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Health Insurance
&
Health
Paul Andrew Bourne
i
Health Insurance &
Health
ii
Health Insurance &
Health
Paul Andrew Bourne Director
Socio‐Medical Research Institute
iii
©Paul A. Bourne, 2011 First Published in Jamaica, 2011 by Paul Andrew Bourne 66 Long Wall Drive Stony Hill, Kingston 9, St. Andrew National Library of Jamaica Cataloguing Data Health Insurance & Health
Includes index ISBN Bourne, Paul Andrew All rights reserved. Published, 2011 Cover designed by Paul Andrew Bourne
iv
Preface The population of Jamaica was estimates to be 2,698,810 people (end of year, 2009), with about
49.3% males (sex ratio = 97.1) and 11% in the older age adulthood category (60+ years old).
There are two features about the Jamaican population that must be noted here 1) the feminization
at older ages and 2) a high rate of growth at older ages (80+ years) compared to other age
cohorts. There is evidence that showed that there is a strong statistical correlation between
people ‘seeking medical care’ and ‘health insurance coverage’ in Jamaica. However, the
relationship between the two aforementioned variables is curvilinear one as people will seek
more medical care with the ownership of more health insurance coverage, and this will fall after
more than 18% of Jamaicans purchasing health insurance coverage. Despite the fact that there is
direct association between health insurance and health care seeking behaviour, in 2007, only
21.2% of Jamaicans were holders of health insurance coverage (572,148 Jamaicans).
With only 21 out of every 100 Jamaica being holders of health insurance in 2007, this
speaks to the high cost of individual health coverage and it justifies the public health care
utilization in this country and the switching from the public health care to the private health care
utilization with increased income and wealth (socioeconomic status). This volume
comprehensively examines health insurance and health among Jamaicans, using survey data for
2002 and 2007.
Health Insurance and Health is but the commencement of those phenomena, and I hope that this will foster more discussion in the future as well as guide research.
Paul Andrew Bourne Director
Socio-Medical Research Institute March 2011
v
Acknowledgement The writing of a book is a time consuming and a tedious process, which is assisted by many people. A book is not a singulate effort and this must be recognized by the author(s), editor(s) and/or publisher(s). Like many other authors, I am indebted to many people who contributed in different ways to the completion of this book. These individuals are 1) Mrs. Evadney Bourne, 2) Kimani Bourne, 3) Kerron Bourne, 4) Paul Andrew Bourne, Jnr, who stayed up with me on countless nights, and longer on Saturdays and Sundays. Ms. Neva South-Bourne, whose tireless efforts and endless patience in proofreading some of the chapters as well as Mrs. Cindi Scholefield. I am also indebted to the Derek Gordon Databank, University of the West Indies, Mona (Jamaica) that made the dataset available from which many of the chapters emerged. The majority of the chapters are published works in different journals, and I am grateful for their permission to use the materials in this book (North American Journal of Medical Sciences, Health, Current Research Journal in Social Sciences, International Journal of Collaborative Research on Internal Medicine and Public Health, HealthMed Journal, and Journal of Clinical and Diagnostic Research; Journal of Applied Sciences Research). Finally, I would like to thank all my co-authored who wrote different articles with me. Any errors of omission or commission in this book should not be ascribed to anyone or organizations as these are of the author.
vi
Table of Contents
Preface iv
Acknowledgement v
Chapter 1 1
Health insurance coverage in Jamaica: Multivariate analyses using two cross‐sectional survey data for 2002 and 2007
Chapter 2 31
Disparities in self‐rated health, health care utilization, illness, chronic illness and other socioeconomic characteristics of the Insured and Uninsured
Chapter 3 63
Self‐reported health and medical care‐seeking behaviour of uninsured Jamaicans
Chapter 4 87
Variations in health, illness and health care‐seeking behaviour of those in the upper social hierarchies in a Caribbean society
Chapter 5 113
Health of children less than 5 years old in an Upper Middle Income Country: Parents’ views
Chapter 6 137
Health Inequality in Jamaica, 1988‐2007
Chapter 7 172
Social determinants of self‐reported health across the Life Course
Chapter 8 194
Sociomedical Public Health in Jamaica
Chapter 9 226
vii
Modelling social determinants of self‐evaluated health of poor older people in a middle‐income developing nation
Chapter 10 252
Self‐rated health of the educated and uneducated classes in Jamaica
Chapter 11 278
Retesting and refining theories on the association between illness, chronic illness and poverty: Are there other disparities?
Chapter 12 304
Variations in social determinants of health using an adolescence population: By different measurements, dichotomization and non‐dichotomization of health
Chapter 13 331
Childhood Health in Jamaica: changing patterns in health conditions of children 0‐14 years
Chapter 14 359
The uninsured ill in a developing nation Chapter 15 391 Determinants of self‐rated private health insurance coverage in Jamaica
Chapter 16 415
Difference in social determinants of health between men in the poor and the wealthy social strata in a Caribbean nation
viii
Health Insurance &
Health
1
Chapter 1 Health insurance coverage in Jamaica: Multivariate analyses using two cross-
sectional survey data for 2002 and 2007
Paul Andrew Bourne
Health insurance is established as an indicator of health care-seeking behaviour. Despite this reality, no study existed in Jamaica that examines those factors that determine private health insurance coverage. This study bridges the gap in the literature as it seeks to determine correlates of private health insurance coverage. The aim of this study is to understand those who possess Health insurance coverage in Jamaica so as to aid public health policy formulation. This study used two secondary cross-sectional data from the Jamaica Survey of Living Conditions (JSLC). The JSLC was commissioned by the PIOJ and the Statistical Institute of Jamaica (STATIN) in 1988. The surveys were taken from a national cross-sectional survey of 25 018 respondents (for 2002) and 6,782 people (for 2007) from the 14 parishes across Jamaica. The JSLC is a self-administered questionnaire where respondents are asked to recall detailed information on particular activities. The questionnaire was modelled from the World Bank’s Living Standards Measurement Study (LSMS) household survey. There are some modifications to the LSMS, as JSLC is more focused on policy impacts. The surveys used stratified random probability sampling technique to draw the original sample of respondents. Descriptive statistics were used to provide background information on the sample, and logistic regression was to determine predictors of private health insurance coverage. Health insurance coverage can be predicted by socio-demographic factors (such as area of residence; education, marital status, social support, social class, gender, age), and economic (consumption and income). The findings revealed some similarities and dissimilarities between data for 2002 and 2007. Area of residence, consumption, educational level, marital status, income and social support were determinants over the two periods. Asset ownership was a factor in 2002 but not in 2007. For 2007, age, gender and social class were factors and not for 2002. A dissimilarity in this study was with social support. It was found that in 2002, social support was negatively correlated with Health insurance coverage and this shifts to a positive correlate in 2007. In 2002, age and gender were not associated with Health insurance coverage but these became significant predictors in 2007. Interestingly, poor health status is not correlated with private health insurance coverage. More health insurance coverage is owned by urban than by other town or rural residents. Health insurance coverage is more structured for employed people who are in the private or public sectors more within urban and other towns than rural areas indicating that rural residents, who are faced high poverty and self-employment, will be more likely in continuing their choice in home remedy or non-traditional medicine in order to address their ill-health. Health which is strongly correlated with income means that poor individuals, families, societies, nations, will be less healthy and will need assistance in the form of health insurance to be able to reduce mortality.
2
Introduction
Health is more than the absence of diseases (WHO, 1948); as the absence of diseases is an
antithesis (negative definition) of health and does not capture the positive aspects to this
phenomenon. In the preamble to its Constitution in 1946, the WHO noted that health includes
social, psychological and physical wellbeing; indicating that any measurement of health must
include non-epidemiologic factors and that this must recognize the positive ingredients in the
construction of health. One scholar coined the terms ‘Biopsychosocial model’ to explain the
different facets that must be understood, evaluated and treated in addressing the care of
unhealthy patients (Engel, 1960). Engel’s ‘Biopsychosocial model’ was employed to mean that
health includes biological, social, psychology and other determinants. While one scholar opined
that this definition of health as forwarded by the WHO as well as by extension Engel was too
broad and elusive, and creates a difficulty to measure (Bok, 2004), the WHO’s conceptual
definition of health recognizes the importance of social and behavioural factors in determining
health status. They cannot be omitted in medical care treatment nor should we seek a
measurement in order to operationalizing health as this will not be in keeping with the construct
of the comprehensive phenomenon.
Caldwell (1993) wrote that the behavioural and lifestyle practices are a major determinant
in health (see also, Bourne, 2009), and that this in explaining mortality is not new. Caldwell’s
perspective does not only highlight the role that people play in their own quality of life; but that
their actions (or inactions) hold a crucible part of their health status. Smoking, alcohol
consumption, physical inactivity, wreckless driving, unhealthy diets and other choices are all
decisions people take in life that will either negatively or positively influence their health status,
and later will become a public health challenge. The tendency of people to become involved in
3
particular lifestyle practices account for pre-mature mortality for many of them. Material
deprivation, psychosocial stressors, high levels of risky behaviour, unhealthy living conditions,
social exclusion, perceived lack of control, limited access to good-quality health care,
constrained choices and physical inactivity account for higher levels of dysfunctions. According
to the WHO (2005), 60% of all death are owing to chronic illness, and that 80% of chronic
dysfunctions occur in low-to-middle income countries, which speaks to the growing lifestyle
practices (or lack). Material deprivation and psychosocial stressors increased the risk of diseases
for poor people and people in general which is embedded in the statistics of the WHO
publication.
According to the WHO (2005, p. 66), 95% of Jamaicans with chronic dysfunctions
experienced financial difficulties owing to their illness “…and [that] a high proportion of people
admitting such difficulties avoided some medical treatment as a result (p. 66). It was also noted
that in India diabetic patients spent significantly more of their annual salary on medical care. The
statistics from the WHO (2005) showed that 25% of the poor’s annual income is spent on private
care compared to 4% of people with higher incomes. People are aware that illnesses are
inevitable, owing to the high cost of medical care in order to access health care services they will
then use health insurance coverage. Health care costs can be so high that people become poor;
and the recurring nature of some ailments can deplete people’s income and wealth to the point of
poverty. It is this reality that accounts for health insurance coverage. Health insurance coverage
is a by-product for people because it is demanded for lower treatment costing when illnesses
occur. Therefore, health insurance coverage not only lowers treatment cost of illnesses but also
lowers the psychosocial stressor on income, and the family’s wellbeing.
4
Morrison (2000) titled an article ‘Diabetes and hypertension: Twin Trouble’ in which he
established that diabetes mellitus and hypertension have now become two problems for
Jamaicans and in the wider Caribbean. This situation was equally collaborated by Callender
(2000) at the 6th International Diabetes and Hypertension Conference, which was held in Jamaica
in March 2000. The researcher found that there was a positive association between diabetic and
hypertensive patients - 50% of individuals with diabetes had a history of hypertension
(Callender, 2000, p. 67). Those diseases are not only lifestyle causing, they can be expensive to
treat especially if they are severe. Hence, health insurance coverage is sought in keeping with the
probability of illness.
Health insurance is therefore a health care-seeking behaviour and it can be used to
indicate people’s perception of a futuristic likelihood of illness. It can estimate people’s fear of
their inability to afford medical costs, their preparation for not wanting to deplete income, lower
wealth and the lack of it can account for some premature mortality. From the findings of a cross-
sectional study conducted by Powell et al. (2007) of some 1,338 Jamaicans, 19.0% of
respondents perceived that their economic wellbeing to be ‘very bad’. In addition, when they
asked, “Does your salary and the total of your family’s salary allow you to satisfactorily cover
your needs?” 57.4% of them felt that this “does not cover” their expenses (Powell et al., 2007, p.
29). In addition, out of a maximum score of 10, those in the lower class scored 5.9 for how do
they ‘feel about the state of their health’ compared to a score of 6.6 for those in the upper class
and a score of 6.7 for the middle class. This again goes to the rationale of demanding health
insurance coverage for the poor people. Bourne (2009) found that there is no significant
statistical relationship between health insurance and health care seeking behaviour or health
5
insurance and good health of Jamaicans, suggesting that it is not inaffordability of health care
that drives health insurance coverage; but something else.
An extensive review of health literature in Jamaica found no study that has examined
determinants of health insurance coverage. Health insurance in Jamaica was a private good up to
2007, and so it could only be had by those who were employed. Hence using data up to 2007
would be examining Health insurance coverage of employed Jamaicans. The aim of this study is
to have an understanding of those who possess Health insurance coverage in Jamaica, so as to
aid public health policy formulation. In keeping with the aim, this study sought to determine
correlates of Health insurance coverage in Jamaica, using cross-sectional data for 2002 and 2007.
Methods
This study used two secondary cross-sectional data from the Jamaica Survey of Living
Conditions (JSLC). The JSLC was commissioned by the Planning Institute of Jamaica (PIOJ)
and the Statistical Institute of Jamaica (STATIN) in 1988. These two organizations are
responsible for planning, data collection and policy guideline for Jamaica, and have been
conducting the JSLC annually since 1989. The two cross-sectional surveys used for this study
were conducted in 2002 and 2007 (World Bank, 2002; PIOJ & STATIN, 2003; PIOJ & STATIN,
2008). The surveys were taken from a national cross-sectional survey of 25 018 respondents (for
2002) and 6,782 people (for 2007) from the 14 parishes across Jamaica. The surveys used
stratified random probability sampling technique to drawn the original sample of respondents.
The non-response rate for the 2002 survey was 29.7% and 26.2% for the 2007 survey. The
sample was weighted to reflect the population (World Bank, 2002; PIOJ & STATIN, 2003; PIOJ
& STATIN, 2008).
6
The JSLC is a self-administered questionnaire where respondents are asked to recall
detailed information on particular activities. The questionnaire was modelled from the World
Bank’s Living Standards Measurement Study (LSMS) household survey. There are some
modifications to the LSMS, as JSLC is more focused on policy impacts (World Bank, 2002).
The questionnaire covers demographic variables, health, immunization of children 0–59 months,
education, daily expenses, non-food consumption expenditure, housing conditions, inventory of
durable goods and social assistance. Interviewers are trained to collect the data from household
members. The survey is conducted between April and July annually.
Descriptive statistics such as mean, standard deviation (SD), frequency and percentage
were used to analyze the socio-demographic characteristics of the sample. Chi-square was used
to examine the association between non-metric variables, and an Analysis of Variance
(ANOVA) was used to test the relationships between metric and non-dichotomous categorical
variables. Logistic regression examined the relationship between the dependent variable and
some predisposed independent (explanatory) variables, because the dependent variable was a
binary one (self-reported health status: 1 if reported good health status and 0 if poor health).
The results were presented using unstandardized B-coefficients, Wald statistics, Odds
ratio and confidence interval (95% CI). The predictive power of the model was tested using the
Omnibus Test of Model and Hosmer & Lemeshow (2000) to examine goodness of fit. The
correlation matrix was examined in order to ascertain whether autocorrelation (or
multicollinearity) existed between variables. Based on Cohen & Holliday (1982) correlation can
be low (weak) - from 0 to 0.39; moderate – 0.4-0.69, and strong – 0.7-1.0 (see also, Cohen &
Cohen, 2003; Cohen, 1988). This was used to exclude (or allow) a variable in the model. In
7
addition, variables were excluded from the model if they had in excess of 20% of the cases
missing. Odds Ratio (OR) was used to interpret each significant variable.
Multivariate regression framework (Asnani et al., 2008; Hambleton et al., 2005) was
utilized to assess the relative importance of various demographic, socio-economic characteristics,
physical environment and psychological characteristics, in determining the health status of
Jamaicans; and this has also been employed outside of Jamaica (Cohen & Holliday, 1982; James,
2001; Ross et al., 1990). This approach allowed for the analysis of a number of variables
simultaneously; and is used to examine health insurance coverage. Secondly, the dependent
variable is a binary dichotomous one and this statistic technique has been utilized in the past to
do similar studies. Having identified the determinants of health status from previous studies,
using logistic regression techniques, final models were built for Jamaicans as well as for each of
the geographical sub-regions (rural, peri-urban and urban areas) and sex of respondents using
only those predictors.
Models
The current study will employ multivariate analyses in the study of health and medical care
seeking behaviour of Jamaicans. The use of this approach is better than bivariate analyses as
many variables can be tested simultaneously for their impact (if any) on a dependent variable.
HIt=f(Ht, Ai, Gi, HHi, ARi, lnC, ∑Di, EDi, MRi, Si, HTi, lnY, CRi, MCt, SSi, Ti , CIi, Pi, Eni, HSB, εi) (1)
Where HIi is health insurance coverage of person i, Ht (ie self-rated current health status
in time t) is a function of age of respondents, Ai ; sex of individual i, Gi; household head
of individual i, HHi; area of residence, ARi; house tenure of individual i, HTi; logged
8
consumption per person per household member, lnC; summation of durable goods and
asset owned, ∑Di; Education level of individual i, EDi; marital status of person i, MRi;
social class of person i, Si;; logged income, lnY; crowding of individual i, CRi; medical
expenditure of individual i in time period t, MCt; social support of individual i, SSi; social
assistance (ie welfare) individual i, Ti; crime index, CIi; physical environment of
individual i, Eni, health care seeking behaviour and an error term (ie. residual error).
The final models that were derived from the general Equation (1) that can be used to
predict Health insurance coverage of Jamaicans are Equation (2) and Equation (3):
HIt(Jamaicans, 2002) =f(ARi, lnC, EDi, MRi, lnY, SSi, ∑Di, HSB, εi)
(2)
HIt(Jamaicans, 2007) =f(ARi, lnC, EDi, MRi, lnY, SSi, Ai, Gi, Si, HSB, εi) (3)
Measures
An explanation of some of the variables in the model is provided here. Self-reported is a dummy
variable, where 1 (good health) = not reporting an ailment or dysfunction or illness in the last
4 weeks, which was the survey period; 0 (poor health) if there were no self-reported ailments,
injuries or illnesses (Bourne & Rhule, 2009). While self-reported ill-health is not an ideal
indicator of actual health conditions because people may underreport, it is still an accurate proxy
of ill-health and mortality (Idler & Kasl, 1991; Idler & Benyamini, 1997; Bourne & Rhule,
2009). Social supports (or networks) denote different social networks with which the individual
is involved (1 = membership of and/or visits to civic organizations or having friends who visit
ones home or with whom one is able to network, 0 = otherwise). Psychological conditions are
9
the psychological state of an individual, and this is subdivided into positive and negative
affective psychological conditions (Diener, 2000; Harris & Lightsey, 2005). Positive affective
psychological condition is the number of responses with regard to being hopeful, optimistic
about the future and life generally. Negative affective psychological condition is number of
responses from a person on having lost a breadwinner and/or family member, having lost
property, being made redundant or failing to meet household and other obligations. Health status
is a binary measure (1=good to excellent health; 0= otherwise) which is determined from
“Generally, how do you feel about your health”? Answers for this question are in a Likert scale
matter ranging from excellent to poor. Health care-seeking behaviour is derived from the
question: Have you visited a health care practitioner, pharmacist or healer in the past four 4
weeks, with an option of yes or no. For the purpose of the regression was coded as 1=yes,
0=otherwise. Crowding is the total number of individuals in the household divided by the
number of rooms (excluding kitchen, verandah and bathroom). Age is a continuous variable in
years.
Results Demographic characteristic and bivariate analyses
In 2002 the sample was 25,018 respondents: 12,332 males (49.3%) and 12,675 females (50.7%).
In 2007 the sample was 6,782 respondents with there being marginally more females (51.3%)
than males (48.7%; Table 1.1). The findings in Table 1.1 revealed that urbanization was taken
place in 2002, there were 13.4% of respondents living in urban zones and this shifted to 29.5% in
2007. The percentage of Jamaicans dwelling in rural areas declined from 61% in 2002 to 49.0%
in 2007. In 2002, 12.5% of respondents indicated that they had an illness in the 4-week survey
period and this increased by 2.4% in 2007. Sixty-four percent of respondents reported having
visited a health care facility (including a healer), and this increased to 66% in 2007. The social
10
class categorization of Jamaicans remained relatively the same over the studied period; and the
percentage of respondents who had health insurance coverage increased from 11.0% in 2002 to
20.2% in 2007. The mean number of visits made to health care institutions (including healers)
declined from 1.7 days (SD=1.4 days) to 1.4 days (SD=1.1 days). On the other hand, crowding
increased by 135% in 2007 over 2002; and medical care expenditure also increased by 29.1%
over the period (Table 1.1).
Based on Table 1.2, the mean annual income of respondents in 2002 was Ja $331,488.32
(SD = JA $304,040.77) and this increased by 108.6% in 2007: Ja $691,560.45 (SD = Ja
$128,742.65). On disaggregating income by area of residence, it was revealed that there was
significant statistical difference between income of respondents and their area of residents. On
average, urban respondents received 1.6 times more income than rural residents in 2007 and this
was similar in 2002 (approximately 1.5 times more). The disparity in income between urban and
other town respondents was lower (in 2007 – 1.1 times more and this was the same in 2002) than
that between urban and rural dwellers.
A cross-tabulation between health status and self-reported illness revealed a significant
statistical correlations - χ2(df = 2) = 1,289.23, p < 0.001 (Table 1.3). Table 1.3 revealed that an
individual who reported poor health status was 9.3 times more likely to have an illness than those
stating a dysfunction. On the other hand, an individual who reported good health status was 2.0
more likely not to report an illness than those reporting at least one ailment. Based on Table 1.3,
more males (85.4%) reported good health status than females (79.2%) - (χ2(df = 2) = 44.666, p <
0.001) - and the converse was true for poor health status, with 5.5% of females compared to
4.2% of males.
Based on Table 1.4, there was a change in pattern of 5-leading recurring illnesses in
Jamaica. In 2002, hypertension was the leading cause of self-reported dysfunctions (21.6%)
followed by cold (19.9%); unspecified ailments (18.1%); diabetes mellitus (11.6%) and asthma
(9.6%). However in 2007, the leading prevalence of self-reported ailments shifted to unspecified
11
ailments (23.4%) followed by hypertension (20.6%); cold (14.9%), diabetes mellitus (12.3%)
and 9.5% asthma cases. Furthermore, a significant statistical relationship was found between
diagnosed recurring illness was gender in both years: In 2002 (χ2(df = 1) = 125.469, p < 0.001, n
= 3,063) and in 2007 (2 χ2(df = 1) = 40.916, p < 0.001, n= 999; Table 1.4). Table 1.4 showed that
diabetes mellitus and hypertension were significant more among for females than males and that
arthritis, unspecified illnesses, asthma diarrhoea and cold were more prevalent among males than
females.
Table 1.5 showed that there was a significant statistical correlation between medical care-
seeking behaviours and gender: In 2002 (χ2(df = 1) = 9.006, p = 0.003) and in 2007, (χ2(df = 1) =
3.004, p < 0.048). In 2002 data revealed that more females sought medical care (66%) than
males (60.7%); and this was the case in 2007: 67.6% for females and 62.3% for males (Table
1.5).
In 2007, there was a significant statistical correlation between health care-seeking
behaviour of Jamaicans and health insurance coverage (χ2(df = 1) = 16.712, p < 0.001). The
association was a very weak one (r = 0.128). However, the findings revealed that 76.2% (n =
189) of people with private health insurance visited a health care practitioner compared to 62.0%
(n = 468) those who do not have health insurance coverage.
Multivariate analyses In 2007, health insurance coverage in was correlated with logged consumption (OR = 1.90, 95%
CI = 1.12 - 3.23); logged income (OR = 1.71, 95% CI = 1.02 - 2.87); durable goods (OR = 1.09,
95% CI = 1.02 - 1.17); marital status (married: OR = 3.91, 95% CI = 2.47 - 6.20); area of
residence (urban areas: OR = 2.24, 95% CI = 1.23 - 4.09); education (secondary: OR = 2.97,
12
95% CI = 1.46 - 6.00; tertiary: OR = 18.76, 95% CI = 8.12 - 43.43); and social support (OR =
0.54, 95% CI = 0.36 - 0.80; Table 1.7).
For 2002, health insurance coverage model was a predictive model (χ2 (df = 24) =
451.35, p < 0.001; Hosmer and Lemeshow goodness of fit χ2=5.91, P = 0.66), with 92.4% of the
data being correctly classified (41.1% - correct classification of cases of self-rated Health
insurance coverage and 98.4% of cases of self-rated no private health insurance coverage; Table
1.7). The model (Table 1.7) can explain 44.7% of the variability in Health insurance coverage of
Jamaicans (for 2002).
Health insurance coverage in Jamaica for 2007 can be determined by 10 variables. These
were logged consumption (OR = 1.00, 95% CI = 1.00 - 1.00); logged income (OR = 1.00, 95%
CI = 1.00 - 1.00); marital status (married: OR = 1.84, 95% CI = 1.52 - 2.22); area of residence
(urban areas: OR = 1.30, 95% CI = 1.08 - 1.57); education (secondary or tertiary: OR = 1.45,
95% CI = 1.09 - 1.92); and social support (OR = 1.33, 95% CI = 1.04 - 1.70); age (OR = 1.01,
95% CI = 1.01 - 1.02); social class (upper class: OR = 1.61, 95% CI = 1.08 - 1.57) and by gender
(male: OR = 0.81, 95% CI = 0.69 - 0.95).
For 2007, the factors that determine health insurance coverage in Jamaica is a predictive
model (χ2 (df = 20) =590.07, p < 0.001; Hosmer and Lemeshow goodness of fit χ2=7.25, P =
0.51), with 79.4% of the data being correctly classified (40.4% - correct classification of cases of
self-rated Health insurance coverage and 96.4% of cases of self-rated no private health insurance
coverage). For 2007, the model can explain 49.1% of the variability in private health insurance
coverage.
Discussion
13
There are some sociodemographic determinants of Health insurance coverage in Jamaica that
have remained predictors. These include area of residence, consumption, education, marital
status, income and social support. Durable goods were a predictor of health insurance coverage
in 2002; however, this is ceased to be the case in 2007. Over time, health insurance coverage
was determined by some additional factors such as age, gender, and social class. Of the 6
predictors of Health insurance coverage in Jamaica that continued to be factors in both periods,
there is dissimilarity. Social support which was a negative determinant in 2002 reversed to a
positive one in 2007. It is expected that those with more social support would be less likely to
purchase health insurance coverage as there is a higher probability that they can be assisted in
times of medical needs by the social networks with which they are apart. The church, civic
associations and societies, family, friends and associates are more likely to extend a helping hand
in time of medical need, and this account for the unwillingness of people to purchase private
health insurance because this socio-economic support is present.
In 2007, the findings revealed that Health insurance coverage was positively correlated to
social support which invalidates the aforementioned perspective. The inflation rate in Jamaica
rose by 194% in 2007 over 2006, which indicates that net disposable individual and household
income would have fallen substantially and that each individual would have seen an erosion of
his purchasing power coupled with higher cost of living. The direct correlation between social
support and Health insurance coverage can be explained by social institutions encouraging its
members to purchase insurance to offset the increased costs. They probably may be less likely to
offer the same level of assistance to all its members like the previous period when costings were
lower. The economic cost will create a challenge for those social networks to spread their limited
financial resources over a wider cross-section of people with diverse needs. This then is a part of
14
the explanation why Health insurance coverage was the highest in Jamaica in 2007 (21.2%) over
the 2 decades; and in 2007, medical care-seeking behaviour was 66% which fell by 5.7% over
2006.
The current study revealed that married people were more likely to purchased private
health insurance than those who were never married and that there is no significant difference in
purchase of health insurance between those who were divorced, separated or widowed and those
who were never married. In 2002, the findings showed that married people were 4 times more
buy Health insurance coverage compared to those who were never married and that this ratio fell
to 2 times more in 2007. This lower of disparity in ownership of Health insurance coverage
between the married and never married cohorts in Jamaica is an indication of people’s
willingness to subsidize medical care cost with private health insurance coverage; the lowering
of their disposable income owing to increased cost of living; increased awareness of seeking
medical care and the high cost of doing so; and the changing typology of diseases which require
continuous monitoring by health care practitioners and how this is likely to erode income and
wealth, and that this would be best mitigated against through the provision of health insurance.
An another interest finding that is embedded in the disparity of more married than
unmarried people owning private health insurance is the explanation for why married people
have a greater health status than unmarried people. Health insurance coverage is an indicator of
health care-seeking behaviour, which goes to the core of married people’s willing to address
health concerns owing to their recognition of the family (ie children and spouse) depending on
them for care, protection and financial support. According to Moore et al. (1997, 29), people who
reside with a spouse have a different base of support that those in other social arrangements (See
also Smith & Waitzman 1994; Lillard & Panis 1996). Cohen & Wills (1985) found that
15
perceived support from one’s spouse increased wellbeing (see also Smith & Waitzman 1994),
while Ganster et al. (1986) reported that support from supervisors, family members and friends
was related to low health complaints. Koo, Rie & Park (2004) findings revealed that being
married was a ‘good’ cause for an increase in psychological and subjective wellbeing in old age.
Smith & Waitzman(1994) offered the explanation that wives found dissuade their husband from
particular risky behaviours such as the use of alcohol and drugs, and would ensure that they
maintain a strict medical regimen coupled with proper eating habit (see also Ross et al., 1990;
Gore, 1973). In an effort to contextualize the psychosocial and biomedical health status of
particular marital status, one demography cited that the death of a spouse meant a closure to
daily communicate and shared activities, which sometimes translate into depression that affect
the wellbeing more of the elderly who would have had investment must in a partner (Delbés &
Gaymu 2002, p. 905).
Embedded in Smith and Waitzman finding is the positive effecting of marriage on men’s
health status. This speaks to culture of men’s unwillingness to seek medical care, and the role of
the spouse in reducing this practice. The current study found that men were 19.2% less likely that
women own health insurance, indicating once again their unwillingness to seek medical care.
Health literature has established that women are more likely to seek medical care than men
(Stekelenburg et al., 2009; PIOJ & STATIN, 2001) and that this was concurred by the current
study. Interestingly, in 2002, for every 156 females that sought medical care there were 100
males; but in 2007, the ratio widens to 160 females for every 100 males. Although females
sought more health care services than males, statistics revealed that the latter group spent more
days in illness (mean = 10.3 days) than females (mean number of days suffered from illness =
9.3 days) (PIOJ & STATIN, 2008).
16
Poor health status which is an indicator of health conditions means that females were
more likely to seek medical care to address those concerns compared to males who were
suffering from the different illnesses. Of the 3 specified chronic illnesses (arthritis, diabetes
mellitus, and hypertension) females are influenced by the more severe types, and thus explain the
greater probability of them seeking medical care and buying health insurance coverage than
males. This research found that in 2002, females were 2.1 times more likely to report having
hypertension and 1.5 times more likely to claim that they have diabetes mellitus than males. In
2007, the disparity in self-reported hypertension fell to 1.7 times and increased to 2 times for
diabetes mellitus. For arthritis, the disparity was narrowly greater for males than females. In
2002, for every 120 males that reported arthritis there were 100 females and this was 111 males
for every 100 females in 2007.
Men are not only unwilling culturally to display emotions, fear, weakness and illness,
they are equally reserved about speaking of their health conditions. Such a position is embedded
in the culture, which states that boys should ‘suppress reaction to pain’ and to speak of illness to
lower ones maleness (Chevannes, 2001, p. 37). Chevannes’s work explains the current findings
as well to provide in-depth information on statistics published in the Jamaica Survey of Living
Conditions (JSLC). The JSLC (2000) reported that men were 0.7 times less likely to self-report
sicknesses, injuries and/or ailments compared to their female counterparts. In a number of
societies, traditional females seek health-care more than males, which allow for a better
monitoring and diagnostic assessment of their health conditions as against men.
Higher income means the individual, family, society and nation has more to it disposable
to cover non-consumption items such as health insurance. Easterlin argued that “those with
higher income will be better able to fulfill their aspiration and, and other things being equal, on
17
an average, feel better off” (Easterlin, 2001a, p. 472), indicating a bivariate relationship between
subjective well-being and income. Stutzer & Frey (2003) found that the association between
subjective wellbeing and income to be a non-linear one. According to Stutzer & Frey (2003) “In
the data set for Germany, for example, the simple correlation is 0.11 based on 12, 979
observations” (p. 9). The current study concur with Easterlin that greater income can purchase
other goods, which accounts for the positive correlation between income and private health
insurance coverage. This is also in keeping with Brown et al.’s study (2008) which had income
as a predictor of health care-seeking behaviour. The current research went further than Brown et
al (2008) and Easterlin (2001) studies as it found that those who consume more on food and non-
food items are more likely to own Health insurance coverage than those who consume less.
Hence, it is expected that wealthy will be significantly more likely to own Health insurance
coverage than the poor.
In Jamaica, statistics from the Planning Institute of Jamaica and Statistical Institute of
Jamaica (2007) revealed that poverty is substantially a rural phenomenon and that the more of
the wealthy live in urban area, then more urban dwellers having Health insurance coverage is
reinforcing the literature that more money provide access to a wider spread of goods and services
outside of basic necessities. The current research has provided more interest information in the
literature as wide gap that existed in 2002 between the wealthy and the poor in regards to
ownership of private health insurance, narrowed in 2007.
Another interesting finding of this study is the positive significant correlation between
health insurance coverage and educational attainment. In 2002, those with tertiary level
education were 19 times more likely to own health insurance coverage in Jamaica and this
narrowed substantially to 1.4 times more than those with primary and below education. The
18
narrowing of the gap of those who owned health insurance coverage between the tertiary and the
primary level education can due to knowledge of ill-health, lowered income, the role of the
media in information the populace about the role of health insurance coverage in reducing
medical cost on seeking health care. Interestingly private health insurance companies in Jamaica
have expanded health insurance schemes to Credit Unions, and so this is giving greater access of
this product to the poor who are mostly members of the Union.
The positive significant correlation of age and health insurance coverage in Jamaica can
be accounted for by the biological changes and the high cost of medical care due to this futuristic
probability. Organism aged naturally, which explains biological ageing. Ageing is synonymous
with reduced functional limitations (or increased health conditions), suggesting that the older
people become they will be more willing to purchase Health insurance coverage due to the future
cost of medical care and the high likeliness of illness because of health conditions. Gompertz’s
law in Gavriolov & Gavrilova (2001) showed that there is fundamental quantitative theory of
ageing and mortality of certain species (the examples here are as follows – humans, human lice,
rat mice, fruit flies, and flour beetles (see also, Gavriolov & Gavrilova, 1991). Gompertz’s law
went further to establish that human mortality increase twofold with every 8 years of an adult
life, which means that ageing increases in geometric progression. This phenomenon means that
human mortality increases with age of the human adult, but that this becomes less progress in
advance ageing. Thus, biological ageing is a process where the human cells degenerate with
years (i.e. the cells die with increasing in age), which is well established in evolutionary biology
(Medawar 1946; Carnes and Olshansky, 1993; Carnes et al., 1999; Charlesworth, 1994).
A study on the elderly in the Caribbean Food and Nutrition Institute’s magazine Cajanus
found that 70% of individuals who were patients within different typologies of health services in
19
Jamaica were senior citizens (Caribbean Food and Nutrition Institute1999; Anthony 1999), and
this emphasize the need of elderly to purchase health insurance in order to cover the cost of
health care. A study conducted by Costa, using secondary data drawn from the records of the
Union Army (UA) pension programme that covered some 85% of all UA, showed there is an
association between chronic conditions and functional limitation – which include difficulty
walking, bending, blindness in at least one eye and deafness (Costa 2002). Again this is
reiterating the need to seek medical care owing to ageing, and justifying the positive correlation
between age and health insurance coverage in this study.
Interestingly health insurance is among the greatest predictor of health care-seeking
behaviour in the United States (Call & Ziegenfuss, 2007), and this is not the case in Jamaica as
only 21 out of every 100 Jamaicans possessed health insurance coverage in 2007. However of
those who claimed to have private health insurance coverage, 8 out of 10 visited health care
facilities, suggesting that those with this facility would be a great predictor of health care-seeking
behaviour. It should be noted that Jamaica does not have a national health insurance coverage
which is opened to the general populace. Instead (in 2007), the government introduced a national
health insurance coverage in which people with particular ailments can access services and
medication at particular public institutions free and a national health insurance scheme which
caters to the elderly Jamaicans (ages 60 years and older).
Conclusion
The socioeconomic determinants of Health insurance coverage in Jamaica have expanded in
2007 over 2002. Area of residence, consumption, income, educational attainment, marital status
and social support have remained factors in 2007 over 2002; but age, gender and social class are
currently new sociodemographic variables that explain private health insurance in Jamaica.
20
Furthermore, females seeking more medical care in Jamaica has been fundamentally linked to
culture and this is undoubtedly so; but this study has found that the typology of their health
conditions is another pivotal rationale for this disparity. The reported health conditions with
which males reported more of than females are illnesses that can be substantially over the
counter with non-traditional medicine, and so further goes to the reason for their low access of
traditional health care services.
In Jamaica, the employment typology in area of residents is different and contributes to
the disparity in private health insurance coverage. Employment in rural area is substantially self-
employment (ie farming) and this type of employment is not designed around private health
insurance coverage. Health insurance coverage is more structured for employed people who are
in the private or public sectors more within urban and other towns than rural areas indicating that
rural residents, who are faced high poverty and self-employment, will be more likely in
continuing their choice in home remedy or non-traditional medicine in order to address their ill-
health. Health which is strongly correlated with income means that poor individuals, families,
societies, nations, will be less healthy and will need assistance in the form of health insurance to
be able to reduce mortality. In concluding, the information with which this provided can be used
by public health services in formulating programmes that can be address the concerns of males
and rural poor.
