Data Quality in Jamaica

Data Quality in Jamaica
Paul Andrew Bourne

Data Quality in Jamaica

Paul Andrew Bourne

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First published in Jamaica, 2011 by Paul A. Bourne

© Paul A Bourne

ISBN

All rights of this book are reserved. No part of this publication may be reproduced (electronically or otherwise), stored in retrieval system, or transmitted in any other form (photocopying, recording or otherwise) with the prior permission of the publisher.

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TABLE OF CONTENTS page

List of Tables List of Figures Preface Acknowledgement Dedication PART I: HEALTH STATUS: USAGE OF HEALTH DATA Introduction 1

v ix x xii xiv 1

A theoretical framework of good health status of Jamaicans: using econometric analysis to model good health status 5 An Epidemiological Transition of Health Conditions, and Health Status of the OldOld-To Oldest-Old in Jamaica: A comparative analysis using two cross-sectional surveys 26 Self-evaluated health and health conditions of rural residents in a middle-income nation 56 Disparities in self-rated health, health care utilization, illness, chronic illness and other socio-economic characteristics of the Insured and Uninsured 83 Variations in social determinants of health using an adolescence population: By different measurements, dichotomization and non-dichotomization of health 113 Self-rated health status of young adolescent females in a middle-income developing country 140 Health of females in Jamaica: using two cross-sectional surveys 159

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Health of children less than 5 years old in an Upper Middle Income Country: Parents’ views 179 Health of males in Jamaica 204

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PART II: ERRORS IN DATA Introduction 230

10 Dichotomising poor self-reported health status: Using secondary cross-sectional survey data for Jamaica 232 11 Paradoxes in self-evaluated health data in a developing country 12 The validity of using self-reported illness to measure objective health 13 The image of health status and quality of life in a Caribbean society 253 278 298

Paul A. Bourne, Donovan A. McGrowder, Christopher A.D. Charles, Cynthia G. Francis 14 The quality of sample surveys in a developing nation 317

Paul A. Bourne, Christopher A.D. Charles, Neva South-Bourne, Chloe Morris, Denise Eldemire-Shearer, Maureen D. Kerr-Campbell

Part III: DATA QUALITY 15 Practices, Perspectives and Traditions 349

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List of Tables page Table 1.1.1: Good Health Status of Jamaicans by Some Explanatory Variables Table 1.1.2: Good Health Status of Elderly Jamaicans by Some Explanatory Variables Table 1.1.3: Good Health Status of Middle Age Jamaicans by Some Explanatory Variables 22 23 24

Table 1.1.4: Good Health Status of Young Adults Jamaicans by Some Explanatory Variables 25 Table 2.2.1. Socio-demographic characteristics of sample Table 2.2.2. Self-reported illness by sex of respondents, 2002 and 2007 Table 2.2.3. Self-reported illness by marital status, 2002 Table 2.2.4. Self-reported illness by marital status, 2007 Table 2.2.5. Self-reported illness by Age cohort, 2002 and 2007 Table 2.2.6. Mean age of oldest-old with particular health conditions Table 2.2.7. Diagnosed Health Conditions by Aged cohort Table 2.2.8. Self-reported illness (in %) by health status. Table 2.2.9. Health care-seeking behaviour and health status, 2007 Table 2.2.10. Health care-seeking behaviour by health status controlled for aged cohort Table 2.2.11. Logistic regression on Good Health status by variables Table 3.3.1. Demographic characteristics, 2002 and 2007 Table 3.3.2: Self-reported health conditions by particular social variables Table 3.3.3. Health care-seeking behaviour by sex, self-reported illness, health coverage, social hierarchy, education, age and length of illness, 2002 and 2007 Table 3.3.4. Stepwise Logistic regression: Social and psychological determinants of self-evaluated health, 2002 and 2007 49 50 51 52 53 65 67 43 44 45 46 47 48

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71

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Table 3.3.5. Stepwise Logistic regression: R-squared for Social and psychological determinants of self-evaluated health, 2002 and 2007 Table 4.4.1. Crowding, expenditure, income, age, and other characteristics by health insurance status Table 4.4.2. Health, health care seeking behaviour, illness and particular demographic characteristics by health insurance status Table 4.4.3. Age cohort by diagnosed illness Table 4.4.4. Illness by age of respondents controlled for health insurance status Table 4.4.5. Age cohort by diagnosed health condition, and health insurance status Table 4.4.6. Logistic regression: Explanatory variables of self-rated moderate-to-very good health Table 4.4.7. Logistic regression: Explanatory variables of self-reported illness Table 4.4.8. Logistic regression: Explanatory variables of health care seeking behaviour Table 4.4.9. Logistic regression: Explanatory variables of self-reported diagnosed chronic illness Table 4.4.10. Multiple regression: Explanatory variables of income Table 4.4.11. Logistic regression: Explanatory variables of health insurance status Table 5.5.1: Demographic characteristic of studied population Table 5.5.2: Particular demographic variables by area of residence Table 5.5.3: Logistic regression: Variables of antithesis of illness among adolescence population Table 5.5.4: Logistic and Ordinal Logistic regression: Factors explaining self-reported health status of adolescents Table 5.5.5: Self-rated health status and antithesis of illness Table 6.6.1: Descriptive analysis of variables of target cohort Table 6.6.2: Socio-demographic and psychological variables of self-related health status of the sample
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72

102

103 104 105 106

107 108 109

110 111 112 134 136

137

138 139 157

158

Table 7.7.1. Sociodemographic characteristics of sample by area of residence, 2002 and 2007 174 Table 7.7.2. Self-rated health status by self-reported illness, 2007 Table 7.7.3. Self-rated health status by income quintile, 2007 Table 7.7.4. Self-reported diagnosed health condition by per capita income 175 177 178 196 197 198 222 223 224

Table 8.8.1. Socio-demographic characteristic of sample, 2002 and 2007
Table 8.8.2. Health status by self-reported illness Table 8.8.3. Health status by self-reported diagnosed illness Table 9.9.1. Sociodemographic characteristics of sample, 2002 and 2007 Table 9.9.2. Health status and self-rated illness Table 9.9.3. Predictors of poor self-reported illness by some explanatory variables, 2002

Table 9.9.4. Predictors of not self-reporting an illness by some explanatory variables, 2007 225 Table 9.9.5. Model summary for 2002 logistic regression analysis Table 9.9.6. Model summary for 2007 logistic regression analysis Table 10.10.1. Socio-demographic characteristic of sample Table 10.10.2. Very poor or poor and moderated-to-very poor self-reported health status of sexes (in %) Table 10.10.3. Odds ratios for very poor or poor and moderate-to-very poor self-reported health of sexes by particular variables Table 10.10.4. Odds ratios of poor health status by age cohorts Table 11.11.1. Socio-demographic characteristic of sample by sex of respondents Table 11.11.2. Socio-demographic characteristic of sample by educational level Table 11.11.3. Socio-demographic characteristic of sample by self-reported illness Table 11.11.4. Stepwise Logistic Regression: Good self-rated health status by socio-demographic, economic and biological variables
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226 227 249

250

251 252 273 274 275

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Table 11.11.5. Stepwise Logistic Regression: Self-reported illness by socio-demographic and biological variables Table 12.12.1. Life expectancy at birth for the sexes, self-reported illness, and mortality, 1989-2007 Table 12.12.2. Life expectancy at birth of population and sex of children by self-reported illness Table 13.13.1 Demographic characteristics of sample for CLG and JSLC, 2007

277

292

293 312

Table 13.13.2 Quality of life and health status by gender of respondents, CLG and JSLC 313 Table 13.13.3 Quality of Life and health status by area of residence, CLG and JSLC Table 13.13.4 Quality of life, health status and standardized health status Table 13.13.5 QoL by economic situation of individual and family, CLG Table 14.14.1. Health and curative care visits: 2000-2007 Table 14.14.2: Proportion of Survey (Sample) vs. Proportion of Population Table 14.14.3. Descriptive characteristic of samples: Sub-national and National surveys Table 14.14.4. Characteristic of samples: Sub-national and National surveys 314 315 316 344 345 346 347

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List of Figures page

Figure 2.2.1. Diagnosed health conditions, 2002 and 2007 Figure 2.2.2. Self-reported illness (in %) by Income Quintile, 2002 and 2007 Figure 7.7.1. Mean scores for self-reported diagnosed health conditions, 2002 and 2007 Figure 8.8.1. Mean age of health conditions of children less than 5 years old Figure 8.8.2. Health status by Parent-reported illness (in %) examined by gender

54 55 176 199 200

Figure 8.8.3. Health status by parent-reported illness (in %) examined by area of residence 201 Figure 8.8.4. Health status by parent-reported illness (in %) examined by social classes
Figure 8.8.5. Health status by health care-seeking behaviour

202
203

Figure 9.9.1. Mean age for males with particular self-reported diagnosed illness Figure 12.12.1. Life expectancy at birth for the population by self-reported illness (in %)

228 294

Figure 12.12.2. Life expectancy at birth for female by self-reported illness of female (in %) 295 Figure 12.12.3. Life expectancy at birth for male by self-reported illness of male (in %) Figure 12.12.4. Mortality (in No of people) and self-reported illness/injury (in %) 296 297

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PREFACE

For centuries, academics, researchers, government agents and policy specialists have relied on cross-sectional data, results and statistics from International Agencies (World Bank; World Health Organization, WHO; United Nations, UN; International Labour Organization, ILO; et cetera), Statistical Institute of Jamaica (STATIN) and Planning Institute of Jamaica (PIOJ) as well as reputable Universities (Oxford, Cambridge, Harvard, Yale, Stanford, University of the West Indies, et cetera). The fundamental assumption is that the quality of the data is high, reliable and accurate for usage. Since 1989, STATIN and PIOJ have been collecting selfreported data from Jamaicans to guide and formulate policies. The data are published in the Jamaica Survey of Living Conditions (JSLC). Although the JSLC is a collection of results from a modified questionnaire of World Bank’s Living Standard Household Survey, academics, researchers and governmental agencies have been using the data, there is a fundamental assumption that the data quality is reliable, valid and accurate for usage. Relying on an assumption of data quality is unscientific, non-verification, cannot detect and correct errors. One of the basic tasks of demography is the production of reliable demographic estimates. Despite the available demographic tools available to demographers, epidemiologists, and statisticians, they have been using Survey Data published by the STATIN and PIOJ, without data quality verification (ie. Content Error Testing). Data quality in Jamaica may be good (ie Census and JCLC), but this is based on low coverage errors. There are two main types of errors in data, coverage and content, but much attention has been placed on coverage errors examinations. Coverage errors refer to the completeness of inclusion of people or events in a sample. This error can be rectified through better sampling selection, sampling frame, which has been done for the selection of samples for the JSLC. The gradual development and consistent updating of sampling frames, from which the people are drawn for the JSLC, reduced the coverage errors from identification, modification and rectification. Thus the statistical methods relating to coverage errors have been utilized as recently as on the 2007-2009 JSLC, making the errors lesser and increasing the completeness of the sample.

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Demographers and Epidemiologists are concerned about pursuing reliable data in order to increase the quality of their estimates. As such, they evaluate the ‘Content’ of the collected data, to identify and correct any ‘Content Errors’. This is performed using matching census records with records from surveys, as apart of the data quality verification and reliability process. In an effort to correct errors in age data, demographers (such as Preston, Elo, Rosenwaike and Hill; Caldwell; Ewanks) have used matching studies to assess content errors, testing the consistency of the data. The assumption here is that data are not of a high quality because they have been collected from the source(s). The same holds true in Jamaica. It is within the aforementioned context that we must examine the quality of surveys, censuses, and other data collection methods in Jamaica and not hide behind tradition, credentials, status and past reputation. By accepting that data are of a high quality denotes that we are failing to continuously utilize science in the pursuit of truth as truth is not constant over time (or indefinitely continuous). This volume is designed primarily to clarify the quality of sample survey data in Jamaica, particularly the Jamaica Survey of Living Conditions (JSLC). Science is about inquiry, which means that it can be used to question the cosmology and foundations of current epistemology. The JSLC publishes collected data on different issues reported by Jamaicans, suggesting that the estimates from this could be incorrect, unreliable or of low quality without content verification. Quality is data is critical to the quality estimates, indicating that low quality data can result in erroneous findings (or estimates). The gradual development of health science cannot rest on the pillows of unsubstantiated data. It is this unscientific and crucial assumption that can create fundamental flaws in policy formulation and intervention programmes. This book recognizes the likeliness of such a situation and seeks to evaluate the content of health data, because the importance of the health is critical to national development and so cannot be felt to unverified data. Readers who seek supplementary coverage of areas which are in this volume can review odds ratio, confidence interval, multivariate analysis (logistic and multiple regressions), theoretical and conceptual framework, as these will provide more information on technical issues used in this book. The majority of the chapters were taken from publications in different journals. All the chapters were carefully selecting in keeping with the general theme and focus of the volume,
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“Data Quality in Jamaica’. Initially the materials appearing in these pages were rehearsed in a graduate class in Public Health at the University of the West Indies, Mona and with other scholars in health sciences. Chapters 12 and 13 were co-authored with other Caribbean and International scholars, aiding in the coverage of the material and the scope of the volume. All the other chapters were solely written by the author.

Paul Andrew Bourne

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ACKNOWLEDGEMENT

The pursuit of science cannot rest on unsubstantiated (or unverified) data. Science is about the pursuit of truth, which denotes that nothing is with verification. Facts cannot be established on unverifiable information (such as myth, tradition, customs, religious cosmology), but it about is reaching out to establish truths that are based on logic, gradual development, reliability, generality, and validity. Thus, the use of health data cannot rely on tradition, authority, and credibility as the health affects development, which makes it reliability. Effective policies cannot be fashioned around inaccurate and lowly reliable data as this will void the cost of data collection. While science is a gradually developed with trial and errors, verification of data paramount the final results. Thus, quality data is crux upon which science holds its value. As the quality of the data collected holds more of the depth of the scientific estimates than the logic and other scientific approaches. For decades (from 1989), in Jamaica, we have been using survey data, relying accuracy of the data collector and institutions. This denotes that while we advance estimates and fact from the data, there exists a scientific unanswered question “How is the data quality of survey, particularly the JSLC?” Within the value of science, unanswered questions are good as they for the basis upon which future studies are conducted, as this will advance knowledge on health matters in Jamaica. The question of ‘How is data quality in Jamaica?” in respect to the content errors are still unanswered. This book, therefore, owes itself to the pursuit of truth more that the establishment of tradition and/or the sanctioning of authority. Thus, the author acknowledges the search for truth as this the birth of knowledge that can guide effective policy and intervention programmes.

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DEDICATION
This book is dedicated to the

‘Pursuit of Truth’

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Part I
HEALTH STATUS: USAGE OF HEALTH DATA

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INTRODUCTION

Many researchers, scholars and academics utilize secondary cross-sectional survey data, because of the high cost and time allocation in conducting primary research. Secondary cross-sectional surveys are in response to affordability and time, which create a barrier to primary data collection. The question that is frequently asked, therefore, by user of those data is “How reliable is the content and coverage of the already collected data?” Some researchers rely on the credibility of the data collectors (such as WHO, UN, ILO, World Bank, Statistical Agencies, NASA, established Universities) in answering the aforementioned question. While those Organizations are of a high standard, science is not about the non-verification of objects, events and data estimates, particularly data collected from other sources. The reliance on the reliability and validity of data source go to the crux of trustworthiness and not science. This assumption violates the premise of science, verification of issues. Although science rest on gradual development of issues before conclusions are finalized, many of the aforementioned Organizations have been in existence for some time and have access to more resources than single scholar (or researcher), particularly in developing nations, but this does not denote an arbitrary and unquestionable reliance on them, their data, estimates and findings. The meaning of unquestionable facts destroys all the pillows upon which science are based, retard logic and further scientific discoveries. Science is about the pursuit of truths, indicating that questioning is a normal component in validation, consistency and reliability. Outside of the verification of truths, there can be no science as everything is mere proposition. It is the logic, gradual development, continuous inquiry, verification, validity, consistency and reliability that distinguish sciences from mythology, customs, traditions and opinions. In Jamaica, researchers, scholars, academics and ordinary citizens rely on the estimates and results of STATIN, PIOJ, the University of the West Indies and other established International Organizations. There is an undeniable reality that those Agencies have long contributed to scientific estimates, results and cosmologies, but this is not sufficient to end scientific inquiry on their conceptualizations and results. Many discoveries emerged out of the questioning of the establishments, epistemology, cosmology, customs, traditions, authority, and
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not accepting things because they were stated. Knowledge is not consistent over all time intervals, making its changeable on new information at a specified interval. Science is about the continuation of truth searching, making it a persistent quest of all things including the establishments, customs, tradition, knowledge, authority, and ‘natural philosophy’. Facts and knowledge are changeable with logic, gradual development of new facts, justification of knowledge, refutation of the old knowledge, testing of old and the establishment of new principles, laws, and methods. Science cannot co-exist indefinitely with unanswered, nonjustifiable and opinionated issues as “What is in an interval (i.e. in time)?” can change with systematic, logical and conceptual inquiry. Simply put, cosmologies (or world views) are based on a set of propositions that are flexible. With more knowledge about something, the truth changes and different paradigms are established to explain events, object, situations and knowledge. Hence, knowledge is only hidden in time, changeable with time and empiricism. If knowledge is not stationary throughout time, then the reliability of result can be questioned, irrespective of the credibility of the data source. Since 1989, Jamaicans and other scholars have been using the data of STATIN and/or PIOJ, with some never questioning the content of the results. However, statisticians have questioned the coverage of the data source that has led to modifications of the sampling frames and the decreasing of coverage errors. This has increased the generalizability of sample frame, size and data estimates. Clearly we should question issue to advance science, knowledge of what is. With the lowering of coverage errors in JSLC, this does not frame any purity about the content. Because the instrument of the JSLC is a modification of the World Bank’s Living Standard Household Survey, this does not mean unquestionable estimates, results and content. While the instrument provide some reliability about questionnaire, reliability does not end with questionnaire and sample design. Caribbean demographers (such as Paul Bourne, Sharon Priestley, Julian Devonish), who are cognizant of content errors in surveys as well as censuses, have neglected to provide a framework for understanding data quality in Jamaica. They as well as other non-demographers have relied on traditions, authority, agencies and the industrialized nations to stipulate data quality, without questioning estimates, results and data sources. The author, who is a trained demographer, has published plethora of articles from health using the JSLC. Because science is about the pursuit of truths, the author is therefore concerned
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about data quality, particularly in the JSLC, as the correctness of the estimates relies on data source. There are two main types of data errors (such as coverage and content errors). On many occasions statisticians have evaluated coverage errors that have increased the quality of the sample estimates. Their efforts and works have increased the generality of the sample survey, but do not ruled out other errors. This means that the quality of the JSLC data is currently higher in Jamaica, increasing reliability and provision for better generalizability of the population. Like statisticians, the author is questioning the quality of the JSLC data. This in no way speaks of the questioning of the credibility of workers – including data gathers, statisticians and workers. Instead of the author’s questioning of the content of the JSLC data on health, is just an inquiry that validate and/or improve the estimates and results. Prior to beginning a comprehensive inquiry of the data quality, the author presents works that use the data on health. This volume is separated into two Parts. Part I is the presentation of different topics on health using the JSLC dataset. It is worth adding here chapters on health for readers to understanding the estimates and results and how this volume will enhance those estimates and findings.

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CHAPTER

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A Theoretical Framework of Good Health Status of Jamaicans: Using Econometric Analysis to Model Good Health Status
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. 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=1.051.46; 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.

I NTRODUCTION 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 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

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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 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):

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H t = ƒ (H t-1 , G o , B t , MC t , ED) ……………………………………………….. Model (1) where H t – current health in time period t , stock of health (H t-1 ) in previous period , B t – smoking and excessive drinking, and good personal health behaviours (including exercise – G o ), MC t ,- 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)]. H t = H* (H t-1 , P mc , P o , ED, Et , R t , A t , G o ) ………………………..…………… Model (2) Model (2) expresses current health status H t as a function of stock of health (H t-1 ), price of medical care P mc , the price of other inputs P o , education of each family member (ED), all sources of household income (Et ), family background or genetic endowments (G o ), retirement related income (R t ), asset income (A t ). 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. 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;
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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 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

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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. The proposed model that this research seeks to evaluate is displayed below [Model (3)]:
H t = f(H t-1 ,P mc , ED i , R t , A t , Q t , HH t , C i , En i , MS i , HI i , HT i , SS i , LL i ,X i , CRi , D i , O i , Σ(NP i ,PP i ), M i ,N i , FS i , A i , W i , ε i )….. Model (3)

The current health status of a Jamaica, H t , is a function of 23 explanation variables, where H t is current health status of person i, if good or above (i.e. no reported health conditions four

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week leading up to the survey period), 0 if poor (i.e. reported at least one health condition); H t-1 is
stock of

health for previous period; lnPmc is logged cost of medical care of person i; ED i 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; HI i is health insurance coverage of person i, 1 if have a health insurance policy, 0 if otherwise; HT i 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; M i 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 N i 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), W i . 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 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.
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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, see Model 4). The factors are retirement income, logged medical expenditure, marital status, 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 1.1.1).
Ht = f(H t-1, Rt , P mc , ED i , MSi, HI i , SS i,AR i, X i , Σ(NP i,PP i), M i,N i, ε i)...……………………………..... Model (4)

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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) (see Model 5). These factors were education, marital status, health insurance, area of residence, gender, psychological conditions, number of males in household, number of children in household and previous health status (see Table 1.1.2).
Ht = f(H t-1, ED i, MSi , HI i, ,ARi , X i, Σ(PP i), M i,N i, ε i)...…………………………………………………..... 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

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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, and number of children in household and previous health status (see Table 1.1.3)
Ht = f(Ht-1, P mc , En i , HI i, X i , Σ(NP i),N i, εi)...........................................……………………………..... Model (6)

Based on table 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 1.1.4).
Ht = f(H t-1, W i, HI i, Σ(NP i), εi )...............................................…………………………….....Model (7)

From table 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).

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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.

H t = f(H t-1 , R t , P mc , ED i , MS i , HI i , SS i ,AR i , X i , Σ(NP i ,PP i ), M i ,N i , ε i )...………………………..... Model (4) H t = f(H t-1 , ED i , MS i , HI i , ,AR i , X i , Σ(PP i ), M i ,N i , ε i )...………………………………………..... Model (5) H t = f(H t-1 , P mc , En i , HI i , X i , Σ(NP i ),N i , ε i )....................................……………………………..... Model (6) H t = f(H t-1 , Wi , HI i , Σ(NP i ), ε i ).......................................................……………………….…….......Model (7) H t = f(H t-1 ,P mc , ED i , R t , A t , Q t , HH t , C i , En i , MS i , HI i , HT i , SS i , LL i ,X i , CR i , D i , O i , Wi ,ε i )……………………………………………………………………….. Σ(NP i ,PP i ), M i ,N i , FS i , A i , 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 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.

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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 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
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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 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
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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 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]
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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.

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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23. Bourne PA. Social and environmental correlates of self-evaluated health of poor aged Jamaicans. HealthMed journal 2010;4(2):284-296. 24. Homer D, Lemeshow S. Applied Logistic Regression, 2nd edn. John Wiley & Sons Inc., New York, 2000. 25. Cohen L, Holliday M. Statistics for Social Sciences. London, England: Harper and Row, 1982. 26. Asnani MR, Reid ME, Ali SB, Lipps G, Williams-Green P. Quality of life in patients with sickle cell disease in Jamaica: rural-urban differences. Rural and Remote Health. 2008; 8: 1-9. 27. WHO. The Social Determinants of Health; 2008. Available at http://www.who.int/social_determinants/en/ (accessed April 28, 2009). 28. Kelly M, Morgan A, Bonnefog J, Beth J, Bergmer V. The Social Determinants of Health: developing Evidence Base for Political Action, WHO Final Report to the Commission; 2007.
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29. Wilkinson RG, Marmot M. Social Determinants of Health. The Solid Facts, 2nd ed. Copenhagen: World Health Organization; 2003. 30. Solar O, Irwin A. A Conceptual Framework for Analysis and Action on the Social Determinants of Health. Discussion paper for the Commission on Social Determinants of Health DRAFT April 2007. Available from http://www.who.int/social_determinants/resources/csdh_framework_action_05_07.pdf (Accessed April 29, 2009). 31. Graham H. Social Determinants and their Unequal Distribution Clarifying Policy Understanding The MilBank Quarterly 2004;82 (1), 101-124. 32. 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. 33. Bourne PA, Eldemire-Shearer D. Differences in social determinants of health between men in the poor and the wealthy social strata in a Caribbean nation. North Am J of Med Sci 2010; 2(6):267-275.

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Table 1.1.1: 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 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

-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

-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

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

-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

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 1.1.2: 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 -20.37 1.22 40192.9 1.24 0.00 0.96 1.000 0.327 0.00 3.38 0.00 0.30 -0.10 0.12 -0.22 0.89 -0.06 0.00 -0.16 -0.49 -0.33 -3.35 0.33 0.40 Std Error 0.15 0.17 0.22 0.65 0.04 0.00 0.12 0.15 0.15 0.22 0.14 0.21 Wald statistic 0.47 0.47 1.00 1.86 2.16 0.93 1.80 11.00 4.82 241.88 5.32 3.48 P 0.495 0.491 0.317 0.172 0.142 0.335 0.180 0.001 0.028 0.000 0.021 0.062 Odds Ratio 0.90 1.12 0.81 2.44 0.95 1.00 0.86 0.61 0.72 0.04 1.39 1.49 CI (95%) Lower 0.67 0.81 0.53 0.68 0.88 1.00 0.68 0.46 0.54 0.02 1.05 0.98 Upper 1.22 1.56 1.23 8.76 1.02 1.00 1.08 0.82 0.97 0.05 1.83 2.27

38.60

-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

-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

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 1.1.3: 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
2

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

0.03 -0.29 -0.57 0.50 -0.09 0.00 0.31

-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

χ (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 1.1.4: 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

2
An Epidemiological Transition of Health Conditions, and Health Status of the Old-Old-To-Oldest-Old in Jamaica: A comparative analysis using two crosssectional surveys

There is a paucity of information on the old-old-to-oldest-old in Jamaica. In spite of studies on this cohort, there has never been an examination of the epidemiological transition in health condition affect this age cohort. The aims of the current study are 1) provide an epidemiological profile of health conditions affecting Jamaicans 75+ years, 2) examine whether there is an epidemiological transition in health conditions affecting old-old-to-oldest-old Jamaicans, 3) evaluate particular demographic characteristics and health conditions of this cohort, 4) assess whether current self-reported illness is strongly correlated with current health status, 5) mean age of those with particular health conditions, 6) model health status and 7) provide valuable information upon which health practitioners and public health specialists can make more informed decisions. In 2007, 44% of old-to-oldest-old Jamaicans were diagnosed with hypertension, which represents a 5% decline over 2002. The number of cases of diabetes mellitus increased over 570% in the studied period. The poor indicated having more health conditions than the poorest 20% of the sample. The implications of the shift in health conditions will create a health disparity between 75+ year adults and the rest of the population.

Introduction
The elderly population (ages 60+ years) constituted 10.9% of Jamaica’s population, which means that this age cohort is vital in public health planning [1]. Eldemire [2] opined that “The majority of Jamaican older persons are physically and mentally well and living in family units”. This view was substantiated in an early study; when Eldemire [3] found that approximately 81 percent of the seniors reported that they were physically competent to care for themselves, without any form of external intervention. Eldemire’s work revealed that 88.5 percent being physiologically independent.
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Many elderly persons are more than physically independent as Eldemire [3] found 65.5 percent of them supported themselves, with males reporting a higher self-support (82.6%) compared to females, 47.7%. A study conducted by Franzini and colleague [4] found that social support was directly related to self-reported health, which is collaborated by Okabayashi et al’s study [5]. The aforementioned situation can explain why many elderly are offered socioeconomic support. Eldemire [3] found that approximately 71 percent of children were willing to accept responsibility for their parents, with seniors who were older than 75 years being likely to need support. Seniors ages 75-84 years are referred to as old-old and those 85+ are referred as oldest-old. The 2001 Population Census of Jamaica found approximately 66 percent of the elderly live in private households [6], which imply that the aged are physically and mentally competent. This is in keeping with Eldemire’s studies [2, 3]. The functional independence of the elderly is not atypical to Jamaica as DaVanzo and Chan [7], using data from the Second Malaysian Family Life Survey which includes 1,357 respondents of age 50 years and older living in private households, noted that some benefits of co-residence range from emotional support, companionship, physical and financial assistance [8]. Embedded in DaVanzo and colleague’s work is the issue of ‘Is it functional independence or stubbornness?’ that accounts for the elderly persons’ report that they are physically and mentally well in order that family and onlookers will not request that they live in home care facilities. This brings into focus the issues of health status and health conditions of elderly Jamaicans. Physical disability and health problems increase with age [9]. Bogue [9] opined that demand for medical care increases with ageing and that this is owing to health deteriorations. He [9] also noted that as an individual age, the demands on their children increases and likewise
27

their demand on the public services also increases. Statistics revealed that 15.5% of Jamaicans reported suffering from an illness/injury in 2007; this was 2.8 times more for individuals ages 65+ and 2.4 times for those people ages 60+ years [10]. This further goes to concurs with Bogue’s perspective that ageing is associated with increased illness. Concurrently, in 2007, 51.9% of Jamaicans who reported an illness, in the 4-week period of the survey, indicated that this was recurring compared to 75.1% of the elderly. The elderly also sought more medical care (72%) compared to the general population (66%), purchased more medication (78.3% compared to the general population, 73.3%) and had more health insurance coverage (27.8%) compared to the general population (21.1%) [10]. The aforementioned findings only concur with the work of Bogue, and still does not provide us with changing in health conditions of the elderly in particular the old-old-to-oldest old. Using a sub-sample of 3,009 elderly Jamaicans, Bourne [11] found that the general wellbeing was low; but, within the context of Bogue’s work, raised the question of the old-old or the oldest-old’s health status. Bourne [12], using a sub-sample of 1,069 respondents ages 75+ years, found that 51.3% of those 75-84 years had poor health status compared to 52.6% of the oldest-old. There was no significant statistical difference between the poor health status of oldold and oldest-old Jamaicans. While poor health status comprised of health conditions, Bourne’s works do not provide us with an understanding of the health conditions over time and whether these are changing or not. A study on elderly Barbadians by Hambleton and colleagues [13] found that current health conditions (diseases) were the most influential predictor of current health status and adds value to discourse that health conditions provide some understanding of health status. However, this finding does not clarify the epidemiological transition of health conditions affecting the old-old-to-oldest-old Caribbean nationals, in particular Jamaicans.
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An extensive review of health and ageing literature in the Caribbean revealed no study that has examined an epidemiological transition of health conditions of people 75+ years. In Jamaica, 4% of the population in 2007 were older than 75+ years, indicating that over 100,000 Jamaicans have reached 75 years or older. This is a critical group that must be studied for public health planning as more elderly have chronic dysfunctions than any other age cohort in the population. The aims of the current study are 1) provide an epidemiological profile of health conditions affecting Jamaicans 75+ years, 2) examine whether there is an epidemiological transition in health conditions affecting old-old-to-oldest-old Jamaicans, 3) evaluate particular demographic characteristic and health conditions of this cohort, 4) assess whether current selfreported illness is strongly correlated with current health status, 5) mean age of those with particular health conditions, 6) model health status and 7) provide valuable information upon which health practitioners and public health specialists can make more informed decisions.

Materials and Methods
The current study utilized a sub-sample of approximately 4% from each nationally crosssectional survey that was conducted in 2002 and 2007. The sub-sample was 282 people ages 75+ years from the 2007 cross-sectional survey (6,783 respondents) and 1,069 people ages 75+ years from the 2002 survey (25,018 respondents). Living Conditions which began in 1989. 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. This means that the country was
29

The survey is known as the Jamaica Survey of

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 (i.e. LFS) was selected for the JSLC [14, 15]. The sample was weighted to reflect the population of the nation. The JSLC 2007 [14] 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 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 Age: The length of time that one has existed; a time in life that is based on the number of years lived; duration of life. Or it is the total number of years which have elapsed since birth [16]. Elderly (or aged, or seniors): The United Nations defined this as people ages 60 years and older [17].
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Old-Old. An individual who is 75 to 84 years old [9] Oldest-old. A person who is 85+ years old [9]. Health conditions (i.e. 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. Good health status is a dummy variable, where 1=good to very good health status, 0 = otherwise Income Quintile can be used to operationalize social class. Social class: 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). 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

31

and a dichotomous categorical variable. The level of significance used in this research was 5% (i.e. 95% confidence interval).

Result
Sociodemographic characteristics of sample Of the sample for 2002, 57.6% was female compared to 57.4% females in 2007. The mean age in 2002 was 81.3 years (SD = 5.6 years), and this was 81.4 years (SD = 5.4 years) in 2007. More than two-thirds of the 2002 sample dwelled in rural areas, 20.8%. In 2007, the percent of sample who resided in urban areas increased by 169.7%, and a reduction by 25.9% of those who dwelled in rural zones compared to a marginal reduction of 4.3% in semi-urban areas (Table 2.2.1). Concurrently, in 2007, 51.6% of sample reported suffering from an illness which was a 22% increase over 2002. Five percent more people sought medical care in 2007 over 2002 (ie 69.2%). Illness (or health conditions) A number of shifts in diagnosed health conditions were observed in this study. The number of cases of hypertension and arthritis were observed between the two periods. The most significant increase in health conditions was in diabetes mellitus cases (i.e. 576%) (Figure 2.2.1). A cross tabulation between self-reported illness and sex revealed that there was no significant statistical correlation between the two variables (Table 2.2.3). Although no statistical associated existed between the self-reported illness and sex, the percent of men who reported an illness in 2007 over 2002 increased by 30.5% compared to 16.4% for females. No significant statistical relationship existed between self-reported illness and marital status (Tables 2.2.4, 2.2.5). In spite of the aforementioned situation, the divorced sample
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reported the greatest percentage of increased in self-reported illness (16.7%) followed to married people (15.7%); separated individuals (11.6%), widowed (5.8%) and those who were never married reported the least increase in self-reported illness (5.2%). No significant statistical correlation existed between self-reported illness and age cohort of respondents – P >0.05 – (Table 2.2.5). Although the aforementioned is true, the percent of old-old who reported illness in 2007 over 2002 increased by 23.6% compared to a 16.6% increased in the oldest-old cohort over the same period. A cross tabulation between diagnosed self-reported health conditions and age of respondents revealed a significant association between the two variables (Table 2.2.6). On examination, in 2002, the lowest mean age was recorded by people who indicated that they had arthritis. However, for 2007, the mean age was the lowest for old-old-to-oldest-old who had reported the common cold. A shift which is evident from the finding is the mean age of those with diabetes mellitus in 2002 (79.5 yrs. ± 2.5 yrs), which was the second lowest age of person with illness in 2002 to the greatest mean age for people with the same dysfunction in 2007 (90.20 yrs ± 3.54 yrs) (Table 2.2.6). Based on Table 2.2.7, no significant statistical association was found between diagnosed health conditions and age cohort of the sample – P >0.05. In spite of this reality, some interesting findings are embedded in the data across the two years. The findings revealed an exponential increase in diabetes mellitus and the common cold. However, the most significant increase occurred in diabetic cases in the oldest-old. Reductions were recorded in hypertension, arthritis and unspecified categorization. A cross-tabulation between self-reported illness (in %) and Income Quintile revealed a significant statistical correlation between both variables for 2002 (χ2 (df = 4) = 11.472, P =0.022)

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and 2007 (χ2 (df = 4) = 10.28, P < 0.05). Based on Figure 2.2.2, the poor had highest selfreported cases of illness compared to the other social groups. Although this was the case for 2002 and 2007, the wealthy reported more illnesses than the wealthiest 20% of sample. Concurrently, the poorest 20% reported the greatest increase in self-reported illness for 2007 over 2002 (19.4%) with the wealthy segment of the sample reported the least increase (2.7%). The first time that the Jamaica Survey of Living Conditions (JSLC) collected information on self-reported illness and general health status (health status) of Jamaicans was in 2007. Based on that fact, this study will not be able to compare the health status of the sample for the two studied years; however, this will be the basis upon which future studies can compare. The crosstabulation between the two aforementioned variables was a significantly correlated one (χ2 (df = 2) = 39.888, P < 0.001) (Table 2.2.8). Health care-seeking behaviour A cross tabulation of health care seeking behaviour and aged cohort revealed no statistical relationship between the two variables for 2002 (χ2(df=1) = 0.004, P = 0.947) and for 2007 (χ2(df=1) = 1.308, P = 0.253). Table 2.2.9 revealed that there is a significant statistical relationship between health careseeking behaviour and health status of the sample (χ2 (df = 2) = 10.539, P = 0.005, cc=0.265). Further examination showed that 57.1% of old-old-to-oldest-old sought medical care, and as health status decreases the percent of sample seeking medical care increases. Of those who reported poor health, 86.7% of them have sought medical care in the 4-week period of the survey. When the aforementioned association was further investigated by aged cohort, the difference was explained by old-old (χ2 (df = 2) = 11.296, P = 0.004, cc=0.305) and not oldestold (χ2 (df = 2) = 0.390, P = 0.823) (Table 2.2.10).

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Controlling health care-seeking behaviour and health status by aged cohort revealed that the old-old are more likely to seek more medical care with reduction in their good health status; but this is not the case for the oldest-old. With one-half of the cells in oldest-old category being less than 5 items, the non-statistical association possibly is a Type II Error. Type II Error indicates that there is no statistical significant relationship between variables when there is a probability that an association does exists.

Multivariate analysis: Predictors of good health status Good health status of old-old-to-oldest-old Jamaicans can be predicted by self-reported illness (Table 11). Based on Table 2.2.11, self-reported illness is a negative predictor of good health status (OR = 0.176, 95% CI = 0.095 - 0.328). Twenty-four percent of the variability in good health status can be explained by self-reported illness. Concurrently, no other variable except self-reported illness was significantly correlated with good health status. Furthermore, 75.9% of the data were correctly classified: 90.5% of good health status and 42.0% of those who has stated otherwise (poor to fair health status). In addition, an old-old-to-oldest-old Jamaican is 0.824 times less likely to reported good health status.

Discussion
Ageing is directly correlated with increased functional disability [18]. This can be concurred with the disproportionate number of elderly who continue to outnumber other age cohorts in visits medical care facilities and number of cases in chronic dysfunctions. Statistics from the Planning Institute of Jamaica and Statistical Institute of Jamaica revealed that elderly Jamaicans disproportionately outnumber other ages in diabetes mellitus, hypertension, arthritis and mortality [10, 16, 17]. The Jamaican Ministry of Health data showed that the prevalence of

35

chronic diseases is greatest for those 65+ years. Is the aforementioned information sufficient enough for public health policy makers, health care practitioners and academics as a reference to a comprehensive understanding of the old-old-to-oldest-old in Jamaica? The answer is a resounding no as this study will show. Bogue [9] showed that functional capacity, demand for medical care and health problems increase with ageing which concurs with Erber’s work [18] and other research [19]. The current study found that 10.3% more old-old-to-oldest-old Jamaicans reported at least one health condition in 2007 over 2002 and this was associated with at 1.7% increase health care-seekers. In 2007, 73 out of every 100 old-old-to-oldest-old Jamaicans sought medical care which is the national figure (66 out of every 100 Jamaicans). The research found that significant statistical association existed between medical care and health status of sample. Interestingly in this study though, is the fact that as the old-old’s health status fall to poor 89 out of every 100 of them sought care compared to 53 out of every 100 old-old who had good health. A critical finding of this study is the fact that after an individual reaches 85 years and beyond, there is no difference in seeking health care. Intertwined in this finding is the psychological reluctance of prolonged life at the onset of illness compared to those in the old-old categorization as many of oldest-old believe that they have lived a long time and so they are able to transcend this life. People’s cognitive responses to ordinary and extraordinary situational events in life are associated with different typologies of wellbeing [20]. Positive mood is not limited to active responses by individual, but a study showed that “counting one’s blessings,” “committing acts of kindness”, recognizing and using signature strengths, “remembering oneself at one’s best”, and “working on personal goals” are all positive influences on wellbeing [21,22]. Happiness is not a mood that does not change with time or situation; hence, happy people can experience negative
36

moods [23]. Within the context of the aforementioned, an individual who has lived or is living for 85+ years consider this as a blessing and so they are comfortable with that blessing, which accounts for the psychological reluctance to prolong life if this is accompanied by severity of illness. The World Health Organization opined that the among the challenges of the 21st century will how to prevent and postpone dysfunctions and disability in order to maintain the health, independence and mobility for aged population. The current research found that 42 out of every 100 old-old-to-oldest old Jamaican reported an illness in 2002 and this increased to 52 out of every 100. The substantiate matter is not merely the increase in dysfunctions; but it is the epidemiological transition in typology of diseases. Health conditions were not only reported, they were substantially diagnosed by a medical practitioner. An alarming finding was the exponential increase in number of diabetic (576%) and cold cases (330.77%) in 2007 over 2002, indicating the challenge of revamping lifestyle at older ages. It should be noted here that the average age for an old-old-to-oldest-old having diabetes mellitus increased from 79.5 years to 90.0 years, and therefore this reinforces the point that psychological reluctance to live with critical changes that diabetes mellitus may cause. The challenge for the old-old-to-oldest in Jamaica is not merely the lifestyle changes that follow diabetes mellitus; but the complication from having more than one illnesses and the issues surrounding the diseases. These issues include blindness, renal failure and micro-vascular

complications. Forty-four out of every 100 persons in the sample had hypertension in 2007, and the fact that diabetes mellitus and hypertension are strongly related, the old-old-to-oldest-old will be experiencing many health problems. A study by Callender [27] found that 50% of individuals

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with diabetes had a history of hypertension and given that Morrison [28] opined that these are twin problems for the Caribbean, it is more problematic for the people 75+ years. Studies have shown that ageing is directly correlated with increased health conditions, this research found that such a reality dissipates after 75+ years. While this study is not able to provide an explanation for this finding, factors such as sex, marital status, poverty and area of residence are no longer contributions to health disparity which contradicts other studies [29-34]. Poverty, which is critical to health determinant [35,36] and the fact that it explains incapacity to afford food, health care and other necessities, may seem improbable as not being a predictor of good health of old-old-to-oldest old Jamaicans. However, it is associated with health conditions for this sample. This means that health status is wider than dysfunction, and how this cohort feels about life is even broader than the challenge of physical incapacity. In spite of this claim, health conditions are a strong predictor of health status for the old-old-to-oldest-old in Jamaica. This concurs with Hambleton and colleagues’ work [13] which found that 33.6% of the total explanatory power (38.2%) of health status of elderly Barbadians was accounted for by current health conditions. Embedded in Hambleton et al. [13] and the current study is the critical role that current health conditions play in determining health status.

Conclusion
This study provides information upon which public health and health practitioners can make more informed decisions about this age group. A fundamental way for this impetus to proceed is the immediate diabetes education in the elderly population in particular those 75+ years. On a panel titled ‘Diabetes Education for the Elderly’ at the 11th Annual international Conference on ‘Diabetes and Ageing’ conference in 2005 at the Jamaica Conference Centre, Merrins [37] called
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for diabetes care treatment for elderly which indicates that the issue of diabetes education is not new but that it is even more important today within the context of the current findings. With over 570% more diabetic cases found in the old-old-to-oldest elderly in Jamaica, this means that on average 96% more cases are diagnosed each year. This is a massive increase in such cases, and cannot go unabated. The increase in diabetes mellitus could be accounted for by the new persons who become 75 years each year or a higher percentage cases that were formerly undetected become diagnosed. Which ever is the case, a public health promotion thrust is required to test all Jamaicans 75+ within the context of a disease prevention agenda and healthy life expectancy. Hence, the implications of the shift in health conditions will create a health disparity between 75+ year adults and the rest of the population. This requires better management of older persons [38], which will also require that people 75+ with good health be tested for diabetes mellitus.

