomaa M. Health and disease in developing countries. London and Oxford: MacMillan; 1994: pp. 85-94. 32. Wagstaff A Poverty, equity, and health: Some research findings. In: Equity and health: Views from Pan American Sanitary Bureau. Pan American Health Organization, Occasional publication No. 8, Washington DC, US; 2001: pp.56-60. 33. Bourne PA. Impact of poverty, not seeking medical care, unemployment, inflation, selfreported illness, health insurance on mortality in Jamaica. North American Journal of Medical Sciences 2009; 1(3):99-109. 34. Alleyne GAO. Equity and health. In: Equity and health: Views from Pan American Sanitary Bureau. Pan American Health Organization, Occasional publication No. 8, Washington DC, US; 2001: pp.3-11. 35. Barrett R.D., Lalta S. Health financing innovations in the Caribbean: EHPO and the National Health Fund of Jamaica. New York and Washington DC: Sustainable Development Department, Technical Paper Series, Inter-American Development Bank; 2004.
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36. Kalimo E, et al. Need, use and expenses of health services in Finland, 1974-76. Helsinki, Social Insurance Institution; 1983. 37. Phillmore P. Shortened lives: premature death in North Tyneside. Bristol, University of Bristol; 1989 (Briston Papers in Applied Social Studies No. 12).

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Table 14.1. Demographic characteristic of sample, n=736 Characteristic Sex Male Female Marital status Married Never married Divorced Separated Widowed Social hierarchy Poorest 20% Second poor Middle Second wealthy Wealthiest 20% Area of residence Urban Semi-urban Rural Injury in last 4-weeks Yes No Self-reported diagnosed illness Acute conditions Influenza Diarrhoea Asthma Chronic conditions Diabetes mellitus Hypertension Arthritis Other Health care-seeking behaviour Yes No Health care utilization Public hospital (yes) Private hospital (yes) Public health care centres (yes) Private health care centres (yes) Other (yes) Purchased medication Yes

n 298 438 161 276 14 10 62 170 146 165 142 111 176 128 432 23 712 124 26 73 69 147 40 189 446 280 146 27 96 212 8 411

% 40.5 59.5 30.8 52.8 2.7 1.9 11.9 23.1 19.8 22.4 19.3 15.4 23.9 17.4 58.7 3.1 96.9 18.6 3.9 10.9 10.3 22.0 6.8 28.3 61.4 38.6 29.9 5.5 19.6 43.4 1.6 58.6

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Table 14.2. Health care-seeking behaviour, health care utilization, self-reported illness and area of residence by social hierarchy
Characteristic Poorest 20% n (%) Second poor n (%) Social hierarchy Middle n (%) Second wealthy n (%) Wealthiest 20% n (%)

P 0.046

Health care-seeking behaviour Yes No Health care utilization Public hospitals Yes No Private hospitals Yes No Public health care centres Yes No Private health care centres Yes No
Self-reported diagnosed illness

90(54.2) 76(45.8)

86(59.7) 58(40.3)

98(60.1) 65(39.9)

91(65.0) 49(35.0)

81(71.7) 32(28.3) 0.337

32(37.2) 54(62.8) 5(5.7) 83(94.3) 29(33.3) 59(67.0) 28(31.5) 61(68.5)

30(35.3) 55(64.7) 5(5.9) 80(94.1) 21(24.7) 64(75.3) 35(41.2) 50(58.8)

35(35.7) 63(64.3) 3(3.1) 95(96.9) 21(21.4) 77(78.6) 49(50.0) 49(50.0)

30(33.7) 59(66.3) 6(6.7) 83(93.3) 15(17.0) 73(83.0) 52(58.4) 37(41.6)

19(23.5) 62(76.5) 0.451 8(9.9) 73(90.1) 0.016 10(12.3) 71(87.7) 0.001 48(59.3) 33(40.7) 0.200

Acute conditions Influenza Diarrhoea Asthma Chronic conditions Diabetes mellitus Hypertension Arthritis Other Area of residence Urban Semi-urban Rural Length of illness (i.e. in days) mean± SD

24(15.0) 3(1.9) 21(13.1) 15(9.4) 38(23.8) 15(9.4) 44(27.5)

25(19.1) 9(6.9) 17(13.0) 15(11.5) 23(17.6) 8(6.1) 34(26.0)

34(22.7) 7(4.7) 17(11.3) 9(6.0) 37(24.7) 7(4.7) 39(26.0)

26(20.3) 3(2.3) 6(4.7) 15(11.7) 27(21.1) 6(4.7) 45(35.2)

15(15.2) 4(4.0) 12(12.1) 15(15.2) 22(22.2) 4(4.0) 27(27.3) <0.0001 59(52.2) 21(18.6) 33(29.2) 14.9±21.8

19(11.2) 21(14.4) 35(21.2) 42(29.6) 16(9.4) 25(17.1) 30(18.2) 36(25.4) 135(79.4) 100(68.5) 100(60.6) 64(45.1) 10.6±11.6 12.9±22.7 11.1±15.9 31.5±116.3

0.006

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Table 14.3. Monthly food expenditure, per capita consumption, length of illness, number of visits made to health practitioner, medical expenditure and self-reported diagnosed illness by area of residence Area of residence Characteristic P Urban Semi-urban Rural n (%) n (%) n (%) 280.71±192.00 277.45±162.97 237.07±145.59 †Monthly food expenditure 0.002 mean ± standard deviation 2425.23±1992.1 1923.62±1241.6 1441.30±1179.8 < 0.0001 Per capita consumption mean ± standard deviation 8 0 5 9.5±19.1 13.5±23.0 17.7±65.4 Length of illness in day 0.256 mean ± standard deviation 1.4±0.7 1.4±1.3 1.4±1.0 Number of visits made to 0.927 health care practitioner in last 4-weeks mean ± standard deviation †Medical expenditure Public 3.47±7.07 4.72±16.51 4.78±18.65 0.787 mean ± standard deviation Private 13.58±13.21 15.38±15.60 13.14±35.37 0.851 mean ± standard deviation Self-reported diagnosed 0.162 illness Acute conditions Influenza 19(12.3) 34(28.8) 17(17.9) Diarrhoea 3(1.9) 4(3.4) 19(4.8) Asthma 21(13.6) 9(7.6) 43(10.9) Chronic conditions Diabetes mellitus 16(10.4) 13(11.0) 40(10.1) Hypertension 37(24.0) 24(20.3) 86(21.7) Arthritis 10(6.5) 6(5.1) 24(6.1) Other 48(31.2) 28(23.7) 113(28.5)
†Quoted in USD (USD 1.00 = Ja. $ 80.47 at the time of the survey)

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Table 14.4. Self-reported diagnosed health conditions of uninsured ill respondents by age cohort Age cohort Characteristic Children Young adults Other-aged Young old adults n (%) n (%) n (%) n (%) Self-reported diagnosed illness Acute conditions Influenza 83(45.6) 10(15.6) 19(9.1) 6(5.1) Diarrhoea 13(7.1) 2(3.1) 6(2.9) 2(1.7) Asthma 42(23.1) 11(17.2) 13(6.2) 4(3.4) Chronic conditions Diabetes mellitus 1(0.5) 2(3.1) 32(15.3) 21(17.9) Hypertension 0(0.0) 4(6.3) 55(26.3) 41(35.0) Arthritis 0(0.0) 0(0.0) 12(5.7) 18(15.4) Other 43(23.6) 35(54.7) 72(34.4) 25(21.4)

Old-old n (%)

Oldest-old n (%)

P < 0.0001

6(8.1) 2(2.7) 2(2.7) 10(13.5) 36(48.6) 9(12.2) 9(12.2)

0(0.0) 1(4.5) 1(4.5) 3(13.6) 11(50.0) 1(4.5) 5(22.7)

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Table 14.5: Logistic regression: Variables of moderate-to-very good health status of uninsured ill respondents Variable Age Average Medical Expenditure Male Middle Class Upper class †Lower class Married Divorced, separated or widowed †Never married Logged Income Urban area Other town †Rural area Head household Dummy health care-seekers Chronic illness
Model χ2 (12) = 83.70, P < 0.001 -2 Log likelihood = 482.96 Nagelkerke R2 = 0.23

Coefficien t -0.033 0.000 -0.511 -0.807 -1.029 0.140 -0.421

Std. Wald Error statistic 0.008 18.605 0.000 0.244 0.387 0.553 0.278 0.349 1.668 4.374 4.345 3.465 0.253 1.455

95.0% C.I. Odds ratio 0.967*** 1.00 0.60* 0.45* 0.36 1.00 1.15 0.66 1.00 0.95 - 0.98 1.00 - 1.00 0.37 - 0.97 0.21 - 0.95 0.12 - 1.06 0.67 - 1.98 0.33 - 1.30

1.053 0.696 0.844 0.218 -0.803 -0.456

0.332 0.300 0.342 0.250 0.255 0.351

10.063 5.365 6.092 0.761 9.882 1.696

2.87** 2.01* 2.33* 1.00 1.24 0.45** 0.63*

1.50 - 5.49 1.11 - 3.62 1.19 - 4.54 0.76 - 2.03 0.27 - 0.74 0.32 - 0.86

Hosmer and Lemeshow goodness of fit χ2= 3.72, P = 0.88
Overall correct classification = 75.1% Correct classification of cases of self-rated moderate-to-very good health status = 93.4% Correct classification of cases of not self-rated not moderate-to-very good health status = 26.5% †Reference group *** P < 0.0001, **P < 01, *P < 0.05

 

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Table 14.6: Logistic regression: Variables of self-reported health care-seekers of uninsured ill respondents Variable Chronic illness Age Moderate-to-very good health Secondary education Tertiary education †Primary and below education Male Crowding Logged income Length of illness Married Divorced, separated, or widowed †Never married Urban area Other town †Rural area
Model χ2 (13) = 47.85, P < 0.001 -2 Log likelihood = 486.1 Nagelkerke R2 = 0.15

Coefficient 0.812 0.024 -0.857 1.117 1.278

Std. Error 0.277 0.008 0.281 0.762 1.222

Wald statistic Odds ratio 8.609 2.25** 9.593 9.274 2.148 1.094 1.03** 0.42** 3.06 3.59 1.00