21
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Table 1.1. Demographic characteristic of samples: 2002 and 2007
Variable
2002
2007
Number Percent Number Percent Gender Male 12,332 49.3 3,303 48.7 Female 12,675 50.7 3,479 51.3 Area of residence Urban 3,357 13.4 2,002 29.5 Other 6,401 25.6 1,458 21.5 Rural 15,260 61.0 3,322 49.0 Illness Yes 3,010 12.5 980 14.9 No 21,103 87.5 5,609 85.1 Visits health care facilities Yes 1,966 63.9 658 65.5 No 1,113 36.1 347 34.5 Social class Poor 9,931 39.7 2,697 39.4 Middle 4,984 19.9 1,351 19.9 Upper 10,099 40.0 2,734 40.3 Private Health Insurance Coverage Yes 2,671 11.0 1,314 20.2 No 21,546 89.0 5,203 79.8 Health status Good 5,397 82.2 Fair 848 12.9 Poor 320 4.9
25
Table 1.2. Income, Crowding, Age, by Area of residence: 2002 and 2007 Characteristic Year Category Mean SD p-value Income Ja$ 2002† Urban $440,451.50 $521,519.38 < 0.001 Other towns $385,625.70 $276,644.12 Rural $284,810.20 $231,540.04 Total $331,488.32 $304,040.77 2007†† Urban $865,674.20 $673,512.10 < 0.001 Other towns $771,300.50 $597,582.65 Rural $551,633.70 $389,765.68 Total $691,560.45 $128,742.65 Crowding 2002 Urban 2.0 persons 1.4 persons > 0.05 Other towns 2.0 persons 1.4 persons Rural 2.0 persons 1.4 persons Total 2 persons 1.4 persons 2007 Urban 4.3 persons 2.4 persons < 0.001 Other towns 4.6 persons 2.3 persons Rural 5.0 persons 2.5 persons Total 4.7 persons 2.5 persons Age 2002 28.2 yrs 22.0 yrs 2007 29.9 yrs 21.8 yrs No of visits to health care facilities
2002 1.7 days 1.4 days
2007 1.4 days 1.1 days Medical expenditure 2002† $1,144.14 $2,946.02 2007†† $1,477.07 $4,711.15 †Ja $40.97 = US $1.00 ††Ja $80.47 = US $1.00
26
Table 1.3. Health status by self-reported illness, and gender: 2007
Characteristic Category Health status (%) Total Good Fair Poor Self-reported dysfunction1 0 89.1 8.7 2.2 5569 ≥ 1 42.8 36.8 20.4 976 Total 5381 845 319 6545 Gender2 Male 85.4 10.4 4.2 3195 Female 79.2 15.3 5.5 3370 Total 5397 848 320 6565 1 χ2(df = 2) = 1,289.23, p < 0.001, c=0.405 2 χ2(df = 2) = 44.666, p < 0.001, c=0.082
27
Table 1.4. Self-reported diagnosed recurring illness by gender and years (2002, 2007)
Year
Sex Self-reported diagnosed recurring illness (%) Total
Cold
Diarrhoea
Asthma
Diabetes
Hypertension
Arthritis
Other
No
2002
Male 22.9 3.1 11.4 9.3 12.9 7.6 20.1 12.5
1252
Female
17.8 2.4 8.3 13.2 27.6 6.3 16.6 7.7 1811
Total 610 83 294 356 661 209 553 297 3063
2007
Male 17.2 2.7 11.7 7.7 14.4 6.0 25.4 14.9
402
Female
13.4 2.7 8.0 15.4 24.8 5.4 22.1 8.2 597
Total 149 27 95 123 206 56 234 109 999
28
Table 1.5. Medical Care-Seeking Behaviour by Gender, 2002, 2007 Medical Care-Seeking Behaviour
2002
2007
Male
Female
Male
Female
Yes1 60.7 66.0 62.3 67.6
No2 39.3 34.0 37.7 32.4
Total 1266 1813 406 599 1 χ2(df = 1) = 9.006, p = 0.003, n = 3,079 2 χ2(df = 1) = 3.004, p = 0.048, n= 1,005
29
Table 1.6. Health insurance coverage by Area of Residence, 2007
Health Insurance
Area of Residence
Total Urban Other towns Rural
No coverage 72.0 77.9 85.5 79.8
Private Coverage 19.2 15.1 7.1 12.4
Public Coverage 8.7 7.0 7.4 7.7
Total 1939 1401 3177 6517 χ2(df = 4) = 184.347, p < 0.001, n = 6,517
30
Table 1.7. Logistic Regression: Predictors of Private Health Coverage in Jamaica Characteristic 2002 2007
OR 95% CI OR 95% CI Age 1.00 0.98-1.02 1.01 1.01-1.02*** Log consumption 1.90 1.12-3.23* 1.00 1.00-1.00* Log income 1.71 1.02-2.87* 1.00 1.00-1.00*** Log medical expenditure 0.99 0.81-1.21 1.00 1.00-1.00 Household head 4.61 0.21-99.16 1.03 0.86-1.23 Medical care seeking behaviour 0.88 0.42-1.83 1.65 1.07-2.41* Sex Male 0.88 0.60-1.30 0.81 0.69-0.95* Marital status Separated, divorced or widowed 1.38 0.49-3.88 1.19 0.87-1.64 Married 3.91 2.47-6.20*** 1.84 1.52-2.22*** †Never married 1.00 1.00 Area of residence Urban 2.24 1.23-4.09** 1.30 1.08-1.57* Other towns 1.19 0.75-1.89 1.11 0.90-1.36 †Rural 1.00 1.00 Education Secondary 2.97 1.46-6.00** 1.45 1.09-1.92* Tertiary 18.8 8.11-43.43*** †Primary or below 1.00 1.00 House tenure: owned 1.76 0.16-19.4 Social class Middle 0.88 0.32-2.41 0.96 0.63-1.46 Upper 1.88 0.68-5.24* 1.61 1.04-2.49* †Lower 1.00 1.00 Social support 0.54 0.36-0.80** 1.33 1.04-1.70* Health status Good health 0.93 0.56-1.53 1.05 0.84-1.31 Durable goods index (excluding land) 1.09 1.01-1.17* Physical environment 0.78 0.48-1.27 Crime index 1.01 0.99-1.03 Asset ownership (ie land or property) 0.79 0.51-1.22 Psychological condition Negative affective conditions 0.96 0.91-1.02 Log crowding 1.33 0.88-2.02 1.07 0.98-1.16 Social welfare 0.79 0.52-1.20 Time spent in health care facilities Public 0.96 0.79-1.20 1.00 1.00-1.00 Private 1.43 0.02-85.3 1.00 1.00-1.00 Illness 4.01 0.44-36.43 1.14 0.90-1.43 Injury 0.68 0.36-1.75 1.12 0.57-2.20 N 25,007 6,565 Chi2 451.3 590.1 Nagelkerke R2 0.45 0.49 LR 776.4 4,126.8 *P< 0.05, **P< 0.01, ***P< 0.001
31
Chapter 2
Disparities in self-rated health, health care utilization, illness, chronic illness and other socioeconomic characteristics of the Insured and Uninsured
Paul A. Bourne
This study examines self-rated health status, health care utilization, income distribution, and health insurance status of Jamaicans, and the disparity by the insured and uninsured. It also models self-rated health status, health care utilization, income distribution, and how these differ between the insured and uninsured. Cross-sectional data from the 2007 Jamaica Survey of Living Conditions (JSLC), conducted by the Planning Institute of Jamaica (PIOJ) and the Statistical Institute of Jamaica (STATIN), were used to analyse the information for this study. The JSLC is a modification of the World Bank’s Living Standard Household Survey, with a sample of 6,783 respondents. Analytic models, using multiple logistic and linear regressions, were used to determine factors which explain self-rated health status, health care utilization, and income distribution. Disparities in self-rated health status, health care utilization, and income distribution were examined by the insured and uninsured. Majority (61.1%) of those who reported being diagnosed with a chronic condition were 60+ years old (diabetes mellitus, 59.3%; hypertension, 60.2%; arthritis, 67.9%) and 2.4% were children. The mean age of those with chronic illness was 62.3 years (SD = 16.2), and this was 61.5 years (SD = 16.5) for the uninsured and 63.8 years (SD = 15.8) for those with insurance coverage. Only 20.2% of respondents had health insurance coverage (private, 12.4%; NI Gold, public, 5.3%; other public, 2.4%). Most of the chronically ill were uninsured (67%). More people with chronic illnesses who had health insurance coverage were elderly, (65.9%), compared to uninsured chronically ill elderly (58.4%). Majority of health insurance was owned by those in the upper class, (65%), and 19%, by those in the lower socioeconomic strata. Insured respondents were 1.5 times (Odds ratio, OR, 95% CI = 1.06 – 2.15) more likely to rate their health as moderate-to-very good compared to the uninsured, and they were 1.9 times (95% CI = 1.31-2.64) to seek more medical care, 1.6 times (95% CI = 1.02-2.42) more likely to report having chronic illness, and more likely to have greater income (β = 0.094) than the uninsured. Illness is a strong predictor of why Jamaicans seek medical care (R2 = 71.2% of 71.9%), and health insurance coverage accounted for less than one-half percent of the variance in health care utilization. However, health care utilization is a strong predictor of self-reported illness, but it was weaker than illness explaining health care utilization (61.1% of 66.5%). Public health insurance was mostly had by those with chronic illnesses (76%) compared to 44% private health coverage and 38% had no coverage (χ2 = 42.62, P < 0.0001). With the health status of the insured being 1.5 times more than the uninsured, their health care utilization being 1.9 times more than the uninsured and illness being a strong predictor of health care seeking, any reduction in the health care budget in developing nations denotes that vulnerable
32
groups (such as elderly, children and the poor) will seek less care, and this will further increase the mortality among those cohorts. Introduction This study examines self-rated health status, health care utilization, income distribution, and
health insurance status of Jamaicans, and the disparity between the insured and uninsured. It also
models self-rated health status, health care utilization, income distribution, and how these differ
between the insured and uninsured. The current findings revealed that 20.2% of Jamaicans had
health insurance coverage (i.e. 2,140,316 Jamaicans are uninsured, using end of year population
for 2007), suggesting that a large percent of the population are having to use out of pocket
payment or government’s assistance to pay their medical bills.
The health of individuals within a society goes beyond the individual to the
socioeconomic development, standard of living, production and productivity of the nation.
Individuals’ health is therefore the crux of human’s development, survivability and explains the
rationale as to why people seek medical care on the onset of ill-health. In seeking to preserve
life, people demand and utilize health care services. Western societies are structured that people
meet health care utilization with a combination of approaches. These approaches can be any
combination of out of pocket payment, health insurance coverage, government assistance and
families’ aid.
In Latin America and the Caribbean, health care is substantially an out of pocket
expenditure aided by health insurance policy and government’s health care policy. Within the
context of the realities in those nations, the health of the populace is primarily based on the
choices, decisions, responsibility and burden on the individual. Survival in developing nations
33
are distinct from Developed Western Nations as Latin America and Caribbean peoples’
willingness, frequency, and demand for health care as well as health choices are based on
affordability. Affordability of health care is assisted by health insurance coverage; as the
provisions of care offered by the governmental policies mean that the public health care system
will be required to meet the needs of many people. Those people will be mostly children, elderly
and other vulnerable groups.
The public health care system in many societies often time involve long queues, long
waiting times, frustrated patients and poor people who are dependent on the service. In order to
circumvent the public health care system, people purchase health insurance policies as a means
of reducing futuristic health care cost as well as an avoidance of the utilization of public health
care. Uninsurance in any society means a dependency on the public health care system,
premature mortality and oftentimes public humiliation. The insured on the other hand are able to
circumvent many of the experiences of the poor, elderly, children and other vulnerable cohorts
who rely on public health care system. Insurance in developing nations, and in particular
Jamaica, is private system between the individual and a private insurance company. Because of
the nature of health insurance and insurance, people buy into a pool which is usually
accommodated through employment. Such a reality excludes retired elderly, unemployed,
unemployable, and children of those cohorts. In seeking to understand health care non-utilization
and high mortality in developing nations, insurance coverage (or lack of) becomes crucial in any
health discourse.
There is high proportion of uninsured in the United States and this is equally the reality in
many developing nations, particularly in Jamaica [1-6]. According to the World Health
Organization (WHO), 80% of chronic illnesses were in low and middle income countries, and
34
60% of global mortality is caused by chronic illnesses [7]. It can be extrapolated from the
WHO’s findings that
uninsurance is critical in answering some of the health disparities within and among groups and
the sexes in the society. The realities of the health inequalities between the poor and the wealthy
and the sexes in a society and those in the lower income strata having more illnesses and in
particular chronic conditions [7-12] is embedded in financial deprivation.
The WHO stated that “In reality, low and middle income countries are at the centre of
both old and new public health challenges” [7]. The high risk of death in low income countries
is owing to food insecurity, low water quality, low sanitation coupled with in access to financial
resources [11, 13]. Poverty makes it insurmountable for poor people to respond to illness unless
health care services are free. Hence, the people who are poor will suffer even more so from
chronic diseases. The WHO captures this aptly “...People who are already poor are the most
likely to suffer financially from chronic diseases, which often deepens poverty and damage long
term economic prospects” [7]. This goes back to the inverse correlation between poverty and
higher level education, poverty and non-access to financial resources, and now poverty and
illness. According to the WHO [7], “In Jamaica 59% of people with chronic diseases
experienced financial difficulties because of their illnesses...” and emphasize the importance of
health insurance coverage and the public health care system for vulnerable groups.
Previous studies showed that health insurance coverage is associated with health care
utilization [1-6], and this provides some understanding of health care demand (or the lack of) in
developing countries. Studies have been conducted on the general health of the insured and/or
uninsured, health care utilization and other health related issues [1-6] have used a piecemeal
approach, which means that there is a gap in the literature that could provides more insight into
35
the insured and uninsured. While the current body of health literature provide pertinent
information on health and health care utilization and how these differ based on the insured and
uninsured, health choices are complex and requires more than piecemeal inquiry.
Materials and methods
Data methods This study is based on data from the 2007 Jamaica Survey of Living Conditions (JSLC),
conducted by the Planning Institute of Jamaica (PIOJ) and the Statistical Institute of Jamaica
(STATIN). The JSLC is an annual and nationally representative cross-sectional survey that
collects information on consumption, education, health status, health conditions, health care
utilization, health insurance coverage, non-food consumption expenditure, housing conditions,
inventory of durable goods, social assistance, demographic characteristics and other issues [14].
The information is from the civilian and non-institutionalized population of Jamaica. It is a
modification of the World Bank’s Living Standards Measurement Study (LSMS) household
survey [15].
Overall, the response rate for the 2007 JSLC was 73.8%. Over 1994 households of
individuals nationwide are included in the entire database of all ages [16]. A total of 620
households were interviewed from urban areas, 439 from other towns and 935 from rural areas.
This sample represents 6,783 non-institutionalized civilians living in Jamaica at the time of the
survey. The JSLC used complex sampling design, and it is also weighted to reflect the
population of Jamaica.
Statistical analysis
36
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 an Analysis of Variance (ANOVA) was used to test the equality of means
among non-dichotomous categorical variables. Means and frequency distribution were
considered significant at P < 0.05 using chi-square, independent sample t-test, and analysis of
variance f test, multiple logistic and linear regressions.
Analytic Models
Cross-sectional analyses of the 2007 JSLC were performed to compare within and between sub-
populations and frequencies. Logistic regression examined the relationship between the
dichotomous binary dependent variable and some predisposed independent (explanatory)
variables. A pvalue < 0.05 was selected to established statistical significance.
Analytic models, using multiple logistic and linear regressions, were used to ascertain
factors which are associated with (1) self-rated health status, (2) health care utilization, (3) self-
reported illness, (4) self-reported diagnosed chronic illness, and income. For the regressions,
design or dummy variables were for all categorical variables (using the reference group listed
last). Overall model fit was determined using log likelihood ratio statistic, odds ration and r-
squared. Stepwise regressions were used to determine the contribution of each significant
variable. All confidence interval (CIs) for odds rations (ORs) were calculated at 95%.
Results
37
Demographic characteristic of sample The sample was 6,783 respondents (48.7% males and 51.3% females). Children constituted
31.3%; other aged adults, 31.3%; young adults, 25.9%; and elderly, 11.9%. The elderly
comprised 7.7% young-old, 3.2% old-old and 1.0% oldest-old. Majority of the sample had no
formal education (61.8%); primary, 25.5%; secondary, 10.8% and tertiary, 2.0%. Two-thirds of
the sample sought health in the last 4-weeks; 69.2% were never married; 23.3% married; 1.7%
divorced; 0.9% separated and 4.9% were widowed respondents. Almost 15% reported an illness
in the last 4-weeks (43.3% had chronic conditions, 30.4% had acute conditions and 26.3% did
not specify the condition). Of those who reported an illness in the last 4- weeks, 87.9% provided
information on the typology of conditions: cold, 16.7%; diarrhea, 3.0%; asthma, 10.7%; diabetes
mellitus, 13.8%; hypertension, 23.1%; arthritis, 6.3%; and specified conditions, 26.3%. Marginal
more people were in the upper class (40.3%) compared to the lower socioeconomic strata
(39.8%). Only 20.2% of respondents had health insurance coverage (private, 12.4%; NI Gold,
public, 5.3%; other public, 2.4%). Majority of health insurance was owned by those in the upper
class (65%) and 19% by those in the lower socioeconomic strata.
Bivariate analyses
Sixty-one percent of those with chronic conditions were elderly compared to 16.6% of
those with other conditions (including acute ailments). Only 39% of those with chronic
conditions were non-elderly compared to 83.4% of those with other conditions – (χ2 = 187.32, P
< 0.0001).
Thirty-three percent of those with chronic illnesses had health insurance coverage
compared to 17.8% of those with acute and other conditions - (χ2 = 26.65, P < 0.0001).
38
Furthermore examination of self-reported health conditions by health insurance status revealed
that diabetics recorded the greatest percent of health insurance coverage (43.9%) compared to
hypertensive, (28.2%); arthritic (25.5%); acute conditions’ patients (17.0%) and other health
conditions respondents (18.8%). Sixty-seven percent of respondents who reported being
diagnosed with chronic conditions sought medical care in the last 4-weeks compared to 60.4% of
those with acute and other conditions (χ2 = 4.12, P < 0.042). Those with primary or below
education were more likely to have chronic illnesses (45.0%) compared to secondary level
(6.1%) and tertiary level graduants (11.1%) - (χ2 = 23.50, P < 0.0001). There was no
statistical association between typology of illness and social class - (χ2 = 0.63, P = 0.730): upper
class, 44.6%; middle class, 41.1% and lower class, 43.0%.
This study found significant statistical association between health insurance status and (1)
educational level (χ2 = 45.06, P < 0.0001), (2) social class (χ2 = 441.50, P < 0.0001), and (3) age
cohort (χ2 = 83.13, P < 0.0001). Forty-two percent of those with at most primary level education
had health insurance coverage compared to 16.3% of secondary level and 42.2% of tertiary level
respondents. Thirty-three percent of upper class respondents had health insurance coverage
compared to 16.7% of those in the middle class and 9.4% of those in the lower socioeconomic
strata. Almost 33% of the oldest-old had health insurance coverage compared to 15.1% of
children; 18.4% of young adults; 23.6% of other aged- adults; 28.6% of young-old and 24.9% of
old-old. A significant statistical association was found between health insurance status and area
of residence (χ2 = 138.80, P < 0.0001). Twenty-eight percent of urban dwellers had health
insurance coverage compared to 22.1% of semi-urban respondents and 14.5% of rural residents.
Furthermore, similarly a significant relationship existed between health care seeking behaviour
and health insurance status (χ2 = 33.61, P < 0.0001). Fourteen percent of those with health
39
insurance sought medical care in the last 4-weeks compared to 9.0% of those who did not have
health insurance coverage. Likewise a statistical association was found between health insurance
status and typology of illness (χ2 = 26.65, P < 0.0001). Fifty-eight percent of those with
insurance coverage had chronic illnesses compared to 38.3% of those without health insurance.
Concurringly, 42% of those with insurance coverage had acute or other conditions compared to
62% of those who did not have health insurance coverage. Further examination revealed that
other public health insurance was mostly had by those with chronic illnesses (76%) compared to
NI Gold (public, 65%) and 44% private health coverage (χ2 = 42.62, P < 0.0001). Private health
coverage was most had by those with non-chronic illnesses (56%) compared to 35% with NI
Gold (public) and 25% other public coverage.
No significant statistical difference was found between the average medical expenditure
of those who had insurance coverage and non-insured (t = 0.365, P = 0.715) – mean average
medical expenditure of those without health insurance was USD 10.68 (SD = 33.94) and insured
respondents’ mean average medical expenditure was USD 9.93 (SD = 18.07) - (Ja. $80.47 = US
$1.00 at the time of the survey).
There was no significant statistical relationship between health care utilization (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 =
40
15.8) compared to 32.5 years for those with non-chronic conditions. Concurringly, uninsured
chronically ill respondents’ mean age was 61.5 years (SD = 16.5) compared to 28.6 years (SD =
25.9) for those with non-chronic illnesses.
Table 2.1 examines information on crowding index, total annual food expenditure,
annual non-food expenditure, income, age, time in household, length of marriage, length of
illness and number of visits made to medical practitioner by health insurance status.
Self-rated health status, health care seeking behaviour, illness, educational level, social
class, area of residence, and health conditions, health care utilization by health insurance status
are presented in Table 2.2.
Table 2.3 presents information on age cohort of respondents by diagnosed health
conditions. A significant statistical association was found between the two variables χ2 = 436.8,
P < 0.0001.
Table 2.4 examines illness by age of respondents controlled for by health insurance
status. There existed a significant statistical relationship between illness and age of respondents,
but none between the uninsured and insured, P = 0.410.
Table 2.5 presents information on the age cohort by diagnosed health conditions, and
diagnosed health conditions controlled by health status.
There is a statistical difference between the mean age of respondents and the typology of
self-reported illnesses (F = 99.9, P < 0.0001). Those with cold, 19.2 years (SD = 23.9);
diarrhoea, 30.3 years (SD = 31.4); asthma, 22.9 years (SD = 22.1); diabetes mellitus, 60.9 years
(SD = 16.0); hypertension, 62.5 years (SD = 16.8); arthritis, 64.3 years (SD = 14.5), and other
conditions, 38.3 years (SD = 25.3).
Analytic Models
41
Nine variables account for (Table 2.6), 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 care
utilization and illness. Self-reported illnesses accounted for 62.2% of the explained variability of
moderate-to-very good health status.
Table 2.7 shows information on the explanatory factors of self-reported illnesses. Seven
factors accounted for 66.5% of the variability in self-reported illnesses. Ninety-two percent of
the variability in self-reported illnesses was accounted for by health care utilization (health care
seeking behaviour).
Three variables emerged as statistically significant correlates of health care utilization.
They accounted for 71.9% of the variance in health care utilization. Most of the variability can
be explained by self-reported illnesses (71.2%, Table 2.8).
Self-reported diagnosed chronic illnesses can be explained by 5 variables (gender, marital
status, health insurance status, age and length of illness), and they accounted for 27.7% of the
variance in self-reported diagnosed chronic illness (Table 2.9).
Sixty-two percent of the variability in income can be explained by crowding index, social
class, household head, health insurance status, self-rated health status, health care utilization,
area of residence and marital status). Most of the variability in income can be explained by social
class (Table 2.10).
Table 2.11 presents information on the explanatory variables which account for health
insurance coverage. Six variables emerged as significant determinants of health insurance
coverage (age, income, chronic illness, health care utilization, marital status and upper
42
socioeconomic class). The explanatory variables accounted for 19.4% of the variability in health
insurance coverage. Income was the most significant determinant of health insurance coverage
(explained 43% of the explained variance, 19.4%).
Discussion The current study revealed that 15 out of every 100 Jamaicans reported having an illness in the
last 4-weeks, and 57% of those with an illness had chronic conditions. Sixty-one out of every
100 of those with chronic illnesses were 60+ years; 67% of the chronically ill sought medical
care when compared to 66% of the population. Most of the chronically ill respondents were
uninsured (67%). The chronically ill had mostly primary level education, and there was no
statistical association between typology of illness and social class. Almost 2 in every 100
chronically ill Jamaicans were children (less than 19 years), and most of them were uninsured.
Nine percent more of the chronically ill who the other aged adult cohort did not have health
insurance coverage. Insured respondents were 1.5 times more likely to rate their health as
moderate-to-very good compared to the uninsured, and they were 1.9 times more likely to seek
more medical care, 1.6 times more likely to report having chronic illnesses, and more likely to
have greater income than the uninsured. Illness is a strong predictor of why Jamaicans seek
medical care (R2 = 71.2% of 71.9%), and health insurance coverage accounted for less than one-
half percent of the variance in health care utilization. However, health care utilization is a strong
predictor of self-reported illness, but it was weaker than illness explaining health care utilization
(61.1% of 66.5%). Public health insurance was most common among those with chronic illnesses
(76%) compared to 44% private health coverage and 38% had no coverage. Those in the upper
43
income strata’s income was significant more than those in the middle and lower socioeconomic
group, but chronic illnesses were statistically the same among the social classes.
Health disparities in a nation are explained by socioeconomic determinants as well as
health insurance status. Previous research showed that health care utilization and health
disparities are enveloped in unequal access to insurance coverage and social differences [2, 4,
17-19]. The present paper revealed that health insurance coverage is mostly had by those in the
upper class, with less than 20 in every 100 insured being in the lower socioeconomic class.
Although this study found that those in the lower class does not have more chronic illness than
those in the wealthy class, 86 out of every 100 uninsured respondents indicated that their health
status was poor.
Health insurance coverage provides valuable economic relief for chronically ill
respondents as this allows them to access needed health care. Like Hafner-Eaton’s research [2],
this paper found that health insurance status was the third most powerful predictor of health care
utilization. Forty-nine to every 100 chronically ill persons use the public health care facilities.
This mean that health insurance coverage appeases the health care burden of its holder, but the
insured in Jamaica are mostly wealthy, older, chronically ill, married, and seek more medical
care than the uninsured. The uninsured ill are therefore less likely to demand health care, and this
economic burden of health care is either going to be the responsibility of the state, the individual
or the family. The difficulty here is that the uninsured are more likely to be in the lower-to-
middle class, of working age or children, experienced more acute illness, 38 out of every 100
chronically ill are in the lower class, these provide a comprehensive understanding of the insured
and uninsured that will allow for explanations in health disparities between the socioeconomic
strata and sexes. With 43 out of every 100 people in the lower socioeconomic strata self-reported
44
being diagnosed with chronic illness, health insurance coverage, public health system and other
policy intervention aid in their health, and health care utilization.
Among the material deprivation of the poor is uninsurance. Those in the wealthy
socioeconomic group in Jamaica were 3.5 times more likely to be holder of health insurance
coverage than those in the lower socioeconomic strata. And Gertler and Sturm [3] identified that
health insurance cause a switching from public health to the private health system, which
indicates that a reduction in public health expenditure and health insurance will significantly
influence the health of the poor. This research showed that only 19% of those with health
insurance were in the lower class. Therefore issue of uninsurance creates futuristic challenges for
the poor in regard to their health and health care utilization. As on the onset of illness, those in
the lower income strata without health insurance must first think about their illness and weight
this against the cost of losing current income in order to provide for their families as well as
parents of ill children must also do the same. The public health care system will relieve the
burden of the poor, and while those with health insurance are more likely to utilize health care,
this is a futuristic product in enhancing a decision to utilize health care. But outside of those
issues, their choices (or lack), the cost of public health care, national insurance scheme and
general price index in the society further lowers their quality of life. Although the poor may be
dissatisfied with the public health care system (waiting time, crowding, discriminatory practices
by medical practitioners), better health for them without health coverage is through this very
system. It can be extrapolated therefore from the present data that there are unmet health needs
among some people in the lower socioeconomic strata. As those who do not have health
insurance, want to avoid the public health care system owing to dissatisfaction or
inafffordability, and will only seek health care when their symptoms are severe and sometimes
45
the complications from the delay make it difficult to be addressed on their visits. Among unmet
health needs of the poor will be medication. Even if they attend the public health care system and
are treated, the system does not have all the medications which is an indication that they are
expected to buy some. The challenge of the poor is to forego purchasing medication for food,
and this means their conditions would not have been rectified by the health care visitation.
By their very nature, the socioeconomic realities of the poor such as lower access to
education, proper nutrition, good physical milieu, poor sanitation and lower health coverage,
cripple their future health status, this accounts for high premature mortality and hinders health
care utilization. It is this lower health care utilization which accounts for their increase risk of
mortality as the other deprivations such as proper sanitation and nutrition exposes them to
disease causing pathogens which means that their inability to afford health insurance increased
their reliance on the public health care system. The present findings showed that the uninsured
are mostly poor and within the context of Lasser et al.’s work [20] that they receive worse access
to care, are less satisfied with the care they receive and medical services than the insured in the
US, this is an indication of further resistant of the poor from willingly demanding health care as
this rehashes their dissatisfaction and humiliation. Despite the dissatisfaction and humiliation,
their choices are substantially the public health care system, abstinence from care, risk of death,
and the burden of private health care. Apart of the rationales why those in the lower
socioeconomic strata have fewer health coverage than those in the wealthy income group are (1)
inafffordability, (2) type of employment (mostly part time, seasonal, low paid and uninsured
position) which makes it too difficult for them to be holders of health insurance and this retards
the switch from public-to-private health care utilization. Recently a study conducted by Bourne
and Eldemire-Shearer [21] found that 74% of those in the poorest income quintile utilized public
46
hospitals compared to 58% of those in the second poor quintile and 31% of those in the
wealthiest 20%. Then, if public health becomes privatized or become increasingly more
expensive for recipients, the socioeconomically disadvantaged population (poor, elderly, children
and other vulnerable groups) will become increasingly exposed to more agents that are likely to
result in their deaths, increased utilization of home remedy as well as the widening of the health
outcome inequalities among the socioeconomic strata.
Illness and particularly chronic condition can easily result in poverty, before mortality
sets in. With the World Health Organization (WHO) opined that 80% of chronic illnesses were in
low and middle income countries and that 60% of global mortality is caused by chronic illness
[7], leveling insurance coverage can reduce burden of care for those in the lower socioeconomic
strata. The importance of health insurance to health care utilization, health status, productivity,
production, socioeconomic development, life expectancy, poverty reduction strategy and health
intervention must include increase health insurance coverage of citizenry within a nation. The
economic cost of uninsured people in a society can be measured by the lost of production,
payment of sick time, mortality, lowered life expectancy and cost of care for children, orphanage
and elderly who become the responsibility of the state from the death of the poor. Therefore the
opportunity cost of reduced public health care budget is the economic cost of the aforementioned
issues, and goes to the explanation of premature mortality in a society.
Particularly the chronically ill, they benefit from health insurance coverage not because
of the reduced cost of health care, but the increased health care utilization that result from health
coverage. From the findings of Hafner-Eaton’s work [2], the chronically ill in the United States
were 1.5 times more likely to seek medical care and while this is about the same for Jamaicans,
health insurance is responsible to their health care utilization and not the condition or illness.
47
According to Andrulis [22], “Any truly successful, long-term solution to the health problems of
the nation will require attention at many points, especially for low-income populations who have
suffered from chronic underservice if not outright neglect” Embedded in Andrulis’s work is the
linkage between poverty, poor health care service delivery, differences in health outcomes
among the socioeconomic groups, higher mortality among particular social class, deep-seated
barriers in health care delivery and the perpetuation of those and how they can increase health
differences among the socioeconomic strata. The relationship between poverty and illness is well
established in the literature [7, 8, 23] as poverty means deprivation from proper nutrition, safe
drinking water, and those issues contribute to lower health, production, productivity, and more
illness in the future. Free public health care or lower public health care cost does not mean equal
opportunity to access, eliminate the barriers to equal opportunity, neither does it increase health
and wellness for the poor and remove lower health disparities among the socioeconomic groups.
However, lower-income, increase price indices, removal of government subsidy from public
health care, increased uninsurance, lower health care utilization, increase poverty, premature
mortality and lower life expectancy of the population and particular subpopulations.
Increases in diseases (acute and chronic) are owing to lifestyle practices of people.
Lifestyle practices are voluntary lifestyle choices and practices [24]. The poor are less educated,
more likely to be unemployed, undernourished, deprived from financial resources, and their
voluntary actions will be about survival and not diet, nutrition, exercise and other healthy
lifestyle choice. Lifestyle choices such as diet, proper nutrition, and sanitation, safe drinking
water are costly, which oftentimes occurs because of poverty, some people can afford to make
these choices. It follows therefore that those in the lower socioeconomic strata’s voluntary action
will be unhealthy choices which are cheaper. Poverty therefore handicaps its people, and
48
predetermines unhealthy lifestyle choices, which further accounts for greater mortality, lower life
expectancy, health insurance coverage and private health care utilization.
Conclusion
Poverty is among the social determinants of health, health care utilization, and health insurance
coverage in a society. While the current study does not support the literature that chronic
illnesses were greater among those in the lower socioeconomic strata, they were less likely to
have health insurance coverage compared to the upper class. Poverty denotes socioeconomic
deprivation of resources which appears in a society, and goes to the crux of health disparities
among the socioeconomic groups and sexes. Health care utilization is associated with health
insurance coverage as well as government’s assistance, and this embodies the challenges of those
in the vulnerable groups.
Within the current global realities, many governments are seeking to reduce their public
financing of health care which would further shift the burden of health care to the individual, and
this will even increase premature mortality among those in the lower socioeconomic strata.
Governments in developing nations continue to invest in improving public health measures such
as safe drinking water, sanitation, mass immunization) and the training of medical personnel,
building clinics and hospitals and there is definite a need to include health insurance coverage to
their public health measure as this will increase access to health care utilization. Any increase in
health care utilization will be able to improve health outcome, reduce health disparities between
the socioeconomic groups and the sexes that will see improvements in the quality of life of the
poor.
49
In summary, with the health status of the insured being 1.5 times more than the
uninsured, their health care utilization being 1.9 times more than the uninsured and illness being
a strong predictor of health care seeking, any reduction in the health care budget in developing
nations denotes that vulnerable groups (such as elderly, children and poor) will seek less care,
and this will further increase the mortality among those cohorts.
Conflict of interest
The authors have no conflict of interest to report.
Disclaimer
The researchers would like to note that while this study used secondary data from the Jamaica Survey of Living Conditions, none of the errors in this paper should be ascribed to the Planning Institute of Jamaica or the Statistical Institute of Jamaica, but to the researchers.
50
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21. Bourne PA, Eldemire-Shearer D. Public hospital health care utilization in Jamaica. Australian J of Basic and Applied Scie 2009; 3:3067-3080.
22. Andrulis DP. Access to care is the centerpiece in the elimination of socioeconomic disparities in health. Ann Intern Med 1998; 129:412-416.
23. Foster AD. Poverty and illness in low-income rural areas. The American Economic Review 1994; 84:216-220.
24. Barnekow-Bergkvist M, Hedberg GE, Janlert U, Jansson E. Health status and health behaviour in men and women at the age of 34 years. European J of Public Health 1998; 8:179-182.
52
Table 2.1. 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, < 0.0001Total annual food expenditure1 3476.09±2129.97 3948.12±2257.97 t = - 6.81, < 0.0001Annual non-food expenditure1 3772.91±3332.50 6339.40±5597.60 t = - 21.33, < 0.0001Income1 7703.62±5620.94 12374.89±9713.00 t = - 22.75, < 0.0001Age (in year) 28.7±21.4 35.0 ±22.7 t = - 9.40, < 0.0001Time in household (in years) 11.7±1.6 11.8±1.3 t = - 1.62, 0.104Length of marriage 16.9±14.3 18.3±13.8 t = - 1.55, 0.122Length of illness 14.7±51.1 14.1±36.2 t = - 0.217, 0.828No. of visits to medical practitioner 1.4±1.0 1.5±1.2 t = - 0.659, 0.5111Expenditures and income are quoted in USD (Ja. $80.47 = US $1.00 at the time of the survey)
53
Table 2.2. 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 (87.4) Secondary 80 (9.9) 23 (6.7) 9 (5.7) 577 (11.1) Tertiary 46 (5.7) 4 (1.2) 4 (2.5) 74 (1.4)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 (20.9) 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)
54
Table 2.3. 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 (48.1) 51 (53.7) 3 (2.4) 0 (0.0) 0 (0.0) 54 (23.1) 218 (24.5) Children
Young adults 14 (94) 2 (7.4) 16 (16.8) 3 (2.4) 6 (2.9) 1 (1.8) 43 (18.4) 85 (9.6)
Other-aged adults 22 (14.8) 6 (22.2) 18 (18.9) 44 (35.8) 76 (36.9) 17 (30.4) 85 (36.3) 268 (30.1)
Young old 8 (5.4) 2 (7.4) 7 (7.4) 49 (39.8) 61 (29.6) 22 (39.3) 32 (13.7) 181 (20.3)
Old Elderly 8 (5.4) 3 (11.1) 2 (2.1) 19 (15.4) 49 (23.8) 14 (25.0) 13 (5.6) 108 (12.1)
Oldest Elderly 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
55
Table 2.4. 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 ± 26.8F statistic 73.1, P < 0.0001 23.3, P < 0.0001
56
Table 2.5. 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 (2.6) 110 (26.5) 100 (38.6) 21 (23.3) 37 (29.4)Young-old 49 (9.7) 132 (34.3) 37 (8.9) 82 (31.7) 12 (13.3) 50 (39.7)Old-old 26 (5.1) 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
57
Table 2.6. 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
58
Table 2.7. 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
59
Table 2.8. Logistic regression: Explanatory variables of health care seeking behaviour Explanatory variable Coefficient
Std error
Odds ratio 95.0% C.I.
R2
Health insurance coverage (1= insured)
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 2.9. 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
61
Table 2.10. Multiple regression: Explanatory variables of income
Explanatory variable
Unstandardized Coefficients
Standardized Coefficients
95% CI
B Std. Error Beta 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
62
Table 2.11. 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
63
Chapter 3
Self-reported health and medical care-seeking behaviour of uninsured Jamaicans
Paul A. Bourne
On examination of the literature in Latin America and the Caribbean, and in particular Jamaica, no study could be found that investigated the health and health care-seeking behaviour of uninsured people. This study bridges the gap in the literature, by evaluating uninsured Jamaicans’ medical care-seeking behaviour and good health status. The study extracted a sample of 5,203 uninsured respondents 15 years and older from a national probability cross-sectional survey of 6,782 Jamaicans. Descriptive statistics were used to provide background information on the sample; cross-tabulations evaluated bivariate analyses, and logistic regression was used to model health and medical care-seeking behaviour. Good health of uninsured Jamaicans is correlated -reported biological condition (OR =0.114, 95% CI = 0.090 -0 .145) followed by age (OR =0.952, 95% CI = 0.946- 0.959); gender (OR = 1.501, 95% CI = 1.221–1.845); consumption (OR = 1.000, 95% CI = 1.000–1.000); social class (upper class OR = 0.563, 95% CI = 0.357–0.888); education (secondary and above OR = 0.622, 95%CI = 0.402–0.963), and area of residence (other towns OR = 1.351, 95% CI = 1.026–1.778). Medical care-seeking behaviour is associated with age (OR = 1.020, 95% CI = 1.006 – 1.033); poor health status (OR = 2.303, 95% CI = 1.533–3.461), and marital status (married OR = 0.518, 95% CI = 0.325–0.824). The findings are far reaching and provide an understanding of the uninsured, and the information can be used to aid public health intervention and education programmes. Introduction Poverty is among the reasons for some people in developing nations not seeking medical care;
and it also explains premature death owing to low health care utilization. The World Health
Organization (WHO) [1] opined that 80% of chronic illnesses were in low and middle income
countries, suggesting that poverty interfaces with illness and creates other socio-economic
challenges. Poverty does not only impact on illness, it causes premature deaths, lower quality of
64
life, lower life and healthy life expectancy, low development and other social ills such as crime,
high pregnancy rates, and social degradation of the community. According to Bourne &
Beckford [2], there is a positive correlation between poverty and unemployment; poverty and
illness; and crime and unemployment. Sen [3] encapsulated this well when he put forward the
idea that low levels of unemployment in the economy are associated with higher levels of
capabilities. The WHO [1] opined that 60% of global mortality is caused by chronic illness, and
within the context that four-fifths of chronic dysfunctions are in low-to-middle income countries,
health insurance coverage reduces the burden of out-of-pocket medical expenditure for the
individual and the family.
Jamaica is among those countries classified as developing nations. Hence, the challenges
which were stated earlier also influence the quality of life of some people within the society. In
1988, Jamaica’s unemployment rate was 18.9% and 2 decades later (2007), it fell by 67.2% (to
6.2%) which indicates close to full-employment. [4] This significant reduction in unemployment
rates cannot be the only indicator used to evaluate the socio-economic status of Jamaica, or for a
hasty conclusion to be drawn that the quality of life of Jamaicans is better in 2007 compared to
1988. In 1988 the inflation rate in Jamaica was 8.8% and this increased by over 90%, suggesting
that the economic cost of living for Jamaicans was substantially higher than twenty years earlier.
It is important to note that the inflation rate in 2007 (16.8%) increased by 194.7% over 2006. A
national representative probability sample cross-sectional survey of 1,338 Jamaicans which was
conducted in 2007 revealed that 68.7% of respondents claimed that their current economic
situation was at most the same compared to 12 months ago, and of this figure 25% mentioned
that it was worse. [5] Furthermore, 62% of the sample indicated that their salaries were not able
65
to satisfactorily cover their basic needs, and 71.9% claimed that they were concerned about the
likelihood of being unemployed in the next 12 months. Those realities, then, explain why in
2007, the number of Jamaicans seeking medical care fell to 66% over 70% in the previous year;
while the self-reported figures rose to an unprecedented 15.5%.
In Jamaica, rural poverty is twice (15.3%) that of urban poverty (6.2%). [4] This may
create the impression that urban poverty is low and does not demand an examination. Poverty is
poverty and whether it occurs in rural, peri-urban and urban areas; its effect is the same. Hence,
when poverty is coupled with unemployment, chronic illnesses will require health care for either
preventive or curative measures which must lead to a financial commitment that can erode their
resources or that of their families. [5] In 2007, statistics on health in Jamaica showed that 50.8%
of people in the poorest income quintile (i.e. below the poverty line) indicated that they were
unable to afford to seek medical care, compared to 36.7% of those just above the poverty line
and 7.1% of those in the wealthiest income quintile. [4] It is private health insurance and social
security that facilitate access to medical care for the poor and do assist in reducing the financial
commitment of individuals and families for those with chronic or recurring illnesses. Twenty-
one of every 100 Jamaican in 2007 has health insurance coverage, suggesting that the majority of
people pay for medical care out of their pockets.
Many studies have examined the insured and health care demand of the general populace
[6-10] but on reviewing the literature no study was found in Latin America and the Caribbean, in
particular Jamaica, that has investigated the uninsured in regards to their medical care-seeking
behaviour and health status. According to Call & Ziegenfuss, [7] health insurance is a significant
predictor of access to medical care services, and people who do not have access to health
66
insurance have less possibilities of accessing health care services. This was contradicted by
Bourne [11] who found that health insurance is not significant when correlated with the medical
care-seeking behaviour of Jamaicans or a predictor of the good health of Jamaicans [11] or
female Jamaicans. [12] Call & Ziegenfuss [7] added that rural residents are more restricted from
access to health insurance coverage than urban citizens, suggesting that medical care-seeking
behaviour would be lower for rural than urban residents. While Call & Ziegenfuss’ perspectives
provide us with basic information about the insured, it is inadequate for this cohort of people
based on the findings of Bourne [11], and Bourne & Rhule [12].
For 2007, statistics revealed that 21.2% of Jamaicans had health insurance coverage and
66% sought medical care, indicating that most of the people who utilized medical care services
did not use health coverage. Within the context of the global economic downturn, increased job
redundancies and prices of commodities, the uninsured will be asked to pay more for medical
care. Apart from the increased odds of not utilizing health care services, little is known about the
uninsured in Latin American and the Caribbean, and in particular Jamaica. This study will
bridge the gap in the literature, by evaluating their health status, medical care-seeking behaviour,
and the medical conditions of uninsured Jamaicans in order to establish whether there are
differences in the three geographical regions, and to use the information for public health
intervention and policy formulation. The researcher used data from the 2007 Jamaica Survey of
Living Conditions to evaluate medical care-seeking behaviour, medical conditions, purchased
medication, and the health status of uninsured Jamaicans as well as building two models for good
health status and health care-seeking behaviour of this uninsured group.
Methods and materials
67
Data The current study extracted a sample of 5,203 respondents 15 years of age and over from a
national probability cross-sectional survey (Jamaica Survey of Living Conditions, JSLC) of
6,782 Jamaicans [13-15]. The cross-sectional survey was conducted between May and August
2007 from the 14 parishes across Jamaica and included 6,782 people of all ages [16]. The JSLC
used stratified random probability sampling technique to draw the original sample of
respondents, with a non-response rate of 26.2%. The sample was weighted to reflect the
population. [13-15]
Study instrument
The JSLC used an administered questionnaire where respondents were asked to recall detailed
information on particular activities. The questionnaire was modelled on the World Bank’s Living
Standards Measurement Study (LSMS) household survey. There are some modifications to the
LSMS, as the JSLC is more focused on policy impacts. The questionnaire covers demographic
variables, health, and other issues. Interviewers were trained to collect the data from household
members. Data on 5, 203 individuals who indicated not having health insurance coverage was
used in data analysis.
Statistical methods Descriptive statistics such as mean, standard deviation, frequency and percentage were used to
analyze the socio-demographic characteristics of the sample. Chi-square analyses were used to
examine the association between non-metric variables for area of residence, and gender of
respondents. Logistic regression analyses examined 1) the relationship between good health
68
status and some socio-demographic, economic and biological variables; as well as 2) a
correlation between medical care-seeking behaviour and some socio-demographic, economic and
biological variables. The statistical package SPSS for Windows version 16.0 (SPSS Inc;
Chicago, IL, USA) was used to analyze the data. A p-value less than 5% was used to indicate
statistical significance.
