Data Quality in Jamaica

Paul Andrew Bourne

i

Data Quality in Jamaica

Paul Andrew Bourne

ii

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.

iii

TABLE OF CONTENTS

page

List of Tables v

List of Figures ix

Preface x

Acknowledgement xii

Dedication xiv

PART I: HEALTH STATUS: USAGE OF HEALTH DATA 1

Introduction

1 A theoretical framework of good health status of Jamaicans: using econometric analysis to model good health status 5

2 An Epidemiological Transition of Health Conditions, and Health Status of the Old-Old-To Oldest-Old in Jamaica: A comparative analysis using two cross-sectional surveys 26

3 Self-evaluated health and health conditions of rural residents in a middle-income nation 56

4 Disparities in self-rated health, health care utilization, illness, chronic illness and other socio-economic characteristics of the Insured and Uninsured 83

5 Variations in social determinants of health using an adolescence population: By different measurements, dichotomization and non-dichotomization of health 113

6 Self-rated health status of young adolescent females in a middle-income developing country 140

7 Health of females in Jamaica: using two cross-sectional surveys 159

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

9 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 253

12 The validity of using self-reported illness to measure objective health 278

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

v

List of Tables page

Table 1.1.1: Good Health Status of Jamaicans by Some Explanatory Variables 22 Table 1.1.2: Good Health Status of Elderly Jamaicans by Some Explanatory Variables 23 Table 1.1.3: Good Health Status of Middle Age Jamaicans by Some Explanatory Variables 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 43 Table 2.2.2. Self-reported illness by sex of respondents, 2002 and 2007 44 Table 2.2.3. Self-reported illness by marital status, 2002 45 Table 2.2.4. Self-reported illness by marital status, 2007 46 Table 2.2.5. Self-reported illness by Age cohort, 2002 and 2007 47 Table 2.2.6. Mean age of oldest-old with particular health conditions 48 Table 2.2.7. Diagnosed Health Conditions by Aged cohort

49 Table 2.2.8. Self-reported illness (in %) by health status. 50 Table 2.2.9. Health care-seeking behaviour and health status, 2007 51 Table 2.2.10. Health care-seeking behaviour by health status controlled for aged cohort 52 Table 2.2.11. Logistic regression on Good Health status by variables 53 Table 3.3.1. Demographic characteristics, 2002 and 2007 65 Table 3.3.2: Self-reported health conditions by particular social variables 67 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 69 Table 3.3.4. Stepwise Logistic regression: Social and psychological determinants of self-evaluated health, 2002 and 2007 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 72 Table 4.4.1. Crowding, expenditure, income, age, and other characteristics by health insurance status 102 Table 4.4.2. Health, health care seeking behaviour, illness and particular demographic characteristics by health insurance status 103 Table 4.4.3. Age cohort by diagnosed illness 104 Table 4.4.4. Illness by age of respondents controlled for health insurance status 105 Table 4.4.5. Age cohort by diagnosed health condition, and health insurance status 106 Table 4.4.6. Logistic regression: Explanatory variables of self-rated moderate-to-very good health 107 Table 4.4.7. Logistic regression: Explanatory variables of self-reported illness 108 Table 4.4.8. Logistic regression: Explanatory variables of health care seeking behaviour 109 Table 4.4.9. Logistic regression: Explanatory variables of self-reported diagnosed chronic illness 110 Table 4.4.10. Multiple regression: Explanatory variables of income 111 Table 4.4.11. Logistic regression: Explanatory variables of health insurance status 112 Table 5.5.1: Demographic characteristic of studied population 134 Table 5.5.2: Particular demographic variables by area of residence 136 Table 5.5.3: Logistic regression: Variables of antithesis of illness among adolescence population 137 Table 5.5.4: Logistic and Ordinal Logistic regression: Factors explaining self-reported health status of adolescents 138 Table 5.5.5: Self-rated health status and antithesis of illness 139 Table 6.6.1: Descriptive analysis of variables of target cohort 157

Table 6.6.2: Socio-demographic and psychological variables of self-related

health status of the sample 158

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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 175

Table 7.7.3. Self-rated health status by income quintile, 2007 177

Table 7.7.4. Self-reported diagnosed health condition by per capita income 178

Table 8.8.1. Socio-demographic characteristic of sample, 2002 and 2007 196

Table 8.8.2. Health status by self-reported illness 197

Table 8.8.3. Health status by self-reported diagnosed illness 198

Table 9.9.1. Sociodemographic characteristics of sample, 2002 and 2007 222 Table 9.9.2. Health status and self-rated illness 223 Table 9.9.3. Predictors of poor self-reported illness by some explanatory variables, 2002 224 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 226 Table 9.9.6. Model summary for 2007 logistic regression analysis 227 Table 10.10.1. Socio-demographic characteristic of sample 249 Table 10.10.2. Very poor or poor and moderated-to-very poor self-reported health status of sexes (in %) 250 Table 10.10.3. Odds ratios for very poor or poor and moderate-to-very poor self-reported health of sexes by particular variables 251 Table 10.10.4. Odds ratios of poor health status by age cohorts 252

Table 11.11.1. Socio-demographic characteristic of sample by sex of respondents 273 Table 11.11.2. Socio-demographic characteristic of sample by educational level 274 Table 11.11.3. Socio-demographic characteristic of sample by self-reported illness 275 Table 11.11.4. Stepwise Logistic Regression: Good self-rated health status by socio-demographic, economic and biological variables 276

viii

Table 11.11.5. Stepwise Logistic Regression: Self-reported illness by socio-demographic and biological variables 277 Table 12.12.1. Life expectancy at birth for the sexes, self-reported illness, and mortality, 1989-2007 292 Table 12.12.2. Life expectancy at birth of population and sex of children by self-reported illness 293 Table 13.13.1 Demographic characteristics of sample for CLG and JSLC, 2007 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 314

Table 13.13.4 Quality of life, health status and standardized health status 315

Table 13.13.5 QoL by economic situation of individual and family, CLG 316

Table 14.14.1. Health and curative care visits: 2000-2007 344

Table 14.14.2: Proportion of Survey (Sample) vs. Proportion of Population 345 Table 14.14.3. Descriptive characteristic of samples: Sub-national and National surveys 346 Table 14.14.4. Characteristic of samples: Sub-national and National surveys 347

ix

List of Figures

page

Figure 2.2.1. Diagnosed health conditions, 2002 and 2007 54 Figure 2.2.2. Self-reported illness (in %) by Income Quintile, 2002 and 2007 55

Figure 7.7.1. Mean scores for self-reported diagnosed health conditions, 2002 and 2007 176

Figure 8.8.1. Mean age of health conditions of children less than 5 years old 199 Figure 8.8.2. Health status by Parent-reported illness (in %) examined by gender 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 202 Figure 8.8.5. Health status by health care-seeking behaviour 203 Figure 9.9.1. Mean age for males with particular self-reported diagnosed illness 228 Figure 12.12.1. Life expectancy at birth for the population by self-reported illness (in %) 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 %) 296 Figure 12.12.4. Mortality (in No of people) and self-reported illness/injury (in %) 297

x

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 self-

reported 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.

xi

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,

xii

“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

xiii

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.

xiv

DEDICATION

This book is dedicated to the

‘Pursuit of Truth’

1

Part I

HEALTH STATUS: USAGE OF HEALTH DATA

2

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

3

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, non-

justifiable 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

4

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.

5

CHAPTER

1 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=1.05-1.46; rural area:), education (secondary: 95%CI=1.17-1.58; tertiary: 95%CI=1.47-2.82; primary or below: OR=1.00), social support (95%CI=0.75-0.96), gender (95%CI=1.281-1.706), psychological affective conditions (negative affective: 95%CI=0.939-0.98; positive affective: 95%CI:1.05-1.11), number of males in household (95%CI:1.07-1.24), number of children in household (95%CI=1.12-1.27) and previous health status. There are disparities in the social determinants of health across the life course, which emerged from the current findings. The findings are far reaching and can be used to aid policy formulation and how social determinants of health are viewed in the future.

INTRODUCTION

Health is a multidimensional construct which goes beyond dysfunctions (illnesses, ailment or

injuries) [1-14]. Although World Health Organization (WHO) began this broaden conceptual

framework in the late 1940s [1], Engel [3] was the first to develop the biopsychosocial model that

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

6

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, self-

rated 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):

7

Ht = ƒ (Ht-1, Go, Bt, MCt, ED) ……………………………………………….. Model (1)

where Ht – current health in time period t, stock of health (Ht-1) in previous period, Bt –

smoking and excessive drinking, and good personal health behaviours (including exercise – Go),

MCt,- use of medical care, education of each family member (ED), and all sources of household

income (including current income).

Grossman’s model was good at the time; however, one of the drawbacks to this model was

the fact that some crucible factors were omitted by the aforementioned model. Based on that

limitation, using literature, Smith and Kington [10] refined, expanded and modified Grossman’s

work as it omitted important variables such as price of other inputs and family background or

genetic endowment which are crucible to health status. They refined Grossman’s work to include

socioeconomic variables as well as some other factors [Model (2)].

Ht = H* (Ht-1, Pmc, Po, ED, Et, Rt, At, Go) ………………………..…………… Model (2)

Model (2) expresses current health status Ht as a function of stock of health (Ht-1), price

of medical care Pmc, the price of other inputs Po, education of each family member (ED), all

sources of household income (Et), family background or genetic endowments (Go), retirement

related income (Rt ), asset income (At).

It is Grossman’s work that accounts for economists like Veenhoven’s [20] and Easterlin’s

[19] works that used econometric analysis to model factors that determine subjective wellbeing.

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;

8

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

9

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)]:

Ht = f(Ht-1,Pmc, ED i, Rt, At, Qt, HHt, C i, Eni , MSi, HIi , HTi , SSi, LLi,Xi , CRi , Di, Oi , Σ(NP i,PPi), M i,N i, FSi, Ai , Wi, ε i )….. Model (3)

The current health status of a Jamaica, Ht, is a function of 23 explanation variables, where

Ht is current health status of person i, if good or above (i.e. no reported health conditions four

10

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

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

educational level of person i, 1 if secondary, 1 if tertiary and the reference group is primary and

below; Rt is retirement income of person i, 1 if receiving private and/or government pension, 0 if

otherwise; HI i is health insurance coverage of person i, 1 if have a health insurance policy, 0 if

otherwise; HTi is house tenure of person i, 1 if rent, 0 if squatted; Xi is gender of person i, 1 if

female, 0 if male; CRi is crowding in the household of person i; Σ(NPi,PPi) NPi is the summation

of all negative affective psychological conditions and PPi is the summation of all positive

affective psychological conditions; Mi is number of male in household of person i and Fi is

number of female in household of person i; Ai is the age of the person i and Ni is number of

children in household of person i; LLi is living arrangement where 1= living with family

members or relative, and 0=otherwise and social standing (or social class), Wi.

Statistical analysis

Statistical analyses were performed using Statistical Packages for the Social Sciences (SPSS) for

Windows, Version 16.0 (SPSS Inc; Chicago, IL, USA). A single hypothesis was tested, which

was ‘health status of rural resident is a function of demographic, social, psychological and

economic variables.’ The enter method in logistic regression was used to test the hypothesis in

order to determine those factors that influence health status of rural residents if the dependent

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.

11

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(Ht-1, Rt, Pmc, EDi, MSi, HIi, SSi,ARi, Xi, Σ(NP i,PPi), Mi,N i, ε i)...……………………………..... Model (4)

12

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(Ht-1, EDi, MSi, HIi, ,ARi, Xi, Σ(PP i), Mi,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

13

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, Pmc, Eni, HIi, Xi, 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(Ht-1, Wi, 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).