95% CI 1.31 - 3.88 1.01 - 1.04 0.24 - 0.74 0.69 - 13.60 0.33 - 39.42

-0.358 0.114 0.000 0.000 -0.733 -0.692

0.244 0.053 0.000 0.002 0.274 0.384

2.154 4.694 4.138 0.013 7.181 3.248

0.70 1.12* 1.00* 1.00 0.48** 0.50 1.00

0.43 - 1.13 1.01 -1.24 1.00 - 1.00 1.00 - 1.00 0.28 - 0.82 0.24 - 1.06

0.171 -0.336

0.286 0.302

0.359 1.238

1.19 0.72 1.00

0.68 - 2.08 0.41 - 1.29

Hosmer and Lemeshow goodness of fit χ2= 8.11, P = 0.62
Overall correct classification = 69.0% Correct classification of cases of self-reported health care-seekers = 89.4% Correct classification of cases of self-reported health care-nonseekers = 32.2% †Reference group *** P < 0.0001, **P < 01, *P < 0.05

 

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Chapter 15
Determinants of self-rated private health insurance coverage in Jamaica

Paul A. Bourne & Maureen D. Kerr-Campbell The purpose of the current study was to model the health insurance coverage of Jamaicans; and to identify the determinants, strength and predictive power of the model in order to aid clinicians and other health practitioners in understanding those who have health insurance coverage. This study utilized secondary data taken from the dataset of the Jamaica Survey of Living Conditions which was collected between July and October 2002. It was a nationally representative stratified random sample survey of 25,018 respondents, with 50.7% females and 49.3% males. The data was collected by way of a self-administered questionnaire. The non-response rate for the survey was 29.7% with 20.5% not responding to particular questions, 9.0% not participating in the survey and another 0.2% being rejected due to data cleaning. The current research extracted 16,118 people 15 years and older from the survey sample of 25,018 respondents in order to model the determinants of private health insurance coverage in Jamaica. Data were stored, retrieved and analyzed using SPSS for Windows 15.0. A p-value of less than 0.05 was used to establish statistical significance. Descriptive analysis was used to provide baseline information on the sample, and cross-tabulations were used to examine some non-metric variables. Logistic regression was used to identify, determine and establish those factors that influence private health insurance coverage in Jamaica. This study found that approximately 12% of Jamaicans had private health insurance coverage, of which the least health insurance was owned by rural residents (7.5%). Using logistic regression, the findings revealed that twelve variables emerged as statistically significant determinants of health insurance coverage in this sample. These variables are social standing (two wealthiest quintile: OR=1.68, 95% CI=1.23-2.30), income (OR=1.00, 95%CI=1.00-1.00), durable goods (OR=1.16, 95%CI=1.12-1.19), marital status (married: OR=1.97, 95%CI=1.61-2.42), area of residence (Peri-urban: OR=1.45, 95%CI=1.1991.75; urban: OR=1.83, 95%CI=1.40-2.40), education (secondary: OR=1.57, 95%CI=1.20-2.06; tertiary: OR=9.03, 95%CI=6.47-12.59), social support (OR=0.64, 95%CI=0.53-0.76), crowding (OR=1.14, 95%CI=1.02-1.28), psychological conditions (negative affective: OR=0.97, 95%CI=0.94-1.00; positive affective: OR=1.11, 95%CI=1.06-1.16), number of males in household (OR=0.85, 95%CI=0.77-0.93), living arrangements (OR=0.62, 95%CI=0.41-0.92) and retirement benefits (OR=1.55, 95%CI=1.03-2.35). This study highlighted the need to address preventative care for the wealthiest, rural residents and the fact that social support is crucial to health care, as well as the fact that medical care costs are borne by the extended family and other social groups in which the individual is (or was) a member, which explains the low demand for health insurance in Jamaica. Private health care in Jamaica is substantially determined by affordability and education rather than illness, and it is a poor measure of the health care-seeking behaviour of Jamaicans.

 

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1. INTRODUCTION

Literature on private health insurance or health insurance in the Caribbean, and in particular Jamaica, has been substantially on (i) population density – i.e. coverage, (ii) coverage offerings, (iii) cost of care – i.e. health economics, and (iv) acceptance (or lack of) by health service providers of certain insurance coverage. Having extensively perused the literature review on private health insurance and health care reform in Jamaica, it is obvious that no study has been conducted identifying the different factors that explain health insurance coverage in this nation. The individual utilization pattern of health insurance coverage is highly associated over time with older adults [1, 2] as they prepare for the degeneration of the body; but, what else do we know about those who have private health insurance in Jamaica? Do insurers attract healthy patients, and are high risk individuals more likely to become insured as against their low risk (i.e. less health conditions) counterparts? Health insurance is a constituent of health seeking

behaviour, suggesting that it is equally important in any study of health, quality of life, and wellbeing. In this study the researchers will critically examine factors that can be used to predict private health insurance coverage by using a logistic regression technique to explain the independent effect; and in the process the researchers will investigate the lives of respondents in order to understand those who reported having private health insurance coverage. Instead of providing an elaborate and extensive description of ‘health insurance’, we will give a simplified meaning of this construct. Health insurance is protection against medical costs owing to the possibility of injuries, dysfunctions and other happenings that hinder the body from performing at some functional standard. In keeping with this definition, a health insurance policy is the contract that is signed by an insurer (i.e. insurance provider) and an individual or a

 

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group, in which the insurer agrees to pay a specific sum (i.e. a premium). Hence, the population’s health service is partially dependent on health insurance coverage or the welfare system of the state. Jamaica does not have a public health insurance system, but one for the elderly and those who have particular chronic health conditions, such as diabetes mellitus, hypertension, cancer or a combination. In September 2001, the Cabinet of Jamaica accepted and approved a proposal for the establishment of a National Health Fund (NHF) that would assists patients as well as the elderly in Jamaicans. The individual benefits of the NHF (i.e. public health insurance options) for the elderly and for those with particular chronic health conditions was officially commenced in 2003 (i.e. August 1, 2003), and so there are only data on private health insurance coverage from 1988-2002. Despite the fact that Jamaica has instituted a free healthcare service delivery programme for its child population (below 18 years in 2006), the quality of care which is relatively good is still surrounded by a certain socio-psychological milieu as well as inequality in health care offerings in the private versus the public sector. This explains the rationale why some people seek private health care and by extension private health insurance coverage [3] to meet the impending higher medical cost of care [1, 4-7] and a particular quality of service – environment, customer service and length of service. The current study will be examined within the theoretical framework used by Franc, Perronnin, & Pierre. [8] 1.2 Theoretical Framework A South African Health Inequalities Survey (SANHIS) carried out in 1994 of 3,489 women ages 16 to 64 years was used to model the determinants of health insurance coverage. Kirigia et al. [8] sought to model health insurance demand among South African women. They used binary logistic regression analyses to estimate health insurance coverage among the sample and various determinants of health insurance coverage. Health insurance coverage of the sample was

 

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determined by socio-demographic characteristics, health rating, environment rating, bad health choices (i.e. smoking and alcohol consumption), and contraceptives. These were embodied in the mathematical formula, (Eq. (1) : Pij= (α + β1Health rating + β2Environment rating + β3 Residence + β4 Income + β5 Education + β6Age + β7 Age squared + β8 Race + β9 Household size + β10 Occupation + β11 Employment + β12Smoking + β13 Alcohol use + β14Contraceptive use = β15Marital status + εi ……………Eq. (1)

where Pij = 1 if individual I owns insurance (j=1) and equal otherwise (j=0); α is intercept terms; (β’s) are the estimated coefficients; and εi is the stochastic error term. The conceptual framework of Kirigia et al.’s work [8] was on two risks of health care. They believed that these risks are (1) the risk of becoming ill, with the associated loss in quality of life, cost of medical care, loss of productive times, more serious cases, mortality, and (2) the risk of total or incomplete or delayed recovery [8]. This denotes that a person’s decision to buy health insurance would be based on differentials between the level of expected utility of the insurance and the expected utility without insurance. It is this binary nature dependent variable and the desire to determine the effect of particular independent variables that justified the binary logistic regression technique. Eq. (1) allows for the estimation of the individual probability of having or not having health insurance by some explanatory variables. Kirigia et al. [8] did not stipulate whether health insurance was public or private coverage, and this was addressed in another research paper. Using the same principle of econometric analysis as Kirigia et al, a group of researchers used a single multiple regression equation that identified explanatory variables and the powers of particular factors that can be used to determine determinants of those who have private health

 

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insurance [9]. This captures a standard utility theory model of a demand for private health insurance coverage, Eq. (2): Y = β0+ β1P+β2I+β3Z ………………………………..…………………………..Eq (2) where the standard utility theory is expressed in the quantity demanded of health insurance,
Y, can be written as a function of the user price of health insurance, P, income, I, and a vector of other factors, Z or (with time subscripts suppressed); and β1 and β2 represent, respectively, the price and income elasticity of the demand for private health insurance.

Like Kirigia et al., [8] self-rated private health insurance coverage is a binary variable (1= yes and 0= otherwise), which denotes that a logistic regression model will be used to estimate the determinants and determine their impact on the dependent variable, as was done by Ahking, Giaccotto, and Santerre [9] - Eq. (3). Instead of having a vector factor which envelopes individual characteristics, this research isolates those factors including income, unlike Eqs. (1) and (2), and added more variables such as psychological conditions, living arrangements and social support.

HIi = ƒ(Yi, HCi, Eni, MSi, ARi , Ei, SSi, Oi, Pi, Gi, NPi, PPi, Mi, Fi , Di, EWi , Ai, Ri, YPi, Pmci, LLi, CRi,)……………………………………………………………………………………………..…. .(3) where Eq (3) is Private Health Insurance coverage, HIi, is a function of Yi is average current income per person in household i; HCi is health conditions of person i; Eni is physical environment of person i; MSi is marital status of person i; ARi is area of residence of person i; Ei is educational level of person i; SSi is social support of person i; Oi is average occupancy per person i; Pi is property ownership of person i; Gi is gender per person i; NPi is negative affective psychological conditions per person i; PPi is positive affective psychological conditions per

 