The correlation matrix was examined in order to ascertain if autocorrelation and/or
multicollinearity existed between variables. Based on Cohen and Holliday [17] correlation can
be low (weak) - from 0 to 0.39; moderate – 0.4-0.69, and strong – 0.7-1.0. The approach in
addressing collinearity (r > 0.6) was to independently enter variables in the model to determine
which one should be retained during the final model construction. The method of retaining or
excluding a variable from the model was based on the variables’ contribution to the predictive
power of the model and its goodness of fit. [18-24] Wald statistics were used to determine the
magnitude (or contribution) of each statistically significant variable in comparison with the
others, and the Odds Ratio (OR) for the interpreting of each significant variable.
Models
The current study will employ multivariate analyses in the study of the health status (Equation
[1]) and medical care seeking behaviour of Jamaicans (Equation [2]). The use of this approach is
better than bivariate analyses as many variables can be tested simultaneously for their impact (if
any) on a dependent variable.
Ht=f(Ai, Gi, HHi, ARi, lnC, EDi, MRi, Si, ∑MCt, SRIi, εi) 1
Where Ht (i.e. self-rated good current health status in time t) is a function of age of
respondents Ai; sex of individual i, Gi; household head of individual i, HHi; area of
69
residence, ARi; logged consumption per person per household member, lnC; Education
level of individual i, EDi; marital status of person i, MRi; social class of person i, Si;
summation of medical expenditure of individual i in time period t, MCt; self-reported
illness, SRIi, and an error term (i.e. residual error).
MCSBi=f(PHt ,Ai, Gi, HHi, ARi, lnC, EDi, MRi, Si, CRi, εi) 2
Where MCSBi is medical care-seeking behaviour of individual i is a function of PHt (ie
self-rated poor current health status in time t of individual i); age of respondents Ai; sex
of individual i, Gi; household head of individual i, HHi; area of residence, ARi; logged
consumption per person per household member, lnC; education level of individual i, EDi;
marital status of person i, MRi; social class of person i, Si; logged consumption per
person per household member i, lnC; crowding of person i, CRi; and an error term (i.e.
residual error).
From Equation (1) was derived Equation (3) and Equation (4):
Ht=f(Ai, lnC, SRIi, Si, EDi, ARi, Gi, εi) 3 MCSBi=f(PHt ,Ai, MRi, εi) 4 Measures
An explanation of some of the variables in the model is provided here. Self-reported illness
status is a dummy variable, where 1 = reporting an ailment or dysfunction or illness in the last
4 weeks, which was the survey period; 0 if there were no self-reported ailments, injuries or
illnesses. [11, 12, 25] While self-reported ill-health is not an ideal indicator of actual health
conditions because people may under-report, it is still an accurate proxy of ill-health and
mortality. [26, 27] Health status is a binary measure where 1=good to excellent health; 0=
otherwise which is determined from “Generally, how do you feel about your health”? Answers
70
for this question were on a Likert scale matter ranging from excellent to poor. Age group was
classified as children (ages less than 15 years); young adults (ages 15 through 30 years); other
aged adults (ages 30 through 59 years); young-old (ages 60 through 74 years); old-old (ages 75
through 84 years) and oldest-old (ages 85+ years). Medical care-seeking behaviour was taken
from the question ‘Has a health care practitioner, healer , or pharmacist been visited in the last 4
weeks?’ with there being two options Yes or No. Medical care-seeking behaviour therefore was
coded as a binary measure where 1=Yes and 0= otherwise.
Results Socio-demographic characteristics of sample The sample was 5,203 uninsured respondents (49.2% males and 50.8% females). Of the sample,
32.9% were children; 26.9% young adults; 30.0% other aged adults; 10.8% elderly. The
majority of those sampled had good health status (82.9%); 73% were never married; 62.0%
visited medical care-seeking behaviour; 60.3% had at most no formal education; 52.2% lived in
rural areas; 21.0% in semi-urban areas and 26.8% in urban areas. Fifty-nine percent of the
sample purchased the prescribed medication, and 14.2% reported an illness. Of those who
reported ailments, 89.5% revealed that they were diagnosed by health care practitioners.
Approximately 17% indicated cold; 3.5% diarrhoea; 9.8% asthma; 19.7% hypertension; 5.5%
arthritis; 25.3% and unspecified dysfunctions. Forty-five percent of the sample were poor
(23.1% below the poverty line), 20.9% in the middle class, and 34.1% were classified as wealthy
(14.8% in the wealthiest group).
A significant statistical correlation was found between medical care-seeking behaviour
and health status (χ2 (df = 2) =36.199, P < 0.001, n=752). Seventy-six percent (N= 160) of those
71
who reported poor health status sought medical care compared to 68.0% (n = 174) of those who
reported fair health status and 50.6% (n= 170) of those who indicated good health status.
Table 3.1 revealed that significantly more rural residents were poor (58.7%) compared to
34.9% of semi-urban and 26.5% of urban dwellers. Only 21.2% of rural respondents were in the
upper class which was significantly lower than those in semi-urban areas (42.6%) and the
percentage is even greater in urban zones (52.5%).
A cross-tabulation between health status and area of residence revealed a statistical
correlation (P<0.001). Further examination showed that substantially more rural respondents
indicated poor health status (6.3%) than semi-urban (3.3%) and urban (3.9%) (see Table 3.1).
Significantly more rural dwellers reported being diagnosed with a recurring illness (15.9%) than
semi-urban (11.8%) and urban respondents (12.7%). No significant statistical correlation was
found between medical care-seeing behaviour and area of residence (P= 0.375).
Seventeen percent of females reported a recurring illness which was significantly more
than the 12% for males (Table 3.2). Of the diagnosed recurring illness, approximately twice as
many females reported diabetes mellitus (11.3%) and hypertension (24.6%) than males (6.1%)
and 12.6% respectively. While more males indicated cold (18.1%); diarrhoea (3.6%); asthma
(11.3%); arthritis (6.5%); and unspecified (27.5%) compared to females – cold (15.6%);
diarrhoea (3.4%); asthma (8.8%); arthritis (4.7%), and 23.7% unspecified ailments.
A cross-tabulation between health status and self-reported illness found that there was a
significant statistical correlation (χ2 (df = 2) = 989.552, P < 0.001). The association was a
moderately strong one (contingency coefficient = 0.401). Further examination of the results
revealed that 89.4% (n=3,964) of those who reported no illness had good health status, and only
43.7% of respondents with an ailment indicated poor health status. Approximately 22% of
72
individuals with at least one dysfunction had poor health status compared to 2.3% of those who
did not have an illness (Table 3.3).
A significant statistical correlation existed between self-reported illness and age cohort
(χ2 (df = 5) = 407.365, P < 0.001, n = 5,189). The findings revealed that 12.4% children reported
at least one illness compared to 5.5% of young adults and following this age cohort self-reported
illness increased to 14.7% for other aged adults; 33.3% of young old; 49.7% of old-old and
51.2% of oldest-old.
Multivariate Analysis
Table 3.4 examines variables that seek to explain the good health status of insured
Jamaicans. Good health statuses of uninsured Jamaicans are correlated with socio-demographic,
economic and biological factors. The correlates of good health status of uninsured Jamaicans are
statistically significant (χ2 (df = 15) =993.114, P < 0.001; -2 Log likelihood = 2554.359;
Nagelkerke R2 =0.390; Hosmer and Lemeshow goodness of fit χ2=11.159), and 84.6% of the data
were correctly classified: 94.9% of cases in good health status were correctly classified and
46.6% were cases with poor health status.
Table 3.5 presents information on variables that determine (or not) the medical care-
seeking behaviour of uninsured Jamaicans. The correlates that explain medical care-seeking
behaviour of uninsured respondents are statistically significant χ2 (df = 14) = 47.79, P < 0.001; -2
Log likelihood = 648.32; Nagelkerke R2 =0.117; Hosmer and Lemeshow goodness of fit
χ2=4.480), and 67.5% of the data were correctly classified: 88.1% of data correctly classified
medical care-seeking behaviour and 30.0% of data otherwise.
Discussion
73
Caribbean societies, in particular Jamaica, have seen an increase in illnesses such as HIV/AIDS,
malignant neoplasm, diabetes mellitus, hypertension, ischaemic heart disease, and arthritis [28-
33] which require continued treatment. Although this is a reality, only 21.2% of Jamaicans had
health insurance coverage in 2007, indicating that the majority of people are without health
insurance coverage and many people will not be able to afford medical care.
The current study found that approximately one-half of Jamaicans who do not have
health insurance were poor compared to 34.1% of the wealthy and 20.9% of those in the middle
class. Substantially more Jamaicans below the poverty line (23.1%) did not have health
insurance compared to 14.8% of those in the wealthiest 20%. In addition, 33% were children
compared to 11% who were older than 60 years. Although there is a preponderance of Jamaicans
who are poor and uninsured, this research found that there was no statistical difference between
medical care-seeking behaviour and social class; medical care-seeking behaviour and sex; and
health care-seeking behaviour and area of residence. Embedded in this finding is the dominance
of a non-medical care-seeking behaviour culture in Jamaica, and it is so fundamental that
education, social class and income are not able to retard the practice. This is captured in an
analysis of the study that had 44 out of every 100 respondents indicating that they were ill
enough to seek medical care compared to 34 out of every 100 in the population; and 18 out of
every 100 stated they preferred home remedies compared to 30 in 100 in the populace.
Sixty-six out of every 100 Jamaicans sought medical care, comprising the poorest 20%-
to-wealthiest 20% in 2007. The current study revealed that 45 out of every 100 poor people were
not covered by health insurance compared to 17 out of 50 for the wealthy and 21 out of 100 for
the middle class. Concomitantly, 33 out of every 100 children (less than 15 years) and 60 out of
every 100 Jamaicans who had no formal education were not covered by health insurance. The
74
rationale which accounts for the fact that there is no significant difference in medical care-
seeking behaviour among the social classes is embedded in the removal of user fees in the health
care system; and how this has narrowed the health care-seeking behaviour gap between the poor
and the wealthy.
In 2007, the government of Jamaica introduced national health insurance coverage for
those who suffer from particular illnesses, as well as for those who are older than 60 years. This
social security coverage commissioned by the Jamaican government eliminates health insurance
for ‘unwell’ patients, suggesting that health is conceptualized as diseases, which is not in keeping
with an operationalization of health offered by the WHO. [34] According to the WHO, health
does not only mean the absence of disease, but it must include social, psychological and physical
wellbeing. The health insurance coverage offered by the government explains the low uninsured
group among the Jamaican elderly. Hence, this means that most of those who possess health
insurance would have private coverage; the high ‘unwell’ Jamaicans therefore justify the high
non-insured group in the nation. This paper examines the uninsured or the ‘unwell’.
This analysis has found that good health status can be determined by age, consumption,
self-reported illness, social class, education, area of residence and gender of respondents, which
concurs with other studies. [35-39] While this study is the first of its type in Jamaica, its findings
are similar to other multivariate studies that have examined the health status of people. Using
data for elderly Barbadians, Hambleton et al.’s work [35] found that dysfunction accounted for
the most explanatory power in health status, which is confirmed by this analysis. The model that
was developed for the good health status of uninsured Jamaicans was based on the 7
aforementioned variables with a coefficient of determination of the current study being 39.0%
(Nagelkerke R2 =0.390). This predictive model seems weak; but Hambleton et al’s work on
75
elderly Barbadians had a coefficient of determination of 38.2%, indicating that the analysis of
this paper is relatively good in keeping with a non-Jamaican study of a similar nature.
In spite of the similarities, there are some notable differences with other studies. Eight-
three out of every 100 uninsured Jamaicans reported at least good health status; 20 out of every
100 were hypertensive; 9 out of 100 diabetic and 6 out of 100 arthritic compared to the
percentage of respondents in the population with particular health conditions: hypertension, 22
out of every 100; diabetes mellitus, 12 out of every 100; and, arthritis, 9 out of every 100. It is
interesting to note that Jamaicans have a preference for private health care utilization [15] but
this is not the case for the uninsured. In 2007, 52 out of every 100 Jamaican visited private health
care services compared to 6 out of every 100 of the uninsured. The percentage of uninsured who
visited public health care facilities (34 out of every 100) was also lower than in the general
populace (41 out of every 100).
The analysis of this study went further than that of other identified studies as it found that
uninsured Jamaicans who resided in rural areas reported a greater percentage of illnesses,
followed by urban, than other town residents. Marmot [35] opined that income influences health
as it provides access to more resources, medical services, and lower infant mortality. The
analysis of this work concurs with Marmot [35] and PAHO et al. [9] as consumption (which can
proxy income) is positively correlated with good health status. With this reality, there seems to
be a paradox, as those in the wealthy classes had lower good health status than those in the poor
classes.
Income undoubtedly provides access to more resources, better physical conditions and
opens the way to better quality of water and food; it also offers individuals, societies or nations
the highest quality medical services which cannot be accessed by the poor. [35] There is another
76
side to this discourse in that the lifestyle practices of the wealthy help to erode the advantages of
income. According to Bourne, McGrowder & Holder-Nevins, [41] health behaviour which is a
function of one’s culture suggests that the wealthy will continue their involvement in parties and
other social arrangements which will involve the use of alcoholic beverages, smoking and other
risky lifestyle practices that reduce the advantage of income. While income can buy access to
better medical services, this paper highlights that it cannot buy good health. It is clear from the
current study that wealthy uninsured Jamaicans are using their income the wrong way in regards
to its negative impact on health. Insufficient money is associated with more illness; however,
this study has revealed that there is no statistical difference between the wealthy and the poor
seeking medical care. Although the wealthy substantially used private health care facilities and
the poor utilized public health facilities, [15] embedded in this analysis therefore is the fact that
the quality of primary level care in Jamaica is of a high standard.
While there is no difference between the wealthy uninsured and the poor uninsured
seeking medical care, the study revealed that those with poor health status were 2.3 times more
likely to seek health care services than those in good health. The analysis of this work showed
that 22 out of every 100 uninsured Jamaicans who indicated at least one health condition
reported poor health status. Hence this study highlights the fact that there is a disparity between
respondents’ conceptualization of health status and that of illness, as 44% of uninsured ill
respondents indicated that they had good health status.
The JSLC report revealed that the prevalence of recurrent (chronic) diseases is highest
among individuals 65 years and over. [41] According to PIOJ & STATIN [42] individuals 60-64
years were 1.5 times more likely to report an injury than children less than five years old, and the
figure was even higher for those 64 years and older (2.5 times more). It should be noted here that
77
this increase in self-reported cases of injuries/ailments does not represent an increase in the
incidence of cases as the JSLC for 2004 said that the proportion of recurring/chronic cases fell
from 49.2% in 2002 to 38.2% in 2004 [43]. Eldemire [44] found that 34.8% of new cases of
diabetes and 39.6% of hypertension were associated with senior citizens (i.e. ages 60 and over).
Bourne, McGrowder, & Crawford [39] found that the poor health status of people 60 to 64 years
was 33.2% and this increased to 36.1% for elderly 65 to 69 years, 49.4% for elderly 70 to 74
years and 51.7% for those 75 years and older, emphasizing the positive correlation between
increased ailments and ageing of the Jamaican elderly.
An analysis of the current study revealed that there is no significant difference among the
populations across the 3 geographical areas in Jamaica in regards to health care-seeking
behaviour, suggesting that the uninsured medical care-seeking behaviour is the same in the 3
geographical areas. This research concurs with the finding of a study by Call & Ziegenfuss [7]
meaning that the uninsured in Jamaica are not atypical as they are in keeping with those in
Minnesota, United States. Further, no significant correlation was found among urban, semi-
urban, rural and educational levels of uninsured Jamaicans which were similar to that of Call &
Ziegenfuss.
Many studies have shown that married people (or those in unions) had greater health
status than those who were never married. [45-51] The current work disagreed with those
findings as it found that there was no significant statistical correlation between good health status
of married uninsured people, and those who were never married, or separated, divorced or
widowed. Analysis of this research revealed that those who were married were 48.2% less likely
to seek medical care than those who were never married. The answer to this lies in the lifestyle
practices of these people. Smith & Waitzman [49] offered the explanation that wives were able
78
to dissuade their husband from particular risky behaviours such as the use of alcohol and drugs,
and would ensure that they maintain a strict medical regimen coupled with proper eating habits.
[50,51] Koo, Rie & Park’s findings [48] revealed that being married was a ‘good’ cause for an
increase in psychological and subjective wellbeing in old age. This study is the first of its kind
in the Caribbean, in particular Jamaica, which models the health care-seeking behaviour of
uninsured respondents, and so there is nothing to compare it with. The coefficient of
determination for this model was 11.9%, which means that although it is low its validation will
need further research.
Limitation of study
A single cross-sectional study cannot be used to examine causality, as well as a snap shot survey
cannot effectively capture the continuous matter of the variables. The severity of illness was
excluded from the medical care-seeking behaviour model because of missing cases and this
could have been critical to the study.
Conclusion
The findings of this research are far reaching and provide an understanding of the uninsured, and
the information can be used to aid public health intervention and education programmes.
Conflict of interest
There is no conflict of interest to report.
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Table 3.1: Socio-demographic characteristics of sample Variable
Area of residence P Urban n (%)
Semi-urban n (%)
Rural n (%)
Sex 0.284 Male 662 (47.4) 544 (49.9) 1354 (49.9) Female 735 (52.6) 547 (50.1) 1361 (50.1) Social class < 0.001 Poor 370 (26.5) 381 (34.9) 1594 (58.7) Middle 294 (21.0) 245 (22.5) 546 (20.1) Upper 733 (52.5) 465 (42.6) 575 (21.2) Age group 0.002 Children 418 (29.9) 334 (30.6) 961 (35.4) Young adults 411 (29.4) 306 928.0) 646 (23.8) Other aged adults 416 (29.8) 344 (31.5) 803 (29.6) Young old 93 (6.7) 72 (6.6) 199 (7.3) Old-old 48 (3.4) 27 (2.5) 82 (3.0) Oldest-old 11 (0.8) 8 (0.7) 24 (0.9) Health status < 0.001 Good 1137 (81.7) 956 (87.6) 2202 (81.6) Fair 201 (14.4) 99 (9.1) 329 (12.2) Poor 54 (3.9) 36 (3.3) 169 (6.3) Education < 0.001 No formal 841 (60.4) 687 (63.1) 1599 (59.1) Basic 174 (12.5) 118 (10.8) 362 (13.4) Primary/preparatory 168 (12.1) 158 (14.5) 429 (15.8) Secondary/High 166 (11.9) 111 (10.2) 300 (11.1) Tertiary 43 (3.1) 14 (1.3) 17 (0.6) Marital status 0.012 Married 177 (18.3) 132 (17.5) 382 (21.9) Never married 721 (74.5) 562 (74.6) 1245 (71.4) Divorced 18 (1.9) 17 (2.3) 15 (0.9) Separated 5 (0.5) 8 (1.1) 20 (1.1) Widowed 47 (4.9) 34 (4.5) 82 (4.7) Self-reported illness 0.001 Yes 176 (12.7) 128 (11.8) 432 (15.9) No 1215 (87.30 958 (88.2) 2280 (84.1) Medical care-seeking behaviour 0.375 Yes 120 (66.3) 78 (59.5) 270 (60.9) No 61 (33.7) 53 (40.5) 173 (39.1) Number of visits to medical facilities
1.4 days (SD = 0.7)
1.4 days (SD= 1.3)
1.4 days (SD = 1.0)
0.846
83
Table 3.2: Sociodemographic characteristic by Sex Variable Sex P
Male Female Self-reported illness < 0.001 Yes 298 (11.7) 438 (16.6) No 2256 (88.3) 2197 (83.4) Diagnosed Self-reported illness < 0.001 Cold 56 (18.1) 69 (15.6) Diarrhoea 11 (3.6) 15 (3.4) Asthma 35 (11.3) 39 (8.8) Diabetes mellitus 19 (6.1) 50 (11.3) Hypertension 39 (12.6) 109 (24.6) Arthritis 20 (6.5) 21 (4.7) Other (unspecified) 85 (27.5) 105 (23.7) No 44 (14.2) 35 (7.9) Medical care-seeking behaviour 0.101 Yes 182 (58.5) 286 (64.4) No 129 (41.5) 158 (35.6) Purchase medication 0.251 Prescribed medicine 170 (56.9) 259 (60.1) Partial prescription 3 (1.0) 13 (3.0) Prescribed/over the counter 9 (3.0) 15 (3.5) Over counter 20 (6.7) 25 (5.8) Prescribed/did not buy 9 (3.0) 17 (3.9) None prescribed required 88 (29.4) 102 (23.7) Number of visits to medical facilities Mean (SD) 1.3 days (0.7) 1.4 days (1.1) 0.252
84
Table 3.3. Health status by Self-reported dysfunction
Health Status
Self-reported Dysfunction
Total n (%)
No ailment n (%)
At least one ailment n (%)
Good
3964 (89.4) 320 (43.7) 4284 (82.9)
Fair
372 (8.4) 255 (34.8) 627 (12.1)
Poor
100 (2.3) 158 (21.6) 258 (5.0)
Total 4436 733 5169 χ2 (df = 2) =989.552, P < 0.001
85
Table 3.4. Ordinary Logistic Regression: Correlates of Good Health Status of Uninsured Jamaicans
Variable Coefficient Std Error
Wald statistic
Odds ratio 95.0% C.I.
Age -0.049 0.004 191.667 0.95 0.95 -0.96*** Logged consumption per capita 0.000 0.000 11.692 1.00 1.00 - 1.00** Self reported illness -2.168 0.121 323.527 0.11 0.09 -0.15***
Middle class
0.086
0.154
0.314
1.09
0.81 - 1.47 Upper class -0.575 0.233 6.107 0.56 0.36 - 0.89* †Lower class
Married
0.138
0.129
1.154
1.00
1.15
0.89 -1.48 Divorced/separated/widowed -0.217 0.192 1.277 0.81 0.55 - 1.17 †Never married
Primary schooling
19.089
40192.970
0.0001.00
0.00 -0.00 Secondary and above -0.475 0.223 4.525 0.62 0.40 - 0.96* †No formal education
Urban area
-0.115
0.124
0.870
1.00
0.89
0.70 -1.14 Other town 0.301 0.140 4.593 1.35 1.03 -1.78* †Rural area
Man
0.406
0.105
14.872
1.00
1.50
1.22 -1.85*** Household head 0.097 0.113 0.741 1.10 0.88 -1.37 Cost of public medical care 0.000 0.000 0.040 1.00 1.00 - 1.00 Cost of private medical care 0.000 0.000 3.003 1.00 1.00 -1.00χ2 (df = 15) =993.114, P < 0.001 -2 Log likelihood = 2554.359 Nagelkerke R2 =0.390 Hosmer and Lemeshow goodness of fit χ2=11.159, P = 0.693 Overall correct classification = 84.6% Correct classification of cases of good health status = 94.9% Correct classification of cases of poor health status = 46.6% †Reference group *P < 0.05, **P < 0.01, ***P < 0.001
86
Table 3.5. Ordinary Logistic Regression: Correlates of Medical Care-Seeking Behaviour of Uninsured Jamaicans Variable
Coefficient Std.
Error Wald
statistic Odds ratio 95% C.I.
Man -0.282 0.205 1.894 0.76 0.51 - 1.13 Age 0.019 0.007 8.213 1.02 1.01 - 1.03**
Middle class
0.544
0.284
3.675
1.72
0.99 - 3.00 Upper class 0.683 0.427 2.558 1.98 0.86 - 4.57 †Lower
Poor health
0.834
0.208
16.139
1.00
2.30
1.53 - 3.46***
Urban area
0.070
0.248
0.079
1.07
0.66 - 1.75 Other town -0.243 0.260 0.877 0.78 0.47 - 1.31 †Rural
Crowding
0.111
0.067
2.749
1.00
1.12
0.98 - 1.27 Per capita consumption 0.000 0.000 0.017 1.00 1.00 - 1.00
Secondary and above
0.431
0.571
0.569
1.54
0.50 - 4.71 †No formal education
Married
-0.659
0.237
7.720
1.00
0.52
0.33 -0 .82** Divorced, separated/widowed -0.453 0.332 1.864 0.62 0.33 - 1.22 †Never married
Head household
-0.210
0.218
0.933
1.00
0.81
0.53 - 1.24χ2 (df = 14) = 47.79, P < 0.001 -2 Log likelihood = 648.32 Nagelkerke R2 =0.117 Hosmer and Lemeshow goodness of fit χ2=4.480, P = 0.811 Overall correct classification = 67.5% Correct classification of cases of medical care-seeking behaviour = 88.1% Correct classification of cases of no medical care-seeking behaviour = 30.0% †Reference group *P < 0.05, **P < 0.01, ***P < 0.001
87
Chapter 4 Variations in health, illness and health care-seeking behaviour of those in the
upper social hierarchies in a Caribbean society
Paul Andrew Bourne
Little research exists in the Caribbean, and in particular Jamaica, on the upper class, and no study emerged from a search of the literature examining health, illness, and health care-seeking behaviour of this group. To provide pertinent information on the upper class in regards to their general health status, illnesses, typology of illnesses, health care seeking behaviours and factors which determine their (1) moderate-to-very good health status, (2) illness, and (3) health care seeking behaviour in order to make available to policy specialists and public health practitioners information on this group, to be used as a guide in their decision making policies. A sample of 2,734 respondents from the wealthiest 20% and second wealthy social hierarchies was extracted from a cross-sectional survey of 6,783 respondents. An administered questionnaire was used to collect the data, which were stored and analyzed using SPSS for Windows 16.0 (SPSS Inc; Chicago, IL, USA). The questionnaire was modelled on the World Bank’s Living Standards Measurement Study (LSMS) household survey. The majority of the sample stated at least good health status (83.3%), with 0.5% indicating very poor health status, and 15.3% who indicated an illness in the last 4-week period. Four variables emerged as statistically correlated with moderate-to-very good health status of those in the upper class (i.e. second wealthy and wealthiest 20%) - Model fit χ2 = 57.54, P < 0.0001. The model explained 33.2% of the variance in moderate-to-very good health status, and that the model is a good fit for the data. Three variables emerged as statistically correlated with self-reported illness - Model fit χ2 = 1087.7, P < 0.0001. The significant variables (i.e. health care-seeking behaviour, good health status, and marital status) accounted for 72.4% of the variability in self-reported illness. Three variables emerged as statistically significant correlates of health care-seekers - Model fit χ2 = 995.45, P < 0.0001. The statistically significant correlates (i.e. good health status, self-reported illness, marital status) accounted for 76.4% of the variance in health care-seeking behaviour of the upper class. Rural residents continue to have lower moderate-to-very good health status when compared to the general population, and the second wealthy and the wealthiest 20% in Jamaica. Although only 4 percent of the upper social hierarchy utilizes the public health care system, there is still a demand for public health services for this group, and it must be taken into account as a part of the general planning for the health care system of the country. Introduction Studies have long established health disparities between the poor and the wealthy classes, and
this is no different in Latin America and the Caribbean [1-17]. According to the World Health
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Organization [7], 80% of chronic illnesses were in low and middle income countries, which
illustrate the dichotomy between illness and material deprivation. The dichotomy between illness
and poverty is not only limited to low-to-middle income nations, as a study in the Netherlands
found that those who were chronically ill were more likely to be poor [15], and this was also
found in other European nations [16,17]. The association between insufficient money and health
is not limited to illness, but the WHO [7] opined that 60% of global mortality is caused by
chronic illness, which raised another issue, the relationship between poverty and premature
mortality.
Marmot [8] postulated that money makes a difference in health, infant mortality and
general morality. The association between income and health expands beyond the direct
relationship between income and access to good physical and social milieu, good nutrition and
access to high quality health care services, to the indirect association between income and health
through access to education, employment, material resources and occupational class. Clearly
there are inequalities in health between those in the upper class and those in the lower class [18,
19], but limited studies existed on the wealthy and the wealthiest 20% in nations. In keeping
with public health aims, many studies have been carried out on the poor; poverty and illness;
poverty and productivity; chronic illness, capabilities and poverty, but what about the second
wealthy and the wealthiest 20% in regard to their health, illness, health care-seeking behaviour
and factors which influence health, illness and health care-seeking behaviour?
Public health is about improvements in the health conditions of all members of a society
and not just a particular group. Embedded in the mandate of public health is the access to
information which will guide policy formulation, intervention and health education programmes,
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and so information is equally needed on the affluent groups. Limited information, if any, exists
in the Caribbean on the health of the second wealthy and wealthiest 20% classes. While general
statistics indicate that the upper class has a greater health status and more access to material
resources than the poor class, the former group constitutes a percentage of the population and
must be studied like the poor class. The current study revealed that the prevalence rate of the
upper class utilizing public health care facilities (i.e. hospitals and health centres) was 4%,
suggesting that this group must be planned for, as they utilize and demand public health care
resources like other social classes. Concurringly, this research showed that 3% of those in the
wealthy social class had chronic illnesses, and that 1% had diabetes mellitus, which denotes that
public health must make available resources for this group. Within the context that the upper
social class utilizes public health care resources, it is surprising that no studies exist in Jamaica
that have examined health, illness, and the health care seeking-behaviour of this social group.
The current study aims to provide pertinent information on the upper class in regards to
their general health status, illness, typology of illness, health care seeking behaviours and factors
which determine their (1) moderate-to-very good health status, (2) illness, and (3) health care
seeking behaviour, in order to make available to policy specialists and public health practitioners
information on this group, which will serve as a guide for their decision-making policies.
Methods and materials
Sample
A sample of 2,734 respondents from the wealthiest 20% and second wealthy social hierarchy
was extracted from a cross-sectional survey of 6,783 respondents: 50.5% in the wealthiest 20%
and 49.5% in the second wealthy group. The survey was carried out jointly by the Planning
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Institute of Jamaica and the Statistical Institute of Jamaica [20]. The method of selection of the
sample from each survey was based solely on rural residence. The survey (Jamaica Survey of
Living Conditions) was begun in 1989, collecting data from Jamaicans in order to assess
government policies. Each year since 1989, the JSLC has added a new module in order to
examine that phenomenon which is critical within the nation. In 2002, the foci were on 1) social
safety net and 2) crime and victimization; while for 2007, there was no focus. The current sample
was extracted from the 2007 dataset.
The survey was drawn using stratified random sampling. This design was a two-stage
stratified random sampling design where there was a Primary Sampling Unit (PSU) and a
selection of dwellings from the primary units. The PSU is an Enumeration District (ED), which
is composed of a minimum of 100 residences in rural areas and 150 in urban areas. An ED is an
independent geographical unit that shares a common boundary. This means that the country was
grouped into strata of equal size based on dwellings (EDs). Based on the PSUs, a listing of all the
dwellings was made, and this became the sampling frame from which a Master Sample of
dwellings was compiled, which in turn provided the sampling frame for the labour force. One
third of the Labour Force Survey (i.e., LFS) was selected for the JSLC [20]. The sample was
weighted to reflect the general population of the nation.
The JSLC 2007 [20] was conducted in May and August of that year. An administered
questionnaire was used to collect the data, which were stored and analyzed using SPSS for
Windows 16.0 (SPSS Inc; Chicago, IL, USA). The questionnaire was modelled on the World
Bank’s Living Standards Measurement Study (LSMS) household survey. There are some
modifications to the LSMS, as the JSLC is more focused on policy impacts. The questionnaire
covered areas such as socio-demographic variables, for example education, daily expenses (for
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the past 7-day period), food and other consumption expenditures, inventory of durable goods,
health variables, crime and victimization, social safety net, and anthropometry. The
questionnaire contains standardized items such as socio-demographic variables, excluding crime
and victimization, which were added in 2002 and later removed from the instrument, with the
exception of a few new modules each year. The non-response rate for the survey for 2007 was
27.7%. The non-response includes refusals and cases rejected in data cleaning.
Measures
Self-rated health status: is measured using people’s self-rating of their overall health status [21],
which ranges from excellent to poor. The question that was asked in the survey was “How is
your health in general?” And the options were very good; good; fair; poor and very poor. For the
purpose of the model in this study, self-rated health was coded as a binary variable (1= good, 0 =
Otherwise) [21-28]. The binary good health status was used as the dependent variable.
Self-reported illness (or self-reported dysfunction): The question was asked: “Is this a diagnosed
recurring illness?” The answering options are: Yes, Influenza; Yes, Diarrhoea; Yes, Respiratory
diseases; Yes, Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No. A binary
variable was later created from this construct (1=no 0=otherwise) in order to be applied in the
logistic regression.
Age is a continuous variable which is the number of years alive since birth (using last birthday).
Age groups were classified as children, young adults, other adults, young-old (or young-elderly),
old-old, and oldest-old: children – 0 to 14 years; young adults – 15 to 30 years; other adults – 31
to 59 years; young-old – 60 to 74 years; old-old - 75 – 84 years and oldest-old – 85+ years.
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Medical care-seeking behaviour was taken from the question ‘Has a health care practitioner or
pharmacist been visited in the last 4 weeks?’ with there being two options: Yes or No. Medical
care-seeking behaviour therefore was coded as a binary measure where 1= Yes and 0 =
otherwise.
Crowding is the total number of individuals in the household divided by the number of rooms
(excluding kitchen, verandah and bathroom).
Sex: This is a binary variable where 1= male and 0 = otherwise.
Social supports (or networks) denote different social networks with which the individual is
involved (1 = membership of and/or visits to civic organizations, or having friends who visit
one’s home or with whom one is able to network, 0 = otherwise).
Statistical Analysis
Descriptive statistics such as mean, standard deviation (SD), frequency and percentage were used
to analyze the socio-demographic characteristics of the sample. Chi-square was used to examine
the association between non-metric variables, and t-test and an Analysis of Variance (ANOVA)
were used to test the relationships between metric and/or dichotomous and non-dichotomous
categorical variables. Box-plots were used to examine what was happening among age, self-
reported illness, and social hierarchy as well as age, typology of illness and social hierarchy (i.e.
poorest 20% and wealthiest 20%). Multiple logistic regression techniques were conducted to
identify parameters and their estimates. Stepwise logistic regression technique was used to
determine the contribution of each significant determinant to the model. A p-value less than 0.05
(two-tailed) was selected to indicate statistical significance (i.e. 95% confidence interval).
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Results
Table 4.1 presents information on the socio-demographic characteristics of the sample. One
percent of the sample reported an injury. Of those who reported an injury, 67.9% stipulated the
injury experienced in the last 4weeks. Domestic accidents and incidents accounted for 47.3% of
the injuries experienced. Fifteen percent of the sample indicated an illness in the last 4 weeks. Of
those who reported an illness, 89.1% stipulated the typology of the health condition.
When the respondents were asked if they had purchased the prescribed medication,
67.7% said yes. Of those who did not purchase the medication, 9.5% claimed they were unable
to afford it; 39.7% said they were not ill enough; 27.6% remarked that they used a home remedy;
5.2% indicated that they did not have the time and 18.1% stated other. Seventy-one percent of
the sample sought medical care in the last 4weeks, 32.5% had health insurance coverage (i.e.
23.7% private). The majority of the sample stated at least good health status (83.3%), with 0.5%
indicating very poor health status.
Of the sample, only 10.6% indicated where the medical visit took place in the last
4weeks. Of those who responded (n=288), 27.4% indicated a public hospital, 61.8% said a
private health care centre and 12.5% remarked that it was a public health care centre. Twenty-
nine percent of those who responded to typology of medical facility used in the last 4weeks had
chronic conditions and attended a public facility. The prevalence rate of the upper class utilizing
public health care facilities (i.e. hospitals and health centres) was 4% (3% had a chronic illness;
of the 3%, 1% had diabetes mellitus).
There was no significant statistical association between marital status and social
hierarchy (i.e. second wealthy or wealthiest 20%) – χ2 = 8.518, P = 0.744.
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Table 4.2 shows information on particular variables and social hierarchy. A significant
statistical relationship existed between area of residence and social hierarchy. Those in the
wealthiest 20% were more likely to be urban dwellers (48.6%) than those in the second wealthy
social group (36.9%) - χ2 = 57.002, P < 0.0001.
Rural dwellers were more likely to be wealthy (59.1%) compared to semi-urban residents
(50.1%) and urban respondents (42.1%). Concurringly, urban settlers were more likely to be in
the wealthiest 20% (57.9%) compared to semi-urban (49.9%) and rural respondents (40.9%) – P
< 0.0001.
There was a significant statistical association between educational level and social
hierarchy (χ2 = 30.53, P < 0.0001). Those in the wealthiest 20% were more likely to be educated
at the tertiary level (5.3%), as compared to those in the second wealthy social group (1.9%).
Likewise there was a statistical relationship between health insurance coverage and social
hierarchy (χ2 = 113.27, P < 0.0001). Forty-two percent of those in the wealthiest 20% had health
insurance coverage compared to 22.6% of those in the second wealthy social group.
There were significant statistical differences between those in the wealthy and the
wealthiest 20% (1) age ( t = - 4.745, P < 0.001) – mean age of the wealthy 30.14 ± 21.1, and the
wealthiest 20% 33.9 ± 20.4; (2) crowding (t = 15.991, P < 0.0001 – mean household crowding
for those in the wealthy group was 4.2 ± 2.2 compared to 3.0 ± 1.6 for those in the wealthiest
20%, and (3) total expenditure (t = - 16.219, P < 0.0001) – mean total expenditure for those in
the wealthy group was USD 9,713.00 ± USD 5,327.88 and those in the wealthiest 20% was USD
14,915.29 ± USD 10,550.99. Furthermore, there was a significant statistical difference between
mean duration of illness of those in the second wealthy social group (23.8 days ± 96) and those
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in the wealthiest 20% (9.9 days ± 18.7) – t = 1.985, P = 0.048; but none between duration of
marriage and social hierarchy (wealthy, 16.7 years ± 14.6; wealthiest 20%, 17.3 ± 13.6) – t = -
0.593, P = 0.553.
Multivariate analyses
Table 4.3 shows information on particular variables that are correlated (or not) with self-reported
moderate-to-very good health status of the sample. Four variables emerged as statistically
correlated with moderate-to-very good health status of those in the upper class (i.e. second
wealthy and wealthiest 20%) - Model fit χ2 = 57.54, P < 0.0001. The model explained 33.2% of
the variance in moderate-to-very good health status, and the model is a good fit for the data
(Hosmer and Lemeshow goodness of fit χ2 = 2.87, P = 0.94, -2LL = 194.22). Eighty-one percent
of the data were correctly classified: 94.9% of those who had indicated moderate-to-very good
health status and 33.3% of those that were classified into poor and very poor health status.
Table 4.4 presents information on variables that either correlated or did not correlate with
self-reported illness of the sample. Three variables emerged as statistically correlated with self-
reported illness - Model fit χ2 = 1087.7, P < 0.0001. The significant variables (i.e. health care-
seeking behaviour, good health status, and marital status) accounted for 72.4% of the variability
in self-reported illness. The model is a good fit for the data (Hosmer and Lemeshow goodness of
fit χ2 = 8.11, P = 0.42, -2LL = 649.69). Ninety-five percent of the data were correctly classified:
72.2% of those who were classified as having an illness and 99.6% of those who did not report
an illness.
Table 4.5 displays variables that seek to explain the variability in self-reported health
care-seeking behaviour of the sample. Three variables emerged as statistically significant
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correlates of health care-seekers - Model fit χ2 = 995.45, P < 0.0001. The statistically significant
correlates (i.e. good health status, self-reported illness, marital status) accounted for 76.4% of the
variance in health care-seeking behaviour of the upper class. The model was a good fit for the
data - Hosmer and Lemeshow goodness of fit χ2 = 3.64, P = 0.90. Ninety-five percent of the data
were correctly classified: 96.2% of those who had selected seeking medical care in the last 4
weeks and 95.3% of those who did not seek medical care.
Discussion
The present work revealed that 88 out of every 100 respondents in the upper class in Jamaica
indicated that their health status was at least good, with only 5 in every 1,000 experiencing very
poor health statuses. One in every 100 had an injury and 15 per 100 had an illness in the last 4-
week period. The prevalence rate of self-reported diagnosed acute health conditions was 36 per
1,000 and 96 per 1,000 for chronic conditions. Twenty-four per 1,000 had diabetes mellitus; 28
out of every 1,000 had hypertension and 7 per 1,000 reported having been diagnosed with
arthritis. Seventy-one percent sought medical care; there was no significant statistical association
between (1) self-reported injury and being second wealthy or in the wealthiest 20% as well as (2)
between self-reported illness and social hierarchy (i.e. second wealthy or wealthiest 20%). The
mean length of time experiencing the current illness (in days) was greater for those in the second
wealthy class, as compared to those in the wealthiest 20%. Although only 1% of the sample
reported an injury in the study, 47.3% of the injuries were owing to domestic accidents and
domestic incidents, and 21.1% were due to motor vehicle accidents. Four percent of the sample
utilized public health care facilities for their last medical visit, and 11.8% of the sample were
elderly (ages 60 years and beyond), 24.6% children (ages less than 15 years); 49.6% of those in
the wealthiest 20% dwelled in urban areas compared to 36.9% of those in the second wealthy
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social group. Those in the wealthiest 20%, according to average total expenditure, were 1.5 times
more than those in the second wealthy class and they were 2.9 times more educated at the tertiary
level. Concurringly, rural upper class respondents had the lowest moderate-to-very good health
status; those with good health status were 48% less likely to seek medical care; those with
illnesses were 449 times more likely to seek medical care, and married upper class respondents
were 45% less likely to seek health care, while married wealthy residents were 2.3 times more
likely to report an illness.