14

Limitations to the Models

Good Health Status of Jamaicans [ie Model (4)], elderly [ie Model (5)], middle age adults

[ie Model (6)], and young adults [ie Model (7) are derivatives of Model (3). Good Health Status

[ie Model (4) – Model (7)] cannot be distinguished and tested over different time periods, person

differential, and these are important components of good health.

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

The current work is a major departure from Grossman’s theoretical model as he assumed

that factors affecting good health Status over the life course are the same, this study disagreed

with this fundamental assumption. This study revealed that predictors of good health status are

not necessarily the same across the life course, and differently from that of the general populace.

Despite those critical findings, healthy time gained can increase good health status directly and

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.

15

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

16

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, 21-

23, 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 Eldemire-

Shearer’s work [33] was only among men across different social strata in Jamaica (poor and

17

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]

18

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.

19

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4. Engel GL. The clinical application of the biopsychosocial model. Am J of Psychiatry. 1980; 137:535-544. 5. Bourne P. Using the biopsychosocial model to evaluate the wellbeing of the Jamaican elderly. West Indian Med J. 2007b; 56: (suppl 3), 39-40. 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. Longest BB, Jr. Health Policymaking in the United States, 3rd. Chicago, IL: Health Administration Press; 2002. 8. Bourne PA. Quality of Life of Jamaican women. West Indian Med J. 2008; 57: (suppl 4), 49. 9. Grossman M. The demand for health- a theoretical and empirical investigation. New York: National Bureau of Economic Research; 1972. 10. Smith JP, Kington R. Demographic and economic correlates of health in old age. Demography. 1997; 34:159-170. 11. Abel-Smith B. An introduction to health: Policy, Planning and Financing. Harlow: Pearson Education; 1994. 12. Bourne PA. Health Determinants: Using Secondary Data to Model Predictors of Well-being of Jamaicans. West Indian Med J. 2008; 57:476-481. 13. Bourne PA. Medical Sociology: Modelling Well-being for elderly People in Jamaica. West Indian Med J. 2008; 57:596-604. 14. Bourne PA. Health measurement. Health 2010;2(5):465-476.

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20. Veenhoven R. Happiness in nations, subjective appreciation of in 56 nations 1946-1992. Rotterdam, Netherlands: Erasmus University; 1993. 21. Hutchinson G, Simeon DT, Bain BC, Wyatt GE, Tucker MB, LeFranc E. Social and health determinants of wellbeing and life satisfaction in Jamaica. International J of Social Psychiatry. 2004; 50:43-53. 22. Bourne PA. Modelling social determinants of self-evaluated health of poor old people in a middle-income developing nation. J Biomedical Sci and Engineering, 2010;3: 700-710. 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.

22

Table 1.1.1: Good Health Status of Jamaicans by Some Explanatory Variables

Variable Coefficient Std Error.

Wald

statistic P

Odds Ratio

CI (95%)

Lower Upper Middle Quintile -0.03 0.10 0.09 0.764 0.97 0.81 1.17 Two Wealthiest Quintiles -0.11 0.10 1.26 0.261 0.90 0.74 1.09 Poorest-to-poor Quintiles*

Retirement Income

-0.38

0.17

4.88

0.027

0.68

0.49

0.96 Household Head 0.17 0.29 0.37 0.543 1.19 0.68 2.08 Logged Medical Expenditure -0.05 0.02 5.10 0.024 0.95 0.91 0.99 Average Income 0.00 0.00 1.56 0.212 1.00 1.00 1.00 Average Consumption 0.00 0.00 0.16 0.689 1.00 1.00 1.00 Environment 0.01 0.07 0.02 0.891 1.01 0.88 1.16 Separated or Divorced or Widowed -0.97 0.10 87.36 0.000 0.38 0.31 0.46 Married -0.55 0.08 53.05 0.000 0.58 0.50 0.67 Never married*

Health Insurance

-3.31

0.12

776.64

0.000

0.04

0.03

0.05

Other Towns

0.21

0.08

6.64

0.010

1.24

1.05

1.46 Urban Area -0.01 0.13 0.00 0.952 0.99 0.78 1.27 Rural Area*

House Tenure - Rent

-1.08

0.88

1.48

0.224

0.34

0.06

1.93 House Tenure - Owned -0.42 0.55 0.58 0.447 0.66 0.23 1.93 House Tenure- Squatted*

Secondary Education

0.31

0.08

15.81

0.000

1.36

1.17

1.58 Tertiary Education 0.71 0.17 18.09 0.000 2.03 1.45 2.82 Primary and below*

Social Support

-0.17

0.07

6.33

0.012

0.85

0.75

0.96 Living Arrangement -0.06 0.13 0.20 0.659 0.95 0.73 1.22 Crowding -0.01 0.04 0.08 0.772 0.99 0.91 1.07 Land ownership -0.07 0.07 0.90 0.342 0.93 0.81 1.08 Gender 0.39 0.07 28.67 0.000 1.48 1.28 1.71 Negative Affective -0.04 0.01 14.96 0.000 0.96 0.94 0.98 Positive Affective 0.07 0.01 26.26 0.000 1.08 1.05 1.11 Number of males in household 0.14 0.04 13.36 0.000 1.15 1.07 1.24 Number of females in household 0.06 0.04 2.36 0.124 1.06 0.98 1.14 Number of children in household 0.17 0.03 29.16 0.000 1.19 1.12 1.27 Constant 1.89 0.65 8.31 0.004 6.59

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

23

Table 1.1.2: Good Health Status of Elderly Jamaicans by Some Explanatory Variables

Coefficient Std

Error Wald

statistic P Odds Ratio CI (95%)

Lower Upper Middle Quintile -0.10 0.15 0.47 0.495 0.90 0.67 1.22 Two Wealthiest Quintiles 0.12 0.17 0.47 0.491 1.12 0.81 1.56 Poorest-to-poor quintiles

Retirement Income

-0.22

0.22

1.00

0.317

0.81

0.53

1.23 Household Head 0.89 0.65 1.86 0.172 2.44 0.68 8.76 Logged Medical Expenditure -0.06 0.04 2.16 0.142 0.95 0.88 1.02 Average Income 0.00 0.00 0.93 0.335 1.00 1.00 1.00 Environment -0.16 0.12 1.80 0.180 0.86 0.68 1.08

Separated or Divorced or Widowed

-0.49

0.15

11.00

0.001

0.61

0.46

0.82

Married -0.33 0.15 4.82 0.028 0.72 0.54 0.97 Never married*

Health Insurance

-3.35

0.22

241.88

0.000

0.04

0.02

0.05

Other Towns

0.33

0.14

5.32

0.021

1.39

1.05

1.83

Urban 0.40 0.21 3.48 0.062 1.49 0.98 2.27 Rural areas*

House tenure - rented

-20.37

40192.9

0.00

1.000

0.00

0.00

House tenure - owned 1.22 1.24 0.96 0.327 3.38 0.30 38.60 House tenure – squatted*

Secondary Education

-0.46

0.11

16.06

0.000

0.63

0.51

0.79 Tertiary Education 0.81 0.35 5.45 0.020 2.26 1.14 4.47 Primary or below*

Social support

-0.08

0.11

0.47

0.495

0.93

0.75

1.15 Living arrangement 0.26 0.18 2.11 0.146 1.30 0.91 1.84 Crowding -0.05 0.09 0.29 0.593 0.95 0.80 1.14 Landownership 0.17 0.13 1.72 0.190 1.19 0.92 1.54 Gender 0.47 0.12 14.67 0.000 1.60 1.26 2.04 Negative Affective -0.03 0.02 1.97 0.160 0.97 0.94 1.01 Positive Affective 0.07 0.02 9.26 0.002 1.07 1.03 1.12 Number of male 0.18 0.07 6.75 0.009 1.19 1.04 1.36 Number of females 0.05 0.07 0.49 0.485 1.05 0.91 1.21 Number of children 0.22 0.06 12.09 0.001 1.24 1.10 1.40 Constant -1.32 1.44 0.83 0.362 0.27

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

24

Table 1.1.3: Good Health Status of Middle Age Jamaicans by Some Explanatory Variables

Coefficient Std

Error Wald

statistic P Odds Ratio CI (95%)

Lower Upper Middle Quintile 0.03 0.15 0.04 0.834 1.03 0.76 1.40 Two Wealthiest Quintiles -0.29 0.15 3.67 0.055 0.75 0.56 1.01 Poorest-to-poor Quintiles*

Retirement Income

-0.57

0.36

2.44

0.119

0.57

0.28

1.16 Household Head 0.50 0.45 1.24 0.265 1.66 0.68 4.01 Logged Medical Expenditure -0.09 0.04 6.44 0.011 0.91 0.85 0.98 Average Income 0.00 0.00 0.53 0.465 1.00 1.00 1.00 Environment 0.31 0.12 7.41 0.006 1.37 1.09 1.71

Separated or Divorced or Widowed

-0.20

0.23

0.77

0.380

0.82

0.53

1.28 Married -0.18 0.11 2.68 0.102 0.84 0.68 1.04 Never married*

Health Insurance

-3.04

0.17

320.76

0.000

0.05

0.03

0.07

Other Towns

0.11

0.12

0.75

0.387

1.11

0.87

1.42 Urban -0.01 0.19 0.00 0.963 0.99 0.68 1.44 Rural areas*

House tenure - rented

17.94

20029.78

0.00

0.999

0.00

House tenure - owned -1.33 1.12 1.43 0.232 0.26 0.03 2.35 House tenure – squatted*

Secondary education

0.19

0.13

2.11

0.146

1.20

0.94

1.55 Tertiary education 0.34 0.23 2.23 0.135 1.41 0.90 2.21 Primary or below*

Social support

-0.08

0.10

0.57

0.450

0.93

0.76

1.13 Living Arrangement -0.19 0.21 0.87 0.351 0.83 0.55 1.24 Crowding -0.05 0.06 0.65 0.419 0.95 0.85 1.07 Landownership -0.13 0.11 1.47 0.226 0.88 0.71 1.08 Gender 0.51 0.11 21.41 0.000 1.66 1.34 2.06 Negative Affective -0.08 0.02 24.66 0.000 0.92 0.90 0.95 Positive Affective 0.05 0.02 4.51 0.034 1.05 1.00 1.10 Number of males in house 0.03 0.06 0.23 0.630 1.03 0.92 1.14 Number of female in house 0.08 0.06 2.09 0.149 1.08 0.97 1.21 Number of children in house 0.10 0.04 5.47 0.019 1.11 1.02 1.21 Constant 3.29 1.25 6.89 0.009 26.77

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

25

Table 1.1.4: Good Health Status of Young Adults Jamaicans by Some Explanatory Variables

Coefficient Std Error Wald

statistic P Odds Ratio

CI (95%)

Lower Upper

Middle Quintile

-0.06

0.19

0.10

0.747

0.94

0.65

1.37 Two Wealthiest Quintiles -0.59 0.18 11.10 0.001 0.55 0.39 0.78 Poorest-to-poor quintiles*

Household Head

-0.25

0.39

0.41

0.520

0.78

0.36

1.68

Logged Medical Expenditure

0.01

0.04

0.09

0.760

1.01

0.93

1.10 Average Income 0.00 0.00 3.29 0.070 1.00 1.00 1.00 Environment -0.03 0.13 0.04 0.840 0.97 0.75 1.26 Health Insurance -3.73 0.21 321.51 0.000 0.02 0.02 0.04

Other Towns

0.23

0.15

2.42

0.120

1.26

0.94

1.69 Urban -0.05 0.18 0.07 0.788 0.95 0.68 1.34 Rural area*

Secondary education

-0.06

0.41

0.02

0.886

0.94

0.43

2.09 Tertiary education -0.39 0.47 0.70 0.405 0.68 0.27 1.69 Primary and below*

Social support

-0.14

0.13

1.22

0.269

0.87

0.68

1.12 Crowding 0.04 0.06 0.65 0.420 1.05 0.94 1.16 Gender 0.19 0.15 1.60 0.206 1.20 0.90 1.60 Negative Affective -0.04 0.02 4.22 0.040 0.96 0.93 1.00 Positive Affective 0.07 0.03 6.81 0.009 1.07 1.02 1.13

Number of males in house

0.13

0.07

3.67

0.055

1.13

1.00

1.29

Number of females in house

0.06 0.06 0.87 0.351 1.06 0.94 1.20

Married

0.08

0.22

0.13

0.717

1.09

0.70

1.68

Never married* Constant

2.75

0.67

16.62

0.000

15.57

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

26

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 cross-sectional 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.