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person i; Mi is number of males per household per person i; Fi is number of females per household per person i; Di is the number of children per household per person i; EWi is durable goods; Ai is age of person i; Ri is retirement benefits of person i; YPi is social standing of person i; Pmci is cost of medical care of person i, LLi is living arrangements of person i; and CRi is crowding. The current study found the following determinants of private health insurance of Jamaica (Eq (4)): HIi = ƒ (Yi, ARi, MSi, SSi, Ei, ∑(NPi, PPi), Mi , EWi, Ri, YPi,LLi,CRi,)..........................(4) where Eq (4) is Private Health Insurance Coverage, HIi, is a function of Yi is average current income per person in household i; HCi is health conditions of person i; ARi is area of residence of person i; MSi is marital status of person i; SSi is social support of person i; Gi is gender per person i; Ei is educational level of person i; NPi is negative affective psychological conditions per person i; PPi is positive affective psychological conditions per person i; EWi is durable goods of person i; Di is the number of children per household per person i; Ri is retirement benefits of person i, YPi is social standing of person i, LLi is living arrangements and CRi is crowding. 2. MATERIALS AND METHODS 2.1 Method This study utilized secondary data taken from the dataset of the Jamaica Survey of Living Conditions which was collected between July and October 2002. It was a nationally

representative stratified random sample survey of 25,018 respondents, with 50.7% females (N=12,675) and 49.3% males (N=12,332). The data was collected by way of an administered
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questionnaire. The non-response rate for the survey was 29.7% with 20.5% not responding to particular questions, 9.0% not participating in the survey and another 0.2% being rejected due to data cleaning. The current research extracted a sub-sample of 16,118 people 15 years and older from the survey sample of 25,018 respondents in order to model the determinants of private health insurance coverage in Jamaica. The rationale for the use of the 2002 data set instead of the 2007 is primarily because of the sample population. In 2002, the institutions that were principally responsible for the data collection used 10% of the national population to gather pertinent data on the labour force, and this was for the Survey of Living Conditions. It represents the largest data collected on the Jamaican population, and data was also collected on crime and victimization and the environment, these being included for the first time, and omitted in subsequent surveys. Given the nature of crime, violence and victimization in the nation, we opted to use a survey that had crime and the environment as among data collected. Another condition for the selection of this dataset was the fact that it was a large population, as against other years when the population was less than 3,000. Within the context of a non-response rate that ranges from 10 to 30 per cent, a larger rather than a smaller sample size coupled with some pertinent variables was preferred to a smaller sample size without the two critical aforementioned variables. Data were stored, retrieved and analyzed using SPSS for Windows 15.0. A p-value of less than 0.05 was used to establish statistical significance. Descriptive analysis will be done on the sampled population in order to provide background information on the respondents; and the enter method of logistic regression will be used to establish the determinants of self-reported private health insurance in Jamaica. Using the principle of parsimony, the final model will consist of only those statistically significant variables. Where multicollinearity existed (r > 0.7), variables were independently

 

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entered into the model to aid in determining which one should be retained during the final model construction (i.e. the decision therefore was based on the variable’s contribution to the predictive power of the model and the goodness of fit). 2.2 Measure Health conditions: The summation of reported ailments, injuries or illnesses in the last four weeks, which was the survey period; where higher values denote greater health conditions; it ranges from 0 to 4 conditions. Health status is a dummy variable, where 1 (good health) = not reporting an ailment or dysfunction or illness in the last four weeks, which was the survey period; 0 (poor health) if there were no self-reported ailments, injuries or illnesses. While selfreported ill-health is not an ideal indicator of actual health conditions as people may under-report their health condition, it is still an accurate proxy of ill-health and mortality. Household crowding: This is the average number of persons living in a room. Physical Environment: This is the number of responses from people who indicated suffering landsides; property damage due to rains, flooding or soil erosion. Psychological conditions are the psychological state of an individual, sub-divided into positive and negative affective psychological conditions.18-19 Positive affective psychological condition signifies the number of responses with regard to being hopeful and optimistic about the future and life generally. Negative affective psychological condition means number of responses from a person on having lost a breadwinner and/or family member, loss of property, being made redundant, or failing to meet household and other obligations. Income is proxied by total individual expenditure in USD. The rate was USD1=Ja. $50.97 in 2002 at the survey period. Average income (i.e. per person per household) is total expenditure divided by the number of persons in the household. Age: The number of years lived, which is

 

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also referred to age at last birthday. This is a continuous variable, ranging from 15 to 99 years. Age group is classified into three categories. These are young adults (ages 15 to 30 years), middle aged adults (ages 31 to 59 years) and the elderly (ages 60+ years). Retirement benefits were measured by those who received retirement income. Private Health Insurance Coverage: This is a dummy variable, where 1 denotes self-reported ownership of private health insurance coverage and 0 is otherwise. Durable goods: This variable is the summation of the self-reported durable goods owned by an individual excluding houses, buildings and property.   where Di ownership of durable goods. Living arrangements are a dummy variable where, 1=living alone, 0= living with family members or relative. Social support (or network) denotes different social networks with which the individual has been or is involved (1= membership of and/or visits to civic organizations or having friends that visit one’s home or with whom one is able to network, 0=otherwise). Crime:   ranges from 1 to 28, where higher values denote greater

where Ki represents the frequency with which an individual has witnessed or experienced a crime, where i denotes 0, 1 and 2, in which 0 indicates not witnessing or experiencing a crime, 1 means witnessing 1 to 2, and 2 symbolizes seeing 3 or more crimes. Ti denotes the degree of the different typologies of crime witnessed or experienced by an individual ( where j=1 …4, where 1=valuables stolen, 2=attacked with or without a weapon, 3= threatened with a gun, and
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4= sexually assaulted or raped. The summation of the frequency of crime by the degree of the incident ranges from 0 and a maximum of 51. Social standing is proxied by per capita population quintile (from poorest-to-wealthiest) 3. RESULTS 3.1 DEMOGRAPHIC CHARACTERISTICS OF SAMPLE The sample was 16,619 respondents (i.e. 48.6% males and 51.4% females; with 39.2% young adults, 42.7% middle aged adults and 18.1% elderly). Some 25.8% of the sample resided in periurban areas; 60.2% in rural zones; 14.0% were from urban areas; 16.8% were below the poverty line (i.e. poorest 20%); while 18.2% were just above the poverty line compared to 21.2% in the wealthy quintile and 24.1% in the wealthiest 20%. Of the sample, 97.6% responded to the health status question. Of those who responded to the health status question, 80.6% indicated at least good health and 19.4% poor health. Ninety-seven percentage points of the sample (n=16,118) responded to the health insurance coverage question, of that 11.9% revealed having health insurance coverage. Based on Table 15.1, poverty is substantially a rural phenomenon. The findings revealed that 21.2% of rural residents were below the poverty line (i.e. poorest 20%) compared to 10.7% of peri-urban dwellers and 9.5% of urban settlers. Health insurance was greatest among urban residents: Some 20.8% of urban dwellers had health insurance compared to 17.6% for peri-urban settlers and 7.5% of rural residents. A significant statistical difference was found between area of residence and crime, and income in this sample. Peri-urban residents spent the most statistically on medical care (USD39.16 ± USD85.77, 95%CI: USD31.39-USD46.94) compared to urban (USD30.25± USD61.47, 95%CI: USD22.66USD37.83) and rural residents (USD29.33±USD54.15, 95%CI: USD26.58-USD32.06) (Table
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15.1) On examination of the cross tabulation between good health status and social standing, a statistical correlation was found (P= 0.001) (Table 15.2). Table 15.2 showed that the worst health was reported by those in the wealthiest quintile (21.8%), the poorest (19.9%), the poor (18.6%) and so on. There is a positive statistical correlation between ageing and self-reported poor health (or health conditions) of Jamaicans (P =0.001) (Table 15.3). Further examination of Table 15.6 revealed that 10.3% of young adults reported poor health compared to 17.4% of middle aged adults and 43.6% of the elderly.

3.2 Multivariate Analysis Table 15.4 presents information on the variables which are correlated (or non-correlated) with private health insurance coverage in Jamaica of people 15 years and older. Using logistic regression, twelve variables emerged as statistically significant determinants of health insurance coverage in this sample (Table 15.7). These variables are social standing (two wealthiest quintiles: OR=1.68, 95% CI=1.23-2.30), income (OR=1.00, 95%CI=1.00-1.00), durable goods (OR=1.16, 95%CI=1.12-1.19), marital status (married: OR=1.97, 95%CI=1.61-2.42), area of residence (Peri-urban: OR=1.45, 95%CI=1.199-1.749; urban: OR=1.831, 95%CI=1.395-2.402), education (secondary: OR=1.57, 95%CI=1.20-2.06; tertiary: OR=9.03, 95%CI=6.47-12.59), social support (OR=0.64, 95%CI=0.53-0.76), crowding (OR=1.14, 95%CI=1.02-1.28), psychological conditions (negative affective: OR=0.97, 95%CI=0.94-1.00; positive affective: OR=1.11, 95%CI=1.06-1.16), number of males in household (OR=0.85, 95%CI=0.77-0.93),

 

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living arrangements (OR=0.62, 95%CI=0.41-0.92) and retirement benefits (OR=1.55, 95%CI=1.03-2.35). The model [Eqn (4)] had statistically significant predictive power (model χ2 = 1604.389, P=0.001; Hosmer and Lemeshow goodness of fit χ2= 5.280, P = 0.727), and correctly classified 91.3% of the sample (Correct classification of cases of reported health insurance coverage =32.0% and correct classification of cases with no insurance coverage = 98.3%).

4. DISCUSSION This study found that health insurance coverage is influenced by social standing, durable goods, income, marital status, area of residence, education, social support, crowding, psychological conditions, retirement benefits, living arrangements and the number of males in the household, and that those with good health are more likely to purchase health insurance than those with poor health. Continuing, rural residents, elderly and poorest, are the least likely to purchase health insurance coverage in Jamaica. In the literature, it is well documented that the majority of uninsured workers in South Dakota were either employed or self-employed [6]. The poor, elderly and many rural residents are more likely to be employed on a seasonal basis in the informal sector, and these occupations and employment types do not have private health insurance, suggesting a further rationale for why unemployed people within a particular socio-economic status would be less likely to be holders of health insurance coverage. In this study, it was revealed that more uninsured Jamaicans were poor, elderly and from rural zones, and these were the ones most likely to be unemployed in Jamaica. The current study was not able to validate the direct claim of employability of the uninsured, but the elderly can indirectly validate the literature that more
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unemployed people do not have health insurance. In addition to the aforementioned fact, another finding was that poor health is associated with low income, owing to the difficulties it creates with accessing crucial health care [6]. This research disagrees with the literature that the poor have lower health statuses, suggesting that they have more health-related conditions than the wealthy. The rich engage in highly involved particular lifestyle practices that expose them to health hazards, and this is not equally comparable to the poor environment of the poor, justifying why they reported the least health status. Pacione [10] has shown that the quality of the physical environment affects the quality of life (or health or wellbeing) of people, but that lifestyle behavioural practices play a significant role in determining one’s health [11] like the physical milieu. [12,13] Moreover, the high cost of health care is a deterrent for the poor to have health insurance coverage; [6] and we concur with the literature as we found a positive statistical association between self-rated health insurance coverage and income. However, in this study we have refined the income variable, as there is a ceiling to income and its relation with the purchase of health coverage in Jamaica. The current work has revealed that those in the wealthy-to-wealthiest quintiles were twice as likely to purchase health insurance coverage as the poor-to-poorest people. Within the context that those in the wealthiest quintile purchased the most health insurance and indicated the lowest health status, it can be inferred that the purchase of health insurance is in keeping with their life style and the perceived role of income in buying good health, as against preventative behaviour. Health insurance coverage is an elderly phenomenon, [6] and this work does not concur with the literature. The argument put forward is that younger people are healthier, and so do not see the need to invest in health coverage, as the risk of becoming ill is low, hence the willingness to engage in risky behaviour compared to their older counterparts, [6] suggesting that the