References
1. Statistical Institute of Jamaica (STATIN). Demographic statistics, 2007. Kingston: STATIN; 2008. 2. Eldemire D. A situational analysis of the Jamaican elderly, 1992. Kingston: Planning Institute of Jamaica; 1995. 3. Eldemire D. The elderly and the family: The Jamaican experience. Bulletin of Eastern Caribbean Affairs. 1994; 19:31-46. 4. Franzini L, Fernandez-Esquer ME. Socioeconomic, cultural, and personal influences on health outcomes in low income Mexican-origin individuals in Texas. Soc Sci and Med. 2004; 59:16291646. 5. Okabayashi H, Liang J, Krause N, Akiyama H, Sugisawa H. Mental health among older adults in Japan: Do sources of social support and negative interaction make a difference? Soc Sci and Med. 2004; 59:2259-2270. 6. Statistical Institute of Jamaica (STATIN). Population Census 2001, Jamaica. Volume 1:Country Report. Kingston, Jamaica: STATIN; 2001.
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7. DaVanzo J, Chan A. Living arrangements of older Malaysians: Who coresides with their adult children. Demography. 1994;31:9113. 8. Pan American Health Organization, (PAHO), World Health Organization, (WHO). Health of the elderly aging and health: A shift in the paradigm. USA: PAHO,WHO; 1997. 9. Bogue DJ. Essays in human ecology, 4. The ecological impact of population aging. Chicago: Social Development Center; 1999. 10. Planning Institute of Jamaica, (PIOJ), Statistical Institute of Jamaica, (STATIN). Jamaica Survey of Living Conditions, 2007. Kingston: PIOJ, STATIN; 2008. 11. Bourne PA. Medical Sociology: Modelling Well-being for elderly People in Jamaica. West Indian Med J. 2008; 57:596-04. 12. Bourne PA. Good health status of older and oldest elderly in Jamaica: Are there differences between rural and urban areas? The Open Med J. 2009;2:18-27. 13. Hambleton IR, Clarke K, Broome HL, Fraser HS, Brathwaite F, Hennis AJ. Historical and current predictors of self-reported health status among elderly persons in Barbados. Rev Pan Salud Public. 2005;17: 342-352. 14. 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. 15. 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. 16. Statistical Institute of Jamaica (STATIN). STATIN; 2006. Demographic Statistics, 2005. Kingston:

17. World Health Organization, (WHO). Definition of an older or elderly person. Washington DC: 2009. 18. Erber J. Aging and older adulthood. New York: Waldsworth; 2005. 19. Planning Institute of Jamaica, (PIOJ), Statistical Institute of Jamaica, (STATIN). Jamaica Survey of Living Conditions, 1989-2006. Kingston: PIOJ, STATIN;1989-2007. 20. Lyubomirsky S. Why are some people happier than others? The role of cognitive and motivational process in wellbeing. Am Psychologist. 2001;56:239-249.
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21. Sheldon K, Lyubomirsky S. How to increase and sustain positive emotion: The effects of expressing gratitude and visualizing best possible selves. J of Positive Psychology. 2006;1:73-82. 22. Abbe A, Tkach C, Lyubomirsky S. 2003. The art of living by dispositionally happy people. J of Happiness Studies. 2003;4:385-404. 23. Diener E, Seligman MEP. 2002, Very happy people. Psychological Sci. 2002;13: 81–84. 24. WHO. Health promotion glossary. Geneva: World Health Organization; 1998. 25. WHO. Primary prevention of mental, neurological and psychosocial disorder. Geneva: WHO; 1998. 26. WHO. The world health report, 1998: Life in the 21st century a vision of all. Geneva: WHO;1998. 27. Callender J. Lifestyle management in the hypertensive diabetic. Cajanus. 2000;33:67-70. 28. Morrison E. Diabetes and hypertension: Twin trouble. Cajanus. 2000;33:61-63. 29.WHO. The Social Determinants of Health. Washington DC: WHO; 2008. 30. Victorino CC, Guathier AH. The social determinants of child health: variations across health outcomes – a population-based cross-sectional analysis. BMC Pediatrics. 2009, 9:53 31. Kelly M, Morgan A, Bonnefog J, Beth J, Bergmer V. The Social Determinants of Health: developing Evidence Base for Political Action, WHO Final Report to the Commission; 2007. 32. Wilkinson R, Marmot M, eds. Social Determinants of Health. The Solid Facts. 2nd ed. Copenhagen Ø: World Health Organization; 2003. 33. Solar O, Irwin A. A Conceptual Framework for Analysis and Action on the Social Determinants of Health. Discussion paper for the Commission on Social Determinants of Health. Geneva: WHO; 2007. 34. Graham H. Social Determinants and their Unequal Distribution Clarifying Policy Understanding The MelBank Quarterly. 2004; 82:101-124. 35. 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. 36. Alleyne GAO. Equity and health: Views from the Pan American Sanitary Bureau. In: Pan American Health Organization, (PAHO). Equity and health. Washington DC: PAHO; 2001. p. 311.

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37. Herd P, Goesling B, House JS. Socioeconomic Position and Health: The Differential Effects of Education versus Income on the Onset versus Progression of Health Problems. J of Health & Soci Behavior. 2007; 48:223-238 38. Merrins C. Special considerations in providing medical nutrition therapy to the elderly with diabetes. West Indian Med J. 2005; 54:39.

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Table 2.2.1. Socio-demographic characteristics of sample Variable 2002 Frequency Sex Male Female Marital status Married Never married Divorced Separated Widowed Income Quintile Poorest 20% Poor Middle Wealthy Wealthiest 20% Self-reported illness Yes No Health care-seeking behaviour Yes No Area of residence Rural Semi-urban Urban Educational level Primary or below Secondary Tertiary Health insurance coverage Yes No Age Mean (SD) Public health care expenditure Mean (SD) Private health care expenditure Mean (SD) 453 616 304 255 18 22 442 239 216 195 194 225 441 601 306 136 731 222 116 662 309 24 % 42.4 57.6 29.2 24.5 1.7 2.1 42.5 22.4 20.2 18.2 18.1 21.0 42.3 57.7 69.2 30.8 68.4 20.8 10.9 66.5 31.1 2.4 26.7 73.3 81.37 yrs (±5.38yrs)
Ja $368.89.54 (±Ja.$1518.66) Ja. $1856.04 (±Ja.$4347.78)

2007 Frequency 120 162 88 66 6 7 105 56 51 74 58 43 141 132 102 38 83 56 143 % 42.6 57.4 32.4 24.3 2.2 2.6 38.6 19.9 18.1 26.2 20.6 15.2 51.6 48.4 72.9 27.1 50.7 19.9 29.4

48 4.6 998 998 81.29 yrs (±5.6yrs)
Ja $341.54 (±Ja.$1165.60) Ja. $1436.23 (±Ja.$2060.42)

43

Table 2.2.2. Self-reported illness by sex of respondents, 2002 and 2007 20021 Self-reported illness Male N (%) Yes No Total 174 (39.3) 269 (60.7) 443 Female N (%) 267 (44.6) 332 (55.4) 599 Male N (%) 60 (51.3) 57 (48.7) 117 Female N (%) 81 (51.9) 75 (48.1) 156 20072

1 χ2 (df = 1) = 2.927, P =0.087 2 χ2 (df = 1) = 0.011, P =0.916

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Table 2.2.3. Self-reported illness by marital status, 2002 Marital status* Self-reported illness Married N (%) Yes No Total 140 (46.8) 159 (53.2) 299 Never married N (%) 88 (34.8) 165 (65.2) 253 Divorced N (%) 9 (50.0) 9 (50.0) 18 Separated N (%) 10 (45.5) 12 (54.5) 22 Widowed N (%) 190 (43.2) 250 (56.8) 440

* χ2 (df = 4) = 9.027, P =0.060

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Table 2.2.4. Self-reported illness by marital status, 2007 Marital status* Self-reported illness Married N (%) Yes No Total 55 (62.5) 33 (37.5) 88 Never married N (%) 26 (40.0) 39 (60.0) 65 Divorced N (%) 4 (66.7) 2 (33.3) 6 Separated N (%) 4 (57.1) 3 (42.9) 7 Widowed N (%) 51 (49.0) 53 (51.0) 104

* χ2 (df = 4) = 8.589, P =0.072

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Table 2.2.5. Self-reported illness by Age cohort, 2002 and 2007 20021 Self-reported illness Old-Old N (%) Yes No Total 333 (42.8) 445 (57.2) 778 Oldest-Old N (%) 108 (40.9) 156 (59.1) 264 Old-Old N (%) 110 (52.9) 98 (47.1) 208 Oldest-Old N (%) 31 (47.7) 34 (52.3) 65 20072

1 χ2 (df = 1) = .289, P =0.591 2 χ2 (df = 1) = .535, P =0.465

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Table 2.2.6. Mean age of oldest-old with particular health conditions

20021 Health conditions Cold Diarrhoea Asthma Diabetes mellitus Hypertension Arthritis Other Total Mean Age (±SD) 80.00 86.00 0.00 79.50 80.13 79.32 81.64 80.14 (±2.50) (±0.84) (±0.69) (±1.75) (±4.73)

20072

Mean Age (±SD) 77.63 85.00 81.00 90.92 81.21 79.13 83.90 82.75 (±1.77) (±9.66) (±5.20) (±4.84) (±4.95) (±3.54) (±6.82) (±4.50)

F statistic [7,134] = 2.085, P = 0.049

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Table 2.2.7. Diagnosed Health Conditions by Aged cohort 20021 Diagnosed Health conditions Aged cohort Old-Old % Cold Diarrhoea Asthma Diabetes mellitus Hypertension Arthritis Other 1.5 0.0 0.0 3.0 47.8 35.8 11.9 0.0 Oldest-Old % 0.0 8.3 0.0 0.0 58.3 8.3 25.0 0.0 Aged cohort Old-Old % 7.2 2.7 1.8 11.1 44.1 12.6 11.7 2.7 Oldest-Old % 0.0 3.2 3.2 16.1 45.2 6.5 22.6 3.2 20072

No

1 χ2 (df = 1) = 10.028, P =0.074 2 χ2 (df = 1) = 5.382 P =0.613
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Table 2.2.8. Self-reported illness (in %) by health status. Health Status Good Self-reported illness Yes No Total χ2 (df = 2) = 39.888, P < 0.001, cc=0.357 n (%) 21 (25.3) 62 (74.7) 83 Fair n (%) 60 (55.0) 49 (45.0) 109 Poor n (%) 60 (74.1) 21 (25.9) 81

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Table 2.2.9. Health care-seeking behaviour and health status, 2007 Health Status Good Health care-seeking behaviour No Yes Total χ2 (df = 2) = 10.539, P = 0.005, cc=0.265 n (%) 9 (42.9) 12 (57.1) 21 Fair n (%) 21(35.6) 38 (64.4) 59 Poor n (%) 8 (13.3) 52 (86.7) 60

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Table 2.2.10. Health care-seeking behaviour by health status controlled for aged cohort Health status Aged cohort Good Old-old1 Health CareSeeking Behaviour No Fair Bad Total

7 (46.7)

18 (36.7)

5 (10.9)

30 (27.3)

Yes Total Oldest-old2 Health CareSeeking Behaviour No

8 (53.3) 15

31 (63.3) 49

41 (89.1) 46

80 (72.7) 110

2 (33.3)

3 (30.0)

3 (21.4)

8 (26.7)

Yes Total
1 2

4 (66.7) 6

7 (70.0) 10

11 (78.6) 14

22 (73.3) 30

χ (df = 2) = 11.296, P =0.004, cc=0.305 χ (df = 2) = 0.390, P =0.823

2 2

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Table 2.2.11. Logistic regression on Good Health status by variables Variable Self-reported illness Age Middle Class Upper class †Poor Coefficient -1.735 -0.041 -0.083 0.391 Std. Error 0.317 0.030 0.414 0.759 Wald statistic 29.950 1.910 0.040 0.264 Odds ratio 0.176 0.960 0.921 1.478 95.0% C.I. 0.095 - 0.328*** 0.905 - 1.017 0.409 - 2.072 0.334 - 6.546

Married Divorced, separated or widowed †Never married

0.297 -0.110

0.393 0.376

0.574 0.086

1.346 0.896

0.624 - 2.907 0.428 - 1.872

Urban area Other town †Rural area Constant

0.347 -0.398 2.979

0.350 0.414 2.456

0.981 0.922 1.471

1.414 0.672 19.667

0.712 - 2.808 0.298 - 1.513 -

χ2 =40.083, p < 0.001 -2 Log likelihood = 283.783 Nagelkerke R2 =0.222 Overall correct classification = 75.9% Correct classification of cases of good self-rated health = 90.5% Correct classification of cases of not good self-reported health = 42.0% †Reference group *P < 0.05, **P < 0.01, ***P < 0.001

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Figure 2.2.1. Diagnosed health conditions, 2002 and 2007

Figure 1 expresses the percentage of people who reported being diagnosed with particular health conditions in 2002 and 2007. Each number denotes a different health condition: cold, 1; diarrhoea, 2; asthma,3; diabetes mellitus, 4; hypertension, 5; arthritis, 6; other (unspecified), 7; and non-diagnosed illness, 8.

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Figure 2.2.2. Self-reported illness (in %) by Income Quintile, 2002 and 2007

Figure 2 expresses the percentage of people who reported an illness by income quintiles for 2002 and 2007. Q1 denotes the poorest 20% to the wealthiest 20% (ie Q5).

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CHAPTER

3
Self-evaluated health and health conditions of rural residents in a middleincome nation

In Jamaica, in 1989, the national poverty rate was 30.5% and this exponentially fell by 208.1% in 2007, but in the latter year, rural poverty was 4 times more than peri-urban and 3 times more than urban poverty rate. Yet there is no study on health status and health conditions in order to examine changes among rural residents. The present study aims to (1) examine epidemiological shifts in typology of health conditions in rural Jamaicans, (2) determine correlates and estimates of self-evaluated health status of rural residents, (3) determine correlates and estimates of selfevaluated health conditions of rural residents and (4) assist policy makers in understanding how intervention programmes can be structure to address some of the identified inequalities among rural residents in Jamaica. In 2002, 14% of respondents indicated having an illness in the 4week period of the survey compared in 17% in 2007. For 2002, there are 12 determinants of health: 11 social and 1 psychological determinants. In 2007, there were 7 determinants of health: 6 social and 1 biological variables. The determinants accounted for 22.6% of the explanatory power of the health model for 2002 and 44.7% for 2007. Sixty-eight percentage points of the health status model can be accounted for by self-reported illness (i.e. R squared = 30.4%). With the exponential increase in diabetes mellitus and health inequalities that exists today in rural Jamaica, public health and other policy makers need to use multidimensional intervention strategy to address those inequalities.

Introduction
The health of a population is critical to all forms of development. This is a justifiable rationale for governments’ investment in health care and the health system. Despite governments in Latin America and the Caribbean increased investment in health since the 1980 [1], there are still many inequities in health among and within their nations [2]. This is evident in the health disparities indicators as well as the social determinants of health [3-6]. The advancement in technology and medical sciences have not abated the disparities in infant mortality, poverty, health service
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utilization, and health differentials among Latin America and Caribbean nations as well as among the social hierarchies. Casas et al. [4] cited that the improvements in health in the region are not in keeping with the region’s economic development rates and the same can be said between the wealthy and the poor. In Jamaica, which is an English-speaking country in the region, in 1989 the national poverty rate was 30.5% and this exponentially fell by 208.1% in 2007, but in the latter year, rural poverty was 4 times more than peri-urban and 3 times more than urban poverty rate [9]. Statistics from the WHO for 2007 showed that both life expectancy and healthy life expectancy at birth was at least 4 years more for females than males [7]. Many empirical studies have found that rural residents had lower health status and/or more health conditions, greater levels of poverty and lower levels of education compared to their urban counterparts [8-18], and these are also the case in Jamaica [19]. Those disparities speak to socio-economic and health inequalities in many states. Although there is empirical evidence which revealed that health inequalities and inequities do exist between rural and urban residences as well as among social hierarchies and between the sexes in Latin America and the Caribbean in particular Jamaica, only few studies were found that have examined the health status of rural people in the region [14, 19-28]. The different researches in the region on rural health have not investigated epidemiological transition of health conditions in the rural areas, and in order to tackle the identified health disparities and inequalities, intervention techniques must be based on analytic research on the cohort and not a general understanding of the nation. Inequity and/or inequalities in health can only be addressed in the region if they are understood through research within each nation, and that policy makers cannot rely on finding of studies outside of the region or their countries in order to effectively remedy the challenges that
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they face. The relationship between poverty and ill health is empirically established, but the focus of the region since the 1980s has been poverty reduction and while this has been materializing, the health disparities are still evident today [3]. Embedded in the literature therefore are income maldistribution, working conditions and health outcome inequalities, health determinants inequalities, lower material wellbeing and poverty direct influence on health. Poverty also indirectly influences health service utilization, quality of received care and healthy life expectancy. With poverty been substantially a rural phenomenon, investment in health in rural areas require an understanding of the health and changes occurring in health conditions among the residents. It follows therefore that a research for a nation with area of residence between an explanatory variable does not provide a comprehensive insight into many of the issues that are embodied in a particular municipality (or area of residence). For decades (since the 1980s), Jamaican statistical agencies have been collected data on health status of the people and these are used to guide policies, but with disproportionately more people in rural areas in poverty and poverty influences inequalities and/or inequities in a group, then this is rationale for the research of rural Jamaicans.

The WHO [8] opined that 80% of chronic illnesses were in low and middle income countries, suggesting that illness interfaces with poverty and other socio-economic challenges. Poverty does not only impact on illness, it causes pre-mature deaths, lower quality of life, lower life and unhealthy life expectancy, low development and other social ills such as crime, high pregnancy rates, and social degradation of the community. According to Bourne & Beckford [15], there is a positive correlation between poverty and unemployment; poverty and illness; and crime and unemployment. Embedded in those findings are the challenges of living in poverty, and the perpetual nature of poverty and illness, illness and poverty, poverty and unemployment,
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economic deprivation and psychological frustration of poor families. Sen [18] encapsulated this well when he forwarded that low levels of unemployment in the economy is associated with higher levels of capabilities. This highlights the economic challenge of unemployment and equally explains the labour incapacitation on account of high levels of unemployment, which goes back to the WHO’s perspective that chronic illnesses are more experienced by low-tomiddle income peoples. According to WHO [8], 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.

Within the aforementioned findings, area of residence in particular rural area is too much of an important variable to be treated as an explanatory concept. The health outcome inequalities will be decline merely by investing in the health sector of the general population. Montgomery [17] opined that urban causes of mortality and disability provide understanding into urban-rural health differentials. The paper provides answers some of urban health disparities in developing countries and compares those situations with those faced by rural residents. Montgomery’s findings [17] were generally on developing countries and while it does give some insights to the urban-rural health inequalities, it cannot be used to formulate policies or intervention strategies specifically for Jamaica. The rationale embedded in this argument is the fact that not all developing countries are at the same socio-economic stage of development, and therefore requires research for any chosen intervention techniques that they decide to utilize to effect health changes. Concurrent investment in health is critical to economic development [29]; once again this has not result in removal of health inequalities in Latin America and the Caribbean in particular Jamaica [3-5]. Therefore more research is needed to understand the health outcome in rural zones in order to the health disparity gaps in the region and within political states. The
59

present study aims to (1) examine epidemiological shifts in typology of health conditions in rural Jamaicans, (2) determine correlates and estimates of self-evaluated health status of rural residents, (3) determine correlates and estimates of self-evaluated health conditions of rural residents and (4) assist policy makers in understanding how intervention programmes can be structure to address some of the identified inequalities among rural residents in Jamaica.

Materials and Method

The current study extracted samples of 15,260 and 3,322 rural residents from two surveys collected jointly by the Planning Institute of Jamaica and the Statistical Institute of Jamaica for 2002 and 2007 respectively [30,31]. The method of selection of the sample from each survey was solely based on rural residence. The survey (Jamaica Survey of Living Conditions) was begun in 1989 to collect data from Jamaicans in order to assess policies of the government. 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; 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 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

60

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 labor force. One third of the Labor Force Survey (i.e., LFS) was selected for the JSLC [30, 31]. The sample was weighted to reflect the population of the nation. The JSLC 2007 [30] 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 it has data on selfreported 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 modeled 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-days), 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 that were added in 2002 and later removed from the instrument, with the except of a few new modules each year. The non-response rate for the survey for 2007 was 26.2% and 27.7%. The non-response includes refusals and rejected cases in data cleaning. Measurement 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
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variable was later created from this construct (1=no 0=otherwise) in order to be applied in the logistic regression. This was used to indicate health status (i.e. dependent variable) for 2002. Self-rated health status: is measured using people’s self-rate of their overall health status [32], which ranges from excellent to poor health status. The question that was asked in 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 and fair, 0 = Otherwise) [33-38]. The binary good health status was used as the dependent variable for 2007. Covariates Age is a continuous variable which is the number of years alive since birth (using last birthday) Social hierarchy: This variable was measured based on income quintile: The upper classes were those in the wealthy quintiles (i.e. quintiles 4 and 5); middle class was quintile 3 and poor class was those in lower quintiles (i.e. quintiles 1 and 2). Medical care-seeking behaviour was taken from the question ‘Has a health care practitioner, or pharmacist being 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). Age is a continuous variable in years.

Sex. This is a binary variable where 1= male and 0 = otherwise.

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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 the psychological state of an individual, and this is subdivided into positive and negative affective psychological conditions [39, 40]. 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.

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, t-test and an Analysis of Variance (ANOVA) were used to test the relationships between metric and/or dichotomous and non-dichotomous categorical variables. The level of significance used in this research was 5% (i.e. 95% confidence interval).

Results
Demographic Table 3.3.1 examines the demographic characteristics of the samples for 2002 and 2007. The samples were 15,260 and 3,322 rural respondents for 2002 and 2007 respectively. The findings revealed that 96.3% of the sample for 2002 respondents to the question ‘Have you had any illness in the past 4-weeks and the rate was 97% for 2007. In 2002, 14% of those who responded
63

to the question of illness claimed yes compared to 17% in 2007. When the respondents were asked to state the experienced health conditions, in 2002, 1.3% answered compared to 14.8% in 2007. Self-reported health conditions showed that exponential increases in influenza and respiratory conditions in 2007 over 2002. Hypertensive and arthritic cases fell by 44.1% and 75.7% respectively, while diabetes mellitus increased by 150% over the studied period. Eight-one percentage points of sample claimed to have at least a good health status and 6% at least poor health. Of those who indicated at least good health, 37% stated very good (or excellent) health compared to 1.1% who claimed very poor health of those who indicated at least poor health status. When respondents were asked ‘Why did you not seek medical care for your illness?’ in 2002, 23.2% stated could not afford it; 41.3% was not ill enough and 22.2% used home remedy. For 2007, 17.4% claimed that they were unable to afford it, 43.3% was not ill enough and 16.8% stated used home remedy.

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Table 3.3.1. Demographic characteristics, 2002 and 2007 2002 Variable Sex Male Female Marital status Married Never married Divorced Separated Widowed Social hierarchy Lower Middle Wealthy Self-reported illness Yes No Self-reported health conditions Acute Influenza Diarrhoea Respiratory diseases Chronic Diabetes mellitus Hypertension Arthritis Other Medical care-seeking behaviour Yes No Medical care utilization Public hospitals Private hospitals Public health care centres Private health care centres Health insurance coverage Yes No Age Median, in years, range)
Length of illness, in days, Median (range)

2007 % 50.6 49.3 25.6 66.6 0.6 1.1 6.3 47.8 20.8 31.4 13.5 86.5 n 1,654 1,668 513 1,462 22 20 112 1,828 650 844 536 2,688 % 49.8 50.2 24.1 68.7 1.0 0.9 5.3 55.0 19.6 25.4 16.6 83.4

n 7,727 7,524 2,460 6,436 56 104 610 7,298 3,169 4,791 1,987 12,713

1 4 6 10 82 48 40 1,302 740 499 80 285 528

0.5 2.1 3.1 5.2 42.9 25.1 20.9 63.8 36.4 39.1 6.3 22.3 41.3

80 19 51 64 118 30 130 349 202 127 8 76 158

16.3 3.9 10.4 13.0 24.0 6.1 26.4 63.3 36.7 37.2 2.3 22.3 46.3

1,036 7.0 13,714 93.0 23 (0 to 99) 7 (0 to 90)

464 14.5 2,715 85.5 25 (0 to 99) 7 (0 to 99)

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Bivariate analyses Table 3.3.2 presents self-reported health conditions by sex, age, health care-seeking behaviour, and length of illness of sample. Females were more likely to indicated suffering from the different health conditions than males except for respiratory diseases. Of those who stated a particular health conditions, those with chronic illness such as hypertension and arthritis were more likely to send more time suffering from the diseases.

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Table 3.3.2: Self-reported health conditions by particular social variables Health conditions Acute conditions Influenza Diarrhoea Respiratory Diabetes mellitus Chronic Hyperte Arthritis nsion

Variable

Other

P

2002
Sex (%) Male Female Total Age - in years- Mean (SD) Health care-seeking behaviour Yes (%) Total Length of illness –in days – Mean (SD) Sex (%) Male Female Total Age - in years- Mean (SD) Health care-seeking behaviour Yes (%) Total n Length of illness –in days – Mean (SD) 0.045
0.0 100.0 1 80.0 (0.0) 25.0 75.0 4 1.8 (1.7) 83.3 16.7 6 14.0 (24.6) 20.0 80.0 10 63.7 (13.2) 30.5 69.5 82 68.7 (13.7) 79.3 82 16 (11) 20.8 79.2 48 68.4 (12.60 83.3 48 18 (11) 35.0 65.0 40 56.0 (23.4) 65.0 40 19 (12)

< 0.0001

0.0 10 3 (0)

75.0 14 4 (2)

100.0 6 11 (5)

88.9 9 12 (11)

0.05 0.045

2007
<0.0001
42.5 57.5 80 19.5 (24.8) 41.3 80 8 (6) 36.8 63.2 19 20.1 (28.5) 56.9 43.1 51 24.3 (23.8) 20.3 79.7 64 56.5 (17.4) 27.1 72.9 118 64.0 (17.1) 46.7 53.3 30 68.3 (12.0) 46.7 30 112 (217) 43.1 56.9 130 36.0 (25.0) 70.5 129 57 (188)

<0.0001 < 0.0001

52.6 19 5 (2)

62.7 51 42 (172)

75.0 64.4 64 118 76 (135) 104 (239)

0.004

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Table 3.3.3 examines health care-seeking behaviour by sex, self-reported illness, health coverage, social hierarchy, educational levels, age and length of illness for 2002 and 2007. Based on Table 3, the mean age of someone who sought medical care is greater than someone who does not. There is no significant statistical association between medical care-seeking behaviour and self-reported illness, but there is a relationship between length of illness and medical careseeking behaviour.

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Table 3.3.3. Health care-seeking behaviour by sex, self-reported illness, health coverage, social hierarchy, education, age and length of illness, 2002 and 2007 2002 2007 Variable Health care-seeking behaviour Health care-seeking behaviour Yes No P Yes No P N (%) N (%) N (%) N (%) 0.011 0.112 Sex Male 511 (39.2) 333 (45.0) 134 (38.4) 89 (44.1) Female 791 (60.8) 407 (55.0) 215 (61.6) 113 (55.9) 0.360 0.130 Self-reported illness Yes 1261 (97.0)) 713 (96.6) 335 (96.3) 199 (98.5) No 39 (3.0) 25 (3.4) 13 (3.7) 3 (1.5) 0.197 0.013 Health insurance coverage Yes 89 (6.9) 40 (5.4) 270 (77.4) 173 (86.1) No 1210 (93.1) 700 (94.6) 79 (22.6) 28 (13.9) <0.0001 0.104 Social hierarchy Lower 545 (41.9) 363 (49.1) 167 (47.9) 115 (56.9) Middle 248 (19.0) 157 (21.2) 79 (22.6) 41 (20.3) Wealthy 509 (39.1) 220 (29.7) 103 (29.5) 46 (22.8) <0.0001 0.623 Educational level Primary or below 402 (40.5) 208 (41.5) 336 (96.3) 191 (94.6) Secondary 569 (57.4) 279 (55.7) 11 (3.2) 9 (4.5) Tertiary 21 (2.1) 14 (2.8) 2 (0.6) 2 (1.0) Age Mean (SD) – in years 46.4 (27.4) 40.4 (28.3) <0.0001 43.5 (27.5) 37.9 (146.8) 0.025 Length of illness Mean (SD) – in days 12 (11) 10 (9) <0.0001 7 (20) 5 (15) 0.01

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Multivariate analyses Table 3.3.4 represents information on social and psychological determinants of health of rural residents for 2002 and 2007. Based on Table 4, in 2002, there are 12 determinants of health: 11 social and 1 psychological determinants. On the other hand, in 2007, there were 7 determinants of health: 6 social and 1 biological variables. The determinants accounted for 22.6% of the explanatory power of the health model for 2002 and 44.7% for 2007. Sixty-eight percentage points of the health status model can be accounted for by self-reported illness (i.e. R squared = 30.4%).

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Table 3.3.4. Stepwise Logistic regression: Social and psychological determinants of self-evaluated health, 2002 and 2007 2002 2007 Explanatory variables: Coefficient Std. Odds 95% CI Coefficient Std. Odds 95% CI Error ratio Error ratio Income 0.000 0.000 1.00 1.00-1.00 0.000 0.000 1.00 1.00-1.00 Age -0.044 0.002 0.96 0.93-0.96 -0.052 0.004 0.95 0.94-0.96 Middle NS NS NS NS 0.321 0.196 1.38 0.94-2.02 Wealthy -0.311 0.090 0.73 0.61-0.88 NS NS NS NS †Lower 1.00 1.00 Total Durable good 0.058 0.013 1.06 1.03-1.09 NS NS NS NS Separated, divorced or widowed -0.367 0.109 0.69 0.56-0.86 NS NS NS NS Married -0.307 0.077 0.74 0.63-0.86 NS NS NS NS †Never married 1.00 NS NS NS NS Tertiary -0.175 0.065 0.84 0.72-0.98 NS NS NS NS †Primary or below 1.00 Social support -0.229 0.070 0.80 0.70-0.90 NID NID NID NID Male 0.803 0.011 2.23 1.95-2.56 0.563 0.134 1.76 1.35-2.28 Negative affective conditions -0.062 0.037 0.94 0.92-0.96 NID NID NID NID Number of females in household 0.123 0.025 1.13 1.05-1.22 NID NID NID NID Number of children in household 0.056 0.006 1.06 1.01-1.11 NID NID NID NID Length of illness -0.039 0.193 0.96 0.95-0.97 NS NS NS NS Crowding NS NS NS NS -0.081 0.029 0.92 0.87-0.98 Medical care-seeking = yes NS NS NS NS -1.01 0.26 0.36 0.21-0.60 Self-reported illness -2.225 0.15 0.11 0.08-0.15 -LL 6,381.3 1,562.6 n 12,666 2,817 Nagelkerke R square 0.226 0.447 2 χ 1220.5 670.0 NS – not significant (P > 0.05) NID – not in dataset and/or could not be measured based on the available data

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Table 3.3.5 shows the contribution of each explanatory variable to the model for 2002 and 2007. Based on Table 5, of the social and psychological determinants of health, age explains more the variability in health than another other determinant. Income contributed at most 0.2% to health of respondents. Using the not reporting an illness to measure health of rural respondents, age accounted for 77% of the health; but when self-reported health status is used to measure health, age accounted for only 11.5%. Table 3.3.5. Stepwise Logistic regression: R-squared for Social and psychological determinants of self-evaluated health, 2002 and 2007 2002 2007 Explanatory variables: R squared R squared Income 0.1 0.2 Age 17.4 11.5 Middle NS 0.4 Wealthy 0.1 NS Total Durable good 0.2 NS Separated, divorced or widowed 0.1 NS Married 0.2 NS Tertiary 0.1 NS Social support 0.2 NS Male 2.2 1.2 Negative affective conditions 0.4 NID Number of females in household 0.5 NID Number of children in household 0.1 NID Length of illness 1.0 NS Crowding NS 0.2 Medical care-seeking = yes NS 0.8 Self-reported illness 30.4 NS – not significant (P > 0.05) NID – not in dataset

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Discussion
The current health status of rural respondents was good (i.e. 81 out of every 100), but 17 out of every 100 had an illness. Inspite of reporting an illness, the present study found that 36 out of every 100 ill respondents had not sought medical care. Of those who did not utilize medical care although they indicated an illness, at least 41% claimed financial inadequacies and in 2007, 17% used home remedy. The results revealed that rural respondents have a conceptualization of illness and the fact that medical care outside of the home should be utilized based on length of illness and not mere ailments. Concurrently, illness accounts for most of current health status which emphasizes the dominant of the biomedical perspective in viewing health and health care in rural Jamaica. While self-reported chronic health conditions fell by over 41% in 2007 over 2002, the percent of those who reported acute conditions increased by over 436%. Of the increased cases of acute conditions, respiratory diseases accounted for 235% while influenza accounted for 3160% increase over 2002. Although overall self-reported chronic health conditions see a decline for 2007 over 2002, diabetes mellitus was the only condition that showed an increase in the study (i.e. 150%). Interestingly, the current findings showed that 107.1% more rural residents were covered by health insurance in 2007 over 2000, but this was corresponding to a minimal reduction in those seek medical care. The number of rural residents who were classified into the lower (i.e. working) class increased by 15.1% and a 19.1% of those in the wealthy class. With income being positively correlated with good health, an increase in the number of people the lower class highlights reduction in health for 2007. Males continue to report better health status than females, but this fell from 2.3 times more in 2002 to 1.8 times in 2007, which suggests that the reduction in income is substantially influence the quality of life of rural males.
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The current findings concur with the literature that showed that severity of illness (or length of illness), age, and health coverage are positively related to medical care seeking behaviour than illness [41-43]. Statistics from national cross-sectional surveys in Jamaica since 1989 [9] revealed that females were approximately more likely to report an illness and utilize medical care than males. When the absolute figures from the surveys were cross-tabulation, it was found that the statistical association which existed in 2002 disappeared in 2007. This is not atypical to Jamaica as a qualitative study in Pakistan on street children found that boys would attend formal health care are more likely to attend based on severity of illness and if it affects their economic livelihood [41]. Another study conducted in Anyigba, North-Central, Nigeria found that [42] found that 85 out of every 100 respondents waited for less than a week after the onset of illness to seek medical, and that 57 out of every 100 indicated that they would recover without treatment. In this research it was revealed that 43 out of every 100 rural residents indicated that they were not ill enough which suggests that they would recover in time. Health care facilities in Jamaica are primarily operated by females, and with the perception in the culture that males must be masculine, which include exhibiting strength, power and avoiding weakness, this is a justification of the rationale for severity of illness account for medical care-seeking behaviour as against actual illness [41-43]. Dunlop et al’s finding which found that females utilize health care facilities more than males [44] partially concurs with this research that found this to be the case in 2002. In 2002, 1.6 times more females sought medical care than males, but the study found that there was no significant association between sex and medical care-seeking behaviour for 2007. The explanation of this is embodied in the two things, (1) income, (2) inflation and (3) the increased number of people who were classified into the lower class.
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Income is positively correlated with social hierarchy, health, and employment status [16, 45-50]. Income which is among the social determinants of health, is directly associated with health through material wellbeing, but it is also associated with occupational and social hierarchies. The poor receives less of the income than the middle and wealthy classes, which denotes that an increased in the number of people in the lower class, income will be reduced and so will health status. It should be noted here that poverty which affect health, is exponential greater in rural Jamaica and that there are more females in rural household. The health careseeking disparity which is diminished can be explained by the inflation over the study. In 2007, inflation increased by 194% over 2006 [20] and coupled with the lower income, rural respondents in particular females who are more likely to unemployment, owns less material resources and increasingly are becoming single parents [9], would justify the narrowing of the health care-seeking gap that existed in 2002.Williams et al. [42] found that medical care-seeking behaviour did not differ significant between the sexes, which is in keeping with the situation for 2007 in this study. The WHO [8] found that poverty is associated with increased health conditions. Empirical evidence existed that showed the poverty is related to low levels of choices, income, access to health care services, and opportunities, which is highlighted in this study. Latin America and the Caribbean governments have increased investment in health care and in the 2006, the Jamaican government introduced the removal of public health care utilization fees for children (0 to 18 years) and expanded the a drug for the elderly programme to all people who suffer from particular chronic illnesses. While these undoubtedly increase the health outcomes which would have been lower if those opportunities were not present, health inequalities still exist among rural residents.
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With all the investment in health from decentralization of the health care system, drug for the elderly programmes, removal of health care user fees to health public care interventions, there is a rise in acute health conditions in particular influenza and respiratory diseases. The good news is the reduction in chronic health conditions. This good news is nothing to celebrate as diabetes mellitus has increased exponentially in the last one half decade. The reduction in number of hypertensive and arthritic cases correspond to lowered ages in reporting having those illnesses. The mean age of reporting hypertension has declined by 5 years (to 64 years) and 7.2 years (to 56.5 years). Furthermore, Morrison [51] postulated that hypertension and diabetes are now twin problems in the Caribbean and although the current study has shown a reduction in self-reported hypertensive people in rural Jamaica, 24 out of every 100 health conditions were accounted for by hypertension. Diabetes mellitus accounted for 13 out of every 100 health conditions, which speaks to a future health rural problem. Another researcher found that 50% of people with diabetes had a history of hypertension, and this future highlights a health challenge for policy makers and public health practitioners. The lowered ages of reporting particular chronic illnesses indicate that rural residents will be living longer with those conditions and this measure increase burden on the health care system in the future. A critical issue which emerged from this study is the value that rural residents ascribed to illness in determining their health status. There is a strong negative statistical correlation between self-reported illness and good health status. The findings indicated that 68% of the explanatory power of good health status can be accounted for by illness. This is not atypical as a research by Hambleton et al. on Barbadian elderly found that illness accounted for 88.0% of health status. It can be extrapolated from those findings that (1) the older one gets, he/she places more emphasis on illness in the evaluation of health status, (2) the relationship between illness and health
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appears to more causal than an associative one, (3) the biomedical approach to measuring health still predominates people’s perception, and (4) the culture which fashions the conceptualization of health is influences health care-seeking. Those issues are principally among the reasons that care is curative and not preventative in Jamaica and this is captured in the finding which showed that health care-seeking behaviour is negatively correlated with good health. Rural respondents who seek medical care are 64% less likely to report good health status, indicating embedded cultural dominance of the biomedical approach in the conceptualization of health. The dominance of the biomedical approach to the study of health in Jamaica is even high among medical researchers as a study conducted in 2007/08 examined medical history; health careseeking behaviour; health (i.e. diseases, medication consumption), mental health, sexual practices, dietary habits; lifestyle (i.e. violence and injury; smoking, narcotic and alcohol behaviour), community and home milieu, suggesting the greater weight on health from the perspective of illness, its treatment and measureable outcome as against people’s assessment of their health status [53]. Another limitation of the ‘Jamaica Health and Lifestyle Survey II’ was the omission of area of residence disaggregation of the collected though limited health data. The current study bridges this gap, and goes further by using self-assessed heath status in addition to self-rated health, health care-seeking behaviour and provide other pertinent health matters on rural Jamaicans.

Conclusion
Health inequalities in rural Jamaica still exist today. The current study found that in the future health care institutions will be called to invest more in the health system in order to address the health challenges of increased diabetes mellitus as well as respiratory diseases. On the other hand, despite investments in health by governments, progress in technology, public health
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services, increased levels of education and income since the last century, decision makers, public health practitioners and other health care providers need to recognize that increased life expectancy and lowered infant mortality rates have not addressed the challenges of in the health of rural population in Jamaica. General financial investment in health to control communicable diseases that are particularly detrimental for children such as diarrhoea and respiratory diseases are on the increase in rural areas, which means that the level of economic development since the 20th Century does not provide answers to the differences in health outcomes within a country. The identified health disparities in rural Jamaica denote that investment in health and health intervention strategies are not effectively addressing the health inequalities which are underlying in the health statistics. This means that the health inequalities in those areas in Jamaica will fuel future public health challenges for the societies, as well as increase the economic burden of health care system. The analyses provided in the current study clearly highlight the need for thinking that will incorporate the health realities of rural population in the agenda of policy makers.

The way forward
The present work highlights the lingering dominance of the biomedical perspective that influences health and health care in rural Jamaica. Hence the way forward for government and policy makers including health care practitioners as well as public health educators in order to reduce health inequalities is a multi-dimensional approach to health and health care as the current mechanism is working. The researcher is proposing (1) mobile clinics, (2) community and house visits from medical practitioners, (3) restructuring health care facilities to reflect a new preventative thrust, (4) introduced preventative care approach as a subject in all schools, (5) that the focus should not only be on the extreme of income poverty and health care access, but on
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opportunities, empowerment, security of poor and rural residents, (6) there is a need for a social security network that nutritious foods to rural residents, and (7) there is a need for the modification to the way public health programmes are fashioned and operated as well as a widening and new definition of the boundaries of public health intervention. These new mechanisms will be costly, but a reorganization of expenditure means that some of the money spent for curative care will be reduce as preventative care is the focal point and not curative health treatment. Another important thing which is needed is research on the value system of rural residents and this should be done using a longitudinal study in order to provide information for health care intervention strategies.

References
1. Pan American Health Organization, (PAHO). Investment in health: Social and economic returns, Scientific and Technical Publication, No. 582. Washington DC: PAHO, WHO; 2001. 2. Pan American Health Organization, (PAHO). Equity and health: Views from the Pan American Sanitary Bureau, Occasional Publication, No. 8. Washington DC: PAHO, WHO; 2001. 3. Alleyne GAO. Health and economic growth. In: Pan American Health Organisation. Equity and health: Views from the Pan American Sanitary Bureau, Occasional Publication No. 8. Washington DC; 2001: pp. 265-269. 4. Casas JA, Dachs NW, Bambas A. Health disparities 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. 5. Wagstaff A (2001). 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, pp.56-60. 6. Suarez-Berenguela RM. Health system inequalities and inequities in Latin America and the Caribbean: Findings and policy implications. In: Pan American Health Organization, (PAHO). Investment in health: Social and economic returns, Scientific and Technical Publication, No. 582. Washington DC: PAHO, WHO; 2001.pp.119-142. 7. World Health Organization, (WHO). World health statistics, 2009. Geneva: WHO; 2009: p. 48. 8. World Health Organization. Preventing Chronic Diseases a vital investment. Geneva: WHO; 2005.