27

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 socio-

economic 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

28

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 old-

old 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.

29

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 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.

Materials and Methods The current study utilized a sub-sample of approximately 4% from each nationally cross-

sectional 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). The survey is known as the Jamaica Survey of

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

30

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 self-

reported 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 non-

response 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].

31

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

32

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

33

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)

34

and 2007 (χ2 (df = 4) = 10.28, P < 0.05). Based on Figure 2.2.2, the poor had highest self-

reported 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 cross-

tabulation 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 care-

seeking 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 oldest-

old (χ2 (df = 2) = 0.390, P = 0.823) (Table 2.2.10).

35

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

36

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

37

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

38

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

39

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.

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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.

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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.

41

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. 3-11.

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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.

43

Table 2.2.1. Socio-demographic characteristics of sample Variable

2002

2007

Frequency

%

Frequency

%

Sex Male 453 42.4 120 42.6 Female 616 57.6 162 57.4 Marital status Married 304 29.2 88 32.4 Never married 255 24.5 66 24.3 Divorced 18 1.7 6 2.2 Separated 22 2.1 7 2.6 Widowed 442 42.5 105 38.6 Income Quintile Poorest 20% 239 22.4 56 19.9 Poor 216 20.2 51 18.1 Middle 195 18.2 74 26.2 Wealthy 194 18.1 58 20.6 Wealthiest 20% 225 21.0 43 15.2 Self-reported illness Yes 441 42.3 141 51.6 No 601 57.7 132 48.4 Health care-seeking behaviour Yes 306 69.2 102 72.9 No 136 30.8 38 27.1 Area of residence Rural 731 68.4 83 50.7 Semi-urban 222 20.8 56 19.9 Urban 116 10.9 143 29.4 Educational level Primary or below 662 66.5 Secondary 309 31.1 Tertiary 24 2.4 Health insurance coverage Yes 48 4.6 26.7 No 998 998 73.3 Age Mean (SD) 81.29 yrs (±5.6yrs) 81.37 yrs (±5.38yrs) Public health care expenditure Mean (SD)

Ja $341.54 (±Ja.$1165.60) Ja $368.89.54 (±Ja.$1518.66)

Private health care expenditure Mean (SD)

Ja. $1436.23 (±Ja.$2060.42) Ja. $1856.04 (±Ja.$4347.78)

44

Table 2.2.2. Self-reported illness by sex of respondents, 2002 and 2007

Self-reported

illness

20021

20072

Male Female Male Female

N (%) N (%) N (%) N (%)

Yes 174 (39.3) 267 (44.6) 60 (51.3) 81 (51.9)

No 269 (60.7) 332 (55.4) 57 (48.7) 75 (48.1)

Total 443 599 117 156

1 χ2 (df = 1) = 2.927, P =0.087

2 χ2 (df = 1) = 0.011, P =0.916

45

Table 2.2.3. Self-reported illness by marital status, 2002

Self-reported illness

Marital status*

Married Never married Divorced Separated Widowed

N (%) N (%) N (%) N (%) N (%)

Yes 140 (46.8) 88 (34.8) 9 (50.0) 10 (45.5) 190 (43.2)

No 159 (53.2) 165 (65.2) 9 (50.0) 12 (54.5) 250 (56.8)

Total 299 253 18 22 440

* χ2 (df = 4) = 9.027, P =0.060

46

Table 2.2.4. Self-reported illness by marital status, 2007

Self-reported illness

Marital status*

Married Never married Divorced Separated Widowed

N (%) N (%) N (%) N (%) N (%)

Yes 55 (62.5) 26 (40.0) 4 (66.7) 4 (57.1) 51 (49.0)

No 33 (37.5) 39 (60.0) 2 (33.3) 3 (42.9) 53 (51.0)

Total 88 65 6 7 104

* χ2 (df = 4) = 8.589, P =0.072

47

Table 2.2.5. Self-reported illness by Age cohort, 2002 and 2007

Self-reported

illness

20021

20072

Old-Old Oldest-Old Old-Old Oldest-Old

N (%) N (%) N (%) N (%)

Yes 333 (42.8) 108 (40.9) 110 (52.9) 31 (47.7)

No 445 (57.2) 156 (59.1) 98 (47.1) 34 (52.3)

Total 778 264 208 65

1 χ2 (df = 1) = .289, P =0.591

2 χ2 (df = 1) = .535, P =0.465

48

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

Health

conditions

20021

20072

Mean Age (±SD) Mean Age (±SD)

Cold 80.00 - 77.63 (±1.77)

Diarrhoea 86.00 - 85.00 (±9.66)

Asthma 0.00 - 81.00 (±5.20)

Diabetes mellitus 79.50 (±2.50) 90.92 (±4.84)

Hypertension 80.13 (±0.84) 81.21 (±4.95)

Arthritis 79.32 (±0.69) 79.13 (±3.54)

Other 81.64 (±1.75) 83.90 (±6.82)

Total 80.14 (±4.73) 82.75 (±4.50)

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

49

Table 2.2.7. Diagnosed Health Conditions by Aged cohort

Diagnosed

Health

conditions

20021

20072

Aged cohort Aged cohort

Old-Old Oldest-Old Old-Old Oldest-Old

% % % %

Cold 1.5 0.0 7.2 0.0

Diarrhoea 0.0 8.3 2.7 3.2

Asthma 0.0 0.0 1.8 3.2

Diabetes mellitus 3.0 0.0 11.1 16.1

Hypertension 47.8 58.3 44.1 45.2

Arthritis 35.8 8.3 12.6 6.5

Other 11.9 25.0 11.7 22.6

No 0.0 0.0 2.7 3.2

1 χ2 (df = 1) = 10.028, P =0.074

2 χ2 (df = 1) = 5.382 P =0.613

50

Table 2.2.8. Self-reported illness (in %) by health status.

Self-reported illness

Health Status

Good Fair Poor

n (%) n (%) n (%)

Yes 21 (25.3) 60 (55.0) 60 (74.1)

No 62 (74.7) 49 (45.0) 21 (25.9)

Total 83 109 81

χ2 (df = 2) = 39.888, P < 0.001, cc=0.357

51

Table 2.2.9. Health care-seeking behaviour and health status, 2007

Health care-seeking behaviour

Health Status

Good Fair Poor

n (%) n (%) n (%)

No 9 (42.9) 21(35.6) 8 (13.3)

Yes 12 (57.1) 38 (64.4) 52 (86.7)

Total 21 59 60

χ2 (df = 2) = 10.539, P = 0.005, cc=0.265

52

Table 2.2.10. Health care-seeking behaviour by health status controlled for aged cohort

Aged cohort

Health status

Total

Good

Fair

Bad

Old-old1 Health Care-

Seeking Behaviour No 7 (46.7) 18 (36.7) 5 (10.9) 30 (27.3)

Yes 8 (53.3) 31 (63.3) 41 (89.1) 80 (72.7) Total 15 49 46 110 Oldest-old2 Health Care-

Seeking Behaviour No 2 (33.3) 3 (30.0) 3 (21.4) 8 (26.7)

Yes 4 (66.7) 7 (70.0) 11 (78.6) 22 (73.3) Total 6 10 14 30

1 χ2 (df = 2) = 11.296, P =0.004, cc=0.305

2 χ2 (df = 2) = 0.390, P =0.823

53

Table 2.2.11. Logistic regression on Good Health status by variables

Variable Coefficient Std. Error

Wald statistic Odds ratio 95.0% C.I.

Self-reported illness -1.735 0.317 29.950 0.176 0.095 - 0.328*** Age

-0.041

0.030

1.910

0.960

0.905 - 1.017

Middle Class

-0.083

0.414

0.040

0.921

0.409 - 2.072

Upper class

0.391

0.759

0.264

1.478

0.334 - 6.546

†Poor Married

0.297

0.393

0.574

1.346

0.624 - 2.907 Divorced, separated or widowed

-0.110

0.376

0.086

0.896

0.428 - 1.872

†Never married Urban area

0.347

0.350

0.981

1.414

0.712 - 2.808 Other town

-0.398

0.414

0.922

0.672

0.298 - 1.513

†Rural area Constant

2.979

2.456

1.471

19.667

-

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

54

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.

55

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).

56

CHAPTER

3 Self-evaluated health and health conditions of rural residents in a middle-income 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 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. In 2002, 14% of respondents indicated having an illness in the 4-week 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

57

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

58

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,

59

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-to-

middle 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

60

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

61

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 self-

reported 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

62

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.

63

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

64

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.

65

Table 3.3.1. Demographic characteristics, 2002 and 2007 Variable

2002 2007 n % n %

Sex Male 7,727 50.6 1,654 49.8 Female 7,524 49.3 1,668 50.2 Marital status Married 2,460 25.6 513 24.1 Never married 6,436 66.6 1,462 68.7 Divorced 56 0.6 22 1.0 Separated 104 1.1 20 0.9 Widowed 610 6.3 112 5.3 Social hierarchy Lower 7,298 47.8 1,828 55.0 Middle 3,169 20.8 650 19.6 Wealthy 4,791 31.4 844 25.4 Self-reported illness Yes 1,987 13.5 536 16.6 No 12,713 86.5 2,688 83.4 Self-reported health conditions Acute Influenza 1 0.5 80 16.3 Diarrhoea 4 2.1 19 3.9 Respiratory diseases 6 3.1 51 10.4 Chronic Diabetes mellitus 10 5.2 64 13.0 Hypertension 82 42.9 118 24.0 Arthritis 48 25.1 30 6.1 Other 40 20.9 130 26.4 Medical care-seeking behaviour Yes 1,302 63.8 349 63.3 No 740 36.4 202 36.7 Medical care utilization Public hospitals 499 39.1 127 37.2 Private hospitals 80 6.3 8 2.3 Public health care centres 285 22.3 76 22.3 Private health care centres 528 41.3 158 46.3 Health insurance coverage Yes 1,036 7.0 464 14.5 No 13,714 93.0 2,715 85.5 Age Median, in years, range) 23 (0 to 99) 25 (0 to 99) Length of illness, in days, Median (range) 7 (0 to 90) 7 (0 to 99)

66

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.