 

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futuristic end for health insurance coverage becomes even more critical after 30 years when more people will have families, as well as the fact that the purchase of health insurance may materialize owing to futuristic changes in the economic circumstances of the individual. There is a statistical relationship between socioeconomic conditions and the health status of Barbadians, which is not the case in Jamaica. A study by Hambleton et al., [11] of elderly Barbadians revealed that 5.2% of the variation in reported health status was explained by the traditional determinants of health. Furthermore, when this was controlled for current experiences, the percentage fell to 3.2% (a drop of 2%). When the current set of socioeconomic conditions was used, they accounted for some 4.1% of the variation in health status, while 7.1% were due to lifestyle practices compared to 33.5% which were as a result of current diseases. [11] Despite this fact, it is obvious from the data that there are other indicators which explain health status; people do not necessarily pay attention to this fact although they may have more income or access to more economic resources. This explains the rationale for more health conditions being reported by the wealthiest as well as the group that purchased the most health insurance, where the thinking is that money can buy health. A study published in the Caribbean Food and Nutrition Institute on the elderly in the Caribbean found that 70% of individuals who were patients within different typologies of health services were senior citizens. [14, 15, 16] Among the many issues that the research reported on are the five major causes of morbidity and mortality, taken from the Caribbean Epidemiology Centre, which are of paramount importance to this discussion, and their influence on the elderly cerebrovascular, cardiovascular, neoplasm, diabetes, hypertension and acute respiratory infection - and these dysfunctions are highly costly to treat. It should be noted that many of these dysfunctions are owing to lifestyle behaviour. Hence, the purchase of private health insurance
 

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coverage by these people when they become old and approach retirement is in keeping with the cost of health care and the high likelihood of becoming ill. Eldemire, [17] on the other hand, opined that the elderly are not as sick as some people are making them out to be – “The majority of Jamaican older persons are physically and mentally well and living in family units” [17]; but the fact is they are preparing for the eventuality of health conditions owing to the principle of the degeneration of the body with the onset of old age. Eldemire is somewhat right. The current study found that for every 1 young adult who reported poor health, there were approximately 2 middle aged adults and 4 elderly persons. Simply put, there were elderly people with poorer health than other age cohorts; but of the elderly, more of them indicated good health status (56.4%). The mere fact of living longer (life expectancy post retirement is at least 15 years), suggests that the aged population will require more for medical care if they become ill. [18] With ageing the issue is not if they become ill but when. A group of scholars found that there is a direct association between ageing and health conditions, [19] a concept with which this study concurs. And this provides the explanation for the purchase of private health insurance more than other age cohorts, because they are at a different stage from other age cohorts in a population. Health conditions are crucial to the purchase of primary health insurance coverage, and this is highlighted by ageing. Eldemire’s works [17, 18] have shown that ageing in an individual does not translate to high physical impairments, but that with ageing come particular changes in the profile of dysfunctions – Alzheimer’s disease, dementia, cerebrovascular, cardiovascular, neoplasm, diabetes, hypertension and acute respiratory infection. [21] A study conducted by Costa [22], using secondary data drawn from the records of the Union Army (UA) pension programme that covered some 85% of all UA, shows that there is an association between chronic
 

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conditions and functional limitation – which includes difficulty walking and bending, blindness in at least one eye and deafness [22]. Among the significant findings is – (i) the predictability between congestive heart failure in men and functional limitation (i.e. walking and bending). Although Costa’s study was on men, this applies equally to women, as biological ageing reduces physical functioning, and so any chronic ailment will only further add to the difficulties of movement of the aged, be it man or woman. One study has contradicted the works of Eldemire, and it showed that a large percentage of the elderly suffer from at least one health condition. Women are more involved in health seeking behaviour, compared to their male counterparts, [20] irrespective of the age factor, and this is owing to the cultural background in which they live. Unlike women, across the world men have a reluctance to ‘seek health-care’ compared to their female counterparts. It follows in truth that women have bought themselves additional years in their younger years, and it is a practice that they continue throughout their lifetime which makes the gap in age differential what it is – approximately a 4-year differential in Jamaica. In keeping with the preventative care approach to health care, it would be expected that women would purchase more health insurance coverage than them, but this is not the case in Jamaica as gender was not a predictor of health status. However, the more men in a household, the less an individual will purchase health insurance coverage. The Planning Institute of Jamaica in collaboration with the Statistical Institute of Jamaica has shown that while the general health status is commendable, chronic illnesses are undoubtedly eroding the quality of life enjoyed by people who are 65 years and older [23, 24]. The JSLC report reveals that the prevalence of recurrent (chronic) diseases is highest among individuals 65 years and over. [23] The findings show that in 2000, the prevalence of self-reported illness/injury for people aged 65 years and over was 41.7%, for those 60 to 64 years it was 27.6% compared to
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19.8% for children less than five years old.

However, the prevalence of self-reported

illness/injury for those 50 to 59 years was 18.8%. Some 36.6% of individuals 65 years and over reported injuries/illnesses in 2002 which is a 5.6% reduction in self-reported prevalence of illnesses/injuries over 2000, but the self-reported prevalence of illness/injuries rose by 25.8% to 62.4% in 2004. [25, 26] It should be noted here that this increase in self-reported cases of injuries/ailments does not represent an increase in the incidence of cases, as according to the JSLC for 2004,the proportion of recurring/chronic cases fell from 49.2% in 2002 to 38.2% in 2004 [26]. In addition, the PIOJ and STATIN [23] in (JSLC 2000) opined that individuals 60-64 years of age were 1.5 times more likely to report an injury than children less than five years of age, and the figure was even higher for those 64 years of age and older (2.5 times more). In this paper, the findings concurred with the literature that health conditions are significantly greater; but other issues account for them not demanding more health insurance coverage than middle age adults. This is reinforced in the findings that showed that people who received retirement benefits were approximately twice as likely to purchase health insurance coverage as those who did not receive any retirement benefits. Embedded in this finding is the fact that health insurance is a matter of affordability and education, and not illness, which justifies why rural residents had the lowest health insurance coverage, yet still the poorest 20% good health status was greater than that of those in the wealthiest 20%. Statistics revealed that poverty in 2007 for the nation was 9.9%, and rural poverty was 15.3% compared to 4% in peri-urban and 6.2% in urban areas [27], accounting for the lowest private health insurance coverage in that group. 5. CONCLUSION In summary, married Jamaicans are more likely to purchase health insurance coverage compared to those who were never married, with urban residents being more likely to purchase health
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insurance than rural dwellers. An individual who has attained tertiary level education was more likely to purchase health insurance than one with at most primary level education, and those who lived alone were less likely to purchase health insurance coverage than those who dwelled with relatives or family members. Moreover the wealthiest were more likely to purchase health insurance, but were less healthy, and this indicates that income does not buy good health. Therefore, this study highlighted the need to address preventative care for the wealthiest, and the fact that social support is crucial to health care, along with the fact that medical care costs are borne by the extended family and other social groups in which the individual is (or was) a member, which explains the low demand for health insurance in Jamaica.

Conflict of interest
The authors have no conflict of interest to report.

Disclaimer
The researchers would like to note that while this study used secondary data from the Jamaica Survey of Living Conditions, none of the errors in this paper should be ascribed to the Planning Institute of Jamaica or the Statistical Institute of Jamaica, but to the researchers. Acknowledgement The author would like to take this opportunity to thank the Data Bank in Sir Arthur Lewis Institute of Social and Economic Studies, the University of the West Indies, Mona, Jamaica for making the dataset (ie Jamaica Survey of Living Conditions, 2002) available accommodated the current study.

 

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Reference 1. Ettner, S.L. (1997) Adverse selection and the purchase of Medigap insurance by the elderly. Journal of Health Economics, 16,543-562. 2. Liu, T., and Chen, C. (2002) An analysis of private health insurance purchasing decisions with national health insurance in Taiwan. Social Science & Medicine, 55,755-774. 3. Dong, H., Kouyate, B., Cairns, J., Mugisha, F., Sauerborn, R. (2003) Willingness-to-pay for community-based insurance in Burkina Faso. Health Economics, 12,849-862. 4. Carrin, G. (2003) Social health insurance in developing countries: A continuing challenge. International Social Security Review, 55, 57-69. 5. Thomasson, M.A. (2006) Racial differences in health coverage and medical expenditure in the United States. Social Science History, 30,529-550. 6. South Dakota Dept. of Health, the Lewin Group. (2002) Health Insurance Coverage in South Dakota: Final Report of the State Planning Grant Program. OA: South Dakota Dept. of Health. 7. Varghese, R,K,, Friedman, C., Ahmed, F., Franks, A.L., Manning, M., and Seeff, L.C. (2005) Does health insurance coverage of Office Visits Influence Colorectal Cancer Testing. Cancer Epidemiology Biomarkers & Prevention 14,744-747. 8. Kirigia, J.M., Sambo, L.G., Nganda, B., Mwabu, G.M., Chatora, R., and Mwase, T. (2005) Determinants of health insurance ownership among South African women. BMC Health Services Research, 5, 1-17. 9. Ahking, F.W, Giaccotto, C., and Santerre R. (2009) The aggregate demand for private health insurance coverage in the U.S. Journal of Risk and Insurance, The American Risk and Insurance Association, 76, 133-157 10. Pacione, M. (2003) Urban environmental quality of human wellbeing–a social geographical perspective. Landscape and Urban Planning, 65, 19-30. 11. Hambleton, I.R., Clarke, K., Broome, H.L., Fraser, H.S., Brathwaite, F., and Hennis, A.J. (2005) Historical and current determinants of self-rated health status among elderly persons in Barbados. Rev Panam Salud Publica 2005, 17:342-353. 12. P. Bourne Determinants of well-being of the Jamaican Elderly. M.S. thesis, The University of the West Indies, Mona Campus, Jamaica. 13. Bourne, P. (2007) Using the biopsychosocial model to evaluate the wellbeing of the Jamaican elderly. West Indian Medical Journal, 56, (suppl 3), 39-40. 14. CAJANUS. (1999) Health of the Elderly. Caribbean Food and Nutrition Institute Quarterly 32,217-240. 15. CAJANUS. (1999) Focus on the elderly. Caribbean Food and Nutrition Institute Quarterly, 32,179-240. 16. Anthony, B.J. (1999) Nutritional Assessment of the elderly. Caribbean Food and Nutrition Institute Quarterly, 32:201-216. 17. Eldemire, D. (1995) A situational analysis of the Jamaican elderly, 1992. The Planning Institute of Jamaica, Kingston. 18. Eldemire, D. (1997) The Jamaican elderly: A socioeconomic perspective & policy implications. Social and Economic Studies, 46, 175-193. 19. Zimmer, Z., Martin, L.G., and Lin, H-S. (2003) Determinants of old-age mortality in Taiwan. (http://www.popcouncil.org/pdfs/wp/181.pdf) 20. Rice, P.L. (1998) Health psychology. Brooks/Cole Publishing, CA. 21. Eldemire, D. (1996) Level of Mental Impairment in the Jamaican Elderly and the Issues
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of Screening Levels, Caregiving, Support Systems, Carepersons, and Female Burden. Molecular and Chemical Neuropathology, 28, E1-E5. 22. Costa DL. Chronic diseases rates and declines in functional limitation. Demography 2002, 39:119-138. 23. Planning Institute of Jamaica, (PIOJ) and Statistical Institute of Jamaica, (STATIN). (2001) Jamaica Survey of Living Conditions 2000. PIOJ and STATIN, Kingston. 24. Planning Institute of Jamaica, (PIOJ) and Statistical Institute of Jamaica, (STATIN). (1998) Jamaica Survey of Living Conditions 1997. PIOJ and STATIN, Kingston. 25. Planning Institute of Jamaica, (PIOJ) and Statistical Institute of Jamaica, (STATIN). (2003) Jamaica Survey of Living Conditions 2002. PIOJ and STATIN, Kingston. 26. Planning Institute of Jamaica, (PIOJ) and Statistical Institute of Jamaica, (STATIN). (2005) Jamaica Survey of Living Conditions 2004. PIOJ and STATIN, Kingston. 27. Planning Institute of Jamaica, (PIOJ) and Statistical Institute of Jamaica, (STATIN). (2008) Jamaica Survey of Living Conditions 2007. PIOJ and STATIN, Kingston.