Marmot [8] asked the question “Does money matter for health? If so, why?” and opined
that it does in terms of access to good nutrition, material resources, lower infant mortality, health
care choices, and a good physical environment compared to those in the lower socioeconomic
group. Clearly there are differences in health outcomes between the social hierarchies [1-17], but
does money matter for health between the second wealthy and the wealthiest 20%? The current
study found that money does not matter for health between the wealthy and the wealthiest 20%.
Money does not matter for the general health status of the wealthy and the wealthiest 20%, but
also for self-reported injuries and illnesses (i.e. both acute and chronic conditions). Embedded in
this finding is the reality that there is a basic amount of money necessary, and any more than that
will not improve the health of the individual. This work showed that those in the wealthiest 20%
on average spent almost 2 times more than those in the second wealthy class, and are about 3
times more educated at the tertiary level, but this does not produce additional improvements in
health for the wealthiest 20%.
The present paper found that a large health disparity occurred between upper class
respondents and geographic area of residents, which concurs with the findings of Vila et al.’s
work. Vila et al.’s research [9] used self-reported health status (i.e. fair-to-poor health status) and
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found that lower socioeconomic class residents of Milwakee had the greatest fair-to-poor health
status with those in the upper class indicated the least fair-to-poor health status. Concurringly,
they also found that upper socioeconomic group had the greatest health in the city, which was
different in this research. In this study, upper socioeconomic group who resided in semi-urban
areas were the healthiest, and had lower total annual expenditure than those upper class
respondents who lived in urban areas. The huge health disparity was found between the upper
class rural and semi-urban dwellers, suggesting that lifestyle practices in semi-urban geographic
areas was greatest and was remarkably different from that of upper class rural respondents.
However, the health disparity is among those who dwell in particular geographical areas,
and those who have health insurance coverage, and not between the wealthy and the wealthiest
20%. Rural upper class Jamaicans had the least moderate-to-very good health status. This health
disparity is substantial as upper class semi-urban residents were 4.8 times more likely to report
moderate-to-very good health status, and those who dwelled in urban areas were 4.3 times more
likely to report moderate-to-very good health status compared to those in the rural areas. Such
inequality in health emphasized that the lifestyle of rural residents is such that money does not
equate their health status with those of their other wealthy urban and semi-urban peers. This is
embedded in the present work as there is no significant statistical correlation between self-
reported illness and area of residence, or area of residence and health care seeking behaviour of
the upper class. It follows that it is not money and illness that separate the rural from the other
affluent respondents, but this must be therefore embedded in the cultural differences between
people. Another finding which emerged from the current research is the fact that married upper
class respondents reported more illness than those who were never married, yet the former group
sought less medical attention than the latter group. Although married upper class respondents
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reported more illness, there was no statistical correlation between marital status and moderate-to-
very good health status. A plethora of studies have examined the health status of married and
non-married respondents and the verdict is that the former group’s health status is greater [29-
35], which means that money removes this health disparity.
According to Moore et al. [35], people who reside with a spouse have a different base of
support which aids in better health choices and justifies greater health status, as against those
without social support from a marital union. This was also found in earlier studies by Smith and
Waitzman [31] and Lillard and Panis [34]. Cohen and Wills [36] found that perceived support
from one’s spouse increased well-being, while Ganster et al. [37] reported that support from
supervisors, family members and friends was related to low health complaints. Another study
found that being married was a ‘good’ cause for an increase in psychological and subjective
well-being in old age [38]. Smith and Waitzman [31] offered the explanation that wives were
likely to dissuade their husbands from particular risky behaviours such as the use of alcohol and
drugs, and would ensure that they maintained a strict medical regimen coupled with proper
eating habits. On the contrary, this paper revealed that married affluent Jamaicans were more
likely to report illness, as compared to never-married wealthy respondents, but that this does not
translate into better health status for one group over the other.
Using the relationship of the absence of illness to health of the wealthy-to-wealthiest 20%
of Jamaicans, this should denote that the wealthiest should be healthier than the second wealthy.
Clearly, there is a cognitive disparity between the image of health and illness. Illness is well
established to be a narrow approach to the conceptualization of health [39-46], and this is what
emerged as the case for the upper class. According to the WHO [39], health is social,
psychological and physical wellbeing and not merely the absence of illness. Clearly upper class
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respondents subscribe to this conceptualization as experiencing illness was correlated with low
moderate-to-very good health status, but illness was not a factor which determines the moderate-
to-very good health status of those in the upper class.
Ferrer and Palmer’s work [14] revealed marginal health variabilities between those
people in the second wealthy and the wealthiest 20%, and using self-reported to measure health
status, this study found no statistical association between self-reported health and the two social
hierarchies. The present work goes further than Ferrer and Palmer’s research that used health
status and investigated general illness and particular health conditions and those in the second
wealthy and the wealthiest 20%. Ferrer and Palmer’s research did not examine illness or
particular typology of illness. Statistics revealed that 15.5% of Jamaicans reported an illness in
the last 4weeks in 2007 [47] compared to 15.3% of those in the upper class. Seemingly there is
no difference between self-reported illness in the population and those in the upper class, but
further examination of the diagnosed health conditions revealed some differences between the
population and the subpopulation. For the population, the prevalence rates for people with
asthma were 87 per 1,000; diabetes mellitus, 120 per 1,000; hypertension, 224 per 1,000 and
arthritis, 88 per 1,000 [47] compared to those in the upper class, being asthma, 12 per 1,000;
diabetes mellitus, 24 per 1,000; hypertension, 28 per 1,000 and arthritis, 7 per 1,000. The
findings of this study highlight that those in the affluent social hierarchy have a lower prevalence
of chronic illness than people in the general population of Jamaica, which concurs with the
literature that those in the lower socioeconomic group were more likely to experience more
chronic illness than the affluent. Although those in the wealthy-to-wealthiest 20% group in
Jamaica had a lower prevalence of chronic health conditions compared to the general population,
they had a prevalence rate of 37 per 1,000 for other health conditions.
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The other conditions constitute ailments such as prostate and breast cancers, ischemic
heart disease, malignant neoplasm of the trachea, bronchus and other heart diseases. Statistics on
the mortality of males 5 years and older revealed that cerebro-vascular diseases, diabetes
mellitus, ischemic heart diseases, malignant neoplasm of the prostate, hypertensive disease,
chronic lower respiratory infections, other heart diseases and malignant neoplasm of the trachea
and HIV were among the 10 leading causes of death [48]. For females 5 years and older it was
about the same as the 10 leading causes of death for males, except for malignant neoplasm of the
prostate and malignant neoplasm of the trachea, these being replaced by malignant neoplasm of
the breast and pneumonia.
Although the upper class clearly has lower prevalence rates of particular chronic
illnesses, compared to the general population, and more than those in the poorest 20% [47],
diabetes mellitus, hypertension and other health conditions are high among them and may
explain the levels of mortality among those therein. Chronic illnesses are linked to lifestyle
causes, and though they have lower rates of chronic illness than people in the lower
socioeconomic group, the reality among the upper class is that their lifestyle explains their
particular morbidity and mortality. A study by Wilks et al. [49] found that 64.3% of Jamaicans
were currently using alcohol (i.e. liquor, wine, beer or stout, and mixed alcoholic coolers), 13.5%
used marijuana, 14.5% smoked cigarettes, and the rates were even greater for males than
females. Concurringly, 71% of those in the upper class consumed alcohol (i.e. 84.3% of males
and 48.7% of females); 9.8% smoked cigarettes (i.e. 12.4% of males and 6.7% of females);
10.4% smoked marijuana (i.e. 16.9% of males and 2.2% of females) and 10.5% used illegal
drugs (17.1% of males and 2.7% of females) [49]. Furthermore, the percentage of upper class
males who consumed alcohol was more than for those males in the lower (76.1%) and the middle
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class (79.4%) [49]. Unhealthy lifestyle practices are therefore responsible for the composition of
illnesses which are experienced by the upper class and account for many of their ailments.
Furthermore, it is clear from the findings that among the upper socioeconomic class there are no
vulnerable groups, but what is equally evident is that socioeconomic status accounted for a major
role in determining the health status of upper class Jamaica as was found for all socioeconomic
classess in Blanc et al.’s work [11].
Conclusion
While poverty is associated with illness and illness is more related to poverty and lower health
status for the poor than for those in the upper class, the same is not true of the relationship
between the wealthy and the wealthiest 20% in Jamaica. It follows that money and wealth,
beyond a certain amount, does not add any further improvements to good health status. Income
and wealth beyond that which is accessible to the second wealthy in Jamaica do not provide
those beyond that with any greater health status. However, what emerged from the current work
is that the health disparity between the rural areas’ affluent people and others is vast, suggesting
that there are some underlying cultural conditions which exist among the rich of different
geographical areas, and which do not disappear because the individual is wealthy. Another
pertinent finding is that the wealthy spent more days in illness compared to the wealthiest 20%,
but this does not translate into lower moderate-to-very good health status. A part of the
justification for this non-health disparity is owing to their conceptualization of health compared
to the image of illness.
There are affluent Jamaicans who utilize the public health care system, and many of them
have diabetes mellitus. Within the context of the utilization of the public health care system by
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the wealthy, although the percentage is very small, the current finding are important to public
health policy makers in understanding the service utilization of this group and their health, and
illness profile.
In summary, money and wealth beyond that which is accessible by the second wealthy in
Jamaica will show no further disparity in moderate-to-very good health status. The paper
highlighted the fact that health insurance coverage is not a good measure of health care-seeking
behaviour and illness is not a good proxy for the health status of the upper class. However, the
health disparity which existed for the general society among the different areas of residents is the
same for the upper class. Rural residents continue to have lower moderate-to-very good health
status than the general population, and the second wealthy and the wealthiest 20% in Jamaica.
Although only 4 percent of the upper social hierarchy utilizes the public health care system, there
is still a demand for public health services for this group, and it must be taken into account as a
part of the general planning for the health care system of the country.
Conflict of interest
The author has no conflict to interest to report
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Table 4.1. Demographic characteristics of sample Characteristics Frequency %Social hierarchy Second wealthy 1352 49.5 Wealthiest 20% 1382 50.5Sex Male 1356 49.6 Female 1378 50.4Area of residence Urban 1184 43.3 Semi-urban 706 25.8 Rural 844 30.9Injury Yes 28 1.1 No 2622 98.9Self-reported typology of injury Motor vehicle accident 4 21.1 Domestic accident 7 36.8 Industrial accident 5 26.3 Domestic incident 2 10.5 Other (unspecified events) 1 5.3Self-reported illness Yes 405 15.3 No 2237 84.7Self-reported diagnosed illness Acute conditions Influenza 56 15.5 Diarrhoea 8 2.2 Respiratory 34 9.4Chronic condition Diabetes mellitus 66 18.3 Hypertension 76 21.1 Arthritis 19 5.3 Other 102 28.3Educational level Primary or below 2311 87.3 Secondary 241 9.1 Tertiary 95 3.6Length of time married median (inn years) 12 (Range = 1, 71)Number of visits to medical practitioners in last 4-weeks mean (SD)
1.4 (1.1)
Length of illness median (in days) 5 (Range = 0,200)
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Table 4.2. Particular variables by social hierarchy Social hierarchy P Wealthy Wealthiest 20% Area of residence n (%) n (%) χ2 = 57.002, P < 0.0001 Urban 499 (36.9) 685 (49.6) Semi-urban 354 (26.2) 352 (25.5) Rural 499 (36.9) 345 (25.0)Sex χ2 = 0.074, P = 0.407 Male 667 (49.3) 689 (49.9) Female 685 (50.7) 693 (50.1)Self-reported diagnosed health condition χ2 = 5.190, P = 0.520 Acute conditions Influenza 32 (17.9) 24 (13.2) Diarrhoea 3 (1.7) 5 (2.7) Asthma 12 (6.7) 22 (12.2) Chronic conditions Diabetes mellitus 33 (18.4) 33 (18.1) Hypertension 38 (21.2) 38 (18.1) Arthritis 8 (4.5) 11 (6.0) Other (unspecified) 53 (29.0) 49 (26.9)Health care-seeking behaviour χ2 = 1.272, P = 0.154 Yes 141 (68.4) 155 (73.5) No 65 (31.6) 56 (26.5)Self-reported illness χ2 = 0.000, P = 0.520 Yes 200 (15.3) 205 (15.3) No 1105 (84.7) 1132 (84.7)Self-reported health status χ2 = 8.815, P = 0.066 Very good 567 (43.2) 531 (40.0) Good 536 (40.8) 565 (42.5) Fair 157 (12.0) 185 (13.9) Poor 42 (3.2) 45 (3.4)
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Very poor 11 (0.8) 3 (0.2)
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Table 4.3. Logistic regression: Moderate-to-very good health status by particular variables
Coefficient Std. Error Wald P Odds ratio
95% CI
Age -0.051 0.013 15.260 0.000 0.95 0.93, 0.98 Male -0.351 0.387 0.822 0.365 0.70 0.33, 1.50 Self-reported illness -19.926 13414.774 0.000 0.999 0.00 0.000, Married -0.353 0.433 0.666
0.415
0.70 0.30, 1.64
Divorced, separated or widowed -0.383 0.549 0.487 0.485 0.68 0.23, 2.00
†Never married 1.00 Health insurance 0.997 0.408 5.976
0.015
2.71 1.22, 6.02
Medical expenditure 0.000 0.000 4.712 0.030 1.00 1.00, 1.00 Urban area 1.474 0.439 11.258
0.001
4.37 1.85, 10.34
Other town 1.584 0.511 9.622 0.002 4.88 1.79, 13.26†Rural area 1.00 Head of household 0.031 0.410 0.006
0.940
1.03 0.46, 2.30
Per capita consumption 0.000 0.000 0.206 0.650 1.00 1.00, 1.00Model fit χ2 = 57.54, P < 0.0001 Hosmer and Lemeshow goodness of fit χ2 = 2.87, P = 0.94 -2LL = 194.22 Nagelkerke R2 =0.332 †Reference group
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Table 4.4. Logistic regression: Self-reported illness by particular variables
Variable Coefficient Std.
Error
Wald
statistic P Odds ratio
95.0% C.I.
Age 0.013 0.008 2.769 0.096 1.01 1.0, 1.03 Male -0.415 0.233 3.188 0.074 0.66 0.42, 1.04 Married 0.821 0.260 9.960
0.002
2.27 1.37, 3.79
Divorced, separated or wid -0.141 0.421 0.113 0.737 0.87 0.38, 1.98 †Never married 1.00 Health insurance -0.259 0.244 1.132
0.287
0.77 0.48, 1.24
Urban area -0.347 0.257 1.832
0.176
0.71 0.43, 1.17
Other town -0.219 0.294 0.551 0.458 0.80 0.45, 1.43 †Rural area 1.00 Head of household 0.408 0.243 2.810
0.094
1.50 0.93, 2.42
Per capita consumption 0.000 0.000 0.595 0.440 1.00 1.00, 1.00 Good health status -1.872 0.248 56.921
0.000
0.15 0.10, 0.25
Health care-seekers 6.080 0.417 212.549 0.000 437.11 193.02, 989.89 Model fit χ2 = 1087.7, P < 0.0001 Hosmer and Lemeshow goodness of fit χ2 = 8.11, P = 0.62 -2LL = 649.69 Nagelkerke R2 =0.724 †Reference group
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Table 4.5. Logistic regression: Self-reported health seeking behaviour by particular variable
Coefficient
Std. Error
Wald statistic P
Odds ratio
95.0% C.I.
Age 0.014 0.008 3.080 0.079 1.02 1.00, 1.03 Male -0.109 0.260 0.175 0.676 0.90 0.54, 1.49 Married -0.601 0.295 4.151
0.042
0.55 0.31, 0.98
Divorced, separated or wid -0.291 0.445 0.429 0.513 0.75 0.31, 1.79† Never married 1.00 Health insurance 0.463 0.269 2.954
0.086
1.59 0.94, 2.69
Urban area 0.134 0.287 0.218
0.640
1.14 0.65, 2.01
Other town -0.034 0.328 0.011 0.918 0.97 0.51, 1.84 †Rural area 1.00 Head of household -0.069 0.270 0.066
0.797
0.93 0.55, 1.58
Per capita consumption 0.000 0.000 0.042 0.837 1.00 1.00, 1.00 Self-reported illness 6.108 0.417 214.598 0.000 449.37 198.47, 1017.42 Good health status -0.658 0.266 6.147
0.013
0.52 0.31, 0.87
Model fit χ2 = 995.45, P < 0.0001 Hosmer and Lemeshow goodness of fit χ2 = 3.64, P = 0.90 -2LL = 446.41 Nagelkerke R2 =0.764 †Reference group
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Chapter 5 Health of children less than 5 years old in an Upper Middle
Income Country: Parents’ views
Paul Andrew Bourne
Health literature in the Caribbean, and in particular Jamaica, has continued to use objective indices such as mortality and morbidity to examine children’s health. The current study uses subjective indices such as parent-reported health conditions and health status to evaluate the health of children instead of traditional objective indices. The study seeks 1) to examine the health and health care-seeking behaviour of the sample from the parents’ viewpoints; and 2) to compute the mean age of the sample with a particular illness and describe whether there is an epidemiological shift in these conditions. Two nationally representative cross-sectional surveys were used for this study (2002 and 2007). The sample for the current study is 3,062 respondents aged less than 5 years. For 2002, the study extracted a sample of 2,448 under 5 year olds from the national survey of 25,018 respondents, and 614 under 5 year olds were extracted from the 2007 survey of 6,728 respondents. Parents-reported information were used to measure issues on children under 5 years old. In 2007, 43.4% of the sample had very good health status; 46.7% good health status; 2.5% poor health and 0.3% very poor health status. Almost 15% of children had illnesses in 2002, and 6% more had illnesses in 2007 over 2002. In 2002, the percentage of the sample with particular chronic illnesses was: diabetes mellitus (0.6%); hypertension (0.3%) and arthritis (0.3%). However, none was recorded in 2007. The mean age of children less than 5 years old with acute health conditions (i.e. diarrhoea, respiratory diseases and influenza) increased over 2002. In 2007, 43.4% of children less than 5 years old had very good health status; 46.7% good health status; 7.1% fair health status; 2.5% poor and 0.3% very poor health status. The association between health status and parent-reported illness was - χ2 (df = 4) = 57.494, P < 0.001 – with the relationship being a weak one, correlation coefficient = 0.297. A cross-tabulation between health status and parent-reported diagnosed illness found that a significant statistical correlation existed between the two variables - χ2 (df = 16) = 26.621, P < 0.05, cc = 0.422, - with the association being a moderate one, correlation coefficient = 0.422. A cross tabulation between health status and health care-seeking behaviour found a significant statistical association between the two variables - χ2 (df = 4) = 10.513, P < 0.033 - with the correlation being a weak one – correlation coefficient = 0.281.Rural children had the least health status. The health disparity that existed between rural and urban less than 5 year olds showed that this will not be removed simply because of the abolition of health care utilization fees.
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Introduction
In many contemporary nations, objective indices such as life expectancy, mortality and
diagnosed morbidity are still being widely used to measure the health of people, a society and/or
a nation [1-6]. The World Health Organisation (WHO) in the Preamble to its Constitution in the
1940s wrote that health is more important than disease, as it expands to the social, psychological
and physical wellbeing of an individual [7]; and lately that during the 21st century the emphasis
must be on healthy life expectancy [8,9]. In keeping with its opined emphasis, the WHO
formulated a mathematical approach that diminished life expectancy by the length and severity
of time spent in illness as the new thrust in measuring and examining health. Although healthy
life expectancy removes time spent in illness and severity of dysfunctions, it fundamentally rests
on mortality. The WHO therefore, instead of moving forward, has given some scholars, who are
inclined to use objective indices in measuring health, a guilty feeling about continuing this
practice.
The Caribbean, and in particular Jamaica, continues to use mortality and morbidity to
measure the health of children or infants [1-6]. The use of mortality, morbidity and life
expectancy is the practice of Caribbean scholars, and is widely used in Jamaica by the: Ministry
of Health (MOHJ) [10]; Statistical Institute of Jamaica (STATIN) [11]; Planning Institute of
Jamaica (PIOJ) [12]; PIOJ and STATIN [13] as well as the Pan American Health Organization
(PAHO) [14] in measuring health. In spite of the conceptual definition opined by the WHO in
the Preamble to its Constitution in 1946, the health of children who are less than 5 years old in
Jamaica is still measured primarily by using mortality and morbidity statistics. Recently a book
entitled ‘Health Issues in the Caribbean’ [15] had a section on Child Health; however the
articles were on 1) nutrition and child health development [16] and 2) school achievement and
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behaviour in Jamaican children [17], indicating the void in health literature regarding health
conditions.
An extensive review of health literature in the Caribbean region found no study that has
used national survey data to examine the health status of children less than 5 years of age. The
current study fills this gap in the literature by examining the health status of children less than 5
years of age using cross-sectional survey data which are based on the views of patients. The
objectives of this study are 1) to examine the health and health care-seeking behaviour of the
sample; and 2) to evaluate the mean age of the sample with a particular illness and to describe
whether there is an epidemiological shift in these conditions.
Materials and methods
Sample
The current study used two secondary nationally representative cross-sectional surveys (for 2002
and 2007) to carry out this work. The sub-samples are children less than 5 years old, and the only
criterion for selection was being less than 5 years old. The sample in the current study is 3,062
respondents of ages less than 5 years. For 2002, a sub-sample of 2,448 less than-5 year olds was
extracted from the national survey of 25,018 respondents in 2002, and information on 614 less
than-5 year olds was extracted from the 2007 survey. The survey (Jamaica Survey of Living
Conditions) began in 1989 to collect data from Jamaicans in order to assess government policies.
Since 1989, the JSLC has added a new module each year in order to examine that phenomenon,
which is critical within the nation [18, 19]. In 2002, the focus was on 1) social safety nets, and
2) crime and victimization, while for 2007, there was no focus.
Methods
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Stratified random sampling technique was used to draw the sample for the JSLC. This design
was a two-stage stratified random sampling design where there was a Primary Sampling Unit
(PSU) and a selection of dwellings from the primary units. The PSU is an Enumeration District
(ED), which comprises a minimum of 100 residences in rural areas and 150 in urban areas. An
ED is an independent geographical unit that shares a common boundary. This means that the
country was grouped into strata of equal size based on dwellings (EDs). Based on the PSUs, a
listing of all the dwellings was made, and this became the sampling frame from which a Master
Sample of dwellings was compiled, which in turn provided the sampling frame for the labour
force. One third of the Labour Force Survey (i.e. LFS) was selected for the JSLC [18, 19]. The
sample was weighted to reflect the population of the nation [18-20].
The JSLC 2007 was conducted in May and August of that year; while the JSLC 2002 was
administered between July and October of that year. The researchers chose this survey based on
the fact that it is the latest survey on the national population, and that that it has data on the 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 (less than 5 years old) to 100 females (less than 5 years
old), which shifted to 116.2 less than-5 year old males to 100 less than-5 year old females. The
sample over the 6 year period (2002 to 2007) revealed internal migrations to urban zones (Table
5.1): In 2002, 59.6% of respondents resided with their parents and/or guardians in rural areas,
which declined to 5.07%. The percentage of children less than 5 years of age whose parents
were in the poorest 20% fell to 25.4% in 2007 over 29.6% in 2002. In 2007 over 2002, 1.7 times
less children less than 5 years of age were taken to public hospitals, compared to 1.2 times less
taken to private hospitals (Table 5.1). Approximately 6% more children less than 5 years were
ill in 2007 over 2002. Based on Table 5.1, less than-5 year olds with particular chronic illnesses
had: diabetes mellitus (0.6%); hypertension (0.3%) and arthritis (0.3%). However, none was
recorded in 2007.
There were some occasions on which the response rates were less than 50%: In 2002,
health care-seeking behaviour was 14.3%; parent-reported diagnosed health conditions, 14.2%;
and visits to health care institutions, 8.9% (Table 5.1). For 2007, the response rate for health
care-seeking behaviour was 20.2%; parent-reported diagnosed health conditions, 20.2%, and less
than 11% for cost of medical care.
Health conditions
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Based on Table 5.1, the percentage of less than-5 year olds with particular acute conditions saw a
decline in colds and asthmatic cases, as well as chronic conditions. Figure 5.1 revealed that in
2007 the mean age of children less than 5 years old with acute health conditions (i.e. diarrhoea,
respiratory diseases and influenza) increased over 2002. On the other hand, the mean age of
those with unspecified illnesses declined from 1.76 years (SD = 1.36 years) to 1.64 years (SD =
1.36 years). Concomitantly, the greatest mean age of the sample was 2.71 years (SD = 1.21
years) for asthmatics in 2007 and 2.59 years (1.24 years) in 2002. It should be noted here that
the mean age of a child less than 5 years of age in 2002 with diabetes mellitus was 1.50 years
(2.12 years).
Health status
In 2002, the JSLC did not collect data on the general health status of Jamaicans, although this
was done in 2007. Therefore, no figures were available for health status for 2002. In 2007,
43.4% of children less than 5 years old had very good health status; 46.7% good health status;
7.1% fair health status; 2.5% poor and 0.3% very poor health status. The response rate for the
health status question was 96.9%.
Ninety-seven percent of the sample was used to examine the association between health
status and parent-reported illness - χ2 (df = 4) = 57.494, P < 0.001 – with the relationship being a
weak one, correlation coefficient = 0.297. Table 5.2 revealed that 24.2% of children less than 5
years of age who reported an illness had very good health status, compared to 2 times more of
those who did not report an illness. One percent of parents indicated that their children (of less
than 5 years) who had no illness had poor health status, compared to 5.6 times more of those
with illness who had poor health status.
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Health conditions, health status and medical care-seeking behaviour
A cross-tabulation between health status and parent-reported diagnosed illness found that a
significant statistical correlation existed between the two variables - χ2 (df = 16) = 26.621, P <
0.05, cc = 0.422, - with the association being a moderate one, correlation coefficient = 0.422
(Table 5.3). Based on Table 5.3, children less than 5 years old with asthma were less likely to
report very good health status (5.9%), compared to those with colds (30.5%); diarrhoea (22.2%);
and unspecified health conditions (22.7%).
When health status by parent-reported illness (in %) was examined by gender, a
significant statistical relationship was found, P < 0.001: males - χ2 (df = 4) = 25.932, P < 0.05, cc
= 0.320, and females - χ2 (df = 4) = 39.675, P < 0.05, cc = 0.356. The health statuses of males
less than 5 years old in the very good and good categories were greater than those of females
(Figure 5.2). However, the females had greater health statuses in fair and poor health status than
males, with more males reporting very poor health status than females.
Based on Figure 5.3, even after controlling health status and parent-reported illness (in
%) by area of residence, a significant statistical association was found: urban - χ2 (df = 3) =
10.358, P < 0.05, cc = 0.238; semi-urban - χ2 (df = 3) = 9.887, P = 0.021, cc = 0.273, and rural -
χ2 (df = 3) = 45.978, P < 0.001, cc = 0.365. Concomitantly, children less than 5 years of age were
the least likely to have very good health status (19.4%) compared to rural (25.8%) and 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 =
121
0.021, cc = 0.393; middle class - χ2 (df = 3) = 8.821, P = 0.032, cc = 0.259, and wealthy class - χ2
(df = 3) = 10.691, P = 0.02, cc = 0.234. Based on Figure 5.4, middle class children who are less
than 5 years old had the greatest very good health status (37%) compared to the wealthy class
(26.8%) and the poor-to-poorest classes (16.1%). Fourteen percent of poor-to-poorest class
children who are less than 5 years old had at most poor health status compared to 0% of the
middle class and 4.9% of the wealthy class, while 1.8% of poor-to-poorest classes less than 5
years of age had very poor health status.
When health status and parent-reported illness was examined by age, sex, social class,
and area of residence, the correlation was a weak one – correlation coefficient = 0.295, P <
0.001, n=583.
A cross tabulation between health status and health care-seeking behaviour found a
significant statistical association between the two variables - χ2 (df = 4) = 10.513, P < 0.033 -
with the correlation being a weak one – correlation coefficient = 0.281. A child less than 5 years
old was 2.44 times more likely to be taken for medical care if he/she had at most poor health
status. On the other hand, a child who had very good health status was 1.97 times more likely not
to be taken to health care practitioners (Figure 5.5).
In 2007, an examination of the health care-seeking behaviour and parent-reported illness
of the sample revealed no statistical correlation - χ2 (df = 1) = 0.430, P = 0.618. Sixty-two
percent of the sample, who was ill, was taken to health care practitioners, while 38.5% were not.
On the other hand, more were taken for medical care than in 2007 in the 4-week period of the
survey. No statistical correlation was noted for the aforementioned variables in 2002 - χ2 (df = 1)
= 1.188, P = 0.276. Of those who reported ill, 63.7% were taken to health care practitioners.
Discussion
122
Infant mortality has been declining since the 1970s, and this has further decreased since 2004
[14]; this, as the literature shows, is not a good measure of health. The current study found that,
using general health status, children less than 5 years of age in Jamaica had good health. The
findings revealed that 90 out of every 100 less than-5 year olds had at least good health status,
with 44 out of every 100 having very good health status. In spite of the good health status of less
than-5 year olds in Jamaica in 2007, 20.8% of them had an illness in the 4-week period of the
survey, which is a 5.9% increase over 2002. It is interesting to note the shift in this study away
from specific chronic illnesses. In 2002, 30 out of every 1,000 less than-5 year olds in Jamaica
were diagnosed with hypertension and arthritis (i.e. parent-reported), with 60 out of 1,000 having
been parent-reported with diabetes mellitus. None such cases were found in 2007, suggesting
that in the case of the children who had those particular chronic illnesses, their parents had either
migrated with them or they had died. Concomitantly, the country is seeing a reduction in
children less than 5 years old with colds; however, marginal increases were seen in diarrhoea,
asthma and unspecified health conditions over the last 6 years. Although there were increased
reported cases of illness over the studied period, in 2007, 62 out of every 100 ill children were
taken to medical practitioners, and this fell from 64 in every 100 in 2002. One of the arguments
put forward by some people is that what retards or abates health care-seeking behaviour is
medical cost. With the abolition of health care user fees for children since 2007, the culture must
be playing a role in parents and/or guardians not taking children who are ill to medical care
facilities for treatment.
Medical cost cannot be divorced from the expenditure that must be incurred in taking the
child to the health care facility. In 2007, 25 out of every 100 children less than 5 years of age had
parents and/or guardians who were less than the poverty line. Although this has declined by
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4.2% since 2002, it nevertheless means that there are children whose parents are incapacitated by
other factors. Marmot [21] opined that the financial inability of the poor is what accounts for
their lowered health status, compared to other social classes. The current study concurs with the
findings of Marmot, as it was revealed that children less than 5 years of age from poor
households had the least health status. This means that poverty is not merely eroding the health
status of poor Jamaicans, but that equally it is decreasing the health status of poor children.
Rural poverty in Jamaica is at least twice as great as urban poverty, and approximately 4
times more than semi-urban [13], which provides another explanation for the poor health status
of children less than 5 years of age. The current study found that 3.2% of those children dwelling
in urban zones recorded at most poor health status, compared to 13.6% of rural children,
suggesting that the health status of the latter group is 4.3 times worse than the former. This
means that poverty in rural zones is exponential, eroding the quality of life of children who are
less than 5 years old. Poverty in semi-urban areas was 4% which is 2.5 times less than that for
the nation; and those less than 5 years of age recorded the greatest health status, supporting
Marmot’s perspective that poverty erodes the health status of a people. Hence, the decline in
health care-seeking behaviour for this sample is embedded in the financial constraints of parents
and/or guardians as well as their geographical challenges. The terrain in rural zones in Jamaica is
such that medical care facilities are not easily accessible to residents compared to urban dwellers.
With this terrain constraint comes the additional financial burden of attending medical care
facilities at a location which is not in close proximity to the home of rural residents, and this
accounts for the vast health disparity between rural and urban children. As a result of the above,
the removal of health care utilization fees for children less than 18 years of age does not
correspond to an increased utilization of medical care services, or lowered numbers of unhealthy
124
children less than 5 years of age. If rural parents are plagued with financial and location
challenges, their children will not have been immunized or properly fed, and their nutritional
deficiency would explain the health disparity that exists between them and urban children who
have easier access to health care facilities.
The removal of health care utilization fees is not synonymous with an increased
utilization of medical care for children less than 5 years old, as 46.5% of the sample attended
public hospitals for treatment in 2002, and after the abolition of user fees in April 2007
utilization fell by 1.7 times compared to 2002. In order to understand stand why there is a switch
from health care utilization to mere survival, we can examine the inflation rate. In 2007, the
inflation rate was 16.8% which is a 133% increase over 2002 (i.e. 7.2%), which translates into a
24.7% increase in the prices of food and non-alcoholic beverages, and a 3.4% increase in health
care costs [22]. Here the choice is between basic necessities and health care utilization, which
further erodes health care utilization in spite of the removal of user fees for children.
Health status uses the individual self-rating of a person’s overall health status [23], which
ranges from excellent to poor. Health status therefore captures more of people’s health than
diagnosed illness, life expectancy, or mortality. However, how good a measure is it? Empirical
studies show that self-reported health is an indicator of general health. Schwarz & Strack [24]
cited that a person’s judgments are prone to systematic and non-systematic biases, suggesting
that it may not be a good measure of health. Diener, [25] however, argued that the subjective
index seemed to contain substantial amounts of valid variance, indicating that subjective
measures provide some validity in assessing health, a position with which Smith concurred [26].
Smith [26] argued that subjective indices do have good construct validity and that they are a
125
respectably powerful predictor of mortality risks [27], disability and morbidity [27], though these
properties vary somewhat with national or cultural contexts. Studies have examined self-reported
health and mortality, and have found a significant correlation between a subjective and an
objective measure [27-29]: life expectancy [30]; and disability [28]. Bourne [30] found that the
correlation between life expectancy and self-reported health status was a strong one (correlation
coefficient, R = 0.731); and that self-rated health accounted for 53% of the variance in life
expectancy. Hence, the issue of the validity of subjective and objective indices is good, with
Smith [26] opining that the construct validity between the two is a good one.
The current research found that parent-reported illness and the health status of children
less than 5 years of age are significantly correlated. However, the statistical association was a
weak one (correlation coefficient = 0.297), suggesting that only 8% of the variance in health
status can be explained by parent-reported children’s illnesses. This is a critical finding which
reinforces the position that self-reported illnesses (or health conditions) only constitute a small
proportion of people’s health. Therefore, using illness to measure the health status of children
who are less than 5 years of age is not a good measure of their health, as illness only accounts for
8% of health status. However, based on Bourne‘s work [30], health status is equally as good a
measure of health as life expectancy. One of the positives for the using of health status instead
of life expectancy is its coverage in assessing more of people’s general health status by using
mortality or even morbidity data.
Conclusion
In summary, the general health status of children who are less than 5 years old is good;
however, social and public health programmes are needed to improve the health status of the
126
rural population, which will translate into increased health status for their children. The health
disparity that existed between rural and urban children less than 5 years of age showed that this
will not be removed simply because of the abolition of health care utilization fees. In keeping
with this reality, public health specialists need to take health care to residents in order to further
improve the health status of children who are less than 5 years old.
Conflict of interest
The author has no conflict of interest to report.
Disclaimer
The researcher would like to note that while this study used secondary data from the Jamaica Survey of Living Conditions, 2007, none of the errors that are within this paper should be ascribed to the Planning Institute of Jamaica or the Statistical Institute of Jamaica as they are not there, but owing to the researcher.
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13. PIOJ, and STATIN. (1989-2009) Jamaica Survey of Living Conditions, 1988-2008. PIOJ and STATIN, Kingston.
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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].
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Table 5.1. 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 5.2. 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 5.3. 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,
Figure 5.1. Mean age of health conditions of children less than 5 years old, 2002 and 2007
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Figure 5.2. Health status by Parent-reported illness (in %) examined by gender
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Figure 5.3. Health status by parent-reported illness (in %) examined by area of residence
134
Figure 5.4. Health status by parent-reported illness (in %) examined by social classes
135
Figure 5.5. Health status by health care-seeking behaviour
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137
Chapter 6 Health Inequality in Jamaica, 1988-2007
Paul A. Bourne
In Jamaica, mortality for men is not only greater than that of women as indicated by the life expectancy but of the five leading causes of death (malignant neoplasms; cerebrovascular disease; heart disease; diabetes mellitus and homicides), the rates for men were greater in four of the five categories (malignant neoplasms; cerebrovascular; heart disease and homicides). Despite these realities, men seek less medical care than women while staying longer in hospitals for curative care. Hence, this study examines medical seeking behaviour, self-reported ill-health, and sex differential in medical seeking health in nation. The current research used secondary data. The data were extracted from the Jamaica Survey of Living Conditions (JSLC) on medical care seeking behaviour, self-reported illness (or ill-health) and the sex composition of those who reported ill-health. The JSLC was born out of the World Bank’s Living Standard Survey. Data were also taken from the Ministry of Health’s Annual Report, which provided statistics on actual percentage of Jamaicans who visited public hospitals. The current study used 19 years of published data extracted from the JSLC (1988-2007). Scatter diagrams and best fitted lines were used to examine correlations and trends. Over a 2-decade period, 1988 to 2007, only a small percentage of Jamaicans reported ill-health (between 9 to 19 %) and 15.5% in 2007, which is an increase of 3.3% over the previous year. Despite this low figure, increasingly more men sought medical care over the study period (41.1%) compared to women (29%). Nevertheless, health care seeking behaviour is still sex bias – 68.1% of women and 62.8% of men who reported health conditions. For men, more of medical care seeking behaviour is explained by ill-health (r-squared=35.4%) than women (r-squared 8.8%). This study is one of the first to examine and provide some explanation on sex differentials in health care behaviour and self-reported illness/injury in Jamaica. We found that while more men who report ill-health have been seeking medical care, the gap between the sexes in regard health seeking behaviour has been narrowing.
Introduction
Globally, in 1950-1955, life expectancy for women was 47.9 years compared to 45.2 years for
men. One-half of a century later, the disparity increased to 4.2 years (68.1 years for women and
63.9 years for men). For the Caribbean, in the same aforementioned period, life expectancy was
53.5 years and 50.8 years for women and men respectively; and 50 years later, the disparity
138
increased to 5.5 years (70.9 years for women and 65.4 years for men) which was greater than that
for the world. Life expectancy which is an indicator of mortality and morbidity are also a
measure for health status; and speaks to the quality of labour for the society. Although there are
some morbidity that are not life threatening, health literature showed that healthy life is not
equivalent to lived years. The World Health Organization being aware of this disparity
developed the DALE (ie disability adjusted life expectancy) to discount life expectancy for the
time lost due to illness. Based on this information, statistics revealed that developing countries
lost 9 years of life expectancy owing to unhealthy years (or illness); and this is still within the
cultural context of men’s unwillingness to seeking medical care. While this provides a general
framework for the rationale of the disparity in life expectancy of the sexes, it does not afford us a
comprehensive understanding of health inequality.
There has always been a health differential between the sexes in Jamaica [1] Dating
back to 1880, which was the first time that life expectancy data was recorded for men and
women in the island, women outlived men. Statistics for Jamaica showed that for the period
1880 and 1882, women lived approximately 3 years more than men and 122 years later (2002-
2004), they outlived them by 6 years, which is an additional 3 years. Globally, women live
longer than men by 8 years which is 2 years more than that of the life expectancy sex differential
in Jamaica. Women are not only living longer than their male counterparts, but they are enjoying
greater quality of life[2] A study of 3,009 older people done in 2007 in Jamaica[3] revealed that
elderly women had a higher quality of life (3.3 ± 2.2) than men (2.8 ± 1.8; p value = 0.001),
which concurred with the earlier work done by the WHO in 1998. But, studies that have
examined well-being have shown that men experienced a greater economic wellbeing than
139
women[4], despite not having a higher subjective wellbeing. What is explaining this health
differential between the sexes?
Life expectancy which is calculated using mortality data indicate that men are
experiencing particular pathogen causing diseases which are accounting for the greater increase
in mortality and lower life expectancy than women. An epidemiological profile of selected health
conditions and services in Jamaica for 1990-2002 was conducted by the Health Promotion and
Protection Division, Ministry of Health in 2005 which indicated that malignant neoplasm was the
leading cause of death in Jamaica. It was 39% greater for men than women. The second leading
cause of death, cerebrovascular disease, was 14% higher for men than women; heart diseases rate
was 71.2 per 100,000 for men and 66.1 per 100,000 for women, and diabetes mellitus was
greater for women than men. The statistics revealed that mortality caused by diabetes mellitus
was 64% higher for women than men.
Jamaica is not unique in regard to i) women outliving men, ii) particular morality is
greater for men than women, and ii) some of the leading causes and death are sex specific[2] The
issue of higher mortality differential between the sexes at older ages begins with boys suffering
more illnesses and injuries than girls[5] The World Health Organization (WHO) offered a potent
finding that age-and sex differential in mortality dates back to 1955[2] This indicates that higher
mortality in the world’s population tend to favour men, and justifies the longer life that they live
compared to men.
In demography, life expectancy is used to measure health. But this approach fails to
capture health as one can be alive but enjoy optimum health – living with varying levels of
morbidity. There is an argument that morbidity is accounted for in mortality, and this so.
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However, some dysfunctions are not death causing, and so quality of life (health) will be lower
with these health conditions. It is owing to this reality that the World Health Organization
(WHO) introduced what is known as healthy life expectancy which discounts life expectancy by
morbidity.
Healthy Life Expectancy
One of the drawbacks to the use of life expectancy is its absence to capture ‘hale’ years
of life. Traditionally when life expectancy is measured, it uses mortality data to predetermine
the number of years of life yet to be lived by an individual, assuming that he/she subscribes to
the same mortality patterns of the group. The emphasis of this approach is on length of life and
not on the quality of those years lived. Hence changes in life expectancy are primary due to
mortality movements, and imply changes in external conditions of the socio-biological
environment. These changes include the components of public health, the physical milieu, and
technological/medical advancement. With all the aforementioned conditions that have improved
over the last century, increased life expectancy in the world is not surprising to scholars. One
way of evaluating population ageing in the world or in any geopolitical space is ‘life
expectancy’. Today, it should come as no surprise to people that many developing nations have
been experiencing increased gains in additional years of life for members with its population in
comparison to 20th century.