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9. Planning Institute of Jamaica, (PIOJ), Statistical Institute of Jamaica, (STATIN). Jamaica Survey of Living Conditions, 1989-2007. Kingston: PIOJ, STATIN; 1989-2008. 10. Rodman H. Lower class families: The cultures of Poverty in Negro Trinidad. London: Oxford University press; 1971. 11. Lanjouw P, Ravallion M. Poverty and household size. The Economic Journal, 1995; 105: 1415-1434. 12. Fields GS. Poverty, inequality, and development. Cambridge, England: Cambridge University Press; 1980 13. WHO. Poverty reduction strategy papers: Their for health, second synthesis report. Geneva: WHO; 2004. Retrieved on 29th October 2009 from http://www.who.int/hdp/en/prsp.pdf. 14. 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. 15. Bourne PA, Beckford O. Poverty, Illness and Unemployment in Jamaica. Paper presented at the Caribbean Studies Association, CSA, 34th Annual Conference Hilton, Kingston, Jamaica, June 1-4, 2009. 16. 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. 17. Montgomery MR. Urban poverty and health in developing countries. Population Bulletin 2009; 64(2):1-20. 18. Sen, A. (1979). Poverty: An ordinal approach to measurement. Econometricia 44, 219-231. 19. Bourne PA. A theoretical framework of good health status of Jamaicans: using econometric analysis to model good health status over the life course. North American Journal of Medical Sciences. 2009; 1: 86-95. 20. 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; 1:99-109. 21. Bourne PA. (2009). An epidemiological transition of health conditions, and health status of the old-old-to-oldest-old in Jamaica: a comparative analysis. North American Journal of Medical Sciences. 2009; 1:211-219. 22. Bourne, P.A. (2009). Socio-demographic determinants of Health care-seeking behaviour, self-reported illness and Self-evaluated Health status in Jamaica. International Journal of Collaborative Research on Internal Medicine & Public Health 1(4), 101-130. 23. 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. 24. Bourne PA. Health Determinants: Using Secondary Data to Model Predictors of Well-being of Jamaicans. West Indian Medical J. 2008; 57:476-481. 25. Bourne PA. Medical Sociology: Modelling Well-being for elderly People in Jamaica. West Indian Medical Journal 2008; 57:596-604. 26. Asnani MR, Reid ME, Ali SB, Lipps G, Williams-Green P. Quality of life in patients with sickle cell disease in Jamaica: rural-urban differences. Journal of Rural and Remote Health 2008; 8: 890-899. 27. Hutchinson G, Simeon DT, Bain BC, Wyatt GE, Tucker MB, LeFranc E. Social and Health determinants of well-being and life satisfaction in Jamaica. International Journal of Social Psychiatry 2004; 50(1):43-53.

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28. Bourne PA, McGrowder DA. Rural health in Jamaica: examining and refining the predictive factors of good health status of rural residents. Rural and Remote Health 2009; 9 (2), 1116. 29. WHO. Macroeconomics and health: Investing in health for economic development. Geneva: WHO; 2001. 30. 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. 31. 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. 32. Kahneman D, Riis J. Living, and thinking about it, two perspectives. Quoted in: Huppert, F.A., Kaverne, B. and N. Baylis, The Science of Well-being, Oxford University Press; 2005. 33. Finnas F, Nyqvist F, Saarela, J. Some methodological remarks on self-rated health. The Open Public Health J 2008; 1: 32-39. 34. Helasoja V, Lahelma E, Prattala R, Kasmel A, Klumbiene J, Pudule I. The sociodemographic patterning of health in Estonia, Latvia, Lituania and Finland. European J of Public Health 2006; 16:8-20. 35. Molarius A, Berglund K, Eriksson C, et al. Socioeconomic conditions, lifestyle factors, and self-rated health among men and women in Sweden. European J Public Health 2007; 17:12533. 36. Leinsalu M. Social variation in self-rated health in Estonia: a cross-sectional study. Soci Sci and Medicine 2002; 55:847-61. 37. 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. 38. Idler EL, Kasl SV. Self-ratings of health: Do they also predict change in functional ability. Journal of Gerontology: Social Sciences 1995; 50B:S344-S353. 39. Diener E. Subjective well-being: The science of happiness and a proposal for a national index. American Psychological Association 2000; 55: 34-43. 40. Harris PR, Lightsey OR Jr. Constructive thinking as a mediator of the relationship between extraversion, neuroticism, and subjective wellbeing. European Journal of Personality 2005; 19: 409-426. 41. Ali M, de Muynck A. Illness incidence and health seeking behaviour among street children in Rawalpindi and Islamabad, Pakistan – a qualitative study. Child: Care, Health & Development 2005; 31(5):525-532. 42. Williams RE, Black CL, Kim H-Y, Andrews EB, Mangel AW, Buda JJ, Cook SF. Determinants of health-care-seeking behaviour among subjects with irritable bowel syndrome. Alimentary Pharmacology & Therapeutics 2006; 23(11):1667-1675. 43. Chevannes B. Learning to be a man: Culture, socialization and gender identity in five Caribbean communities. Kingston, Jamaica: University of the West Indies Press; 2001. 44. Dunlop DD, Manheim LM, Song J, Chang RW. Gender and ethnic/racial disparities in health care utilization among older adults. J of Gerontology: Soci Sci 2002; 57B (3): S221-S233. 45. Grossman M. The demand for health - a theoretical and empirical investigation. New York: National Bureau of Economic Research, 1972.

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46. Smith JP, Kington R. Demographic and Economic Correlates of Health in Old Age. Demography 47. Wilkinson RG, Marmot M. Social Determinants of Health. The Solid Facts, 2nd ed. Copenhagen: World Health Organization; 2003. 48. Graham H. Social Determinants and their Unequal Distribution Clarifying Policy Understanding The Milbank Quarterly 2004; 82 (1), 101-124. 49. 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. 50. Stronks K, Van de Mheen H, Van de Bos J, Mackenbach JP. The interrelationship between income, health and employment status. Int J of Epidemiol 1997; 26:592-600. 51. Morrison E. Diabetes and hypertension: Twin trouble. Cajanus 2002; 33:61-63. 52. Callender J. Lifestyle management in the hypertensive diabetic. Cajanus 2000; 33:67-70. 53. Wilks R, Younger N, Tulloch-Reid M, McFarlane S, Francis D. Jamaica health and lifestyle survey 2007/08. Kingston: Epidemiology research unit, Tropical Medicine Research Institute, University of the West Indies, Mona; 2008.
1997; 34:159-70.

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CHAPTER

4
Disparities in self-rated health, health care utilization, illness, chronic illness and other socio-economic characteristics of the Insured and Uninsured

Previous studies which have examined health status as regards the insured and uninsured have used a piecemeal approach. This study elucidates information on the self-rated health status, health care utilization, income distribution and health insurance status of Jamaicans. It also models self-rated health status, health care utilization and income distribution, and how these differ between the insured and uninsured. The majority of health insurance was owned by those in the upper class, (65%) compared to 19% for those in the lower socio-economic strata. No significant statistical difference was found between the average medical expenditure of those who had insurance coverage and the non-insured. 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) more likely to seek medical care, 1.6 times (95% CI = 1.02-2.42) more likely to report having chronic illness, 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 half a percent of the variance in health care utilization. Health care utilization is a strong predictor of self-reported illness, but it was weaker than illness in explaining health care utilization (61.1% of 66.5%). Public health insurance was mostly acquired by those with chronic illnesses: (76%) compared to 44% private health coverage and 38% without coverage. The findings highlighted that any reduction in the health care budget in developing nations means that vulnerable groups (such as the elderly, children and the poor) will seek less care, and this will further increase mortality among those cohorts.

Introduction This study examines the 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

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health insurance coverage, suggesting that a large percentage of the population are obliged to make out-of-pocket payments or use government 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 development and survivability, and explains the rationale as to why people seek medical care at the onset of ill-health. In seeking to preserve life, people demand and utilize health care services. Western societies are structured so that people meet health care utilization with a mixture of approaches. These approaches can be any combination of out-of-pocket payments, health insurance coverage, government assistance and assistance from the family. In Latin America and the Caribbean, health care is substantially an out-of-pocket expenditure aided by health insurance policies and government health care regimes. Within the context of the realities in those nations, the health of the populace is primarily based on the choices, decisions, responsibilities and burdens of the individual. Survival in developing nations is distinct from Developed Western Nations, as Latin American and Caribbean peoples’ willingness, frequency, and demand for health care, as well as their health choices, are based on affordability. Affordability of health care is assisted by health insurance coverage, as the provision of care offered by governmental policies means that the public health care system will be required to meet the needs of many people. Those people will be mostly children, the elderly and those who belong to other vulnerable groups. The public health care system in many societies often involves long queues, extended 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

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of reducing future health care costs as well as an avoidance of the utilization of public health care. Not having insurance in any society means a dependency on the public health care system, premature mortality, vulnerability of disadvantaged groups, and often public humiliation. The insured, on the other hand, are able to circumvent many of the experiences of the poor, the elderly, children and other vulnerable cohorts who rely on the public health care system. Insurance in developing nations, and in particular Jamaica, is a private arrangement between the individual and a private insurance company. Such a reality excludes the retired, the elderly, the unemployed, the 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 a 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 are in low and middle income countries, and 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 the different groups and sexes in the society. The realities of health inequalities between the poor and the wealthy and the sexes in a society, with those in the lower income strata contracting 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 and low sanitation coupled with inadequate access to financial resources [11, 13]. Poverty makes it impossible for poor people to respond to illness unless health care services are free. 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
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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 this emphasizes 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 which 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 provide more insight into the insured and uninsured. This study elucidates information on the self-rated health status, health care utilization, income distribution, and health insurance status of Jamaicans. It also models self-rated health status, health care utilization, income distribution, and how these differ between the insured and uninsured.

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].
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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 1,994 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 a complex sampling design, weighted to reflect the population of Jamaica.

Statistical analyses

Statistical analyses were performed using the Statistical Packages for the Social Sciences, Version 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 in this study as well as chi-square, independent sample t-tests, and analysis of variance f-tests, multiple logistic and linear regressions.

In analyzing the multiple logistic and linear regressions, correlation matrices were examined to determine multicollinearity. 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, we used SUDDAN
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statistical software (Research Triangle Institute, Research Triangle Park, NC), and this was adjusted for the survey’s complex sampling design. A p-value < 0.05 (two-tailed) was used to establish statistical significance

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 variables and some predisposed independent (explanatory) variables.

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 used for all categorical variables (using the reference group listed last). Overall model fit was determined using log likelihood ratio statistics, odds ratios and r-squared. Stepwise regressions were used to determine the contribution of each significant variable to the overall model. All confidence intervals (CIs) for odds ratios (ORs) were calculated at 95%.

Results Demographic characteristics 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 the elderly, 11.9%. The latter comprised 7.7% young-old, 3.2% old-old and 1.0% oldest-old. The majority of the sample had

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no formal education (61.8%); primary, 25.5%; secondary, 10.8% and tertiary, 2.0%. Two-thirds of the sample had sought health care 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: colds, 16.7%; diarrhoea, 3.0%; asthma, 10.7%; diabetes mellitus, 13.8%; hypertension, 23.1%; arthritis, 6.3%; and specified conditions, 26.3%. Marginally more people were in the upper class (40.3%) compared to the lower socio-economic strata (39.8%). Only 20.2% of respondents had health insurance coverage (private, 12.4%; NI Gold, public, 5.3%; other public, 2.4%). The 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). Furthermore examination of self-reported health conditions by health insurance status revealed that diabetics recorded the greatest percentage of health insurance coverage (43.9%) compared to hypertensives, (28.2%); people with arthritis (25.5%); those with acute conditions (17.0%) and respondents with other health conditions (18.8%). Sixty-seven percent of respondents who

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reported being diagnosed with chronic conditions had 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 graduates (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 associations 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. 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 insurance had 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. Concurring

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with this, 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 taken out 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 mostly acquired 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 the 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 = 15.8) compared to 32.5 years for those with non-chronic conditions. Similarly, 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.

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Table 4.4.1 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, health conditions and health care utilization by health insurance status are presented in Table 4.4.2. Table 4.4.3 presents information on the 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 4.4.4 examines illness by age of respondents controlled by health insurance status. There was a significant statistical relationship between illness and age of respondents, but none between the uninsured and insured, P = 0.410. Table 4.4.5 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 colds, 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 Nine variables (see Table 4.4.6), account for 32.8% of the variance in moderate-to-very good self-rated health status of Jamaicans The variables are medical expenditure, health insurance status, area of residence, household head, age, crowding index, total food expenditure, health

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care utilization and illness. Self-reported illnesses accounted for 62.2% of the explained variability of moderate-to-very good health status. Table 4.4.7 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 4.4.8). 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 4.4.9). 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 4.4.10). Table 4.4.11 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 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 (accounting for 43% of the explained variance, 19.4%).

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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 as 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 of the chronically ill who were in the other aged adult cohorts 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 half a 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 in 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, whereas 38% had no coverage at all. The income of those in the upper income strata was significantly more than those in the middle and lower socio-economic group, but chronic illnesses were statistically the same among the social classes. Health disparities in a nation are explained by socio-economic 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,

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17-19]. The present paper revealed that health insurance coverage is mostly acquired by those in the upper class, with less than 20 in every 100 insured being in the lower socio-economic class. Although this study found that those in the lower class did not suffer from more chronic illnesses 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. The uninsured ill are therefore less likely to demand health care, and this economic burden of health care is going to be the responsibility of either the state, the individual or the family. The difficulty here is that the uninsured are more likely to be in the lower-to-middle class, of working age or children, experiencing more acute illness; 38 out of every 100 chronically ill individuals are in the lower class, and these provide a comprehensive understanding of the insured and uninsured that will allow for explanations in health disparities between the socio-economic strata and sexes. With 43 out of every 100 people in the lower socio-economic strata self-reporting being diagnosed with chronic illness, health insurance coverage, public health systems and other policy interventions aid in their health, and health care utilization. Among the material deprivations of the poor is uninsurance. Those in the wealthy socioeconomic group in Jamaica were 3.5 times more likely to be holders of health insurance coverage than those in the lower socio-economic strata. And Gertler and Sturm [3] identified that health insurance causes a switching from public health to the private health system, which indicates that a reduction in public health expenditure and health insurance will significantly

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influence the health of the poor. This research showed that only 19% of those with health insurance were in the lower class. Therefore, the issue of uninsurance creates future challenges for the poor in regard to their health and health care utilization. At the onset of illness, those in the lower income strata without health insurance must first think about their illness and weigh this against the cost of losing current income, in order to provide for their families; 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 future product in enhancing a decision to utilize health care. But outside of those issues, their choices (or lack of choices), the cost of public health care, national insurance schemes and general price indices in the society all further lower 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 socio-economic strata, as those who do not have health insurance want to avoid the public health care system, owing to dissatisfaction or lack of means, and will only seek health care when their symptoms are severe; sometimes the complications from the delay make it difficult for their complaints to be addressed on their visits. Among the 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 themselves. 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 socio-economic realities of the poor, such as less access to education, proper nutrition, good physical milieu, poor sanitation and lower health coverage,

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cripple their future health status, and this hinders health care utilization while also accounting for high premature mortality. It is this lower health care utilization which accounts for their increased risk of mortality, as the other deprivations such as proper sanitation and nutrition expose them to disease-causing pathogens, which means that their inability to afford health insurance increases 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] they receive worse access to care, and are less satisfied than the insured in the US with the care and medical services that they receive. This is an indication of further reluctance on the part of the poor to willingly demand health care, as this intensifies 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. Some of the reasons why those in the lower socio-economic strata have less health coverage than those in the wealthy income group are (1) inaffordability, (2) type of employment (mostly part-time, seasonal, low paid and uninsured positions) 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 hospitals compared to 58% of those in the second poor quintile and 31% of those in the wealthiest 20%. Then, if public health is privatized and becomes increasingly more expensive for recipients, the socio-economically disadvantaged population (the poor, the elderly, children and other vulnerable groups) will become increasingly exposed to more agents that are likely to result in their deaths, with an increased utilization of home remedies as well as the broadening of the health outcome inequalities among the socio-economic strata.

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Illness, and particularly chronic conditions, can easily result in poverty before mortality sets in. With the World Health Organization (WHO) opining that 80% of chronic illnesses were in low and middle income countries, and that 60% of global mortality is caused by chronic illness [7], levelling insurance coverage can reduce the burden of care for those in the lower socio-economic strata. The importance of health insurance to health care utilization, health status, productivity, production, socio-economic development, life expectancy, poverty reduction strategies and health intervention must include increased health insurance coverage of the citizenry within a nation. The economic cost of uninsured people in a society can be measured by the loss of production, sick leave payment, mortality, lowered life expectancy and cost of care for children, orphanages and the elderly who become the responsibility of the state. Therefore the opportunity cost of a reduced public health care budget is the economic cost of the aforementioned issues, and goes to the explanation of premature mortality in a society. The chronically ill, in particular, benefit from health insurance coverage, not because of the reduced cost of health care, but the increased health care utilization that results 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 for their health care utilization and not the condition or illness. 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 various socio-economic groups, higher mortality among particular social classes, deep-seated barriers in health care delivery and the perpetuation of such barriers, and how they

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can increase health differences among the socio-economic strata. The relationship between poverty and illness is well established in the literature [7, 8, 23] as poverty means being deprived of elements such as proper nutrition and safe drinking water, and these issues contribute to lower health, production, productivity, and more illness in the future. Free public health care or lower public health care costs do not mean equal opportunity to access health care, nor do they eliminate the barriers to such access, or increase health and wellness for the poor, or remove lower health disparities among the socio-economic groups. However, lower income, increased price indices, removal of government subsidies from public health care, increased uninsurance and lower health care utilization, increase poverty and premature mortality, and lower the life expectancy of the population. Increases in diseases (acute and chronic) are largely owing to the 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 of financial resources, and their voluntary actions will be directly related to survival and not diet, nutrition, exercise or other healthy lifestyle choices. Lifestyle choices such as diet, proper nutrition, and sanitation and safe drinking water are costly, and they are choices which, often because of poverty, some people cannot afford to make. It follows therefore that those in the lower socio-economic strata will voluntarily make unhealthy choices because they are cheaper. Poverty therefore handicaps people, and predetermines unhealthy lifestyle choices, which further account for greater mortality, lower life expectancy, and less health insurance coverage and private health care utilization.

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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 socio-economic strata, they were less likely to have health insurance coverage compared to the upper class. Poverty denotes socio-economic deprivation of resources available in a society, and goes to the crux of health disparities among the socio-economic groups and sexes. Health care utilization is associated with health insurance coverage as well as government assistance, and this embodies the challenges of those in 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 further increase premature mortality among those in the lower socio-economic 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, along with the construction of clinics and hospitals, and there is definite a need to include health insurance coverage in their public health measures, as this will increase access to health care utilization. Any increase in health care utilization will be able to improve health outcomes, reduce health disparities between the socio-economic groups and the sexes, and bring about improvements in the quality of life of the poor. 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 behaviour, any reduction in the health care budget in

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developing nations denotes that vulnerable groups (such as children, the elderly and the poor) will seek less care, and this will further increase mortality among those cohorts.

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Table 4.4.1. 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 Income1 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 4.4.2. 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 (87.5) Secondary 80 (9.9) 23 (6.7) 9 (5.7) 577 (11.1) Tertiary 46 (5.7) 4 (1.1) 4 (2.6) 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 (20.8) 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 4.4.3. Age cohort by diagnosed illness
Diagnosed illness Acute condition Cold Age cohort n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) Diarrhoea Asthma Diabetes mellitus Chronic condition Hypertension Arthritis Other Total

Children

97 (65.1)

13 (48.2)

51 (53.7)

3 (2.4)

0 (0.0)

0 (0.0)

54 (23.1)

218 (24.5)

Young adults

14 (9.4)

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 (14.7)

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 (15.5)

49 (23.8)

14 (25.0)

13 (5.5)

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 4.4.4. 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 4.4.5. 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 (35.5) 49 (9.7) 132 (34.3) 26 (5.2) 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 (29.3) 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 4.4.6. Logistic regression: Explanatory variables of self-rated moderate-to-very good health
Explanatory variable Coefficient Std. error Odds ratio 95.0% C.I. R2 0.003 0.005 0.007 0.006

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

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 4.4.7. Logistic regression: Explanatory variables of self-reported illness
Explanatory variable Coefficient Std Error Odds ratio 95.0% C.I. R2
0.001 0.003 0.002 0.037 0.002 0.009 0.611

Average medical expenditure Male Married Age

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 4.4.8. Logistic regression: Explanatory variables of health care seeking behaviour Odds ratio
1.86** 369.92*** 0.51**

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

Coefficient
0.620 5.913 -0.680

Std error
0.179 0.252 0.198

95.0% C.I.
1.31 - 2.64 225.74 - 606.17 0.34 - 0.75

R2
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 4.4.9. 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 4.4.10. Multiple regression: Explanatory variables of income
Unstandardized Coefficients Explanatory variable Constant Crowding index Upper class Middle Class †Lower class Household head B 11.630 0.206 1.265 0.692 Std. Error 0.061 0.008 0.052 0.047 0.625*** 0.649*** 0.347*** β 95% CI 11.511 - 11.750 0.190 - 0.221 1.162 - 1.368 0.599 - 0.784 0.195 0.320 0.133

R2

-0.181 0.137 0.165 0.109 0.145 0.130

0.038 0.042 0.040 0.039 0.046 0.049

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

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

0.012 0.007 0.006 0.003 0.002 0.003

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

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

0.075

0.038

0.044*

0.000 - 0.150

0.001

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 4.4.11. 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

5
Variations in social determinants of health using an adolescence population: By different measurements, dichotomization and non-dichotomization of health

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. 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 nondichotomization 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.

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

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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. 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

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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. 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

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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 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.

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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 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

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(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 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).

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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 3 and 4).

Models 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.
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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 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

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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.

H A=f (A i , G i , AR i , It , lnD i , ED i, US i , S i , HIi , lnY, CR i , lnMC t , SA i , ε i )

(1)

where H A (i.e. self-rated antithesis of diseases) is a function of age of respondents, A i ; sex of individual i, G i ; area of residence, AR i ; 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), lnD i ; Education level of individual i, ED i ; union status of person i, US i ; social class of person i, S i ; 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, lnMC t ; social assistance of individual i, SA i ; 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%.

H ND=f (A i , G i , AR i , It , lnD i , ED i, US i , S i , HIi , lnY, CR i , lnMC t , SA i , ε i ) Where H ND denotes the non-dichotomization of self-rated health status.

(2)

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%.

H D1 =f (A i , G i , AR i , It , lnD i , ED i, US i , S i , HIi , lnY, CR i , lnMC t , SA i , ε i ) Where H D1 is very poor-to-poor self-rated dichotomized health status.

(3)

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%.
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H D2 =f (A i , G i , AR i , It , lnD i , ED i, US i , S i , HIi , lnY, CR i , lnMC t , SA i , ε i ) Where H D2 is good self-rated dichotomized health status.

(4)

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%.

H D1-4 =f (A i , G i , AR i , It , lnD i , ED i, US i , S i , HIi , lnY, CR i , lnMC t , SA i , ε i )

(5)

Where H D3 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%.

H D1-4 =f (A i , G i , AR i , It , lnD i , ED i, US i , S i , HIi , lnY, CR i , lnMC t , SA i , ε i ) Where H D4 is good-to-excellent self-rated dichotomized health status.

(6)

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 Demographic characteristics of studied population Table 5.5.1 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 5.5.2 presents information on the particular demographic characteristic as well as health status and self-reported illness of respondents by area of residence.

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Table 5.5.3 depicts information of variables which explain the antithesis of illness among the adolescence population. Table 5.5.4 shows the different dichotomizations of self-rated health status and nondichotomized self-rated health status, and the various social determinants which explain each. Table 5.5.5 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

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
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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 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

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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 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

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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. 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

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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 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

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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 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

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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. 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.

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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 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.

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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 5.5.1: Demographic characteristic of studied population, n = 1394
Characteristic 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) n 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 Percent 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 5.5.2: Particular demographic variables by area of residence, n = 1,394
Characteristic Self-reported illness Yes No Self-rated health status Very good Good Moderate Poor Very poor Social class Lower Middle Upper Educational level Primary or below Secondary Tertiary Sex Male Female Health insurance coverage Yes No Length of illness, mean ± SD Area of residence Urban Periurban n (%) n (%) 27 (7.1) 15 (5.4) 352 (92.9) 264 (94.6) 162 (42.7) 172 (45.4) 38 (10.0) 7 (1.8) 0 (0.0) 101 (25.6) 88 (22.3) 205 (52.0) 138 (36.6) 213 (56.5) 26 (6.9) 213 (54.1) 181 (45.9) 73 (19.4) 303 (80.6) 6.0 ± 5.7 days 141 (50.4) 132 (47.1) 7 (2.5) 0 (0.0) 0 (0.0) 108 (37.6) 58 (20.2) 121 (42.2) 136 (48.6) 136 (48.6) 8 (2.9) 148 (51.6) 139 (48.4) 37 (13.6) 235 (86.4) 7.8 ± 9.0 days Rural n (%) 47 (6.9) 635 (93.1) 328 (48.4) 297 (43.9) 39 (5.8) 11 (1.6) 2 (0.3) 439 (61.6) 141 (19.8) 133 (18.7) 312 (46.1) 359 (53.0) 6 (0.9) 361 (50.6) 352 (49.4) 84 (12.6) 585 (87.4) 6.4 ± 6.5 days

P, χ2 0.628, 0.931 24.82, 0.002

172.64, < 0.0001

37.79, < 0.0001

1.20, 0.548 9.36, 0.009

F = 0.42, 0.857

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Table 5.5.3: Logistic regression: Variables of antithesis of illness among adolescence population, n = 1,280
Characteristic Age Health care-seeking (1=yes) Health insurance coverage (1=yes) Primary education (reference group) Secondary Tertiary lnMedical Male Social assistance from government Logged family income Rural area (reference group) Urban Periurban Poor-to-Very poor health status (reference group) Moderate-to-Very good health status Good-to-Very good health status Lower class (reference group) Middle class Upper Crowding Model χ2, P -2 LL R2 Hosmer and Lemeshow OR denotes odds ratio, CI (95%) means 95% confidence interval and *P < 0.05 OR 1.1 0.0 1.0 1.0 1.8 1.9 0.8 1.4 1.6 0.8 1.6 1.2 1.0 0.3 12.6 1.6 0.8 0.9 CI (95%) 1.0 - 1.3 0.0 - 0.01* 0.4 - 2.5 0.9 - 3.7 0.3 - 15.1 0.1 - 5.0 0.7 - 2.6 0.6 - 4.4 0.3 - 1.8 0.7 - 3.8 0.5 - 2.9 0.03 - 2.1 6.0 - 26.3* 0.5 - 5.2 0.2 - 3.1 0.8 - 1.1 287.08, < 0.0001 327.56 0.53 χ2 = 4.40, P = 0.82

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Table 5.5.4: Logistic and Ordinal Logistic regression: Factors explaining self-reported health status of adolescents, n = 1,280
Characteristic Very poor-to-poor OR
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

Good OR
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

CI (95%)
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 * 0.4 – 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

Self-rated health status Moderate-to-very good CI (95%) OR CI (95%)
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 -

Good-to-very good OR CI (95%)
0.1 0.05 – 0.2* 0.9 0.9 – 1.1 0.7 0.3 – 2.1 1.2 0.6 – 2.4 1.0 1.0 0.6 – 1.6 0.4 0.2 – 1.3 0.7 0.4 – 1.2 1.2 0.6 – 2.3 1.0 2.2 1.0 – 4.8 0.7 0.3 – 1.6 1.0 0.6 0.4 – 1.0* 3.3 1.53– 8.2* 1.4 0.9 – 2.2 2.0 1.2 – 3.4* 0.9 0.8 – 0.98* 113.11, <0.0001 588.76 0.20 χ2 = 4.61, P = 0.80

All Estimate
1.8 0.02 1.0 0.04 1.0 0.02 0.3 0.5 0.1 1.0 - 0.7 - 0.6 1.0 0.5 - 0.01 - 0.1 - 0.30 0.1

CI (95%)
1.1 – 2.4* - 0.03 – 0.1 0.1 – 2.0* - 0.3 – 0.4 - 0.2 – 0.2 0.4 – 1.0 0.1 – 1.0* - 0.2 – 0.4 - 1.0 - - 0.4* - 1.0 - -0.1

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

0.4 – 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

0.2 – 0.8* - 0.3 – 0.3 - 0.3 – 1.2 - 0.6 – -0.001* - 0.01 – 0.1* 112.94, < 0.0001 2354.33 Pseudo R2 = 0.10 Goodness of fit, χ2=5451.14. P < 0.001

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Table 5.5.5: Self-rated health status and antithesis of illness, n = 1,330
Characteristic Antithesis of illness No Yes χ2 = 125.58, P < 0.0001 Characteristic Self-rated health status Very good Good Moderate Poor Very poor χ2 = 125.58, P < 0.0001 Very good n (%) 18 (2.9) 611 (97.1) Self-rated health status Good Moderate n (%) n (%) 38 (6.4) 560 (93.6) 26 (31.3) 57 (68.7) Poor n (%) 7 (38.9) 11 (61.1) Very poor n (%) 0 (0.0) 2 (100.0)

Good health (Antithesis of illness) No n (%) 18 (20.0) 38 (42.7) 26 (29.2) 7 (7.9) 0 (0.0)

Yes n (%) 611 (49.2) 560 (45.1) 57 (4.6) 11 (0.9) 2 (0.2)

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CHAPTER

6
Self-rated health status of young adolescent females in a middle-income developing country

The study of young female adolescents in Jamaica is sparse and few, in particular on self-related health status. This research seeks to examine the self-related health status of young female 12-17 years and to model factors that influence good self-related health status of young female adolescents. Four variables emerged as accounting for 20.3% of the variability in reported good self-related health status of young females. Good self-related health status are explained by cost of medical care (OR = 0.996, 95% CI = 0.99 - 1.01), private health care insurance coverage (OR = 0.30, 95% CI = 0.01 - 0.09), number of females in household (OR = 0.73, 95% CI = 0.59 - 0.90), and healthcare seeking behaviour (OR = 1.25, 95% CI = 1.04 - 1.52). The majority of the female adolescents reported good self-related health status. The findings are far reaching and can be used to guide policy. Any policy that seeks to address wellbeing of female adolescents must incorporate the advancement of the household, social and economic factors coupled with the needs of the individual.

Introduction
Adolescents and young adults represent a large and growing proportion of the populations of developing countries around the world. In the English-speaking Caribbean countries, adolescents represent about 20% of the population, or approximately 1.2 million persons according to 2007 population data [1]. Adolescence usually refers to the psychological and physiological processes of maturation between the ages of about 12 to 18. It is a transitional period of rapid physical (pubertal), emotional, cognitive and social development [2], and is often characterized by the clarification of sexual values and experimentation with sexual behaviours [3]. While adolescents are generally among the healthiest of any age group, they have special biological needs.
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Worldwide, studies on adolescent sexual behaviour show that the years of adolescence and the transition to adulthood are associated with increases in rates of risky behaviour, including the use of drugs and alcohol, delinquency, and unsafe sexual practices [4, 5]. Early initiation of sexual activity among adolescents has been identified as a major risk factor for a number of negative reproductive health outcomes, including early childbearing and associated implications for maternal and child health outcomes, as well as increased risk for sexually transmitted infections (STIs) including human immunodeficiency virus (HIV) [6]. The last two decades have been marked by significant changes in adolescent health in Caribbean countries. There has been a shift from infectious to social morbidities caused or contributed by individual risk behaviors and environmental factors [7,8] concurrent with rising unemployment, increased poverty, and reduced health services. Until in the last ten years we have known relatively little about the health status of youths residing in the Caribbean. In a study of a clinical population of young people in Jamaica, Smikle et al. [9] found that the mean age at onset of sexual intercourse among males was 12.5 years; 4% of sexually active males reported using condoms consistently. According to the Jamaica Reproductive Health Survey of 2002-03, sexual initiation occurs on average at 13.5 years for young men and 15.8 years for young women [10]. The earlier adolescents begin sexual activity, the less likely they are to use contraception, thus increasing their risk of pregnancy and STIs [11]. Soyibo and Lee [12] reported, among a general population of Jamaican school-attending adolescents, rates of marijuana, cocaine, and heroin use of 10.2%, 2.2%, and 1.13%, respectively; the alcohol use rate was 50.2%, and the tobacco use rate was 16.6%. The country’s adolescent fertility rate has increased in recent years and, at 112 per 1,000 women aged 15-19, is among the highest in the region. Before they reach the age of 20, 37% of Jamaican women have been pregnant at least once, and 81% of these

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pregnancies are unplanned [10]. This concur with another study where more than 75 percent of pregnancies among 15-24-year-olds are unplanned, and about 40 percent of Jamaican women had at least one child before age 20 [13]. Self-rated health is a subjective and general indicator of overall health status. It evaluates the health of an individual based on his/her perception of general overall health. This indicator has been found to capture important information about the individual’s overall health and is a powerful predictor of mortality and functional ability [14]. While self-rating of health is a good measure of objective and subjective health [2], it is also a feasible way to measure health in large-scale surveys [15, 16]. Self-rated health has been extensively studied in older adult population groups, where a range of factors associated with self-rated health status has been identified [17, 18]. Much less is known about the self-rated health status of younger populations such as adolescents in Jamaica, and the available information remains limited in scope. The published literature suggests that young people preferentially employ psychological or behavioural factors as a rating frame for their health [19, 20]. In contrast, for older people, physical well-being plays a more crucial role in assessing their health [21]. Given the observation that young adults differ from older people in their perception of health, a better understanding and a separate analysis of the factors associated with self-rated health status is needed for adolescents. Thus, this research seeks to examine the self-related health status of young female Jamaicans and to determine the factors that influence the health status of young females, ages 12 to 17 years.

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Method
Data The current study is based on data from 2002 Jamaica Survey of Living Conditions (JSLC). The JSLC is an annual nationally representative survey which collects information on health, health conditions, health care utilization, and other socio-demographic characteristics of Jamaicans. It is a modification of the World Bank’s Living Standards Measurement Study (LSMS) household survey [22]. The survey collects information from the non-institutionalized population between JuneOctober 2002. The sample size was 25,018 respondents [23]. The current study uses a subsample of 1,565 young women (ages 12 through 17 years) from the general JSLC survey for 2002. The mean age of respondents was 14.4 years (±1.7 years). The only inclusion criterion for this study was female and age (12 through 17 years). For 2003 to 2006, the Jamaica Surveys of Living Conditions did not collect information on the health status of Jamaica. Data for 2008 to 2009 are not yet ready, at the time of writing this paper the researcher was not given access to the 2007 survey data and so the researcher had to resort to using 2002 survey data to conduct this research 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. An ED is an independent geographic unit that shares a common boundary. This means that the country is
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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 (LFS) was selected for the JSLC. The sample was weighted to reflect the population of the nation. The non-response rate was 26.2%. The non-response includes refusals and rejected cases in data cleaning. Over 1994 households of individuals nationwide are included in the entire database of all ages. 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 weighted to reflect the population of Jamaica. Measure Related health status was operationalized using the number of self-related illness/injury in the last four weeks. It is a dummy variable, where 0 = bad related health status (proxied by selfresponse to having had a least one health condition), 1 = good related health status (proxy by not reporting a health condition). It is taken from the question, ‘Have you had any illness other than due to injury? For example a cold, diarrhoea, asthma attack, hypertension, diabetes, or any other illness? And the options were yes = 1 and no = 2.

Physical environment is the external surroundings and conditions in which the individuals reside. Natural disaster refers to the number of responses from people who indicated suffering landsides, property damage due to rains, flooding, and soil erosion.

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Negative affective psychological condition identifies the number of responses from a person on having loss a breadwinner and/or family member, family having lost its property, household member being made redundant, family having difficulties meeting its financial obligations.

Crime index = Σ ki Tj, The equation represents the frequency with which an individual witnessed or experienced a crime, where i denoted 0, 1 and 2, in which 0 indicated not witnessed or experienced a crime, 1 means witnessed 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, 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 ranged from 0 and a maximum of 51.

Education was proxied by the number of self-reported days that an individual goes to schools.

Household crowding (crowding) is the total number of people who are dwelling in the household divided by the number of rooms that the household occupies excluding kitchen, verandah and bathroom.

Social hierarchy: Income quintiles were used to measure social class, and these range from quintile 1 (poorest 20%) to 5 (wealthiest 20%). Lower is measured by those in quintiles 1 and 2; middle class is represented by those in quintile 3, and upper class indicated those in quintiles 4 and 5.
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Analytic model Multivariate logistic regression was used to fit the data of the current study. The literature was used to identify variables for the current paper as well as the dataset. Sixteen variables (Eqn [1]) were identified based on the literature and the 2002 Jamaica Survey of Living Conditions. We examined correlation matrices to insure that multicollinearity was not an issue. Ht = (P mc , ED,A i , MR, AR, CR, PA, F, EN, C, M, FM; CH, PHS, HSB,Q)……(Eqn [1]) Eqn [1] expresses current health status H t as a function of price of medical care P mc , education of individual, ED; age of the individual, A i , marital status, MR; area of residence, AR; Household crowding (proxy by average occupancy per room), CR; psychological conditions, PA; existing pregnancy, F; natural disaster, EN; average consumption per person, C; number of males in household, M; number of females in household, FM; number of children in household, CH; having private health insurance coverage, PHS; visits to health practitioners, HSB, and per capita population quintile that the individual’s family below, Q. The model was modified because of non-response and low variability. Hence, a number of variables were not including in the final model, which is reflected of the population and the challenges of non-response and low variability. The following variables were omitted from the analysis because the non response rates were high (in excess of 40%). These were positive affective psychological conditions (41.5%, n = 650). Marital status was omitted on two premises; one, non-variability (99.7% of those who responded were never married (n = 672) given their ages; and two, the non-response rate (57.1%, n = 893). Only 1.3% of the population were pregnant (n = 14) and this question had a non-response rate of 29.3% (n = 459).

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The final model was based on those variables that were statistically significant (P <0.05). Using stepwise logistic regression analyses, all variables that were not significant were removed from the final model (P > 0.05). Hence, the final model shows that self-related health status is determined by cost of medical care, Pmc ; number of females in household, FM; having private health insurance coverage, PHS; visits to healthcare practitioners, HSB (Eqn [2]): Ht = (P mc , FM, PHS, HSB)……………………………………………………....(Eqn [2]) Statistical analysis Data was stored and retrieved in the SPSS 16.0; descriptive statistics were used to provide pertinent information on the subsample and logistic regression was used to examine the influence of socio-demographic and psycho-economic variables on self-related health status (or reported health status). The dependent variable was self-related health status and the independent variables were socio-demographic and psycho-economic variables. Means and frequency distribution were considered significant at P < 0.05 using chi-square, independent sample t-test, F-test, and multiple logistic regressions. 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 [23]. 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.

Results
Table 6.6.1 presents information on the sociodemographic characteristic of the sample. The sample had 1,565 respondents: mean age, 14.4 years old (S.D. = 1.7 years); 8.3% reported an
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illness and 1.3% were pregnant. The majority (62%) of the female respondents lived in rural areas, and most (93.8%) had secondary school education. Table 6.6.2 examines information that is associated with good self-related health status of respondents. Four variables emerged as accounting for 20.3% of the variability in good selfrelated health status of young females. The most influential factors that determine self-related health status of young females (ages 12 to 17 years) were family ownership of private health insurance (OR = 0.03, 95%CI: 0.01 - 0.09); the number of females in the household (OR = 0.73, 95%CI: 0.59, 0.90); cost of medical care (OR = 0.996, 95%CI: 1.00, 1.01), and health care seeking behaviour (visits to health care practitioners), (OR = 1.25, 95%CI: 1.04, 1.52).

Discussion
In this study the majority of adolescents reported that they have good self-related health. The determinants of good self-related health status in female adolescents in Jamaica were family owed private health insurance coverage, number of females in household, cost of medical care and healthcare seeking behaviour (visits to health care practitioners). The findings of this study concur with those of another study which assessed youth health in the Caribbean countries including Jamaica where four in five adolescents state that their general health was good [24]. This latter study reported that younger adolescents are more likely to report better health and, by age 16, one in six youths reported fair to poor health status [24]. In addition, almost 10% of the young people (more boys than girls) report having a handicap, disability, or chronic illness that limits their activities. Headaches, physical development and sleep problems are the most common health concerns of young people in the Caribbean [24]. Poor health in adolescents is positively associated with risk factors such as abuse and parental problems and negatively associated with protective factors such as connectedness to family and community [25]. Resnick

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et al. found that parent/family connectedness and perceived school connectedness were protective against every health risk behavior measured, except history of pregnancy [26]. In Jamaica, approximately 9% of the population is covered by private health insurance [27]. Persons in the wealthiest consumption quintile were more than four times more likely to have health insurance coverage than those in the poorest quintile, 35 per cent and 8.5 per cent respectively [28]. The family’s health care insurance coverage was the main determinant of good self-related healthcare status of the female adolescents in Jamaica. Those young females whose family had them on their private health insurance plan indicated a lower self-related health status compared to another young female whose family does not have private health insurance. This suggests that health insurance is purchased in keeping with the high probability of the individual being likely to become ill (or knowing that the individual suffers from a particular health condition). Poverty and lack of health insurance are two powerful socioeconomic influences that predispose young people to a wide variety of health problems. Poor adolescents typically experience more health and health-related problems than non-poor adolescents with respect to acute and chronic conditions that restrict activity; overall self-related fair or poor health; and higher rates of pregnancy, cigarette smoking and depression [29]. Adolescents from poor families and those without health insurance are more likely to seek routine medical care from a public hospital, outpatient clinic, emergency department, or public health center. Uninsured adolescents are more likely to miss school and fall behind academically, which may affect their ability to achieve their full potential [30]. In a study done by Newacheck et al. one in every seven adolescents in the United States, aged 10-18, is uninsured. Uninsured adolescents, as opposed to insured adolescents, are more likely to be members of poor and minority families [31].

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The ability of the families of adolescents to afford healthcare is based on their economic status. An adolescent family economic status can have a strong influence on adolescents’ perceptions of health, their health behaviors and use of health care [32, 33]. The cost of health care was one of the determinant factors of good health status among the female adolescents in Jamaica. In a study by Halcon et al. assessing youth health in the Caribbean Community and Common Market countries including Jamaica, most adolescents (85.9%) reported that they have a place where they usually receive medical care [34]. However, only 36.2% have had a checkup in the last two years. Less than half have seen a dentist in the past two years. If they need contraception, students would go, first to physicians, followed by drug stores, family planning clinics, and public health clinics. Males are consistently less likely to use healthcare services than females; and they are more likely to believe that adults will not provide confidentiality [34]. According to Figueroa et al. health-seeking behaviour and/or access to healthcare in Jamaica appears to have improved between 1993 and 2000 since significantly fewer persons in 2000 than in 1993 reported never having had their blood pressure checked and fewer women reported they had never had a Pap smear. This may be due to a growing health consciousness in sectors of the society [35]. In this study, health seeking behavior was one of the determinants of good health status of female adolescents. The use of healthcare services depends on health status of respondents. The better the health status of an adolescent the lower the health care services utilization and vice-versa. The ability of adolescents to obtain healthcare services is an important indication of whether the healthcare system is meeting their needs. Difficulties experienced by adolescents in accessing healthcare include: long distance to healthcare centre, lack of transport services and long waiting time for the healthcare services [36]. Understanding adolescents' health seeking behaviour is critical for quality service improvement.

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In a study by Halcon et al. of adolescents in Caribbean countries, crowding was a significant concern for a number of young people with 29% reporting 2-4 persons slept in a room and an additional 3.4% indicate more than 5 people slept together [24]. In this study, crowding did not affect the health status of young females neither did negative affective psychological conditions; family assets ownership, household income and consumption, and education. It was also discovered that there was no statistical difference between the health statuses of those who dwelled in rural, urban or other towns. The number of males in the household and the number of children in the household did not influence the quality of life of young females. However, the number of females in the household inversely affects the health status of young female adolescents. Although there is no statistical significance between the health status of poor and wealthy young females, nearly three quarters of young females in the study resided in the rural areas (62 per cent) where incidences of poverty are traditionally higher than those in urban areas. This further substantiates the fact that household economic status is directly linked to health of children, and rural children are perhaps more vulnerable than their urban counterparts. There are several implication associated with phenomenon for young females from rural households. Among them are vulnerability to diseases brought on by nutritional deficiencies, weak immune systems and low academic performance. These invariably impact on their life chances, psychological self actualization and eventually their inability to break the cycle of poverty. Hence, any policy that seeks to address the wellbeing of female adolescents must incorporate the advancement of the household, and the social and economic factors coupled with the needs of the individual.