 

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Table 15.1: Demographic characteristic of sample by area of residence Rural % (n) Age group Young adults Middle age adults Elderly Health insurance coverage Yes No Gender Male Female Per capita income quintile 1=Poorest 20% 2 3 4 5=Wealthiest 20% Marital status Married Never married Divorced Separated Widowed Crowding mean (SD) Crime index Medical expenditure1 mean (SD) Income2 mean (SD)
1 2

Peri-urban % (n) 41.0 (1760) 44.2 (1895) 14.8 (634) 17.4 (726) 82.6 (3442) 46.8 (2006) 53.2 (2283) 10.7 (458) 13.3 (572) 18.7 (800) 22.7 (972) 34.7 (1487) 26.9 (1115) 66.4 (2755) 1.0 (41) 1.2 (49) 4.5 (187) 1.75 ± 1.28 2.34 ± 8.08 $39.16 ± $85.77

Urban % (n)

P

0.001 38.3 (3833) 41.6 (4160) 20.1 (2010) 7.5 (722) 92.5 (8969) 50.4 (5041) 49.6 (4962) 21.2 (2118) 22.0 (2196) 20.8 (2085) 19.8 (1978) 16.2 (1625) 25.5 (2460) 66.6 (6433) 0.6 (56) 1.1 (104) 6.3 (610) 1.77 ± 1.24 1.74 ± 7.37 $29.33±$54.15 39.7 (923) 44.6 (1039) 15.7 (365) 0.001 20.8 (471) 79.2 (1788) 0.001 44.3 (1031) 55.7 (1296) 0.001 9.5 (222) 11.2 (261) 16.7 (388) 24.3 (565) 38.3 (891) 0.001 21.0 (475) 71.6 (1619) 1.2 (26) 1.4 (32) 4.8 (108) 1.72 ± 1.18 2.83 ± 9.30 $30.25± $61.47 0.216 0.001 0.012

$5496.12 ± $4860.97 $7534.74 ± $5544.26 $8779.26 ±$10568.69 0.001

Medical Expenditure is expressed in USD: 1USD= JA$50.97 for the period 2002 Income is expressed in USD: 1USD= JA$50.97 for the period 2002

 

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Table 15.2: Good health status by social standing (Per capita population quintile) Social standing (Per Capita Population Quintile) Good health status Poor Good 1=Poorest 19.9 80.1 2 18.6 81.4 3 17.9 82.1 4 18.4 81.6 5=Wealthiest 21.8 78.2 Total 19.4 80.6

Total

2738

2975

3208

3413

3883

16217

χ2(4) = 23.273, P= 0.001, contingency coefficient = 0.038

 

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Table 15.3: Good health status by age group Age group Young Middle age(15 to age (31 to 30 years) 59 years) Poor 10.3 17.4 Elderly (60+ years) 43.6

Good health status

Total 19.4

Good

89.7

82.6

56.4

80.6

Total

6283

6973

2961

16217

χ2(2) = 1458.12, P= 0.001, contingency coefficient = 0.287

 

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Table 15.4: Logistic regression: Private health insurance coverage by some variables
P Age Middle quintile Two wealthiest quintiles †Poorest 20%-to-poor Household Head Logged medical expenditure Average income Durable goods Separated or Divorced Married †Never married Peri-urban Urban †Rural area Environment House tenure - rented House tenure - owned House tenure – squatted* Secondary Tertiary †Primary and below Social support Sex Crowding Crime index Land ownership Negative affective Positive affective Number of males in house Number of females in house Number of children in house Living arrangement Retirement benefits (1=yes) Poor health status 0.443 0.174 0.001 0.213 0.671 0.009 0.000 0.608 0.000 0.000 0.000 0.116 0.999 0.950 0.001 0.000 0.000 0.722 0.018 0.652 0.665 0.034 0.000 0.001 0.622 0.438 0.017 0.038 0.309 Odds Ratio 1.00 1.24 1.68 1.00 1.80 1.01 1.00 1.16 0.90 1.97 1.00 1.45 1.83 1.00 0.85 0.00 1.04 1.00 1.57 9.03 1.00 0.64 1.03 1.14 1.00 0.96 0.97 1.11 0.85 0.98 0.97 0.62 1.55 0.94 95.0% C.I. Lower Upper 0.99 1.00 0.91 1.71 1.23 2.30 0.71 0.95 1.00 1.12 0.61 1.61 1.10 1.40 0.70 0.00 0.27 1.20 6.47 0.53 0.86 1.02 0.99 0.79 0.94 1.06 0.77 0.89 0.90 0.41 1.03 0.83 4.55 1.08 1.00 1.19 1.33 2.42 1.75 2.40 1.04 4.03 2.06 12.59 0.76 1.24 1.28 1.01 1.16 1.00 1.16 0.93 1.07 1.05 0.92 2.35 1.06

-2Log Likelihood= 3982.175 Nagelkerke R Square= 0.359 Model χ2(8)= 1604.389, P-value=0.001 Hosmer and Lemeshow χ2= 5.280, P=0.727 Overall correct classification = 91.3%: Correct classification of cases of reported health insurance coverage =32.0%; Correct classification of cases with no health insurance coverage =98.3% †Reference group

 

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Chapter 16
Difference in social determinants of health between men in the poor and the wealthy social strata in a Caribbean nation
Paul A. Bourne & Denise Eldemire-Shearer

Studies that have examined social determinants of health have made their investigations on the population, but none have reviewed them from the perspective of particular social hierarchies. The study examined the factors determining the self-reported health of men of different socioeconomic status, by using models derived through econometric analyses. The study used a sample of 6,474 respondents: 2,704 from the two poor quintiles and 3,770 from the two wealthy quintiles. The survey used a random stratified probability sampling technique and involved the use of self-administered questionnaires. Multiple logistic regression technique was used to identify variables which are associated with health conditions of men in the two social hierarchies. The findings revealed that the self-reported health of men in the two wealthiest quintiles were substantially influenced by private health insurance coverage (Odds Ratio (OR) = 32.9, 95%CI: 20.64, 52.45) and age of respondents (OR = 1.03, 95%CI: 1.02, 1.04) This was similar for men in the two poorest income quintiles; private health insurance coverage (OR = 16.97, 95%CI: 10.18, 28.27) and age (OR=1.05, 95%CI: 1.03, 1.06). Negative affective psychological conditions, consumption and medical expenditure affected the self-reported health of those in the two wealthiest quintiles, while positive affective, secondary levels of education and living alone influenced those in the two poorest quintiles. This research serves as a foundation for further work relating to the determinants of self-reported health conditions, inequity across socio-economic strata for men, and how patient care should be addressed. INTRODUCTION

In recent years the World Health Organization (WHO) has increasingly drawn attention to the importance of the relationship between health and social conditions in determining the health of individuals and populations [1]. Social determinants (conditions, in which people are born, live, grow, work and age as well as the health system available to them) produce inequalities in health, and need to be considered in health development. Addressing social determinants and health policy now forms the basis for political action both nationally and internationally [2].

 

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Human poverty is defined as more than income poverty; it is the denial of choices and opportunities for living a tolerable life [3]. Poverty as described above in the Caribbean has been predominantly a rural phenomenon; however, rising levels of urban poverty have also been seen. In 1996 the World Bank estimated 38% of the total population (or 25% including Haiti) in the Caribbean, or more than seven million people, to be poor [4]. One study found that rural poverty in Argentina, Barbados, Boliva, Brazil, Colombia, Jamaica, Suriname, Trinidad and Tobago, and Uruguay was at least twice more than urban poverty [5]. According to the Jamaica Survey of Living Conditions (JSLC), in 2003, the poverty rate stood at 19.1%, and in 2007 it fell to 9.9% [6]. The JSLC for 2001 [6] indicates that the wealthiest 20% of the population accounted for 45.9% of national consumption, while the poorest 20% accounted for only 6.1% of national consumption. On average, the wealthiest 10% of the population consumed approximately 12.5 times more than the poorest 10% [6]. This is a mean per capita annual consumption expenditure of US$ 3,963.53 compared to US$ 314.48. Gafar found that in some Latin American and Caribbean countries, between 2 to 8 percentage of income is estimated to be received by those in the poorest 20% compared to between 42 and 58% that is received by those in the wealthiest 20% [5] which indicates that income inequalities are vast between the poor and the wealthy within those societies, and does account for some of the health disparities between the social hierarchies. According to the WHO’s definition, health is not merely the absence of disease but the highest possible state of physical, social and mental wellbeing. At both a societal and individual level, the aim is to extend healthy life expectancy, as well as productivity and quality of life at older ages for as long as possible [7]. Understanding how the social determinants influence
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health and social wellbeing is an area of considerable research interest. That the unequal distribution of variables such as income, unemployment and education produce health inequalities, has been documented [8-10]. Studies have established a statistical relationship between health status and poverty [11-13], between standard of living and health conditions, health status owing to a particular natural disaster [14,15], and income and health [16]. It is recognized that more information is needed at the social level, and that knowledge needs to be translated into action [17]. People with lower socioeconomic status have worse health in all adult age groups, including older ages [18]. Age has been identified as an important social determinant of health. Among adults, reduced capacity to generate income and the growing risk of illness increase the vulnerability of the elderly to poverty; regardless of their original economic status in developing and industrialized countries [19]. Gender is equally as important a social determinant of health. Men are experiencing poverty. It is important to understand the factors influencing self-reported health. Many studies that have examined those in the poor and wealthy income groups have used a piecemeal approach, and in the Caribbean this is also the case. Studies that have examined social determinants of health [1, 2, 8-17] have made their investigations in the population, but have not reviewed them from the perspective of particular social hierarchies within a nation, in order to establish if the factors are the same, and if not, what the disparities are. It is within this framework that the present study examined factors determining self-reported health among men in the two poorest and the two richest quintiles in Jamaica, in order to provide public health specialists and policy makers with research findings on these cohorts.