Associated with ageing are high probability of increased dysfunctions and the
unavoidable degeneration of the body. This explains why it is germane to analyze healthy life
expectancy and not merely life expectancy. Healthy life expectancy is defined as the number
of years that an individual is expected to live in ‘good’ health. Technological advancement is
141
able to prolong life, but it is not able to remove morbidity and its deterioration in quality of lived
years of the individual. Thus, while life expectancy in the Caribbean is increasing and that this is
in keeping with the rest of the world, there is a simultaneous increase in chronic diseases and
resurgence of infectious disease. This reality highlights the disparity between quantity of years
lived and the quality of those lived years because of sociopsychological conditions- such as
loneliness, bereavement, social support (or the lack of), low self-esteem, and low self-
actualization and so on.
In evaluating health or wellbeing, we must seek to examine more than just the number of
years that an individual is likely to survive as we should be concerned about the quality of those
years. Even though, life expectancy is an indicator of health, the new focus is on healthy life
expectancy. Based on the Healthy People 2010, the new thrust is on increasing quality of years
of life. In attempting to capture ‘quality of years lived’, in 1999, the WHO introduced an
approach that allows us to evaluate this, by the ‘disability adjusted life expectancy’ (DALE)[6]
DALE does not only use length of years to indicate health and wellbeing status of an individual
or a nation, but incorporate the number of years lived without disabilities.
DALE is a modification of the traditional ‘life expectancy’ approach in assessing health.
It uses the number of years lived as its principal component. This is referred to as ‘full health’.
In addition, the number of years of ill-health is weighted based on severity as another component
in the equation. This is then subtracted from the expected overall life expectancy to give what is
referred to as years of hale life. Embedded in this approach is the adjustment of years lived in
‘ill-health’.
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Having arrived at ‘healthy life expectancy’, the WHO has found that poorer countries lost
more from their ‘traditional life expectancy’ than developed nations. The reasons forwarded by
the WHO are the plethora of dysfunctions and the devastating effects of some tropical diseases
like malaria that tend to strike children and young adults. The institution found that these
accounted for a 14 percent reduction in life expectancy for poorer countries and 9 percent for
more developed nations. [6] This is in keeping with a more holistic approach to the measure of
health and wellbeing with which this study seeks to capture. By using the biopsychosocial
model in the evaluation of wellbeing of aged Jamaicans, we will begin to understand factors that
are likely to influence the quality of lived years of the elderly, and not be satisfied with the
increased length of life of the populace. Looking at the life expectancy data for Jamaica, the
figure is 74.1 years for both sexes[6] but by using healthy life expectancy it is 65.1 years[6] Here
life expectancy has been increasing at a faster rate than ‘healthy life expectancy’. Therefore,
Jamaicans are expected to spend some 9 years of their life in ‘poor health’.
In summary, the use of life expectancy to measure health is inadequate and so morbidity
must be taken into consideration. When life expectancy is discounted by morbidity, it provides
an account of the healthy life expectancy of an individual. Hence, the use of life expectancy to
indicate health for men and women is equally insufficient in health analysis. It is evident from
statistics on life expectancy and particular diseases causing mortality that men are experiencing a
lower health status, and what accounts for this reality? Within the context of the aforementioned
issues, and the fact that medical health care seeking has increased from 54.6% in 1989 to 66.0%
in 2007 and that there is a decline of 5.7% over 2006 (Table 6.1), is this offering some
explanation the sex differential in health status? Although less Jamaicans are seeking medical
care of those who reported illnesses, 27.1% more Jamaicans reported dysfunctions (Table 6.1),
143
suggesting that there is greater health differential between the sexes. Hence, for this study,
medical seeking behaviour, self-reported ill-health, and sex differential in medical seeking health
care and self-reported ill-health will be examine to provide a better understanding of the healthy
life expectancy of the sexes in Jamaica.
Materials and Method
The current research used secondary data. The data constitute statistics from the Planning
Institute of Jamaica and the Statistical Institute of Jamaica (in Jamaica Survey of Living
Conditions, JSLC) and Ministry of Health Jamaica (MOH). The data were extracted from the
JSLC on medical care seeking behaviour, self-reported illness (or ill-health) and the sex
composition of those who reported ill-health. The Ministry of Health’s Annual Report provided
data on actual percentage of Jamaicans who visited public hospitals, which was contrasted by the
JSLC’s self-reported visits to public hospitals in order to further examine the sex differentials on
subjective ill-health.
This study used 19 years of published data extracted from the JSLC (1988-2007). The
JSLC was born out of the World Bank’s Living Standard Survey. The JSLC began in 1988 when
the Planning Institute of Jamaica (PIOJ) in collaboration with the Statistical Institute of Jamaica
(STATIN) adopted with some modifications of the World Bank's Living Standards Measurement
Study (LSMS) household surveys. The JSLC has its focus on policy implications of government
programmes, and so each year a different module is included, evaluating a particular programme.
The JSLC is a self-administered questionnaire where respondents are asked to recall detailed
information on particular activities. The questionnaire covers demographic variables, health,
immunization of children 0 to 59 months, education, daily expenses, non-food consumption
144
expenditure, housing conditions, inventory of durable goods, and social assistance. Interviewers
are trained to collect the data, which is in preparation of the household members. The survey is
usually conducted between April and July annually. Furthermore, the instrument is posted on the
World Bank’s site to provide information on the typologies of question and the
(http://www.worldbank.org/html/prdph/lsms/country/jm/docs/JAM04.pdf).
Ministry of Health is the body which is constituted by statutes to regulate all health
institutions in the country. The Ministry of Health (MOH) collects statistics on health, health
services, health utilization, health related matters, and carry out health mandate of the
government. MOH has decentralized its operations. The island is sub-divided in four regions
(South-East; North-East; Western, and Southern), which emerged owing to the passage of the
National Health Service Act of 1997. Each region operates as a semi-autonomous regional body
under the general directs of the central Ministry of Health, which is subject to the directions of
the Minister of Health. The central Ministry of Health collates all the data sent it by the four
health authorities in country. Therefore, data revealed in the Annual Reported of the Ministry of
Health, Jamaica, reflect actual accounts of the health matters in the country.
Scatter diagrams and best fitted lines were used to examine correlations between different
variables, and percentages were also utilized to evaluate events over two decade (1988-2007).
Measure
Sex is the biological composition of being men or women.
Sex differential is the disparity between self-reported ill-health of male or female.
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Medical Care Seeking Behaviour denotes the proportion of self-reported cases of visits for
seeking medical care of those who indicated ill-health.
Self-reported Illness is the percentage of people who have reported cases of dysfunctions (ill-
health or health conditions) as indicated by a respondent in a 4-week reference period.
Poverty is measured using the poverty line. The poverty line estimate is particular attainable
consumption expenditure in excess of a minimum necessary level of expenditure on a
representative bundle of necessary goods and services valued at germane prices. (JSLC 2008)
Results
Some scholars may want to believe that the use of subjective data on health (self-reported ill-
health) cannot be used to proxy health as it is not a good estimate of actual health status. In
order to remove this myth, the researcher will examine the actual figures provided by the
Ministry of Health on visits to public health care facilities and those garnered by the Jamaica
Survey of Living Conditions (JSLC). The JSLC is an annual probability sampled survey which
collects data from Jamaicans based on their recollection of events (self-reported). Based on
Table 6.4, self-reported health as indicated by the JSLC is a good proxy of visits. The data
revealed that in 1997, the difference between Jamaicans recall of events and those actually
happened as recorded by the Ministry of Health was marginally different (1%). Some 7 years
later (2004), the difference between same phenomena was 6.1% suggesting that subjective
assessment of health is a good proxy for actual health. It is within this context, that the researcher
will examine self-reported health data from JSLC to understanding health differential between
the sexes in Jamaica.
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During the periods of the greatest double digits inflation in history of Jamaica (early
1990s) (Table 6.2) in particular inflationary rates that were in excess of 25% (1990-1995),
Jamaicans reported the lowest percentages in ill-health (health conditions). Moreover, in 1991
when inflation was at it peaks, the prevalence of poverty stood at its highest (44.6%), and the
data showed that self-reported illnesses were 13.7%. This figure was the fifth highest self-
reported ill-health in an 18 year period (1989-2007). In the unprecedented inflation of 1991
(80.2%), less men sought medical care (12.0%) over 1990 (16.35) compared to 15.0% in 1991
and 20.3% in 1990. In 1990, it was the first time in the history the of nation that inflation rose to
in excess of 20% and self-reported illness reached its maximum of 18.3%, and medical care
seeking behaviour was at its lowest (38.6%).
In addition, in 1990, both sexes sought the most medical care (Table 6.3). Two years
later (1992), inflation rate fell by 49.9% (to 40.2%) over 1991 which explains the rationale for
the 24.0% decrease in prevalence of poverty; self-reported ill-health declined by 22.6%,
ownership of health insurance increased so to were people seeking medical care and the private
health care utilization. The irony here is that 17.5% less men reported accessing medical care for
their ill-health and 24.7% less women. This indicates that more of those people who did not
report ill-health visited private health care facilities for medical care. In 1993, inflation declined
further by 25.1%; poverty saw a reduction of 28.0%; self-reported health conditions increased by
13.2%; health insurance coverage increased by 12.2%; number of people seeking medical care
increased by 1.8%. In that same period, the number of women who sought care was 3.8 times
more (19.5%) than men (5.1%). Hence, high inflation was reducing visits for medical care and
another matter which emerged from the data during that period, that those who attending public
hospitals began reducing their visits while private hospital users, increased utilization (Table
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6.2). There is a paradox post-2005 as inflation increased by an unprecedented 194.7% in 2007
over 2006 and this explains a corresponding decline in the number of persons who sought
medical care (by 5.7%). Nevertheless, the number of men who visited health care facilities
increased in the period by 21.2% and the number of women was 1.24 times more than men.
The data show that in the last 17 years, women place more emphasis on their health than
men. Between 1988 and 2007, it was only on one occasion that men have indicated having
sought more medical care than women (in 1997) (Table 6.3). The difference between men
seeking medical care and that of women was 0.7%. If health seeking behaviour is a proxy for
preventative care, then it would appear that they were more health conscious. This is the not the
case as in the same period, then spent more days receiving care (mean of 11 days) compared to
10 for women. Hence, this increased in health seeking behaviour was owing to curative and
preventative care. Nevertheless, over the studied period, severity of care for both sexes has been
reality the same. Using mean number of days men received care for illness/injury, the difference
is minute, suggesting that severity of illness between the sexes in Jamaica is the same.
Another interesting finding that emerged from the data is the narrowing of the gap
between public health care utilization and private health care utilization in the nations,
suggesting that costing of living is accounting for more visits to public care facilities. Embedded
in those findings is the affordability in people’s decision to seek medical care. This indicates that
there are some other conditions that are interfacing with men’s and women’s decision to visit
health care facilities for care outside of prices (inflation).
Results: Bivariate Analyses
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Percentage of People Seeking Medical Care by Percentage of People reporting Illness
On examination of Figure 6.1, it was revealed that a negative correlation exists between number
of people who sought medical care and percentage of people who reported ill-health. This
indicates that as more people report health conditions, less of them are likely to seek medical
care. Furthermore, 16.3% of the variability in people seeking medical care can be explained by
illness, suggesting that ill-health is not a good reason for Jamaicans visiting health care
practitioners. On further investigation of people seeking medical care and self-reported
illness/injury, data (Tables 6,2 & 6.3) revealed that on the occasion when the highest percentage
of illnesses were reported, the least number of person sought care for those conditions. This
irony was equally the case for men (16.3%) as well as women (20.3%) (Table 6.3).
Percentage of People Seeking Medical Care by Prevalence of Poverty
On examination of a scatter diagram; it was observed that there is a negative correlation between
the percentage of people seeking medical care and prevalence of poverty. The best fit line
revealed that 57.6% of why people seek health care in Jamaica is determined by poverty (Figure
6.2). Hence, people are highly likely to visit health care facilities in periods of low poverty and
vice versa. This indicates that medical care is not simply about ill-health, it is equally determined
by affordability, suggesting that people will switch to home care in periods of increased poverty.
Irrespective of this knowledge, is there is sex disparity in regard to seeking medical care and
reporting illness?
Percentage of Men Seeking Medical Care by Percentage of Men reporting Illness
149
Generally 16.3% of why Jamaicans visit health care facilities in search of care is owing to their
health conditions. However, for men, 35.4% of why they sought medical care was due to ill-
health as 35.4% of the variability in men seeking medical care can be explained by medical care.
On decomposing the data, when the least percentage of men sought medical care assistance
(37.9%), the most percentage of them reported illness (16.3%) (Table 6.3). Furthermore, when
the lowest percentage of men reported ill-health (health conditions/injuries) (7.4%), this was in
60% of those seeking more medical care. However, in 1999 and 2004, low self-reported illness
was correlated with relatively high health seeking behaviour.
Percentage of Women Seeking Medical Care by Percentage of Women reporting Illness
Health (medical) care seeking behaviour of women is lowly correlated with self-reported illness
(injury) (Figure 6.3). The scatter plot revealed that generally, the more women reported health
conditions the less likely they are to seeking medical care. Some 8.8% of the variability in
medial care seeking behaviour of this cohort can be explained by a change in self-reported health
conditions. Self-reported illness of women accounted for 54% less of the explanatory reason for
seeking medical care compared to that of the both sexes (16.3%), suggesting that women’s health
care behaviour is driven by other factors than ill-health. There are some similarities between
health care seeking behaviour and self-reported illness of both sexes as when women reported
the least percentage of health care seeking behaviour, this was corresponding to the most
reported health conditions (Table 6.3). Furthermore, when the least percentage of ill-health was
reported, this earmarked 59th percentage of the highest seeking medical care behaviour of
women. These were also the case for men.
Deconstruction the Self-Reported Health Status of Jamaicans by Sex, 1989-2006
150
Over the last 2 decades (1988-to-2007), a small proportion of Jamaicans have reported illness (or
dysfunction) (Table 6.5). This has been has high as 168 per 1,000 (in 1989) to a low of 88 per
1,000 (in 1997), and the figure was 155 per 1,000 in 2006 (Table 6.5). On deconstruction the
population self-reported health status, it was revealed that women continue to report more health
conditions than men. In 1989, there 123 women (or women) who reported health conditions to
100 men (or men), and in 2004, the ratio was as high as 153 women per 100 men. This indicates
that 53% more women reported health conditions than men in the latter year and there was an
increase of 30% more women reporting dysfunctions over the 2 decades. Over the studied
period, in 1992, the disparity in self-reported health conditions between men and women was
very close of which there were 114 women to 100 men as it relates to self-reported health
conditions. On the other hand, over the last decade (1997-to-2006), the disparity was 136 or 153
women per 100 men, and in the last 2 years the value has been relatively stable (136 or 137
women per 100 men).
Percentage of People Seeking Medical Care by Percentage with Health Insurance
Health Insurance is one indicator of people’s intent to access care. On examination of the
data (Table 6.2), only a small percentage of Jamaicans in 2007 had health insurance (21.1%).
This meant that more people who will become ill would need to meet their medical expenses out
of savings, current income and assistance from social support agent(s). Table 6.2 revealed in
8.6% of Jamaica had health insurance coverage during the period when the inflation rate was at
its peak (80%) and when it fell to 40.2%, health insurance coverage increased by only 0.4%.
Further investigation of health seeking behaviour and health insurance coverage showed that the
ownership of health insurance was positively related to health seeking behaviour. A bivariate
151
correlation between the two aforementioned factors revealed that 56.1% of the variability in
people seeking medical care was as a result of ownership of health insurance (Figure 6.5).
Ownership of Health Insurance and Prevalence of Poverty
Poverty does not only mean ones inability to purchase consumption items, but also non-
consumption items such as health insurance. On examining a scatter diagram with a best fit line
to establish any correlation between the two aforementioned variables, it was observed that a
moderately strong correlation existed (R-squared = 0.597) – Figure 6.5. This means that 60% of
the variability in ownership of health insurance can be accounted for by prevalence of poverty,
suggesting that poor is less likely to have health insurance coverage.
Discussion
In the conclusion of the health chapter in one of the JSLC’s reports [8] it reads “Sex differentials
with respect to self-reported illness and health seeking behaviours need to be investigated.” This
is the rationale for this study, to provide an assessment of differences in subjective health and
medical seeking behaviour of men and women in Jamaica.
Globally, regionally and in particular Jamaica, women seek more health care than men [1,
2, 4-7] This is not alarming as it commences from at childhood. In 1998, one health organization
wrote that girls are less likely to be injured and have broken bones compared to boys [2], which
continue during the life span. So when the mortality rates show a higher rate for men than
women [2, 6], this is just a continuation of early socialization. Health, therefore, is sex bias. One of
the rationales for the emphasis on health care by women is reason for male’s abstinence, the
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culture. Within many cultures, men are not to display any form of weakness which includes ill-
health. This culturalization has embedded in boys and avoidance of speak of illness/injury and
the image of ill-health is negative and is primarily feministic in nature. This is not limited to
Jamaica or African descendent societies as it is equally the case in European geopolitical zones
such as Norway [9]
Many cultures view (image) of health is the absence of diseases and this is sometimes
linked to cure of the gods or moral rationale, suggesting that ill-health is a weakened biological
state. Men who are culturalized to be strong and macho must now balance ill-health within a
plural culture. The 21 st century has seen the exponential increase in life expectancy of men
compared to women in nineteenth and earlier centuries, but what about high mortality for this
group. There has always been feminization of life expectancy in Jamaica since 1880 (Table 6.1)
and the disparity in life expectancy has double from 1880-1882 to 2002-2004 from 3 years to 6
years respectively. Life expectancy which is an indicator of health does not only speak of longer
life, there are also some cultural changes that account for this increased life span, the social
milieu.
Despite the advancement in medical technology, men continue to outnumber women in
particular mortality rates. These include heart disease and neoplasm to name a few non-
communicable diseases. Heart disease and neoplasms are caused through either lifestyle
behaviour or heredity, and the former explains more of heart disease than the latter. Globally, the
fact that women outlived men by 8 years and in Jamaica by 6 years, lifestyle behaviour
undoubtedly is explaining the higher morbidity in heart diseases of men.
Life style behaviour is expressed in health seeking behaviour, the purchase of health
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insurance, preventative care and not curative care. Jamaica women continue to seek more care
than men, and this concurs with the finding so other studies [2, 3, 10, 11] Women do not take their
health for granted as society labels them with the nurturing role of children as well as ascribe
them softer tasks. This means that health and ill-health are interpreted and viewed from within
the perspective of personal experiences, and expectations. It is through the socialization process
which is carried out by mothers (women) that ill-health and health will be defined which
accounts for ones expectation and some percentage of how the world is viewed and interpreted
by people. In a qualitative study that was done in Nairobi slums, the authors found a strong
correlation between severity of illness and health seeking behaviour of children[12] These
children do not seek care of themselves, but are taken for medical care by their mothers. Another
study on street children (ages 5 to 13 years), who take themselves, like the Nairobi study
attended health care institutions for care dependent on i) severity of illness and ii) if it stops their
economic livelihood[13] Eight percentage of the sampled population of the latter study (in
Pakistan) were boys (men). This speaks to the image of health as viewed by men, and when care
is sought by them.
Ill-health, therefore, based on the image of health seen through the lens of men is weak,
breaches machoism and borders on the fringes of feminism. Within the homophobis world,
despite the gradual reduction of the degree in some societies, men (or boys) do not want to be
labeled weak, homosexual or effeminate. Hence, there is dialectic here as men want to live which
means that they must address ill-health and at the same time they must appear to be macho. Men
are less likely to both report ill-healths as well as seek medical care because of its image and
social labels that they may ascribe to them by society. Women also play a part in this process as
they grown their boys to be strong, ‘tough’, and that they should not show weakness. Ill-health
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is a weakness (or negative health), and so women on seeing men visiting health practitioners
especially if this is frequent construe this as weak, but his is not ascribe to a female for doing the
same thing.
Medical care seeking behaviour is, therefore, construed as indicating ill-health (curative
care) and not preventative care for men. Chevannes[14] wanting to explain how men are as they
are, opined that early socialization played a critical role in shaping men’s masculinity, image of
self and interpretation of the world around them. The image of health as viewed as far back as
prehistoric society is that of sickness, a curse, a plague, a weakness and a state of biological
incapacitation. Men who are culturalized to be strong cannot afford to be seen as weak or
incapacitate by their peers or the opposite sex as the society removes the acclaim of greater,
power and prestige from any such male. This means that men must now report and display less
signs of ill-health (weakness), and the only time that illness must be shown is in times of severity
which is close to death.
Jamaican men displaying low medical care seeking behaviour as cultural underpinnings,
and so does their unhealthy lifestyle practices. Unhealthy lifestyle is undoubtedly explaining
high mortality of men than women. This dates back to prehistoric society, when men must
hunters, heroes, warriors and fierce to defend themselves, their tribes and women. Such events
meant that they would take more risk than women, and this has continued during the centuries.
Although vast amount of information are available on health and health treatment, men continue
to indulge in risky behaviour which accounts for their high morbidity and mortality in some
conditions. The literature speaks to 80% of injuries and between 30-40% of cases with
cardiovascular conditions and diabetes mellitus could have been prevented by lifestyle practices
[5] This explains much of the health conditions and increased in reported ill-health and medical
155
care seeking behaviour. What is the role of education in health differential in the sexes?
Education which is well established has directly correlated with better health [15-22] does
not remove early culturalization by family, peer groups and religious affiliations. The general
education level of the Jamaican populace has been improving since the last 3-decade, but this
does mean the remove of the sex bias health image or stigma of weakness associated with illness.
In 1989, 54.6% of Jamaicans sought care for ill-health and in 2007 that figure has increased by
9.9% (to 60%). In the same period that rate of increase for women was 29.0% compared to
41.1% for men. Nevertheless, in 2007, for every 100 men that sought care for ill-health, 108
women sought medical care. Although, we cannot divorce health from the social milieu, more
men are seeking medical care for illness and this accounts for the faded difference between the
mean numbers of days spent for care in both sexes. The 21 st century has aided men in their
recognition for the need to seek medical care for ill-health, in spite of traditional cosmologies [23,
24]
In contemporary societies, illness for men is not tied to health conditions such as
neoplasm, heart disease, hypertension, mellitus diabetes and stroke, but is synonymous to
sexuality which is a legacy of their socialization[14, 23-29] A medical doctor ascribes to the 21st
century, sex roles that are tied to sex (biological category). This means that being male is linked
to being the stronger sex, fertile, and sexual prowess. Society has not removed from its men that
sex stereotype, and so the image of health for them is substantially tied to sexuality. Men,
therefore, do not see themselves as ill, unless they are impotent. Culturally, because impotency
and infertility are a curse, men will not openly speak about those matters or/and other heath
conditions. Again, male means strength, sexual potency, and these are all at the other end of the
pendulum of ill-health. This explains the reason for the lower purchase of individual health
156
insurance as this symbolizes weakness or preparation of some negative conditions. In spite of
this reality, over the last one-half of a decade, there has been an increase in health insurance
coverage and health seeking behaviour of both sexes. As of 2007, 2.1% more women had health
insurance coverage than men (20.1%), which was more than the national average of 21.1%.
Again this speaks to the differences in image of health held sexes and how their decision is based
on this view. Health insurance is a component of lifestyle practices justify the advantages that
women enjoy compared in men concerning health status. This is also reinforced in the fact (in
2007) that for every 133 women who indicated that they were unable to afford to seek medical
care 100 men [1], showing that men are naturally, owing to their culturalization, unwilling to seek
medical care and this is evident in their lifestyle practices, purchase of health insurance,
reporting ill-health and visits to health care institution for preventative and curative care [1, 5]
According to one scholars income buys health [30], which has some merit. The merit to
this argument is linked to the fact that income affords one the ability and option to purchase
better foods, medical care, a particularly good physical environment that are all positively
correlated with good health[3, 15-18] There is a negative side to affluence and income, as it afford
particular lifestyle that retard good health. Income affords one the lifestyle to purchase cigar,
tobacco, speedy cars, and in the process remove the disadvantage of low income or poverty. In a
study done by a group Caribbean scholars of 1,338 Jamaicans (ages 15 to 99 years), they found
that greatest subjective psychosocial wellbeing was had by the middle class followed by the
wealth and lastly by the poor [31]
Embedded in the income and health debate, is the difficulty of the poor in seeking
medical care (curative and preventative care). This study has shown that there is a moderately
strong correlation between seeking medical care and prevalence of poverty, suggesting that poor
157
men are even less likely to seek care than those in the middle to upper class. When poverty is
coalescing with the cultural biases and image of health, men are likely to suffer more as they
must balance ill-health which is a weakness with in affordability. The issue of affordability is
seen in the percentage of those in the poorest quintile with health insurance in 2007 (6.6%)
compared to 12% in quintile 2, 18% in quintile 3, 22.7% in quintile 4 and 43.4% of those in the
wealthiest quintile. Embedded in this disparity is the poor’s inability to plan for the eventuality
of ill-health coupled with deplorable reality of the physical environment. This physical
environment is such to account for ill-health [32], and when poor nutrition is added to this
situation the poor will become even more ill.
Concluding Comments
In summary, illness is still seen and interpreted by Jamaicans as punishment and negative health,
and this explains their low self-reported health conditions and health care seeking behaviour.
Men who are product of the society must abide within the image of its dictates, which justifies
their unwillingness to seek medical care, report illness, purchase health insurance coverage and
create an image of weakness. In spite of this reality, men have become more involved that
women in seeking medical care over the last 17 years. This means that the society is becoming
increasingly more cognizant that ill-health is more than negative health or is simply equivalent to
weakness, female or less macho men. Although men are substantially driven by health conditions
to seek medical care than women, they are becoming more involved in health care treatment.
Recommendation
Further efforts are needed to eliminate more of the barriers of the negative image of health and
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the use of medical services for ill-health in Jamaica. Medical practitioners, health care workers,
social workers and researchers must integrate the image of men in their treatment, and create an
atmosphere which is conducive to health care for men. A single prevalence study is needed to
ascertain the influence of each of the identified variables in this study and others in order to
understand the role of poverty, health insurance, ill-health, on the health seeking behaviour of
Jamaicans, the media, education as well as confounding variables such as sex, age, religiosity,
area of residence and subjective social class. In addition, a study is necessary to ascertain
whether the increased in self-reported health is owing to unemployment, and how much of ill-
health is accounted for by psychological conditions.
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13. Taff, N., and G. Chepngeno, 2005. Determinants of health care seeking for childhood illness in Nairobi slums. Tropical Medicine and International Health, 10:240-245.
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Table 6.1: Life Expectancy at Birth of Jamaicans by Sex: 1880-2004
Period:
Average Expected Years of Life at Birth
Man Woman
1880-1882 37.02 39.80
1890-1892 36.74 38.30
1910-1912 39.04 41.41
1920-1922 35.89 38.20
1945-1947 51.25 54.58
1950-1952 55.73 58.89
1959-1961 62.65 66.63
1969-1970 66.70 70.20
1979-1981 69.03 72.37
1989-1991 69.97 72.64
1999-2001 70.94 75.58
2002-2004 71.26 77.07
Sources: Demographic Statistics (1972-2006) in Bourne, P. Determinants of well-being of the Jamaican Elderly. Unpublished thesis, The University of the West Indies, Mona Campus; 2007.
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Table 6.2: Inflation, Public-Private Health Care Service Utilization, Incidence of Poverty, Illness and Prevalence of Population with Health Insurance (in per cent), 1988-2007 Year Inflation Public Private Prevalence Illness Health Seeking Mean Utilization Utilization of poverty Insurance Medical Care Days of Coverage Illness 1988 8.8 NI NI NI NI NI NI NI 1989 17.2 42.0 54.0 30.5 16.8 8.2 54.6 11.4 1990 29.8 39.4 60.6 28.4 18.3 9.0 38.6 10.1 1991 80.2 35.6 57.7 44.6 13.7 8.6 47.7 10.2 1992 40.2 28.5 63.4 33.9 10.6 9.0 50.9 10.8 1993 30.1 30.9 63.8 24.4 12.0 10.1 51.8 10.4 1994 26.8 28.8 66.7 22.8 12.9 8.8 51.4 10.4 1995 25.6 27.2 66.4 27.5 9.8 9.7 58.9 10.7 1996 15.8 31.8 63.6 26.1 10.7 9.8 54.9 10.0 1997 9.2 32.1 58.8 19.9 9.7 12.6 59.6 9.9 1998 7.9 37.9 57.3 15.9 8.8 12.1 60.8 11.0 1999 6.8 37.9 57.1 16.9 10.1 12.1 68.4 11.0 2000 6.1 40.8 53.6 18.9 14.2 14.0 60.7 9.0 2001 8.8 38.7 54.8 16.9 13.4 13.9 63.5 10.0 2002 7.2 57.8 42.7 19.7 12.6 13.5 64.1 10.0 2003 13.8 NI NI NI NI NI NI NI 2004 13.7 46.3 46.4 16.9 11.4 19.2 65.1 10.0 2005 12.6 NI NI NI NI NI NI NI 2006 5.7 41.3 52.8 14.3 12.2 18.4 70.0 9.8 2007 16.8 40.5 51.9 9.9 15.5 21.2 66.0 9.9 Source: Bank of Jamaica, Statistical Digest, Jamaica Survey of Living Conditions, Economic and Social Survey of Jamaica, various issues Note: Inflation is measured point-to-point at the end of each year (December to December), based on Consumer Price Index (CPI) NI – No Information Available
163
Table 6.3: Seeking Medical Care, Self-reported illness, and Sex composition of those who report illness and Seek Medical Care in Jamaica (in percentage), 1988-2007
Year
Seeking Medical
Care
Health Insurance Coverage
Seeking Medical Care - Men
Seeking Medical Care -
Women
Reporting Illness-
Men
Reporting Illness- Women
Mean Days Of
IllnessMen
Mean Days Of
Illness Women
1988 NI NI NI NI NI NI NI NI 1989 54.6 8.2 44.7 52.8 15.0 18.5 10.6 11.1 1990 38.6 9.0 37.9 39.2 16.3 20.3 10.2 10.2 1991 47.7 8.6 48.5 47.4 12.1 15.0 10.0 10.3 1992 50.9 9.0 49.0 52.5 9.9 11.3 10.7 10.9 1993 51.8 10.1 48.0 54.7 10.4 13.5 10.7 10.1 1994 51.4 8.8 49.0 53.4 11.6 14.3 10.3 10.4 1995 58.9 9.7 59.0 58.9 8.3 11.3 10.6 10.7 1996 54.9 9.8 50.5 58.5 9.7 11.8 10.0 11.0 1997 59.6 12.6 60.0 59.3 8.5 10.9 11.0 10.0 1998 60.8 12.1 57.8 62.8 7.4 10.1 11.0 11.0 1999 68.4 12.1 64.2 71.1 8.1 12.2 11.0 11.0 2000 60.7 14.0 57.4 63.2 12.4 16.8 9.0 9.0 2001 63.5 13.9 56.3 68.2 10.8 15.9 9 10 2002 64.1 13.5 62.1 65.3 10.4 14.6 10.0 10.0 2003 NI NI NI NI NI NI NI NI 2004 65.1 19.2 64.2 65.7 8.9 13.6 11.0 10.0 2005 NI NI NI NI NI NI NI NI 2006 70.0 18.4 71.7 68.8 10.3 14.1 9.7 10.0 2007 66.0 21.2 62.8 68.1 13.1 17.8 10.6 9.3
Source: Jamaica Survey of Living Conditions, various issues NI - No Information was available
164
Table 6.4: Public Health Care Visits (using the JSLC, data) and Actual Health Care Visits (using Ministry of Health Jamaica, data), 1997 and 2004
Public Health Care Visits in Jamaica
Year Actual Visits, MOH1 Self-reported Visits, JSLC % % 1997 33.1 32.1
2004 52.9* 46.8
Source: Ministry of Health Jamaica and the Jamaica Survey of Living Conditions (JSLC) 1 The Percentages of Actual visits were computed by author *Preliminary data were used to calculate this percentage
165
Table 6.5: Self-reported Health Status per 1,000 by Population, Men and Women; Sex-Ratio of Self-reported Health Status, and Female to Male Ratio of Self-reported Health Status, 1989-2006 Year Self-reported Health Status per
1,000
Male-to-Female ratio of Self-
reported Health Status
Female-to-Male ratio
of Self-reported Health Status
Population
Men Women
1989 168 150 185 81 1231990 183 163 203 80 1251991 137 121 150 81 1241992 106 99 113 88 1141993 120 104 135 77 1301994 129 116 143 81 1231995 98 83 113 73 1361996 107 97 118 82 1221997 97 85 109 78 1281998 88 74 101 73 1361999 101 81 122 66 1512000 142 124 168 74 1352001 134 108 159 68 1472002 126 104 146 71 1402003 - - - - -2004 114 89 136 65 1532005 - - - - -2006 122 103 141 73 1372007 155 131 178 74 136
Computed by Paul Andrew Bourne from Jamaica Survey of Living Conditions from various years
Illness/Injury20.0018.0016.0014.0012.0010.008.00
Seek
ing
Med
ical
Car
e
70.00
60.00
50.00
40.00
30.00
R Sq Linear = 0.163
Figure 6.1: Percentage of Men Seeking Medical Care by Percentage of Men reporting Illness
166
Prevalence of Poverty40.0030.0020.0010.00
Seek
ing
Med
ical
Car
e
70.00
60.00
50.00
40.00
30.00
R Sq Linear = 0.576
Figure 6.2: Percentage of People Seeking Medical Care by Prevalence of Poverty
167
Self-reported Health of Men17.5015.0012.5010.007.50
Hea
lth C
are
Seek
ing
Beh
avio
ur o
f Men
70.00
60.00
50.00
40.00R Sq Linear = 0.354
Figure 6.3: Percentage of Men Seeking Medical Care by Percentage of Men reporting Illness
168
Self-reported Health of Women22.0020.0018.0016.0014.0012.0010.00
Hea
lth C
are
Seek
ing
Beh
avio
ur o
f Wom
en
70.00
60.00
50.00
40.00 R Sq Linear = 0.088
Figure 6.4: Percentage of Women Seeking Medical Care by Percentage of Women reporting Illness
169
Health Insurance21.0018.0015.0012.009.00
Seek
ing
Med
ical
Car
e70.00
60.00
50.00
40.00
30.00
R Sq Linear = 0.561
Figure 6.5: Percentage of people Seeking Medical Care by Percentage with Health Insurance
170
Prevalence of Poverty40.0030.0020.0010.00
Hea
lth In
sura
nce
21.00
18.00
15.00
12.00
9.00
R Sq Linear = 0.597
Figure 6.6: Ownership of Health Insurance and Prevalence of Poverty
171
172
Chapter 7 Social determinants of self-reported health across the Life Course
Paul Andrew Bourne
The socio-psychological and economic factors produced inequalities in health and need to be considered in health development. In spite of this, extensive review of health Caribbean revealed that no study has examined health status over the life course of Jamaicans. With the value of research to public health, this study is timely and will add value to understanding the elderly, middle age and young adults in Jamaica. The aim of this study is to develop models that can be used to examine (or evaluate) social determinants of health of Jamaicans across the life course, elderly, middle age and young adults. The current study used dataset of 2002 Jamaica Survey of Living Conditions (JSLC). It is a cross-sectional survey which used stratified random probability sampling technique to collect data from respondents. Logistic regression analyses were used to model the social determinants of health status of Jamaicans across the life course. Eleven variables emerged as statistically significant predictors of current good health Status of Jamaicans (p<0.05). The factors are retirement income (95%CI=0.49-0.96), logged medical expenditure (95% CI =0.91-0.99), marital status (Separated or widowed or divorced: 95%CI=0.31-0.46; married: 95%CI=0.50-0.67; Never married), health insurance (95%CI=0.029-0.046), area of residence (other towns:, 95%CI=1.05-1.46; rural area:), education (secondary: 95%CI=1.17-1.58; tertiary: 95%CI=1.47-2.82; primary or below: OR=1.00), social support (95%CI=0.75-0.96), gender (95%CI=1.281-1.706), psychological affective conditions (negative affective: 95%CI=0.939-0.98; positive affective: 95%CI:1.05-1.11), number of males in household (95%CI:1.07-1.24), number of children in household (95%CI=1.12-1.27) and previous health status. There are disparities in the social determinants of health across the life course, which emerged from the current findings. The findings are far reaching and can be used to aid policy formulation and how social determinants of health are viewed in the future.
INTRODUCTION
Health is a multidimensional construct which goes beyond dysfunctions (illnesses, ailment or
injuries) [1-14]. Although World Health Organization (WHO) began this broaden conceptual
framework in the late 1940s [1], Engel [3] was the first to develop the biopsychosocial model that
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can be used to examine and treat health of mentally ill patient. Engel’s biopsychosocial model
was both in keeping with WHO’s perspective of health and again a conceptual model of health.
Both WHO and Engel’s works were considered by some scholar as too broad and as such difficult
to measure [15]; although this perspective has some merit, scholars have ventured into using
different proxy to evaluate the ideal conceptual definition forwarded by WHO for some time now.
Psychologists have argued that the use of diseases to proxy health is unidirectional (or
negative) [2], and that the inclusion of social, economic and psychological conditions in health is
broader and more in keeping with the WHO’s definition of health than diseases. Diener was the
first psychologist to forward the use of happiness to proxy health (or wellbeing) of an individual
[16, 17]. Instead of debating along the traditional cosmology health, Diener took the discussion
into subjective wellbeing. He opined that happiness is a good proxy for subjective wellbeing of a
person, and embedded therein is wider scope for health than diseases. Unlike classical economists
who developed Gross Domestic Product per capita (GDP) to examine standard of living (or
objective wellbeing) of people as well this being an indicator of health status along with other
indicators such as life expectancy, Diener and others believe that people are the best judges of
their state. This is no longer a debate, as some economists have used happiness as a proxy of
health and wellbeing [18-20]; and they argued that it is a good measurement tool of the concept.
Theoretical Framework
Whether the proxy of health (or wellbeing) is happiness, self-reported health status, 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
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conceptual framework outlined by WHO and Engel to a theoretical framework for the study of
health. Using data for the world, Grossman established an econometric model that captures
determinants of health. The model read (Model 1):
Ht = ƒ (Ht-1, Go, Bt, MCt, ED) ……………………………………………….. Model (1)
where Ht – current health in time period t, stock of health (Ht-1) in previous period, Bt –
smoking and excessive drinking, and good personal health behaviours (including exercise – Go),
MCt,- use of medical care, education of each family member (ED), and all sources of household
income (including current income).
Grossman’s model was good at the time; however, one of the drawbacks to this model was
the fact that some crucible factors were omitted by the aforementioned model. Based on that
limitation, using literature, Smith and Kington [10] refined, expanded and modified Grossman’s
work as it omitted important variables such as price of other inputs and family background or
genetic endowment which are crucible to health status. They refined Grossman’s work to include
socioeconomic variables as well as some other factors [Model (2)].
Ht = H* (Ht-1, Pmc, Po, ED, Et, Rt, At, Go) ………………………..…………… Model (2)
Model (2) expresses current health status Ht as a function of stock of health (Ht-1), price of
medical care Pmc, the price of other inputs Po, education of each family member (ED), all sources
of household income (Et), family background or genetic endowments (Go), retirement related
income (Rt ), asset income (At).
It is Grossman’s work that accounts for economists like Veenhoven’s [20] and Easterlin’s
[19] works that used econometric analysis to model factors that determine subjective wellbeing.
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Like Veenhoven [20], Easterlin [19] and Smith and Kington [10], Hambleton et al. [6] used the
same theoretical framework developed by Grossman to examine determinants of health of elderly
(ages 65+ years) in Barbados. Hambleton et al.’s work refined the work of Grossman and added
some different factors such as geriatric depression index; past and current nutrition; crowding;
number of children living outside of household; and living alone. Unlike Grossman’s study, he
found that current disease conditions accounted for 67.2% of the explained variation in health
status of elderly Barbadians, with life style risks factors accounting for 14.2%, and social factors
18.6%. One of the additions to Grossman’s work based on Hambleton et al.’s study was actual
proportion of each factor on health status and life style risk factors.
A study published in 2004, using life satisfaction and psychological wellbeing to proxy
wellbeing of 2,580 Jamaicans, Hutchinson et al. [21] employed the principles in econometric
analysis to examine social and health factors of Jamaicans. Other studies conducted by Bourne on
different groups and sub-groups of the Jamaican population have equally used the principles of
econometric analysis to determine factors that explain health, quality of life or wellbeing [5, 8, 22,
23]. Despite the contribution of Hutchinson et al’s and Bourne’s works to the understanding of
wellbeing, there is a gap in the literature on a theoretical framework explains good health status of
the life course of Jamaicans. The current study will model predictors of good health status of
Jamaicans as well as good health status of young adults, middle age adults and elderly in order to
provide a better understanding of the factors that influence each cohort.
METHODS
Participants and questionnaire
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The current research used a nationally cross-sectional survey of 25,018 respondents from the 14
parishes in Jamaica. The survey used stratified random probability sampling technique to draw
the 25,018 respondents. The non-response rate for the survey was 29.7% with 20.5% who did not
respond to particular questions, 9.0% did not participated in the survey and another 0.2% was
rejected due to data cleaning. The study used secondary cross-sectional data from the Jamaica
Survey of Living Conditions (JSLC). The JSLC was commissioned by the Planning Institute of
Jamaica (PIOJ) and the Statistical Institute of Jamaica (STATIN). These two organizations are
responsible for planning, data collection and policy guideline for Jamaica.