67

Table 3.3.2: Self-reported health conditions by particular social variables Variable

Health conditions

P Acute conditions Chronic

Influenza Diarrhoea Respiratory Diabetes mellitus

Hypertension

Arthritis Other

2002 Sex (%) 0.045 Male 0.0 25.0 83.3 20.0 30.5 20.8 35.0 Female 100.0 75.0 16.7 80.0 69.5 79.2 65.0 Total 1 4 6 10 82 48 40 Age - in years- Mean (SD) 80.0 (0.0) 1.8 (1.7) 14.0 (24.6) 63.7 (13.2) 68.7

(13.7) 68.4

(12.60 56.0

(23.4) < 0.0001

Health care-seeking behaviour Yes (%) 0.0 75.0 100.0 88.9 79.3 83.3 65.0 0.05 Total 10 14 6 9 82 48 40 Length of illness –in days – Mean (SD)

3 (0) 4 (2) 11 (5) 12 (11) 16 (11) 18 (11) 19 (12) 0.045

2007 Sex (%) <0.0001 Male 42.5 36.8 56.9 20.3 27.1 46.7 43.1 Female 57.5 63.2 43.1 79.7 72.9 53.3 56.9 Total 80 19 51 64 118 30 130 Age - in years- Mean (SD) 19.5

(24.8) 20.1 (28.5) 24.3 (23.8) 56.5 (17.4) 64.0

(17.1) 68.3

(12.0) 36.0

(25.0) <0.0001

Health care-seeking behaviour < 0.0001 Yes (%) 41.3 52.6 62.7 75.0 64.4 46.7 70.5 Total n 80 19 51 64 118 30 129 Length of illness –in days – Mean (SD)

8 (6) 5 (2) 42 (172) 76 (135) 104 (239) 112 (217) 57 (188) 0.004

68

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 care-

seeking 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 Variable

2002 2007 Health care-seeking behaviour Health care-seeking behaviour

Yes No P Yes No P N (%) N (%) N (%) N (%)

Sex 0.011 0.112 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) Self-reported illness 0.360 0.130 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) Health insurance coverage 0.197 0.013 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) Social hierarchy <0.0001 0.104 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) Educational level <0.0001 0.623 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 Explanatory variables:

2002 2007 Coefficient Std.

Error Odds ratio

95% CI Coefficient Std. Error

Odds ratio

95% CI

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 χ2 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 Explanatory variables:

2002 2007 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 care-

seeking 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 care-

seeking 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

78

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.

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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

84

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 socio-

economic 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

85

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 non-

metric 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 sub-

populations 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) self-

reported 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 socio-

economic 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 socio-

economic 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 (public-

private 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 socio-

economic 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 socio-

economic 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

99

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 Characteristics

Health insurance status P Non-insured mean ± SD

Insured 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 1Expenditures 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 Characteristic

Health insurance status P Coverage No coverage

Private n (%) Public, NI Gold n (%) Other Public n (%) n (%) Health conditions χ2 = 42.62, P < 0.0001 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) Health care seeking behaviour χ2 = 70.09, P < 0.0001 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) Illness χ2 = 67.14, P < 0.0001 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) Education level χ2 = 78.10, P < 0.0001 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) Social class χ2 = 596.08, P < 0.0001 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) Area of residence χ2 = 190.29, P < 0.0001 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) Self-rated health status χ2 = 67.14, P < 0.0001 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) Health care utilization χ2 = 30.06, P < 0.0001 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

Age cohort

Diagnosed illness

Total

Acute condition Chronic condition

Other Cold Diarrhoea Asthma Diabetes mellitus Hypertension Arthritis

n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%)

97 (65.1) 13 (48.2) 51 (53.7) 3 (2.4) 0 (0.0) 0 (0.0) 54 (23.1) 218 (24.5) Children

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 0 (0.0) 1 (3.7) 1 (1.1) 5 (4.1) 14 (6.8) 2 (3.6) 7 (3.0) 30 (3.4) Total 149 27 95 123 206 56 234 890

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Table 4.4.4. Illness by age of respondents controlled for health insurance status Characteristic

Age of respondents 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 Characteristic

Diagnosed health condition

Diagnosed health condition

Acute Chronic Acute Chronic Acute Chronic Uninsured Insured

n (%) n (%) n (%) n (%) n (%) n (%) Age cohort Children 215 (42.6) 3 (0.8) 183 (44.1) 1 (0.4) 32 (35.6) 2 (1.6) Young adults 75 (14.9) 10 (2.6) 58 (14.0) 6 (2.3) 17 (18.9) 4 (3.2) Other aged-adults 131 (25.9) 137 (35.5) 110 (26.5) 100 (38.6) 21 (23.3) 37 (29.3) Young-old 49 (9.7) 132 (34.3) 37 (8.9) 82 (31.7) 12 (13.3) 50 (39.7) Old-old 26 (5.2) 82 (21.3) 20 (4.8) 55 (21.2) 6 (6.7) 27 (21.4) Oldest-old 9 (1.8) 21 (5.5) 7 (1.7) 15 (5.8) 2(2.2) 6 (4.8) Total 505 385 415 259 90 126 χ2 = 317.5, P < 0.0001 χ2 = 234.5, P < 0.0001 χ2 = 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

Average medical expenditure

0.000

0.000

1.00*

1.00 -1.00

0.003

Health insurance coverage (1= insured)

0.410

0.181

1.51*

1.06 - 2.15

0.005

Urban

0.496

0.180

1.64**

1.15 - 2.34

0.007

Other 0.462 0.197 1.59* 1.08 - 2.34 0.006 †Rural 1.00 Household head

0.376

0.154

1.46*

1.08 - 1.97

0.004

Age

-0.046

0.004

0.96***

0.95 - 0.96

0.081

Crowding index

-0.156

0.035

0.86***

0.80 - 0.92

0.010

Total food expenditure

0.000

0.000

1.00***

1.00 - 1.00

0.003

Health care seeking (1=yes)

-0.671

0.211

0.51**

0.34 - 0.77

0.005

Illness

-1.418

0.212

0.24***

0.16 - 0.37

0.204

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

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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

Average medical expenditure

0.000

0.000

1.00*

1.00 - 1.00

0.001

Male

-0.467

0.137

0.63**

0.48 - 0.82

0.003

Married

0.527

0.146

1.69***

1.27 - 2.25

0.002

Age

0.031

0.004

1.03***

1.02 - 1.04

0.037

Total food expenditure

0.000

0.000

1.00**

1.00 -1.00

0.002

Self-rated moderate-to-excellent health

-1.429

0.213

0.24***

0.16 -0.36

0.009

Health care seeking (1=yes)

5.835

0.262

342.11***

204.71 -571.72

0.611

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

Explanatory variable Coefficient Std error

Odds ratio 95.0% C.I.

R2

Health insurance coverage (1= insured)

0.620

0.179

1.86**

1.31 - 2.64

0.003

Self-reported illness

5.913

0.252

369.92***

225.74 - 606.17

0.712

Self-rated moderate-to-excellent health

-0.680

0.198

0.51**

0.34 - 0.75

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 Coefficient Std error Odds ratio 95.0% C.I.

R2 Male -1.037 0.205 0.36*** 0.24 - 0.53 0.048 Married

0.425

0.199

1.53*

1.04 - 2.26

0.012

†Never married 1.00 Health insurance coverage (1= insured)

0.454

0.220

1.58*

1.02 - 2.42

0.008

Age

0.047

0.005

1.05***

1.04 - 1.06

0.201

Logged Length of illness

0.125

0.059

1.13*

1.01 - 1.27

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

Explanatory variable

Unstandardized Coefficients

β 95% CI

B Std. Error R2

Constant 11.630 0.061 11.511 - 11.750 Crowding index

0.206

0.008

0.625***

0.190 - 0.221

0.195

Upper class

1.265

0.052

0.649***

1.162 - 1.368

0.320

Middle Class

0.692

0.047

0.347***

0.599 - 0.784

0.133

†Lower class Household head

-0.181

0.038

-0.108***

-0.256 - -0.106

0.012

Health insurance coverage (1= insured)

0.137

0.042

0.075**

0.054 - 0.220

0.007

Self-rated good health status

0.165

0.040

0.094***

0.088 - 0.243

0.006

Health care seeking (1=yes)

0.109

0.039

0.063**

0.033 - 0.185

0.003

Urban

0.145

0.046

0.079**

0.055 - 0.235

0.002

Other town

0.130

0.049

0.063**

0.033 - 0.226

0.003

†Rural area Married

0.075

0.038

0.044*

0.000 - 0.150

0.001

†Never married F = 144.15, P < 0.0001 R2 = 0.682 †Reference group ***P < 0.0001, **P < 0.01, *P < 0.05

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Table 4.4.11. Logistic regression: Explanatory variables of health insurance status (1= insured) Explanatory variable Coefficient Std. error Odds ratio 95.0% C.I.

R2

Age 0.014 0.006 1.01* 1.00 - 1.03 0.040 Income

0.000

0.000

1.00***

1.00 - 1.00

0.082

Chronic condition

0.563

0.210

1.7**

1.16 - 2.65

0.013

Health care seeking (1=yes)

0.463

0.211

1.59*

1.05 - 2.40

0.010

Married

0.647

0.192

1.91**

1.31 - 2.79

0.024

†Never married Upper class

0.841

0.227

3.46***

1.49 - 3.62

0.025

†Lower class 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 non-dichotomization of health status. The findings provide insights into the social determinants and health, and recommend that we guard against a choiced approach without examining the studied population in question.

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 self-

rated 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 non-

dichotomization, 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 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 [40]. 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 [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 self-

reported 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 non-

metric 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% (two-

tailed) 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 100-

question 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.

HA=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi , ε i) (1)

where HA (i.e. self-rated antithesis of diseases) is a function of age of respondents, Ai;

sex of individual i, Gi; area of residence, ARi; current self-reported illness of individual i,

It; logged duration of time that individual i was unable to carry out normal activities (or

length of illness), lnDi; Education level of individual i, EDi; union status of person i,

USi; social class of person i, Si; health insurance coverage of person i, HIi; logged family

income, lnY; crowding of individual i, CRi; logged medical expenditure of individual i in

time period t, lnMCt; social assistance of individual i, SAi; and an error term (ie. residual

error).

Note that length of illness was removed from the model as it had 93.5% of the cases were

missing as well as union status which had 58.2%.

HND=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi , ε i) (2)

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

Note that length of illness was removed from the model as it had 93.5% of the cases were

missing as well as union status which had 58.2%.

HD1=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, ε i) (3)

Where HD1 is very poor-to-poor self-rated dichotomized health status.

Note that length of illness was removed from the model as it had 93.5% of the cases were

missing as well as union status which had 58.2%.

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HD2=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, ε i) (4)

Where HD2 is good self-rated dichotomized health status.

Note that length of illness was removed from the model as it had 93.5% of the cases were

missing as well as union status which had 58.2%.

HD1-4=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, ε i) (5)

Where HD3 is very moderate-to-very good self-rated dichotomized health status.

Note that length of illness was removed from the model as it had 93.5% of the cases were

missing as well as union status which had 58.2%.

HD1-4=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, ε i) (6)

Where HD4 is good-to-excellent self-rated dichotomized health status.

Note that length of illness was removed from the model as it had 93.5% of the cases were

missing as well as union status which had 58.2%.

Results

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 non-

dichotomized 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

124

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 non-

consumption 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 non-

consumption 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 non-

dichotomized 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 self-

reported 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

128

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 self-

rated 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

129

disparities and variations in these based on the dichotomization, non-dichotomization, sub-

population, 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 non-

dichotomization 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.