 

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MATERIALS & METHODS The current study extracted a sample of 6,474 men; (2,704 from the two poorest quintiles and 3,770 from the two wealthiest quintiles) from the dataset of the Jamaica Survey of Living Conditions (JSLC). The inclusion/exclusion criteria were (1) being males, and (2) being classified in the poor or wealthy social strata. The survey (JSLC) was a nationally representative probability sample in which self-administered questionnaires were used to collect data from the populace [20]. The information is from the civilian and non-institutionalized population of Jamaica. It is a modification of the World Bank’s Living Standards Measurement Study (LSMS) household survey. The survey was drawn using stratified random sampling. The design was a two-stage stratified random sampling design where there was a Primary Sampling Unit (PSU) and a selection of dwellings from the primary units. The PSU is an Enumeration District (ED), which constitutes a minimum of 100 residences in rural areas and 150 in urban areas. The sample was weighted to reflect the population of the nation. The non-response rate for the survey was 27.7%. Measurements Self-reported health conditions: This is a dummy variable, where 1 = self-reported ailment, injury or illness in the last four weeks, which was the survey period, 0 = otherwise. Thus, selfreported health is a binary variable, where 1 = not reporting an illness, and 0 = reporting an ailment. Living arrangement: This is a dummy variable, where 1 = living alone, and 0 = otherwise, , where represents each person in the household, and r is the number of

rooms excluding kitchen, bathroom and verandah.

 

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Age: This is a continuous variable, ranging from 15 to 99 years. Psychological conditions are the psychological state of an individual, and this is subdivided into positive and negative affective psychological conditions. Positive affective psychological condition is the number of responses with regard to being hopeful and optimistic about the future and life generally. Negative affective psychological condition is the number of responses from a person on having lost a breadwinner and/or family member, having lost property, having been made redundant or failing to meet household and other obligations. Natural disaster: This is the number of responses from people who indicated suffering landslides; property damage due to rains, flooding and soil erosion.

where ki represents the frequency with which an individual witnessed or experienced a crime, where i denote 0, 1 and 2, in which 0 indicates not witnessing or experiencing a crime, 1 means witnessing 1 to 2, and 2 symbolizes seeing 3 or more crimes. Tj denotes the degree of the different typologies of crime witnessed or experienced by an individual (where j = 1…4, which 1 = valuables stolen, 2 = attacked with or without a weapon, 3 = threatened with a gun, and 4 = sexually assaulted or raped. The summation of the frequency of crime by the degree of the incident ranges from 0 to a maximum of 51. Consumption: The total sum which is spent by an individual on durable and non-durable good during a 12-month period.

Statistical analysis

 

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Statistical analyses were performed using Statistical Packages for the Social Sciences (SPSS) 16.0 software for Windows (SPSS Inc, Chicago IL). Descriptive statistics were used to provide basic information on the sampled population. Logistic regression analyses were used to establish the model to ascertain parameters, and determine the strength of each statistically significant variable (P < 0.05). The predictive power of the model was tested using the Omnibus Test of Model and Hosmer and Lemeshow [23] was used to examine goodness of fit of the model. The correlation matrix was examined in order to ascertain whether autocorrelation (or multicollinearity) existed between variables. Cohen and Holliday [24] stated that correlation can be low/weak (0 to 0.39); moderate (0.4-0.69), or strong (0.7-1.0). This was used to assist in the exclusion (or retention) of a variable in the model. In support of this, where collinearity existed (r > 0.7), variables were entered independently into the model to assist in determining which one should be retained during the final model construction. The decision to retain (or exclude) was based on the variables’ contribution to the predictive power of the model and its goodness of fit. To derive accurate tests of statistical significance, we used SUDDAN statistical software (Research Triangle Institute, Research Triangle Park, NC), and this adjusted for the survey’s complex sampling design. Analytic model The multivariate model used in this study to examine the sub-sample is a modification of that of Grossman [21] and Smith & Kington [22] which captures the multi-dimensional concept of health status and conditions. The present study further refined the two aforementioned works and in the process added some new factors, such as psychological conditions, crowding, house tenure, and the number of people in the household. Using econometric analysis the study sought to model the self-reported health of men in the two wealthiest and poorest quintiles from a
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general set of social determinants identified in the literature, as seen in the equation below (Equation [1]). Hi = ƒ(Li,Ri,lnC,Eni,ARi,SSi,CRi,( ),lnEi,HHi,Ai,HIi,Mi,Fi,MRi,EDi,lnMEi) ………[1]

Hi is a function of the 17 variables. Li is living alone of person i, 1 if living alone, 0 if not living alone; Ri is retirement income of person i, 1 if receiving private and/or government pension, 0 if otherwise; LnC is the average consumption expenditure of person i, in dollars; En is the natural disaster, 1 if in the lived milieu there has been flooding, soil erosion, landslide, 0 if not; ARi is the area of residence, other towns, KMA with the reference group being rural areas; SSi is social support, 1 if yes, 0 if no; CR is crowding in the household of person i; lnEi is the average total expenditure of the person i in dollars, which is the proxy for income; HHi is household head of person, 1 if yes, 0 if no; Ai is age of person i, in years; HIi is health insurance coverage, 1 if person has a health insurance policy, 0 if otherwise; M is number of males in household of person i; F is number of females in household of person i; MRi is marital status of person i; EDi is educational level of person i; lnMEi is medical expenditure of person i; NPi is

the summation of all negative affective psychological conditions and PP is the summation of all positive affective psychological conditions. The final model consisted of only those variables which are statistically significant (P < 0.05). Equation [2] represents those factors that explain the health conditions of those in the poorest 20% and equation [3] denotes variables which are correlated with the health conditions of those in the wealthiest 20%: Hi = ƒ(Li, ,PPi, Ai,HIi,EDi) ………….……………………………………….[2]

 

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Hi = ƒ(lnCi, NPi, Ai,HIi,lnMEi) ………………………..……………..………[3]

RESULTS Characteristics of sample There are diverse dissimilarities between the demographic characteristics of men in the two poorest quintiles and those in the two wealthiest quintiles. The average consumption per head for the poor was US$301.79 (SD = US$96.16), which represented 22.1% of the average consumption expenditure per head of those in the two wealthiest quintiles. Similarly, the crowding for men in the two wealthiest quintiles was 1 person (SD = 0.798 person) compared to 2.3 persons (SD = 1.4 persons) for those in the two poorest quintiles. Furthermore, 4.6 times more men in the two wealthiest quintiles resided alone, compared to those in the poorest quintiles. There was a remarkable difference in the level of tertiary education of the two sampled groups, as for every 1 man in the two poorest quintiles with tertiary level education there were 88 men in the two wealthiest quintiles. In addition to the aforementioned differences, there are 4 times more men in the two wealthiest quintiles who are receiving retirement income compared to those men in the two poorest quintiles (Table 16.1). Moreover, those in the two wealthiest quintiles are more vulnerable to crime (2.5 ± 8.5; Range = 88, 0) compared to those in the poorest quintiles (1.7 ± 7.3; Range = 88, 0). The disparity was narrower for self-reported health conditions, as for every 100 men in the two poorest quintiles who indicated a health condition there were 109 men in the two wealthiest quintiles. Multivariate Analysis Predicting the health conditions of men in the two poorest quintiles

 

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In the investigation of the factors which predict the health conditions of men in the two poorest quintiles, it was found that the data was a good fit for the model as 89.1% (n = 1,755) of the data were correctly classified; 98.5% of those who indicated no health condition were correctly classified, with 28.7% reporting that they had at least one dysfunction (Table 16.2). Moreover, the 5 factors accounted for 30.6% of the variability in health conditions of this group: -2 log likelihood =1195.541; Nagelkerke R2 = 0.306; χ2 (21) = 360.02, p < 0.001.

Predicting health conditions of men in the two wealthiest quintiles In investigating the self-reported health of men in the two wealthiest quintiles, it was found that the data was a good fit for the model, as 87.6% (n = 2,533) were correctly classified; 99.0% (n = 2,396) of those who indicated no health condition were correctly classified, with 29.0% (n = 76) of those mentioning that they had at least one dysfunction (Table 16.3). Of the 17 variables that the researchers tested, only 5 were statistically significant.