The JSLC is a self-administered questionnaire where respondents are asked to recall detailed
information on particular activities. The questionnaire covers demographic variables, health,
immunization of children 0 to 59 months, education, daily expenses, non-food consumption
expenditure, housing conditions, inventory of durable goods, and social assistance. Interviewers
are trained to collect the data from household members. The survey is conducted between April
and July annually.
Model
The multivariate model used in this study is a modification of those of Grossman and Smith &
Kington which captures the multi-dimensional concept of health, and health status. The present
study further refine the two aforementioned works and in the process adds some new factors such
as psychological conditions, crowding, house tenure, number of people per household and a
deconstruction of the numbers by particular characteristics i.e. males, females and children (ages
≤ 14 years). Another fundamental difference of the current research and those of Grossman, and
Smith and Kington is that it is area specific as it is focused on Jamaican residents.
The proposed model that this research seeks to evaluate is displayed below [Model (3)]:
Ht = f(Ht-1,Pmc, EDi, Rt, At, Qt, HHt, Ci, Eni, MSi, HIi, HTi, SSi, LLi,Xi, CRi, Di, Oi, Σ(NPi,PPi), Mi,Ni, FSi, Ai, Wi, εi)….. Model (3)
The current health status of a Jamaica, Ht, is a function of 23 explanation variables, where
Ht is current health status of person i, if good or above (i.e. no reported health conditions four
week leading up to the survey period), 0 if poor (i.e. reported at least one health condition); Ht-1 is
stock of health for previous period; lnPmc is logged cost of medical care of person i; EDi is
educational level of person i, 1 if secondary, 1 if tertiary and the reference group is primary and
below; Rt is retirement income of person i, 1 if receiving private and/or government pension, 0 if
otherwise; HIi is health insurance coverage of person i, 1 if have a health insurance policy, 0 if
otherwise; HTi is house tenure of person i, 1 if rent, 0 if squatted; Xi is gender of person i, 1 if
female, 0 if male; CRi is crowding in the household of person i; Σ(NPi,PPi) NPi is the summation
of all negative affective psychological conditions and PPi is the summation of all positive
affective psychological conditions; Mi is number of male in household of person i and Fi is
number of female in household of person i; Ai is the age of the person i and Ni is number of
children in household of person i; LLi is living arrangement where 1= living with family
members or relative, and 0=otherwise and social standing (or social class), Wi.
Statistical analysis
177
Statistical analyses were performed using Statistical Packages for the Social Sciences (SPSS) for
Windows, Version 16.0 (SPSS Inc; Chicago, IL, USA). A single hypothesis was tested, which
was ‘health status of rural resident is a function of demographic, social, psychological and
economic variables.’ The enter method in logistic regression was used to test the hypothesis in
order to determine those factors that influence health status of rural residents if the dependent
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variable is a binary one; and linear multiple regression in the event the dependent variable was a
normally distributed metric variable . The final model was established based on those variables
that are statistically significant (ie. p < 0.05) – ie 95% confidence interval (CI), and all other
variables were removed from the final model (p>0.05). Continuing, categorical variables were
coded using the ‘dummy coding’ scheme.
The predictive power of the model was tested using Omnibus Test of Model and Hosmer
and Lemeshow [24] was used to examine goodness of fit of the model. The correlation matrix was
examined in order to ascertain whether autocorrelation (or multi-collinearity) existed between
variables. Cohen and Holliday [25] stated that correlation can be low/weak (0 to 0.39); moderate
(0.4-0.69), or strong (0.7-1.0). This was used in this study to exclude (or allow) a variable in the
model. Where collinearity existed (r > 0.7), variables were entered independently into the model
to determine those that should be retained during the final construction of the model. To derive
accurate tests of statistical significance, we used SUDDAN statistical software (Research Triangle
Institute, Research Triangle Park, NC), and this was adjusted for the survey’s complex sampling
design. Finally, Wald statistics was used to determine the magnitude (or contribution) of each
statistically significant variables in comparison with the others, and the odds ratio (OR) for the
interpreting each significant variables.
Results: Modelling Current Good Health Status of Jamaicans, Elderly, Middle Age and
Young adults
Predictors of current Good Health Status of Jamaicans. Using logistic regression analyses, eleven
variables emerged as statistically significant predictors of current good health status of Jamaicans
(p<0.05, Model 4). The factors are retirement income, logged medical expenditure, marital status,
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health insurance, area of residence, education, social support, gender, psychological affective
conditions, number of males in household, number of children in household and previous health
status (Table 7.1).
Ht = f(Ht-1, Rt, Pmc, EDi, MSi, HIi, SSi,ARi, Xi, Σ(NPi,PPi), Mi,Ni, εi)...……..... Model (4)
The model [ie Model (4)] had statistically significant predictive power (χ2 (27) =1860.639,
p < 0.001; Hosmer and Lemeshow goodness of fit χ2=4.703, p = 0.789) and overall correctly
classified 85.7% of the sample (correct classified 98.3% of cases of good health status and
correctly classified 33.9% of cases of dysfunctions).
There was a moderately strong statistical correlation between age, marital status,
education, retirement income, per capita income quintiles, property ownership, and so these were
omitted from the initial model (ie model 3). Based on that fact, three age groups were classified
(young adults – ages 15 to 29 years; middle age adults – ages 30 to 59 years; and elderly – ages
60+ years) and the initial model was once again tested. There were some modifications of the
initial model in keeping with the age group. For young adults the initial model was amended by
excluding retirement income, property ownership, divorced, separated or widowed, number of
children in household, and house tenure. The exclusion was based on the fact that more than 15%
of cases missing in some categories and a high correlation between variables.
Predictors of current Good Health Status of elderly Jamaicans. From the logistic regression
analyses that were used on the data, eight variables were found to be statistically significant in
predicting good health Status of elderly Jamaicans (P < 0.5) (Model 5). These factors were
education, marital status, health insurance, area of residence, gender, psychological conditions,
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number of males in household, number of children in household and previous health status (Table
7.2).
Ht = f(Ht-1, EDi, MSi, HIi, ,ARi, Xi, Σ(PPi), Mi,Ni, εi)...……………… …..... Model (5)
The model had statistically significant predictive power (model χ2 (27) =595.026, P <
0.001; Hosmer and Lemeshow goodness of fit χ2=5.736, p = 0.677) and overall correctly
classified 75.5% of the sample (correctly classified 94.6% of cases of good or beyond health
status and correct classified 44.7% of cases of dysfunctions).
Predictors of current Good Health Status of middle age Jamaicans. Using logistic regression, six
variables emerged as statistical significant predictors of current good health status of middle age
Jamaican (p < 0.05) (Model 6). These factors are logged medical expenditure, physical
environment, health insurance, gender of respondents, psychological condition, number of
children in household and previous health status (Table 7.3)
Ht = f(Ht-1, Pmc, Eni, HIi, Xi, Σ(NPi),Ni, εi)..........................………..... Model (6)
Based on table 7.3, the model had statistically significant predictive power (model χ2 (27)
=547.543, p < 0.001; Hosmer and Lemeshow goodness of fit χ2=4.318, p = 0.827) and overall
correctly classified 87.2% of the sample (correctly classified 98.3% of cases of good or beyond
health status and correct classified 28.2% of cases of dysfunctions).
Predictors of current Good Health Status of young adult in Jamaica. Using logistic regression, two
variables emerged as statistically significant predictors of current good health status of young
adults in Jamaica (p<0.05) (Model 7). These are health insurance coverage, psychological
condition, social class and previous health status (Table 7.4).
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Ht = f(Ht-1, Wi, HIi, Σ(NPi), εi)................................…………….....Model (7)
From Table 7.3, the model had statistically significant predictive power (model χ2 (19)
=453.733, p < 0.001; Hosmer and Lemeshow goodness of fit χ2=5.185, p = 0.738) and overall
correctly classified 92.6% of the sample (correctly classified 99.0% of cases of good or beyond
health status and correct classified 28.2% of cases of dysfunctions).
Limitations to the Models
Good Health Status of Jamaicans [ie Model (4)], elderly [ie Model (5)], middle age adults
[ie Model (6)], and young adults [ie Model (7) are derivatives of Model (3). Good Health
Status[ie Model (4) – Model (7)] cannot be distinguished and tested over different time periods,
person differential, and these are important components of good health.
Ht = f(Ht-1, Rt, Pmc, EDi, MSi, HIi, SSi,ARi, Xi, Σ(NPi,PPi), Mi,Ni, εi)...………………………..... Model (4) Ht = f(Ht-1, EDi, MSi, HIi, ,ARi, Xi, Σ(PPi), Mi,Ni, εi)...………………………………………..... Model (5) Ht = f(Ht-1, Pmc, Eni, HIi, Xi, Σ(NPi),Ni, εi)....................................……………………………..... Model (6) Ht = f(Ht-1, Wi, HIi, Σ(NPi), εi).......................................................……………………….…….......Model (7) Ht = f(Ht-1,Pmc, EDi, Rt, At, Qt, HHt, Ci, Eni, MSi, HIi, HTi, SSi, LLi,Xi, CRi, Di, Oi, Σ(NPi,PPi), Mi,Ni, FSi, Ai, Wi,εi)……………………………………………………………………….. Model (3)
The current work is a major departure from Grossman’s theoretical model as he assumed
that factors affecting good health Status over the life course are the same, this study disagreed
with this fundamental assumption. This study revealed that predictors of good health status are
not necessarily the same across the life course, and differently from that of the general populace.
Despite those critical findings, healthy time gained can increase good health status directly and
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indirectly but this cannot be examined by using a single cross-sectional study. Health does not
remain constant over any specified period, and to assume that this is captured in age is to assume
that good or bad health change over year (s). Health stock changes over short time intervals, and
so must be incorporated within any health model.
People are different even across the same ethnicity, nationality, next of kin and
socialization. This was not accounted for in the Grossman’s or the current work, as this is one of
the assumptions. Neither Grossman’s study nor the current research recognized the importance of
differences in individuals owing to culture, socialization and genetic composition. Each
individual’s is different even if that person’s valuation for good health Status is the same as
someone else who share similar characteristics. Hence, a variable P representing the individual
should be introduced to this model in a parameter α (p). Secondly, the individual’s good (or bad)
health is different throughout the course of the year and so time is an important factor. Thus, the
researcher is proposing the inclusion of a time dependent parameter in the model. Therefore, the
general proposition for further studies is that the function should incorporate α (p, t) a parameter
depending on the individual and time.
An unresolved assumption of this work which continues from Grossman’s model is that
people choose health stock so that desired health is equal to actual health. The current data cannot
test this difference in the aforementioned health status and so the researcher recommends that
future study to account for this disparity so we can identify factors of actual health and difference
between the two models.
Discussions
183
This study has modelled current good status of Jamaicans. Defining health into two
categories (ie good – not reported an acute or illness; or poor – reported illness or ailment), this
study has found that using logistic regression health status can be modeled for Jamaicans. The
findings revealed that the probability of predicting good health status of Jamaicans was 0.789,
using eleven factors; and that approximately 86% of the data was correctly classified in this study.
Continuing, in Model (4) approximately 98% of those who had reported good health status were
correctly classified, suggesting that using logistic regression to examine good health status of the
Jamaican population with the eleven factors that emerged is both a good predictive model and a
good evaluate or current good health status of the Jamaican population. This is not the first study
to examine current good health status or quality of life in the Caribbean or even Jamaica [6, 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
184
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
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
185
important factors that determine their good health status and less of other determinants that affect
the elderly and middle age adults. It should be noted that young adult is the only age cohort with
which social standing is a determinant of good health. Even though the good health status model
that emerged from this study is good, the low explanatory power indicates that young adults are
unique and further study is needed on this group in order to better understand those factors that
account for their good health. Furthermore, this work revealed that as people age, the social
determinants of health of the population are more in keeping with those of the elderly than at
younger ages. Hence, the social determinants identified by Grossman [9], Smith and Kington [10]
and purported by Abel-Smith [11] as well as the WHO [27] and affiliated researchers [28-32] are
more for the elderly population than the population across the life course.
Conclusions
There are disparities in the social determinants of health across the life course, which emerged
from the current findings. The findings are far reaching and can be used to aid policy formulation
and how we examine social determinants of health. Another issue which must be researched is
whether there are disparities in social determinants of health based on the conceptualization and
measurement of health status (using self-reported health, and health conditions).
Disclosures
The author reports no conflict of interest with this work.
Disclaimer
The researcher would like to note that while this study used secondary data from the Jamaica Survey of Living Conditions (JSLC), none of the errors in this paper should be ascribed to the Planning Institute of Jamaica (PIOJ) and/or the Statistical Institute of Jamaica (STATIN), but to the researcher.
186
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.
187
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Table 7.1: 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
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Table 7.2: 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
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Table 7.3: 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
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Table 7.4: 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
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Chapter 8
Sociomedical Public Health in Jamaica
Paul Andrew Bourne
An extensive review of health and health care-seeking behaviour studies revealed that studies that have examined health care-seeking behaviour and/or health status have used a piecemeal approach by either investigating health or health care-seeking behaviour. The current research seeks to examine (1) demographic characteristics of health care-seekers; (2) sociomedical characteristics of health status; (3) factors that account for health status; (4) factors that explain health care-seeking behaviour and (5) characteristics of those who reported having been diagnosed with particular health conditions. The current study used a sample of 6,783 respondents. The survey was drawn using stratified random sampling. An administered questionnaire was used to collect the data, which were stored and analyzed using SPSS for Windows 16.0 (SPSS Inc; Chicago, IL, USA). Logistic regressions were used to established (1) health status and (2) health care-seeking behaviour model. Two-thirds of the variability in health status was accounted for medical factors such as self-reported illness and length of illness compared to one-third by social conditions. Four variables emerged as statistically significant correlates of self-reported health care-seeking behaviour: self-reported illness, (OR = 358.31, 95% CI = 233.31, 550.30); health status, (OR = 0.46, 95% CI = 0.31, 0.67); health insurance coverage, (OR = 1.74, 95% CI = 1.26, 2.40); age, (OR = 1.01, 95% CI = 1.00, 1.01); and per capita consumption, (OR = 1.00, 95% CI = 1.00, 1.00). The problems which must be addressed by public health policy makers are how to address the high percent of Jamaicans who are current diagnosed with chronic illness (i.e. 43 %) as well the fact that even children are now diagnosed with diabetes mellitus, and use the social conditions to improve health and quality of life of Jamaica.
Introduction
The discipline of public health unlike medicine relies on individuals’ perceptions, beliefs,
customs, idiosyncrasies, culture and practices in order to improve health and quality of life and
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not merely an understanding the aetiology of diseases. Public health is, therefore, left with the
arduous task of comprehending the human experiences and practices, and using them to enhance,
modify and change peoples’ unhealthy behavioural lifestyle. Although Albert [1] opined that
public health can improve health and quality of life of older people, this also extends to all
peoples. In 2005, the Pan-American Journal of Public Health had an exclusive issue which
examined health, well-being, ageing, and proposed a framework for public health action. [2]
Public health can only enhance health and quality of life if it understands the people it serves,
and this denotes that its programmes will only be effective if they are supported by sociomedical
research (including epidemiologic inquiry) on national and sub-national populations.
While peoples’ behaviours share some general similarities across geopolitical boundaries,
a case can also be made equally about the dissimilarities, inequalities and socio-economic
differences in and among people within the same nation. Those similarities and differences are
responsible for the thrust to study and document information on particular phenomena in order to
effectively implement public health programmes that will address the weaknesses, inequalities,
deficiencies and challenges of people. It is for this reason why much information have been
collected and documented on chronic diseases, mortality, disability and health care cost [3-5] as
these pose a challenge to the healthy life expectancy of humans.
The present body of knowledge on mortality, morbidities and disability in the world [3-10],
and in particular the Caribbean, owes much too continuous biomedical research. But by simply
understanding the aetiology of diseases does not mean that technology and medicine can
eradicate the presence of diseases in humans, without an understanding of the social aspects of
the targeted group. Peoples’ beliefs, customs, perception and biases pose a challenge to public
196
health from attaining its mandate because beliefs guide practices.[11] Within the context that
humans’ perspective is important in science and public health, without an understanding of their
image on things, it will be impossible for medicine and the natural sciences to effectively address
medical conditions that are deemed public health problems.
Population health and population health in transition is each a function of social,
environment, psychological and biomedical conditions, and not only disease composition and
history. It is for this very rationale why public health must rely on sociomedical research and
good quality data. [12-14] Hence, this is a justification for researchers’ continuous mode of
investigation of phenomena in order to understand issues experienced by humans. The Caribbean
is no different from the rest of the world in this regards, and this provide some explanation why
Caribbean scholars, the Planning Institute of Jamaica (PIOJ) and the Statistical Institute of
Jamaica (STATIN) continue to embark on social research which include health, lifestyle
practices and data quality [13-20] in order to aid public health practitioners to effectively
understand phenomena and address changes in peoples’ behaviour.
Pappaioanou et al. [12] forwarded a perspective that the capacity of evidence-based public
health must be strengthened in developing countries in order to identified priority health
problems, respond to public health crises, implement effect strategies and evaluate cost effective
interventions. This therefore justifies PIOJ and STATIN, Wilks et al and Bourne’s continuous
examination of self-reported health, lifestyle of people of Caribbean people in order to set the
platform of public health programmes. Books have been dedicated to ‘Health issues in the
Caribbean,’ ‘Equity and Health’, and ‘Investment in Health’ in Latin American and the
Caribbean [21-24], but none of those text or other studies in the region, and in particular Jamaica,
197
have examined in a single research factors that explain health status and health care seeking
behaviour as well as health conditions and the disparities by socioeconomic conditions. An
extensive review of health and health care-seeking behaviour revealed that studies that have
examined health care-seeking behaviour or health status have used a piecemeal approach by
either investigating health, health care-seeking behaviour [25-36] or health conditions. The current
research bridge the gap by examining (1) demographic characteristics of health care-seekers; (2)
sociomedical characteristics of health status; (3) factors that account for health status; (4) health
conditions; (5) factors that explain health care-seeking behaviour and (6) characteristics of those
who reported having been diagnosed with particular health conditions.
Materials and methods Method
The current study used a sample of 6,783 respondents. The sample was drawn from a large
nationally representative cross-sectional survey of 6,783 Jamaicans. [37] The survey was drawn
using stratified random sampling. This design was a two-stage stratified random sampling design
where there was a Primary Sampling Unit (PSU) and a selection of dwellings from the primary
units. The PSU is an Enumeration District (ED), which constitutes of a minimum of 100
dwellings in rural areas and 150 in urban areas. An ED is an independent geographic unit that
shares a common boundary. This means that the country was grouped into a strata of equal size
based on dwellings (EDs). Pursuant to the PSUs, a listing of all the dwellings was made, and
this became the sampling frame from which a Master Sample of dwellings was compiled, which
in turn provided the sampling frame for the labour force. One third of the 2007 Labour Force
Survey (i.e. LFS) was selected for the survey.
198
This study used JSLC 2007 which was conducted by the Statistical Institute of Jamaica
(STATIN) and the Planning Institute of Jamaica (PIOJ) between May and August 2007. The
researchers chose this survey based on the fact that it is the latest survey on the national
population and that it has data on self-rated health status of Jamaicans. An administered
questionnaire was used to collect the data, which were stored and analyzed using SPSS for
Windows 16.0 (SPSS Inc; Chicago, IL, USA). The questionnaire was modelled from the World
Bank’s Living Standards Measurement Study (LSMS) household survey. There are some
modifications to the LSMS, as JSLC is more focused on policy impacts. The questionnaire
covered areas such as socio-demographic, economic and health variables. The non-response rate
for the survey was 26.2%.
Descriptive statistics such as mean, standard deviation (SD), frequency and percentage
were used to analyze the socio-demographic characteristics of the sample. Chi-square was used
to examine the association between non-metric variables, and an Analysis of Variance
(ANOVA) and independent sample t-test were used to examine the relationships between metric
and non-dichotomous categorical variables. Logistic regression examined the relationship
between the dependent variable and some predisposed independent (explanatory) variables,
because the dependent variable was a binary one (self-reported health status: 1 if reported good
health status and 0 if poor health).
The results were presented using unstandardized B-coefficients, Wald statistics, Odds
ratio and confidence interval (95% CI). The predictive power of the model was tested using the
Omnibus Test of Model and Hosmer & Lemeshow [38] were used to examine the goodness of fit
of the model. The correlation matrix was examined in order to ascertain whether autocorrelation
199
(or multicollinearity) existed between variables. Based on Cohen & Holliday [39] correlation can
be low (weak) - from 0 to 0.39; moderate – 0.4-0.69, and strong – 0.7-1.0. This was used to
exclude (or allow) a variable in the model. The correlation matrix was examined in order to
ascertain if autocorrelation or collinearity existed between variables. Where collinearity existed
(r > 0.7), variables were entered independently into the model to help determine which one must
retained during the final model construction (the decision was based on the variable’s
contribution to the predictive power of the model and the goodness of fit) [40].
Wald statistics were used to determine the magnitude (or contribution) of each statistically
significant variable in comparison with the others, and the Odds Ratio (OR) for the interpreting
of each significant variable.
Multivariate regression framework [35,41] was utilized to assess the relative importance of
various demographic, socio-economic characteristics, physical environment and psychological
characteristics, in determining the health status of Jamaicans; and this has also been employed
outside of Jamaica. [33,34,36] This approach allowed for the analysis of a number of variables
simultaneously. Secondly, the dependent variable is a binary dichotomous one and this
statistical technique has been utilized in the past to do similar studies. Having identified the
determinants of health status from previous studies, using logistic regression techniques; final
models were built for Jamaicans as well as for each of the geographical sub-regions (rural, peri-
urban and urban areas) and sex of respondents using only those predictors that independently
predict the outcome. A p-value of 0.05 was used for all tests of significance.
Measure Age is a continuous variable which is the number of years alive since birth (using last birthday)
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Age group is a non-binary measure: children (ages less than 15 years); young adults (ages 15 to
30 years); other-aged adults (ages 31 to 59 years); young elderly (ages 60 to 74 years); old
elderly (ages 75 to 84 years) and oldest elderly (ages 85 years and older).
Self-reported illness (or self-reported dysfunction): The question was asked: “Have you had an
illness such as influenza, asthma et cetera in the past 4-week?”
Health conditions (i.e. parent-reported illness or parent-reported dysfunction): The question was
asked: “Is this a diagnosed recurring illness?” The answering options are: Yes, Influenza; Yes,
Diarrhoea; Yes, Asthma; Yes, Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No.
Self-rated health status: “How is your health in general?” And the options were: Very Good;
Good; Fair; Poor and Very Poor.
Medical care-seeking behaviour was taken from the question ‘Has a health care practitioner,
healer or pharmacist been visited in the last 4 weeks?’ with there being two options: Yes or No.
Self-rated health status: “How is your health in general?” And the options were very good; good;
fair; poor and very poor. For this study the construct was categorized into 3 groups – (i) good;
(ii) fair, and (iii) poor. A binary variable was later created from this variable (1=good and fair
0=otherwise).
Social hierarchy: This variable was measured based on income quintile: The upper classes were
those in the wealthy quintiles (quintiles 4 and 5); middle class was quintile 3 and poor those in
lower quintiles (quintiles 1 and 2).
Results
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Table 8.1 presents information on the demographic characteristic of the sample by area of
residence. The sample was 6 782 respondents: 48.7% males and 51.3% females. Based on Table
8.1, 34.2% of urban residents were in the wealthiest 20% compared to 24.1% of those in the peri-
urban and 10.4% in rural areas. On the other hand, poverty was substantially a rural phenomenon
(29.8%) compared to peri-urban (11.5%) and urban areas (9.3%). There is a significant statistical
association between medication purchased by respondents and area of residence. Twenty percent
of respondents who attended public health-care facilities purchased medication, while 81.4% of
those who visited private health-care facilities purchased medication. Twenty-five percent
(25.2%) of those in rural area who attended public health-care facilities purchased medication
compared to 13.6% of those in peri-urban and 14.2% of those in urban areas. Almost ninety-one
percent (90.5%) of those in urban area who visited private health-care facilities purchased
medication compared to 86.8% of peri-urban and 74.7% of rural residents. Rural residents
reported the most illness (16.6%) compared to urban (13.4%) and peri-urban respondents
(12.9%).
Eighty percent (82.2%) of the respondents indicated at least good health status (with
37.0% said excellent health status) compared to 0.8% who claimed very poor health status. One
percent (1.1%) of the sample was injured in the 4-week period of the survey, while 14.9% was
reported an illness and 43.2% indicated a chronic illness (i.e. Diabetes mellitus, 13.8%;
hypertension, 23.1%; and arthritis, 6.2%) compared to 30.4% reported acute illness (influenza,
16.7%; diarrhoea, 3.0%; and asthma, 10.7%). Almost 66% (i.e. 65.5%) of the sample visited a
health care practitioner (i.e. doctor, nurse, healer, pharmacist) in the 4-week period of the survey;
29.6% was heads of households; married, 23.3%; never married, 69.2%; divorced, 1.7%;
separated, 0.9%; widowed, 4.9%; and the median number of person per room was 4 (range = 1,
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17). The median annual income was USD 7 050.66 (range = USD 261.56, USD 6 523.66) and
median per capita consumption was USD 1 523.88 (range = USD 179.57, USD 20 325.55).
Table 8.2 highlights information on sociomedical characteristics of sample by sex of
respondents. Males were more likely to be married (24.3%) than females (22.4%), and the latter
was more likely to be widowed (7.3%) than the former (2.3%). Females reported more illnesses
(17.5%) than males (12.1%), and they were more likely to have hypertension and diabetes
mellitus than males. However, there was no significant statistical relationship between health
care utilization and sex of respondents: males, 62.3% and females, 67.6% (χ2 = 3.004, P <
0.083).
There was no significant statistical relationship between those who purchased medication
and their sex. Twenty percent (20.2%) of females who visited public health care facilities
purchased medication compared to 19.7% of males (χ2 = 0.023, P = 0.879). Eight-one percent of
females who attended private health-care facilities purchased medication compared to 81.9% of
males (χ2 = 0.100, P = 0.752). Males were more likely to be household heads (32.7%) than
females (26.7%) - χ2 = 29.207, P < 0.0001.
When the significant statistical association between marital status and social standing was
disaggregated by sex, this was explained by females (χ2 = 54.48, P = 0.0001) and not males (χ2 =
24.77, P = 0.074). Almost 49% (48.8%) of divorced females were in the wealthiest 20%
compared to 27.0% of those who are married, 21.8% of widowed; 20.0% of separated as well as
those who were never married respondents.
Table 8.3 shows sociomedical characteristics of sample by marital status. Divorced
respondents were most likely to be in the wealthiest 20% (44.2%) compared to separated
respondents (31.7%); married, 28.3%; never married, 21.8% and widowed respondents, 21.4%
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(χ2 = 67.45, P < 0.0001). Forty percent of the widowed respondents indicated an illness
compared to those who are separated, 29.3%; divorced, 28.6%; married, 24.6% and never
married, 10.8%.
A significant statistical association was found between area of residents and those who
attend public hospitals (χ2 = 7.94, P < 0.019), private hospitals (χ2 = 30.30, P < 0.0001), and
private health care centres (χ2 = 10.19, P < 0.006), while no between area of residents and public
health care centres (χ2 = 4.23, P < 0.13). Rural residents were most likely to visit public hospitals
(37.2%) compared to urban (27.7%) and peri-urban residents (25.6%). With respect to private
hospital utilization, rural residents recorded the least visits (2.3%) than peri-urban (6.8%) and
urban residents (15.0%). Similarly, rural dwellers recorded the least utilization of private health
care centres (46.2%) than urban (52.7%) and peri-urban residents (63.2%).
Rural dwellers recorded the longest time spent in illness (56.6 days ± 169.3) compared to
urban dwellers (9.6 days ± 17.9) and peri-urban residents (53.3 days ± 154.4) – F-statistic = 9.58,
P < 0.0001.
There is a significant statistical association between area of residence and educational
levels (χ2=78.02, P < 0.0001). Sixty-eight percent of those with tertiary level education dwelled
in urban areas compared to 16% of those in peri-urban and 20.6% in rural areas.
No significant statistical association was found between social class and self-reported
illness (χ2=3.28, P < 0.512) as well as between self-reported diagnosed health conditions and
social class (χ2=28.6, P < 0.236).
Figure 8.1 highlights information on self-reported diagnosed illness by marital status of
respondents disaggregated by sex of respondents. A significant statistical association was found
between self-reported diagnosed illness by marital status even when the data was disaggregated
204
by sex (male – χ2 = 52.43, P < 0.001; females - χ2 = 56.2, P < 0.0001), but the relationship was
strong for males (contingency coefficient = 0.425) than females (contingency coefficient =
0.339).
A significant statistical relationship existed between self-reported diagnosed health
conditions and age group (χ2 = 436.8, P < 0.0001). Younger people were more likely to have
acute conditions and older people are likely to have chronic conditions (Figure 8.2). Despite this
fact 1.4% of Jamaica children have diabetes mellitus.
Table 8.5 examines factors that are correlated with self-evaluated health status of
Jamaicans. Of the 13 variables that were tested in the model, 9 emerged as statistically correlated
with health status and that the model was a good fit for the data (Hosmer and Lemeshow
goodness of fit χ2=18.49 (8), P = 0.78; -2LL = 3321.07). The model (i.e. 9 significant correlates
of self-evaluated health status) accounted for 40.3% of the variability in self-reported health
status: 84.8% of the data were correctly classified, 95.3% of those in good or excellent self-
evaluated health status and 47.5% of those in fair to poor health status. Two-thirds of the
variability in health status was accounted for medical factors such as self-reported illness and
length of illness compared to one-third by social factors (i.e. age, sex, per capita consumption,
health care-seeking behaviour, area of residence, marital status and social class). Of the social
factors, consumption accounted for less than 1% of the variance in self-evaluated health status
(i.e. 0.5%) and social class accounted for 0.1%.
Table 8.6 presents information on the self-reported health care-seeking behaviour of
respondents by explanatory variables. Four variables accounted for 71.1% of the variability in
self-reported health care-seeking behaviour. Using logistic regression analyses, 4 variables
emerged as statistically significant correlates of self-reported health care-seeking behaviour: self-
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reported illness, (OR = 358.31, 95% CI = 233.31, 550.30); health status, (OR = 0.46, 95% CI =
0.31, 0.67); health insurance coverage, (OR = 1.74, 95% CI = 1.26, 2.40); age, (OR = 1.01, 95%
CI = 1.00, 1.01); and per capita consumption, (OR = 1.00, 95% CI = 1.00, 1.00). From the
correlation matrix, there is a moderate statistical correlation between self-reported health illness
and self-evaluated health status (r = 0.64).
Discussion
The current study revealed that 29.8% of rural residents were in the poorest 20% (i.e. poorest
income quintile) in Jamaica compared to 11.5% of peri-urban and 9.3% of urban residents. Rural
poverty lies between 2.5 to 3.3 times more than peri-urban and urban poverty, and 1.3 times
more people report illness in those areas than in peri-urban or urban areas. Rural residents are not
only rural and report more illness than other residents; they are 1.9 times less likely to have
health insurance coverage than urban residents and 1.5 times less likely than peri-urban dwellers.
They are also more likely to utilize public hospitals and spent more time nursing in illness than
other residents, and also had the least consumption per person. However, their self-evaluated
health status was the same as urban dwellers but less than that for peri-urban settlers, and there
was no significant statistical correlation among health care-seekers based on their area of
residences. Concurrently, males were more likely to record greater moderate-to-excellent health
status than females; more likely to be married; less likely to be widowed; less likely to report an
illness; less likely to have diabetes mellitus and hypertension; more likely to have asthma,
arthritis; unspecified conditions and influenza than females. Irrespective of the more female than
males reporting having been diagnosed with chronic conditions, there was no significant
correlation between health care seeking behaviour and sex of respondents. The findings
continued as those in the wealthiest 20% were more likely to be divorced people; but those who
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were classified as divorced, separated and widowed were less likely to be healthier than those
who were never married. Those who were never married reported the lowest percent of having
had an illness in the 4-week period of the survey. Two-thirds of the variability was accounted
for medical factors such as self-reported illness and length of illness compared to one-third by
social factors (i.e. age, sex, per capita consumption, health care-seeking behaviour, area of
residence, marital status and social class). Four variables accounted for 71.1% of the variability
in self-reported health care-seeking behaviour, and self-reported illness accounted for 70% of the
explanatory power. People who reported moderate-to-excellent health status were 55% less
likely to seek health care and those who reported an illness were 358.3 times more likely to seek
health care. Less than one-half percent of the variance in health care-seeking behaviour can be
explained by health insurance coverage, and that an individual who indicated that he/she is ill is
81% less likely to stated moderate-to-excellent health status.
Public health is influenced by both the continuous revelations in research as well as
science of people’s behaviour in order to effectively plan behaviour modifications. The
behaviour change required for developing countries must be tailored within the context of the
research findings [42], and cannot be left to the dictates of studies on developed nations. Apart of
the justification for studies on a particular geo-political boundary are based on inequalities,
economic and health disparities among and between people within a nation, and this is
particularly in reference to Latin America and the Caribbean.[43-45] With public health taking
must of its cue from both medical and social sciences, there is obviously a rationale for the social
determinants in the study of public health. The some time ago embarked a thrust of examining
social determinants in understanding health, health conditions and health treatment. In recent
years the World Health Organization (WHO) has increasingly drawn attention to the importance
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of the relationship between health and social conditions in determining the health of individuals
and populations [46]. The social determinants (non-biological factors), produce inequalities in
health and need to be considered in health development. Addressing social determinants and
health policy now includes the basis for political action both nationally and internationally.[47-51]
The findings of the present work highlights and concur with the literature about the
dominant of the biomedical conditions in health. The findings revealed that two-thirds of
variability in health status can be accounted for by self-reported illness and length of illness.
Although this fact speaks to the dominance of biomedical conditions, it does also recognize the
importance of social determinants in health. A study by Hambleton et al. [36] on elderly
Barbadians found that as much as 88% of the variability in self-reported health status could be
explained by current diseases. While the current work has a lower percent of explanation model
which is due to the sample that include young people, it highlights a rationale for the ease of use
of the biomedical conditions and in the process sideline the need for the social determinants in
health, health utilisation and health treatment. Clearly illnesses are fundamental in the health
discourse, and it is also critical in the understanding health care-seeking behaviour of people.
In this research, a respondent who is ill is 358.3 times more likely to seek care. This
highlights not only the dominance of illness to health care-seeking, but the image of health that is
held by Jamaica and how this influence outcome. This is supported by the finding that revealed
that people who self-reported their health status to be moderate-to-good were 54% less likely to
seek health care. Embedded in such a finding is structure of the health care delivery in Jamaica,
which dates back to 130ce to 200ce in Ancient Rome, when health and health care was in
keeping with traditional biomedical model that views the exposure to specific pathogen as the
cause of diseases in organisms. Within this image of health was people’s perception of what
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constituted a need to demand health care services which were illness and this fashioned the
health care industry at the time. Clearly the image of health and health care delivery in Jamaica is
framed around the aetiology of diseases and the not the multidimensional approach to the image
of health which is in keeping with the broad definition offered by the WHO in 1948. [52] The
overemphasis on illness, disability and severity of illness in framing people’s willing to seek
medical care is not atypical to Jamaica as this was found in other societies.[26-31, 53]
Money is well established as being positively correlated with health status. [54] Money
does matter in access to resources, opportunities, choices and quality of care. The current
findings found that people whose consumption expenditure are higher have a greater health
status, which concurs with the literature that money does matter for health. Money does not only
matter for health, it also is important for health seeking behaviour. Despite the positive of
money, those who are most likely to be in the wealthiest 20% had lower health status. This paper
found that divorced, separated and widowed Jamaicans were more likely to have a lower health
status than those who were never married and this was also the case for the upper class with
reference to the lower class. Although the finding does indicate that divorced and separated
respondents were wealthier than other marital statuses, this is a negative for their health status.
Also embedded in this finding is the fact that significant statistical association between social
standing and marital status was among females. This denotes that wealthiest females were most
likely to be divorced which offers an explanation that money can buy health, psychological
comfort, happiness and these would have been the case for these females in the study. Divorced
therefore provides females with more economic resources, but this does not compensate for the
lost of the spouse, and further removes the benefits of the economic gains from health. In
addition divorced females recorded the highest percent of diabetes mellitus among all
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respondents followed by separated women. Hypertension was substantially more among separate
males and widowed females, suggesting that separation from spouse becomes a disbenefit for
Jamaica and therefore account for the unhealthy life style practices which were not identified in
never married and/or married respondents.
There are obvious benefits from having money as this was evident in rural residents
having the least money, the most self-reported illness, and the highest public hospital utilisation.
Despite the income inequalities and economic disparities between rural and other residents in
Jamaica, the former residents are able to experience a self-reported health status which is the
same of those in the affluent urban areas. This means that there are some basic standard of living
enjoyed by rural Jamaica which cushioned the wide income inequalities that exist between them
and urban dwellers. Apart of what creates the cushion for rural residents is the quality of primary
health care facilities offered to them by public hospitals in the country. With most rural residents
utilizing public hospitals, public health offerings have played a critical role in removing some of
the health inequalities that could have been owing to income inequalities. Another factor which
mitigates the negatives of income inequalities among the different area of residents is the
communal settings in rural areas, and how this aids in providing socio-economic support among
residents.
Poverty in rural areas is therefore shared by the wider community as people seek to assist
others in need, vulnerable, less fortunate and economic challenged in life. It is this communal
culture that sees sharing of food, finances and social institutions that helps to retard the negative
of poverty from rural residents. The poor are classified as in the lower socioeconomic status. It is
empirically well established in research that they are less likely to be healthy than those in the
higher socioeconomic groups [55, 56], which is not the case in Jamaica. They have a greater self-
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evaluated health status than those in the higher socioeconomic groups. Concurrent this research
does not concur with the literature that poverty is more common among the chronically ill [57] or
that the poor reported having more illness than the higher socioeconomic class. This was also
highlighted in the fact that rural residents were substantially more likely to poor, but shared the
same health status as those in urban areas. However what emerged from the current findings is
that peri-urban residents had a greater health status than other residents, and this could be due to
the fact that more of them were in the never married group who had the lowest rate of illness as
well as chronic illnesses. Residents in peri-urban area has greater income than those who dwelled
in rural areas but less than those in urban areas which indicates that some money is important in
health, but that is not responsible for greater health. It can be extrapolated from this finding that
peri-urban residents are more involved in healthier lifestyle choices than residents in other
geographic areas, which is accounting for their health more than money and higher formal
education.
This study uncovers a paradox between subjective health and objective health. The
present work found that males reported less illness, had greater self-evaluated health status, but
using statistics on life expectancy in Jamaica females outlive males between 4 to 7 years. [58, 59]
In 1880-1882, Jamaica females outlive males by 2.9 years and in 2002-2004, this was increased
to 5.8 years.[20] For 2007, statistics published by the WHO revealed that this difference was 5
year. [59] This questions the validity of subjective health data in the evaluation of health, and begs
the question “How valid is subjective health data in Jamaica?” A study by Bourne [17] found a
strong statistical correlation between life expectancy at birth for the Jamaicans and self-reported
illness (r = - 0.731); and this association was weaker females (r = - 0.683) than males (r = -
0.796). Hence, there is validity in the use of subjective index to measure health. This suggests
211
that the afore-mentioned disparity in subjective and objective indexes to measure health is not a
paradox, but an issue which needs further examination.
The inverse relationship between health and age is long established in research literature
[1, 34, 35] as well as the shift from acute to chronic conditions in old ages. [60, 61] Morrison [60] in an
article entitled ‘Diabetes and hypertension: Twin Trouble’ forwarded that diabetes mellitus and
hypertension have now become two problems for Jamaicans and in the wider Caribbean.
Callender [61] concurred with Morrison [60] that there is a positive association between diabetic
and hypertensive patients (i.e. 50% of individuals with diabetes had a history of hypertension),
and that this is a public health problem in the Caribbean. This study narrows the chronic
conditions to older people, but also noted that 1.4% of children in Jamaica had diabetes mellitus,
3.5% of young adults and 16.4% of other adults. If Callender’s are true then in a short while one-
half of those afore-mentioned individuals will have dual chronic conditions. A recently
conducted study by Wilks et al. [13] provide some historical background to chronic illnesses in
Jamaica as they found that 31% of Jamaicans indicated that their parent and/or grand parents had
diabetes mellitus; 47% said that hypertension, 17.1% strokes and 15.7% said their parents and/or
grandparents had cancer. Diabetes mellitus and hypertension therefore continue to be silent
killers in Jamaica, and their history dates back to former generations. Public health practitioners
need to urgent begin a campaign of lifestyle practices geared towards children as there is
evidence to support healthy lifestyle practices among all groups, and in particular children, who
are frequently, omitted from healthy lifestyle programmes.
The current study highlighted that there are many inequalities (i.e. systematic, avoidable
and important difference) in health status among Jamaicans and that these need to be rectified in
order to attain the resolution of the World Health Assembly (WHA48.8).[62] Jamaica now has a
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primary health care system which is free to all, but this has still not met equity (i.e. unnecessary
and avoidable differences which are considered to be unfair and unjust) in health care throughout
the society. Free health care for all in Jamaica have not addressed issues such as exposure to
unhealthy, stressful living and working conditions; natural selection or related social mobility;
transient health advantage; gender discrimination; socioeconomic discrimination; inequitable
deployment of resources around the nation; and the organization of some health services around
the country. Inequalities and inequities in Latin America and the Caribbean have been
empirically researched by Pan American Health Organization (PAHO), and further readings can
be had by examining two of its publications [23, 24] as well as Whitehead. [63] It is clear from the
current findings that merely making primary health care free for all will not reduce many of the
public health challenges in a nation and among its people. So while Jamaica has done the former,
there are obvious signs that reaching the poor with health care does not address many other
health inequalities and inequities. Using statistics for 90 countries, the WHO [59] revealed that in
many of these nations there are health disparities and inequities between and among people
which is concurred by Global Forum for Health Research [64] and this study. This reinforces the
need for public health practitioners not to rely on national averages and information which
originates from within the health sector but on sociomedical determinants on groups and sub-
groups within the population.