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Conclusion
The health status of young females in Jamaica is substantially impacted on by family owed private health insurance coverage, number of females in household, cost of medical care and healthcare seeking behaviour (visits to health care practitioner). Embedded in this study is the importance of family through either the purchase of health insurance, coverage of the cost of medical care and health visits of young females. This study provided insights into social factors that determine the good self-related health status of female adolescents, which will enable healthcare practitioners to devise appropriate programs to address the health concerns of this group. Disclosures The author reports no conflict of interest with this work.

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 and/or the Statistical Institute of Jamaica, but to the researchers.

Acknowledgement
The authors 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 available for use in this study, and Dr. Donovan McGrowder for editing and other advice that allowed for the completion the final manuscript.

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20) Piko B. Health-related predictors of self-perceived health in a student population: the importance of physical activity. J Community Health. 2000; 25:125-137. 21) Johnson RJ, Wolinsky FD. The structure of health status among older adults: disease, disability, functional limitation, and perceived health. J Health Soc Behav. 1993; 34:105121. 22) World Bank, Development Research Group, Poverty and Human Resources. Jamaica Survey of Living Conditions, 1988-2000. Basic information. Washington: The World Bank; 2002. http://siteresources.worldbank.org/INTLSMS/Resources/3358986-1181743055198/38773191190214215722/binfo2000.pdf 23) Polit DF. Data analysis and statistics for nursing research. Stamford: Appleton & Lange Publisher; 1996. 24) Halcón LL, Beuhring T, Blum RW. A Portrait of Adolescent Health in the Caribbean 2000. WHO Collaborating Centre on Adolescent Health, Division of General Pediatrics and Adolescent Health, University of Minnesota; 2000. www.paho.org/english/hpp/hpf/adol/monogra.pdf 25) Blum RW, Ireland M. Reducing risk, increasing protective factors: findings from the Caribbean Youth Health Survey. J Adolesc Health. 2004; 35:493-500. 26) Resnick MD, Bearman PS, Blum RW, Bauman KE, Harris KM, Jones J, Tabor J, Beuhring T, Sieving RE, Shew M, Ireland M, Bearinger LH, Udry JR. Protecting adolescents from harm. Findings from the National Longitudinal Study on Adolescent Health. Journal of the American Medical Association. 1997; 278:823-832. 27) Planning Institute of Jamaica, (PIOJ). Economic and Social Survey Jamaica, 1990-2006, Kingston; PIOJ. 1991-2007

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28) Statistical Institute of Jamaica, (STATIN). Demographic Statistics 2005. Kingston: STATIN; 2006. 29) National Adolescent Health Information Center. Fact Sheet on Demographics: Children and Adolescents. San Francisco, California: National Adolescent Health Information Center, University of California, San Francisco; 2000. 30) Byck GR. A comparison of the socioeconomic and health status characteristics of uninsured, statechildren’s health insurance program-eligible children in the United States with those of other groups of insured children: implications for policy. Pediatrics 2000; 106:14-21. 31) Newacheck P, McManus M, Brindis C. Financing health care for adolescents: Problems, prospects, and proposals, Journal of Adolescent Health Care. 1990; 11:398-403. 32) Garbarino J. Children in Danger: Coping With the Consequences of Community Violence. San Francisco, Calif: Josey-Bass Publishers; 1992. 33) Gibbs JT, Huang LN. Children of Color: Psychological interventions with minority youth. San Francisco, California: Josey-Bass Publishers; 1989. 34) Halcón L, Blum RW, Beuhring T, Pate E, Campbell-Forrester S, Venema A. Adolescent health in the Caribbean: a regional portrait. Am J Public Health. 2003; 93:1851-1857. 35) Figueroa JP, Ward E, Walters C, Ashley DE, Wilks RJ. High risk health behaviours among adult Jamaicans. West Indian Med J. 2005; 54:70-76. 36) Booth M, Bernard D, Quine S, Kang M, Usherwood T, Alperstein G, Bennett D. Access to health care among Australian adolescents young people’s perspectives and their sociodemographic distribution. Journal of Adolescent Health. 2006; 34:97-103.

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Table 6.6.1: Descriptive analysis of variables of target cohort

Variables Age Residence

Descriptive Analysis 14.4 (±1.7 years) 62%= Rural 25.4% = Other Town 12.3% = Urban area

Educational level

5.6% = Primary 93.8% = Secondary 0.6% = Tertiary

Average consumption (per year) Average income (per year)

US$652.30 (± $607.37) US$3,699.00 (± $3,167.41)

Crowding Self reported good health Pregnancy (at the time of the survey)

2.3( ±1.5 persons) 91.7% 1.3%

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Table 6.6.2: Socio-demographic and psychological variables of self-related health status of the sample

Characteristic Middle class Upper class Referent group (lower class) Cost of medical care Crowding Environment Negative Affective Conditions Assets owned by household Age Health Insurance Other Towns Urban areas Referent group (Rural areas) Number of male Number of females Number of children Average Consumption Crime Index Average Income Visits to Health practitioners Education

β Coefficient 0.46 -0.36 0.00 -0.02 0.65 -0.03 -0.02 0.004 -3.37 -0.07 -0.05 -0.05 -0.32 0.06 0.00 -0.01 0.00 0.23 0.04

Std Error

Odds ratio

CI (95%)

0.37 0.34 0.00 0.10 0.37 0.04 0.06 0.08 0.46 0.29 0.34 0.12 0.11 0.09 0.00 0.01 0.00 0.10 0.03

1.58 0.70 1.00 0.996* 0.98 1.91 0.97 0.98 1.00 0.03*** 0.94 0.95 1.00 0.95 0.73** 1.06 0.997 0.99 0.997 1.25* 1.04

0.77 - 3.25 0.36 - 1.37 0.99 - 1.01 0.80 - 1.19 0.93 - 3.91 0.91 - 1.04 0.88 - 1.10 0.86 - 1.18 0.01 - 0.09 0.54 - 1.64 0.49 - 1.85 0.75 - 1.20 0.59 - 0.90 0.88 - 1.26 1.00 - 1.01 0.98 - 1.01 1.00 - 1.01 1.04 - 1.52 0.99 - 1.10

Chi-square (19) = 113.87, P < 0.001 -2 Log likelihood = 587.25 Nagelkerke r-squared = 0.203 Overall correct classification = 92.7% Correct classification of cases on good health = 99.2% Correct classification of cases bad health = 18.8% *P < 0.05, **P < 0.01, ***P < 0.001

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CHAPTER

7

Health of females in Jamaica: using two cross-sectional surveys

The 21st Century cannot have researchers examining self-rated health status of elderly, population, children and adolescents and not single out females as they continue to be poorer than males; and are exposed to different socioeconomic situation. Current study 1) examines the health conditions; 2) provides an epidemiological profile of changing health conditions in the last one half decade; 3) evaluates whether self-reported illness is a good measure of self-rated health status; 4) computes the mean age of females having particular health conditions; 5) calculates the mean age of being ill compared with those who are not ill; and 6) assesses the correlation between self-rated health status and income quintile. There is reduction in the mean age of females reported being diagnosed with chronic illness such as diabetes mellitus (60.54 ± 17.14 years); hypertension (60.85 ± 16.93 years) and arthritis 59.72 ± 15.41 years). In 2007 over 2002, the mean age of females with unspecified health conditions fell by 33%. Although healthy life expectancy for females at birth in Jamaica was 66 years which is greater than that for males, improvements in their self-rated health status cannot be neglected as there are shifts in health conditions towards diabetes mellitus and a decline in the mean age at which females are diagnosed with particular chronic illnesses.

Introduction
Life expectancy is among the objective indexes for measuring health for a person, society, or population. In 1880-1882, life expectancy at birth for females in Jamaica was 39.8 years which was 2.79 years more than that for males. One hundred and twenty-two years later, health disparity increased to 5.81 years: in 2002-2004, life expectancy at birth for females was 77.07 years [1]. For the world, the difference in life expectancy for the sexes was 4.2 years more for females than males: for 2000-2005, life expectancy at birth for females was 68.1 years [2]. Within the expanded conceptual framework offered by the World Health Organization (WHO) in
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the late 1940s, health is more than the absence of morbidity as it includes social, psychological and physiological wellbeing [3]. Some scholars [4] opined that using the opposite of ill-health to measure health is a negative approach as health is more than this biomedical approach. Brannon and Feist [4] forwarded a positive approach which is in keeping with the ‘Biopsychosocial’ framework developed by Engel. Engel coined the term Biopsychosocial when he forwarded the perspective that patient care must integrate the mind, body and social environment [5-8]. He believed that mentally patient care is not merely about the illness, as other factors equally influence the health of the patient. Although this was not new because the WHO had already stated this, it was the application which was different from the traditional biomedical approach to the study and treatment of ill patients. Embedded in Engel’s works were wellbeing, wellness and quality of life and not merely the removal of the illness, which psychologists like Brannon and Feist called the positive approach to the study and treatment of health. Recognizing the limitation of life expectancy, WHO therefore developed DALE – Disability Adjusted Life Expectancy – which discounted life expectancy by number of years spent in illness. The emphasis in the 21st Century therefore was healthy life and not length of life (ie life expectancy) [9]. DALE is the years in ill health which is weighted according to severity, which is then subtracted from the expected overall life expectancy to give the equivalent healthy years of life. Using healthy years, statistics revealed that the health disparity between the sexes in Jamaica was 5 years in 2007 [10], indicating that self-rated health status of females on average in Jamaica is better than that for males. This is not atypical to Jamaica as females in many nations had a greater healthy life expectancy than males.

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The discipline of public health is concerned with more than accepting the health disparity as indicated by life expectancy or healthy life expectancy, as it seeks to improve the quality of life of the populace and the various subgroups that are within a particular geographical border. In order for this mandate to be attained, we cannot exclude the study of females’ health merely because they are living longer than males and accept this as a given; and that there is not need therefore to examine their self-rated health status. Many empirical studies that have examined health of Caribbean nationals were on the population [11-15]; elderly [16-25]; children [26, 27]; adolescents [28-30] and females have been omitted from the discourse. A comprehensive search of health literature in Caribbean in particular Jamaica revealed no studies. The values for the healthy life expectancy cannot be enough to indicate the self-rated health status of females neither can we use self-rated health status of population, children, elderly and adolescents to measure that of females. WHO [31] forwarded a position that there is a disparity between contracting many diseases and the gender constitution of an individual, suggesting that population health cannot be used to measure female health. Females have a high propensity than males to contract particular conditions such as depression, osteoporosis and osteoarthritis [31]. A study conducted by McDonough and Walters [32] revealed that women had a 23 percent higher distress score than men and were more likely to report chronic diseases compared to males (30%). It was found that men believed their health was better (2% higher) than that self-reported by females. McDonough and Walters used data from a longitudinal study named Canadian National Population Health Survey (NPHS). Those aforementioned realities justify a study on female health in Jamaica.

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The current study fills the gap in the health literature by investigating health of females in Jamaica. The objectives of the current study are 1) to examine the health conditions; 2) provide an epidemiological profile of changing health conditions in the last one half decade (2002-2007); 3) evaluate whether self-reported illness is a good measure of self-rated health status; 4) compute the mean age of females having particular health conditions; 5) calculate the mean age of being ill compared with those who are not ill; and 6) assess the correlation between self-rated health status and income quintile.

Materials and methods
Sample The current study extracted subsample of females from two secondary cross-sectional data collected by the Planning Institute of Jamaica and the Statistical Institute of Jamaica [33, 34]. In 2002, a subsample of 12,675 females was extracted from the sample of 25,018 respondents and for 2007; a subsample of 3,479 females was extracted from 6,783 respondents. The survey is called the Jamaica Survey of Living Conditions (JSLC) which began in 1989. The JSLC is modification of the World Bank’s Living Standards Measurement Study (LSMS) household survey. A self-administered questionnaire is used to collect the data from Jamaicans. Trained data collectors are used to gather the data; and these individuals are trained by the Statistical Institute of Jamaica 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
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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 (i.e. LFS) was selected for the JSLC. The sample was weighted to reflect the population of the nation. The non-response rate for the survey for 2007 was 26.2% and 27.7%. Measures Self-reported illness (or Health conditions): 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 (self-rated health status): “How is your health in general?” And the options were very good; good; fair; poor and very poor. The first time this was collected for Jamaicans, using the JSLC, was in 2007. 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).

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Statistical analysis The data were collected, stored and retrieved in SPSS for Windows 16.0 (SPSS Inc; Chicago, IL, USA). Descriptive statistics were used to provide information on the socio-demographic variables of the sample. Cross Tabulations were employed to examine correlations between nonmetric variables and Analysis of Variance (ANOVA) were utilized to examine statistical associations between a metric and non-metric variable. The level of significance used in this research was 5% (ie 95% confidence interval). Bryman and Cramer [35] correlation coefficient values were used to determine, the strength of a relation between (or among) variables: 0.19 and below, very low; 0.20 to 0.39, low; 0.40 to 0.69, moderate; 0.70 to 0.89, high (strong); and 0.90 to 1 is very high (very strong).

Results
Demographic characteristic of sample In 2002, 14.7% of sample reported an illness and this increased by 19.1% in 2007. Over the same period, health insurance coverage increased by 81.0% (to 21.0% in 2007); those seeking medical care increased to 67.6% (from 66.0%); the mean age in 2007 was 30.6±21.9 years which marginal increased from 29.4 ± 22.3 years; diabetic cases exponentially increased by 227.7% (in 2007, 15.4%); hypertension decline by 45.5% (to 24.8% in 2007) and arthritic cases fell by 66.1% (to 9.4% in 2007). Urbanization was evident between 2007 and 2002 as the number of females who resided in urban areas increased by 114.7% (to 30.4% in 2007), with a corresponding decline of 19.4% in females zones.

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Table 7.7.1 revealed that the increase in self-reported illness was substantially accounted for by increased cases in the rural sample (from 12.9% in 2002 to 20.0% in 2007). The drastic increase in health insurance coverage in 2007 was due to public establishment of public health insurance coverage. The greatest increase was observed in semi-urban areas 17.8%) followed by urban (9.6%) and rural (7.8%) Table 7.7.1. The increases in self-reported illness can be accounted for by diabetes mellitus, asthma and other dysfunctions. Concurrently, most of the increased cases were diabetic in semi-urban zones (17.1%); other health conditions in semiurban areas (12.4%) and asthma in urban zones (12.0%) (Table 7.7.1). Bivariate analyses There was a significant statistical correlation between self-rated health status and self-reported illness - χ2 (df = 4) = 700.633, P < 0.001; with there being a negative moderate relation between the variables – correlation coefficient = - 0.412(Table 2). Based on Table 7.7.2, 10.7% of those who reported an illness had had very good self-rated health status compared to 40.2% of those who did not indicate an illness. On the other hand, 2.5% of those who did not report a dysfunction had at least poor self-rated health status compared to 19.8% of those who indicated having an illness. Even after controlling self-rated health status and self-reported illness by age, marital status and per capita annual expenditure, a moderate negative correlation was found – correlation coefficient = - 0.362. On further examination of the self-reported illness by age, it was found that in 2002 the mean age of individual who reported an illness was 43.97 ± 26.81 years compared to 27.05 ± 20.41 years for who without an illness – t-test = 30.818, P < 0.001. In 2007, the mean age of reporting an illness was 42.83 ± 26.53 years compared to 28.16 ± 19.95 years for those who did not report an ailment – t-test = 15.263, P < 0.001.
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Based on Figure 7.7.1, there is an increase in the mean age of females being diagnosed with diarrhoea (32.00 ± 36.2 years) and asthma (21.73 ± 20.51 years). However, there is reduction in the mean age of females reported being diagnosed with chronic illness such as diabetes mellitus (60.54 ± 17.14 years); hypertension (60.85 ± 16.93 years) and arthritis 59.72 ± 15.41 years). The greatest decline in mean age of chronically ill diagnosed females was in arthritic cases (by 7.41 years). Concurrently, the mean age of females with unspecified health conditions fell by (33%, from 54.62 ± 21.77 years in 2002 to 36.42 ± 23.69 years in 2007). A cross tabulation between self-rated health status and income quintile revealed a significant statistical correlation - χ2 (df = 16) = 54.044, P < 0.001; with the relationship being a very weak one – correlation coefficient = 0.126 (Table 3). Based on Table 7.7.3, the wealthy reported the greatest self-rated health status (ie very good) compared to the wealthiest 20% (36.7%); with the poorest 20% recorded the least very good self-rated health status. No significant statistical correlation was found between diagnosed self-reported illness and income quintile - χ2 (df = 28) = 36.161, P > 0.001 (Table 7.7.4).

Discussion
Self-rated health status of female Jamaicans can be measured using self-reported illness. The current study found a moderate significant correlation between the two aforementioned variables, suggesting that self-reported illness is a relatively good measure of female’s health. In this study it was revealed that 60 out of every 100 who reported an illness had at most fair self-rated health status, with 20 out every 100 indicated a least poor health. It is evident from the findings that self-rated health status is wider than illness, which concurs with the literature [35, 36], which is keeping with the propositions of the WHO that health must be more than the absence of illness.
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Self-rated health status is people’s self-rated perspective on their general self-rated health status [35], which includes a percentage of poor health (or ill-health). The other components of this status include life satisfaction, happiness, and psychosocial wellbeing. Using a sample of elderly Barbadians, Hambleton et al [37] found 33.5% of explanatory power of self-rated health status is accounted for by illness. There is a disparity between the current study and that of Hambleton et al’s work as more of self-rated health status of the elderly is explained by current illness with this being less for females in Jamaica. Concomitantly, there is an epidemiological shift in the typology of illnesses affecting females as the change is towards diabetes mellitus. In 2007 over 2002, the 15 out of every 100 females reported being diagnosed with diabetes mellitus compared to 5 in 100 in 2002 indicating the negative effects of life behaviour of female’s self-rated health status. Another important finding of the current study is that diagnosed illnesses are not significantly different based on income quintile in which a female is categorized. However, the self-rated health status of females in different social standing (measured using income quintile) is different. Embedded in this finding is the role of income plays in improving self-rated health status [38]. Like Marmot [38], this study found that income is able to buy some improvement in self-rated health status; but this work goes further as it found that income does not reduce the typology in health conditions affecting females. Before this discussion can proceed, the discourse must address the biases in subjective indexes which are found in studies like this one. Any study on subjective indexes in the measurement of health (for example, happiness, life satisfaction; self-rated health status, selfreported illness) needs to address the challenges of biases that are found in self-reported data in particular self-reported health data. The discourse of subjective wellbeing using survey data cannot deny that it is based on the person’s judgement, and must be prone to systematic and non-

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systematic biases [40]. Diener [36] argued that the subjective measure seemed to contain substantial amounts of valid variance, suggesting that there is validity to the use of this approach in the measurement of health (or wellbeing) like the objective indexes such as life expectancy, mortality or diagnosed morbidity. A study by Finnas et al [41] opined that there are some methodological issues surrounding the use of self-reported (or self-rated) health and that these may result in incorrect inference; but that this measure is useful in understanding health, morbidity and mortality. Using life expectancy and self-reported illness data for Jamaicans, Bourne [42] found a strong significant correlation between the two variables (correlation coefficient, R = - 0.731), and that self-reported illness accounted for 54% of the variance in life expectancy. When Bourne [42] disaggregated the life expectancy and self-reported illness data by sexes, he found a strong correlation between males’ health (correlation coefficient, R = 0.796) than for females (correlation coefficient, R = 0.684). Self-reported data therefore do have some biases; but that it is good measure for health in Jamaica and more so for males. In spite of this fact, the current research recognized some of the problems in using self-reported health data (read Finnas et al. [41] for more information), while providing empirical findings using people’s perception on their health. Now that the discourse on objective and subjective indexes is out of the way, the next issue of concern is the reduced aged of reported illness and age of being diagnosed with particular chronic illness. In 2002, the mean age recorded for those who self-reported an illness was 44 years and this fell by 1 year in 2007, indicating that on average females are becoming diagnosed with an illness on average 2 months earlier. When self-reported illness was

disaggregated into acute and chronic health conditions, it was revealed that on average females

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were being diagnosed 7.41 years earlier with arthritis in 2007 over 2002; 4.95 years earlier with hypertension and 1.13 years earlier with diabetes mellitus.

Conclusion
The current study revealed that rural females recorded the highest percentage of self-reported illness. Concurrently, in 2007, 20 out of every 100 females in rural Jamaica reported an ailment which is a 3.7% increase over 2002 compared to a 3.1% increase in urban and 2.2% increase in semi-urban females. Furthermore, poverty was greatest for rural females. In 2002, poverty among rural females was 2.2 times more than urban poverty; and this increased to 3.3 times in 2007. In addition to the aforementioned issues, there is a shift in chronic illnesses occurring in females in Jamaica. Hypertension and arthritis have seen a decline in 2007 over 2002; however, there were noticeable increases in diabetes mellitus over the same period. The greatest increase in cases of diabetes mellitus occurred in semi-urban females followed by urban and rural females. In summing, the current study has revealed that, although healthy life expectancy for females at birth in Jamaica is 66 years, improvements in their self-rated health status cannot be neglected as there are shifts in health conditions (to diabetes mellitus) as well as the decline in ages at which females are being diagnosed with particular chronic illnesses. There is an issue which emerged from the current finding, the increasing cases of unspecified illness among females and this must be examined as to classification in order that public health practitioners will be able to address it before it unfolds into a public health challenge in the future.

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23. Bourne PA, McGrowder DA, Crawford TV. Decomposing Mortality Rates and Examining Health Status of the Elderly in Jamaica. The Open Geriatric Med J. 2009; 2:34-44. 24. Bourne PA. Good Health Status of Older and Oldest Elderly in Jamaica: Are there differences between rural and urban areas? Open Geriatric Medicine Journal. 2009; 2:18-27. 25. Bourne PA. 2008. Medical Sociology: Modelling Well-being for elderly People in Jamaica. West Indian Med J 57:596-04. 26. Walker S. Nutrition and child health development. In: Morgan W, editor. Health issues in the Caribbean. Kingston: Ian Randle; 2005: p. 15-25. 27. Samms-Vaughn M, Jackson M, Ashley D. School achievement and behaviour in Jamaican children. In: Morgan W, editor. Health issues in the Caribbean. Kingston: Ian Randle; 2005: p. 26-37.
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28. Frederick J, Hamilton P, Jackson J, Frederick C, Wynter S, DaCosta V, Wynter H. Issues affecting reproductive health in the Caribbean. In: Morgan W, editor. Health issues in the Caribbean. Kingston: Ian Randle; 2005: p. 41-50. 29. Bourne PA. Demographic shifts in health conditions of adolescents 10-19 years, Jamaica: Using cross-sectional data for 2002 and 2007. North American Journal of Medical Sciences 2009; 1:125-133. 30. Blum RW, Halcon L, Beuhring T, Pate E, Campbell-Forrester S, Venema A. Adolescent heath in the Caribbean: Risk and protective factors. American Journal of Public Health 2003; 93: 456-460. 31. WHO. Ageing and health, epidemiology. Regional Office in Africa: WHO; 2005. 32. McDonough P, Walters V. Gender and health: reassessing patterns and explanations. Social Science and Medicine 2001; 52:547-559. 33. 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. 34.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. 35. Bryman A, Cramer D. Quantitative data analysis with SPSS 12 and 13: a guide for social scientists. London and New York: Routledge; 2005: p. 214-219. 35. Kahneman D, Riis J. Living, and thinking about it, two perspectives. In: Huppert FA, Kaverne B, Baylis N, editors. The science of well-being: Integrating neurobiology, psychology, and social science. London: Oxford University Press; 2005. p. 285-304. 36. Diener E. Subjective well-being. Psychological Bulletin, 1984;95:542–75 37. Hambleton IR, Clarke K, Broome HL, Fraser HS, Brathwaite F, Hennis, A.J. Historical and current predictors of self-reported health status among elderly persons in Barbados. Revista Panamericana de salud Públic 2005; 17, 342-352. 38. 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, pp.31-46. 40. Schwarz N, Strack F. Reports of subjective well-being: judgmental processes and their methodological implications. In: Kahneman D, Diener E, Schwarz N, editors. Well-being: The Foundations of Hedonic Psychology. Russell Sage Foundation: New York; 1999;pp 61-84.
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41. Finnas F, Nyqvist F, Saarela J. Some methodological remarks on self-rated health. The Open Public Health J 2008;1:32-39. 42. Bourne P. Is self-reported health a good measure of objective health? North American J of Medical Sciences. In print.

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Table 7.7.1. Sociodemographic characteristics of sample by area of residence, 2002 and 2007
2002 Variable Rural Marital status Married Never married Divorced Separated Widowed Income quintile Poorest 20% Poor Middle Wealthy Wealthiest 20% Health conditions Diagnosed Acute: Cold Diarrhoea Asthma Diagnosed Chronic: Diabetes mellitus Hypertension Arthritis Other Non-diagnosed Self-reported illness Yes No Health care-seekers Yes No Health insurance Yes, Private Yes, Public No Age Mean (SD) in yrs SemiUrban 568 (25.7) 1452 (65.7) 16 (0.7) 27 (1.2) 147 (6.7) Urban Rural SemiUrban 111 (21.0) 362 (68.6) 16 (3.0) 5 (0.9) 34 (6.4) Urban 2007

1232 (25.7) 3033 (63.3) 25 (0.5) 51 (1.1) 453 (9.4)

243 (19.3) 907 (71.9) 18 (1.4) 22 (1.7) 71 (5.6)

262 (23.9) 723 (65.9) 11 (1.0) 12 (1.1) 89 (8.1)

161 (21.2) 523 (68.9) 16 (2.1) 8 (1.1) 51 (6.7)

1864 (24.8) 1867 (24.8) 1559 (20.7) 1340 (17.8) 894 (11.9)

450 (13.5) 511 (15.3) 652 (19.2) 759 (22.7) 965 (28.9)

206 (11.4) 231 (12.7) 331 (18.2) 441 (24.3) 605 (33.4)

498 (29.9) 437 (26.2) 342 (20.5) 237 (14.2) 154 (9.2)

77 (10.2) 146 (19.4) 161 (21.4) 183 (24.3) 185 (75.2)

97 (9.2) 131 (12.4) 212 (20.0) 265 (25.0) 354 (33.4)

1 (0.7) 3 (2.2) 1 (0.7) 8 (6.0) 57 (42.5) 38 (28.4) 26 (19.4) 1181 (16.3) 6051 (83.7)

0 (0.0) 1 (3.0) 2 (6.1) 0 (0.0) 20 (60.6) 8 (24.2) 2 (6.1) 384 (12.0) 2811 (88.0)

0 (0.0) 0 (0.0) 0 (0.0) 1 (4.2) 10 (41.7) 7 (29.2) 6 (25.0) 228 (12.9) 1540 (87.1)

13 (7.8) 2 (1.2) 20 (12.0) 23 (13.8) 33 (19.8) 9 (5.4) 45 (26.9) 22 (13.2) 324 (20.0) 1298 (80.0)

21 (20.0) 2 (1.9) 6 (5.7) 18 (17.1) 29 (27.6) 7 (6.7) 13 (12.4) 9 (8.6) 104 (14.2) 627 (85.8)

13 (7.8) 2 (1.2) 20 (12.0) 23 (13.8) 33 (19.8) 9 (5.4) 45 (26.9) 22 (13.2) 164 (16.0) 864 (84.0)

791 (66.0) 407 (34.0)

261 (66.8) 130 (33.2)

145 (64.7) 79 (35.3)

215 (65.5) 113 (34.5)

65 (63.1) 38 (36.9)

125 (74.4) 43 (25.6)

540 (7.4) 6723 (92.6) 29.5 (23.0)

539 (16.7) 2690 (83.3) 28.6 (21.2)

341 (19.3) 1430 (80.7) 30.0 (21.0)

114 (7.1) 126 (7.8) 1361 (85.0) 29.9 (22.3)

117 (16.3) 56 (17.8) 547 (76.0) 30.6 (21.1)

191 (18.7) 98 (9.6) 735 (71.8) 31.6 (22.0)

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Table 7.7.2. Self-rated health status by self-reported illness, 2007 Self-rated health status Yes Very good 63 (10.7) Good 176 (29.8) Fair 234 (39.7) Poor 104 (17.6) Very poor 13 (2.2) Total 590 χ2 (df = 4) = 700.633, P < 0.001, correlation coefficient = - 0.412 Self-reported Illness No 1114 (40.2) 1305 (47.1) 281 (10.2) 55 (2.0) 13 (0.5) 2768

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Figure 7.7.1. Mean scores for self-reported diagnosed health conditions, 2002 and 2007

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Table 7.7.3. Self-rated health status by income quintile, 2007 Income Quintile Self-rated health status Poorest 20% 2.00 3.00 4.00 Very good 196 (30.2) 237 (34.0) 225 (32.4) 282 (42.4)

Wealthiest 20% 243 (36.7)

Good

287 (44.2)

320 (45.9)

326 (46.9)

268 (40.3)

284 (42.8)

Fair (moderate)

105 (16.2)

110 (15.8)

107 (15.4)

87 (13.1)

108 (16.3)

Poor

56 (8.6)

23 (3.3)

30 (4.3)

24 (3.6)

26 (3.9)

Very poor

6 (0.9) 650

7 (1.0) 697

7 (1.0) 695

4 (0.6) 665

2 (0.3) 663

Total

χ2 (df = 16) = 54.044, P < 0.001, correlation coefficient = 0.126

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Table 7.7.4. Self-reported diagnosed health condition by per capita income Income Quintile Diagnosed health condition Poorest 20% 2.00 3.00 4.00 Yes, Cold 14 (11.4) 20 (17.5) 21 (15.8) 13 (11.8)

Wealthiest 20% 12 (10.3)

Yes, Diarrhoea

2 (1.6)

5 (4.4)

6 (4.5)

1 (0.9)

2 (1.7)

Yes, Asthma

12 (9.8)

9 (7.9)

11 (8.3)

3 (2.7)

13 (11.1)

Yes, Diabetes

17 (13.8)

14 (12.3)

12 (9.0) 26 (23.6)

23 (19.7)

Yes, Hypertension

35 (28.5)

27 (23.7)

38 (28.6) 24 (21.8)

24 (20.5)

Yes, Arthritis

11 (8.9)

5 (4.4)

6 (4.5)

5 (4.5)

5 (4.3)

Yes, Unspecified

25 (20.3)

27 (23.7)

26 (19.5) 29 (26.4)

25 (21.4)

No Total χ2 (df = 28) = 36.161, P < 0.001

7 (5.7) 123

7 (6.1) 114

13 (9.8) 133

9 (8.2) 110

13 (11.1) 117

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CHAPTER

8
Health of children less than 5 years old in an Upper Middle Income Country: Parents’ views

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. Parent-reported illness status was measured by the question ‘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? Health condition (i.e. parent-reported illness or parent-reported dysfunction) was measured by the question: “Is this a diagnosed recurring illness?” Self-rated health status was measured by “How is your health in general?” And the options were: Very Good; Good; Fair; Poor and Very Poor, and 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:. The health disparity that existed between rural and urban under-5 year olds showed that this will not be removed simply because of the abolition of health care utilization fees.

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

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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 behaviour in Jamaican children [17], indicating the void in health literature regarding health conditions.

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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 below 5 years of age. The current study fills this gap in the literature by examining the health status of children below 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 below 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 under-5 year olds was extracted from the national survey of 25,018 respondents in 2002, and information on 614 under5 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 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
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(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 (below 5 years old) to 100 females (below 5 years old), which shifted to 116.2 under-5 year old males to 100 under-5 year old females. The sample over the 6 year period (2002 to 2007) revealed internal migrations to urban zones (Table 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 below 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 below 5 years of age were taken to public hospitals, compared to 1.2 times less taken to private hospitals (Table 8.8.1). Approximately 6% more children below 5 years were ill in 2007 over 2002. Based on Table 8.8.1, under-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 1). For 2007, the response rate for health careseeking behaviour was 20.2%; parent-reported diagnosed health conditions, 20.2%, and less than 11% for cost of medical care. Health conditions Based on Table 8.8.1, the percentage of under-5 year olds with particular acute conditions saw a decline in colds and asthmatic cases, as well as chronic conditions. Figure 8.8.1 revealed that in
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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 below 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 8.8.2 revealed that 24.2% of children below 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 behavior

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 3). Based on Table 8.8.3, children below 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 8.8.2). 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 8.8.3, 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 8.8.4, 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 below 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 below 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 8.8.5). 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. It was found that 61.5% of the sample who were ill were 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.

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Discussion

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 below 5 years of age in Jamaica had good health. The findings revealed that 90 out of every 100 under-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 under-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 under-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.

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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 below 5 years of age had parents and/or guardians who were below the poverty line. Although this has declined by 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 below 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 below 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 below 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 careseeking 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
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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 under 18 years of age does not correspond to an increased utilization of medical care services, or lowered numbers of unhealthy children below 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 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
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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 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.

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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 rural population, which will translate into increased health status for their children. The health disparity that existed between rural and urban children below 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.

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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 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.
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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. 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.
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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 8.8.1. Socio-demographic characteristic of sample, 2002 and 2007
Variable Sex Male Female Income quintile Poorest 20% Poor Middle Wealthy Wealthiest 20% Self-reported illness Yes No Visits to health care facilities (hospitals) Private, yes Public, yes Area of residence Rural Semi-urban Urban Health (or, medical) care-seeking behaviour Yes No Health insurance coverage Yes, private Yes, public No Self-reported diagnosed health conditions Acute Cold Diarrhoea Asthma Chronic Diabetes mellitus Hypertension Arthritis Other (unspecified) Not diagnosed Number of visits to health care institutions Duration of illness Mean (SD) Cost of medical care Public facilities Median (Range)in USD Private facilities Median (Range)in USD
1 2

2002 n 1216 1231 725 554 474 402 293 345 1969 17 100 1460 682 306 221 128 211 * 2123

% 49.7 50.3 29.6 22.6 19.4 16.4 12.0 14.9 85.0 7.8 46.3 59.6 27.9 12.5 63.3 36.7 9.0 * 91.0

2007 n 330 284 156 140 126 117 75 125 475 5 20 311 125 178 76 48 66 33 496

% 53.7 46.7 25.4 22.8 20.5 19.1 12.2 20.8 79.2 6.7 26.7 50.7 20.4 29.0 61.3 38.7 11.1 5.5 83.4

185 20 46

53.3 5.8 13.3

60 9 17

48.4 7.3 13.7

2 0.6 1 0.3 1 0.3 54 15.6 38 11.0 1.53 (SD = 0.927) 8.51 days (6.952 days) 2.36 (157.26)1 13.76 (117.95)1

0 0 0 0 0 0 22 17.7 16 12.9 1.43 (SD = 0.989) 8.07 days (7.058 days) 0.00 (64.62)2 10.56 (49.71)2

USD1.00 = Ja. $50.87 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 8.8.2. Health status by self-reported illness Self-reported illness Health status Yes n (%) Very good Good Fair Poor Very poor Total
χ2 (df = 4) = 57.494, P < 0.001, cc = 0.297, n = 594

No n (%) 227 (48.3) 217 (46.2) 19 (4.0) 6 (1.3) 1 (0.2) 470

30 (24.2) 61 (49.2) 23 (18.5) 9 (7.3) 1 (0.1) 124

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Table 8.8.3. 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 8.8.1. Mean age of health conditions of children less than 5 years old, 2002 and 2007

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Figure 8.8.2. Health status by Parent-reported illness (in %) examined by gender

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Figure 8.8.3. Health status by parent-reported illness (in %) examined by area of residence

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Figure 8.8.4. Health status by parent-reported illness (in %) examined by social classes

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Figure 8.8.5. Health status by health care-seeking behaviour

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CHAPTER

9
Health of males in Jamaica

Studies in the Caribbean on males have been on marginalization; fatherhood; masculinity and none on the change pattern of diseases, and factors that account for their good health status. The current study fills this gap in the literature by examining males’ health in Jamaica. Study are 1) provide a detailed epidemiological profile of health conditions; 2) indicate the changing pattern of health conditions; 3) calculate the mean age of having reported illness or not; 4) compute the mean age of particular health conditions; 5) state whether the mean age of having particular illness are changing; 6) determine whether there is a significant statistical correlation between health status and self-reported illness; 7) identify factors that correlate with health status; and 8)ascertain the magnitude of each determinant of health status. In 2002, the mean age of a male who reported an illness was 39.32 ± 28.97 years compared to 27.26 ± 20.45 years – t-test = 18.563, P < 0.001. In 2007, the mean age of those with illness marginally increased to 40.64 ± 29.44 years compared to 27.61 ± 19.80 years for those who did not have an illness - t-test = 11.355, P < 0.001. A male who reported good health status with reference to one who indicated poor health status is 17.8 times more likely not to report an illness. Predictors of poor selfreported illness of males in Jamaica for 2002 were age (OR = 1.044; 95% CI = 1.038, 1.049; P < 0.05); urban area (OR = 1.547, 95% CI = 1.172, 2.043; P < 0.05); consumption (OR = 1.183; 95% CI = 1.056, 1.327; P < 0.05). non self-reported illness of males in Jamaica for 2007 can be predicted by good health status (OR = 17.801; 95% CI = 10.761, 29.446; P < 0.05); fair health status (OR = 2.403; 95% CI = 1.461, 3.951; P < 0.05); age (OR = 0.967; 95% CI = 0.957, 0.977; P < 0.05); urban area (OR = 1.579, 95% CI = 1.067, 2.336; P < 0.05); and consumption (OR = 0.551; 95% CI = 0.352, 0.861; P < 0.05). On disaggregating the explanatory power, it was revealed that good health status accounted for 30% (out of 37.6%) of the why males do not report an illness. Interestingly in this work is that the mean age of males who reported being diagnosed with unspecified health conditions has declined by 27 years; but we are not cognizant of what constitutes this category of illness. With average age of contracting this health conditions being 40.7 years, could this group holds some answers to the high mortality of Jamaican males. The way forward must be to research this unspecified health condition grouping as public health cannot plan without research findings.

Introduction
In the Caribbean, studies on males have been masculinity and fatherhood [1-6]; male marginalization [7-10]; survivability [11], broad health concerns [12-25] and those studies
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exclude the health status of males. The Planning Institute of Jamaica, (PIOJ) & Statistical Institute of Jamaica, (STATIN) however since 1988 have provided general self-reported illness and medical care-seeking behaviour of the population and these have been disaggregated by sexes [26]. The information on health issues of males is insufficient upon which public health practitioners can sufficient plan for this cohort. Since 1988, when PIOJ & STATIN began collecting data with a modified World Bank Living Conditions instrument, males has reported less illness than females; visited health carepractitioners less than females; yet their life expectancy has been between 2-6 years less than that of females [27]. These situations suggesting that males’ health cannot be left only up to the aforementioned for planning their health issues. Concurringly, STATIN’s data revealed that of the 5 leading cause of mortality in Jamaica, males outnumbered females in 4 categories [28]; and the morbidity figures published by the Ministry of Health (MOHJ) showed that they outnumbered females in 7 of the 10 leading cause of illnesses (MOHJ) [29,30]. It is evident from the aforementioned data that there is health disparity between the sexes in Jamaica; health goes beyond illness and health care-seeking behaviour. In the late 1940s, the health discourse was such that World Health Organization (WHO) in the Preamble to its Constitution joined the debate and offered a conceptual definition of health [31]. The WHO [31] penned that health is more than the mere absence of diseases to include social, psychological and physiological wellbeing. This was adopted by Engel [32-36] who even coined the term ‘biopsychosocial model’ as the new thrust in mental ill patient care. He like the WHO opined that humans are mind, body and social agents which denote that their care must incorporate all these facets as against the old biomedical approach, which was only concerned about diseases and not wellbeing. This approach has revolutionalized the how health care is
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delivered, measured and planned for. Embedded in Engel’s works are how health should be conceptualized and addressed, and that wellbeing can be attained if it is measured solely using illness. In response to a need to expand the measures of health away from diagnosed illness, mortality and life expectancy (or objective indexes), researchers like Diener [37,38]; Veenhoven [39]; Frey & Stutzer [40-43]; Diener & Seligman [44]; Diener et al. [45]; Hutchinson et al. [21]; Easterlin [46,47] have used happiness, life satisfaction and some health status [20,48]. Those measures are subjective indexes, which the scholars opined assess health more than the negative or narrow objective indexes. In keeping with the limitation of objective indexes, the WHO [49] devised an approach to discount life expectancy by removing time spent in illness to produce what is termed healthy life expectancy (or disability adjusted life expectancy). Disability Adjusted Life Expectancy (DALE) summarizes the expected number of years to be lived in what might be termed the equivalent of "full health." To calculate DALE, the years of ill health are weighted according to severity and subtracted from the expected overall life expectancy to give the equivalent years of healthy life [49]. This approach resulted in Jamaicans losing 9 years of life owing to disabilities. The healthy life expectancy provides yet another account for health status of males; but there is a fundamental weakness that has not been address. Healthy life expectancy is rest on the pillows of mortality patterns and still lacks the coverage that happiness, life satisfaction and health status gives. Healthy life expectancy therefore lacks extensive coverage of an individual’s health; but accompanying the subjective indexes are biases and validity issues. There are empirical evidences to show that self-reported health is an indicator of general health. Schwarz & Strack [50] opined that the person’s judgments are prone to systematic and non206

systematic biases. However, Diener [37] argued that the subjective index seemed to contain substantial amounts of valid variance, suggesting that subjective measures provide some validity in assessing health, this was concurred by Smith [51] with good construct validity and is a respectably powerful predictor of mortality risks [52], disability [53] and morbidity [54], though these properties vary somewhat with national or cultural contexts [52]. Studies using selfreported health and mortality found a significant relationship between a subjective and an objective measure [52, 54]; life expectancy [55]; disability [53]. Bourne [55]) 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 indexes is good, with Smith [51] opined that the construct validity between the two being a good one. Using subjective indexes to measure health, studies have shown that there are many predictors (or variables) of these measures. Income, marital status, education, and other sociodemographic variables [12-18, 20, 21, 40, 46-48, 56] have been found to significant correlate with health status. Those studies have not singled out males in the examination of health issues, suggesting that the experiences of males and females are congruent or similar. WHO [57] forwarded that there is a disparity between contracting many diseases and the gender constitution of an individual. One health psychologist, Phillip Rice [58], in concurring with WHO, argued that differences in death and illnesses are the result of differential risks acquired from functions, stress, life styles and ‘preventative health practices’ [58]. With health disparity between the sexes caused by particular issues with a nation, it is for this reason why health research must examine the sexes differently in order to understand each subgroup.

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The current study fills this gap in the health literature by examining the health of males in Jamaica. The objectives of this study are 1) provide a detailed epidemiological profile of health conditions; 2) indicate the changing pattern of health conditions; 3) calculate the mean age of having reported illness or not; 4) compute the mean age of particular health conditions; 5) state whether the mean age of having particular illness are changing; 6) determine whether there is a significant statistical correlation between health status and self-reported illness; 7) identify factors that correlate with health status; and 8)ascertain the magnitude of each determinant of health status.