DISCUSSION This study makes an important contribution to understanding self-reported health in Jamaican men in two ways. It provides both an econometric model which can be used on subsamples of data sets for routine data collection, and it identifies the variables involved in determining the self-reported health of the poorest and wealthiest Jamaican groups. The study is timely, given the increasing recognition of the contribution of social determinants to health [1]. The findings of this study suggest that age, average consumption, private health insurance coverage, level of education, whether or not the person lived alone, medical expenditure and positive or negative affective psychological conditions were determinants of the self-reported
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health of the wealthiest and poorest men in Jamaica. Age, health insurance and psychological conditions are common to both groups, while consumption and medical expenditure are significant for the wealthiest, and education and living arrangements for the poorest quintiles. These findings are contrary to those of other studies [21, 22], and therefore contribute to the local understanding of the relationship between self-rated health status and the socio-economic status of men in Jamaica. Age was the second most significant predictor of self-reported health for both groups. The Jamaican Healthy Lifestyle Survey Report 2000 [25] noted a prevalence of hypertension of 19.9% among males, which increased with age in both rural and urban populations and in both sexes. The most common chronic diseases identified among elderly males and females were hypertension, arthritis, diabetes, cardiovascular arrest, stroke and cancer. Patients in the 60-andover age groups accounted for 37.2% and 41.1% respectively, of new hypertensive and diabetic cases [26]. Diabetes is one of the leading causes of morbidity and mortality among persons aged 65 and older [27]. Having health insurance was a predictor for both groups of quintiles. Access to services also depends on the capacity to pay, which can exclude men in the poorest quintile and who might have lived all their lives in poverty [28]. The health problems of older men often necessitate prolonged medication and treatment. The high cost of consultations, diagnostic services and particularly medicines are among the most formidable barriers to appropriate and timely care. Deprivation earlier in the life cycle, in terms of education and paid employment, means that older men in the two poorest quintiles are less likely than their counterparts in the two wealthiest quintiles to be literate, to have participated in the formal labour force, or to receive retirement pensions or benefits, such as health insurance coverage. Even when they do receive a
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retirement pension, this is likely to be lower than that of their wealthier counterparts because of the lower average wages that they earned when employed. Thus, many lack the means to meet their needs [28]. In this study 4.8% of men in the two poorest quintiles possessed medical insurance, compared with 3.3% of men in the two wealthiest quintiles, and this was lower than the 7.6% reported in a previous study [29]. This finding suggests that the cost of health care is the individual’s responsibility and for the poorer quintiles emphasizes the reliance on public services. Being in fair or poor health, or having a chronic health condition, is strongly associated with being underinsured. Compared to those in better health, individuals who rate their health as fair or poor are almost three times as likely to be underinsured (19% versus 7%). While this is true regardless of residence, rural non-adjacent residents in poorer health have the highest underinsured rate [30]. Studies have also shown that the lack of health insurance coverage is a significant barrier to treatment, and rural areas have disproportionate populations of uninsured and underinsured [31, 32]. As a result of a large percentage of rural men being employed in small businesses or being self-employed, they are more likely to be uninsured. Bennett and colleagues [33] postulated that rural residents were more likely to be uninsured than urban residents (17.8% versus 15.3%), and that rural respondents were more likely than urban counterparts to report having deferred health care because of cost (15.1% versus 13.1%). This study supports the findings of other studies. The current study found that a positive affective psychological condition was a predictor of self-reported health for those in the two poorest quintiles, while a negative affective condition

 

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was a predictive factor for those in the two wealthiest quintiles. This means that the more a wealthy individual experiences negative affective conditions, he/she is 1.074 times (or 7.4%) more likely to report health conditions, suggesting that increased negative conditions result in more hypertension, diabetes mellitus and other types of illnesses. Positive affective psychological conditions, on the other hand, were inversely correlated with health conditions for those in the two poorest quintiles. There, those in the two poorest quintiles who experienced more positive conditions were 8.3% less likely to report health conditions. Embedded in this finding is the role negative and positive affective conditions play in determining the health conditions of different sub-groups in the Jamaican population. Psychological wellbeing is dependent on a host of factors, including genetic traits, social support systems, personality types, and the presence of positive and negative psychological constructs such as happiness, optimism, morale, depression, anxiety, self-esteem, self-efficacy, and vigour. Psychological wellbeing is particularly important for the prevention or management of cardiovascular disease, but it also has important implications for the prevention and management of other chronic diseases such as diabetes, osteoporosis, hypertension, obesity, cancer and depression [34], which have been identified as significant in the Jamaican population. People’s cognitive responses to ordinary and extraordinary situational events in life are associated with a different typology of wellbeing [35]. It is found that happier people are more optimistic, and as such they conceptualize life’s experiences in a positive manner. A study by Diener and colleagues [36] found that self-reported wellbeing (personal happiness) of the wealthy-affluent (those earning in excess of US 10 million annually) was marginally more than that of the lower wealthy, suggesting that high incomes do not increase happiness by the same proportion. The distinction between the importance of the positive and negative affective
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conditions of the poor and rich respectively, underlines the importance of the state of mind in perceived health. According to Harris and colleagues [37] and Kashdan [25], negative psychological conditions affect subjective wellbeing in a negative manner (i.e. guilt, fear, anger, disgust), and positive factors influence self-reported wellbeing in a direct way. This concurs with findings in studies conducted by Fromson [38] as well as by other scholars [39, 40]. Furthermore, the poor may become more optimistic, even with a decline in their health status. Thus the poor remain hopeful irrespective of their health conditions. The rich, on the other hand, report that a negative affective psychological condition, such as the loss of a family member, is associated with their decline in health. Education was another of the five predictors of self-reported health for those in poor quintiles. For every eighty-eight men in the two wealthiest quintiles attaining a tertiary level of education, there was only one man in the two poorest quintiles. Education is closely associated with an individual’s health status, and high average educational levels are closely associated with higher average life expectancy [41]. Furthermore, educational attainment is linked to many aspects of a person’s wellbeing. Research has shown that higher levels of education usually translate into better health status, higher incomes, and consequently higher standards of living [42] and better cognitive functioning in older age [43]. Men with less education and who are poorer are more likely to experience earlier onset of disease, loss of functioning, and physical impairment [44]. Hayward and colleagues [45] reported onset of diseases and death 5–10 years earlier for persons with lower socioeconomic status. The average number of biological risk factors indicating physiological dysregulation is also higher for poorer people and people with less education [46]. In addition, education significantly affects how effectively people utilize health care. Education further affects health because well-educated people may be more aware of
 

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the benefits and disadvantages of certain types of behaviours associated with personal health [47]. Importantly, marital status did not appear to be a proxy for who a person lives with, as it was not a significant determinant of self-reported health conditions. Smith and Waitzman’s work [48] noted that men’s gains from marriage were greater than those of women [49]. Smith and Waitzman [48] offered the explanation that wives dissuaded their husbands from particular risky behaviours, such as the use of alcohol and drugs, and would ensure that they maintain a strict medical regimen coupled with proper eating habits [50,51] which accounts for them having greater wellbeing than their non-married counterparts. Surprisingly, more men in the two

wealthiest quintiles lived alone. Older men are likely to live alone and be unconnected to any family unit because of irresponsible patterns of sexual behaviour and parenting or unstable relations during their younger years [52]. The wealthiest in the society experience better health, due to their knowledge of health risks and their access to the resources necessary to avoid such risks, and to treat health conditions [53, 54]. But with increasing wealth and development there has been an increase in chronic diseases, as lifestyle changes have had a negative impact [55, 56]. This study found that there was a large gap between the consumption of the groups, with the poorest only consuming 22% of the proportional consumption of the wealthiest. Among the demographic correlates of health is the cost of medical care [1, 2, 21, 22, 57, 58]. The current study concurs with the literature that the cost of medical care is associated with health status; but this is only for wealthy Jamaicans. Medical care expenditure was not associated with self-reported health for the poor to poorest in Jamaica.

 

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Conclusion The key finding which emerged from this is that social determinants of health are not always the same across different social hierarchies. The similarities in social determinants across the two social strata are age of respondents, health insurance coverage, and negative affective psychological conditions. Educational levels and living arrangements are not associated with health for men in the upper social strata, and consumption and medical expenditure are not for those in the lower social strata. This study adds to the literature by showing that social determinants of health are not the same in a particular cohort, or between different social strata.

Conflict of interest
The authors have no conflict of interest to report.

Acknowledgement
The researchers would like to note that while this study used secondary data from the Jamaica Survey of Living Conditions, none of the errors in this paper should be ascribed to the Planning Institute of Jamaica or the Statistical Institute of Jamaica, but to the researchers. REFERENCES 1. World Health Organization (WHO). The Social Determinants of Health; 2008. Available at http://www.who.int/social_determinants/en/ (accessed April 28, 2009). 2. Kelly MP, Morgan A, Bonnefoy J, Butt J, Bergman V. The social determinants of health: Developing an evidence base for political action. Final Report to World Health Organization Commission on the Social Determinants of Health from Measurement and Evidence Knowledge Network; 2007. Available from http://www.who.int/social_determinants/resources/mekn_final_report_102007.pdf (accessed April 29, 2009) 3. United Nations Development Programme. Human development report 1997. New York: OUP; 1997. 4. World Bank. Poverty Reduction and Human Resource Development in the Caribbean. Washington D.C.; 1996. 5. Gafar J. Growth, inequality and poverty in selected Caribbean and Latin American countries, with emphasis on Guyana. J of Latin American Studies 1998; 30:591-617. 6. Planning Institute of Jamaica (PIOJ), Statistical Institute of Jamaica (STATIN). Survey of Living Conditions, 2007. Kingston; PIOJ, STATIN; 2008.

 

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7. World Health Organization (WHO). Preamble to the Constitution of the World Health Organization as adopted by the International Health Conference, New York, and June 19-22, 1946; In Basic Documents, 15th ed. Geneva, Switzerland: WHO, 1948. 8. Graham H. Social Determinants and their Unequal Distribution Clarifying Policy Understanding The Milbank Quarterly, 2004; 82:101-124. 9. Marmot M, Wilkinson RG (Eds.) Social Determinants of Health. 2nd Ed. Oxford University Press; 2003. 10. Solar O, Irwin, A. Towards a Conceptual Framework for Analysis and Action on the Social Determinants of Health. 2005, Geneva: Commission on Social Determinants of Health; 2005. 11. Murray S. Poverty and health. Canadian Medical Association Journal 2006; 174: 923-923. 12. Bloom DE, Canning D. The health and poverty of nations: From theory to practice. Journal of Human Development 2003; 4: 47-72. 13. Smith KR, Waitzman NJ. Double jeopardy: Interaction effects of martial and poverty status on the risk of mortality. Demography 1994; 31: 487-507. 14. Pacione M. Urban environmental quality of human wellbeing–a social geographical perspective. Landscape and Urban Planning 2003; 65: 19-30. 15. Bourne P. Using the biopsychological model to evaluate the wellbeing of the Jamaican elderly. West Indian Medical J 2007; 56 (Suppl 3): 39-40. 16. Benzeval M, Judge K, Shouls S. Understanding the relationship between income and health: How much can be gleamed from cross-sectional data? Social policy and Administration 2001. In Benzeval M, Judge K. Income and health: the time dimension. Social Science and Medicine 2001; 52: 1371-1390. 17. Pettigrew M, Whitehead M, McIntyre SJ, Graham H, Egan M. Evidence for Public Health Policy on Inequalities: 1: The Reality According To Policymakers. Journal of Epidemiology and Community Health 2004; 5:811 – 816. 18. House JA, Lantz PM, Herd P. Continuity and change in the social stratification of aging and health over the life course: Evidence from a nationally representative longitudinal study from 1986 to 2001/2002 (Americans’ Changing Lives Study). Journals of Gerontology: Social Sciences 2005; 60B (Special Issue II): 15-26. 19. Lloyd-Sherlock P. Old age and poverty in developing countries: new policy challenges. World Development 2000; 28: 2157-2168. 20. Statistical Institute Of Jamaica. Jamaica Survey of Living Conditions, 2007 [Computer file]. Kingston, Jamaica: Statistical Institute of Jamaica [producer], 2007. Kingston, Jamaica: Planning Institute of Jamaica and Derek Gordon Databank, University of the West Indies [distributors], 2008. 21. Grossman M. The demand for health- a theoretical and empirical investigation. New York: National Bureau of Economic Research; 1972. 22. Smith JP, Kington R. Demographic and economic correlates of health in old age. Demography 1997; 34: 159-170. 23. Homer D, Lemeshow S. Applied Logistic Regression, 2nd edn. John Wiley & Sons Inc., New York; 2000. 24. Cohen L, Holliday M. Statistics for Social Sciences. London, England: Harper and Row; 1982. 25. Kashdan TB. The assessment of subjective well-being (issues raised by the Oxford Happiness Questionnaire). Personality and Individual Differences 2004; 36: 1225-1232.