131

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 n Percent Sex Male 672 48.2 Female 722 51.8 Union status Married 1 0.2 Common-law 14 2.4 Visiting 73 12.5 Single 494 84.8 Social assistance Yes 232 17.3 No 1108 82.7 Area of residence Urban 394 28.3 Periurban 287 20.6 Rural 713 51.1 Population Income Quintile Poorest 20% 320 23.0 Second poor 328 23.5 Middle income 287 20.6 Second wealthy 263 18.9 Wealthiest 20% 196 14.1 Self-reported illness Yes 89 6.6 No 1251 93.4 Self-reported diagnosed illness Influenza 22 28.9 Diarrhoea 1 1.3 Respiratory illness (ie asthma) 16 21.1 Diabetes mellitus 3 3.9 Hypertension 1 1.3 Other conditions (unspecified) 33 43.4 Health care-seeking behaviour Yes 50 53.8 No 43 46.2 Self-rated health status Very good 631 47.2 Good 601 45.0 Moderate 84 6.3 Poor 18 1.3 Very poor 2 0.1 Health insurance coverage No 1123 85.3 Yes 194 14.7 Age, mean (Standard deviation, SD) 14.2 years (SD = 2.8 years) Length of illness, median (range) 5 days ( 0 – 36 days)

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Table 5.5.2: Particular demographic variables by area of residence, n = 1,394 Characteristic Area of residence P, χ2

Urban Periurban Rural Self-reported illness n (%) n (%) n (%) 0.628, 0.931 Yes 27 (7.1) 15 (5.4) 47 (6.9) No 352 (92.9) 264 (94.6) 635 (93.1) Self-rated health status 24.82, 0.002 Very good 162 (42.7) 141 (50.4) 328 (48.4) Good 172 (45.4) 132 (47.1) 297 (43.9) Moderate 38 (10.0) 7 (2.5) 39 (5.8) Poor 7 (1.8) 0 (0.0) 11 (1.6) Very poor 0 (0.0) 0 (0.0) 2 (0.3) Social class 172.64, < 0.0001 Lower 101 (25.6) 108 (37.6) 439 (61.6) Middle 88 (22.3) 58 (20.2) 141 (19.8) Upper 205 (52.0) 121 (42.2) 133 (18.7) Educational level 37.79, < 0.0001 Primary or below 138 (36.6) 136 (48.6) 312 (46.1) Secondary 213 (56.5) 136 (48.6) 359 (53.0) Tertiary 26 (6.9) 8 (2.9) 6 (0.9) Sex 1.20, 0.548 Male 213 (54.1) 148 (51.6) 361 (50.6) Female 181 (45.9) 139 (48.4) 352 (49.4) Health insurance coverage 9.36, 0.009 Yes 73 (19.4) 37 (13.6) 84 (12.6) No 303 (80.6) 235 (86.4) 585 (87.4) Length of illness, mean ± SD 6.0 ± 5.7 days 7.8 ± 9.0 days 6.4 ± 6.5 days 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 OR CI (95%) Age 1.1 1.0 - 1.3 Health care-seeking (1=yes) 0.0 0.0 - 0.01* Health insurance coverage (1=yes) 1.0 0.4 - 2.5 Primary education (reference group) 1.0 Secondary 1.8 0.9 - 3.7 Tertiary 1.9 0.3 - 15.1 lnMedical 0.8 0.1 - 5.0 Male 1.4 0.7 - 2.6 Social assistance from government 1.6 0.6 - 4.4 Logged family income 0.8 0.3 - 1.8 Rural area (reference group) Urban 1.6 0.7 - 3.8 Periurban 1.2 0.5 - 2.9 Poor-to-Very poor health status (reference group) 1.0 Moderate-to-Very good health status 0.3 0.03 - 2.1 Good-to-Very good health status 12.6 6.0 - 26.3* Lower class (reference group) Middle class 1.6 0.5 - 5.2 Upper 0.8 0.2 - 3.1 Crowding 0.9 0.8 - 1.1 Model χ2, P 287.08, < 0.0001 -2 LL 327.56 R2 0.53 Hosmer and Lemeshow χ2 = 4.40, P = 0.82 OR denotes odds ratio, CI (95%) means 95% confidence interval and *P < 0.05

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

Self-rated health status Very poor-to-poor Good Moderate-to-very

good Good-to-very

good All

OR CI (95%) OR CI (95%) OR CI (95%) OR CI (95%) Estimate CI (95%)

Self-reported illness (1=yes) 2.0 0.3 – 15.6 0.1 0.05 – 0.2* 0.5 0.1 – 4.4 0.1 0.05 – 0.2* 1.8 1.1 – 2.4* Age 1.0 0.9 – 1.2 0.9 0.9 – 1.1 1.0 0.8 – 1.2 0.9 0.9 – 1.1 0.02 - 0.03 – 0.1 Health care-seeking (1=yes) 10.0 1.0 – 96.5* 0.7 0.3 – 1.9 0.1 0.01 – 0.5* 0.7 0.3 – 2.1 1.0 0.1 – 2.0* Health insurance coverage (1=yes) 0.3 0.04 – 2.8 1.1 0.6 – 2.2 3.0 0.4 – 25.5 1.2 0.6 – 2.4 0.04 - 0.3 – 0.4 Primary education (reference group) 1.0 1.0 1.0 1.0 1.0 Secondary 0.7 0.3 – 1.9 0.9 0.6 – 1.5 1.4 0.5 – 3.8 1.0 0.6 – 1.6 0.02 - 0.2 – 0.2 Tertiary 0.0 0 – 0.0 0.4 0.1 – 1.0 5E+007 0.0 - 0.4 0.2 – 1.3 0.3 0.4 – 1.0 Logged Medical expenditure 1.6 0.7 – 3.6 0.6 0.4 – 1.2 0.7 0.4 – 1.2 0.5 0.1 – 1.0* Social assistance from government 0.2 0.03 – 1.7 1.2 0.6 – 2.2 4.8 0.6 – 38.5 1.2 0.6 – 2.3 0.1 - 0.2 – 0.4 Lower class (reference group) 1.0 1.0 1.0 1.0 1.0 Middle class 0.6 0.1 – 2.9 2.1 0.9 – 4.5 1.8 0.3 – 9.6 2.2 1.0 – 4.8 - 0.7 - 1.0 - - 0.4* Upper 14.9 1.9 – 118.3 * 0.7 0.3 – 1.4 0.1 0.01 – 0.5* 0.7 0.3 – 1.6 - 0.6 - 1.0 - -0.1 Rural area (reference group) 1.0 1.0 1.0 1.0 1.0 Urban 1.6 0.4 – 3.0 0.6 0.4 – 1.0* 0.9 0.3 – 2.7 0.6 0.4 – 1.0* 0.5 0.2 – 0.8* Periurban 0.0 0.0 - 0.0 3.3 1.3 – 8.2* 2E+0007 3.3 1.53– 8.2* - 0.01 - 0.3 – 0.3 Male 0.9 0.3 – 2.3 1.5 1.0 – 2.4 1.1 0.4– 3.0 1.4 0.9 – 2.2 - 0.1 - 0.3 – 1.2 Logged family income 0.1 0.04 – 0.4* 1.3 0.9 – 2.0* 8.2 2.8 – 23.8* 2.0 1.2 – 3.4* - 0.30 - 0.6 – -0.001* Crowding 1.6 1.3 – 2.0* 0.9 0.8 – 1.0* 0.6 0.5 – 0.8* 0.9 0.8 – 0.98* 0.1 - 0.01 – 0.1* Model χ2, P 59.66, < 0.0001 113.11, < 0.0001 30.37, < 0.0001 113.11, <0.0001 112.94, < 0.0001 -2 LL 146.38 588.76 175.67 588.76 2354.33 R2 0.38 0.20 0.31 0.20 Pseudo R2 = 0.10 Hosmer and Lemeshow χ2 = 4.6, P = 0.82 χ2 = 4.61, P = 0.80 χ2 = 4.36, P = 0.94 χ2 = 4.61, P = 0.80 Goodness of fit,

χ2=5451.14. P < 0.001 OR denotes odds ratio; *P < 0.05

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Table 5.5.5: Self-rated health status and antithesis of illness, n = 1,330 Characteristic

Self-rated health status Very good Good Moderate Poor Very poor

n (%) n (%) n (%) n (%) n (%) Antithesis of illness No 18 (2.9) 38 (6.4) 26 (31.3) 7 (38.9) 0 (0.0) Yes 611 (97.1) 560 (93.6) 57 (68.7) 11 (61.1) 2 (100.0) χ2 = 125.58, P < 0.0001 Characteristic

Good health (Antithesis of illness) No Yes

n (%) n (%) Self-rated health status Very good 18 (20.0) 611 (49.2) Good 38 (42.7) 560 (45.1) Moderate 26 (29.2) 57 (4.6) Poor 7 (7.9) 11 (0.9) Very poor 0 (0.0) 2 (0.2) χ2 = 125.58, P < 0.0001

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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 June-

October 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

144

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 self-

response 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 kiTj,

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 = (Pmc, ED,Ai , MR, AR, CR, PA, F, EN, C, M, FM; CH, PHS, HSB,Q)……(Eqn [1])

Eqn [1] expresses current health status Ht as a function of price of medical care Pmc,

education of individual, ED; age of the individual, Ai , 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 = (Pmc, 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 self-

related 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

149

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].

150

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.

151

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.

152

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.

153

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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 Descriptive Analysis

Age 14.4 (±1.7 years)

Residence 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) US$652.30 (± $607.37)

Average income (per year) US$3,699.00 (± $3,167.41)

Crowding 2.3( ±1.5 persons)

Self reported good health 91.7%

Pregnancy (at the time of the survey) 1.3%

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

Characteristic β Coefficient

Std Error

Odds ratio

CI (95%)

Middle class 0.46 0.37 1.58 0.77 - 3.25 Upper class -0.36 0.34 0.70 0.36 - 1.37 Referent group (lower class) 1.00 Cost of medical care 0.00 0.00 0.996* 0.99 - 1.01 Crowding -0.02 0.10 0.98 0.80 - 1.19 Environment 0.65 0.37 1.91 0.93 - 3.91 Negative Affective Conditions -0.03 0.04 0.97 0.91 - 1.04 Assets owned by household -0.02 0.06 0.98 0.88 - 1.10 Age 0.004 0.08 1.00 0.86 - 1.18 Health Insurance -3.37 0.46 0.03*** 0.01 - 0.09 Other Towns -0.07 0.29 0.94 0.54 - 1.64 Urban areas -0.05 0.34 0.95 0.49 - 1.85 Referent group (Rural areas) 1.00 Number of male -0.05 0.12 0.95 0.75 - 1.20 Number of females -0.32 0.11 0.73** 0.59 - 0.90 Number of children 0.06 0.09 1.06 0.88 - 1.26 Average Consumption 0.00 0.00 0.997 1.00 - 1.01 Crime Index -0.01 0.01 0.99 0.98 - 1.01 Average Income 0.00 0.00 0.997 1.00 - 1.01 Visits to Health practitioners 0.23 0.10 1.25* 1.04 - 1.52 Education 0.04 0.03 1.04 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 non-

metric 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 semi-

urban 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, self-

reported 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-

168

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

169

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.