Conclusion
Although biomedical conditions accounted for more of health than social determinants, the
current study highlighted the value of the social determinants in the health discourse. The social
issues in this research brought to the fray the fact that separation from one spouse influenced
health status, healthy behaviour and health conditions. It does not cease there as the image of
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health is substantially driven by illness which account for seeking (or not seeking) medical care.
In addition to the afore-mentioned issues, there is a clear public health challenge that exists in
Jamaica which is how to address the unhealthy lifestyle practices of people who have been
separated from their spouses as well as the fact that particular health conditions appear to be
associated with particular social characteristics. Another public challenge is how to change the
image of health in Jamaica from illness to wellness or wellbeing. This public health challenge
must commence with the restructuring of the health care system and its delivery which is
primary driven by the biomedical factors instead of holistic health. Increasing attention must be
placed on this reorganization as if the health care is fashioned more around curative care, then
people will use this image of health care to frame their concept of health and health demands.
Some of the disparities that emerged from the current work from the literature highlights the fact
that public health in Jamaica cannot rely on the research findings in other geo-political
boundaries to craft policies and intervention programmes as the will be ineffective in addressing
its mandate owing to the sociodemographic differences of Jamaicans. Public health therefore
must rely on research findings within it geo-political area while understanding what obtains in
other areas in order to embark on intervention programmes that will improve health and quality
of life of people. Within this context, one of the problems which must be addressed by public
health policy makers is how to address the high percent of Jamaicans who are current diagnosed
with chronic illness (i.e. 43 %) as well the fact that even children are now diagnosed with
diabetes mellitus suggesting that public health must embark on programmes that address living
longer and healthier with (and without) chronic illnesses.
In sum, the inequalities and/or inequities which emerged in this study are social issues
which explain medical conditions and it is this merger of medicine and sociology that is needed
214
to effectively improve the health and quality of life of people. Concurrently, policy makers need
to change the concept of health of Jamaicans and this can be enhanced by (1) leisure and exercise
facilities in communities as well as in health care facilities; (2) reduce the inequalities in working
and living conditions of the vulnerable and disadvantaged groups; (3) address the health-
damaging behaviour of some social groups; (4) administrative reform of professionals in regards
to the dissemination of information to lay people; (5) examine, monitor and evaluate the
implication of health policies on the socioeconomic groups within the society; (6) pollution
control caps; (7) assist in food hygiene, nutrition, sanitation and health education moreso in times
of economic hardships; and (8) commence a databank that collects data on the cultural and
behavioural practices of people in order to effectively formulate health policies. In addition to
the afore-mentioned issues, while the current study is not a representation of the Caribbean,
based on the Pan American Health Organization research on Latin America and the Caribbean
investment in health and health care modernization have not reduced the inequalities and
inequities in nations among different social groups within those nation [23, 24], which is what
emerged from the current work. Clearly, health inequalities and inequities in Latin America and
the Caribbean are very much the same, and any public health intervention programmes that do
not address this reality will be ineffective in aiding health and quality of life of its people. Health
protection therefore must be embedded in science of human behaviour (i.e. social determinants)
as well as an understanding of the pathogenesis of diseases (i.e. sociomedical public health).
Conflict of interest
The author has no conflict of interest to report
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215
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Figure 8.1. Self-reported diagnosed illness by marital status for sex
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Figure 8.2. Self-reported diagnosed illness by age group
220
221
Table 8.1. Demographic characteristic by area of residence Characteristic
Area of residence P Urban Semi-urban Rural
Social standing n (%) n (%) n (%) χ2 = 881.51 P < 0.0001
Poorest 20% 186 (9.3) 167 (11.5) 990 (29.8) Poor 243 (12.1) 273 (18.7) 838 (25.2) Middle 389 (19.4) 312 (21.4) 650 (19.6) Wealthy 499 (24.9) 354 (24.3) 499 (15.0) Wealthiest 20% 685 (34.2) 352 (24.1) 345 (10.4) Self-reported Injury χ2 = 2.25
P = 0.325 Yes 16 (0.8) 16 (1.1) 41 (1.3) No 1933 (99.2) 1408 (98.9) 3186 (98.7) Sex χ2 = 3.67
P = 0.16 Male 943 (47.1) 706 (48.4) 1954 (49.8) Female 1059 (52.9) 752 (51.6) 1668 (50.2) Marital status χ2 = 15.46
P = 0.05 Married 326 (23.3) 217 (21.6) 513 (24.1) Never married 972 (69.3) 702 (70.0) 1462 (68.7) Divorced 32 (3.2) 23 (2.3) 22 (1.0) Separated 9 (0.6) 12 (1.2) 20 (0.9) Widowed 63 (4.5) 49 (4.9) 112 (5.3) Self-evaluated illness χ2 = 15.43
P < 0.0001 Yes 261 (13.4) 183 (12.9) 536 (16.6) No 1690 (86.6) 1231 (87.1) 2688 (83.4) Self-reported diagnosed illness
χ2 = 29.59 P = 0.003
Influenza 25 (10.9) 44 (26.0) 80 (16.3) Diarrhoea 4 (1.9) 4 (2.4) 19 (3.9) Asthma 33 (14.4) 11 (6.5) 51 (10.4) Diabetes mellitus 32 (14.0) 27 (16.0) 64 (13.0) Hypertension 47 (20.5) 41 (24.3) 118 (24.0) Arthritis 16 (7.0) 10 (5.9) 30 (6.1) Other 72 (31.4) 32 (18.9) 130 (26.4) Health insurance coverage χ2 = 138.80
P < 0.0001 Yes 542 (28.0) 310 (22.1) 462 (14.5) No 1397 (72.0) 1091 (77.9) 2715 (85.5) Health care-seeking behaviour
χ2 = 5.21 P = 0.07
Yes 190 (71.2) 119 (63.6) 349 (63.3) No 77 (28.8) 68 (36.4) 202 (36.7) Consumption per capita (in USD)
2632.57±2040.89 2223.76±1753.22 1499.18±1095.70 F =344.31, P < 0.0001
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Table 8.2. Sociomedical characteristic by sex of respondents Characteristic
Sex P Male Female
Consumption per capita (in USD)1 2018.20±1712.76 1962.30±1592.01 t =1.39, P = 0.16 Total Expenditure (on food) (in USD)1 3488.32±2187.43 3616.80±2201.34 t = -2.41, P = 0.016 No. of days in public health care facilities
6.6 ±6.2 6.0±4.7 t = 0.35, P = 0.73
No. of days in private health care facilities
5±0 1±0
Medical expenditure - public (in USD)1
3.67±17.51 8.36±67.69 t = -1.02, P = 0.31
Medical expenditure – private (in USD)1
14.05±21.12 14.15±30.58 t = -0.044, P = 0.97
Self-reported diagnosed illness χ2 = 30.25, P < 0.0001 Influenza 69 (20.2) 80 (14.6) Diarrhoea 11 (3.2) 16 (2.9) Asthma 47 (13.7) 48 (8.8) Diabetes mellitus 31 (9.1) 92 (16.8) Hypertension 58 (17.0) 148 (27.0) Arthritis 24 (7.0) 32 (5.8) Other 102 (29.8) 132 (24.1) Social standing χ2 = 4.35, P = 0.361 Poorest 20% 671 (20.3) 672 (19.3) Poor 640 (19.4) 714 (20.5) Middle 636 (19.3) 715 (20.6) Wealthy 667 (20.2) 685 (19.7) Wealthiest 20% 689 (20.9) 693 (19.9) Self-reported Injury χ2 = 1.68, P = 0.196 Yes 41 (1.3) 32 (0.9) No 3169 (98.7) 3358 (99.1) Marital status χ2 = 61.94, P < 0.0001 Married 522 (24.3) 534 (22.4) Never married 1528 (71.1) 1608 (67.4) Divorced 34 (1.6) 43 (1.8) Separated 16 (0.7) 25 (1.0) Widowed 50 (2.3) 174 (7.3) Self-evaluated illness χ2 = 38.12, P < 0.0001 Yes 388 (12.1) 592 (17.5) No 2820 (87.9) 2789 (82.5) Health care-seeking behaviour χ2 = 3.004, P < 0.083 Yes 253 (62.3) 405 (67.6) No 153 (37.7) 194 (32.4) USD 1.00 = Ja $80.47 at the time of the survey
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Table 8.3. Sociomedical characteristic by marital status of respondents Characteristic
Marital status P Married Never
married Divorced Separated Widowed
Social standing n (%) n (%) n (%) χ2 = 67.45, P < 0.0001 Poorest 20% 153 (14.5) 564 (18.0) 4 (5.2) 9 (22.0) 43 (19.2) Poor 181 (17.1) 928 (20.0) 6 (7.8) 3 (7.3) 36 (16.1) Middle 185 (17.5) 633 (20.2) 18 (23.4) 10 (24.4) 58 (25.9) Wealthy 238 (22.5) 626 (20.0) 15 (19.5) 6 (14.6) 39 (17.4) Wealthiest 20% 299 (28.3) 685 (21.8) 34 (44.2) 13 (31.7) 48 (21.4) Self-reported Injury χ2 = 2.16, P = 0.71 Yes 16 (1.5) 37 (1.2) 0 (0.0) 1 (2.4) 3 (1.3) No 1040 (98.5) 3091 (98.8) 77 (100.0) 40 (97.6) 220 (98.7) Self-evaluated illness χ2 = 233.86, P < 0.0001 Yes 259 (24.6) 338 (10.8) 22 (28.6) 12 (29.3) 90 (40.4) No 795 (75.4) 2789 (89.2) 55 (71.4) 29 (70.7) 133 (59.6) Self-reported diagnosed illness χ2 = 75.36, P < 0.0001 Influenza 18 (7.4) 28 (9.2) 1 (4.8) 1 (8.3) 4 (4.5) Diarrhoea 2 (0.8) 7 (2.3) 1 (4.8) 1 (8.3) 3 (3.4) Asthma 10 (4.1) 31 (10.2) 2 (9.5) 0 (0.0) 1 (1.1) Diabetes mellitus 48 (19.7) 39 (12.8) 10 (47.6) 4 (33.3) 19 (21.6) Hypertension 91 (37.3) 69 (22.6) 3 (14.3) 5 (41.7) 37 (42.0) Arthritis 24 (9.8) 22 (7.2) 1 (4.8) 1 (8.3) 8 (9.1) Other 51 (20.9) 109 (35.7) 3 (14.3) 0 (0.0) 16 (18.2) Health insurance coverage χ2 = 127.20, P < 0.0001 Yes 357 (34.1) 552 (17.9) 27 (35.1) 8 (19.5) 60 (26.9) No 691 (65.9) 2528 (82.1) 50 (64.9) 33 (80.5) 163 (73.1) Health care-seeking behaviour χ2 = 233.86, P < 0.0001 Yes 173 (65.3) 239 (68.1) 15 (68.2) 8 (61.5) 90 (40.4) No 92 (34.7) 112 (31.9) 7 (31.8) 5 (38.5) 133 (59.6) Head of household χ2 = 258.12, P < 0.0001 Yes 564 (53.4) 1163 (37.1) 57 (74.0) 27 (65.9) 181 (80.8) No 492 (46.6) 1973 (62.9) 20 (26.0) 14 (34.1) 43 (19.2)
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Table 8.5. Stepwise logistic regression: Self-evaluated health status by explanatory variables Explanatory variables Coefficient
Std. Error P
Odds ratio
95.0% C.I. R2 change Lower Upper
Illness (1= yes)
-1.648
0.152
0.000
0.192
0.143
0.259
0.266
Age
-0.045
0.003
0.000
0.956
0.951
0.961
0.114
Per capita consumption
0.000
0.000
0.000
1.000
1.000
1.000
0.005
Health care-seeking behaviour
-0.720
0.178
0.000
0.487
0.343
0.690
0.004
Sex (1= male)
0.348
0.091
0.000
1.417
1.184
1.695
0.006
Upper class
-0.345
0.164
0.035
0.708
0.513
0.977
0.001
†Lower class 1.000 Peri-urban
0.340
0.114
0.003
1.405
1.125
1.756
0.002
†Rural 1.000 Length of illness
-0.003
0.001
0.004
0.997
0.995
0.999
0.003
Divorced, separated or widowed
-0.355
0.153
0.021
0.701
0.519
0.947
0.002
†Never married 1.000 Hosmer and Lemeshow goodness of fit χ2=18.49 (8), P = 0.78 Nagelkerke R2 =0.403 -2LL = 3321.07 †Reference group
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Table 8.6. Stepwise logistic regression: Self-reported health care-seeking behaviour by explanatory variables
Explanatory variable Coefficient
Std. Error P
Odds ratio
95.0% C.I. R2 change
Lower Upper Health status (1=moderate-to-excellent)
-0.787
0.191
0.000
0.455
0.313
0.662
0.005
Health insurance
0.554
0.163
0.001
1.741
1.263
2.398
0.004
Self-reported illness
5.881
0.219
0.000
358.313
233.307
550.297
0.700
Age
0.005
0.003
0.037
1.005
1.000
1.010
0.001
Per capita consumption
0.000
0.000
0.021
1.000
1.000
1.000
0.001
Hosmer and Lemeshow goodness of fit χ2=7.12 (8), P = 0.52 Nagelkerke R2 =0.711 -2LL = 1525.53 †Reference group
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Chapter 9 Modelling social determinants of self-evaluated health of poor
older people in a middle-income developing nation
Paul A Bourne
Over the last 2 decades (1988-2007), poverty in Jamaica has fallen by 67.5%, and this is within the context of a 194.7% increase in inflation for 2007 over 2006. It does not abate there, as Jamaicans are reporting more health conditions in a 4-week period (15.5% in 2007) and at the same time this corresponds to a decline in the percentage of people seeking medical care. Older people’s health status is of increasing concern, given the high rates of prostate cancer, genitourinary disorders, hypertension, diabetes mellitus and the presence of risk factors such as smoking. Yet, there is a dearth of studies on the health status of older people in the two poor quintiles. This study examined (1) the health status of those elderly Jamaicans who were in the two poor quintiles, and (2) factors that are associated with their health status. A sample of 1,149 elderly respondents, with an average age of 72.6 years (SD=8.7 years) were extracted from a total survey of 25,018 Jamaicans. The initial survey sample was selected from a stratified probability sampling frame of Jamaicans. An administered questionnaire was used to collect the data. Descriptive statistics were used to examine background information on the sample, and stepwise logistic regression was used to ascertain the factors which are associated with health status. The health status of older poor people was influenced by 6 factors, and those factors accounted for 26.6% of the variability in health status: Health insurance coverage (OR=13.90; 95% CI: 7.98-24.19), age of respondents (OR=7.98; 95% CI: 1.02-1.06), and secondary level education (OR=1.82; 95% CI: 1.35-2.45). Males are less likely to report good health status than females (OR=0.56; 95% CI: 0.42-0.75). Older people in Jamaica do not purchase health insurance coverage as a preventative measure but as a curative measure. Health insurance coverage in this study does not indicate good health but is a proxy of poor health status. The demand of the health services in Jamaica in the future must be geared towards a particular age cohort and certain health conditions, and not only to the general population, as the social determinants which give rise to inequities are not the same, even among the same age cohort.
227
1. INTRODUCTION
Factors determining the poor health status of the elderly in Jamaica can be viewed from the
perspective of a socio-medical dichotomy. Such factors include poverty (resulting in one’s
inability to access loans, quality education and health care), lifestyle (e.g. smoking, sedentary
habits, sexual and dietary practices and physical inactivity), resulting in prostate cancer,
genitourinary disorders, hypertension, diabetes mellitus and premature death. In 2005, the World
Health Organization began a thrust in examining the social determinants of health, and despite
that reality there is a lack of literature in this regard on the elderly poor people in Jamaica. These
parameters were explored in the current research by using a sample of 1,149 elderly poor
Jamaicans.
The findings of this paper reveal that the cost of medical care is positively correlated with
health conditions, and that economic constraints account for the decline in the elderly seeking
medical care. Older people in Jamaica do not purchase health insurance coverage as a
preventative measure but as a curative measure. Health insurance coverage in this study does not
indicate good health, but on the contrary, it is a proxy of poor health status. It is also noted that
income is positively correlated with a higher standard of living and life expectancy. In support of
this claim, studies have shown that life expectancy in many developing countries [1], in
particular the Caribbean (Barbados, Guadeloupe, Jamaica, Martinique, Trinidad and Tobago) has
exceeded 70 years, and they are now experiencing between 8-10% of their population living to
60+ years old. Life expectancy, which is a good indicator of the health status of a populace, is
higher in countries with high GDP per capita. This means that income is able to purchase better
quality products [2], and indirectly affects the length of years lived by people. GDP per capita is
228
used as an objective valuation of standard of living [3-12]. While a country’s GDP per capita
may be low, life expectancy is high because health care is free for the population. Despite this
fact, material living standards undoubtedly affect the health status and wellbeing of people, as
well as the level of females’ educational attainment [6] and the nutrition intake of the poor. On
the other hand, when there is economic growth, the society has more to spend on nutrition, health
care, better physical milieu, better quality food, safer sanitation and education.
Good health is, therefore, linked to economic growth, something which is established in a
plethora of studies by economists. Developing countries (a term synonymous with poverty) do
not only constitute low levels of democracy, civil unrest, corruption [13], high mortality and
crude birth rates, but one must also include nutritional deficiency [14]. The WHO in 1998 put
forward the position that 20% of the population in developing countries do not have access to
enough food to meet their basic needs and provide vital nutrients for survival.
In the Caribbean, and in particular Jamaica, poverty is typical, and many of the ills that
affect other developing nations outside of this region are the same. The poor in this society are
facing insurmountable challenges in buying the necessary health care. In 2007, between 51 and
53% of those in the poor quintiles in Jamaica sought medical care, compared to 61-68 % of those
in the middle-to-wealthiest quintiles. When those who had reported that they were ill were asked
why they had not sought medical care, 51% of those in the poorest quintile indicated that they
‘could not afford it’, with 36.7% of those in the poor quintile giving the same response, and the
percentage declines as the wealth of the person increases to the wealthiest quintile (7.7% of those
in the wealthiest quintile).
229
Over the last 2 decades (1988-2007), poverty in Jamaica has fallen by 67.5% and this is
in the context of a 194.7% increase in inflation for 2007 over 2006. Jamaicans are reporting more
health status in a 4-week period (15.5% in 2007) and at the same time this is associated with a
decline in the percentage of people seeking medical care. Older people’s health status is of
increasing concern, given the high rates of prostate cancer, genitourinary disorders, hypertension,
diabetes mellitus and the presence of risk factors such as smoking in earlier life. Yet, there is a
dearth of studies on the health status of older people in the two poor quintiles.
Works which have examined the social determinants of health have used data for the
population [2,3], but none emerged from a literature research using data for poor old people. This
study examined (1) the health status of those elderly Jamaicans who were in the two poor
quintiles, and (2) factors that are associated with their health status.
2. MATERIALS AND METHODS
2.1 Sample
A sample of 1,149 elderly respondents was extracted from a larger survey of 25,018 Jamaicans.
The sample was based on being 60+ years old, and being classified in the two poorest income
categorizations. The initial survey sample (n = 25, 018) was across the 14 parishes, and was
conducted between June and October 2002. The sample (n=25,018 or 6,976 households out of a
planned 9,656 households) was drawn using a stratified random sampling technique. This design
was a two-stage stratified random sampling design, where there was a Primary Sampling Unit
(PSU) and a selection of dwellings from the primary units. The PSU is an Enumeration District
(ED), which constitutes of a minimum of 100 dwellings in rural areas and 150 in urban zones.
230
An ED is an independent geographic unit that shares a common boundary. This means that the
country was grouped into strata of equal size based on dwellings (EDs). Based on the PSUs, a
listing of all the dwellings was made, and this became the sampling frame from which a Master
Sample of dwellings was compiled, and which provided the frame for the labour force. The
survey adopted was the same design as that of the labour force, and it was weighted to represent
the population of the country.
The survey was a joint collaboration between the Planning Institute of Jamaica and the
Statistical Institute of Jamaica. The data were collected by a comprehensive administered
questionnaire, which was primarily completed by heads of households for all household
members. The questionnaire was adapted from the World Bank’s Living Standards
Measurement Study (LSMS) household surveys, and was modified by the Statistical Institute of
Jamaica with a narrower focus, to reflect policy impacts as well. The instrument assessed: (i) the
general health of all household members; (ii) social welfare; (iii) housing quality; (iv) household
expenditure and consumption; (v) poverty and coping strategies, (vi) crime and victimization,
(vii) education, (viii) physical environment, (ix) anthropometrics measurement and
immunization data for all children 0-59 months old, (x) stock of durable goods, and (xi)
demographic questions.
Data were stored and retrieved in SPSS for Windows, version 16.0 (SPSS Inc; Chicago, IL,
USA). The current study is explanatory in nature. Descriptive statistics were presented to
provide background information on the sampled population. Following the provision of the
aforementioned demographic characteristics of the sub-sample, chi-square analyses were used to
test the statistical association between some variables, t-test statistics and analysis of variance
(i.e. ANOVA) were also used to examine the association between a metric dependent variable
and either a dichotomous variable or non-dichotomous variable respectively. Logistic regression
was used to examine the statistical association between a single dichotomous dependent variable
and a number of metric or other variables (Empirical Model). The logistic regression was used
because in order to test the association between a single dichotomous dependent variable and a
number of explanatory factors simultaneously, it was the best available technique. A p-value <
0.05 (two-tailed) was selected to indicate statistical significance in this study. Where collinearity
existed (r > 0.7), variables were entered independently into the model to determine those that
should be retained during the final model construction. To derive accurate tests of statistical
significance, SUDDAN statistical software was used (Research Triangle Institute, Research
Triangle Park, NC), and this was adjusted for the survey’s complex sampling design.
2.2 Measure
Social determinants. These denote the conditions under which people are born, grow, live, work and age, including the health system.
Crowding. This is the total number of persons living in a room with a particular household.
, where is each person in the household and r is the number of rooms
excluding kitchen, bathroom and verandah.
Age: This is a continuous variable in years, ranging from 15 to 99 years.
Old/Aged/Elderly. An individual who has celebrated his/her 60th birthday or beyond.
Negative Affective Psychological Condition: Number of responses from a person on having lost
a breadwinner and/or family member, loss of property, having been made redundant, failure to
meet household and other obligations.
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232
Private Health Insurance Coverage (or Health Insurance Coverage) proxy Health-Seeking
Behaviour, is a dummy variable which speaks to 1 for self-reported ownership of private health
insurance coverage, and 0 for not reporting ownership of private health insurance coverage.
Gender: Gender is a social construct which speaks to the roles that males and females perform
in a society. This variable is a dummy variable, 1 if male and 0 if otherwise.
Health conditions: The report of having had an ailment, injury or illness in the last four weeks,
which was the survey period. This variable is a binary measure, where 1=self-reported health
status or illnesses, and 0=otherwise (not reporting an illness, injured or dysfunctions).
Poverty: In this study, the definition of poverty is the same as that used to estimate poverty in
Jamaica. It is established from the basis of a poverty line. In order to compute the per capita
poverty line in each geographical area (Kingston Metropolitan Area, Other Towns and Rural
Areas), the cost of living for a basket of goods is divided by an average family of five. The
basket of goods is established by the Ministry of Health based on the normal nutrients of the
average family. Based on a per capita approach, there are five per capita income quintiles, with
the poorest being below the poverty line (quintile 1) and the wealthiest being in quintile 5.
Elderly, Aged or Old persons. Using the same definition offered by the United Nations in the
Report of the World Assembly on Ageing, July 26-August 6, 1982 in Vienna, that the elderly are
persons who are 60+ years old.
Older-poor (elderly-poor, aged-poor). All aged persons below and just above the poverty line
(quintiles 1 & 2) in Jamaica.
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3. RESULTS
3.1 Demographic characteristics of sample
Consistent with the demographic characteristics of the ageing population, the sample was 1,149
of which there were 45% males (N=517) compared to 55% females (N=632). The mean age of
the sample was 72.6 years (SD=8.7 years). Most of the sample were married (40%, N=452),
50.5% (N=580) of the sample were in the poorest 20% of per capita income quintile, 95%
(N=1,087) were not receiving retirement income; those who were heads of households (98.3%,
N=1,129), those who had at most primary education (65.2%, N=700) and those who did not have
health insurance coverage (86.0%, N=973) (Table 9.1 ).
Thirty-seven percent (37.2%) of the sample indicated having had an illness in the last 4-
week period. Approximately 64% of the respondents indicated that they sought health care for
their health conditions. When the respondents were asked if they had visited a health practitioner
for any other reason during the last 12 months, 57.1% reported yes and 30.3% reported going for
‘regular checkups’. Of those who indicated yes, 37.2% visited public health care institutions, and
18.7% went to private clinics, compared to 5.7% who claimed that they attended both health care
facilities. The typologies of illness included colds (1.4%), diabetes mellitus (5.7%), hypertension
(42.9%) and arthritis (31.4%), while 18.6% did not specify their health condition(s). Only 2% of
the respondents had health insurance coverage; 61% purchased the prescribed medication; and
81.8% of those who indicated having not bought their medication reported that they could not
afford it.
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The median number of days for how long an illness lasted was 7 days, with a median
medical expenditure of US $7.85 (US $1.00 = Ja. $50.97).
3.2 Bivariate Correlation of Health Status and Age Cohort
Of the 1,149 sample respondents for this study, 98.8% (N=1,135) were used for the statistical
correlation between health status and gender. Of the 1,135 respondents, there were 688 young-
old, 327 old-old and 120 oldest-old poor Jamaicans. There was a correlation between the two
above-mentioned variables – χ2 (df=2) = 22.863, p-value < 0.001. On an average, 46% of the
aged-poor (N=523) reported that they had at least one illness/injury in the survey period. The
most health status was reported by the oldest-old poor (59.2%, N=71), 52.9% (N=173) and the
least by the young-old (40.6%, N=279). Embedded in these findings is that for every 1 young-
old poor who indicated that he/she had an illness/injury, there are 1.5 oldest-old and 1.3 old-old
poor.
3.3 Multivariate Analysis
The results of the multiple logistic regression model (in Table 9.2), were statistically significant
[Model χ2 (df=18) = 229.47; -2Log likelihood = 1130.37; p-value < 0.001]. Table 9.2 showed
that 26.6% of the variances in the health status of older people in Jamaica were accounted for by
the independent variables used in the multiple logistic regressions. The mold revealed that there
were 6 statistically significant factors that determined health conditions. These predictors are age
(OR=1.04, 95% CI=1.02-1.06), health insurance coverage (OR=13.90, 95% CI=7.98-24.19),
physical environment (OR=1.42, 95% CI=1.06-1.89), cost of medical care (OR=1.00, 95%
235
CI=1.00-1.00), secondary level education (OR=1.82, 95% CI=1.35-2.45) with reference to
primary and below education, and gender of respondents (OR=0.56, 95% CI=0.42-0.75).
Controlling for the effect of other variables, the average likelihood of reporting illness/injury in a
4-week reference period declined by 17 times for those who had dysfunctions.
The model had statistically significant predictor power (Model χ2 (df=18) = 229.47; -
Homer and Lemeshow goodness of fit χ2= 3.739, P=0.880), and correctly classified 70% of the
sample (correctly classified 55.4% of those with dysfunctions and 82.3% of those without
dysfunctions) (Table 9.2). The logistic regression model can be written as: Log (probability of
dysfunctions/probability of not reporting dysfunctions) = -4.185 + 0.039 (Age) + 2.632 (Health
Insurance coverage, 1= yes, 0=no) + 0.348 (Physical Environment, 1=yes, 0=no) + 0.000 (Cost
of Medical Care) + 0.598 (Secondary level education=1, 0=primary and below) – 0.581 (Sex).
4. DISCUSSION
People are living longer [15], which means that on average the elderly are living 15-20 years
after retirement. Demographic ageing at the micro and macro levels implies a demand for certain
services such as geriatric care. In addition to preventative care, there will be a need for particular
equipment and products (i.e. wheelchairs, walkers etc.). Then there are future preparations for
pension and labour force changes, along with the social and economic costs associated with
ageing, as well as the policy based research to better plan for the reality of these age groups. The
World Health Organization (WHO), in explaining the ‘problems’ that are likely to occur because
of population ageing, argues that the 21st Century will not be easy for policy makers as it is
pivotal in the preparation process to postpone ailments and disabilities, and the challenge of
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providing a particular standard of health for the populace [16]. What constitutes population
ageing? Some demographers have put forward the benchmark of 8-10% as an indicator of
population ageing [17]. Within the construct of Gavrilov and Heuveline’s perspective, the
Jamaican population began experiencing this significant population ageing as of 1975 (using 60+
years for ageing) or 2001 (if ageing is 65+ years). The issue of population ageing will double
come 2050, irrespective of the chronological definition of ageing, but what about the elderly
poor health conditions?
Let us examine the disparity between long life and quality of lived years. Ali, Christian &
Chung [18] who are medical doctors, cite the case of a 74 year-old man who had epilepsy, and
presented the findings in the West Indian Medical Journal. They write that “Elderly patients are
frequently afflicted with paroxysmal impairments of consciousness, because they frequently
have chronic medical disorders such as diabetes mellitus and hypertension, and can also be on
many medications….Many elderly patients may have more than one cause for this symptom”
[18].
The case presented by the medical doctors emphasizes the point we have been arguing
that long life does not imply quality of lived years. Although the case study cited here does not
constitute a general perspective on all the elderly, other quantitative studies have concurred with
Ali, Christian and Chung’s general findings. Scientists agree that biological ageing means
degeneration of the human body, and such a reality means that longer life will not mean quality
years. Population ageing is going to be a socioeconomic, psychological and political challenge
today, tomorrow and in the future of developing countries and nations like Jamaica. This
reinforces the position postulated by the WHO that healthy life expectancy [19] is where we
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ought to be going, as the new thrust is not living longer but how many of those years are lived
without dysfunctions. Within the context of healthy life expectancy, studies that will be used to
guide policy are those that incorporate many determinants, and not only biological conditions
[20-25]. But none of those studies examined poor old people. Hambleton [20] and Bourne [23-
25] are Caribbean scholars who have researched social determinants using the population of the
poor, and this gap to date in the literature needs to be addressed, as the elderly constitute a
vulnerable group, and the poor elderly group is even more vulnerable. Any policy which seeks to
reduce poverty must take into account the poor elderly.
‘Ageing in poverty’ implies that persons remain in their local environments with the
ability to live in their own home - wherever that might be - for as long as confidently and
comfortably possible. It inherently includes not having to move from one's current residence in
order to secure the necessary support services in response to changing needs. The ageing of
Caribbean populations has been accompanied by a shift to chronic non-communicable diseases
as major causes of morbidity. While overall national trends have been reported, examination of
local patterns of morbidity are increasingly important, as they have implications for the services
to be provided, the mix of human resources, and the maintenance of health and functional status
that facilitate ageing in place.
Research has shown that crowding is strongly correlated with the wellbeing of the elderly
(ages 60+ years) [23]; however this phenomenon, which is synonymous with poverty, does not
influence the health status of poor elderly Jamaicans. Embedded in this finding is the fact that
older people, in particular those in poor quintiles, interpret people around not as a negative force
but as good social networking and interaction. What, then, influences their health conditions?
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Poverty speaks to a particular environment; Pacione [26] showed that one’s physical
environment affects one’s quality of life, and other scholars have agreed with this finding. The
current study concurs with Pacione and others, in that the physical milieu is positively correlated
with health conditions. Although Michael Pacione’s work was on the general population,
Bourne’s works [23, 24] examined the elderly population (ages 60+ years) and found a negative
association between physical environment and wellbeing, and this study concurred with that of
the aforementioned researcher on the correlation between physical environment and health
conditions. In this study, an important finding is to refine the correlation.
Health insurance coverage is among the many indicators of the health-seeking behaviour
of a populace. For the poor elderly, it is the most significant predictor of health conditions. The
correlation is a strong positive one, indicating that health insurance coverage is a good proxy for
more ill-health than good health. The current research found that those elderly poor who owned
health insurance were 14 times more likely to report dysfunctions (or injuries) than those who
did not. Health insurance is, therefore, a cost reducer for those who are aware that they are ill,
and it is not in demand as a preventative measure. Arising from this fact is the role played by the
costs of medical and curative care. Health is influenced by more than disease-causing pathogens.
[27]
The cost of medical care is positively correlated with health conditions, suggesting that
the more dysfunctions (or injuries) that the elderly poor report, the more they are likely to spend
on medical care. The elderly poor are prevented from seeking preventative care as against
curative care. The latest data published by the Planning Institute of Jamaica and the Statistical
Institute of Jamaica[28] showed that 37.3% of elderly people are at least poor, with 20.6% falling
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in the poorest quintile. This further explains the rationale for the reduction in the demand for
medical care within the context of a precipitous increase in inflation in 2007 over 2006 (194%).
With the steady rise in the cost of health care, as well as the increase in general food and non-
alcoholic beverage prices in Jamaica, coupled with the fact that illness in older age requires care,
the elderly poor are facing increasingly difficult times. The severity of the economic situation
has seen a dramatic increase in the number of Jamaicans not seeking medical care for
illness/injury. Although there is a decline in the general population seeking medical care (66%),
more of the elderly do seek health care (72.3%) and this is owing to recurrent chronic illness
which was shown to affect 74.2% of them28. Illnesses/injuries are precipitously affecting the
elderly, and the data showed that self-reported illness for the elderly was 2.3 times more (36.6%)
than in the general population (15.5%) [28]. In 2007, the elderly poor who constitute 38% of the
poor-to-poorest in the population are mostly household heads (67.3%) and often unemployed,
and within this context they must provide for their own health needs and those of their family,
despite the harsh economic challenges and increased cost of health care.
In 2002, 12.9% of Jamaicans were unable to afford medical care, and approximately 4
years later, the figure had risen by 162.8% to 33.9% in 2007. This is within the context of a
26.3% decline in poverty for the same period. Generally poverty has been falling over the last 2
decades in Jamaica, and inflation has fluctuated, justifying the increased amount spent on food
and beverages [28], and the corresponding reduction in health care expenditure. In Jamaica
remittances, which subsidize income for many households, have fallen by 7.7% and the
reduction is 33% for those in the poor-to-the-poorest income quintiles. If the cost of medical care
is positively correlated with the health status of the elderly poor, then can it be said that the poor
elderly have more ill-health within the context of biological ageing and lowered access to
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employment income? Marmot [2] opined that there is a direct association between income and
poor health, and this further helps us to understand the embedded health challenge of the elderly
poor, as they must meet the increasing costs of medical care, cost of living, lower income,
illnesses and severity of health conditions. On examining the health statistics for 2007 [28], the
indication was that 50.8% of those in the poorest income quintile were unable to afford to seek
medical care, and the figure was 36.7% of those in the poor quintile. In order to understand the
severity of the situation regarding the aged-poor people in Jamaica, let us analyze the
aforementioned within the context of the aged-poor. The official statistical publication for
Jamaica for 2007 [28] showed that 20.6% percent of the elderly people are in the poorest quintile
and 17.7% in the poor quintile which means that a little over half of the aged-poorest in Jamaica
(10.4%) were unable to afford medical care, and 6.5% of the aged-poor had financial difficulty
affording medical care expenditure. One of the choices that must be made by the aged-poor in
Jamaica is a switch from the formal medical care service to utilizing home remedies and over-
the-counter medications, instead of visiting their personal physicians or health care facilities.
Since 1988 when the Jamaican authorities began collecting data on self-reported health
conditions, men have been reporting less health status than women [28]. The reporting of less
illness does not mean that men are healthier than women, as the same statistical report [28]
shows that women seek more medical care than men. Morbidity data for the sexes in Jamaica is
typical, as in Mexico City, Havana and Santiago-Chile at least 60% of females compared to 50%
of males aged 60+ years old reported fair-to-poor health [29]. Continuing, Buenos Aires,
Montevideo and Bridgetown-Barbados had twice the figures of the aforementioned geo-political
zones [29]. This is in keeping with women’s protective role of self, and their willingness to have
a regard for their future health status accounts for a higher health status and not a lower one,
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although they report more dysfunctions than men. If life expectancy were to be used to proxy
good health status, females are healthier than men given that they outlive them by 6 years in
Jamaica and 8 years in the world. Furthermore, in 2000-2005, life expectancy for men was 69.5
years and 74.7 years for women, and come 2045-2050 they both would have gained an additional
2 and one-quarter years more to their life span. The equal and constant rate of change in the life
expectancy of both sexes in Jamaica highlights the fact that men do not enjoy better overall
health status than their female counterparts. More years of life for both sexes means that the life
course opens itself to coronary heart disease, stroke and diabetes mellitus, and so morbidity must
be examined in this discourse.
Studies done by the Ministry of Health reveal that of the five leading causes of mortality
in Jamaica, which are malignant neoplasm, heart disease, diabetes mellitus, homicide and
cerebrovascular diseases [30], more men die from more of the aforementioned conditions than
women. Malignant neoplasms are 39% greater for men than women; cerebrovascular diseases
are 14% higher for females than males; heart disease was 71.2 per 100, 000 for men and 66.1 per
100,000 for women; and diabetes mellitus was 64% more for females than males [30]. The
greater vulnerability of men to particular mortality than women is typical across Latin America
and the Caribbean [29], pointing to gender bias (that is feminization) in visits to health care
facilities, which are embedded in the life expectancy rates and visits to health care institutions.
The matter of reporting less health status, once again, does not imply a healthier person, as health
is not on a continuum, with ill-health on one extreme and good health on the other. Health is
more in keeping with cyclical flow, and changes over the life course with time, experiences and
socio-physical environmental conditions. Hence, asking about ill-health is not a good proxy for
health status, as in 2007 a group of Caribbean scholars conducted a national representative
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prevalence survey of some 1,338 Jamaicans, and found that those who indicated themselves to be
of the lower class had the least self-reported health status [13].
The discipline of gerontology – scientific inquiry into the biological, psychological, and
social aspects of ageing - has shown that ageing is not necessarily without increased health
conditions; it is natural for aged people to complain and die more of dysfunctions than other age
cohorts [31, 32] and that is directly related to their basal metabolic rate [33] and the nature of the
life course of the aged [34]. Here functional ageing is an explanation for the image of ageing,
and it can be measured by normal physical changes, diminished short-term memory, reduced
skin elasticity and a decline in aerobic capacity. It is well established in the research literature
that age is directly correlated with health status for the elderly, and in this study the finding
concurs with the literature. The current research shows that age is the second most significant
predictor of health status for the elderly poor, and explains why the disparity in poor health in
Latin and America and the Caribbean is higher for older persons than younger people [29].
Population ageing is synonymous with more disability and more non-communicable diseases
such as malignant neoplasms, hypertension, diabetes, and heart diseases than younger ages.
Donald Bogue [35] noted that health problems increase with ageing, and that one’s health issues
intensify with ageing. Therefore, an unhealthy lifestyle – tobacco consumption, physical
inactivity, unprotected sex, and unhealthy diet - over the life course will affect the elderly in
latter life, and the declining health of the elderly poor is the same within the sub-categories of the
elderly – young-old, old-old and oldest old.
Issues of the elderly cannot be discussed without an examination of area of residence.
This study found no correlation between the aged-poor’s health status and area of residence.
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Using data since 1989 (from various issues of the Jamaica Survey of Living Conditions),
population ageing is biased by gender as well as by specific area of residence. Over the last
decade (1997-2007), the number of elderly Jamaicans living in rural areas has declined from
54.3% to 46.6% (a rate of 14.1%). For the same period, the rate of increase of the aged populace
in the Kingston Metropolitan Area (100% cities) was 19.5%, down from 27.2% (in 1997) while
the increase in the aged population over the same period in Other Towns was 12.9% over 18.5%
in 1997. Regarding the prevalence of poverty for the region (2007), rural poverty was 3.8 times
more than that in Other Towns, and 2.5 times more than that in the Kingston Metropolitan Area.
Despite the compounding economic challenges of poverty coupled with ageing, the poor-elderly
in Jamaica do not experience a difference in their health status owing to area of residence. Here
the health issues of the aged poor are independent of their area of residence, suggesting that in
the population the poor are age-residence insensitive. This contradicts research literature on the
health status of the elderly which has shown a correlation between the aged and their areas of
residence [23,24,48], indicating that the physical characteristics of the aged poor are the same in
different areas of residence, and therefore do not account for any poor health, disability,
functional inability or psychological conditions.
Like the WHO [36], the researcher believes that although ageing is a biological
phenomenon, it cannot be due only to biological conditions, as ageing relates to bio-psycho-
social [20, 25, 37-49] and environmental conditions [23-26], since people – biological organisms
– must operate in a socio-physical milieu throughout their life span, and this demands an
expansion of biological conditions in the ageing discourse. The very nature of gerontology must
coalesce biopsychosocial and environmental conditions in assessing ageing and the health of the
aged, which are in keeping with the WHO’s Constitution of 1948, and this has also been
244
established in many Caribbean scholarships [20,23-25,42-49]. Within the context of the above-
mentioned challenges for elderly people, when this is coupled with poverty which affects 10.2%
of elderly Jamaicans (N=29,794) in 2007, it intensifies the challenges experienced by elderly
people. With the increased cost of food and non-alcoholic beverages, fuel and household
supplies, housing and household operational expenses, the health status of the older-poor will
continue to deteriorate, as they will not be able to afford health care services. The decline in
medical care-seeking behaviour of Jamaicans speaks to the challenges of older people and the
rise in instances of switching to alternative medicine. This is further intensified by poverty; and
rural poverty, which is more severe than that found in urban areas [50], will further compound
the challenges of the health status of the aged populace. Older people who are poor must operate
within the same biopsychosocial and physical environment during their lifetimes as other
persons.