Materials and methods

The current study used secondary cross-sectional data taken from two nationally representative surveys. A subsample of 12,332 males out of 25,018 respondents and 3,303 males from 6,783 respondents were extracted from 2002 and 2007 surveys respectively. The only criterion upon which the subsample was selected was based on being male. The survey (Jamaica Survey of Living Conditions, JSLC) is a modification of the World Bank Survey on Living Conditions [5961] (PIOJ & STATIN, 1988-2008; World Bank, 2002). The JSLC began collecting data since 1988, and each year a new module is included based on particular sociopolitical issues with the economy leading up to the survey period. A self-administered questionnaire is used to collect the data from Jamaicans. Trained data collectors are used to gather the data; and these individuals are trained by the Statistical Institute of Jamaica. 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
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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 dwelling 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. The sample was weighted to reflect the population of the nation. The non-response rate for the survey for 2007 was 26.2% and 27.7% [59-61]. 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 [17, 18, 62]. 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 [52, 53]. 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 in 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, header, or pharmacist being 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.
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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 analyses were used to examine the association between non-metric variables; and t-test for metric and dichotomous variables and F statistic was utilized for metric and non-dichotomous variables. Logistic

regressions analyses the relationship between 1) poor self-reported illness and some sociodemographic variables (for 2002); as well as 2) not reported an illness and some sociodemographic, economic variables and health status (for 2007). The statistical packages SPSS 16.0 was used for the analysis. Ninety-five percent confidence interval was used for the analysis, and the final models (ie equations) were based those variables that P < 0.05. Odds Ratio (OR) was interpreted for each significant variable. Initially the enter approach was used in logistic regression followed by stepwise to ascertain the contribution of each significant variable for the final models. In order to exclude multicollinearity between particular independent variables, correlation matrix was examined in order to ascertain if autocorrelation (or multicollinearity) existed between variables. Based on Bryman & Cramer [63], 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. Moderately to highly correlated variables were excluded from the model. Another exclusion criterion that was used is 30% of missing cases.

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Results
Demographic characteristic of sample

Table 9.9.1 revealed a shift in percent of divorced (+ 0.8%); widowed (+ 0.7%); separated (0.4%); never married (+1.7%) and married males (-1.4%) between 2002 and 2007. There was also a percentage shift in the sample reported having had an illness in the 4-week period of the survey. Concomitantly, there was a decline in percent of sample with hypertensive and arthritic cases in the chronic illness category, with an increase in diabetic cases. In 2007, 62.3% of males sought medical care compared to 60.7% in 2002. The increase was not limited to medical careseeking behaviour as the percentage of males with health insurance coverage increased by 10.5% to 19.3%. Massive urbanization is occurring in male population as in 2002, 62.7% of males dwelled in rural zones and this decline to 50.1% in 2007, with 16% more males resided in urban zones and 3.4% decline in semi-urban males. In the period (2002-2007), consumption and income increased by 2.24 and 2.17 times respectively. Health statistics In 2007, it was the first time in the 2 decade history on collecting data on Jamaicans that health status was obtained. The findings revealed that 39.0% of sample indicated very good health status; 46.4% good health; 10.4%, fair health and 4.3% poor-to-poorest health, with 0.8% indicated very poor health status. A cross tabulation between health status and self-rated illness revealed a significant statistical correlations - χ2 (df = 4) = 602.354, P < 0.001, with the association being a weak one, correlation coefficient = 0.399. Twenty-one percent of the sample indicated having had an illness that reported poor-to-poorest health status compared to 1.9% of sample that revealed no illness

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recorded poor-to-poorest health status (Table 9.9.2). Continuing, 3.3 times more of the respondents who indicated not having an illness had very good health status compared to those who indicated having an illness. In 2002, the mean age of a male who reported an illness was 39.32 ± 28.97 years compared to 27.26 ± 20.45 years – t-test = 18.563, P < 0.001. In 2007, the mean age of those with illness marginally increased to 40.64 ± 29.44 years compared to 27.61 ± 19.80 years for those who did not have an illness - t-test = 11.355, P < 0.001. Based on Figure 9.9.1, the mean age of males with particular chronic illness has decline over the period. Interestingly, the greatest percentage decline was observed in unspecified health conditions. In 2002, the mean age for males with unspecified health condition was 55.79 ± 28.81 years and this fell to 40.67 ± 27.01 years in 2007. In 2007, the mean age for males with diabetes mellitus was 61.94 ±12.01 years; 66.76 ± 15.95 years for those with hypertension and 70.29 ± 10.85 years for those with arthritis. Further examination revealed that there is statistical difference between the mean of those with chronic illness (P > 0.001); but this existed between the chronic and the acute illnesses as well as the unspecified health conditions: for 2002 – F statistic = 15.62, P < 0.001 and for 2007 – F statistic = 31.601, P < 0.001. Multivariate analysis Predictors of poor self-reported illness by some explanatory variables In 2002, current poor health status of males in Jamaica was found to be significantly correlated with age; area of residence; consumption, social support and marital status (χ2 = 545.320, P < 0.001
-2 Log likelihood = 4277.79) (Table 9.9.3). Table 9.9.3 revealed that predictors of poor self-reported

illness of males in Jamaica for 2002 were age (OR = 1.044; 95% CI = 1.038, 1.049; P < 0.05); urban area (OR = 1.547, 95% CI = 1.172, 2.043; P < 0.05); consumption (OR = 1.183; 95% CI = 1.056, 1.327; P < 0.05). Further analysis show that age was the most significant predictor of poor
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health status accounting for 14.3% of the model (ie 15.1%); with area of residence accounting for 0.2% (Table 9.9.3). In 2007, current poor health status of males in Jamaica was found to be significantly associated with health status; age of respondents; consumption, and area of residence - (χ2 = 463.61, P < 0.001; -2 Log likelihood = 1103.314) (Table 4). Based on Table 9.9.4 revealed that predictors of poor self-reported illness of males in Jamaica for 2002 were age (OR = 1.044; 95% CI = 1.038, 1.049; P < 0.05); urban area (OR = 1.547, 95% CI = 1.172, 2.043; P < 0.05); consumption (OR = 1.183; 95% CI = 1.056, 1.327; P < 0.05). The findings here show that for each year that a male ages, he is 1.04 times more likely to report an illness; and that urban males are 1.6 times more likely to report an illness with reference to rural males. Further analysis show that age was the most significant predictor of poor health status accounting for 14.3% of the model (ie 15.1%); with area of residence accounting for 0.2% (Table 9.9.5). Based on Table 9.9.4, non self-reported illness of males in Jamaica for 2007 can be predicted by good health status (OR = 17.801; 95% CI = 10.761, 29.446; P < 0.05); fair health status (OR = 2.403; 95% CI = 1.461, 3.951; P < 0.05); age (OR = 0.967; 95% CI = 0.957, 0.977; P < 0.05); urban area (OR = 1.579, 95% CI = 1.067, 2.336; P < 0.05); and consumption (OR = 0.551; 95% CI = 0.352, 0.861; P < 0.05). On disaggregating the explanatory power, it was revealed that good health status accounted for 30% (out of 37.6%) of the why males do not report an illness; age accounted for 5.4%; fair health accounted for 0.8%; consumption, 0.9% and area of residence, 0.5% (Table 9.9.6). Concomitantly, Table 4 revealed that a male who reported good health status with reference to one who indicated poor health status is 17.8 times more likely not to report an illness; and that the more a male spent in consumption expenditure, he is 0.449 times less likely not to report an illness.

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Discussion
The current study revealed that men were willing to state their general health status (using response rate, 97%); but that they were unwilling to report the typologies of illness that they were diagnosed with (response rate, 0.7% in 2002 and 12.2% in 2007). Income of males increased by least 2 times in 2007 over 2002; however, health care-seeking behaviour increased by only 1.6%. Embedded in the finding is males reluctance to seek medical care, and this again can be seen in of 8.8% increase in health insurance coverage in 2007 over 2002 7% was due to public health insurance although this is fee. The number of diabetic cases in 2007 increased by 2.3 times over 2002, and there declines in the mean age at which males reported illness. The mean age at which a male who had self-reported being diagnosed with diabetes fell to 61.94 years; hypertension, 66.8 years; arthritis, 70.3 years and unspecified health conditions, 40.7 years from 55.8 years. Hence, why the reluctance to seek medical care with the aforementioned context? Chevannes [1] provided some explanation for men’s general behaviour using social learning theory. He forwarded the perspective that a young male imitates the roles of society members through role modeling as to what constitute acceptable and good roles [1]. Young males are grown to be strong, masculine, brave and fewer traits must shun the appearance of weakness and its associated attributes. The male child therefore as a part of his socialization is to accept that the illness is correlated with weakness, and that he must not be willing participate into health care seeking behaviour unless it is unavoidable. This definition of unavoidable is embedded into severity, and being unable to rectify the complaint outside of health care practitioners. This gender role of sexes is not limited to Jamaica or the Caribbean but a study carried out by Ali and de Muynck [64] on street children in Pakistan found a similar gender
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stereotype. A descriptive cross-sectional study carried out during September and October 2000, of 40 school-aged street children (8-14 years) revealed severity of illnesses and when ill-health threatens financial opportunities that males sought medical care. Another finding was that [65]. Chevannes noted males suppressed response a pain, accounting for a low turn out to health care facilities and justifies a higher mortality rates as on attend medical care facilities it is often too later and death is probable outcome. Hence the lowered age with which are diagnosed with particular chronic illness (such as diabetes mellitus, hypertension and arthritis) does not change this embedded culturalization which began prior to formal schooling and justifies why higher education does not often time change this practice. Understanding the psyche of men and how this is fashioned aids in the comprehension of their reluctance to visit health care facilities. The current findings indicate that urbanization is taken place with males in Jamaica. The migration to urban zones is primarily to facilitate economic opportunities which account for the drastic increase in income. Ali & de Muynck [64] study provides some understanding for the marginal increase in health care seeking behaviour in Jamaica as this figure is accounted for males who were ill to the point of being unable to work and that the ill-health threatens their economic livelihood. Another explanation for males’ withdrawal from visits to health care facilities is due to the gender composition of those facilities. Males are culturalized to be strong, provide for his family and chief among these is to show a female his masculinities which are tied to strength, physique and financial ability. It follows that with the higher percentage of health care workers being females, this retard the males’ masculinity as he conceptualizes visits to these institutions as a show of his weakness. In protection of this masculinity, males will go to any extent to maintain their image, which includes the sacrificing of life. This is embedded in the health
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reported figures for the sexes. In 2002, 14.6% of females reported an illness compared to 10.2% for males, and in 2004 the disparity widens as the figures were 13.6% for females and 8.9% for male [26]. The current work showed the contribution of health status in explaining illness (or nonillness) of males. Current health status therefore accounted for 80.9% (30% out of 37.1%) of the variability in current illness (or lack of), which is to Hambleton et al.’s work. Hambleton et al. found that 87.5% (ie 33.5% out of 38.3%) of current illness account current health status of elderly Barbadians. This work holds some comparability with Hambleton et al.’s study with respect to explanatory power and contribution of illness to health status. Hambleton et al.’s research is not only validating the current study, this work is validating the use of self-rated (or self-reported) illness or health status in measuring health of an individual. Many empirical studies have established the strong correlation between marital status and health status. This work found that there was no significant difference between health status of married males and males who were never married; but that divorced, separated and widowed males were 1.4 times more likely to report an illness. A part of this rationale for the higher probability of increased illness is owing to 1) the lost owing to separation which may be via death or physical separation, 2) the psychological tenet in investment and its lose from parting; and, 3) the financial separation cost which are likely to account for depression, suicide and other forms of illness. A study by Able et al. [66] found that the rate of suicide in male Jamaicans was 9 times higher than that for females, and they opined that a part of this is owing to suppressed feeling of this sex. Although divorce, separation or widowhood have a psychosocial influence on males, being married do not provide a benefit of better health.

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Conclusion
The current study provides a comprehensive examination of males’ health in Jamaica with which can be used by public health and other policy makers in understanding this cohort. Interestingly in this work is that the mean age of males who reported being diagnosed with unspecified health conditions has declined by 27 years; but we are not cognizant of what constitutes this category of illness. With average age of contracting this health conditions being 40.7 years, could this group holds some answers to the high mortality of Jamaican males. The way forward must be to research this unspecified health condition grouping as public health cannot plan without research findings.

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

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Table 9.9.1. Sociodemographic characteristics of sample, 2002 and 2007
Variable n Marital status Married Never married Divorced Separated Widowed Self-reported illness Yes No Self-reported diagnosed illness Cold Diarrhoea Asthma Diabetes mellitus Hypertension Arthritis Other Not diagnosed Income quintile Poorest 20% Poor Middle Wealthy Wealthiest 20% Health care-seeking behaviour Yes No Health insurance coverage Yes No Area of residence Rural Semi-urban Urban Income Median (Range) Age Mean ±SD Consumption Median (Range) Duration of illness Median (Range) Cost of medical care Public Median (Range) Private Median (Range)
In 2002, US $1.00 = Ja. $50.87 In 2007, US $1.00 = Ja. $80.47

2002 % 2007 5421 64 85 234 1217 10699 5 6 3 39 16 19 2454 2345 2440 2482 2611 769 497 1251 10699 25.7 69.4 0.8 1.1 3.0 10.2 89.8 5.7 6.8 3.4 44.3 18.2 21.6 19.9 19.0 19.8 20.1 21.2 60.7 39.3 10.5 89.5 n

2007 % 522 1528 34 16 50 388 2820 69 11 47 31 58 24 102 60 671 640 636 667 689 253 153 612 2560 24.3 71.1 1.6 0.7 2.3 12.1 87.9 17.2 2.7 11.7 7.7 14.4 6.0 25.4 14.9 20.3 19.4 19.3 20.2 20.9 62.3 37.7 19.3 80.7

7727 62.7 3062 24.8 1543 12.5 Ja $251,795.96 (Ja. $6,423,253.16.72) 28.28 ± 21.7 ears Ja $55,508.45 (Ja. $1,992,283.72) 10.5 days (90 days) Ja $150.00 (Ja. S12,000) Ja $800.00 (Ja $ 29,000)

1654 50.1 706 21.4 943 28.5 Ja $545,950.17 (Ja. $5,228,700.28) 29.11 ± 21.6 years Ja $123,697.30 (Ja. $1,621,147.12) 7.1 days (15 days) Ja $294.96 (Ja. $20,000) Ja $1130.39 (Ja $ 13,000)

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Table 9.9.2. Health status and self-rated illness
Self-rated illness Health status Very good Good Fair Poor Very poor Yes 50 (13.0) 129 (33.4) 125 (32.4) 66 (17.1) 16 (4.1) No 1193 (42.6) 1351 (48.2) 205 (7.3) 44 (1.6) 8 (0.3)

Total χ2 (df = 4) = 602.354, P < 0.001; cc = 0.399

386

2801

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Table 9.9.3. Predictors of poor self-reported illness by some explanatory variables, 2002
Variable Age Urban areas Other towns †Rural areas Log Consumption Separated_Div_Wid Married †Never married Physical environment Secondary Tertiary †Primary or below Rented – house tenure Owned †Squatted Social support Constant S.E. 0.003 0.142 0.156 Wald statistic 222.661 9.470 1.312 P 0.000 0.002 0.252 Odds ratio 1.044 1.547 1.195 95.0% C.I. 1.038 1.049 1.172 2.043 0.881 1.622

0.058 0.148 0.097

8.344 4.766 1.388

0.004 0.029 0.239

1.183 1.382 1.121

1.056 1.034 0.927

1.327 1.848 1.355

0.086 0.100 0.212

0.885 0.018 0.087

0.347 0.893 0.768

1.084 1.013 1.064

0.916 0.833 0.703

1.283 1.232 1.612

0.170 0.123

0.017 0.025

0.895 0.876

0.978 1.020

0.700 0.801

1.366 1.298

0.082 0.664

6.231 92.874

0.013 0.000

1.226 0.002

1.045

1.440

χ2 = 545.320, P < 0.001 -2 Log likelihood = 4277.79 Hosmer and Lemeshow goodness of fit χ2=4.324, P = 0.827 Nagelkerke R2 =0.151 Overall correct classification = 88.9% Correct classification of cases of poor self-rated health = 99.8% Correct classification of cases of good self-rated health =1.8% †Reference group

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Table 9.9.4. Predictors of not self-reporting an illness by some explanatory variables, 2007
Variable S.E. Good health status Fair health status †Poor health status Age Middle Class Upper class †Lower class Married Divorced, separated or wid †Never married Health insurance 0.257 0.254 Wald statistic 125.717 11.927 P 0.000 0.001 Odds ratio 17.801 2.403 95.0% C.I. 10.761 1.461 29.446 3.951

0.005 0.257 0.364

39.848 0.011 0.344

0.000 0.918 0.558

0.967 1.027 1.238

0.957 0.620 0.606

0.977 1.701 2.528

0.194 0.313

0.710 0.003

0.399 0.954

0.849 1.018

0.581 0.551

1.241 1.881

0.195

0.016

0.899

0.975

0.665

1.430

Urban area Other towns †Rural areas Log Consumption Constant

0.200 0.216

5.221 2.858

0.022 0.091

1.579 1.440

1.067 0.944

2.336 2.199

0.228 2.596

6.844 10.301

0.009 0.001

0.551 4158.196

0.352

0.861

χ2 = 463.61, P < 0.001 -2 Log likelihood = 1103.314 Hosmer and Lemeshow goodness of fit χ2=4.272, P = 0.832 Nagelkerke R2 =0.376 Overall correct classification = 88.9% Correct classification of cases of poor self-rated health = 99.8% Correct classification of cases of good self-rated health =1.8% †Reference group

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Table 9.9.5. Model summary for 2002 logistic regression analysis
Model Age Age+urban area Age+urban area+consumption Age+urban area+consumption+social support Age+urban area+consumption+social support+ marital status Nagelkerke R Square
0.143 0.145 0.148 0.149

0.151

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Table 9.9.6. Model summary for 2007 logistic regression analysis
Model Good health Good health+age Good health+Age+fair health Good health+Age+fair health+consumption Good health+Age+fair health+consumption+urban area Nagelkerke R Square 0.300 0.354 0.362 0.371 0.376

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Figure 9.9.1. Mean age for males with particular self-reported diagnosed illness

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Part II
ERRORS IN DATA

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INTRODUCTION
Content errors refer to the accuracy of characteristics of data system, assessing the reliability of data sources. This is executed and performed by testing the consistency of data sources, particularly the content. The exorbitant cost and time consuming nature of primary data collection makes it increasingly determinable to avoidance of primary data collection. In response to the challenges of primary data collection, some researchers (academics and scholars) have resorted secondary data sources. Some people use the credibility of the data collector and publisher as the yardstick for measuring the usability of secondary data. The tradition, scope, coverage, authority and traditional contribution of some institutions and agencies make it easier for people to assume the reliability validity of the data estimates, results and data system. Institutions and/or agencies like American Diabetes Association; Centers for Disease Control and Prevention; WHO; Pan American Health Organization, PAHO; United Nations; NASA; ILO; World Bank; Universities – Cambridge; Harvard; Oxford; Princeton; Yale to name a few). Repeatedly science has tested and refuted traditions, cosmologies, and authorities. It is as a result on the unbiasness of science to investigate phenomena, which have led to the modification and refutation of old knowledge. New paradigms emerged when scientists question epistemologies. Thus scientists cannot take the biased position that authority is important in fashioned knowledge. Science seeks to ascertain the truth, which is embedded in the primary assumption that nothing is truth with testing and verification. With this underlying reality, we must question data quality irrespective of the data sources’ former credibility, scholastic accomplishments and authority on knowledge. In keeping with the pillows upon which science operates, inquiry cannot be done only on some data estimates and results that of from specific individuals and/or institutions as this violate the ‘pursuit of truth’. If we assume that we currently know the truth, then more examination of issues surround that matter and the phenomenon in question cannot be tested in the future, which assumes that knowledge is constant. Empirical evidence exists that showed the modification of past knowledge, refutation of some, and paradigm shift of positions. Knowledge is fluid. Fluidity implies that we must be continuously examining knowledge, because the set of propositions that held in the past can change and thereby offer a new knowledge of what we thought was. This is

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one of the tenets that accounts for demographers continuously examining the content of data, particularly on age in surveys and censuses. Surveys represent the summation of peoples’ views and quality of recall. It is sometimes overlooked by people that surveys are critical based on the quality of the recollection of the respondents and their honesty. Knowing this fact, demographers have formulated and developed techniques to examine the quality of data. While it is established that coverage errors are low, because statisticians have continued to improve the quality of the sample frame, sample and representation of the population, demographers in Jamaica have not examined data quality (ie content) outside age box (paradigm). This volume seeks to evaluate content errors in health data, particularly among the JSLC, because of the interconnectivity between health and development of a society. Part II of this volume explores content errors in health data that are likely in the JSLC. The JSLC is not absolute truth as there is no such phenomenon, making an inquiry into the likeliness of content errors apart of the verification of the data and ascertaining the degree of truth that is therein. These inquiries are to strengthen the quality of the data as measures and adjustments can be made in keeping with findings.

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CHAPTER

10
Dichotomising poor self-reported health status: Using secondary cross-sectional survey data for Jamaica

Caribbean scholars continue to dichotomise self-reported health status without empirical justification for inclusion or exclusion of moderate health status in the dichotomisation of poor health. This study will 1) evaluate which cut-off point should be used for self-reported health status; 2) assess whether dichotomisation of self-reported data should be practiced; 3) ascertain any disparity in dichotomisation by some covariates (i.e., marital status, age cohort, social class); and 4) examine the odds of reporting poor or moderate-to-very poor self-reported health status if one has an illness. When moderate self-reported health status was used in poor health status, the cut-off revealed moderate effect on specified covariates across the age cohorts for women. However, for men, exponential effects were used on social class, but not on area of residence or marital status across the different age cohorts. The cut-off point in the dichotomisation of self-reported health status does not make a difference for women and must be taken into consideration in the use of self-reported health data for Jamaica.

Introduction
Logistic regression has been widely used by Caribbean and/or Latin American scholars to examine parameters and weights of determinants of self-reported health status [1-7] or life satisfaction [8]. This is a global practice [9-14]. Embedded in the use of logistic regression in the study of self-reported (rated) health is the dichotomisation of health status. Self-rated health status is a Likert scale variable ranging from very poor to very good health status. This denotes that the dichotomisation of self-reported health must address where moderate health status should be placed.

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The dichotomisation of self-reported health status brings into focus the issue of a cut-off and the validity of one’s choice. 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. Another important issue which is unresolved in the choice of a cut-off is the subjective with which Caribbean scholars have continued to make their decision. Their decision as to what constitutes bad or good (including excellent) health is not purely subjective, as this practice is global one. The decision of a cut-off cannot be subject to international norm if there is no rationale for this approach. Caribbean scholars cannot merely follow tradition in their choice of conceptualisation and operationalisation of a measure, as this is not a scientific enough rationale for the use of a particular measure. Some scholars have opined that self-reported health status should remain a Likert scale measure or in its continuous form as against the dichotomisation of the measure [15-17]. The work of Finnas et al. showed that the five-point Likert scale variable of self-reported health status can be dichotomised. However, there are some methodological issues that must be considered [18]. Finnas and colleagues’ study revealed that the cut-off point of bad versus good self-reported health and the decision as to where moderate self-reported health status be placed does not depend on age. However, when the categorisation of poor self-reported health excludes moderate self-reported health, the covariate of marital status and educational level were found to be highly age-dependent. Within the context of the aforementioned findings, Caribbean scholars need to examine these issues within the available health data in order to be able to empirically make a choice of 1) dichotomisation or 2) non-dichotomisation of self-reported health status. The discourse on whether or not to dichotomise self-reported health status is unresolved., Therefore, dichotomising the measure simply because it has been done so by non-Caribbean
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scholars in developed nations is not a sufficient rationale for following suit in Latin America and the Caribbean. Latin America and the Caribbean are developing nations whose socio-economic situations are different from those in First World Countries, emphasising the justification of why Latin America and Caribbean scholars should examine self-reported health data in order to concretise their choice of dichotomisation or not. Jamaica, which is a part of Latin America and the Caribbean, has been collecting selfreported health data since 1988 [19], and these data have been used repeatedly by scholars to aid public health programmes. An extensive review of the literature did not find a single study that has examined the validity of dichotomisation of self-reported health status. The same was also found for the wider Caribbean, suggesting that scholars have been keeping with the tradition and the practice of using the scholarly information from the developed nations when it comes to dichotomised self-reported health status. The current study fills this gap in the literature, and will be used to guide public health practitioners and other users of self-reported health data on Jamaicans. The objectives of the study are: 1) evaluate which cut-off point should be used for self-reported health status; 2) assess whether dichotomisation of self-reported data should be practiced; 3) ascertain any disparity in dichotomisation by some covariates (i.e., marital status, age cohort, social class); and 4) examine the odds of reporting poor or moderate-to-very poor self-reported health status if one has an illness.

Materials and Methods
Sample This study used secondary cross-sectional survey data, which was collected between May and August, 2007 [20]. The Jamaica Survey of Living Conditions (JSLC), which is used for this

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study, is a joint research conducted by the Planning Institute of Jamaica (PIOJ) and the Statistical Institute of Jamaica (STATIN) [19]. The JSLC is an annual survey that began in 1988. It is a standard exercise; the JSLC’s sample is a proportion of the Labour Force Survey (LFS). In 2007, it was one-third of the LFS. For 2007, the JSLC’s sample was 6,783 respondents. The current study extracted 1,583 respondents from the larger sample as the focus was on participants aged 46+ years. 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. 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. A total of 620 households were interviewed from urban areas, 439 from semi-urban areas and 935 from rural areas, which constituted 6,783 respondents. The sample was weighted to reflect the population of the nation. The non-response rate for the survey for 2007 was 27.7%. Data collection The JSLC is a modification of the World Bank’s Living Standards Measurement Study household survey [21]. Face-to-face interviews over the aforementioned period were used to collect the data. A structured questionnaire was used and already trained interviewers were then trained again specifically for this task. The questions covered demographic characteristics,

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household consumption, health status, health care-seeking behaviour, illnesses, education, housing, social welfare and related programmes, and inventory of durable goods. Statistical analyses Data were stored, retrieved and analyzed using SPSS-PC for Windows version 16.0. Descriptive statistics were used to provide background information on the sample. Cross tabulations were done to examine non-metric dependent and independent variables, which provided the percentages. Percentages were computed for dichotomous health statuses (i.e., very poor or poor health status, and the other very poor to moderate health status); these were employed for calculating the odds ratio in each dichotomisation of self-reported health status. Among men aged 46-54 years, 37.7% of those who reported an illness rated their health status as very poor or poor, as compared to 7.3% of those who did not indicate an illness. Hence, the odds ratio of very poor-to-poor health status was 7.7 [(37.7/62.3)/(7.3/92.7)] indicating that men who reported an illness also have 8 times as high odds of reporting very poor or poor health status than those who did not report a dysfunction. In age cohort 46-54 years, the percentage of men who reported very poor, poor or moderate health status was 81.4% compared to 39.9% of those who did not report an illness. Hence, the odds ratio of very poor, poor or moderate health status versus non-very poor to moderate health status was 9.6 [(81.4/18.6)/ (31.2/68.8)]. The current study expanded on the work of Finnas et al. [18], which examined some of the methodological challenges in self-reported data in Finland. This paper is an expansion of Finnas et al.’s study in a number of respects, such as: 1) their work used age cohort 35-64 years while this study used 45-85+ years; 2) self-reported illness was included among the covariates in the
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examination of self-reported (rated) health status; and 3) social class and access (or lack of access) to material resources play a critical role in directly and indirectly influencing health, and so this was added to this paper. Although higher education plays a vital role in health status, 2% of the sample had tertiary level education and of this, 0.2% was older than 45 years. Most of the sample had at most primary level education (87.3%), which means that the role of tertiary education would contribute marginally to this sample. Hence, the researcher excluded it from the covariate analysis of self-reported health status. Measurement of variables

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 = 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 underreport, it is still an accurate approximation of ill-health and mortality [26, 27]. Self-reported health status (or health status) was measured by the question: Generally, how would you describe your health currently? The options were: very good, good, moderate (or fair), poor, and very poor. Age group was classified as children (aged less than 15 years), youth (aged 15 through 25 years), and other age cohorts ranging in 5 year intervals from 26-30 years, et cetera. 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 two options were yes or no. Medical care-seeking behaviour, therefore, was coded as a binary measure where 1=yes and 0= otherwise. Social class is measured using income quintile where it ranges from poorest 20% to wealthiest 20%.

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The distribution of the different age cohorts for each sex based on self-reported health status is given in Figures 1a and 1b. Figures 1a and 1b will be used to argue the case for a cut-off point for the dichotomisation of self-reported health status in Jamaica.

It is well established in biomedical literature that there is a strong negative correlation between health and age; the current study using self-reported health status by different age cohort controlled for sexes revealed that good health decreases as the individual ages and that more women beyond 80 years old reported very good health status compared to men in the same age cohorts. Health status, therefore, can be simply explained by age cohorts, and the aforementioned findings show that sex must be taken into consideration among the covariates in order to comprehend the effects of particular demographic variables on the statistical interpretations of health data. The other covariates must include education level, marital status, area of residence, and social class.

The issue of dichotomising self-reported health status continues to be debated in Jamaica as researchers continue to grapple with whether to use very poor-to-poor health status versus moderate-to-very poor health status. The issue of using moderate health in poor or good health status is critical as this will aid researchers in understanding whether there should be a cut-off point and where it should be, as this is the crux of the interpretation of the logistic regression model. Based on Figure 1, the very poor-to-poor health status is marginal at ages below 46 years, and so for the purpose of dichotomisation, ages 46 years and older will be used.

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Results
Demographic characteristics Of the sample (6783), 48.7% was male; 51.3% female; 69.2% never married; 14.9% reported having an illness in the survey period (4-week); 49.0% dwelled in rural areas; 82.2% reported at least good health and 4.8% reported at least poor health status (Table 10.10.1). Concomitantly, 61.8% indicated no formal education; 2.0% reported tertiary level education; 20.4% was classified as in the wealthiest 20% and 19.7% was in the poorest 20%. Continuing, the mean age of the sample was 29.9 years (SD = 21.8 years) with 25 percent of the sample being 12 years old; 50 percent being 26 years old and 75 percent being 44 years old; 2.1% of the sample was at least 81 years old. Furthermore, 31% of the sample was less than 15 years old and 18.9% youth. Multivariate analyses Interpretation of the odds ratios Comparatively, for ages 46-54 years, the odds ratio for reporting an illness when an individual is a male who self-reported that he had very poor-to-poor health status was 7.7 times compared to a male who did not report an illness. For women of the same age cohort, those who reported an illness who had reported a health status of very poor-to-poor was 3.3 times more likely to report an illness compared to a female of the same age cohort who did not report a dysfunction. The findings revealed that the odds ratio of an 85+-year-old male reporting an illness when he had indicated very poor-to-poor health status was 7.9 times more than for one who had not indicated a dysfunction. However, the odds ratio of reporting an illness declined for Jamaican males (Table 10.10.2). On the other hand, the odds of a female of the same age who reported an

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illness indicating that she had very poor-to-poor health status was greater at 85+ years than a 4654-year-old female. Generally, using the odds ratio, males benefited more by being married (Table 3) than females (Table 10.10.3). Concomitantly, the variance from adding moderate-to-poor or very poor health status marginally change the odds ratios over very poor-to-poor health status to very moderateto-very poor self-reported health status. This was the same across area of residence for the sexes. A substantial disparity in the odds ratios occurred in social standing for males, while it was relatively the same for females. Table 10.10.3 revealed that by adding moderate self-reported health status to very poor or poor self-reported health status for males, the odds ratios at older ages (i.e., 75+ years) increased exponentially over very poor-to-poor self-reported health status. Using odds ratios, the cut-off point for poor health status (excluding moderate health) increased over the age cohorts. However, when the cut-off point included moderate health status, the odds ratios from ages 46 years to 84 years showed that as respondent’s age within this age cohort, their likeliness of reporting poor health increased; this declined for ages beyond 85+ years. Concurrently, the odds ratios are exponentially higher for the latter dichotomisation than the former (Table 10.10.4).

Discussion
The findings of the current study show that the choice of cut-off for the dichotomisation of selfreported health status marginally matters for age, marital status, and area of residence. These findings concur with Finnas et al.’s work [18]. However, social class matters for males. The odds ratios for males at the different social classes, when moderate heath status is added to poor health status, changed substantially. This suggests that the dichotomisation of self-reporting for males

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will not shift and will produce a different result from if only poor or very poor were the cut-offs for self-reported health status. The findings of the study showed that the poor or poorest 20% of males benefitted exponentially when moderate self-reported health status is added to the cut-off point in dichotomising poor health status (including very poor). Another important finding of this study, which was not examined by Finnas et al., is the validity of using self-reported illness to measure the health status of people. Even though the likelihood of a person with an illness reporting very poor-to-poor health status is greater than one, it should be noted that that likelihood falls at older ages for males and increases at older ages for females. For men, when the cut-off point includes moderate health status, the impact of assessing self-reported illness with poor or very poor health status is higher than if the cut-off was only poor or very poor health status. Embedded in this finding is the vast difference that is created by merely changing the cut-off point from poor health status to moderate-to-very poor health status for males. While this disparity does not emerge for females, health researchers who use sex as a covariate must be aware of this reality when dichotomising self-reported health status. The cutoff point for dichotomising self-reported health does not matter if one is examining the health status of only females, as the marginal difference in odds ratio is insignificant and would not create a classification disparity in interpreting the final results. However, the same cannot be said about males, particularly those of older ages. Therefore, with regards to using self-reported health status, combining people from broad age groups should not be done, as this will not capture the challenges identified in health data on males in Jamaica. Studies have shown that health deteriorates with age [22-30]; indicating the critical role that age plays in the understanding health of people. Therefore, in an examination of poor health status, cautioned must be used by the researcher(s), as people are less likely to report very poor241

to-poor health at ages 15-30 years. On examination of self-reported health status for Jamaicans, the researcher became aware of this fact and so the study of dichotomisation of poor health did not use that age cohort. It is this rationale, and why the researcher concurred with Finnas et al., that it was decided that these should be used as covariates. Within the context of the current study, which revealed that small percentages of particular age cohorts are likely to report very poor-to-poor health status, the researcher chose age cohorts that are more likely to report very poor-to-poor health status as this was critical to study. Unlike Finnas et al.’s work, which cuts off at age 64 years, this study extended as far as to study respondents up to 85+ years. In 2007, 3.8% of Jamaicans were 75+ years (i.e., 101,272); 1% were older than 84 years (26,821), and given that people at these ages are more likely to report poor or very poor health, the researcher believes that stopping the study at age 64 would have excluded a critical proportion of those who are likely to be reporting poor health status. Among the social determinants of health are social class and area of residence [1-6, 3133]. People are not only defined by their ages, but by where they live and the social class in which they belong. The current study revealed that rural Jamaican women indicated the greatest percentage of very poor-to-poor health status, while this was not the case for men. However, the inclusion of moderate health status to poor or very poor health status across the age cohorts by area of residence revealed marginal differences as was the case without the inclusion of moderate health status. Among men of 85+ years, the odds ratio of reporting very poor-to-poor health approximately doubled over the previous age cohort (75-84 years) and this was marginally the same when moderate health was included in the dichotomisation of very poor-to-poor health. For women, this was not the case as the odds ratios were mostly the same for the two dichotomisations.
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Health literature has shown that the poor had the lowest health status [34]. Among men, the effect of social class on health showed no consistent pattern and this was the same for women. However, when moderate health status is included in the cut-off for very poor-to-poor health status, significant changes were observed over the age cohorts. For men, exponential increases occurred with the inclusion of moderate health status to the cut-off point, while this was not the case for women. The current study revealed that the dichotomisation of self-reported health status fundamentally increased the odds ratio, suggesting that the moderate-to-very poor exponentially takes in more men based on how self-reported health status is dichotomised in Jamaica at older ages (75+ years). Embedded in the finding is the disparity between the percentages of sexes who reported moderate health at older ages for men more than women. This study included self-reported illnesses, unlike Finnas et al.’s work, and the findings indicated that cut-off point for dichotomisation of health status was somewhat changed for women, but exponentially changed for men. The findings revealed that women ages 85+ years— when self-reported health status was dichotomised using very poor-to-poor health—had the highest odds of reporting poor health status. When poor health status was expanded to include moderate health status, the younger ages recorded greater odds of indicating moderate-to-very poor health status. This indicates that at longer ages using the latter dichotomisation approach the odds were age-dependent. Men of 85+ years recorded the least odds ratio of very poor-to-poor and moderate-to-very poor health status. There was no clear pattern of age-dependence of selfreported illness for men. Embedded in the findings is the greater likelihood of men to report moderate health than poor health at higher ages (85+ years). This suggests that they are underreporting their true very poor-to-poor health status at higher ages. It follows that the narrower categorisation of age was able to capture this which was lost in a wider categorisation.
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Marital status as a covariate indicated that marriage benefits Jamaicans men more than it does women. Among men, the odds of reporting very poor-to-poor status are less than for those who were unmarried, across the age cohorts. Interestingly, beyond 84 years, the odds ratio of very poor-to-poor health status of men declines, suggesting that the benefits of marriage at this age increases compared to earlier ages. When the cut-off point included moderate health status for men, the odds were relatively the same except for men above age 75. The odds ratios of reporting poor health (i.e., including moderate health status) for those of 75+ years fell substantially, which means that health status for men over 75+ years increased with marriage. Among women, the odds ratio for those less than 55 years who were married was the same as for their unmarried counterparts. It was found that marriage becomes beneficial for women when they are older than 75+ years, compared to unmarried women of the same age. When the dichotomisation of poor health included moderate health, marginal disparities in odds ratios were found among women in different areas of residence compared to when poor health status excluded moderate health. Embedded in this finding is the fact that poor health is weakly agedependent, as there were not clear patterns for the sexes. However, owing to narrowing age groups, this is a new finding which has emerged in health research literature for Jamaica—that marriage substantially benefits women at older ages (75+ years) than their younger counterparts. One of the critical findings of this study is that a narrower definition of poor health status (excluding moderate health status) had odds ratios that were closer across the age groups, suggesting that it would be better to exclude moderate health status from very poor-to-poor health status on dichotomising health status. However, if researchers decide to include moderate as a part of the dichotomisation of poor health status, they should be aware of some of the

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methodological implications of their choice, and how this will impact on the interpretation, in particular for men, within the different social classes.

Conclusion
In summary, the odds ratios vary substantially for men in different social classes as well as for self-reported illness based on the dichotomisation cut-off point for poor health. Among women, there was no clear age dependency based on the cut-off point of poor health; the vast disparity that was present for men was not found for women in the different social classes. Like the study conducted by Finnas et al., this paper agrees that the choice of cut-off point in dichotomising poor health status cannot be made primarily on variables such as age, because sex and social class must also play a factor in this choice, as well as the nature of the study. Concurrently, this study differs from Finnas et al.’s work in that with a narrower classification of poor health, the effect of marital status and area of residence were not found to be highly age-dependent. The current study found that dichotomising 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.

Conflict of interest
There is 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.

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References 1. 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 Med Sci 2009; 1: 86-95. 2. Bourne PA. Good Health Status of Older and Oldest Elderly in Jamaica: Are there differences between rural and urban areas? Open Geriatric Medicine J 2009; 2:18-27. 3. Bourne, Paul A. A Comparative Analysis of Health Status of men 60 + years and men 73 + years in Jamaica: A Multivariate Analysis. Asian Journal of Gerontology and Geriatrics. (in print). 4. Bourne PA, Rhule J. Good Health Status of Rural Women in the Reproductive Ages. Int J of Collaborative Research on Internal Medicine & Public Health 1:132-155. 5. Bourne PA, McGrowder DA. Rural health in Jamaica: Examining and refining the predictive factors of good health status of rural residents. Journal of Rural and Remote Health 2009; 9:1116. 6. 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 2005; 17: 342-352. 7. Reyes-Ortiz CA, Pelaez M, Koenig HG, Mulligan T. Religiosity and self-rated health among Latin American and Caribbean elders. Int J Psychiatry Med 2007; 37:425-43. 8. Hutchinson G, Simeon DT, Bain BC, Wyatt GE, Tucker MB, LeFranc E. Social and Health determinants of well-being and life satisfaction in Jamaica. International Journal of Social Psychiatry 2004; 50:43-53. 9. 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. 10. Idler EL, Kasl S. Self-ratings of health: Do they also predict change in functional ability? J of Gerontology 1995; 50B: S344-S353. 11. Stronks K, Van De Mheen H, Van Den Bos, J Mackenback JP. The interrelationship between income, health and employment status. Int J of Epidemiol 1997; 26:592-600. 12. Molarius A, Berglund K, Eriksson C, et al. Socioeconomic conditions, lifestyle factors, and self-rated health among men and women in Sweden. Eur J Public Health 2007; 17:125-33. 13. Helasoja V, Lahelma E, Prattala R, Kasmel A, Klumbiene J, Pudule I. The sociodemographic patterning of health in Estonia, Latvia, Lituania and Finland. Eur J Public Health 2006; 16:8-20.

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14. Leinsalu M. Social variation in self-rated health in Estonia: A cross-sectional study. Soc Sci Med 2002; 55:847-61. 15. Mackenbach JP, van de Bos J, Joung IM, van de Mheen H, Stronks K. The determinants of excellent health: different from the determinants of ill-health. Int J Epidemiol 1994; 23:1273-81. 16. Manderbacka K, Lahelma E, Martikainsen P. Examining the continuity of self-rated health. Int J Epidemiol 1998; 27:208-13. 17. Manor O, Matthews S, Power C. Dichotomous or categorical response: Analysing selfreported health and lifetime social class. Int J Epidemiol 2000; 29:149-57. 18. Finnas F, Nyqvist F, Saarela J. Some methodological remarks on self-rated health. The Open Public Health Journal 2008; 1: 32-39. 19. Planning Institute of Jamaica, (PIOJ) & Statistical Institute of Jamaica, (STATIN): Jamaica Survey of Living Conditions, 1988-2007. Kingston: PIOJ & STATIN; 1989-2008. 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. 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). 22. Reijneveld SA, Gunning-Schepers LJ. Age, health and measurement of the socio-economic status of individuals. Eur J Public Health 1995; 5:187-92. 23. Shooshtari S, Menec V, Tate R. Comparing predictors of positive and negative self-rated health between younger (25-54) and older (55+) Canadian adults: a longitudinal study of wellbeing. Res Aging 2007; 29:512-54. 24. Bogue DJ: Essays in human ecology, 4. The ecological impact of population aging. Chicago: Social Development Center; 1999. 25. Yashin AI, Iachine IA. How frailty models can be used for evaluating longevity limits: Taking advantage of an interdisciplinary approach. Demography 1997; 34:17-30. 26. Medawar PB. Old age and natural death. Mod. Q. 1946; 2:30-49. In: Medawar PB. ed. The Uniqueness of the Individual. New York: Basic Books; 1958: 17-43.

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27. Carnes BA, Olshansky SJ. Evolutionary perspectives on human senescence. Population Development Review 1993; 19: 793-806. 28. Carnes BA, Olshansky S J, Gavrilov L A, Gavrilova NS, Grahn D. Human longevity: Nature vs. nurture – fact or fiction. Persp. Biol. Med. 1999; 42:422-441. 29. Charlesworth B: Evolution in Age-structured Populations. 2nd ed. Cambridge: Cambridge University Press; 1994. 30. Gavrilov LA, Gavrilova NS: The biology of ¸life Span: A Quantitative Approach. New York: Harwood Academic Publisher; 1991. 31. Shields M, Shooshtari S. Determinants of self-perceived health. Health Rep 2001; 13:35-52. 32. Grossman M: The demand for health – A theoretical and empirical investigation. New York: National Bureau of Economic Research; 1972. 33. Smith JP, Kington R. Demographic and Economic Correlates of Health in Old Age. Demography 1997; 34:159-70. 34. 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.