170

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Table 7.7.1. Sociodemographic characteristics of sample by area of residence, 2002 and 2007 Variable

2002

2007

Rural Semi-Urban

Urban Rural Semi-Urban

Urban

Marital status Married 1232 (25.7) 568 (25.7) 243 (19.3) 262 (23.9) 111 (21.0) 161 (21.2) Never married 3033 (63.3) 1452 (65.7) 907 (71.9) 723 (65.9) 362 (68.6) 523 (68.9) Divorced 25 (0.5) 16 (0.7) 18 (1.4) 11 (1.0) 16 (3.0) 16 (2.1) Separated 51 (1.1) 27 (1.2) 22 (1.7) 12 (1.1) 5 (0.9) 8 (1.1) Widowed 453 (9.4) 147 (6.7) 71 (5.6) 89 (8.1) 34 (6.4) 51 (6.7) Income quintile Poorest 20% 1864 (24.8) 450 (13.5) 206 (11.4) 498 (29.9) 77 (10.2) 97 (9.2) Poor 1867 (24.8) 511 (15.3) 231 (12.7) 437 (26.2) 146 (19.4) 131 (12.4) Middle 1559 (20.7) 652 (19.2) 331 (18.2) 342 (20.5) 161 (21.4) 212 (20.0) Wealthy 1340 (17.8) 759 (22.7) 441 (24.3) 237 (14.2) 183 (24.3) 265 (25.0) Wealthiest 20% 894 (11.9) 965 (28.9) 605 (33.4) 154 (9.2) 185 (75.2) 354 (33.4) Health conditions Diagnosed Acute: Cold 1 (0.7) 0 (0.0) 0 (0.0) 13 (7.8) 21 (20.0) 13 (7.8) Diarrhoea 3 (2.2) 1 (3.0) 0 (0.0) 2 (1.2) 2 (1.9) 2 (1.2) Asthma 1 (0.7) 2 (6.1) 0 (0.0) 20 (12.0) 6 (5.7) 20 (12.0) Diagnosed Chronic: Diabetes mellitus 8 (6.0) 0 (0.0) 1 (4.2) 23 (13.8) 18 (17.1) 23 (13.8) Hypertension 57 (42.5) 20 (60.6) 10 (41.7) 33 (19.8) 29 (27.6) 33 (19.8) Arthritis 38 (28.4) 8 (24.2) 7 (29.2) 9 (5.4) 7 (6.7) 9 (5.4) Other 26 (19.4) 2 (6.1) 6 (25.0) 45 (26.9) 13 (12.4) 45 (26.9) Non-diagnosed - - - 22 (13.2) 9 (8.6) 22 (13.2) Self-reported illness Yes 1181 (16.3) 384 (12.0) 228 (12.9) 324 (20.0) 104 (14.2) 164 (16.0) No 6051 (83.7) 2811 (88.0) 1540 (87.1) 1298 (80.0) 627 (85.8) 864 (84.0) Health care-seekers Yes 791 (66.0) 261 (66.8) 145 (64.7) 215 (65.5) 65 (63.1) 125 (74.4) No 407 (34.0) 130 (33.2) 79 (35.3) 113 (34.5) 38 (36.9) 43 (25.6) Health insurance Yes, Private 540 (7.4) 539 (16.7) 341 (19.3) 114 (7.1) 117 (16.3) 191 (18.7) Yes, Public - - - 126 (7.8) 56 (17.8) 98 (9.6) No 6723 (92.6) 2690 (83.3) 1430 (80.7) 1361 (85.0) 547 (76.0) 735 (71.8) Age Mean (SD) in yrs 29.5 (23.0) 28.6 (21.2) 30.0 (21.0) 29.9 (22.3) 30.6 (21.1) 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

Self-reported Illness

Yes

No

Very good 63 (10.7) 1114 (40.2) Good 176 (29.8) 1305 (47.1) Fair 234 (39.7) 281 (10.2) Poor 104 (17.6) 55 (2.0) Very poor 13 (2.2) 13 (0.5) Total 590 2768 χ2 (df = 4) = 700.633, P < 0.001, correlation coefficient = - 0.412

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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 Self-rated health status

Income Quintile

Poorest 20% 2.00 3.00 4.00 Wealthiest 20% Very good

196 (30.2) 237 (34.0) 225 (32.4) 282 (42.4) 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) 7 (1.0) 7 (1.0) 4 (0.6) 2 (0.3)

Total 650

697

695

665

663

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 Diagnosed health condition

Income Quintile Poorest 20% 2.00 3.00 4.00 Wealthiest 20%

Yes, Cold

14 (11.4) 20 (17.5) 21 (15.8) 13 (11.8) 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

7 (5.7) 7 (6.1) 13 (9.8) 9 (8.2) 13 (11.1)

Total 123 114 133 110 117 χ2 (df = 28) = 36.161, P < 0.001

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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 under-

5 year olds was extracted from the 2007 survey. The survey (Jamaica Survey of Living

Conditions) began in 1989 to collect data from Jamaicans in order to assess government policies.

Since 1989, the JSLC has added a new module each year in order to examine that phenomenon,

which is critical within the nation [18, 19]. In 2002, the focus was on 1) social safety nets, and

2) crime and victimization, while for 2007, there was no focus.

Methods

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 self-

reported 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 care-

seeking 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 semi-

urban 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 care-

seeking behaviour for this sample is embedded in the financial constraints of parents and/or

guardians as well as their geographical challenges. The terrain in rural zones in Jamaica is such

that medical care facilities are not easily accessible to residents compared to urban dwellers.

With this terrain constraint comes the additional financial burden of attending medical care

facilities at a location which is not in close proximity to the home of rural residents, and this

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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.

193

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, 1980-2008. STATIN, Kingston.

12. Planning Institute of Jamaica, (PIOJ). (1981-2009) Economic and Social Survey, 1980-2008. 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

2002 2007 n % n %

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

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Table 8.8.2. Health status by self-reported illness

Health status

Self-reported illness

Yes No

n (%) n (%)

Very good 30 (24.2) 227 (48.3)

Good 61 (49.2) 217 (46.2)

Fair 23 (18.5) 19 (4.0)

Poor 9 (7.3) 6 (1.3)

Very poor 1 (0.1) 1 (0.2)

Total 124 470

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

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Table 8.8.3. Health status by self-reported diagnosed illness

Health status

Self-reported diagnosed illness Cold Diarrhoea Asthma Unspecified No

Very good 18 (30.5) 2 (22.2) 1 (5.9) 5 (22.7) 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

1 (1.7) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0)

Total 59 9 17 22 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 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). 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 care-

practitioners 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 non-

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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 self-

reported 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 [59-

61] (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 socio-

demographic variables (for 2002); as well as 2) not reported an illness and some socio-

demographic, 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 care-

seeking 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

213

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

215

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

216

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 non-

illness) 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.

217

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.

218

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Table 9.9.1. Sociodemographic characteristics of sample, 2002 and 2007 Variable 2002 2007

n % n % Marital status Married 2007 25.7 522 24.3 Never married 5421 69.4 1528 71.1 Divorced 64 0.8 34 1.6 Separated 85 1.1 16 0.7 Widowed 234 3.0 50 2.3 Self-reported illness Yes 1217 10.2 388 12.1 No 10699 89.8 2820 87.9 Self-reported diagnosed illness Cold - - 69 17.2 Diarrhoea 5 5.7 11 2.7 Asthma 6 6.8 47 11.7 Diabetes mellitus 3 3.4 31 7.7 Hypertension 39 44.3 58 14.4 Arthritis 16 18.2 24 6.0 Other 19 21.6 102 25.4 Not diagnosed - - 60 14.9 Income quintile Poorest 20% 2454 19.9 671 20.3 Poor 2345 19.0 640 19.4 Middle 2440 19.8 636 19.3 Wealthy 2482 20.1 667 20.2 Wealthiest 20% 2611 21.2 689 20.9 Health care-seeking behaviour Yes 769 60.7 253 62.3 No 497 39.3 153 37.7 Health insurance coverage Yes 1251 10.5 612 19.3 No 10699 89.5 2560 80.7 Area of residence Rural 7727 62.7 1654 50.1 Semi-urban 3062 24.8 706 21.4 Urban 1543 12.5 943 28.5 Income Median (Range) Ja $251,795.96

(Ja. $6,423,253.16.72) Ja $545,950.17

(Ja. $5,228,700.28) Age Mean ±SD 28.28 ± 21.7 ears 29.11 ± 21.6 years Consumption Median (Range) Ja $55,508.45

(Ja. $1,992,283.72) Ja $123,697.30

(Ja. $1,621,147.12) Duration of illness Median (Range) 10.5 days (90 days) 7.1 days (15 days) Cost of medical care Public Median (Range) Ja $150.00 (Ja. S12,000) Ja $294.96 (Ja. $20,000) Private Median (Range) Ja $800.00 (Ja $ 29,000) Ja $1130.39 (Ja $ 13,000) In 2002, US $1.00 = Ja. $50.87 In 2007, US $1.00 = Ja. $80.47

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Table 9.9.2. Health status and self-rated illness Health status

Self-rated illness Yes No

Very good 50 (13.0) 1193 (42.6) Good 129 (33.4) 1351 (48.2) Fair 125 (32.4) 205 (7.3) Poor 66 (17.1) 44 (1.6) Very poor 16 (4.1) 8 (0.3) Total 386 2801 χ2 (df = 4) = 602.354, P < 0.001; cc = 0.399

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Table 9.9.3. Predictors of poor self-reported illness by some explanatory variables, 2002

Variable S.E.

Wald statistic P Odds ratio 95.0% C.I.

Age 0.003 222.661 0.000 1.044 1.038 1.049 Urban areas 0.142 9.470 0.002 1.547 1.172 2.043 Other towns

†Rural areas Log Consumption

0.156 0.058

1.312 8.344

0.252 0.004

1.195 1.183

0.881 1.056

1.622 1.327

Separated_Div_Wid

0.148

4.766

0.029

1.382

1.034

1.848

Married 0.097 1.388 0.239 1.121 0.927 1.355 †Never married

Physical environment

0.086

0.885

0.347

1.084

0.916

1.283

Secondary 0.100 0.018 0.893 1.013 0.833 1.232 Tertiary 0.212 0.087 0.768 1.064 0.703 1.612 †Primary or below

Rented – house tenure

0.170

0.017

0.895

0.978

0.700

1.366

Owned 0.123 0.025 0.876 1.020 0.801 1.298 †Squatted

Social support

0.082

6.231

0.013

1.226

1.045

1.440

Constant 0.664 92.874 0.000 0.002 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. Wald

statistic P Odds ratio 95.0% C.I. Good health status 0.257 125.717 0.000 17.801 10.761 29.446 Fair health status 0.254 11.927 0.001 2.403 1.461 3.951 †Poor health status

Age

0.005

39.848

0.000

0.967

0.957

0.977

Middle Class

0.257

0.011

0.918

1.027

0.620

1.701 Upper class 0.364 0.344 0.558 1.238 0.606 2.528 †Lower class

Married

0.194

0.710

0.399

0.849

0.581

1.241 Divorced, separated or

wid 0.313 0.003 0.954 1.018 0.551 1.881

†Never married Health insurance

0.195

0.016

0.899

0.975

0.665

1.430

Urban area

0.200

5.221

0.022

1.579

1.067

2.336 Other towns 0.216 2.858 0.091 1.440 0.944 2.199 †Rural areas

Log Consumption

0.228

6.844

0.009

0.551

0.352

0.861 Constant 2.596 10.301 0.001 4158.196

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 Nagelkerke R Square

Age 0.143 Age+urban area 0.145 Age+urban area+consumption 0.148 Age+urban area+consumption+social support 0.149 Age+urban area+consumption+social support+ marital status 0.151

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Table 9.9.6. Model summary for 2007 logistic regression analysis Model Nagelkerke R

Square Good health 0.300 Good health+age 0.354 Good health+Age+fair health 0.362 Good health+Age+fair health+consumption 0.371 Good health+Age+fair health+consumption+urban area 0.376

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

229

Part II

ERRORS IN DATA

230

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

231

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.

233

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

234

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 self-

reported 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

235

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,

236

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

237

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%.

238

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.