 

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26. Planning Institute of Jamaica (PIOJ). Economic and Social Surveys, 2002. Kingston; PIOJ; 2003. 27. Desai MM, Zhang P, Hennessy CH. Surveillance for morbidity and mortality among older adults in United States, 1995-1996. MMWR 46(SS08): 7 - 25. 28. World Health Organization. Active ageing: a policy framework. Geneva: WHO, 2002. 29. Planning Institute of Jamaica (PIOJ), Statistical Institute of Jamaica (STATIN). Jamaica Survey of Living Conditions 2002. Kingston: PIOJ, STATIN, 2003. 30. Maine Rural Health Research Centre, Research and Policy Brief. Rural Residents more likely to be uninsured, January 2009. http://muskie.usm.maine.edu/Publications/rural/pb33.pdf. 31. Beck RW, Jijon CR, Edwards JB. The relationships among gender, perceived financial barriers to care, and health status in a rural population. The Journal of Rural Health 1996; 12; 188-196. 32. Rowland D, Lyons B. Triple jeopardy: Rural, poor, and uninsured. Health Services Research 1989; 23: 975-1004. 33. Bennett K, Olatosi B, Probost J. Health disparities: A rural-urban chart book. Rural Health Research and Policy Centre, 2008. 34. Warburton DE, Gledhill N, Quinney A. Musculoskeletal fitness and health. Can J Appl Physiol 2001; 26: 217-237. 35. Lyubomirsky S. Why are some people happier than others? The role of cognitive and motivational process in well-being. American Psychologist, 2001; 56: 239-249. 36. Diener E, Horwitz J, Emmon RA. Happiness of the very wealthy. Social Indicators Research 1985; 16: 263-274. 37. Harris L, Peter R, Lightsey Jr., OR. Constructive thinking as a mediator of the relationship between extraversion, neuroticism, and subjective well-being. European Journal of Personality 2005; 19: 409-426. 38. Fromson PM. Self-discrepancies and negative affect: The moderating roles of private and public self-consciousness. Social behavior and Personality 2006; 34: 333-350. 39. McCullough ME, Bellah CG, Kilpatrick SD, Johnson JL. Vengefulness: Relationships with Forgiveness, Rumination, Well-Being, and the Big Five. Personality and Social Psychology Bulletin 2001; 27: 601-610. 40. Watson D, Clark LA, Tellegen A. Development and validation of brief measures of positive and negative affect: The PANAS Scale. Journal of Personality and Social Psychology 1988; 54: 1063-1070 41. Wang J, Jamison DT, Bos E, Preker A, Peabody J. Measuring country performance on health: Selected indicators for 115 Countries: 11-19. Human Development Network: Health, Nutrition and Population Series, Washington, DC: The World Bank, Health, Nutrition and Population, May 1999, 359 pages. 42. Elo IT, Preston SH. Educational differentials in mortality in the United States, 1979-85, Social Science Medicine 1996; 42: 47-57. 43. Stern PC, Carstensen LL, eds. The Aging Mind. Opportunities in Cognitive Research, National Research Council, Washington, DC: National Academy Press, 2000. 44. Geronimus AT, Hicken M, Keene D, Bound J. Weathering and age patterns of allostatic load scores among blacks and whites in the United States. Am J Public Health 2006; 96: 826-833. 45. Hayward MD, Crimmins EM, Miles TP, Yu Y. The significance of socioeconomic status in explaining the racial gap in chronic health conditions. Am Sociol Rev 2000; 65: 910-930.

 

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Table 16.1: Demographic characteristics of sample Two Poorest quintiles N=2,704 % Educational attainment Primary and below Secondary & post-sec. Tertiary Marital Status Married Never married Divorced Separated Widowed Household Head No Yes Age group Youth (15 – 25yrs) Older adults (26 -59 yrs) Elderly (60+ yrs) 551 1787 18 593 1902 7 17 83 94 2610 973 1214 517 23.4 75.8 0.1 22.8 73.1 0.3 0.7 3.2 3.5 96.5 36.0 44.9 19.1 84.2 15.8 97.7 2.3 93.6 6.4 95.2 4.8 Two Wealthiest quintile N=3,770 % 603 2,414 291 1,058 2,370 49 51 116 1,505 2,261 1015 2135 620 3,038 637 3,426 339 2,673 1,095 3,462 118 18.2 73.0 8.8 29.0 65.0 1.3 1.4 3.2 40.0 60.0 26.9 56.6 16.4 82.7 17.3 91.0 9.0 70.9 29.1 96.6 3.3

Self-reported health conditions None 2229 At least one 418 Receiving retirement income No 2625 Yes 63 Living Arrangement With family 2532 Alone 172 Ownership of private health insurance No 2508 Yes 127

†Average annual Consumption US $301.79 (SD=US $96.16) US$1,326.50(SD=US $1,054.97) Crowding mean (SD) Crime Index mean(SD) †1US$ = Ja. $50.97 (in 2002) 2.3 persons (1.4 persons) 1.7(7.3); Range=88, 0 1 person (0.798 person) 2.5(8.5); Range=88,0

 

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Table 16.2: Logistic regression: Health conditions of men in the two poorest quintiles by some explanatory variables Explanatory Variables Retirement income Age Household head Log averaged consumption Separated/Div/Widowed Married †Single Other Towns Urban areas †Rural area Health Insurance Natural disaster Secondary & post secondary Tertiary †Primary and below Living arrangement Crowding Negative affective Positive affective Logged medical expenditure Crime index Number of males per household Number of females per household
-2 Log likelihood =1195.541 Nagelkerke R Square = 0.306 Model χ2 (21) = 360.02, P < 0.001

β Coefficient 0.166 0.044 -0.746 -0.033 -0.123 -0.179 -0.237 -0.359 2.831 0.032 0.599 -0.931 0.328 -0.072 0.007 -0.087 0.038 0.014 0.009 0.043

Odds Ratio 1.18 1.05 0.47 0.97 0.88 0.84 1.00 0.79 0.70 1.00 17.0 1.03 1.82 0.39 1.00 1.39 0.93 1.01 0.92 1.04 1.01 1.01 1.04

CI (95%) 0.52 -2.68 1.03 - 1.06*** 0.15 - 1.50 0.54 - 1.73 0.48 - 1.64 0.56 - 1.25 0.50 - 1.26 0.38 - 1.30 10.18 - 28.27*** 0.75 - 1.41 1.24 - 2.68** 0.04 - 4.23 1.02 - 1.88* 0.80 - 1.08 0.96 - 1.06 0.86 - 0.98** 0.93 - 1.16 1.00 - 1.03 0.84 - 1.21 0.87 - 1.26

Overall correct classification = 89.1% Correct classification of cases of no health conditions = 98.5% Correct classification of cases with al least one dysfunction =28.7% †Reference group *P < 0.05, **P < 0.01, ***P < 0.001

 

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Table 16.3: Logistic regression: Health conditions of men in the two wealthiest quintiles by some explanatory variables Explanatory Variables Retirement income Age Household head Log average annual consumption Separated/Div/Widowed Married †Single Other towns Urban †Rural area Health insurance Natural disaster Secondary & post-secondary Tertiary †Primary and below Living arrangement Crowding Negative affective Positive affective Logged medical expenditure Crime index Number of males in household Number of females in household β Coefficient 0.375 0.032 0.396 0.632 -0.227 -0.178 -0.124 -0.188 3.494 -0.142 0.081 -0.243 -0.139 -0.030 0.071 -0.019 0.086 0.007 0.157 0.185 Odds Ratio 1.46 1.03 1.49 1.88 0.80 0.84 1.00 0.88 0.83 1.00 32.90 0.87 1.08 0.78 1.00 0.87 0.97 1.07 0.98 1.09 1.01 1.17 1.20 CI (95%) 0.73 - 2.91 1.02 - 1.04*** 0.46 - 4.85 1.27 - 2.80** 0.49 - 1.29 0.62 - 1.13 0.68 - 1.15 0.59 - 1.16 20.64 - 52.45*** 0.67 - 1.13 0.79 - 1.49 0.46 - 1.32 0.69 - 1.10 0.81 - 1.17 1.04 - 1.11*** 0.93 - 1.04 1.00 - 1.19* 1.00 - 1.02 0.98 - 1.40 0.99 - 1.47

-2 Log likelihood = 2054.45 Nagelkerke R Square = 0.280 Model χ2(21) = 522.79, P < 0.001 Overall correct classification = 87.6%, Correct classification of cases of no health conditions = 99.0% Correct classification of cases with al least one dysfunction =29.0% †Reference group *P < 0.05, **P < 0.01, ***P < 0.001

 

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Health Insurance & Health
By Paul Andrew Bourne
Health insurance is established as an indicator of health care-seeking behaviour. Despite this reality, no study existed in Jamaica that examines those factors that determine private health insurance coverage. This study bridges the gap in the literature as it seeks to determine correlates of private health insurance coverage. The aim of this study is to understand those who possess Health insurance coverage in Jamaica so as to aid public health policy formulation.

In 2007, statistics revealed that 21 out of every 100 Jamaicans had health insurance coverage and 66 out of every 100 sought medical care, indicating that most of the people who utilized medical care services did not use health coverage. Within the context of the global economic downturn, increased job redundancies and prices of commodities, the uninsured will be asked to pay more for medical care. Apart from the increased odds of not utilizing health care services, little is known about the uninsured in Latin American and the Caribbean, and in particular Jamaica.

Married Jamaicans are more likely to purchase health insurance coverage compared to those who were never married, with urban residents being more likely to purchase health insurance than rural dwellers. An individual who has attained tertiary level education was more likely to purchase health insurance than one with at most primary level education, and those who lived alone were less likely to purchase health insurance coverage than those who dwelled with relatives or family members. Moreover the wealthiest were more likely to purchase health insurance, but were less healthy, and this indicates that income does not buy good health.