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Table 10.10.1. Socio-demographic characteristic of sample, n = 6,783 n Sexes Male Female Marital status Married Never married Divorced Separated Widowed Self-reported illness Yes No Self-reported health status Very good Good Moderate Poor Very poor Area of residence Urban Semi-urban Rural Income quintile Poorest 20% Poor Middle Wealthy Wealthiest 20% Education attainment (level) No formal Basic Primary or preparatory Secondary Tertiary 3303 3479 1056 3136 77 41 224 980 5609 2430 2967 848 270 50 2002 1458 3322 1343 1354 1351 1352 1382 4071 783 898 709 131 % 48.7 51.3 23.3 69.2 1.7 0.9 4.9 14.9 85.1 37.0 45.2 12.9 4.1 0.8 29.5 21.5 49.0 19.8 20.0 19.9 19.9 20.4 61.8 11.9 13.6 10.8 2.0

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Table 10.10.2. Very poor or poor and moderated-to-very poor self-reported health status of sexes (in %) Very poor-to-poor Moderate-to-very poor 4655657585+yrs 4655657585+yrs 54yrs 64yrs 74yrs 84yrs 54yrs 64yrs 74yrs 84yrs Men Self-reported illness Yes 37.7 40.0 50.7 46.7 41.7 81.4 87.5 92.5 93.3 91.7 No 7.3 10.4 13.6 21.4 27.3 31.2 39.9 42.4 64.3 72.7 Area of residence Urban 12.1 14.5 21.9 22.0 25.0 49.2 60.9 50.0 55.6 62.5 Semi-urban 18.3 27.0 38.2 50.0 60.0 46.2 65.1 79.4 96.0 90.0 Rural 20.2 24.7 35.3 35.7 30.0 48.3 56.8 70.6 92.9 70.0 Marital status Married 16.8 19.5 31.3 30.0 25.0 48.8 56.4 64.2 60.0 62.5 Not 18.3 25.9 33.8 33.3 35.7 57.2 62.9 72.3 88.9 92.9 Social class 19.6 22.4 28.1 33.3 25 54.6 59.7 65.6 100 100 Poorest20% Poor Middle Wealthy Wealthiest20% Total n 20.7 18.0 18.6 12.0 266 29.4 24.2 22.0 16.4 207 35.1 13.6 11.9 14.5 33.9 22.7 23.1 33.8 23.7 21.7 22.8 14.5 216 42.9 30.3 33.3 20.1 156 37.1 15.3 16.1 17.2 36.9 32.3 25.2 43.8 22.9 26.1 25.8 12.9 172 50.0 33.3 30.0 20.0 50.0 57.1 25.0 18.4 97 23 Women 41.7 18.5 25.0 28.6 32.1 0 30.0 33.3 28.6 31.3 50.0 20.0 119 47.4 17.4 25.0 28.6 34.8 0 31.7 28.6 27.3 38.5 50.0 22.2 43 46.7 47.0 52.0 40.7 266 77.2 44.3 53.0 52.2 64.5 58.8 58.2 65.7 64.0 57.1 61.9 46.2 284 58.8 61.3 62.7 54.5 207 81.8 51.8 60.6 62.3 69.6 69.3 64.3 70.4 74.6 62.7 68.4 53.9 216 81.0 66.7 73.3 50.0 156 79.8 60.0 59.7 72.4 77.4 80.6 68.5 75.0 77.1 69.6 71.0 58.1 172 100.0 71.4 87.5 25.0 97 79.2 59.3 56.3 71.4 75.0 0.0 70.0 77.8 71.4 62.5 80.0 60.0 119 100.0 83.3 85.7 33.3 23 73.7 52.2 41.7 71.4 69.6 0.0 63.3 71.4 63.6 56.8 80.0 55.6 43

Self-reported illness Yes 29.1 No 11.1 Area of residence Urban 9.7 Semi-urban 14.2 Rural 26.8 Marital status Married 18.6 Not 19.0 Social class Poorest20% 28.7 Poor 19.0 Middle 19.0 Wealthy 18.6 Wealthiest20% 9.8 Total n 284

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Table 10.10.3. Odds ratios for very poor or poor and moderate-to-very poor self-reported health of sexes by particular variables Very poor-to-poor Moderate-to-very poor
4654yrs 5564yrs 6574yrs 7584yrs 85+yrs

Self-reported illness Yes 7.7 No 1 Area of residence Urban 0.5 Semi-urban 0.9 Rural 1 Marital status Married 0.9 Not 1 Social class Poorest20% 1.8 Poor 1.9 Middle 1.6 Wealthy 1.7 Wealthiest20% 1 Total n 266 Self-reported illness Yes 3.3 No 1 Area of residence Urban 0.3 Semi-urban 0.5 Rural 1 Marital status Married 1.0 Not 1 Social class Poorest20% 3.7 Poor 2.2 Middle 2.2 Wealthy 2.1 Wealthiest20% 1 Total n 284

Men 1.9 1 0.8 3.5 1 0.6 1

4654yrs

5564yrs

6574yrs

7584yrs

85+yrs

5.7 1 0.5 1.1 1 0.7 1 1.5 2.1 1.6 1.4 1 207 3.4 1 0.3 0.3 1 1.0 1 3.0 1.8 1.6 1.7 1 216

6.5 1 0.5 1.1 1 0.9 1 1.6 3.0 1.7 2.0 1 156 3.3 1 0.3 0.4 1 1.4 1 5.3 2.0 2.4 2.3 1 172

3.2 1 0.5 1.8 1 0.9 1

9.6 1 1.0 0.9 1 0.7 1 1.8 1.3 1.3 1.6 1 266 4.3 1 0.6 0.6 1 1.0 1 2.2 2.1 1.5 1.9 1 284

10.5 1 1.2 1.4 1 0.8 1 1.2 1.2 1.3 1.4 1 207 4.2 1 0.7 0.7 1 1.3 1 2.0 2.5 1.4 1.9 1 216

16.8 1 0.4 1.6 1 0.7 1 1.9 4.3 2.0 2.7 1 156 2.6 1 0.4 0.8 1 1.9 1 2.2 2.4 1.7 1.8 1 172

7.7 1 0.1 1.8 1 0.2 1 large large 7.5 21.0 1 97 2.6 1 0.4 0.8 1 0.0 1 2.3 1.1 1.1 2.7 1 119

4.1 1 0.7 3.9 1 0.1 1 large large 10.0 12.0 1 23 2.6 1 0.3 1.0 1 0.0 1 2.0 1.4 1.0 3.2 1 43

1.5 1.5 3.0 2.2 1.3 1.1 3.0 5.9 1 1 97 23 Women 3.2 1 0.7 0.8 1 0.0 1 2.0 1.6 1.8 4.0 1 119 4.3 1 0.6 0.8 1 0.0 1 1.4 1.3 2.2 3.5 1 43

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Table 10.10.4. Odds ratios of poor health status by age cohorts Poor Health status 46-54yrs 55-64yrs Age cohorts 65-74yrs 75-84yrs 85+yrs

Very poor-to-poor health Yes No Moderate-to-very poor health Yes No Total n 0.091 1 550 0.529 1 423 1.861 1 328 5.444 1 216 5.048 1 66 0.004 1 0.020 1 0.046 1 0.167 1 0.228 1

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CHAPTER

11
Paradoxes in self-evaluated health data in a developing country

Statistics showed that males reported fewer illnesses and greater mortality rates than females, but are outlived by approximately 6 years by their female counterparts, yet their self-rated health status is the same as that of females. This study examines the following questions: (1) Are there paradoxes in health disparity between the sexes in Jamaica? and (2) Is there an explanation for the disparity outside of education, marital status, and area of residence? Good health status was correlated with self-reported illness (OR =0.23, 95% CI = 0.09-0.59), medical care-seeking behaviour (OR = 0.51, 95% CI = 0.36-0.72), age (OR = 0.96, 95% CI = 0.96-0.97), and income (OR = 1.00, 95% CI = 1.00-1.00). Self-reported illness is statistically correlated with sex (OR = 0.25, 95% CI = 0.10-0.62), head of household (OR = 0.33, 95% CI = 0.12-0.96), age (OR = 1.04, 95% CI = 1.01-1.07) and current good self-rated health status (OR = 0.32, 95% CI = 0.120.84). This paper highlights that caution must be used by researchers in interpreting selfreported health data of males.

Introduction
Jamaica began collecting data on the living standard of its people in 1988, and to date, statistics have shown that females continue to report more illnesses than males, seek medical care more frequently than males [1], and outlive males on average by 6 years [2]. A study by Hutchinson et al. [3] on the wellbeing and life satisfaction of Jamaicans showed that women had lower psychological wellbeing and less life satisfaction than men, which highlights some of the paradoxes in the health data. In his study, Bourne [4] found that there was no significant statistical difference between the current good health status of males and females. However, he found that there was no statistical correlation between medical care-seeking behaviour and sex of

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respondents, suggesting that reporting more illnesses does not mean that females are any more willing to address their identified health conditions than males. A research on rural Jamaican women in the reproductive ages of 15 to 49 [5] showed that 79% were never married, 20% were married, 90% had a secondary level education, 45% were poor (i.e., 22% below the poverty line), and 15.3% reported an illness while only 5% had health insurance coverage. In Jamaica, poverty is a rural phenomenon (i.e., in 2007, 15.3% of rural individuals were living below the poverty line compared to 4% of semi-urban Jamaicans and 6.2% of urban peoples). Males’ per capita consumption was 1.2 times more than that of females; female-headed households had a higher prevalence of poverty compared to male-headed households [1], and it follows that socio-demographic and economic challenges faced by females do not discount from them living longer than men. A study by Bourne [6] showed that elderly men in Jamaica are healthier than their female counterparts, suggesting that longer life does not imply healthy life expectancy. Statistics showed that females are more likely to be unemployed [7], poorer, have longer lives, report more illnesses, visit health care practitioners more frequently than men, and are less healthy than men in later life. They are also on average more educated, yet still their health status is generally equal to that of males [8]. Examining mortality data of the sexes for aged Jamaicans, Bourne et al. [9] found that mortality at older ages was between 115 and 120 for males to every 100 females. A study by Abel et al. [10] found that the suicide rate for males was 9 times greater than for females which indicates that mortality for males is not only greater at older ages but that suicide is occurring voluntarily throughout their life span. Using secondary data of 8,373 Jamaican children (aged under 15 years) for 2002 and 2104 for 2007, Bourne [11] found that there was no significant difference between the sexes’
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health conditions. However, female children are taken to health care practitioners more frequently than male children. In a study of 5229 and 1394 adolescents aged 10 to 19 years in Jamaica, Bourne [12] found that mortality for males was greater than for females. A significant statistical correlation existed between health conditions, but none between health conditions and age cohort of the sample. Furthermore, he found that in 2007, 96% of adolescents did not report an illness in the past 4 weeks, 54% sought medical care, and 15% had health insurance coverage. One of the drawbacks of Bourne’s work [12] was the fact that health condition was not disaggregated by sexes. but invaluable information was provided that showed the low willingness of adolescents to seek medical care. Another study on children showed that while there is no significant difference between the health statuses of the sexes, females are taught by society to seek more medical care than male children [11] and that this continues over their life course [1]. The literature highlights the fact that the health status disparity does not commence in childhood, which denotes that females’ longer life and males’ greater health status in later life is a paradox that must be unravelled by researchers. Interestingly, while the literature explains Hutchinson et al’s work as to why women have lower psychological wellbeing and life satisfaction, it does not provide an understanding for the plethora of other studies which showed no significant statistical difference between the general self-rated health of the sexes [4, 8] and childhood [11]. Additionally, the health status of elderly males is better than that of females despite the fact that females report more illness and live longer than males. Another area which is unexplained by their study is the fact that statistics showed that mortality at all ages for males is higher than for females [2]. There is a lack of information on the paradox of health disparity between the sexes in Jamaica and this research seeks to fill this gap in the literature. The current
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research attempts to answer the following questions: (1) Are there paradoxes in the health disparity between the sexes in Jamaica? and (2) Is there an explanation for the disparity outside of education, marital status, and area of residence?

Methods and materials
Data The current study utilised a data set collected jointly by the Planning Institute of Jamaica and the Statistical Institute of Jamaica [13]. The survey was conducted between May and August of 2007. The Jamaica Survey of Living Conditions (JSLC), which began in 1988, is a modification of the World Bank’s Living Standards Measurement [1, 14]. The sample size was 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. The sample was weighted to reflect the population of the nation.

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Instrument An administered instrument in the form of a questionnaire was used to collect the data from respondents. The questionnaire covers socio-demographic variables such as education, age, 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 p-value < 0.05 (two-tailed) was selected to indicate statistical significance. 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 & Holliday [15] and Cohen &
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Cohen [16], low (weak) correlation ranges from 0.0 to 0.39, moderate – 0.4-0.69, and strong – 0.7-1.0. Any correlation that had at least a moderate value was excluded from the model in order to reduce multicollinearity and/or autocorrelation between or among the independent variables [17-21]. Models Health is a multifactorial construct. This indicates that it is best explained with many variables against a single factor. Health is empirically established and is determined by many factors [2237], and therefore the use of multivariate regression technique is best suited to explain this phenomenon than bivariate analyses [22-37]. The current study seeks to establish the sociodemographic, economic and biological correlates of self-rated health, and self-reported illness so as to examine the paradoxes in health disparity between the sexes. The aforementioned construct will be tested in two econometric models. Model [1] is good self-rated health statuses and is associated with socio-demographic, economic and biological variables; and Model [2] is selfreported illness and is related to socio-demographic, economic and self-rated health status. H t =f(A i , G i ,HH i , AR i , I t , J i, lnC, lnD i , ED i, MR i , S i , HIi , lnY, CR i , MC t , SA i , Ti , ε i ) (1)

where H t (i.e., self-rated current health status in time t) is a function of age of respondents, A i ; sex of individual i, G i ; household head of individual i, HH i ; area of residence, AR i ; current self-reported illness of individual i, It ; injuries received in the last 4 weeks by individual i, J i ; logged consumption per person per household member, lnC; logged duration of time that individual i was unable to carry out normal activities, lnD i ; education level of individual i, ED i ; marital status of person i, MR i ; social class of person i, S i ; health insurance coverage of person i, HIi ; logged income, lnY; crowding of

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individual i, CRi; medical expenditure of individual i in time period t, MC t ; social assistance of individual i, SA i ; length of time living in current household by individual i, Ti ; and an error term (i.e., residual error).

It ,=f(A i , G i ,HH i , AR i , J i, lnC, lnD i , ED i, MR i , S i , HIi , lnY, CR i , MC t , SA i , Ti , H t , ε i )

(2)

where It (i.e., self-reported illness in last 4-weeks) is a function of age of respondents, A i ; sex of individual i, G i ; household head of individual i, HH i ; area of residence, AR i ; injuries received in the last 4 weeks by individual i, J i ; logged consumption per person per household member, lnC; logged duration of time that individual i was unable to carry out normal activities, lnD i ; education level of individual i, ED i ; marital status of person i, MR i ; social class of person i, S i ; health insurance coverage of person i, HIi ; logged income, lnY; crowding of individual i, CRi; medical expenditure of individual i in time period t, MC t ; social assistance of individual i, SA i ; length of time living in current household by individual i, Ti ; self-rated current good health status, H t ; and an error term (i.e., residual error).

Models [1] and [2] were modified to [3] and [4] owing to collinearity of consumption and income (r ≥ 0.7) and non-response of injury (over 70%). H t =f(A i , G i ,HH i , AR i , I t , lnD i , ED i, MR i , S i , HI i , lnY, CR i , MC t , SA i , Ti , ε i ) It ,=f(A i , G i ,HH i , AR i , lnD i , ED i, MR i , S i , HIi , lnY, CR i , MC t , SA i , Ti , H t , ε i ) Measurement of variables Health in the current study is measured using (1) self-rated health status (self-rated health), and (2) self-reported illness. Self-rated health status was derived from the question, “Generally, how (3) (4)

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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]. 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 recoded as a binary value, where 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 and no. Medical care-seeking behaviour therefore was coded as a binary measure where 1 = yes and 0 = otherwise. Total annual expenditure was used to measure income. Income quintile was used to measure social standing. The income quintiles ranged from poorest 20% to wealthiest 20%.

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Results
Demographic characteristic of sample The sample was 6,782 respondents: 48.7% males and 51.3 females. The mean age of the sample was 30.0 years (SD = 21.8 years). Almost 15% reported having had an illness in the last 4 weeks and 89.1% reported that the illness was diagnosed by a medical practitioner: cold (14.9%), diarrhoea (2.7%), asthma (9.5%), diabetes mellitus (12.3%), hypertension (20.6%), arthritis (5.6%), and unspecified (23.4%). Bivariate analyses The findings showed that females were more likely to (1) be widowed (7.3% females to 2.3% males); (2) be older (mean age: 30.6 years females to 29.1 years males) – t = -2.8, P = 0.05; (3) report illness (17.5% females to 12.1% males); and (4) spend on medical expenditure (Table 11.11.1). However, there was no significant statistical difference between the sexes (1) seeking medical care, (2) their social standing, and (3) their educational levels. Tertiary level graduates were substantially more likely to be in the wealthiest class (54%), and dwelled in urban areas (63.4%). Concomitantly, they reported more illness than secondary level respondents (9.2% tertiary to 5.4% secondary), but less than those with primary education level or below (16.2%) (Table 11.11.2). Table 11.11.3 showed significant statistical associations between (1) marital status and self-reported illness (P < 0.05), (2) area of residence and self-reported illness (P < 0.05), and (3) medical care expenditure and self-reported illness (P < 0.05). There was a significant statistical association between health care-seeking behaviour (in %) and social standing of respondents – χ2 =17.12, P = 0.002. The findings revealed that as
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social standing increases from poorest 20% to wealthiest 20%, health care-seeking behaviour (in %) increases: poorest 20% = 54.7% health care-seeking behaviour; poor = 63.2%; middle class = 66.4%; wealthy = 68.4%, and wealthiest 20% = 73.5%. Multivariate analyses Good health status of Jamaicans was correlated with self-reported illness (OR = 0.23, 95% CI = 0.09-0.59), medical care-seeking behaviour (OR = 0.51, 95% CI = 0.36-0.72), age of respondents (OR = 0.96, 95% CI = 0.96-0.97), and income (OR = 1.00, 95% CI = 1.00-1.00) (Table 4). The model is a good fit for the data – χ2 = 114.7, P < 0.001, Hosmer and Lemeshow Test P= 0.776. Furthermore, the aforementioned variables accounted for 20% of the variability in the good health status of Jamaicans (R-squared = 0.20) (Table 11.11.4). The self-reported illness of respondents is statistically correlated with sex (OR = 0.25, 95% CI = 0.10-0.62), head of household (OR = 0.33, 95% CI = 0.12-0.96), age of respondents (OR = 1.04, 95% CI = 1.01-1.07), and current good self-rated health status (OR = 0.32, 95% CI = 0.12-0.84) (Table 5). The model is a very good fit for the data – χ2 = 33.7, P < 0.001, Hosmer and Lemeshow Test P = 0.766 (Table 11.11.5).

Discussion
There are enough empirical studies that agree that there was a positive statistical correlation between income, education, married people, social class and health status of people. The current study concurs with the literature that there is a positive association between income and health status. However, this paper did not find a significant statistical correlation between education, marital status, social class and self-rated health of Jamaicans. The current work highlights a number of disparities between the literature and this paper. Many studies have shown that income is strongly and positively correlated with health status [22, 24]. However, this study
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disagreed with those findings, as it found that income’s contribution was 1% of the explanatory power of 20%. Furthermore, income contributed the least to current good self-rated health status of Jamaicans. Hambleton et al. [23], studying elderly Barbadians, found that self-reported illness accounted for the most variability in health status, which concurs with the current study and therefore emphasises the secondary role that income plays in influencing health status. In Jamaica, medical care-seeking behaviour is not an indicator of preventative care, as those who sought health care were 49% less likely to report good health, and those who did not have an illness spent more on health care compared to those who indicated an ailment. Embedded in this finding is the concept of health that Jamaicans hold regarding how medical care is still synonymous with illnesses, but the fact that those who are not sick spent more on health care and are healthier indicates that preventative care is being practiced by Jamaicans. Apart from these findings that emerged in the data, a number of health disparities were identified and some could be considered paradoxical events. The study found that men were 75% less likely to report an illness than women. However, there was no significant statistical difference between the health statuses of the sexes. Males reported greater income than females, yet there was no significance between their health care expenditure and health care-seeking behaviour. Is it a paradox that males reported fewer dysfunctions, attend health care institutions as equally frequently as females, and have a health status that is no better than that of females? The paradox does not cease there, as males are outlived by females, experience greater mortality at all ages than females, and again indicate fewer ailments than females. Is this a paradox? Comparatively, using statistics from the Ministry of Health in Jamaica (actual visits to public hospitals), and statistics from the Planning Institute of Jamaica and Statistical Institute of Jamaica (i.e., self-reported visits) to measure the validity of self-reported health data in 1997, it
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was shown that 33.1% of Jamaicans attended public hospitals [38] compared to 32.1% who actually reported having attended public hospitals. Furthermore, in 2004, 52.9% of Jamaicans visited public hospitals [38] compared to 46.8% who reported having visited public hospitals. When the data was disaggregated by sex, in 2004, actual visits for females were 69.8% compared to 65.7% self-reported; while for males, actual visits were 30.2% compared to self-reported visits of 64.2%. Using curative visits from the Ministry of Health data, 33% of males visited health care facilities to address particular illness, yet only 9% of males reported that they had an illness. Embedded in the data are the extent to which males under-report their illnesses, which further emphasises the paradoxes in the health data. Self-rated health data for females is therefore highly accurate, but this is not the case for males. It was a paradox in the health data to find that males reported fewer illnesses, experienced greater mortality at all ages, and had greater income, yet their health status was the same as that of females. There are clearly paradoxes in the health data between the sexes in Jamaica. If males are under-reporting their illnesses by approximately 50%, statistics on health data are rendered inaccurate, and so caution must be taken in using self-reported health data for males. The reasons for this paradox can be unravelled when one takes a closer look at Jamaican culture and society. Caribbean males and Jamaicans in particular, are persuaded by society to be strong and brave. Masculinity is tied to these attributes and so justifies the emphasis of physique and strength in the Jamaican culture. The converse explains why they neglect weakness or the appearance of weakness, which includes illnesses. Ill health is conceptualised as weakness and within the context of socialisation and adapting to societal norms, males will not openly speak of illness, they avoid medical care-seeking behaviour and only visit health care institutions when an illness becomes severe.
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Statistics from the Ministry of Health showed that since 2000–2004, females outnumber males by 2 to 1 in terms of visits to health care institutions [38]. However, using reported data for the same period, the figures were: in 2000 – 57.4% males and 63.2% females; in 2001 – 56.3% males and 68.2% females; in 2001 – 62.1% males and 65.3% females and 2004 – 64.2% males and 65.7% females. Clearly, the self-reported data is not in keeping with the actual data, and this denotes that males are over-stating their health care visits. On the other hand, using 2004’s data on actual visits, 69.8% of Jamaican females utilised health care facilities compared to 66% of females who actually reported health care visits. Within the context of over-statement of health care-seeking behaviour and understatement of illness by males in Jamaica, this goes to the crux of the socialisation issue and society’s influence on health care. A Caribbean anthropologist, Chevannes [39], opined that Caribbean males suppressed responses to pain, which justifies a low turnout to health care facilities and higher mortality rates. This is not atypical of Caribbean males. Ali & de Muynck [40], in examining street children in Pakistan, found a similar gender stereotype. A descriptive cross-sectional study carried out during September and October 2000 of 40 school-aged street children (8-14 years) showed that only severe illness that threatens financial opportunities will cause males to seek medical care. Ali & de Muynck’s study therefore provides some understanding for the reluctance of males seeking medical care despite having greater income. With 49% of Jamaicans being males, within the context of socialisation and societal pressures and norms, this explains the fact that income has a weak correlation with health status. This negative emotional irresponsiveness to medical care-seeking in Jamaica is not limited to males, as females are a part of the current study which found no significant statistical difference between them and males seeking health care.

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Another paradox embedded in the health data is the fact that people who spent more on medical care reported fewer illnesses – males reported fewer ailments, yet they are not healthier than females. Once again the explanation for this is embodied in the socialisation and societal norms, including the negative view that Jamaicans have of health care, health reporting and male unwillingness to separate caring about health from weakness, weakness from femininity, and hence how men respond to the interviewers. There is evidence that males are under-reporting their illnesses in the JSLC’s cross-sectional survey, which means that the self-reported health data of males cannot be trusted. The researcher is proposing that a part of the rationale of the under-statement of illnesses by males in Jamaica owes to the sex of the interviewers. Most interviewers employed by the Statistical Institute of Jamaica to collect data from Jamaicans are females, and within the context of not wanting to exhibit weakness, males are understating their illness in order to create the perception that they are strong and healthy. The issue appears to be extensive because statistics from the Ministry of Health for 2004 showed that for curative visits, females outnumber males by 2 to 1 [38]. Although the researcher was unable to obtain the Ministry of Health Annual Report for 2007, the 2006 report showed the same ratios as for 2000– 2004, which implies that gender of the interviewers is a contributing factor when collecting data on men’s health in Jamaica. Is it a paradox that the educated are wealthier, have greater income and still are not healthier than the poor with less financial resources? This study would suggest not, as the weak relationship between health status and educational level disappears on the inclusion of income. The current work does show that a bivariate relationship exists between education and healthier people, but that when income and education are placed in a single model, education no longer becomes significantly associated with good health status. The current findings concur with the
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literature which found that when subjective wellbeing, which is a measure of subjective health, was controlled for income and other variables, the statistical correlation between education and health disappears [41-43]. Smith & Kington [4] wrote, “Good health is an outcome that people desire, and higher income enables them to purchase more of it.” This implies that (1) health can be bought and (2) those with lower incomes will have a lower health status. Although the literature has concurred with this study (that income is positively associated with health), income’s contribution to health in Jamaica is weak, indicating that while more income is correlated with better health status, Smith & Kington’s perspective must be refined, as there was no significant statistical correlation between socio-economic class and health status. In Jamaica, there is no statistical difference between the health statuses of the socio-economic classes and this is equally the case when health is measured using health conditions. On the other hand, there is a clear paradox in the health data of the current study, as income is correlated with better health status, yet the wealthy classes do not have greater health status or fewer reported illness than the lower socio-economic classes. The rationale that accounts for the paradoxes that emerged from the current study is due to lifestyle practices of the wealthy and the acceptance of the state of the poor. Marmot [44] opined that poverty is associated with greater infant mortality, more ill-health, material and social deprivation, poor conditions, and greater inequality in occupation, employment and income inequality. Within the inequalities that favour the wealthy, income means that they can afford, purchase and buy goods. Wilkinson [45] found a weak relationship between average income and life expectancy in wealthy nations and Sen [46] found that increased life expectancy in Britain between 1901 and 1960 occurred during slow growth of per capita GDP (Gross
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Domestic Product). Sen went on to say that the improvement in life expectancy was owing to support policies such as sharing of health care and limited food supply. Another found a nonlinear increase in the probability of dying with increased income [47], suggesting that income fulfils two roles: (1) provides access to better socio-material resources, and (2) retards the positives of access to become a negative. The paradox in income can be seen in the fact that while wealthy Jamaicans have more income and access to more socio-material and political resources, their health status is not greater than the under-privileged, poor and poorest 20%. Additionally, the contribution of income to health status is minimal, which is not the case in the literature. It was expected that Jamaicans who sought more health care must have been experiencing more ill-health, but this was not the case. Having established that health data collected from males indicates a low validity, with 49% of the sample being males, it follows that paradoxes identified in the current study highlight the difficulties in interpreting health data in Jamaica.

Conclusion
There are some paradoxes in self-reported health data in Jamaica. Although some of these paradoxes are highlighted in this paper, caution now must be used by researchers in interpreting self-reported health data collected from males, as they are clearly under-reporting illnesses and over-stating their health care-seeking behaviour. In spite of the paradoxes in the data, selfreported health collected on females in Jamaica is of high quality. This denotes that the paradoxes within the health data have provided critical answers to males’ reluctance in visiting health care facilities, their unwillingness to openly speak about illnesses and the fact that they have concealed information on their health. Therefore, a new approach is needed in soliciting information from males about their health status.
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Conflict of interest
There is 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, but rather to the researcher.

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References
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20. Hamilton JD. Time series analysis. New Jersey: Princeton University Press; 1994. 21. Kleinbaum DG, Kupper LL, Muller KE. Applied regression analysis and other multivariable methods. Boston: PWS-Kent Publishing; 1988. 22. Grossman M. The demand for health – A theoretical and empirical investigation. New York: National Bureau of Economic Research; 1972. 23. Hambleton IR, Clarke K, Broome HL, Fraser HS, Brathwaite F, Hennis AJ. Historical and current predictors of self-reported health status among elderly persons in Barbados. Rev Pan Salud Public; 2005; 17: 342-352. 24. Smith JP, Kington R. Demographic and Economic Correlates of Health in Old Age. Demography; 1997; 34:159-70. 25. 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; 1:99-109. 26. Bourne PA. An epidemiological transition of health conditions, and health status of the oldold-to-oldest-old in Jamaica: a comparative analysis. North American Journal of Medical Sciences; 2009; 1:211-219. 27. Bourne PA. Good Health Status of Older and Oldest Elderly in Jamaica: Are there differences between rural and urban areas? Open Geriatric Medicine Journal; 2009; 2:18-27. 28. Bourne PA. A Comparative Analysis of Health Status of men 60+ years and men 73+ years in Jamaica: A Multivariate Analysis. Asian Journal of Gerontology and Geriatrics. (In print). 29. Bourne PA, McGrowder DA. Rural health in Jamaica: Examining and refining the predictive factors of good health status of rural residents. Journal of Rural and Remote Health 9 (2); 2009; 1116. 30. Asnani MR, Reid ME, Ali SB, 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. 31. CSDH. Closing the gap in a generation: Health equity through action on the social determinants of health. Final Report of the Commission on Social Determinants of Health. Geneva, World Health Organization; 2008. 32. Kelly M, Morgan A, Bonnefog J, Beth J, Bergmer V. The Social Determinants of Health: developing Evidence Base for Political Action, WHO Final Report to the Commission; 2007. 33. Wilkinson R, Marmot M. Social Determinants of Health. The Solid Facts. Second edition. Geneva: World Health Organization; 2003. 34. Solar O, Irwin A. A Conceptual Framework for Analysis and Action on the Social Determinants of Health. Discussion paper for the Commission on Social Determinants of Health DRAFT; April 2007. 35. Graham H. Social Determinants and their Unequal Distribution Clarifying Policy Understanding. The Milbank Quarterly; 2004; 82:101-124. 36. Petticrew M. Whitehead M, McIntyre SJ, Graham H, Egan M. Evidence for Public Health Policy on Inequalities: 1: The Reality According To Policymakers. J of Epidemiol and Community Health; 2004; 5: 811–816. 37. Ross CE, Mirowsky J. Refining the association between education and health: The effects of quantity, credential, and selectivity. Demography; 1999; 36:445-460.

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38. Ministry of Health, Jamaica (MOHJ). Ministry of Health, Jamaica: Annual Report, 2004. Kingston; MOHJ; 2005. 39. Chevannes B. Learning to be a man: Culture, socialization and gender identity in five Caribbean communities. Kingston: The University of the West Indies Press; 2001. 40. Ali M, de Muynck A. 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. 41. Clemente F, Sauer WJ. Life satisfaction in the United States. Social Forces 1976; 54:621631. 42. Spreitzer E, Synder EE. Correlates of life satisfaction among the aged. J of Gerontology; 1974; 29:454-458. 43. Toseland R, Rasch J. Correlates of life satisfaction: An AID analysis. Int J of Aging and Human Development; 1979-1980; 10:203-211. 44. 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. 45. Wilkinson R. Unhealthy societies: The afflictions of inequality. London: Routledge; 1996. 46. Sen A. Development as Freedom. New York: Alfred A Knopf; 1999. 47. Deaton A. Health inequality and economic development. Working paper, Princeton University Research Program in Development Studies and Center for Health and Wellbeing; 2001.

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Table 11.11.1. Socio-demographic characteristic of sample by sex of respondents Characteristic Sex Male % Educational level Primary or below Secondary Tertiary Total Social standing Poorest 20% Poor Middle Wealthy Wealthiest 20% Total Marital status Married Never married Divorced Separated Widowed Total Area of residence Urban Semi-urban Rural Total Medical care-seeking behaviour Yes No Total Self-reported illness Yes No Total Age Mean (SD) in years Medical Expenditure1 Mean (SD) in US$ 1 Rate in 2007:1US$= Ja$80.47 87.9 10.5 1.6 3207 20.3 19.4 19.3 20.2 20.9 3303 24.3 71.1 1.6 0.7 2.3 2150 28.5 21.4 50.1 3303 62.3 37.7 406 12.1 87.9 3208 29.1 (21.5) 9.31 (15.48) Female % 86.6 11.0 2.4 3385 19.3 20.5 20.6 19.7 19.9 3479 22.4 67.4 1.8 1.0 7.3 2384 30.4 21.6 47.9 3479 67.6 32.4 599 17.5 82.5 3381 30.6 (21.9) 11.19 (36.51) Total % 87.3 10.8 2.0 6592 > 0.05 19.8 20.0 19.9 19.9 20.4 6782 < 0.05 23.3 69.2 1.7 0.9 4.9 4534 29.5 21.4 49.0 6782 65.6 34.5 1005 < 0.05 14.9 85.1 6589 29.9 (21.8) 10.46 (30.23) > 0.05 P > 0.05

> 0.05

< 0.05 > 0.05

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Table 11.11.2. Socio-demographic characteristic of sample by educational level
Characteristic
Primary Secondary

Educational level
Tertiary Total P

%

%

%

Social standing Poorest 20% Poor Middle Wealthy Wealthiest 20% Total Marital status Married Never married Divorced Separated Widowed Total Area of residence Urban Semi-urban Rural Total Medical care-seeking behaviour Yes No Total Self-reported illness Yes No Total Health insurance coverage None Private coverage Public coverage Total Age Mean (SD) in years Medical Expenditure1 Mean (SD) in US$ 1 Rate in 2007:1US$= Ja$80.47

< 0.05 20.3 20.0 19.4 19.9 20.3 5752 25.5 66.1 1.9 1.0 5.5 4048 28.8 22.0 49.2 5752 65.7 34.3 953 16.2 83.8 5736 79.8 12.0 8.2 5682 32.0 (22.6) 10.44 (30.78) 19.7 21.7 24.5 20.3 13.7 709 0.0 99.7 0.0 0.3 0.0 344 30.0 19.2 50.8 709 60.0 40.0 40 5.4 94.6 705 83.7 11.7 4.6 689 14.6 (1.7) 12.31 (18.73) 3.8 7.6 16.0 19.1 53.4 131 16.9 81.5 1.5 0.0 0.0 130 63.4 16.4 20.6 131 66.7 33.3 12 9.2 90.8 130 57.8 35.9 6.3 128 26.4 (10.6) 5.79 (5.51) 19.9 20.0 19.9 19.9 20.2 6592 < 0.05 23.4 69.1 1.7 0.9 5.0 4522 < 0.05 29.6 21.6 48.8 6592 >0.05 65.5 34.5 1005 < 0.05 14.9 85.1 6571 < 0.05 79.8 12.5 7.7 6499 30.0 (21.8 10.46 (30.23)

< 0.05 >0.05

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Table 11.11.3. Socio-demographic characteristic of sample by self-reported illness
Self-reported illness P

Yes %

No %

Total %

Social standing Poorest 20% Poor Middle Wealthy Wealthiest 20% Total Marital status Married Never married Divorced Separated Widowed Total Area of residence Urban Semi-urban Rural Total Medical care-seeking behaviour Yes No Total Health insurance coverage None Private coverage Public coverage Total Age Mean (SD) in years Medical Expenditure1 Mean (SD) in US$
1

 0.05 19.7 18.1 20.9 20.4 20.9 980 35.9 46.9 3.1 1.7 12.5 721 26.6 18.7 54.7 980 65.1 34.9 970 20.0 20.4 19.8 19.7 20.2 5609 20.9 73.4 1.4 0.8 3.5 3801 30.1 21.9 47.9 5609 77.4 22.6 31 19.9 20.0 19.9 19.8 20.3 6589 < 0.05 23.3 69.2 1.7 0.9 4.9 4522 < 0.05 29.6 21.5 48.9 6589 >0.05 65.4 34.6 1001 < 0.05 75.3 80.6 11.5 12.7 13.3 6.8 978 5525 42.0 28.0 (27.7) (20.0) 9.30 38.80 (18.27) (126.09) 79.8 12.5 7.7 6503 < 0.05 < 0.05

Rate in 2007:US$1.00 = Ja$80.47

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Table 11.11.4. Stepwise Logistic Regression: Good self-rated health status by sociodemographic, economic and biological variables R-squared Variable Self-reported illness Medical care-seeking Age Income SE 0.48 0.18 0.01 0.00 P 0.002 0.000 0.000 0.007 0.000 Odds ratio 95.0% C.I. 0.23 0.51 0.97 1.00 16.03 0.09-0.59 0.36-0.72 0.96-0.97 1.00-1.00 0.02 0.02 0.15 0.01

Constant 0.54 -2 LL = 857.3 Hosmer and Lemeshow Test P = 0.776 Χ2 = 114.7, P < 0.001 R-squared = 0.20 N=6049 (89.2%)

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Table 11.11.5. Stepwise Logistic Regression: Self-reported illness by socio-demographic
and biological variables R-square Variable Male Head Household Age SE 0.47 0.54 0.01 P 0.003 0.043 0.010 Odds ratio 0.25 0.33 1.04 0.32 95.0% C.I. 0.10-0.63 0.12-0.96 1.01-1.07 0.12-0.84 0.059 0.024 0.021 0.075

Good Health 0.49 0.020 -2 LL = 177.7 Hosmer and Lemeshow Test P = 0.766 χ2 = 33.7, P < 0.001 R-squared = 0.19 N=6049 (89.2%)

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CHAPTER

12
The validity of using self-reported illness to measure objective health

There is a longstanding discourse on whether self-reported health is a good measure of objective health. This has never been empirical examined in Jamaica. Study seeks to 1) examine the relationship between particular subjective and objective indexes; 2) investigate the validity of a 4-week subjective index in measuring objective indexes; 3) evaluate the differences that exist between the measurement of subjective and objective indexes by the sexes; and 4) provide policy makers, other researchers, public health practitioners as well as social workers with research information with which can be used to inform their directions. A strong significant association was found between life expectancy at birth for the Jamaican population and self-reported illness (r = -0.731); and this was weaker females (r = - 0.683) than males (r = - 0.796). However, the relationship between mortality and self-reported illness was a weak non-linear one. Selfreported illness in a 4-week reference period is a good measure of objective health that selfreported illness for males was a better measure for objective health than for females.

Introduction
There is a longstanding discourse on whether self-reported health is a good measure of objective health. Objective health indexes include mortality, life expectancy and diagnosed morbidity, which provide a great degree of precision in the measurement of health. Those measures have been used for centuries by mathematicians, demographers and epidemiologists to provide insights into the health of an individual, community or population. While the objective health indexes do have a high probability of mathematical empiricism, which make for validity and reliability in comparisons across different population characteristics, they are narrow in evaluating a range of issues affecting the health of people. Life expectancy germinates from
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mortality data, which speaks to lived years and not quality of the lived time. Like life expectancy and mortality, morbidity is caused by some disease causing pathogens that further justify the causal relation between morbidity and health. Historically, policy makers including doctors relied on research findings on the causes of particular dysfunctions in order to formulate measures to address their reduction or eradication. Health therefore was viewed as the absence of diseases; hence, the alleviation of morbidity meant a healthy person or population. But the absence of diseases still does not imply that an individual or population is healthy, as this is the further extreme of the health continuum. It was this gap in the discourse and the accepted limitation of objective indexes of health that led the World Health Organization (WHO), in the late 1940s, to forward a conceptual definition of health [1]. The WHO’s definition of health stipulated that it goes beyond the mere absence of diseases to social, psychological and physical wellbeing. Health was no longer the absence of diseases but different tenets of ‘wellbeing’. Although WHO’s perspective outlined the way forward, and sought to provide a platform for which an expansion in objective health could begin, some scholars opined that it was too vague and elusive a conceptualization [2,3]. In spite of those critiques, some researchers began using subjective indexes to measure health instead of the traditional objective indexes. The subjective measures are 1) happiness; 2) life satisfaction, 3) self-reported health status, and self-reported illness [4-15]. Diener [5, 6] postulated that happiness can be used to measure subjective wellbeing (ie health). He opined that happiness expends beyond and implicitly takes into account more aspects of an individual’s life than the objective indexes. Happiness like life satisfaction, selfreported health has a common denominator, people’s perception of their general quality of life. Although this is in keeping with that comprehensive broad conceptual definition of health

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forwarded by the WHO – more than the narrow biomedical approach diagnosed morbidity, life expectancy or mortality – the debate about the validity of those subjective indexes continue. Scientific literature on health has revealed that self-rated health status is highly reliable a measure to proxy health and that this ‘successfully crosses cultural lines’ [16]. O’Donnell and Tait [17] concluded that self-reported health status can be used to indicate wellbeing as they found that all respondents who had chronic diseases reported very poor health. Another group of scholars concurred with the aforementioned findings when their findings revealed that the statistical association between happiness and subjective wellbeing (ie self-reported health) was a strong one - correlation coefficient r = 0.85 in the 18 OECD countries [18]. In that same study, the research found a weak relation between objective measures of health and self-reported health. This highlights the disparity in measures, the need for more empirical studies and implicitly has not address the biasness in the subjectivity of the subjective indexes. The subjective indexes introduced the issue of biasness in recall and perception as subjectivity denotes people’s perceptions. Perception is highly biased as people can provide an inflated or deflated account of their state in an interview or on a self-administered questionnaire. It is for this reason why empirical researchers avoid and decry its utilization in the measurement of health. Although subjective indexes are in keeping with the WHO’s widened definition of health, their biasness must be understood as challenges for researchers. The discourse on subjective wellbeing, using survey data, cannot be denied that it is based on person’s judgement, and therefore must be prone to systematic and non-systematic biases [19]. In an earlier work, Diener [5] argued that the subjective measure seemed to contain substantial amounts of valid variance; suggesting that this indicated the validity of subjective indexes. Kahneman [20] devised a procedure of integrating and reducing the subjective biases

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when he found that instantaneous subjective evaluations are more reliable than assessments of recall of experiences. This highlights the biasness therefore that remain in cross-sectional survey that asked people to remember over a long time. Embedded in the aforementioned findings are whether particular subjective indexes that comprised of recall over 2-4 weeks is a good measure for objective indexes of health. Embodied in the literature is the need to carry out empirical research on subjective and objective indexes with emphasis on subjective indexes that are not on instantaneous assessment. Using data for Jamaica, the aims of this study are to 1) examine the relationship between particular subjective and objective indexes; 2) investigate the validity of 2-4 week subjective index (self-reported illness over a 4-week period) in measuring objective indexes (ie life expectancy and mortality); 3) evaluate the differences that exist between the measurement of subjective and objective indexes by the sexes; and 4) provide policy makers, other researchers, public health practitioners as well as social workers with research information with which can be used to inform their directions.

Materials and method
The current study utilized secondary published data from the Statistical Institute of Jamaica [21], and the Planning Institute of Jamaica and the Statistical Institute of Jamaica [22]. Life expectancy and mortality were from the Statistical Institute of Jamaica, and self-reported illness from the Planning and Statistical Institutes of Jamaica. Generally, data were for two decades (1989-2007); however, life expectancy data were only available for some of those years. Life expectancy for some years was taken from the Human Development Reports [23]. Data were stored, retrieved and analyzed using SPSS for Windows 16.0 (SPSS Inc; Chicago, IL, USA). Descriptive statistics were used to provide background information on data.