239

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

240

illness indicating that she had very poor-to-poor health status was greater at 85+ years than a 46-

54-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 moderate-

to-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 self-

reported 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

241

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 cut-

off 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 poor-

242

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, 31-

33]. 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.

243

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 self-

reported 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 under-

reporting 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 age-

dependent, 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

245

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.

246

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Table 10.10.1. Socio-demographic characteristic of sample, n = 6,783 n %

Sexes Male 3303 48.7 Female 3479 51.3 Marital status Married 1056 23.3 Never married 3136 69.2 Divorced 77 1.7 Separated 41 0.9 Widowed 224 4.9 Self-reported illness Yes 980 14.9 No 5609 85.1 Self-reported health status Very good 2430 37.0 Good 2967 45.2 Moderate 848 12.9 Poor 270 4.1 Very poor 50 0.8 Area of residence Urban 2002 29.5 Semi-urban 1458 21.5 Rural 3322 49.0 Income quintile Poorest 20% 1343 19.8 Poor 1354 20.0 Middle 1351 19.9 Wealthy 1352 19.9 Wealthiest 20% 1382 20.4 Education attainment (level) No formal 4071 61.8 Basic 783 11.9 Primary or preparatory 898 13.6 Secondary 709 10.8 Tertiary 131 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 46-

54yrs 55-64yrs

65-74yrs

75-84yrs

85+yrs 46-54yrs

55-64yrs

65-74yrs

75-84yrs

85+yrs

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 Poorest20% 19.6 22.4 28.1 33.3 25 54.6 59.7 65.6 100 100

Poor 20.7 29.4 42.9 50.0 33.3 46.7 58.8 81.0 100.0 100.0 Middle 18.0 24.2 30.3 30.0 20.0 47.0 61.3 66.7 71.4 83.3 Wealthy 18.6 22.0 33.3 50.0 57.1 52.0 62.7 73.3 87.5 85.7 Wealthiest20% 12.0 16.4 20.1 25.0 18.4 40.7 54.5 50.0 25.0 33.3 Total n 266 207 156 97 23 266 207 156 97 23

Women Self-reported illness Yes 29.1 35.1 37.1 41.7 47.4 77.2 81.8 79.8 79.2 73.7 No 11.1 13.6 15.3 18.5 17.4 44.3 51.8 60.0 59.3 52.2 Area of residence Urban 9.7 11.9 16.1 25.0 25.0 53.0 60.6 59.7 56.3 41.7 Semi-urban 14.2 14.5 17.2 28.6 28.6 52.2 62.3 72.4 71.4 71.4 Rural 26.8 33.9 36.9 32.1 34.8 64.5 69.6 77.4 75.0 69.6 Marital status Married 18.6 22.7 32.3 0 0 58.8 69.3 80.6 0.0 0.0 Not 19.0 23.1 25.2 30.0 31.7 58.2 64.3 68.5 70.0 63.3 Social class Poorest20% 28.7 33.8 43.8 33.3 28.6 65.7 70.4 75.0 77.8 71.4 Poor 19.0 23.7 22.9 28.6 27.3 64.0 74.6 77.1 71.4 63.6 Middle 19.0 21.7 26.1 31.3 38.5 57.1 62.7 69.6 62.5 56.8 Wealthy 18.6 22.8 25.8 50.0 50.0 61.9 68.4 71.0 80.0 80.0 Wealthiest20% 9.8 14.5 12.9 20.0 22.2 46.2 53.9 58.1 60.0 55.6 Total n 284 216 172 119 43 284 216 172 119 43

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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 46-

54yrs 55-64yrs

65-74yrs

75-84yrs

85+yrs 46-54yrs

55-64yrs

65-74yrs

75-84yrs

85+yrs

Men Self-reported illness Yes 7.7 5.7 6.5 3.2 1.9 9.6 10.5 16.8 7.7 4.1 No 1 1 1 1 1 1 1 1 1 1 Area of residence Urban 0.5 0.5 0.5 0.5 0.8 1.0 1.2 0.4 0.1 0.7 Semi-urban 0.9 1.1 1.1 1.8 3.5 0.9 1.4 1.6 1.8 3.9 Rural 1 1 1 1 1 1 1 1 1 1 Marital status Married 0.9 0.7 0.9 0.9 0.6 0.7 0.8 0.7 0.2 0.1 Not 1 1 1 1 1 1 1 1 1 1 Social class Poorest20% 1.8 1.5 1.6 1.5 1.5 1.8 1.2 1.9 large large Poor 1.9 2.1 3.0 3.0 2.2 1.3 1.2 4.3 large large Middle 1.6 1.6 1.7 1.3 1.1 1.3 1.3 2.0 7.5 10.0 Wealthy 1.7 1.4 2.0 3.0 5.9 1.6 1.4 2.7 21.0 12.0 Wealthiest20% 1 1 1 1 1 1 1 1 1 1 Total n 266 207 156 97 23 266 207 156 97 23

Women Self-reported illness Yes 3.3 3.4 3.3 3.2 4.3 4.3 4.2 2.6 2.6 2.6 No 1 1 1 1 1 1 1 1 1 1 Area of residence Urban 0.3 0.3 0.3 0.7 0.6 0.6 0.7 0.4 0.4 0.3 Semi-urban 0.5 0.3 0.4 0.8 0.8 0.6 0.7 0.8 0.8 1.0 Rural 1 1 1 1 1 1 1 1 1 1 Marital status Married 1.0 1.0 1.4 0.0 0.0 1.0 1.3 1.9 0.0 0.0 Not 1 1 1 1 1 1 1 1 1 1 Social class Poorest20% 3.7 3.0 5.3 2.0 1.4 2.2 2.0 2.2 2.3 2.0 Poor 2.2 1.8 2.0 1.6 1.3 2.1 2.5 2.4 1.1 1.4 Middle 2.2 1.6 2.4 1.8 2.2 1.5 1.4 1.7 1.1 1.0 Wealthy 2.1 1.7 2.3 4.0 3.5 1.9 1.9 1.8 2.7 3.2 Wealthiest20% 1 1 1 1 1 1 1 1 1 1 Total n 284 216 172 119 43 284 216 172 119 43

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Table 10.10.4. Odds ratios of poor health status by age cohorts

Poor

Health status

Age cohorts

46-54yrs 55-64yrs 65-74yrs 75-84yrs 85+yrs

Very poor-to-poor health

Yes 0.004 0.020 0.046 0.167 0.228

No 1 1 1 1 1

Moderate-to-very poor

health

Yes 0.091 0.529 1.861 5.444 5.048

No 1 1 1 1 1

Total n 550 423 328 216 66

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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 = 0.12-0.84). This paper highlights that caution must be used by researchers in interpreting self-reported 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 non-

dichotomous 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) non-

response 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 [22-

37], 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 socio-

demographic, 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 self-

reported illness and is related to socio-demographic, economic and self-rated health status.

Ht=f(Ai, Gi,HHi, ARi, It, Ji, lnC, lnDi, EDi, MRi, Si , HIi , lnY, CRi, MCt, SAi, Ti , ε i) (1)

where Ht (i.e., self-rated current health status in time t) is a function of age of

respondents, Ai ; sex of individual i, Gi; household head of individual i, HHi; area of

residence, ARi; current self-reported illness of individual i, It; injuries received in the last

4 weeks by individual i, Ji; logged consumption per person per household member, lnC;

logged duration of time that individual i was unable to carry out normal activities, lnDi;

education level of individual i, EDi; marital status of person i, MRi; social class of person

i, Si; 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, MCt; social

assistance of individual i, SAi; length of time living in current household by individual i,

Ti; and an error term (i.e., residual error).

It,=f(Ai, Gi ,HHi, ARi, Ji, lnC, lnDi, EDi, MRi, Si, HIi, lnY, CRi, MCt, SAi, Ti , Ht, ε i) (2)

where It (i.e., self-reported illness in last 4-weeks) is a function of age of respondents, Ai

; sex of individual i, Gi; household head of individual i, HHi; area of residence, ARi;

injuries received in the last 4 weeks by individual i, Ji; logged consumption per person

per household member, lnC; logged duration of time that individual i was unable to carry

out normal activities, lnDi; education level of individual i, EDi; marital status of person i,

MRi; social class of person i, Si; health insurance coverage of person i, HIi; logged

income, lnY; crowding of individual i, CRi; medical expenditure of individual i in time

period t, MCt; social assistance of individual i, SAi; length of time living in current

household by individual i, Ti; self-rated current good health status, Ht; 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%).

Ht=f(Ai, Gi,HHi, ARi, It, lnDi, EDi, MRi, Si, HIi, lnY, CRi, MCt, SAi, Ti, ε i) (3)

It,=f(Ai, Gi ,HHi, ARi, lnDi, EDi, MRi, Si, HIi, lnY, CRi, MCt, SAi, Ti, Ht, ε i) (4)

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

260

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 re-

coded 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

264

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 non-

linear 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, self-

reported 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.

270

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Table 11.11.1. Socio-demographic characteristic of sample by sex of respondents Characteristic Sex

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

9.31 (15.48) 11.19 (36.51)

10.46 (30.23)

> 0.05

1Rate in 2007:1US$= Ja$80.47

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

Primary Secondary Tertiary Total P

% % % Social standing < 0.05 Poorest 20% 20.3 19.7 3.8 19.9 Poor 20.0 21.7 7.6 20.0 Middle 19.4 24.5 16.0 19.9 Wealthy 19.9 20.3 19.1 19.9 Wealthiest 20% 20.3 13.7 53.4 20.2 Total 5752 709 131 6592 Marital status < 0.05 Married 25.5 0.0 16.9 23.4 Never married 66.1 99.7 81.5 69.1 Divorced 1.9 0.0 1.5 1.7 Separated 1.0 0.3 0.0 0.9 Widowed 5.5 0.0 0.0 5.0 Total 4048 344 130 4522 Area of residence < 0.05 Urban 28.8 30.0 63.4 29.6 Semi-urban 22.0 19.2 16.4 21.6 Rural 49.2 50.8 20.6 48.8 Total 5752 709 131 6592 Medical care-seeking behaviour >0.05 Yes 65.7 60.0 66.7 65.5 No 34.3 40.0 33.3 34.5 Total 953 40 12 1005 Self-reported illness < 0.05 Yes 16.2 5.4 9.2 14.9 No 83.8 94.6 90.8 85.1 Total 5736 705 130 6571 Health insurance coverage < 0.05 None 79.8 83.7 57.8 79.8 Private coverage 12.0 11.7 35.9 12.5 Public coverage 8.2 4.6 6.3 7.7 Total 5682 689 128 6499 Age Mean (SD) in years 32.0

(22.6) 14.6 (1.7)

26.4 (10.6)

30.0 (21.8

< 0.05

Medical Expenditure1 Mean (SD) in US$

10.44 (30.78)

12.31 (18.73)

5.79 (5.51)

10.46 (30.23)

>0.05

1Rate in 2007:1US$= Ja$80.47

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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 0.05 Poorest 20% 19.7 20.0 19.9 Poor 18.1 20.4 20.0 Middle 20.9 19.8 19.9 Wealthy 20.4 19.7 19.8 Wealthiest 20% 20.9 20.2 20.3 Total 980 5609 6589 Marital status < 0.05 Married 35.9 20.9 23.3 Never married 46.9 73.4 69.2 Divorced 3.1 1.4 1.7 Separated 1.7 0.8 0.9 Widowed 12.5 3.5 4.9 Total 721 3801 4522 Area of residence < 0.05 Urban 26.6 30.1 29.6 Semi-urban 18.7 21.9 21.5 Rural 54.7 47.9 48.9 Total 980 5609 6589 Medical care-seeking behaviour >0.05 Yes 65.1 77.4 65.4 No 34.9 22.6 34.6 Total 970 31 1001 Health insurance coverage < 0.05 None 75.3 80.6 79.8 Private coverage 11.5 12.7 12.5 Public coverage 13.3 6.8 7.7 Total 978 5525 6503 Age Mean (SD) in years 42.0

(27.7) 28.0

(20.0) < 0.05

Medical Expenditure1 Mean (SD) in US$ 9.30 (18.27)

38.80 (126.09)

< 0.05

1Rate 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 socio-demographic, economic and biological variables Variable SE P Odds ratio

95.0% C.I.