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Scatter diagrams were employed to establish 1) statistical associations, and 2) linearity and nonlinearity between variables under examination. Multiple regression, using the enter method, was employed to a predictive model of linear associations. Models were built for 1) general life expectancy and self-reported illness of Jamaicans; 2) life expectancy and self-reported illness of the sexes. A 95% confidence interval would be used to examine whether a variable is statistical significant or not. LEp = ƒ (SPI p, ε ) LEm = ƒ (SPIm, ε ) LEf = ƒ (SPI f, ε ) [1] [2] [3]

Where LEp (life expectancy at birth for the population at a given period) is a function of selfreported illness (SPI p) of population at a given period and some residual error (ε). LEm is life expectancy at birth for males at a given period SPIm is self-reported illness for males at a given period LEf is life expectancy at birth for females at a given period SPIm is self-reported illness for females at a given period Measure Self-reported illness. The percent of people who reported having had an illness/injury in the 4week period of the survey for a given year. Mortality. The number of death of people in Jamaica for a given year. Life expectancy at birth. The average number of years of new-born would live if subject to the mortality patterns of the cross-sectional population at the time of his/her birth. Subjective health is self-evaluated (or assessed) illness of an individual.

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Objective health. This variable constitutes life expectancy and mortality of a given population at a particular time.

Results
In 1989, life expectancy at birth for the Jamaican population was 72.5 years and this has increased to 73.12 year in 2007 (Table 12.12.1). Disaggregating population life expectancy at birth revealed that in 1989, a female child was likely to outlive a male-child by 3 years. One and one-half decades later this difference increased to 6 years. Over the 2 decades, the self-assessed difference in ill status of females increased from 3.5% (in 1989) to 4.7% in 2007. Concurrently, general self-reported illness over a 4-week period declined from 16.8% to 15.5%, with a mean self-reported illness of 12.5% (SD = 2.6%). Mortality declined by 9.2%; with a mean mortality over the 2 decades being 15,829 people (SD = 1,616 people). Life expectancy of population by self-reported illness (for a 4-week period) Assessing illness from a 4-week period, Figure 12.12.1 found a strong significant association between life expectancy at birth for the Jamaican population and self-reported illness (correlation coefficient, r = -0.731). Fifty-four percent of life expectancy can be accounted for by selfreported illness (R2 = 0.535). Based on Table 12.12.2, if all other things remain constant (ie not change) which denotes that self-reported illness would be naught, a Jamaican child at birth on average would be expected to live for 75.6 years (95% confidence interval: 73.9, 77.3 years). With every 1% increase in self-reported illness, life expectancy is expected to decline by 0.17 years (ie 2 months).

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Life expectancy of female child at birth by self-reported illness of females (for a 4-week period) Life expectancy at birth of female Jamaica and self-reported illness of female (assessed based on a 4-week period) are moderately negatively correlated with each other (correlation coefficient, r = - 0.683). Forty-seven percent of the variance in life expectancy at birth of a female child in Jamaica can be explained by 1% change in self-reported illness of females (Figure 12.12.2). Table 12.12.2 revealed that if self-reported illness were equals to zero, life expectancy of a female child at birth on average would be 83.3 years (9% % Confidence interval = 75.4, 91.3 years). With every 1% increase in self-reported illness, life expectancy will decline by 0.53 years (or 6 months) (95% confidence interval = -1.031, -0.024 years).

Life expectancy of male child at birth by self-reported illness of males (for a 4-week period)

Life expectancy at birth for a male is strongly associated with self-reported illness of males (in %) – correlation coefficient, r = - 0.796. Sixty-three percent of the variance in life expectancy at birth of a male can be explained by self-reported illness (in %) (Figure 12.12.3). If self-reported illness were zero, average life expectancy of a male child in Jamaica would be 72.7 years (95% Confidence interval = 71.3, 74.1 years) (Table 2). With each

additional increase in self-reported illness (ie 1%), life expectancy of a male will decline by 0.17 year (2 months) – (95% confidence interval = 0.289, 0.055). Mortality and self-reported illness of population (in %) Based on Figure 1 the data for mortality (in number of people) and self-reported illness (in %) is best fitted by a non-linear curve. Concomitantly, when self-reported illness of the population (in %) is less than 11%, the significant statistical correlation between self-reported illness and mortality is a negative one. When self-reported illness lies between 11% and 16%, mortality
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begins to increase indicating the direct statistical association between both variables. When selfreported illness exceeds 16%, the association between the two variables changed to a negative one.

Limitation
The use of a single variable to explain the objective indexes may create the impression that only one explanatory variable is important. This is a limitation of the study as the researcher wants to examine one independent variable (ie self-reported illness in a 4-week reference period) in order to establish whether it is a good measure of objective indexes and whether differences exist between the sexes.

Discussion
Empirical analyses have examined the subjective and objective wellbeing phenomenon, and have provided some platform for a partial resolution of the matter. Using cross-sectional data, researchers established that there was a significant statistical relation between subjective wellbeing (self-reported wellbeing) and objective wellbeing [5, 6, 19]. Diener [5] found a strong correlation between the two variables, which disagreed with Kahneman and Riis [18], who found correlation coefficient between subjective happiness and self-reported health to be strong; but the statistical association between self-reported health and objective health. The current research concurs with both Diener and not Kahneman and Riis in one instance as the correlation between self-reported illness (ie subjective index) and objective health (ie life expectancy) for the population was a strong one, correlation of coefficient, r = 0.731. The evidence here is both that the association is a strong one and that it is negative, suggesting that life expectancy deteriorates with more self-reported illness. This justifies the increase in life expectancy at birth for
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Jamaicans in 2007 over 1989 as the percentage of self-reported illness declined by 1.3%. However on the other hand, when the objective index is mortality, the statistical association between objective health and self-reported illness (ie subjective index) was very weak. The studies of Diener and Kahneman and Riis assume that the sexes operate in the same manner which means that what applies to the general populace is the same across the sexes. This study did not make that assumption; instead the researcher examined whether there was a disparity between the sexes and if there were any, what these were. This work revealed that strong significant correlation between objective health (ie life expectancy at birth for Jamaicans) and self-reported illness of both sexes differs by male and female. The findings showed that selfreported illness was more an explanation of life expectancy of males than of females. Interestingly to note that self-reported illness accounted for less than one-half of life expectancy of females but close to two-thirds for males. Kahneman [20] suggested that instantaneous self-assessment of health is a good measure of subjective health unlike self-evaluations that occur over a longer period of time. This study found that self-reported illness over a 4-week period of time is not immediate and is still a good measure of life expectancy; but not mortality. Embedded in this finding is the fact that subjective index can be instantaneous unlike Kahneman’s finding. The current study did not examine beyond a 4-week period and while it was not immediate does not say that we can totally disregard time in recall. The matter may not show any difference for the general population; but this would be different for particular age cohorts – elderly. Evolutionary biology has shown that cells degenerate with ageing, suggesting that functional capacity in particular mental faculties will not on average be as good as in earlier years [24-29]. It is within the context of ageing that

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Kahneman’s perspective may be even more potent as a 4-week period will not seek challenges in recall for the young or middle age people but this could be so for the aged. Gaspart [30] opined on the difficulty of using objective quality of life in measuring wellbeing and put forward a perspective that self-reported wellbeing should replace this measurement. He wrote, “So its objectivism is already contaminated by post-welfarism, opening the door to a mixed approach, in which preferences matter as well as objective wellbeing” [30] which speaks to the necessity of using a measure that captures more of the multidimensional construct of health than the traditional income per capita. Wellbeing depends on both the quality and the quantity of life lived by people, which argues more for subjective indexes than objective ones [14]. The current study revealed that self-reported health is a good measure of life expectancy but a poor measure of mortality in Jamaica. Therefore those studies that have used self-rated illness (or health conditions) [31-34] to evaluate health of Jamaicans or particular subgroupings with the population were good in capturing health; but that researchers must be cognizant of the differences that do exist between the validity of particular objective indexes used and self-reported illness as well as the sex disparity in validity of subjective index in measuring health. Self-reported illness therefore is a good measure of health as self-rated health status or life expectancy. But the former is a better measure for health of males than females. Hence, this must be taken into consideration in the interpretation of health. Simply put, using self-reported illness to evaluate health of females is less reliable than of assessing males’ health; and that subjective health (self-reported illness) is a good measure of objective health (life expectancy) in Jamaica.

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Conclusion
Life expectancy at birth is widely used to measure quality of life in a country or of a people in particular geographic region. It is among the objective indexes used by some demographers and economists to evaluate health status of people and a population. This study found that selfreported illness in a 4-week reference period is a good measure of objective health (life expectancy at birth for the population of Jamaica). However, self-reported illness is a poor measure of mortality. On disaggregating life expectancy and self-reported illness data by sexes, it was revealed that self-reported illness for males was a better measure for objective health than for females. The literature revealed that subjective indexes of health is a good measure if people are asked to report on their health current and not over any long period of time. The current study disagrees with the literature that for subjective index (ie self-reported illness) to be a good measure of health it must be instantaneous as this work found that subjective index over a 4week was a good measure of life expectancy. This does not denote that the period extends beyond 4 weeks; but that 1) self-reported illness is a good measure of objective index (life expectancy); 2) subjective index is a better measure of objective index (life expectancy) for males than females; 3) subjective index is not a good measure for mortality, and 4) self-reported illness can be used to measure health as self-rated health status, happiness, or life satisfaction.

Conflict of interest The author has no conflict of interest to report at this time.

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References 1. 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. 2. Crisp R. Wellbeing. The Stanford Encyclopedia of Philosophy; 2008. 3. Bok S. Rethinking the WHO definition of health. Harvard Center for Population and Development Studies, Harvard School of Public Health. Working Paper Series 2004; 14(7). 4. Di Tella R, MacCulloch R, Oswald AJ. 1998. The Macroeconomics of Happiness, mimeo, Harvard Business School. 5. Diener E. Subjective well-being. Psychological Bulletin 1984; 95: 542–75. 6. Diener E. Subjective well-being: the science of happiness and a proposal for a national index. Am Psychologist 2000; 55: 34–43. 7. Borghesi S, Vercelli A. Happiness and health: two paradoxes. DEPFID Working papers; 2008. 8. Kashdan TB. The assessment of subjective well-being (issues raised by the Oxford Happiness Questionnaire). Personality and Individual Differences 2004; 36:1225-1232. 9. Blanchflower DG, Oswald AJ. 2004. Well-Being Over Time In Britain And The USA. J of Public Economics 2004; 88:1359-1386. 10. Frey BS, Stutzer A. happiness and economics. Princeton University Press: Princeton; 2002. 11. Grossman M. The demand for health – a theoretical and empirical investigation. New York: National Bureau of Economic Research; 1972. 12. Hambleton IR, Clarke K, Broome HL, Fraser HS, Brathwaite F, Hennis AJ. Historical and current predictors of self-reported health status among elderly persons in Barbados. Rev Pan Salud Public 2005; 17: 342-352. 13. Hutchinson G, Simeon DT, Bain BC, Wyatt GE, Tucker MB, LeFranc E. Social and Health determinants of well-being and life satisfaction in Jamaica. Inter J of Social Psychiatry 2004; 50:43-53. 14. Easterlin RA. Income and happiness: towards a unified theory. Economic J 2001; 111:465484.

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15. Graham C. Happiness and health: Lessons – A Question – For Public Policy. Health Affairs 2008; 27:72-87. 16. Ringen S. Wellbeing, measurement, and preferences. Scandinavian Sociological Association 1995; 38, 3-15.

17. O’Donnell V, Tait H. Wellbeing of the non-reserves Aboriginal population. Canada Catalogue 2003; 89-589.

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18. Kahneman D, Riis J. Living, and thinking about it, two perspectives. In: Huppert FA, Kaverne B, Baylis N. The Science of Well-being. Oxford University Press: New York; 2005.

19. Schwarz N, Strack F. Reports of subjective well-being: judgmental processes and their methodological implications. In: Kahneman D, Diener E, Schwarz N, editors. Well-being: The Foundations of Hedonic Psychology. Russell Sage Foundation: New York, 1999: 61-84. 20. Kahneman D. Objective happiness. In: Kahneman D, Diener E, Schwartz N, editors. Wellbeing: Foundations of hedonic psychology. Russell Sage: Foundation, New York; 1999. 21. Statistical Institute of Jamaica, (STATIN). Demographic statistics, 1989-2007. Kingston, STATIN; 1989-2008. 22. Planning Institute of Jamaica, (PIOJ), Statistical Institute of Jamaica, (STATIN). Jamaica Survey of Living Conditions, 1989-2007. Kingston: PIOJ, STATIN; 1989-2008. 23. United Nations Development Programme, (UNDP). Human Development Report 1990-2003. New York: UNDP; 1990-2003. 24. Gavrilov LA, Gavrilova NS. The reliability theory of aging and longevity. J. theor. Biol 2001; 213:527-545. 25. Gavrilov LA, Gavrilova NS. The biology of life Span: A Quantitative Approach. New York: Harwood Academic Publisher; 1991. 26. Charlesworth B. Evolution in Age-structured Populations, 2nd ed. Cambridge: Cambridge University Press; 1994 27. Carnes BA, Olshansky JS. Evolutionary perspectives on human senescence. Population Development Review 1993; 19: 793-806. 28. Carnes BA, Olshansky SJ, Gavrilov L A, Gavrilova NS, Grahn D. Human longevity: Nature vs. nurture - fact or fiction. Persp. Biol. Med 1999; 42: 422-441.

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29. Medawar PB. Old age and natural death. Mod Q 1946; 2:30-49. 30. Gaspart F. Objective measures of wellbeing and the cooperation production problem. Social Choice and Welfare 1998; 15:95-112. 31. Bourne PA. Childhood Health in Jamaica: changing patterns in health conditions of children 0-14 years. North American Journal of Medical Sciences. 2009;1:160-168. 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 American Journal of Medical Sciences. 2009;1: 86-95. 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;1:99-109. 34. Bourne PA. (2009). An epidemiological transition of health conditions, and health status of the old-old-to-oldest-old in Jamaica: a comparative analysis. North American Journal of Medical Sciences. 2009;1:211-219.

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Table 12.12.1. Life expectancy at birth for the sexes, self-reported illness, and mortality, 19892007 Year Life expectancy at birth (e0) Ill-health (in %) Mortality Male Female Total Male Female Total 1989 69.97 72.64 72.5 15.0 18.5 16.8 16400 1990 69.97 72.64 72.5 16.3 20.3 18.3 14900 1991 69.97 72.64 72.5 12.1 15.0 13.7 13300 a 1992 73.6 9.9 11.3 10.6 13200 1993 73.7a 10.4 13.5 12.0 13900 1994 11.6 14.3 12.9 13500 1995 74.1a 8.3 11.3 9.8 15400 1996 9.7 11.8 10.7 15800 1997 8.5 10.9 9.7 15100 1998 75.0a 7.4 10.1 8.8 17000 1999 70.94 75.58 73.25 8.1 12.2 10.1 18200 2000 70.94 75.58 73.25 12.4 16.8 14.2 17400 2001 70.94 75.58 73.25 10.8 15.9 13.4 17800 2002 71.26 77.07 74.13 10.4 14.6 12.6 17000 2003 71.26 77.07 74.13 NI NI NI 16900 2004 71.26 77.07 74.13 8.9 13.6 11.4 16300 2005 73.33 NI NI NI 17000 2006 73.24 10.3 14.1 12.2 16400 2007 73.12 13.1 17.8 15.5 14900
a

These were taken from the United Nations Development Programme

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Table 12.12.2. Life expectancy at birth of population and sex of children by self-reported illness Explanatory variable Coefficient Std. Error Beta t-statistic P 95% CI

Population Constant Self-reported illness

75.604 -0.173

0.738 0.054

-0.731

102.425 -3.217

< 0.001 0.011

73.934, 77.274 -0.295, -0.051

F statistic [1, 9] = 10.350, P = 0.011 R = - 0.731 R2 = 0.535

Female children Constant Self-reported illness

83.363 -0.528

3.375 0.213

-0.684

24.700 -2.478

< 0.001 0.042

75.382, 91.344 -1.031, -0.024

F statistic [1, 7] = 6.138, P = 0.042 R = - 0.684 R2 = 0.467

Male children Constant Self-reported illness

72.718 -0.172

0.587 0.050

-0.796

123.840 -3.478

< 0.001 < 0.010

71.330, 74.107 -0.289, -0.055

F statistic [1, 7] = 12.096, P = 0.010 R = - 0.796 R2 = 0.633

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74.50

Life expectancy at birth: both sex (in years)

74.00

73.50

73.00

R Sq Linear = 0.535 72.50 8.00 10.00 12.00 14.00 16.00 18.00 20.00

Illness/Injury (in %)

Figure 12.12.1. Life expectancy at birth for the population by self-reported illness (in %). Life expectancy at birth of Jamaicans and self-reported illness (assessed based on a 4-week period) are strongly negatively correlated with each other (correlation coefficient, r = - 0.731). Fifty-four percent of the variance in life expectancy at birth for the population of Jamaica can be explained by 1% change in selfreported illness.

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78.00

Life expectancy: female (at birth in years)

77.00

76.00

75.00

74.00

73.00
R Sq Linear = 0.467

72.00 12.00 14.00 16.00 18.00 20.00 22.00

Self-reported Health of female (in %)

Figure 12.12.2. Life expectancy at birth for female by self-reported illness of female (in %). There is a negative moderate correlation between life expectancy at birth of a female and self-reported illness of female (in %) – correlation coefficient = 0.683. Forty-seven percent of the variance in life expectancy at birth of a female can be accounted for by 1% change in self-reported illness females (in %).

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Life expectancy: male (at birth in years)

71.25

71.00

70.75

70.50

70.25

R Sq Linear = 0.633

70.00

8.00

10.00

12.00

14.00

16.00

18.00

Self-reported Health of male (in %)

Figure 12.12.3. Life expectancy at birth for male by self-reported illness of male (in %).

There is a strong negative significant statistical correlation between life expectancy at birth of a male and self-reported illness of male (in %) - correlation coefficient, r = - 0.796. Sixty-three percent of the variance in life expectancy at birth of a male can be explained by self-reported illness (in %).

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19000.00

18000.00

Mortality (in No. of people)

17000.00

16000.00

15000.00

14000.00
R Sq Cubic =0.106

13000.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00

Illness/Injury (in %)

Figure 12.12.4. Mortality (in No of people) and self-reported illness/injury (in %)

Based on Figure 1 the data for mortality (in number of people) and self-reported illness (in %) is best fitted by a non-linear curve. Concomitantly, when self-reported illness of the population (in %) is less than 11%, the significant statistical correlation between self-reported illness and mortality is a negative one. When self-reported illness lies between 11% and 16%, mortality begins to increase indicating the direct statistical association between both variables. When self-reported illness exceeds 16%, the association between the two variables changes to a negative one.

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CHAPTER

13
The image of health status and quality of life in a Caribbean society

Paul A. Bourne, Donovan A. McGrowder, Christopher A.D. Charles, Cynthia G. Francis

Health is defined as the presence or absence of illness. This conceptualization of health status is dominant in health treatment and in fashioning the health care system. However, very little research has been done on how Jamaicans view health status and quality of life (QoL). This article seeks to understand how Jamaicans conceptualize health status and QoL because definitional content has implications for their health. The majority of the respondents in the CLG (54%) and the JSLC (82.2%) surveys reported good health status. There was a strong statistical relationship between area of residence and health status (P < 0.0001) unlike the relationship between area of residence and quality of life (P < 0.137). The respondents dichotomized health status and QoL and a significant relationship was found between both variables (P < 0.0001). The respondents’ dichotomization of health status and QoL is explained by the significant relationship between health status and self reported illness (P < 0.0001) where respondents view health status as the absence or presence of illness, excluding QoL. Health status means the presence or absence of illness and excludes QoL which is not in keeping with previous findings. This distinction is culturally determined.

Introduction
The satisfaction of basic needs constitutes quality of life (QoL) which is related to health. Maslow’s theory of human motivation posits that there are five basic interrelated needs. These are: physiological needs, safety needs, need of love and affection, need to belong , need for esteem and need for self actualization. All of these operate in a hierarchy of prepotency [1-3]. Each of these needs in the hierarchy has to be satisfied before the higher need can be met [1].
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Understanding these needs is important because the greater the acquisition of knowledge of people’s natural way of being, the less difficult it becomes to guide people about how to fulfill their greatest potential, how to respect the self, how to love and be productive, how to be good and happy [2]. Maslow also posits that healthy people with healthy psyches transcend their environment. This transcendence occurs because these people are guided by internal values and rules that foster a self-governing character, detachment and independence [3]. Maslow’s theory can be used to motivate people to become healthy [4]. Scores on belief in an internal locus of control and neuroticism were predicted by Maslow’s need for satisfaction [5]. Biopsychosocial health can be explained by the hierarchy of needs. Maslow argues that people have the potential for growth and innate goodness, and are able to strive when faced with adversity. Therefore, positive psychology influences health [6]. The hierarchy of needs also explains gender differences in the meaning of health. Women associated a comfortable life, pleasure, values and happiness with health, unlike men who associated health with national security and family. The values of women satisfied their fundamental needs, while those of men satisfied their higher order needs. This difference suggests that men can be motivated to engage in healthy behaviour after they have fulfilled their more fundamental needs, compared to women who may strive for health before they are motivated by other needs [7]. However, there is no gender difference in self-actualization scores, but women score lower on perceived selfpresentation, confidence, physical self-efficacy and perceived physical ability [8]. Biological and psychological health is related to the hierarchy of needs. For example, geriatric patients have a hierarchy of needs. Therefore, caring for these patients requires that their self-actualization and self-esteem needs are met, and not just their physiological health [9]. In addition, the unmet physiological and safety needs of patients who suffer from chronic
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vestibular dysfunction means that these patients cannot progress to higher order needs. This lack of progress leads to psychosocial problems that have to be addressed [10]. Maslow’s hierarchy of needs is also important for health education [11] because the status of people’s basic needs influences their health-promoting self care behaviour. Some 64% of the variance in healthpromoting self-care behaviour was influenced by the physical: love, belonging, need, satisfaction and self-actualization [12]. Unhealthy behaviour and health disparities based on race and class can be reduced through health promotion programmes that respond to the basic needs of people, which will allow them to achieve self-actualization [13]. This self-actualization influences the quality of life. The hierarchy of needs was applied to the development of the quality of life in 88 countries between 1964 and 1994. There is a significant association between the predictions of Maslow’s theory and the quality of life, including part of the S-shaped course and the sequence of needs achievement [14] which influences health. Published evidence on the health status and quality of life of Jamaicans is lacking, and not much research has been done in this area in the English-speaking Caribbean. This study examined how Jamaicans conceptualize health and quality of life, and investigated any possible relationship between the two variables.

Materials and Methods
The current study utilized two different cross-sectional probability surveys which were conducted in 2007 to examine the health status and quality of life of Jamaicans. These two national surveys were conducted throughout the 14 parishes of Jamaica. The studies were conducted by (1) the Centre for Leadership and Governance (CLG), Department of Government, the University of the West Indies (UWI), Mona, and (2) the Planning Institute of Jamaica (PIOJ) and the Statistical Institute of Jamaica (STATIN) – Jamaica Survey of Living Conditions (JSLC).
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The sample for the current study was 8,120 participants: 1,338 from the CLG and 6,782 from the JSLC. Each survey was independently collected by the organization, and both the CLG and the JSLC collected data at the same time. During the months of July and August 2007, CLG conducted a stratified probability sample of 1,338 respondents. The sampling design used for the study was that used by STATIN. Face-to-face interviews were used to collect the data on an instrument which took about 90 minutes. The instrument consisted of questions about Abraham Maslow’s hierarchy of needs (physiological needs, safety needs, social needs, self-esteem and self-actualization) which were used to determine the participant’s quality of life [3]. The instrument was administered as part of a larger CLG study. It was vetted by senior scholars, researchers, and interviewers from STATIN and the Social Development Commission (SDC). After the vetting phase, the questionnaire was pre-tested in a number of communities across the 14 parishes of Jamaica, as well as among UWI faculty members and the student population. Modifications were made at a training symposium, based on the comments of the different interviewers and the remarks of trained researchers. All the interviewers employed by the CLG’s team were data collectors from either STATIN or SDC. The interviewers who are trained data collectors underwent further training with the CLG team for a 3-day period. The project manager of CLG travelled across the country to verify the data collection process. A data template was created before the data was entered and data entry clerks were trained to work with the instrument. Three different groups independently entered the data, which was cross-referenced and reviewed for accuracy by two members of the research team, who also validated the data entry process and cleaned the data. The JSLC was commissioned by PIOJ and STATIN in 1988, and these organizations have been collecting data since 1989 [15]. The JSLC is done through the administering of
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questionnaires modelled on the World Bank’s Living Standards Measurement Study (LSMS) household survey [16]. The JSLC questionnaire consists of variables dealing with

demographics, health, the immunization of children aged 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 annually between April and July. Measure Quality of life was defined as the overall self-reported life satisfaction of an individual. It was measured as the mean summation of the five-item needs from Abraham Maslow’s hierarchy. These items were physiological needs, safety needs, social needs, self-esteem and selfactualization [1]. Each item was on a 10-point Likert scale. Using Cronbach alpha for the fiveitem scale, reliability was 0.841 (or α = 84%). QoL i = 1/5*∑Ni where i is each need (i.e. I = 1, 2, 3, 4, 5) where the QoL index is: 0≤QoL i ≤10. Cohen and Holliday stated that correlation can be very low/weak (0.0-0.19); weak (0.2-0.39); moderate (0.4-0.69), strong (0.7-0.89) and very strong (0.9-1.0) [17]. Cohen and Holliday’s interpretation will be applied to categorizing Qoli into five groups: very poor (values range from 0 to 1.9); poor (values from 0.2 to 3.9); moderate (values from 4.0 to 6.9), good (values ranging from 7.0 to 8.9) and very good (values ranging from 9 to 10). Health status was measured by the question “Generally, how do you feel about your health?” Answers to this question were on a Likert scale ranging from excellent to poor.

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Results
In examining the demographic characteristics of the sample as well as QoL and health status forty three percent of the CLG’s respondents (n = 1338) were males compared to 49% for the JSLC (n = 6,782; Table 13.13.1). Fifty-four percent of CLG’s respondents indicated at least good QoL (of which 10.3% claimed very good) compared to 82.2% of those in the JSLC who indicated at least good health status (of which 37% mentioned very good). A statistical relationship was found between QoL and gender [QoL – χ2 (df = 4) = 11.9, P < 0.018], and health status and gender [JSLC – χ2 (df = 4) = 46.5, P < 0.0001; Table 13.13.2]. A cross-tabulation between QoL and area of residence revealed no significant statistical relationship [QoL – χ2 (df = 4) = 6.98, P < 0.137; Table 13.13.3]. However there was a significant relationship between health status and area of residence [JSLC – χ2 (df = 4) = 27.51, P < 0.0001]. Using the standardized health status and QoL a significant statistical association was found between the two variables [χ2 (df = 4) = 388.9, P < 0.0001; Table 13.13.4]. In addition, a statistical relationship was found between the two variables [χ2 (df = 16) = 85.477, P < 0.0001; Table 13.13.5]. Using data from JSLC’s survey, a statistical relationship was found between the health status and self-reported illness of respondents’ variables [χ2 (df = 4) = 1323.470, P < 0.0001]. The statistical association was moderate, as given by the contingency coefficient with a value of 0.450. Of those who indicated that they had an illness (n = 976), 3.0% claimed very poor; 17.4% said poor; 36.8% indicated moderate; 31.3% mentioned good and 11.6% reported very good health status. In the same way, of those who indicated that they had not experienced an illness in the last 4-week period (n = 5569), 0.4% reported very poor health status; 1.8% said poor; 8.7% moderate health status; 47.7% claimed good and 41.4% reported very good health status.
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Discussion
This study examined how Jamaicans view health status and QoL. The majority of the

respondents in the CLG and JSLC surveys stated that they had good health status. The JSLC survey had the greater majority with 28.2% more of the respondents stating that they had good health status than their counterparts in the CLG survey. For both surveys there was no

significant gender difference in terms of QoL as there was a weak statistical relationship between gender and QoL. This latter finding suggests that men and women view their quality of life or basic needs similarly, despite the patriarchal nature of Jamaican society and the attendant gender inequality. There was also a weak statistical relationship between the economic situation of the respondents and their families, and QoL. This finding suggests that the respondents in their selfreports did not view their economic status as influencing their QoL. Therefore, in the

respondents’ understanding of their basic needs there are other explanatory factors that will have to be explored in future research. There was a significant difference in the health status of the respondents in rural and urban areas because there was a strong statistical relationship between area of residence and health status, unlike the relationship between area of residence and QoL. These findings suggest that the respondents, in their self-reporting, view their health status and their QoL dichotomously, which is different from the results obtained in previous studies [18, 19]. Moreover, in the current study a significant relationship was found between QoL and health status. This finding suggests that although QoL and health status are related, they are viewed by the respondents as dichotomous domains in their lives.

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The dichotomous conceptualization of QoL and health status may be explained by the finding that a significant relationship was found between health status and self-reported illness. The respondents in this study viewed their health status based on the absence or presence of an illness, and did not include QoL. The respondents’ exclusion of basic needs in their health status suggests that their conceptualization of health as the presence or absence of illness is culturally determined, because this is different from the findings of previous studies [18, 19]. Therefore, within the Jamaican culture QoL is multi-dimensional and health status is one-dimensional, so these conceptualizations are antonymous. The preponderance of illness accounting for most of health is not atypical to Jamaica, as a study conducted by Hambleton et al. [20] involving elderly Barbadians (60 years) revealed similar results. Hambleton et al.’s work found that 88% of the variability in health status was accounted for by current illness. While this study cannot allude to the generalizability of this to the Caribbean, clearly in both of the aforementioned nations, health still carries a narrow definition. This narrow definition of health was the justification of the World Health Organization’s (WHO) concept of health in 1948 [21]. The WHO postulated a definition which states that health is more than the absence of disease, as it includes social, psychological and physical wellbeing [21]. Health is therefore more than the absence of illness. This is a negative approach to the image and study of health, and does not encompass wellbeing or the positive side to health [22, 23]. Both the WHO in the preamble to its Constitution in 1948, [21] and Engel [24-26], have sought to conceptualize and provide a rationale for the image and study of health that extends beyond illness or the antithesis of disease. Despite the contributions of social scholars as well as the WHO and Engel to the discourse of health, in contemporary Jamaica the image of health is still the antithesis of illnesses.

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Bok [27] opined that the WHO’s conceptualization of health is too broad, and therefore poses a problem to operationalize in research. Embedded in Bok’s claim is the difficulty in quantifying social and psychological conditions in health, and explaining the use of diagnosed illnesses, mortality and life expectancy instead of wellbeing. Like other scholars [28-30], Bok sees health as an objective phenomenon which explains the use of life expectancy, diagnosed illness and mortality. Life expectancy relies on mortality data, and while it is an objective measure of the health of people or a society, it is similar to the use of the antithesis of illness and not wellbeing. It is this narrow approach to the use of life expectancy that justifies the World Health Organization’s (WHO) introduction of healthy life expectancy [31]. Recognizing the limitations of life expectancy, the WHO discounted life expectancy for disability. Disability Adjusted Life Expectancy (DALE) summarizes the expected number of years of life of an individual, which might be termed the equivalent of "full health." To calculate DALE, the years of ill health are weighted according to severity, and subtracted from the expected overall life expectancy, to give the equivalent years of healthy life [31]. This study has contributed to our understanding of health status by highlighting the culture-bound conceptualization of health status in Jamaica, which is different from how it is conceptualized in the literature which includes QoL. Another contribution is the generalization of the findings, with the combination of the findings from two large-scale random national surveys. However, there are a couple of limitations. We did not measure the factors influencing how Jamaicans conceptualize illness which would inform interventions. Also, the CLG and JSLC surveys relied on self-reports so there was the possibility of social desirability bias, where the respondents might have told the interviewers what they wanted to hear to get their approval. QoL is concerned with how people assess their lives which includes a wide range of issues from health, life satisfaction, momentary moods, economic wellbeing, happiness to needs
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satisfaction and a global assessment of all aspects of life [32-34]. QoL, therefore, is subjective wellbeing, and its coverage extends beyond illness [35]. Health status, however, is synonymous with physical health (illness) which means that collecting data on illness and self-rated health status is one of the same and therefore adds nothing new to understanding general health as defined by the WHO [21]. In keeping with the broad definition of health forwarded by the WHO, QoL should be used in addition to illness or self-rated health status, as self-reported illness and self-rated health status are the same events.

Conclusion
This study examined how Jamaicans conceptualize health status and QoL. Jamaicans view their health status and their QoL as distinct domains in their lives. This surprising distinction is culturally determined because the difference has not been empirically observed elsewhere except Barbados. The absence or presence of illness influences how Jamaicans conceptualize their health status. The exclusion of QoL or basic needs from their conceptualization of health status should be noted by medical practitioners and researchers when they assess the health of Jamaicans. The aforementioned findings highlight that collecting data on health status and illness in Jamaica is one and the same, and therefore other subjective indices such as QoL, life satisfaction and happiness would yield more information than health status and/or illness. If health is multifaceted, then health status would not be a good measure of this broad conceptualization. Further research is needed to uncover the reasons for the one-dimensional view Jamaicans have of their health status, and how this conceptualization affects their health.

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References
1. Maslow AH. A theory of human motivation. Psychological Review 1943; 50: 370-396. 2. Maslow AH. Towards a Psychology of Being. Princeton, New Jersey: D Van Nostrand; 1962. 3. Maslow AH. Health as transcendence of environment. Journal of Humanistic Psychology 1961; 1: 1-7. 4. Lester D. Maslow and the possibility of becoming healthy. Psychological Reports 1971; 28: 777-778. 5. Lester D, Hvezda J, Sullivan S, Plourde R. Maslow hierarchy’s of needs and psychological health. Journal of General Psychology 1983; 109: 83-85. 6. Moore KA. Positive psychology and health: Situational dependence and personal striving. In Frydenberg E. Beyond Coping: Meeting Goals, Visions, and Challenges. New York: Oxford University Press; 2002 : 107-125. 7. Kristiansen CM. Gender differences in the meaning of health. Social Behavior 1989; 4: 185188. 8. Sumerlin JR, Berretta SA, Privette G, Bundrick CM. Subjective biological self and self actualization. Perceptual & Motor Skills 1994; 79: 1327-1337. 9. Majercsik E. Hierarchy of needs of geriatric patients. Gerontology 2005; 51: 170-173. 10. Haybach PJ. Maslow hierarchy of needs and the individual with chronic vestibular dysfunction. ORL Head Neck Surgery Nurs 1994; 12: 14-17. 11. Nolte A. The relevance of Abraham Maslow’s work to health education. Health Education 1976; 7: 25-27.

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12. Acton GJ. Basic need status and health promoting self-care behavior in adults. Western Journal of Nursing Research 2000; 7: 796-811. 13. Green BL, Lewis RK, Bediako SM. Reducing and eliminating health disparities: A targeted approach. Journal of the National Medical Association 2005; 97: 25-30. 14. Hagerty MR. testing Maslow’s Hierarchy of Needs: National quality of life across time. Social Indicators Research 1999; 46: 249-271. 15. Planning Institute of Jamaica, (PIOJ), Statistical Institute of Jamaica, (STATIN). Jamaica Survey of Living Conditions, 1989-2007. Kingston: PIOJ, STATIN; 1989-2008. 16. Statistical Institute Of Jamaica. Jamaica Survey of Living Conditions, 2007. Kingston, Jamaica: Statistical Institute of Jamaica, 2007. Kingston, Jamaica: Planning Institute of Jamaica and Derek Gordon Databank, University of the West Indies; 2008. 17. Cohen L, Holliday M. Statistics for Social Sciences. London, England: Harper and Row; 1982. 18. 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. 19. Finnas F, Nyqvist F, Saarela J. Some methodological remarks on self-rated health. The Open Public Health Journal 2008; 1: 32-39. 20. Hambleton IR, Clarke K, Broome HL, Fraser HS, Brathwaite F, Hennis AJ. Historical and current predictors of self-reported health status among elderly persons in Barbados. Rev Pan Salud Public 2005; 17: 342-352. 21. 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. Constitution of the World Health Organization, 1948. In Basic Documents, 15th ed. Geneva, Switzerland: WHO; 1948.
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22. Longest BB. Health Policymaking in the United States, 3rd. Chicago: Foundation of the American College Healthcare; 2002. 23. Brannon L, Feist J. Health psychology. An introduction to behavior and health, 6th ed. Los Angeles: Wadsworth; 2007. 24. Engel G. A unified concept of health and disease. Perspectives in Biology and Med 1960; 3: 459-485. 25. Engel G. The care of the patient: art or science? Johns Hopkins Med J 1977; 140: 222-232. 26. Engel G. The need for a new medical model: A challenge for biomedicine. Science 1977; 196: 129-136. 27. Bok, S. 2004. Rethinking the WHO definition of health. Working Paper Series, 14. http://www.golbalhealth.harvard.edu/hcpds/wpweb/Bokwp14073.pdf (Retrieved: 26/05/09). 28. Seigel, J. S., and D. A. Swanson, eds. The methods and materials of demography, 2nded. San Diego: Elsevier Academic Press; 2004. 29. Rowland DT. Demographic methods and concepts. New York: Oxford University Press; 2003. 30. Newell C. Methods and models in demography. New York: The Guilford Press; 1988. 31. World Health Organization. WHO Issues New Healthy Life Expectancy Rankings: Japan Number One in New ‘Healthy Life’ System. Washington D.C. & Geneva: WHO; 2000. 32. Cummins RA. Moving from the quality of life concept to a theory. J of Intellectual Research 2005;49:699-706. 33. Kim-Prieto C, Diener E, Tamir M, Scollon C, Diener M. Integrating the diverse definitions of happiness: A time-sequential framework of subjective well-being. J of Happiness Studies 2005; 6:261-300.

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34. Diener E. Subjective well-being. Psychological Bulletin 1984; 95:542-575. 35. Murphy H, Murphy EK. Comparing quality of life using the World Health Organization Quality of Life measure (WHOQOL-100) in a clinical and non-clinical sample: Exploring the role of self-esteem, self-efficacy and social functioning. J of Mental Health 2006;15:289-300.

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Table 13.13.1 Demographic characteristics of sample for CLG and JSLC, 2007 Variable Gender Male Female Social class Working Middle Upper Educational level Primary or below Secondary Tertiary QoL Very poor Poor Moderate Good Very good Health status Very poor Poor Moderate Good Very good Current economic situation compared to 1 year ago Very good Good Moderate Poor Very poor Area of residence Urban Rural Age Mean (SD) NA – Data not available CLG n 574 723 766 476 57 60 892 339 13 59 536 575 136 JSLC n 3303 3479 2697 1351 2734 5752 709 131

% 42.9 54.0 59.0 36.6 4.4 4.6 69.1 26.3 1.0 4.5 40.6 43.6 10.3

% 48.7 51.3 39.8 19.9 40.3 87.3 10.8 2.0

NA

NA

50 270 848 2967 2430

0.8 4.1 12.9 45.2 37.0

58 361 660 164 90

4.4 27.1 49.5 12.3 6.8

NA

291 21.7 1041 77.8 35.0 years (13.6)

2002 29.8 4780 70.5 29.9 years (21.7)

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Table 13.13.2 Quality of life and health status by gender of respondents, CLG and JSLC

CLG Variable Male n (%) QoL and health status Very poor Poor Moderate Good Very good Total 6 (1.1) 18 (3.2) 222(39.3) 245 (43.4) 74 (13.1) 565 7 (1.0) 40 (5.6) 292 (41.0) 316 (44.3) 58 (8.1) 713 Gender Female n (%) Male

JSLC Gender Female n (%)

n (%)

24 (0.8) 111 (3.5) 331 (10.4) 1482 (46.4) 1247 (39.0) 3195

26 (0.8) 159 (4.7) 517 (15.3) 1485 (44.1) 1183 (35.1) 3370

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Table 13.13.3 Quality of Life and health status by area of residence, CLG and JSLC

CLG Variable Area of residence Non-urban n (%) QoL and Health status Very poor Poor Moderate Good Very good Total 9 (0.9) 47 (4.6) 435(42.4) 432 (42.1) 104 (10.1) 1027 4 (1.4) 12 (4.2) 98 (34.3) 140 (49.0) 32 (11.2) 286 Urban n (%)

JSLC Area of residence Non-urban n (%) Urban n (%)

42 (0.9) 215 (4.7) 554 (12.0) 2072 (44.9) 1735 (37.6) 4618

8 (0.4) 55 (2.8) 294 (15.1) 895 (46.0) 695 (35.7) 1947

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Table 13.13.4 Quality of life, health status and standardized health status

Classification

QoL

JSLC

Standardized JSLC n 50 n 10 54 171 596 488 1319

n Very poor Poor Moderate Good Very good Total 13 59 536 575 136 1319

270 848 2967 2430 6565

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Table 13.13.5 QoL by economic situation of individual and family, CLG

Economic situation of family QoL Much worse n (%) Very poor Poor Moderate 3 (5.1) 2 (3.4) 34 (57.6)
A little worse Same A little better Much better

n (%) 3 (1.2) 24 (9.4) 124 (48.8)

n (%) 2 (0.4) 18 (3.8) 192 (41.0)

n (%) 2 (0.5) 6 (1.5) 155 (37.5)

n (%) 3 (2.7) 7 (6.3) 29 (26.1) 49 (44.1) 23 (20.7) 111

Good

17 (28.8)

88 (34.6)

213 (45.6)

200 (48.4)

Very good

3 (5.1)

15 (5.9)

43 (9.2)

50 (12.1)

Total

59

254

468

413

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CHAPTER

14
The quality of sample surveys in a developing nation
Paul A. Bourne, Christopher A.D. Charles, Neva South-Bourne, Chloe Morris, Denise Eldemire-Shearer, Maureen D. Kerr-Campbell

In Jamaica, population census began in 1844 and many inter-censal ratios performed on the census data show that there is a generally high degree of accuracy of the data. However, statistics from the Jamaican Ministry of Health show that there are inaccuracies in health data collected from males using sample surveys. The objectives of the present research are to (1) investigate the accuracy of a national sample survey, (2) explore the feasibility and quality of using a subnational sample survey to represent a national survey, (3) aid other scholars in understanding the probability of using national sample surveys and sub-national sample surveys, (4) assess older men’s evaluation of their health status, and (5) determine whether dichotomization changes selfevaluated health status. In Study 1, 50.2% of respondents indicated at least good self-evaluated health status compared to 74.0% in Study 2. Statistical associations were found between health status and survey sample [χ2 (df = 5) = 380.34, P < 0.001]; self-reported illness and study sample [χ2 (df = 1) = 65.84, P < 0.01, phi = 0.16]; health care-seeking behaviour and study samples [χ2 (df = 1) = 21.83, P < 0.05, phi = 0.10]. Substantially more respondents reported an illness in Study 1 (34.3%) than in Study 2 (i.e. 17.5%). Clearly, inconsistencies exist in the health data which indicates that care should be taken in using sample surveys.