R-squared

Self-reported illness

0.48

0.002

0.23

0.09-0.59

0.02

Medical care-seeking

0.18

0.000

0.51

0.36-0.72

0.02

Age

0.01

0.000

0.97

0.96-0.97

0.15

Income

0.00

0.007

1.00

1.00-1.00

0.01

Constant

0.54

0.000

16.03

-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

Variable SE P Odds ratio 95.0% C.I.

R-square

Male 0.47 0.003 0.25 0.10-0.63 0.059 Head Household

0.54

0.043

0.33

0.12-0.96

0.024

Age

0.01

0.010

1.04

1.01-1.07

0.021

Good Health

0.49

0.020

0.32

0.12-0.84

0.075

-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. Self-reported illness in a 4-week reference period is a good measure of objective health that self-reported 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, self-

reported 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 non-

linearity 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 (SPIp, ε) [1]

LEm = ƒ (SPIm, ε) [2]

LEf = ƒ (SPIf, ε) [3]

Where LEp (life expectancy at birth for the population at a given period) is a function of self-

reported illness (SPIp) 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 4-

week 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 self-

reported 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 self-

reported 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 self-

reported 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

287

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 sub-

groupings 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 self-

reported 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 4-

week 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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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. Statistics Canada Catalogue 2003; 89-589. 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. Well-being: 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.

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Table 12.12.1. Life expectancy at birth for the sexes, self-reported illness, and mortality, 1989-2007 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 1992 73.6a 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 75.604 0.738 102.425 < 0.001 73.934, 77.274 Self-reported illness -0.173 0.054 -0.731 -3.217 0.011 -0.295, -0.051 F statistic [1, 9] = 10.350, P = 0.011 R = - 0.731 R2 = 0.535 Female children Constant 83.363 3.375 24.700 < 0.001 75.382, 91.344 Self-reported illness -0.528 0.213 -0.684 -2.478 0.042 -1.031, -0.024 F statistic [1, 7] = 6.138, P = 0.042 R = - 0.684 R2 = 0.467 Male children Constant 72.718 0.587 123.840 < 0.001 71.330, 74.107 Self-reported illness -0.172 0.050 -0.796 -3.478 < 0.010 -0.289, -0.055 F statistic [1, 7] = 12.096, P = 0.010 R = - 0.796 R2 = 0.633

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Illness/Injury (in %)20.0018.0016.0014.0012.0010.008.00

Life

exp

ecta

ncy

at b

irth:

bot

h se

x (in

yea

rs)

74.50

74.00

73.50

73.00

72.50

R Sq Linear = 0.535

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 self-reported illness.

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Self-reported Health of female (in %)

22.0020.0018.0016.0014.0012.00

Life e

xpec

tancy

: fem

ale (a

t birt

h in y

ears

)78.00

77.00

76.00

75.00

74.00

73.00

72.00

R Sq Linear = 0.467

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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Self-reported Health of male (in %)18.0016.0014.0012.0010.008.00

Life

exp

ecta

ncy:

mal

e (a

t birt

h in

yea

rs) 71.25

71.00

70.75

70.50

70.25

70.00R Sq Linear = 0.633

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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Illness/Injury (in %)20.0018.0016.0014.0012.0010.008.00

Morta

lity (in

No. o

f peo

ple)

19000.00

18000.00

17000.00

16000.00

15000.00

14000.00

13000.00

R Sq Cubic =0.106

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 self-

presentation, 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 health-

promoting 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 self-

actualization [1]. Each item was on a 10-point Likert scale. Using Cronbach alpha for the five-

item scale, reliability was 0.841 (or α = 84%).

QoLi = 1/5*∑Ni where i is each need (i.e. I = 1, 2, 3, 4, 5)

where the QoL index is: 0≤QoLi ≤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 self-

reports 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 multi-

faceted, 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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Table 13.13.1 Demographic characteristics of sample for CLG and JSLC, 2007 Variable CLG JSLC

n % n % Gender Male 574 42.9 3303 48.7 Female 723 54.0 3479 51.3 Social class Working 766 59.0 2697 39.8 Middle 476 36.6 1351 19.9 Upper 57 4.4 2734 40.3 Educational level Primary or below 60 4.6 5752 87.3 Secondary 892 69.1 709 10.8 Tertiary 339 26.3 131 2.0 QoL Very poor 13 1.0

NA

Poor 59 4.5 Moderate 536 40.6 Good 575 43.6 Very good 136 10.3 Health status Very poor

NA 50 0.8

Poor 270 4.1 Moderate 848 12.9 Good 2967 45.2 Very good 2430 37.0 Current economic situation compared to 1 year ago

Very good 58 4.4 NA Good 361 27.1

Moderate 660 49.5 Poor 164 12.3 Very poor 90 6.8 Area of residence Urban 291 21.7 2002 29.8 Rural 1041 77.8 4780 70.5 Age Mean (SD) 35.0 years (13.6) 29.9 years (21.7) NA – Data not available

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

Variable

CLG JSLC

Gender Gender

Male Female Male Female

n (%) n (%) n (%) n (%)

QoL and health status

Very poor 6 (1.1) 7 (1.0) 24 (0.8) 26 (0.8)

Poor 18 (3.2) 40 (5.6) 111 (3.5) 159 (4.7)

Moderate 222(39.3) 292 (41.0) 331 (10.4) 517 (15.3)

Good 245 (43.4) 316 (44.3) 1482 (46.4) 1485 (44.1)

Very good 74 (13.1) 58 (8.1) 1247 (39.0) 1183 (35.1)

Total 565 713 3195 3370

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

Variable

CLG JSLC

Area of residence Area of residence

Non-urban Urban Non-urban Urban

n (%) n (%) n (%) n (%)

QoL and Health status

Very poor 9 (0.9) 4 (1.4) 42 (0.9) 8 (0.4)

Poor 47 (4.6) 12 (4.2) 215 (4.7) 55 (2.8)

Moderate 435(42.4) 98 (34.3) 554 (12.0) 294 (15.1)

Good 432 (42.1) 140 (49.0) 2072 (44.9) 895 (46.0)

Very good 104 (10.1) 32 (11.2) 1735 (37.6) 695 (35.7)

Total 1027 286 4618 1947

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

Classification QoL JSLC Standardized JSLC

n n n

Very poor 13 50 10

Poor 59 270 54

Moderate 536 848 171

Good 575 2967 596

Very good 136 2430 488

Total 1319 6565 1319

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

QoL

Economic situation of family

Much worse

A little worse

Same A little better Much better

n (%) n (%) n (%) n (%) n (%)

Very poor 3 (5.1) 3 (1.2) 2 (0.4) 2 (0.5) 3 (2.7)

Poor 2 (3.4) 24 (9.4) 18 (3.8) 6 (1.5) 7 (6.3)

Moderate 34 (57.6) 124 (48.8) 192 (41.0) 155 (37.5) 29 (26.1)

Good 17 (28.8) 88 (34.6) 213 (45.6) 200 (48.4) 49 (44.1)

Very good 3 (5.1) 15 (5.9) 43 (9.2) 50 (12.1) 23 (20.7)

Total 59 254 468 413 111

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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 sub-national 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 self-evaluated 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 sub-

national 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 self-

evaluated 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 call-

backs 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 sub-

national 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 chi-

square 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;

325

(4) marital status; (5) household heads; (6) medical care-seeking behaviour and (7) self-

diagnosed 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

327

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 non-

dichotomized. 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

328

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.

329

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 care-

seeking 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 non-

Caribbean 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 self-

evaluated 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, self-

reported 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 self-

evaluated 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 non-

response 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

336

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

340

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 Curative Care Visits

Male Female Female: male Male Female Female: male

2000 30.5 69.5 2.3:1 35.0 65.0 1.9:1

2001 30.6 69.4 2.3:1 34.6 65.4 1.9:1

2002 30.3 69.7 2.3:1 34.2 65.8 1.9:1

2003 30.3 69.7 2.3:1 33.5 66.5 2.0:1

2004 30.2 69.8 2.3:1 32.9 67.1 2.0:1

2005 30.4 69.6 2.3:1 33.3 66.7 2.0:1

2006 30.4 69.6 2.3:1 33.5 66.5 2.0:1

2007* 30.5 69.5 2.3:1 33.6 66.4 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 Group Survey 2001 Census (St.

Catherine) 2001 Census (Jamaica)

(yrs). n % n % N %

55-59 469 23.45 6577 26.7 38645 23.9

60-64 413 20.6 5179 21.1 31828 19.7

65-69 374 18.7 4391 17.8 28901 17.9

70-74 345 17.2 3594 14.6 24856 15.4

75-79 189 9.45 2402 9.78 17711 11.0

80+ 210 10.5 2399 9.77 19552 12.1

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Table 14.14.3. Descriptive characteristic of samples: Sub-national and National surveys Characteristic Sub-national survey

(i.e. Study 2) National survey

(i.e. Study 1) n = 2,000 % n = 483 %

Area of residence Urban 981 49.1 227 47.0 Rural 1019 51.0 256 53.0 Age group (in years) 55-59 469 23.5 120 24.8 60-64 413 20.7 87 18.0 65-69 374 18.7 88 18.2 70-74 345 17.3 68 14.1 75-79 189 9.5 61 12.6 80+ 210 10.5 59 12.2 Marital status Single 686 34.3 150 31.8 Married 894 44.7 217 46.1 Separated 112 5.6 14 3.0 Common-law 136 6.8 49 10.4 Widowed 172 8.6 42 8.7 Household head Yes 1763 88.2 376 77.8 No 237 11.8 107 22.2 Self-rated health status Excellent (or very good) 357 19.0 61 12.9 Good 1038 55.4 177 37.3 Fair (or moderate) 480 25.6 149 31.4 Poor 0 0.0 75 15.8 Very poor 0 0.0 12 2.5 Self-evaluated diagnosed illness Cold - - 14 8.5 Asthma 5 0.3 8 4.8 Diabetes mellitus 129 6.5 24 4.8 Hypertension 193 9.2 24 14.5 Arthritis 20 1.0 46 27.9 Diarrhoea - - 2 1.2 Other: Unspecified - - 22 13.3 Other: Cancer 336 16.8 Heart disease 106 5.3 Kidney/bladder 118 5.9 Prostate problem 143 7.2

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Table 14.14.4. Characteristic of samples: Sub-national and National surveys Characteristic Sub-national survey

(i.e. Study 2) National survey

(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 289 31.6 NS NS In 12 to 35 months 356 38.9 NS NS In 36 and beyond months 269 29.4 NS NS

Provision of care Home remedy 155 44.3 66 13.7 Public clinic 124 35.4 73 15.2 Hospitals 40 11.4 149 30.8 Private doctor 31 8.9 195 40.3

Self-evaluated illness Yes 350 17.5 162 34.3 No 1650 82.5 310 65.7

NS – Not stated (i.e. was not collected in this Study)

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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, cross-

sectional 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.

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.