Introduction
This paper examines the accuracy of a national survey, assesses the usefulness of using a sub-national sample survey to understand the national survey, and attempts to act as a guide to fellow researchers. The article used self-evaluated data from older men on their health status and seeks to elucidate whether self-evaluated health status changes with the dichotomization process. Since 1844 census taking has been an irregular decentenial event in Jamaica (with none done in

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the 1930s). Statisticians and researchers have performed many inter-censual ratios on the data which showed a high degree of accuracy.1 This occurrence is also the case in North America, Britain, Japan, India, Africa and Europe.1 Vital registration statistics (data on births, deaths, marriages) have been used for years in the computation of the life expectancy, health and prosperity of nations. Census-taking and civil registrations are highly expensive data collection processes, which accounts for the use of sample surveys. The first national sample survey was in 1953 to aid inter-censal planning. The use of survey data by nations in planning denotes that planners, in particular researchers, rely on the completeness and accuracy of the data. Sample surveys are widely utilised to examine social conditions. They are also used for much more than the understanding of social conditions, including life expectancy, mortality patterns, fertility, termination of marriage, population projections, other demographic computations and health statistics.1-6 Unlike a census, a survey collects standardized data from a specific population with the purpose of generalizing this to a wider population.7 In the sampling and data collection processes, errors are highly likely to enter into the data. Quality of sample survey data is important for more than the accuracy of using sampling design in a particular task. The guidance that sample survey methods provide to researchers is embedded within people. It follows that sample surveys must rely on the accuracy of recall and the truth of information provided by research participants. This information not only influences people socially but it impacts on the quality and quantum of their lives. It is within this context that the accuracy of sample surveys is crucial to researchers, policy makers, and non-academics as they seek to enhance the quality and quantum of human experience. This mindfulness requires that researchers take into account the broadest possible range of reasons within the parameters of the research that brings validity and reliability into disrepute.
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Jamaica is among many countries that collect sample survey data to guide, formulate, assess and understand their populations. In 1988, Jamaica began collecting sample survey data on the living standard of its people. The survey is referred to as the Jamaica Survey of Living Conditions (JSLC). The JSLC is a modification of the World Bank’s Living Standards Measurement Study (LSMS) household survey, and provides policy makers, including the government, with vital information on policy implementations and their effect on the living standard of people. Health, which is more than disease,8 means that the JSLC coverage is comprehensive enough to allow for the assessment of the health of Jamaicans. Using Jamaican Ministry of Health Annual Reports on the actual visits made to health care facilities as well as visits for curative care, Table 1 shows that on average 30% of males visited health care facilities and 34% received curative care. However, survey statistics for the same period showed that on average health care visits for males were 62% and self-reported illness was 10%.9 This highlights inconsistencies in the data sources. Within the context of disparities which exist in the data sources, it brings into question the reliability and validity of health survey data, which are collected from males in Jamaica. A critical assessment of the literature has revealed that there is a paucity of research investigating the validity of sample survey data in the Caribbean in general and Jamaica in particular. The Caribbean, like many other regions, has come to rely on sample survey data in government planning as well as health planning. People’s lives therefore cannot be based on inaccuracies from sample survey data, and so an examination of the accuracy of surveys will allay many of the fears of critical stakeholders and non-researchers. Validity is vital for understanding and interpreting studies already published, as well as guiding new studies and survey approaches. The objective measurements are infrequently used to validate costly
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questionnaires such as the JSLC. Agreement with reality trumps reliability and validity, where without question the data is taken as accurate over time, and it is accepted that the instrument is measuring what it purports to measure. The current study examines the accuracy of the 2007 JSLC by using another independent sample survey in the same period. The objectives of the present research are to (1) investigate the accuracy of a national sample survey, (2) explore the feasibility and quality of using a subnational sample survey to represent a national survey, (3) aid other scholars in understanding the probability of using national sample surveys and sub-national sample surveys, (4) assess older men’s evaluation of their health status, and (5) determine whether dichotomization changes selfevaluated health status.

Methods and Materials
Data For the current study, the data used in the analysis were originally collected in 2007 from two different studies: (1) the Jamaica Survey of Living Conditions (JSLC) and (2) the Survey of Older men (SOM). In order to cross-validate self-evaluated data from men in Jamaica, because complete data were available from JSLC and only data on older men (ages 55+ years) from SOM, participants 55+ years were selected from each sample, as this was comparable in both samples. Two thousand, four hundred and eight-three were used for the current study: in Study 1 (i.e. JSLC) 483 participants and 2,000 participants in Study 2 (i.e. SOM). The mean age in Study 1 was 67.7 years (SD = 9.3 years) and in Study 2 it was 67.0 years (SD = 8.2 years). Urban dwellers comprised 47.0% (n=227) in Study 1 and 49.1% in Study 2 (n = 981) compared to 53.0% and 50.9% in rural areas respectively.
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Sample Study 1 Data from the Jamaica Survey of Living Conditions (JSLC) for 2007, commissioned by the Planning Institute of Jamaica and the Statistical Institute of Jamaica, were used to provide the analyses for this study.9 These two organizations are responsible for planning, data collection and developing policy guidelines for Jamaica, and they have been conducting the JSLC annually since 1989. 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.10 The sample for this study was 1,343 respondents who are classified as being the poorest 20 percent in Jamaica (or the poorest). The JSLC used a stratified random probability sampling technique that was drawn to the original sample of respondents, with a non-response rate of 26.2%. The JSLC survey was based on a complex design with multiple stratifications to ensure that it represented the population, marital status, area of residence and social class. The sample was weighted to reflect the population. The instrument used by the JSLC was an administered questionnaire where respondents were 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 the JSLC is more focused on policy impacts. 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 were trained to collect data from household members.
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Study 2 The study used primary cross-sectional survey data on men 55 years and older from the parish of St. Catherine in 2007 (for May and June); it is also generalizable to the island.11-13 The survey was submitted and approved by the University of the West Indies Medical Faculty’s Ethics Committee. A stratified multistage probability sampling technique was used to draw the sample (2,000 respondents), and a 132-item questionnaire was used to collect the data. The instrument was sub-divided into general demographic profiles of the sample, past and current health status, health-seeking behaviour, retirement status, social and functional status. The Statistical Institute of Jamaica (STATIN) maintains a list of enumeration districts (ED) or census tracts. The parish of St. Catherine is divided into a number of constituencies made up of a number of enumeration districts (ED). The one hundred and sixty-two enumeration districts in the parish of St. Catherine provided the sampling frame. The enumeration districts were listed and numbered sequentially and selection of clusters was arrived at by the use of a sampling interval. Forty enumeration districts (clusters) were subsequently selected with the probability of selection being proportional to population size (Table 14.14.2). The sample population does not only speak to the parish of St. Catherine; it is generalizable to the island of Jamaica. The sampling frame was men fifty-five years and older in the parish of St Catherine, and this parish was chosen as previous data and surveys11-13 suggested that it had the mix of demographic characteristics (urban, rural and age-composition) which typify Jamaica. Enumeration districts (ED’s) consisted of not more than 400 households, and they were used as primary sampling units (PSUs). Interviewers were trained by University of the West Indies staffers (i.e. Department of Community Health and Psychiatry) and large groups were
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sub-divided into smaller groupings with a supervisor who monitored the interviewers in an effort to maintain accuracy. All interviewers were given a map of their respective EDs and they were taken across the geographic boundary of that ED. Stratified random sampling was used to predetermine those who should be interviewed from particular households. Enumerators commenced at a fixed point as was stipulated by the Statistical Institute of Jamaica (STATIN) and the interviewers proceeded based on their map of the predetermined persons in a clockwise direction. This approach was used in order to exhaust the EDs. In the event a chosen participant from a household did not wish to participate in the interview, the interviewer would go to the next identified household on his/her map. For males 55+ years who were not at home when the interview was being conducted, a maximum of 3 callbacks were used in order to establish a link for a possible interview. In cases where the interviewer had exhausted all the call-backs and the participants were still unavailable, a replacement was used from the adjacent household assuming that the person satisfied the criterion of the study (i.e. male 55+ years). A strict definition of the household was used as a measure of standardizing those who should be interviewed for the study. A household was where any individual slept in the dwelling for at least 3 nights and ate at least one meal per week from the same pot as other individuals. Hence a resident for selection had to be male 55+ years that slept in the same dwelling as other individuals for at least 3 nights and ate at least once a week from the same pot as other individuals in that dwelling. Validity The current study validated the self-evaluated health data used in the JSLC by using a subnational sample survey which was collected during the same time as the former survey. The JSLC questionnaire could not be assessed as it would be expensive to do so, and the objective of the latter study sought to examine the health status, health literacy, health decisions and typology
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of dysfunctions that older men have. Both studies used a 5-point self-rated health status question (Generally, how would you rate your current health status?). The responses ranged from excellent (i.e. very good) to very poor and this allowed for the validation of health status.

Reliability One of the purposes of using matching studies is to examine content errors which assess the reliability of the data sources.1-4 Testing the consistency of information derived from the National Survey (i.e. JSLC) will be done using a survey of older men (ages 55+ years). The older men study was conducted in St. Catherine and this will be used to assess the consistency of the information in the National Survey. Consistency (or inconsistency) was evaluated by using chisquare analysis. If there was no association between the variable and the different sample, then the information in one survey was consistent with the information of the other survey. Conversely, statistical association denoted inconsistency of results. Ethics No ethical clearance was sought for the Jamaica Survey of Living Conditions. However, one was sought and obtained for the sub-national sample survey. The University of the West Indies Ethical Board approved the sub-national sample survey, and each participant was given a written informed consent prior to his/her participation. Statistical Analysis Data were stored, retrieved and processed using SPSS for windows 16.0 and a 5 percent level of significance was used to test significance (i.e. 95% confidence interval). Descriptive statistics were used to provide background information on the samples. Validity was assessed by comparing levels of self-evaluated data on (1) area of residence; (2) age group; (3) health status;
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(4) marital status; (5) household heads; (6) medical care-seeking behaviour and (7) selfdiagnosed illnesses. The researcher also used chi-square to measure associations between the two sample survey results. A statistical association from a chi-square result should be interpreted as differences between Study 1 (i.e. national sample survey) and Study 2 (i.e. St. Catherine sample survey). On the other hand, no relationship should be interpreted as demonstrating any difference between the aforementioned study samples. Contingency coefficient and chi-square were used to examine the statistical association between variables.

Measurement of variables Self-rated health status is measured using people’s evaluation of their overall health status, 14 which ranges from excellent to poor health status. 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 purposes of the model in this study, self-rated health was coded as a binary variable (1 = good and fair 0 = Otherwise) (also see studies that have treated self-rated health status as a binary variable).15-20 Age is a continuous variable which is the number of years alive since birth (using last birthday). For the present study ages range from 55 years and older. Data errors for this work are classified into two groups: coverage errors and content errors. Coverage errors arise due to incompleteness of inclusion of people in a data system.2, 4 This includes misplacement of events in a time or when events are incorrectly classified in one defined boundary, when there should have been an estimate in another defined unit. Content errors denote inaccuracy in the characterization of recorded units in a data system.2, 4 Sampling errors denote negative errors of failure to include elements that should properly belong to a sample against a population. These arise owing to coverage errors. Non-sampling errors are all

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errors which are theoretically outside of those caused by sample against a population. These include (1) non-response, (2) content errors, and (3) interviewers’ biases.

Results
Demographic characteristic of sample The sample was 2,483 respondents [483 for the national survey (i.e. Study 1) and 2,000 for the sub-national survey (i.e. Study 2)]. In Study 1, the mean age was 67.7 years (SD = 9.3 years); while the mean age in Study 2 was 67.0 years (SD = 8.2 years). In Study 1, 50.2% of respondents indicated at least good self-evaluated health status compared to 74.0% in Study 2 (Table 14.14.3). In Study 1, 34.3% of the sample indicated an illness compared to 17.5% in Study 2. The percentage of respondents who indicated having sought more medical care was also more in Study 1 (i.e. 65.5%) than in Study 2 (i.e. 45.7%,(Table 4). The bivariate analysis follows. Bivariate Analyses There was no association between area of residence and study used [χ2 (df = 1) = 0.66, P > 0.05]. This was also the case for age and study sample [χ2 (df = 5) = 8.66, P > 0.25]. However, relationships were found between (1) marital status and study sample [χ2 (df = 4) = 15.38, P < 0.01, contingency coefficient = 0.08] and (2) household head and study sample [χ2 (df = 1) = 33.71, P < 0.01, phi = 0.12]. Furthermore, for Study 1, 78% of respondents indicated that they were heads of their households compared to 88% of those in Study 2. In regard to marital status and study sample, 10% of those in Study 1 revealed that they were in common-law unions

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compared to 7% of those in Study 2. Similarly percentage point disparity was found in separated unions (i.e. 3% in Study 1 and 6% in Study 2). A cross-tabulation between self-evaluated health status and study sample revealed a statistical association [χ2 (df = 5) = 380.34, P < 0.001]. The relationship between the two variables was weak (contingency coefficient = 0.37 or 37%). Substantially more respondents in Study 2 indicated at least good health compared to those in Study 1. The converse was equally true as more people in Study 1 reported at least poor health compared to those in Study 2. When self-evaluated health status was dichotomized, i.e. good versus poor health (with moderate or fair health being included in poor health], the relationship between it and the study sample became weaker (i.e. phi = 0.21 or 21%; χ2 (df = 1) = 105.68, P < 0.05) than when health status was nondichotomized. When self-evaluated health status was dichotomized, 49.8% of respondents in Study 1 indicated moderate-to-very poor health status compared to 26% of those in Study 2. A statistical relationship was found between medical care-seeking behaviour and study samples [χ2 (df = 1) = 21.83, P < 0.05, phi = 0.10]. In Study 1, 65% of respondents claimed to have gone to seek medical care compared to 46% in Study 2. Likewise an association was found between self-reported illness and the study sample [χ2 (df = 1) = 65.84, P < 0.01, phi = 0.16]. Substantially more respondents reported an illness in Study 1 (34.3%) than in Study 2 (i.e. 17.5%).

Discussion
Accuracy of national surveys and sub-sample surveys A number of scholars as well as the Statistical Institute of Jamaica have found and purported that stratified sampling of the parish of St. Catherine is a good measure as a
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representative sample of the wider Jamaican population.1-13 They found that St. Catherine has a diverse population with congruent characteristics similar to the wider Jamaican population. The current study found that while a sample of older men in St. Catherine had similar characteristics to the national sample (Jamaica Survey of Living Conditions), some disparities still exist between the two samples. If a sample of St. Catherine is similar to that of Jamaica, then there must not be any disparity in medical expenditure, health status, marital status, being the household head and self-reported illness. In fact, this study found that even among some demographic characteristics like household head and marital status, differences were there between both surveys. The non-validation in some of the demographic characteristics for both surveys was also found in the self-evaluated health data. The current research found that there was a difference between the self-evaluated health data for the St. Catherine and the national sample. In the St. Catherine sample, none of the respondents indicated having poor health, compared to 18.4% of those in the national sample. The disparity was more so in the category of good health. In the national sample, 37.3% revealed having good health, compared to 55.4% in the St. Catherine cohort. This denotes that 1.5 times more respondents indicated that they had good health in the latter sample, suggesting that there is either overstatement or understatement in describing health status among older men in Jamaica. While the current study does not accept that any one of the two samples is correct over the other, it is evident from the significant inconsistencies between the two samples that health data from older men is incorrectly reported by them. Embedded in the health data from older men therefore are non-sampling errors4, 21-24 from a finite population, suggesting that public health planning with inaccurate health data will yield low quality health outcomes.

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Quality of national surveys and sub-sample surveys, and health status of older men Among non-sampling errors is attitudinal information.1-5 Demographers like Colin Newell4 believed that despite the possibility of extensive training for sample data collectors, their attitude and appearance can affect the quality of the information that they receive (or do not receive) from the interviewee. Caribbean societies, in particular Jamaica, have not been examining the quality of data collected owing to attitudinal biases. Many of the data collectors in Jamaica are females, and within the context that males do not want to appear weak, effeminate or sickly, males reporting illnesses to females clearly are distorting the quality of the data. One Caribbean anthropologist argued that Caribbean males are socialized to be tough, strong, and display no signs of weakness.25 Another Caribbean scholar opined that sickness is interpreted by Caribbean males as a signal of weakness, 26 which justifies their reluctance to speak openly about illnesses. Males’ unwillingness to speak about illnesses crosses gender types, as they must preserve their masculinity both among other males as well as females. Some non-Caribbean researchers found that only 10.5% of men who suffer from erectile dysfunctions sought medical care, 27 suggesting that males prefer not to speak about or display signs of weakness. Illness which is an indicator of weakness for males means that health careseeking behaviour is usually a last resort, and is most times used in case of severity of illness.28 Statistics from the Ministry of Health (Jamaica) showed that on average 30 out of every 100 males sought medical care, which denotes that older males were substantially under-reporting their illness in the St. Catherine study. Although 34 out of every 100 older males sought medical care as indicated by the national survey, within the context that there is a positive relationship with ageing and poor health status, it can be extrapolated and projected from the Ministry of
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Health (Jamaica) data29, 30 that this group should not have indicated only 4% more dysfunctions compared to the general population. The definition of illness for many Jamaicans does not include the common cold or an upset stomach, to name a few conditions, as these can be addressed by using a home remedy. Of Jamaicans who reported an illness, statistics showed that 30.2% utilized a home remedy, compared to 28.4% of males, and at least twice more females seeking medical treatment, 9 which highlights the role of culture in defining and changing health care seeking in Jamaica. If illnesses do not disrupt males’ economic livelihood, many of them are highly unlikely to seek health care as they do not see the need to attend at traditional medical facilities, thinking that the matter can be rectified at home as they are not ill enough. This is not exclusive to Jamaica, as in Pakistan27 young males were more likely to seek medical care only if their illness interfered with their economic livelihood. This explains why many males in Jamaica on average spend more time receiving medical care than females, 9 and accounts for the higher mortality,12 as during the time it takes them to visit health care institutions the dysfunction would have increased to being chronic, untreatable and incurable, thus making it highly unlikely for medical practitioners to make a difference. The culture therefore retards many Caribbean as well as nonCaribbean males from truthfully reporting health matters, and the fact that females are collecting information from them about health matters further accounts for the increased non-sampling errors (i.e. inaccuracies in data as they under-state dysfunctions to create an impression of strength which is tied to their perception of health). The findings from the present research revealed an exponential disparity between selfevaluated illnesses between the two samples. Approximately 2 times more older men reported illnesses in the national survey compared to the St. Catherine survey. Therefore, there is a
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difficulty in validating the self-evaluated health data collected from older men in Jamaica. Statistics from the Ministry of Health (Jamaica) showed that 34 out of every 100 males received curative care, and with the same number from the national survey, it follows that in the St. Catherine study there were substantially more under-reported illnesses. While we can extrapolate an exact value for the number of older males who received curative care within the biology of an organism, as the body ages this is associated with increased illness and reduced function, and therefore the researchers suggest that a greater percentage of older males should have reported an illness that was higher that the national average. The reality of the unreliability and invalidity of health data is further highlighted in the self-reported typology of health conditions between the two sample surveys. In both sample surveys there was no consensus on the typology of dysfunction. In some cases the disparities were huge as was evident for arthritic cases. One percentage point of respondents indicated that they had arthritis in the St. Catherine sample compared to 28% in the national sample. It should be noted that statistics from the Ministry of Health (Jamaica) between 2002 and 200629, 30 showed that females received 2 times more curative care than males. However, selfreported data from the Planning Institute of Jamaica and the Statistical Institute of Jamaica showed that 1.5 times was the greatest disparity, with females reporting having more illnesses and this was 1.4 times more in 2007 (17.8% of females to 13.1% of males). On the other hand, statistics from the Ministry of Health (Jamaica) on curative visits showed that since 2000 an average of 34% of males received care.29,30 This further re-enforces the inaccuracies in selfevaluated health data provided by males in which the case is using elderly samples for two different sources over the same period. Emerging from the study is that the inaccuracies are not limited to older men but that this is generalizable to the populace of males across the nation.
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Another area in which disparity was found is in medical care-seeking behaviour. In the national survey 66% of older men reported that they visited health care practitioners compared to 46% of the sample in the St. Catherine study. For 2000 and 2006, statistics from the Ministry of Health (Jamaica) showed that approximately 30% of males had been visiting health care agencies. Accompanying ageing come increased visits to medical care facilities; but the figure of 66% of older men seeking medical care as revealed by the national survey seems rather high within the context of the socialization already discussed. The discrepancy may be due to the participants’ belief that that they should give government agencies the data they want rather than data that is correct. This further brings into question the quality of self-evaluated health data collected from males in Jamaica, and how future studies must be interpreted, ergo they must incorporate the findings of the current study in their analyses. Dichotomization of self-evaluated health status In the validation process of the health data what emerged is the loss of some of the original information from dichotomized self-evaluated health status. Using non-dichotomized self-evaluated health status, the relationship between this and the study samples was 37%, and when self-evaluated health status was dichotomized the association fell to 21%. This concurs with studies that found that it is better to use the continuous nature of self-evaluated health status than the dichotomized variable,31-33 as in the dichotomization process some of the original information will be lost. The current research showed that 16% of the original information is lost owing to the dichotomization process. This highlights a rationale for the non-dichotomization of self-evaluated health status in Jamaica, as data losses denote the lowering of the quality of health data, fostering challenges in policy implementation from a dichotomized health status.

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Some studies have found that dichotomizing self-rated health and using logistic regression is acceptable34-36 and many studies in Jamaica have followed this procedure,39-44 but clearly using this operational definition in examining the health of males will not produce the same interpretation, as some of the original information would have been lost. Recently a study by Bourne44 found that dichotomizing self-rated health is acceptable for females as there was minimal variance; however a great deal of variance was found in dichotomizing health for males. Another study found that when poor self-rated health status was narrowly defined (excluding moderate health), there was minimal impact on the estimated effects of the covariates45 and this was re-enforced by Bourne’s work.44 However, Bourne’s finding somewhat disagreed with Finnas’47 conclusions, as he found substantial disparity for males when health was classified from very poor-to-moderate compared to very poor-to-poor . Validity and reliability of using national surveys and sub-sample surveys Inaccuracies are found in the present study as already outlined but these exclude errors associated with coverage and content. The national survey (i.e. JSLC) undoubtedly used complex statistical techniques to design its sample and has reduced coverage errors. The JSLC updated its sample frame in 2007,9 which adds to the quality of coverage and further reduces coverage errors as more people would be included in the sample, in order to be better able to select a sample which is more representative of the population. By widening the sampling frame, negative errors of failure to include elements are reduced, as more elements that belong to the population will be included, and therefore can be selected for the national survey sample.46 But the quality of the sample coverage does not mitigate against content errors which appear to be present in the health data. Content error also plays a role in influencing sample outcomes and thereby the quality of data collection.1-4; 21-24 Content errors are a part of response errors and so cannot be neglected in
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the sampling process as they act jointly with coverage errors to lower the quality of data collection.46 It can be extrapolated that the inaccuracies found in the health data of older men cannot be neglected as this will influence health outcomes, the interpretations of those outcomes and intervention initiatives. This is also a public health challenge, as not having quality data denotes that policies will address inaccuracies and will further retard all forms of development in the nation. Surveys on health are among the epidemiological studies executed and they provide critical information on various health issues such as dysfunctions, duration of illness, hospitalization and self-rated health, among other variables. Validity of data assists with understanding the quality of health data and this is agreed with by many demographers1-4 and non-demographers in the Caribbean.44 Wilks et al.’s work47 examines the validity of nonresponse and concretizes the position that quality health data is based on precision in sample size and non-response, and the current study goes further to show that content errors will affect the outcome of the collected data. Interestingly, Wilks et al.’s study is among many researches that have embarked on sampling errors while avoiding the importance of content quality. Examining non-response errors assumes that content errors are non-existent or minimal, and even in Wilks et al.’s work within the context of the current findings there are content errors, as the study collects data from males who are less likely to report quality information on their health status. Empirical studies have established that the quality of data in developing countries is relatively low.1-4,
48

Jamaica is a middle-income developing nation in the Caribbean with a

history of high quality data from statistical data sources. Using intercensal surveys and demographic ratios, it has been shown that the data collected in the censuses and the Jamaica Survey of Living Conditions are of high quality. The longstanding nature of data collection and
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the continuous updating of the sample frame have aided in the reduction of sampling errors and in the process have reduced coverage errors.9, 11 In spite of the efforts of the statistical agency to reduce sampling errors, content errors have still been found to be present in the data; this is more so a gender phenomenon. Inconsistencies in health data collected from males showed that data collected from them are not accurate and cannot be relied on. This raises the question of the incentive for males to truthfully report on their health. Yates22 purported that people can have motives that retard them from providing or revealing the truth. Studies on the reliability and validity of data sources in developing nations continue to emphasize the reduction of coverage errors (i.e. sampling errors). While this is important in data quality, content errors have been substantially left unexplored as a means of providing explanations for the low quality of data in those developing nations. In the Caribbean, like many developing countries, males are socialized to be strong, brave, macho, not to show emotion and not to display weakness, which explains their unwillingness to visit health care institutions for mere checkups and speak openly about illnesses affecting them. The issue of a motive that would account for their unwillingness to speak the truth about health matters is therefore embedded in the (1) culture and (2) definition of illness and its interpretation regarding their status. Males are sufficiently socialized to suppress weaknesses and within the context of those societies, they must exhibit to females that they are strong, brave, and healthy which explains their incorrect response related to health matters when asked by females. Yates, (cit.) while not stating that those matters are exclusive to males, provided us with justification for low data quality in the event that those issues are present in a sample. There are several reasons that may explain the problems with the reliability and validity of the health data in the present study. There is the case of social desirability bias where the
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participants say whatever is required to get the approval of the interviewers. Some participants do not even care about getting social approval - they just want to help the researcher so they tell the interviewers what they think will help them. The possibility of collecting inaccurate data increases when government agencies are involved because of the declining trust that citizens have in government and public institutions in Jamaica. The data given may reflect a rejection of governmental authority and status. The converse may also be true where a researcher who is unaffiliated to a government agency receives accurate data. There is also the issue of the time when the interviewer seeks to interview males because this can adversely affect the data provided if the interviewers are competing with the important social and recreational times and activities of the men. Males are culturally competitive which makes for strength, dominance, physique and endurance critical to composition25 that will be used to indicate to other males that they are healthier, superior and stronger than the next competitive male. The challenge therefore is how do researchers develop an approach to collect data from males in which they have no motive to conceal the truth, and give accurate answers; and concurrently ensure that interviewers’ biases can be eliminated, or minimized so that data quality is not reduced in the data collection process. The challenge of mailing questionnaires to males in developing countries, in particular Jamaica, is that the response rate would be very low and possibly so minimal that data analysis would become problematic in providing pertinent information. The low reliability and validity of health data collected from males poses much public health context as they experience the greatest mortality, and not understanding their health is to further challenge public health practitioners and policy makers to institute measures that will mitigate against their wellbeing.

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To our knowledge a mail survey has never been conducted in Jamaica. We have reservations about the likely success of this kind of survey as mentioned before, especially with men who are already under-reporting their health status. Despite our reservations, the best way to know if a mail survey will work is to do one. However, there should be in-built incentives to increase the response rate. The pin number for a specified dollar amount of cell phone credit should be sent to the cell phone of participants whose completed questionnaires are received in the mail. The foregoing possibility highlights the fact that telephone surveys are also an underutilized method of data collection in a country where cell phone usage is widespread. The use of the cell phone has the advantage of allowing the participants to talk to the interviewers at any place and time that is convenient to them, which should improve the response rate. The validity and reliability would also be enhanced if the telephone interviewer is male. Survey researchers more often than not do not use a mixed method research design. Sometimes the discrepancies within a survey and between surveys are reduced by qualitative methods such as individual interviews, focus groups and participant observation among other methods. These methods will repopulate and enrich the text by writing back the individuals and their characteristics into published research while maintaining the use of regular statistical procedures.49 No one research method has a monopoly on reality so researchers should be eclectic in their methodological approach while being mindful that a bundle of techniques is not synonymous with intellectual sophistication and clarity.50 Reliability and validity can be also be enhanced and discrepancies explained by the recognition that keeping things simple is best and doing less is more; there is greater clarity in using fewer variables for more highly targeted research problems. Health researchers should also be willing to question what was taught about the existing research methodologies and statistics.51
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In addition, future health researchers should take account of the role of mediator and moderator variables in influencing the relationship between the independent and dependent variables, because the measured influence of mediator and moderator variables can sometimes explain the discrepancies between surveys.52

Conclusion
The current study finds that there are many inconsistencies in health status data collected from older men in Jamaica. Generally, while this work did not examine males in Jamaica, using statistics from the Ministry of Health (Jamaica), it appears that the findings can be extrapolated to males. The wider implications for these findings are the challenges of using self-evaluated health data from males in planning their health, and that currently we do not understand men’s health. In researching men’s health the question is not simply to validate the instrument, but there are challenges in data collection that are unresolved, and which increase non-sampling errors. Public health practitioners use self-reported health data from the national surveys and other sub-national surveys and they should understand the challenges faced in interpreting health data on males. Quality health data from males are not produced by them reporting on their health status in national or sub-national surveys, as a part of this problem is the data collectors. Studies have not examined the influence of sex composition on inaccuracies in health data, and this is clearly causing some noise in health statistics. The quality of health data in Jamaica, and by extension all nations, is influenced by the attitudes of respondents towards data collectors, the circumstances surrounding the interviewer, the culture and the belief system of the respondents. These continue to interface with health data quality and are still under-studied in the Caribbean as a part of the explanation in understanding men’s health. This should be a public health concern like epidemics, pandemics and sanitation, as poor quality data will affect policies,
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programmes and the implementation of strategies in alleviating particular health concerns faced by people, in particular males. Improvement in quality of life through better health care must also integrate better quality data collection, as quality care requires accurate health data. Jamaica is a middle-income developing nation in the Caribbean that has been collecting data for centuries, and it boasts 20 years of collecting data on health status. Accompanying the collection of data for a long time is the efficiency and accuracy in using statistical techniques for gathering data. The Planning Institute and the Statistical Institute of Jamaica have continued to modify their sampling frame in an effort to reduce sampling errors. The widening and updating of the sampling frame have reduced coverage errors, as more people will be captured in national sample surveys. The current study has found that there are still errors in the quality of the health data collected from males, despite updating the sampling frame in 2007 in an effort to attain completeness of data coverage. Despite the afore-mentioned errors, the quality of the national survey, within the context of this study, is moderately good, and care should be taken in interpreting health data for males owing to the inconsistencies which emerged from this study. It is clear from the inconsistencies in the health data collected by the relevant agencies that the reliability of self-reported health data from males will pose a problem in public health planning. Sample surveys are used for teaching health care professionals; examining health care staff requirements; community health care; planning health care; planning and determining the future care of patients; evaluation of public health policies; health care interventions; the construction of community centres, hospitals and public clinics; and clinical and health service provisions. Then there are two other issues that emerged from the present findings, firstly, as dichotomizing self-evaluated health for males loses some of the original information, and secondly, that a sample of St. Catherine is not the same as sampling the nation, and so a sample
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from the parish of St. Catherine does not reliably reflect the detailed characteristics of the wider Jamaican population. Thus, care should be taken in the usage of sub-national samples to generalize about a population and more so when it comes to data collected from males in regards to their health. Clearly, there are inconsistencies in the health data collected from men in surveys, and this needs to be factored into their health intervention, and planning for their health status. These findings can inform further surveys, and should stimulate an approach of how to collect data from males on their health status. If public health is to rely on research in order to effectively implement and attain its objectives, the data collected should be reliable and valid, and the current findings must be taken into consideration in aiding the process.

Disclosures The authors report not conflict of interest with this work.

Acknowledgement
The authors 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 (Jamaica Survey of Living Conditions, 2007) available for use in this study.

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Table 14.14.1. Health and curative care visits: 2000-2007 Year Health Care Visits Male 2000 2001 2002 2003 2004 2005 2006 2007* 30.5 30.6 30.3 30.3 30.2 30.4 30.4 30.5 Female 69.5 69.4 69.7 69.7 69.8 69.6 69.6 69.5 Female: male 2.3:1 2.3:1 2.3:1 2.3:1 2.3:1 2.3:1 2.3:1 2.3:1 Curative Care Visits Male 35.0 34.6 34.2 33.5 32.9 33.3 33.5 33.6 Female 65.0 65.4 65.8 66.5 67.1 66.7 66.5 66.4 Female: male 1.9:1 1.9:1 1.9:1 2.0:1 2.0:1 2.0:1 2.0:1 2.0:1

Figures were computed by Paul A Bourne from Jamaica, Ministry of Health (Jamaica) Annual Report 2004 and 2006 *Preliminary data from the Jamaica Ministry of Health were used to compute those percentages and ratios.

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Table 14.14.2: Proportion of Survey (Sample) vs. Proportion of Population Age 2001 Census (St. 2001 Census Survey Group Catherine) (Jamaica) (yrs). 55-59 60-64 65-69 70-74 75-79 80+ n 469 413 374 345 189 210 % 23.45 20.6 18.7 17.2 9.45 10.5 n 6577 5179 4391 3594 2402 2399 % 26.7 21.1 17.8 14.6 9.78 9.77 N 38645 31828 28901 24856 17711 19552 % 23.9 19.7 17.9 15.4 11.0 12.1

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Table 14.14.3. Descriptive characteristic of samples: Sub-national and National surveys Characteristic Sub-national survey National survey (i.e. Study 2) (i.e. Study 1) n = 2,000 % n = 483 Area of residence Urban 981 49.1 227 Rural 1019 51.0 256 Age group (in years) 55-59 469 23.5 120 60-64 413 20.7 87 65-69 374 18.7 88 70-74 345 17.3 68 75-79 189 9.5 61 80+ 210 10.5 59 Marital status Single 686 34.3 150 Married 894 44.7 217 Separated 112 5.6 14 Common-law 136 6.8 49 Widowed 172 8.6 42 Household head Yes 1763 88.2 376 No 237 11.8 107 Self-rated health status Excellent (or very good) 357 19.0 61 Good 1038 55.4 177 Fair (or moderate) 480 25.6 149 Poor 0 0.0 75 Very poor 0 0.0 12 Self-evaluated diagnosed illness Cold 14 Asthma 5 0.3 8 Diabetes mellitus 129 6.5 24 Hypertension 193 9.2 24 Arthritis 20 1.0 46 Diarrhoea 2 Other: Unspecified 22 Other: Cancer 336 16.8 Heart disease 106 5.3 Kidney/bladder 118 5.9 Prostate problem 143 7.2

% 47.0 53.0 24.8 18.0 18.2 14.1 12.6 12.2 31.8 46.1 3.0 10.4 8.7 77.8 22.2 12.9 37.3 31.4 15.8 2.5 8.5 4.8 4.8 14.5 27.9 1.2 13.3

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Table 14.14.4. Characteristic of samples: Sub-national and National surveys Characteristic Sub-national survey National survey (i.e. Study 2) (i.e. Study 1) n = 2,000 % n = 483 % Medical care-seeking behaviour Yes 914 45.7 106 65.5 No 1086 54.3 58 34.4 Sought medical care In less than 12 months In 12 to 35 months In 36 and beyond months Provision of care Home remedy Public clinic Hospitals Private doctor Self-evaluated illness Yes No
NS – Not stated (i.e. was not collected in this Study)

289 356 269

31.6 38.9 29.4

NS NS NS

NS NS NS

155 124 40 31

44.3 35.4 11.4 8.9

66 73 149 195

13.7 15.2 30.8 40.3

350 1650

17.5 82.5

162 310

34.3 65.7

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Part III

Data Quality: Practices, Perspectives and Traditions

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CHAPTER

15
Practices, Perspectives and Traditions
Data quality is a philosophy, theory, principle, legacy which underpins science and the pillows upon which intellectual inquiry are based. The science is science, because of the processes, systematic rigors, logic and verification of data. It is the final estimates, results and/or findings that are lauded as science, but the underlying issues are hidden in the systematic processes that validate the truth. The methods of discovering the truths are hinged on the scientific methods applied to the observations, events and subjects (i.e. data sources). Each day we are systematically processing data, before decisions are taken. The better the information, the less likely it is to make errors. There is a fundamental assumption in the aforementioned issue; quality information is all we need to make more accurate decisions. This is simplistic and naïve. The world is continuously revolving around data, data and more data, these are quality data. Every time we engage in thinking, we are process data. The knowledge that emerged from the data sources is limited to the quality of the data. Data collection, therefore, holds the key to understanding different cosmology, providing clarifications for all epistemologies and is the primary source for scientific discoveries. While it is simply undeniable that NASA, the defense force, meteorology, judiciary, politics, and banking and insurance industry rely on data sources, the quality of the data source is equally important as the estimates, outcomes and purpose of the data gathered. Embedded in data usage is the assumed accuracy of the material that is sometimes overlooked by people, because of the past contribution of the data sources. Apart of the rationale for the justification of the blind usage of data from ‘credible sources’ is low statistical skills of some researchers. Some people who use data do not the prerequisite statistical techniques to identify errors, and correct them. As such, they are slaves to alleged credibility of the data sources instead of using the scientific method of data gathering and data verifications. Many people have not done an introductory course in statistics and/or demography, making them unexposed to the techniques (or tools) for indentifying and correcting errors in data. Within the limitations of their statistical skills, it is easier for them to rely on the establishment for data quality than validating the data as well as the estimates. In many
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instances, the traditional accuracy of data collection are determined by tradition, authority, credibility and reputation of the data gather instead of using the tools that are available to demographers and statisticians for aiding the quality of data. This does not imply for one minute that unreliable data have been used by non-demographers and/or statisticians, but that trustworthiness is used as an avenue in the pursuit of truths, because some people give into the establishments (i.e. authority, credibility, traditions, and titles) as against the scientific methods in process of knowledge building. It follows that the questioning of cosmologies, epistemologies, traditions and authority are healthy in validation of things or the creation of new paradigms. Truth cannot hide for testing, validation and question and still claim truth, fact or soundness. It is the rigorous testing of issues that establish truths, not the failure to systematic question ‘What is?’ It has been repeatedly proven facts change because of new data that justify a different knowledge. With the likelihood of modifying a paradigm or the emergence of an alternative paradigm, truth as continuously altered and recreated to meet the new data. Data validation cannot be taken with scant regards as the science of everything is hidden in the data, making quality data a priority and not an afterthought. The quality of the data speaks to the quality of the estimates, the contribution to knowledge and the unbiased fear will all for the opening up of the data, data processes, method and estimates to scrutiny. The same quality of time that is invested in the estimates must be employed into the data collection, validation, and testing. The coverage and content of the data must be open to scrutiny, for others to confirm, refute or question the accuracy of the estimates.

The methods of evaluating data quality are more taught to economists, demographers, epidemiologists and to a lesser extent undergraduate student, which limits the likeliness of people investing in data quality as they spend in data collection, for policy planning. As a result of the mathematical complexities in errors identification and correction, the average person is oftentimes ignorant of the techniques but may have some intuition that data quality must be examined with the same ferociousness as the estimates. The mere assumption of credibility, authority, establishment, cosmology and credentials cannot be used to determine the quality of data, thereby making people vulnerable to processes of science and not relying on the sideshow. Owing to the aforementioned realities, in addition to the blind beliefs, there is a high reliance
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‘common sense’, tradition, establishment (including universities, WHO, PAHO, NASA, World Bank) as the ultimate source in reliable estimates that some people fail to think that those agencies’ estimates can be questioned for accuracy. Science is established on data, not on credence, character, status, past traditions, authority or fame. It is also a gradual development hold onto the tenets of verification, questioning and logic. The potency of the estimates (i.e. results or outcome) is fashioned by the quality of the data, data gathers, and rigors adhere to during the scientific process of inquiry. The practice of researchers is to questioning the estimates, data sources, data system, apparatus, method, and biases that are likely to result in a particular outcome. The practices, therefore, is understand the set of propositions that led to the outcome, question the scientificness of the process and any errors that can create erroneous findings. The perspective must be that the data cannot be flawed but the estimates are accurate or vice versa. Whether data collection is via way of census, sample, retrospective, longitudinal, crosssectional or other approaches, if you collect data from human subject based on their perspective and/or recall, because the human element is present there is a probability of error in the data, estimates and predictions. It is this perspective that justifies data quality inquiry. There is a long tradition that errors can be present in national registration systems (i.e. births, deaths, marriages, migrations) as they relate to coverage (completeness), then content errors are likely, inaccuracy in data because of the quality of human recall or any other external barriers. Empirical evidence supports content errors in data collection. For centuries, demographers have been examining age values in sample and/or census to determine the probability of content errors. This is also the case in Jamaica. Demographers noted that there are age misreporting in census and national survey data in Jamaica, but limited content errors to age data. The majority of data collected to aid social scientists are from recall of people, memorization, perspectives, and responses of the subjects. The primary assumption here is that people recall is good. Researchers have shown that the recall beyond a particular time interval may be different. The use of any method to collect data on recall is susceptible to errors (i.e. content errors). Then when we ask people to recall their health, health conditions, and other sensitize matters, how sure are we that they accurate report the data and that the data are not erroneously stated, from which policies are frames.

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Jamaica is among Caribbean nations with a long history of high quality census and national survey data, but this begs the question ‘Can we further reduce the errors?’ This proposition does not eliminate the probability of errors, but that errors are present in current estimates. Such recognition accepts the biasness, subjectivity and likely errors that are associated with human recall, making errors identification and correction a scientific pursuit as the search for truths. The trust is bordered by time and quality data. Quality data here refers to good recall, good interviewers, good sampling frame, and an inbuilt mechanism to valid the entire process. This volume has empirically showed that data quality have variations, clarifications, amendments that influence estimates. The evidence is in that the quality of health data for female is higher than that for males, suggesting the degree of caution in interpreting the estimate of health data from males. Many recommendations were forwarded to address the challenges in health data for males; these can be incorporated into the research process to enhance the quality of the data and the resulting estimates. In data gathering, the human element is such that it can erode the positives of low coverage errors (completeness in sample selection). The benefits of using secondary data hold many negatives, which create challenges in accuracy of data. This raises the question of validity and reliability of data accuracy, knowledge, facts, truths, and cosmologies. Even in the validity of general data source as it relates to health matters, there are emerging issues about dichotomization, cut-offs and conceptualization of health from particular perspectives of Jamaicans – being male or female. Clearly Jamaican males and females do not have the same world view on health, affecting the data given and outcomes. These differentials must be, therefore, incorporated into interpreting health estimates and policy formulations.

Any thinking that supports the neglect of understanding the views of the studied population without being cognizant of the worldview is not gradually pursuing truth investigation, but it is on the path of indoctrination as was the case in time of religious cosmologies. The information of this volume can be used as a panacea to increase the estimates of policy formulation that rely on health recall data.

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Suggested Readings Sampling, Sampling Errors, and Other Errors in Survey Data

Biemer, P.A., R.M. Groves, L.E., Lyberg, N.A. Mathiowetz, and S. Sudman (Eds). 1991. Measurement errors in surveys. New York: Wiley-Interscience. Cox, P.R. 1976. Demography, 5th ed. Cambridge: Cambridge University Press.

Fink, A (Ed). 1995. The survey kit. Volumes 1-9. Thousand Oaks, CA: Sage.

Groves, R.M. 1989. Survey errors and survey costs. New York: Wiley-Interscience.

Kish, L. 1965/1995. Survey sampling. New York: John Wiley and Sons.

Preston, S.H., P. Heuveline, and M. Guillot. 2001. Demography: Measuring and modeling population processes. Oxford: Blackwell Publishers.

Siegel, J.S. 2002. Applied demography: Applications to Business, Government, Law, and Public Policy. Chapter 4. San Diego: Academic Press. Siegel, J.S., D.A. Swanson (Eds). 2004. The methods and materials of demography, 2nd ed. San Diego: Elsevier Academic Press.

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ABOUT THE AUTHOR

Paul Andrew Bourne is the Director of Socio-Medical Research Institute, Jamaica. He has co-written monographs on Corruption, Political Culture in Jamaica, Other subjects, and authored books on Growing Old in Jamaica, Analyzing Quantitative Data, Understanding Health and Health Measurement, and Sexual Expressions in Jamaica. Dr. Bourne has authored and co-authored plethora of journal articles on health status, health measurement, sexual and reproductive health, and ageing matters. His works have been published in top journals, and recently his thrust has been on data quality in national surveys, particularly in Jamaica.