Even among the WHO commissioned studies [51-53], as well as other studies on the
social determinants of health [2,3, 20-25], the population of the poor elderly were not examined.
Likewise in the Caribbean, scholars have examined the social determinants of the population or
the elderly population, with poverty being an independent variable [20, 23-25]. Any policy that
seeks to address the health status of the elderly poor must take into consideration, or concentrate
and/or rely on, not only the population in general, but the cohort of the elderly in particular. The
experiences and demands of the elderly are not the same as the general population, and the
current study shows that social determinants of health are somewhat different for the general
elderly population and the poor elderly cohort. The WHO [51] opined that the social
determinants of health for the most part account for the health inequities between and within
nations, which substantiates the differences that emerged between the elderly in other studies
245
[20, 23-24] and the current study of the poor elderly. These findings are far-reaching, and can be
used to guide policy and research. The elderly-poor in Jamaica are experiencing ‘health poverty’
which cannot be alleviated by unresearched policies or research policies on the general
population, but by the elderly cohorts in particular.
5. Conclusion
In summary, the number of elderly persons who reported health conditions in Jamaica is
3 times more than that for the nation (i.e. 12.6%), suggesting that health care expenditure for
Jamaicans is substantially used to address health care needs for the aged population. With the
number of elderly come 2025 estimated to be 14.5% over 10.9% for 2007, health care
expenditure will be primarily absorbed in caring for this age cohort. Public health practitioners
must begin programmes to deal with this pending reality. Ageing is a process which denotes that
the high number of health conditions affecting the elderly would have started earlier, based on
some of the decisions that they undertook (or did not) leading up to their current age. Hence,
there is a need to have a public health campaign geared towards the promotion of healthy
lifestyle practices for ages close to sixty years, in conjunction with one for children and for the
working-age population. The programme should target check-ups, preventative care, signs of the
onset of particular health conditions, and the distinction between ill health and good health care
practices. The demand of the health services in Jamaica in the future must be geared towards a
particular age cohort and certain health conditions, and not only to the general population, as the
social determinants which give rise to inequities are not the same even among the same age
cohort.
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6. Disclosure
The author reports no conflict of interest for this study.
7. Disclaimer
The researcher would like to note that while this study used secondary data from the Jamaica Survey of Living Conditions, none of the errors in this paper should be ascribed to the Planning Institute of Jamaica or the Statistical Institute of Jamaica, but to the researcher.
8. Acknowledgement
The dataset for this study was made available from the databank of SALISES (Sir Arthur Lewis Economic Institute), Faculty of Social Sciences, the University of the West Indies, Mona, Jamaica and for this the researcher is indebted and greater appreciate this gesture.
247
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33. Ruggiero C, Metter EJ, Melenovsky V, Cherubini A, Najjar SS, Ble A, Senin U, Longo DL, Ferrucci L. (2008). High Basal Metabolic Rate Is a Risk Factor For Mortality: The Baltimore Longitudinal Study of Aging. The Journal of gerontology: Biological Sciences 63(7):668-706.
34. WHO. (2001). Life course perspectives on coronary heart disease, stroke and diabetes: Key issues and implications for policy and research. Summary Report of A Meeting of Experts 2-4 may 2001. Geneva: WHO.
35. Bogue DJ. (1999). Essays in human ecology, 4. The ecological impact of population aging. Chicago: Social Development Center.
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Table 9.1: Socio-demographic characteristics of sample Description
N
Percent
Gender Male 517 45.0 Female 632 55.0 Marital status Married 452 40.0 Never married 357 31.6 Divorced 10 0.9 Separated 22 1.9 Widowed 290 25.6 Per capita Income quintile Poorest 580 50.5 Poor 569 49.5 Retirement Income No 1087 95.0 Yes 57 5.0 Household head No 20 1.7 Yes 1129 98.3 Health Insurance coverage No 973 86.0 Yes 158 14.0 Educational Level Primary and below 700 65.2 Secondary 363 33.8 Tertiary 10 0.9
Age 72.63 years (SD=8.7 years) Total Medical Care Expenditure $1,067.64 (SD=$2,000.00) Per capita consumption $30,998.07 (SD=$9,833.00) US $1.00 = JA$50.97
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Table 9.2: Logistic Regression: Socio-demographic correlates of health status of poor older people in Jamaica, N=1,033 OR 95.0% C.I. Variable Age 1.04 1.02 - 1.06*** Retirement income 0.75 0.38 - 1.49 Per capita consumption 1.00 1.00 - 1.02
Separated, divorced or widowed 1.07 0.74 - 1.55 Married 1.11 0.77 - 1.58 Never married (reference group)
Health insurance
1.00
13.90 7.98 - 24.19*** Environment 1.42 1.06 - 1.89* Household head 3.34 0.37 - 30.01 Cost of medical care 1.00 1.00 - 1.05**
Secondary 1.82 1.35 - 2.45*** Tertiary 0.43 0.07 - 2.63 Primary and below (reference group)
Semi-urban
1.00
0.78 0.51 - 1.19 Urban areas 0.86 0.50 - 1.49 Rural areas (reference group)
Sex
1.00
0.56 0.42 - .75*** Living arrangement 1.20 0.77 - 1.88 Crowding 0.89 0.78 - 1.02 Crime index 1.00 0.98 - 1.03 Positive affective 0.96 0.90 - 1.01
Model Chi-square (df =18) = 229.47, p-value < 0.0001 -2Log likelihood = 1130.37; Nagelkerke R-square = 0.266 Hosmer and Lemeshow test P = 0.880 *P < 0.05, **P < 0.01, ***P < 0.001
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Chapter 10
Self-rated health of the educated and uneducated classes in
Jamaica
Paul Andrew Bourne
Education provides choices, opportunities, access to resources and it is associated with an increased likelihood of higher income. Does this holds true in developing nations like Jamaica, and does the educated class experience greater self-rated health status than the uneducated classes? The current study will identify the socio-demographic correlates of self-rated health status of Jamaicans, examine the effects of these variables, explore self-rated health status and self-reported diagnosed recurring illness among the educated and uneducated classes, compute mean income among the different educational types, and determine whether a significant statistical correlation exists between the different educational cohorts. The current study utilised the data set of Jamaica Survey Living Conditions which is a cross-sectional survey. It is a national probability survey, and data were collected across the 14 parishes of the island. Stratified random sampling techniques were used to draw the sample. Self-rated health statuses of respondents are correlated with age, income, crowding, sex, marital status, area of residence, and self-reported illness (es) – χ2= 1,568.4, P < 0.001. Respondents with tertiary level educations were most likely to be classified in the wealthiest 20% (53.4%) and there was no significant statistical difference between their health status and the lower educated classes. There is a need for a public health care campaign that is specifically geared towards the educated classes as their educational achievement is not translating itself into better health care-seeking behaviour and health status than the uneducated classes.
Introduction
Health is imperative for socio-economic and political development of people, a society and a
nation. It is within this context that a study of health is critical as it relates to the wider society.
Traditionally, the concept of health is measured using life expectancy, mortality, and diagnosed
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illness. In the social sciences, researchers have used self-rated health status [1-9], and self-
reported illness [10-17] to measure health. Apart from those terminologies, other synonyms such
as self-assessed health, self-reported health, perceived health, self assessment of health, global
health status, and health status have all been used to speak about health. It follows from the
aforementioned perspective that all those terms imply the same measurement of health or health
status. Self-rated health status is among the subjective indexes used to measure health, and some
scholars argue that they are not a good assessment of health when it comes to life expectancy,
per capita income, or mortality [18-20].
The subjective/objective indexes of measuring health emerged as scholars sought to
ensure that the measurement of health was a reliable and valid one. Some scholars opined that
the self-assessment of one’s health status was more comprehensive than objective assessment [3,
5, 21] as it included one’s health and general life satisfaction. Studies have shown that subjective
indexes are a good measurement for mortality [2, 22-24] and life expectancy [25]. Concurringly,
a recently conducted study by Bourne [25] found that self-assessed illness was not a good
measure of mortality; however, it was was very useful when it came to the subject of life
expectancy in Jamaica.
The subjective indexes in measuring health open themselves up to systematic and
unsystematic biases [26]. People’s perception can be biased as they may inflate or deflate their
status in an interview or on a self-administered instrument (i.e., questionnaire). Another aspect of
bias in subjective evaluation of health is the matter of recall. It is well established in research
literature that as people age, their mental faculties decline [27-32], suggesting that some people
will have difficulties recalling experiences which happened in the past. Within the context of the
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time recollection, bias can occur in subjective indexes. Kahneman [33] devised a procedure of
integrating and reducing the subjective biases when he found that instantaneous subjective
evaluations are more reliable than assessments of recollection of experiences. Contrary to
Kahmeman’s work, Bourne’s [25] results show that self-assessed health for a 4-week period is a
good measure of life expectancy (objective index). In spite of the fact that subjective indexes are
a good measure of objective health, the former still contains biases, which Diener [34] opines
still have valid variance.
It is well established in health research that there is a correlation between or among
different socio-demographic, psychological and economic variables [4, 6-17, 20] and self-rated
health status. The correlates include education, marital status, area of residence, education,
income, psychological conditions (i.e., positive and negative psychological affective conditions),
and other variables. Freedman & Martin [35], using data from 1984 and 1993’s panel survey of
Income and Program Participation, noted that there was an association between educational level
and physical functioning of people over 65 years. Another study by Koo, Rie & Park [36], using
multivariate regression, concluded that education was a predictor of increased subjective
wellbeing (t [2523] = 7.83, P<0.001], which means that education was more than associated
with health. Concomitantly, another research found that the number of years of school (i.e., the
Quantity Theory) was a crucial predictor of health status of an individual [37] which indicates
that tertiary level graduates are more likely to be healthier than non-tertiary level educated
people.
While education provides choices, opportunities, access to resources and is associated
with increased likelihood of achieving a higher income, does it hold true in developing nations
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like Jamaica that the educated class has greater self-rated health status than the uneducated
classes? A paucity of information (research literature) exists in Jamaica on the educated and
uneducated classes and their self-rated health status, self-reported illness(es), the areas in which
the educated and uneducated classes reside, health care-seeking behaviour among the different
educational classes and the self-rated health status of Jamaicans and its correlates.
The current study is important, as it uses a statistical technique which accommodates all
items in self-rated health status categories as opposed to dichotomising self-rated health.
Dichotomising self-rated health status in good and poor health means that some of the original
information will be lost; and this explains why some researchers argue for the maintenance of the
Likert nature of the measuring tool over dichotomisation [38-40]. Secondly, the study is
significant as it included more variables: (1) educational levels and area of residence, (2)
educational levels and health care-seeking behaviour, (3) health insurance coverage and
educational levels, (4) self-reported illness(es) and educational levels, (5) social standing and
educational levels. The objectives of the current study therefore are to (1) identify the socio-
demographic and economic correlates of self-rated health status of Jamaicans, (2) examine the
effects of these variables, (3) explore self-rated health status and self-reported diagnosed
recurring illness among the educated and uneducated classes, (4) calculate the mean age of
respondents in the different educational categories, (5) compute mean income among the
different educational types, and (6) determine whether a significant statistical correlation exists
between the different educational cohorts.
Materials and methods
Data
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A joint survey on the living conditions of Jamaicans was conducted between May and August of
2007 by the Planning Institute of Jamaica (PIOJ) and the Statistical Institute of Jamaica
(STATIN) [41]. The survey is called the Jamaica Survey of Living Conditions (JSLC) which
began in 1988 and is now conducted annually. The JSLC is a modification of the World Bank’s
Living Standards Measurement Study (LSMS) which is a household survey [42]. The current
study used the JSLC’s data set for 2007 in order to carry out the analyses of the data [43]. It had
a sample size of 6,783 respondents, with a non-response rate of 26.2%.
The JSLC is a cross-sectional survey which used stratified random sampling techniques
to draw the sample. It is a national probability survey, and data was collected across the 14
parishes of the island. The design for the JSLC was a two-stage stratified random sampling
design where there was a Primary Sampling Unit (PSU) and a selection of dwellings from the
primary units. The PSU is an Enumeration District (ED), which constitutes a minimum of 100
residences in rural areas and 150 in urban areas. An ED is an independent geographic unit that
shares a common boundary. This means that the country was grouped into strata of equal size
based on dwellings (EDs). Based on the PSUs, a listing of all the dwellings was made, and this
became the sampling frame from which a Master Sample of dwellings was compiled. This, in
turn, provided the sampling frame for the labour force. One third of the Labour Force Survey
(i.e. LFS) was selected for the JSLC. The sample was weighted to reflect the population of the
nation.
Instrument
A self-administered instrument (i.e., questionnaire) was used to collect the data from
respondents. The questionnaire covers socio-demographic variables such as education, age, and
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consumption, as well as other variables like social security, self-rated health status, self-reported
health conditions, medical care, inventory of durable goods, living arrangements, immunisation
of children 0–59 months, and other issues. Many survey teams were sent to each parish
according to the sample size. The teams consisted of trained supervisors and field workers from
the Statistical Institute of Jamaica.
Statistical Analyses
The Statistical Packages for the Social Sciences – SPSS-PC for Windows version 16.0 (SPSS
Inc; Chicago, IL, USA) – was used to store, retrieve and analyze the data. Descriptive statistics
such as median, mean, percentages, and standard deviation were used to provide background
information on the sample. Cross tabulations were used to examine non-metric dependent and
independent variables. Analysis of variance was used to evaluate a metric and a 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 95% confidence
interval was used to examine whether a variable is statistically significant or not.
There was no selection criterion used for the current study. On the other hand, for the
model, the selection criteria were based on 1) the literature; 2) low correlations, and 3) non-
response rate. The correlation matrix was examined in order to ascertain if autocorrelation and/or
multicollinearity existed between variables. Based on Cohen and Holliday [44] and Cohen and
Cohen [45], low (weak) correlation ranges from 0.0 to 0.39, moderate – 0.4-0.69, and strong –
0.7-1.0. This was used to exclude (or allow) a variable in the model. Any correlation that had at
least a moderate value was excluded from the model in order to reduce multicollinearity and/or
autocorrelation between or among the independent variables [46-51]. Another approach in
addressing and/or reducing autocorrelation was to include in the model all variables that were
identified from the literature review with the exception of those where the percentage of missing
cases were in excess of 30%.
The current study used the ordinal nature of the dependent variable (self-rated health
status or self-rated health) which denotes that none of the original data will be lost as is the case
in dichotomising self-rated health. Ordered regression model is written as:
, s = 1, …k, (1)
Where x is the vector of covariates with coefficient to be estimated, k is the number of
cut-points for the dependent variable, and αs, αl stand for the intercepts in the regression models.
Anderson [52] opined that ø1=1 and øk, and that other constraints are possible. In the current
study, the researcher set ø1=1 and 0= ø1< ø2 < …< øk =1 to correspond to the levels from very
good to very poor, and other levels of health are relative to “very good”. Based on Anderson’s
arguments, the monotone increase of ‘ø’s are dealt with by varying the sign for β. Within this
context, a positive estimation of coefficient denotes that those with this characteristic would be
negatively associated with good health status and those without would positively associated with
good health status (or self-rated health status). Simply put, positive estimation of coefficients
means poor health and negative estimation of coefficients denotes better self-reported health
status.
Measurement of variables
Dependent variable
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259
Self-rated health status (i.e., self-rated health) was derived from the question, “Generally, how is
your health?” with the options being very good, good, fair (or moderate), poor, or very poor. The
ordinal nature of this variable was used as was the case in the literature [38-40].
Independent variables
Information on self-reported illness was derived from the question, “Have you had any illnesses
other than injury?” The examples given include cold, diarrhoea, asthma attack, hypertension,
arthritis, diabetes mellitus or other illness. A further question about illness asked, “(Have you
been ill) In the past four weeks?” The options were yes and no. This variable was re-coded as
binary value, 1 = yes and 0 = otherwise.
Information about self-reported diagnosed recurring illness was derived from the question, “Is
this a diagnosed recurring illness?” The options were: (1) yes, cold; (2) yes, diarrhoea; (3) yes,
asthma; (4) yes, diabetes mellitus; (5) yes, hypertension; (6) yes, arthritis; (7) yes, other; (8) no.
Information on medical care-seeking behaviour was taken from the question, “Has a health care
practitioner, healer, or pharmacist been visited in the last 4 weeks?” The options were yes or no.
Medical care-seeking behaviour therefore was coded as a binary measure where 1 = yes and 0 =
otherwise.
The term crowding refers to the average number of person(s) per room excluding the kitchen,
bathroom, and veranda (i.e., total number of people in household divided by the total number of
rooms excluding kitchen, bathroom and veranda).
Total annual expenditure was used to measure income.
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Income quintile was used to measure social standing. The income quintiles ranged from poorest
20% to wealthiest 20%.
Results
Demographic characteristic of sample and bivariate analyses The sample was 6,783 respondents: 48.7% males and 51.3% females. Eighty-two percent of
respondents rated their health status as at least good compared to 4.9% who rated it as poor.
Fifteen percent of respondents reported some form of illness within the last 4 weeks. Of those
who recorded an ailment, 89% reported that the dysfunction was a diagnosed recurring one. The
most frequently recurring illness was unspecified conditions (23.4%) followed by hypertension
(20.6%), cold (14.9%), diabetes mellitus (12.3%), and others (Table 10.1).
The median age of the sample was 29.9 years (range = 99 years). The median annual
income was US $7,050.66 (rate in 2007: 1US$ = Ja$80.47; range = US $4,406.20), and median
crowding was 4.0 persons per room (range = 16 persons).
A cross-tabulation between educational level and area of residence revealed a significant
statistical correlation – χ2(df = 40 = 78.02, P < 0.001 (Table 10.2). Based on Table 10.2, 0.8% of
rural respondents had tertiary level education and 5.4 times more urban residents had tertiary
level education compared to rural respondents.
No significant statistical correlation existed between educational level and sex of
respondents – χ2 (df = 2) = 5.61, P > 0.05 (Table 10.3). Similarly, no significant statistical
association was found between purchased prescribed medication and educational levels of
respondents - χ2 (df = 10) = 11.9, P > 0.05.
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A significant statistical difference was found between mean age of respondents who are
at different educational levels – F statistic [2, 6589] = 214.64, P < 0.001. The mean age of
respondents with primary level of education and below was 32.0 years (SD = 22.6, 95% CI =
31.4-32.6) compared to 14.6 years (SD = 1.7, 95% CI = 14.5-14.8) for those with secondary
education level and 26.4 years (SD = 10.6, 95% CI = 24.6-28.2) for those with tertiary education
level.
A cross-tabulation between self-reported illness and educational level revealed a
significant statistical association - χ2 (df = 2) = 61.33, P < 0.001. Respondents with primary
education level and below recorded the greatest percent of people with illness(es) (16.2%)
followed in descending order by tertiary level (9.2%) and secondary level respondents (5.4%).
The statistical correlation was a weak one – correlation coefficient = 0.10.
A significant statistical correlation existed between self-reported diagnosed recurring
illness and educational level – χ2 (df = 14) = 42.56, P < 0.001 (Table 10.4). Respondents with
secondary level education (37.5%) had the highest percent of unspecified health conditions
followed in descending order by tertiary (33.3%) and primary level respondents (22.7%).
Hypertension was substantially a phenomenon occurring among those with primary education
level and below: 21.6%, compared to 8.3% of tertiary level individuals. Similarly, diabetes
mellitus (12.8%) was more prevalent among primary level respondents compared to 5.0% of
secondary level respondents. On the other hand, asthma was the greatest among tertiary level
respondents (33.3%) compared to secondary level (22.5%) and primary level respondents
(8.7%).
Respondents with tertiary level education were most likely to be classified in the
wealthiest 20% (53.4%) compared to those with secondary education who were more likely to be
262
in the middle class and those with primary level education were either in the poorest 20%
(20.3%) or in the wealthiest 20% (20.3) (Table 10.4) – χ2 (df = 8) = 124.53, P < 0.001.
Of the 20.2% of respondents who had health insurance coverage, tertiary level people
were more likely to have private coverage (35.9%) followed by primary or below (12.0%) and
secondary level individuals (11.6%) – χ2 (df = 4) = 76.95, P < 0.001 (Table 10.4).
Concurringly, a significant statistical difference existed between the mean age among the
different educational levels in which respondents were categorised (Table 10.4) – F statistic [2,
6589] = 214.6, P < 0.001: mean age for those with at most primary level education was 32.0
years (SD = 22.6) compared to a mean age of 26.4 years (SD = 10.6) for those with tertiary level
education. When educational level of respondents was disaggregated into no formal, basic, and
primary to tertiary, the mean age of respondents with no formal education was 42.7 years (SD =
18.0), 2.7 years (SD = 1.9) for basic school level respondents, and 9.0 years (SD = 2.2) for those
who have primary level education – F statistic [4,6587] = 2207.9, P < 0.001
Multivariate analysis
Self-rated health statuses of respondents are correlated with (1) age, (2) income, (3) crowding,
(4) sex, (5) marital status, (6) area of residence, and (7) self-reported illness(es) – χ2= 1,568.4, P
< 0.001; and that the data is a good fit for the model – LL = 9,218.0. The 7 socio-demographic
and economic correlates accounted for 33% of the variability in self-rated health status (Table
10.5). Based on the Table 10.5, the older the respondents get, the more likely they are to rate
their health status as poor and this was the same for crowding and for those who report an illness
(health condition). Urban residents are more likely to report poor self-rated health status than
rural residents. However, there was no statistical difference between self-rated health status for
rural and semi-urban residents. Married people are more likely to report better self-rated health
263
status than widowed people, people with more income are more likely to report better health
status, and males are more likely than females to report better health status. However, no
significant statistical difference was found between self-rated health status among the educated
and uneducated cohorts.
Discussion
The current study concurs with the literature in that self-reported illness has the most influence
on self-rated health status of people [8]. In a study of elderly Barbadians (ages 60+ years),
Hambleton et al. [8] found that current illness accounted for 87.7% of the variance in self-rated
health status. In another study on married people in Jamaica, Bourne and Francis [53] found that
73% of self-reported illnesses explains the variability in self-reported health status. Embedded in
the current finding is whether self-rated health is examined on elderly or married people.
Current self-reported illnesses accounted for a critical proportion of self-rated health and can be
used to measure health. Within this context, self-reported illness is a good measure of self-rated
health, and this has been established by other studies [10-17, 25]. A recently conducted research
found that self-reported illness accounted for 54% (r-square) of the variance in life expectancy of
Jamaicans [25], and this increased to 63% for males. Subjective indexes such as self-rated health
and self-reported illness can be used to measure health, but the latter is a better measure and this
must be taken into consideration in the interpretation of findings using this measurement.
The challenges noted by some researchers in using self-rated health are: (1) bias and (2)
the dichotomisation of the measure. While bias is synonymous with subjective assessment or
evaluation of any construct, the validity of using the measure is high. Diener [34] noted in 1984
that there are still some valid variances, which was validated in a recent study by Bourne [25].
Health literature has long established that subjective indexes such as self-rated health, happiness,
264
and life satisfaction are good measures of health as they are more comprehensive (including
social activities and relationships, psychological conditions, emotions, spirituality, life
satisfaction) while still incorporating the objective component [3, 21, 34]. This is justified by
studies that found strong statistical correlations between subjective health and objective indexes
such as life expectancy [25] and mortality [2, 22-24]. It should be noted here that subjective
indexes (e.g., self-reported illness) and mortality are lowly correlated in Jamaica [25], which
suggests that health literature among regions has revealed different findings. This denotes that
the wholesale use of what is obtained in one nation cannot be applied to another without
understanding socio-demographic characteristics. However, Jamaica, like other nations, can use
subjective indexes to assess health status of its people and by extension its entire population.
The issue of the dichotomisation of self-rated health, because some of the original values
will be lost, is now resolved by this study as self-rated health was dichotomised and findings
were similar to those who had dichotomised the dependent variable (i.e., self-rated health status).
What are the similarities and dissimilarities between the two statistical approaches in
operationalising subjective health?
Studies in the Caribbean found that age, marital status, crowding, sex of respondents,
area of residence, income and illnesses were statistically correlated with subjective health [8, 10-
17, 53], which is validated by the current study. Even some non-Caribbean studies have found
the aforementioned variables to be statistically associated with subjective health [7, 9], indicating
that dichotomising self-rated health status does not fundamentally change most of the socio-
demographic, economic, and biological variables.
265
Examining data on married people by way of dichotomising self-rated health status,
Bourne [25] found that men had a greater self-reported health status than women, and in the
current study (non-dichotomisation of self-rated health status), males had a higher health status
than females. On the other hand, in Bourne’s work [25], he found in descending order self-
reported illnesses, age, income and sex to be the only factors of self-reported good health while
in the non-dichotomised study more variables accounted for health status. Nevertheless, ranking
of the correlates were similar in both studies as in the current. The factors in descending order
were self-reported illness, age, crowding, income, sex and the others, indicating the closeness of
the statistical approaches. Married people are a component of the general populace and they have
socio-demographic and economic experiences which differ from some unmarried people.
The literature showed that income is strongly correlated with self-rated health. However,
in Jamaica this is clearly not the case. In Jamaica, income plays a secondary role to illness and
age and when self-rated health is non-dichotomised, it becomes an even weaker variable.
Although income affords one particular choices (or lack thereof), the educated class in Jamaica
received more income than uneducated classes, yet the former class is not healthier than the
latter. This finding is contrary to the literature that showed the association between higher
education and health [7-9]. Education influences social standing and income, but it does not
directly influence good health status in Jamaica. Concurringly, the current work found that
education is positively correlated with more health insurance coverage. However, health
insurance coverage is not significantly associated with better health status. Embedded here is the
fact that health insurance coverage in Jamaica is not an indicator of health care-seeking
behaviour but a product that is purchased for the eventuality of the onset of illness, as it will
lower out-of-pocket medical care expenditure.
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Education provides its recipients with knowledge, access to knowledge, access to income
and other empowerment, but it does not mean that the educated classes are more concerned about
their health, and this can be measured using health care-seeking behaviour and knowledge about
the illnesses that are affecting the individual. The current paper found that 25 out of every 100
educated Jamaicans are aware of their health condition(s), and this is greater than that for
uneducated classes. Jamaicans with the least level of education were most cognizant of their
ailments and sought medical care just as much as did educated Jamaicans. Education, therefore,
does not denote empowerment to seek medical care, which is embedded in the culture, in
particular for men. Education is still unable to break the bondages of the perceptions of society
which purport that health is weakness, and that to display weakness as a man removes his
masculinity. This continues to shackle Jamaicans, particularly men, who still subscribe to the
traditional notion that illness is correlated to weakness and that men should not display
weakness. It is this cultural perspective that bars many men from visiting health care facilities,
except in cases of severe illness or if they are married [25]. Hence, mortality being greater for
men is not surprising [54] as many men will die prematurely because of the fact that they are
reluctant to visit health care institutions. This reluctance to seek medical care is not limited to
males. In 1988, when Jamaica began collecting data on the living conditions of its people,
females sought more medical care than males, but the disparity ranged between -2 to 6%. In
2007, 68% of females sought medical care compared to 63% of males, which means that higher
education, which is substantially a female phenomenon in Jamaica, is not fundamentally
improving the health status of females or even males.
Educated Jamaicans are more likely to live in urban areas and those with primary
education levels or below are more likely to live in semi-urban zones. The current findings found
267
that semi-urban respondents were more likely to have better health status, although they are more
likely to have at most primary level education. In 2007, statistics revealed that 15.3% of
Jamaicans in rural areas were below the poverty line compared to 4% of semi-urban and 6.2% of
urban Jamaicans [41], indicating that poverty is more synonymous with rural areas, yet there is
no significant statistical difference between the self-rated health status of rural and urban
Jamaicans. Income makes a difference in health, as those with more means can access more and
greater resources including health care, but clearly income beyond a certain amount is retarding
the health status of Jamaicans. This study cannot stipulate a baseline income that people should
receive in order to prevent a decline in health status. However, there is clearly a state of
contentment among the poor and very poor who were equally as healthy as the wealthy. The
health disparity between them and the educated showed no significant statistical difference and
this emphasises that wealth does not automatically transfer itself into health. Another issue which
is evident in the data is the variability in the measurement of health among the social classes, as
the poorest 20% reported less illness than the wealthiest 20% [41], yet the former group still
dwells in slums, inner-city neighbourhoods, and violent communities, and they have lower levels
of education. Despite Diener’s findings [34] that the variance is minimal, Bourne’s work showed
a strong association between subjective health (i.e., self-reported illness) and life expectancy – a
correlation coefficient between 50 and 60% for a single variable is strong. However, this
highlights that there are still some challenges embedded in the use of self-rated health status.
Conclusion
While the dichotomisation of self-rated health status loses some of the original data, when self-
rated health is non-dichotomised, socio-demographic and biological variables accounted for 33%
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of the explanation of the variance and this was 44% using dichotomisation for married Jamaica,
suggesting dichotomisation of health status still holds some validity. Another critical finding that
emerged from the current work is that education is not improving the health status of Jamaicans.
However, it is correlated with better social standing and higher income. Income is significantly
associated with better health status and it played a secondary role to self-reported illness and age
of respondents. Education is associated with more health insurance coverage, but that health
insurance coverage cannot be used to measure health care-seeking behaviour or measure better
health status of Jamaicans. In summary, there is a need for a public health care campaign that is
specifically geared towards the educated classes as their educational achievement is not
translating itself into better health care-seeking behaviour and health status than the uneducated
which suggests that societal pressures are barring Jamaicans from better health status choices.
Conflict of interest The author has no conflict of interest to report.
Acknowledgement
Disclaimer
The researcher would like to note that while this study used secondary data from the Jamaica Survey of Living Conditions, 2007, none of the errors that are within this paper should be ascribed to the Planning Institute of Jamaica or the Statistical Institute of Jamaica, but rather to the researcher.
269
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Table 10.1. Demographic characteristic of sample, n=6,783 Characteristic n %Sex Male 3303 48.7Female 3479 51.3Marital status Married 1056 23.3Never married 3136 69.2Divorced 77 1.7Separated 41 0.9Widowed 224 4.9Social standing Poorest 20% 1343 19.8Poor 1354 20.0Middle 1351 19.9Wealthy 1352 19.9Wealthiest 20% 1382 20.4Area of residence Urban 2002 29.5Semi-urban 1458 21.5Rural 3322 49.0Self-reported illness Yes 980 14.9No 5609 85.1Self-reported diagnosed recurring illness Cold 149 14.9Diarrhoea 27 2.7Asthma 95 9.5Diabetes mellitus 123 12.3Hypertension 206 20.6Arthritis 56 5.6Unspecified 234 23.4Not reported as diagnosed 109 10.9Health care-seeking behaviour Yes 658 65.5No 347 34.5Self-rated health status Very good 2430 37.0Good 2967 45.2Moderate 848 12.9Poor 270 4.1Very poor 50 0.8
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Table 10.2. Educational level by area of residence, n = 6,592 Characteristic Area of residence Total Educational level Urban Semi-urban Rural % % % %Primary and below 84.8 89.0 88.0 87.3Secondary 10.9 9.6 11.2 10.8Tertiary 4.3 1.5 0.8 2.0Total 1952 1421 3219 6592Chi-square (df = 4) = 78.02, P < 0.001, cc = 0.11
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Table 10.3. Education level by sex of respondents, n = 6,592 Characteristic Sex Total
Male Female % % %
Educational level Primary and below 87.9 86.6 87.3Secondary 10.5 11.0 10.8Tertiary 1.6 2.4 2.0Total 3207 3385 6592Chi-square (df = 2) = 5.61, P > 0.05
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Table 10.4. Self-reported diagnosed recurring illness and social standing by educational level Characteristic
Educational Level Total Primary or below
Secondary Tertiary
% % % %Self-reported diagnosed recurring illness1
Cold 15.0 17.5 0.0 14.9Diarrhoea 2.9 0.0 0.0 2.7Asthma 8.7 22.5 33.3 9.5Diabetes mellitus 12.8 5.0 0.0 12.3Hypertension 21.6 0.0 8.3 20.6Arthritis 5.9 0.0 0.0 5.6Unspecified condition 22.7 37.5 33.3 23.4Not diagnosed 10.5 17.5 25.0 10.9Total 947 40 12 999Social standing (income quintile)2 Poorest 20% 20.3 19.7 3.8 19.9Poor 20.0 21.7 7.6 20.0Middle 19.4 24.5 16.0 19.9Wealthy 19.9 20.3 19.1 19.9Wealthiest 20% 20.3 13.7 53.4 20.2Total 5752 709 131 6592Health Insurance coverage3 No 79.8 83.7 57.8 79.8Private 12.0 11.6 35.9 12.5Public 8.1 4.6 6.3 7.7Total 5682 689 128 6499Age4 Mean (SD) in years 32.0 (22.6) 14.6 (1.7) 26.4 (10.6) 30.0 (21.8)Health care-seeking behaviour5 Yes 65.7 60.0 66.7 65.5No 34.3 40.0 33.3 34.5Total 953 40 12 1005Income6 Mean (SD) in US$7 8,381.88
(6,641.28)9,580.20
(7,712.81)14,071.67 (9,31.10)
8,623.84 (6,874.54)
1Chi-square (df = 14) = 42.56, P < 0.001, cc=0.20 2Chi-square (df = 8) = 124.53, P < 0.001, cc=0.14 3Chi-square (df = 4) = 76.95, P < 0.001, cc=0.11 4F statistic [2,6589] = 214.6, P < 0.001 5Chi-square (df = 2) = 0.6, P > 0.05 6F statistic [2,6589] = 52.4, P < 0.001 7Rate in 2007:1US$= Ja$80.47
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Table 10.5. Ordinal logistic regression: Socio-demographic and biological differentials of self-rated health status of Jamaicans Characteristic Estimate
Std. Error Wald P
95% CI Upper Lower
Excellent self-rated health 0.0 0.0 Good self-rated health (ø1) 0.540 0.345 2.456 0.117 -0.135 1.216 Fair self-rated health (ø2) 3.504 0.625 31.465 0.000 2.279 4.728 Poor self-rated (ø3) 5.935 0.985 36.327 0.000 4.005 7.865 Very poor (ø4) 8.659 1.425 36.909 0.000 5.865 11.452 Age 0.045 0.008 34.055 0.000 0.030 0.060 Income -3.79E-007 0.000 10.636 0.001 -6.06E-007 -1.51E-007 Crowding 0.083 0.025 11.130 0.001 0.034 0.132 Primary or below -0.187 0.252 0.553 0.457 -0.681 0.307 Secondary 0.042 0.267 0.025 0.874 -0.481 0.566 Tertiary (=0) Sex (female=0) -0.221 0.077 8.290 0.004 -0.372 -0.071 Married -0.554 0.200 7.704 0.006 -0.945 -0.163 Never married -0.352 0.192 3.342 0.068 -0.729 0.025 Divorced -0.469 0.319 2.171 0.141 -1.094 0.155 Separated -0.109 0.369 0.087 0.768 -0.832 0.615 Widowed (=0) Poorest 20% 0.203 0.163 1.554 0.213 -0.116 0.523 Poor 0.013 0.140 0.009 0.925 -0.262 0.288 Middle 0.028 0.126 0.048 0.826 -0.219 0.274 Wealthy -0.238 0.122 3.782 0.052 -0.477 0.002 Wealthiest 20% (=0) Urban 0.217 0.090 5.789 0.016 0.040 0.395 Semi-urban 0.008 0.085 0.008 0.927 -0.159 0.174 Rural (=0)
Private insurance -0.175 0.110 2.542 0.111 -0.389 0.040
Public insurance 0.026 0.149 0.032 0.859 -0.265 0.318
Public insurance – other
0.387
0.209
3.433
0.064
-0.022
0.796
No insurance coverage (=0)
Illness 2.377 0.401 35.152 0.000 1.591 3.163Nagelkerke r-square = 0.33 Chi-square = 1,568.4, P < 0.001 LL = 9,218.0 n=4,433
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Chapter 11 Retesting and refining theories on the association between
illness, chronic illness and poverty: Are there other disparities?
Paul Andrew Bourne Poverty is well established as being associated with illness and chronic illness. Studies which have examined this phenomenon have done so using objective indices such as life expectancy, infant mortality and general morality. This study (1) examined subjective indices such as self-reported illness and self-reported health, (2) re-tested the theories that chronic illnesses are more likely to be greater in number among the poor and that illnesses are positively correlated with poverty, and (3) evaluated other social characteristics that account for the poverty-illness theory. The current study used a secondary cross-sectional dataset from the Jamaica Survey of Living Conditions (JSLC). The JSLC used an administered questionnaire where respondents were asked to recall detailed information on particular activities. The questionnaire was modelled on the World Bank’s Living Standards Measurement Study (LSMS) household survey. The cross-sectional survey was conducted between May and August 2002 in the 14 parishes across Jamaica and included 25,018 people of all ages. The statistical package SPSS 16.0 was used for the analysis. A p-value less than 5% (2-tailed) was used to indicate statistical significance. Those in the two wealthy social hierarchies were 18% less likely to report chronic illnesses compared to those in the two poor social hierarchies. Males were 69% less likely to report chronic illness compared to females as well as 56% less likely to indicate an illness. When the chronic illnesses were disaggregated by sex of respondents, the prevalence rate of females with hypertension was 2.2 times more than hypertensive males; 3.2 times more than male arthritic patients, and 3.0 times more than male diabetics. Forty-five percent of those with chronic illnesses were married. While poverty has declined in Jamaica since the 1990s, the health disparity between the poor and the upper social hierarchy continues to this day. The information provided in this research has far-reaching implications, and may be used to guide policies, frame interventions and provide a focus for future research in Jamaica.
Introduction
Empirically there are many studies which have found and established a statistical association
between poverty and illness [1-8]. Some research has shown that those in the lower
socioeconomic status are less healthy than those in the wealthy socioeconomic groups [9, 10]. A
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study by Van Agt et al. [8] found that poverty was greater among chronically ill people than the
non-chronically ill, and the WHO [4] concurred with Van Agt et al. [8] when it opined that 80%
of chronic illnesses were in low and middle income countries. Poverty is not only associated with
illness and ill-health, but also higher rates of mortality. According to the WHO [4], 60% of
global mortality is caused by chronic illness, and this should be understood within the context
that four-fifths of chronic dysfunctions are in low-to-middle income countries. The rationales
given for the poverty and illness theory are (1) money (insufficient financial resources); (2)
medical expenditure; and (3) other types of socio-political incapacity [3, 8, 11]. Sen [11]
encapsulated this well when he opined that high levels of unemployment in the economy are
associated with higher levels of capabilities, pointing to money and other incapacities of those
who are likely to be unemployed in the society. The poor are therefore more likely to be
unemployed, to be ill, to suffer from more chronic illnesses, to have insufficient money, low
levels of educational attainment, to experience a greater percentage of infant and other mortality
and to live in an inadequate physical environment, compared to those in the wealthy social
hierarchies.
Using objective indices such as infant mortality and life expectancy to measure the health
of a population, studies in Latin America and the Caribbean concur with the aforementioned
research. Cass et al. [12] found that infant mortality in Peru for those in the poorest quintile (i.e.
poorest 20%) was almost 5 times more than that for those in the wealthiest quintile (i.e.
wealthiest 20%). Another study revealed that out-of-pocket medical expenditure accounts for
some people becoming poor and that a greater percentage of these people do not have health
insurance coverage [2]. One study highlighted the fact that life expectancy between the poorest
20% and the wealthiest 20% was 6.3 years and this was 14.3 years for disability-free life
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expectancy [13]. The relationship between poverty and illness is longstanding, and the Director
of the Pan American Health Organization in 2001 wrote that it is still evident in contemporary
societies [14]. He however went further to state that poverty affects mental as well as physical
health, and concurs with the literature that those in the lower socioeconomic status have greater
levels of illnesses (i.e. psychopathology).
It has been clearly understood and well-established for centuries that poverty is
associated with illness, and that it affects those individuals by constricting their capacity, which
further affects their health. The poor have less access to money and other resources than the
wealthy, and are also deprived of a good health outcome in the future. A study by Mayer et al.
[15] provided evidence that there is a strong relationship between health and future economic
growth, suggesting that current poverty contracts future health and economic prosperity. Mayer
et al.’s work provides pertinent insight into the retardation of poverty, but also gives an
understanding of how poverty affects health, production, productivity and how it poses a present
and future problem for public health policy makers. How is this of concern to public health
policy makers in Jamaica?
A recent study conducted by Bourne [16] found that (1) moderate and direct correlation
between the prevalence of poverty (in %) and unemployment (R2 = 0.48); (2) direct association
existed between not seeking medical care (in %) and prevalence of poverty (in %) – R2 = 0.58;
(3) a strong statistical relationship between prevalence of poverty and mortality – R2 = 0.51; and
(4) a non-linear relationship between not seeking medical care and illness. From Bourne’s
findings, the challenges for public health specialists as well as policy makers are a reality in
Jamaica, as in other nations. If poverty is associated with unemployment and not seeking medical
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care, and not seeking medical care is related to illness, it appears to be a non-issue to re-test the
established theory of poverty and illness and poverty and chronic illness in Jamaica, but this is
not the case as there is self-reported illness may not give the same result as diagnosed illnesses.
None of the aforementioned studies that have examined poverty and illness have used
self-reported data to test the poverty and illness, and poverty and chronic illness phenomena. The
aims of the current study are to investigate (1) poverty and self-reported illness, (2) poverty and
self-reported chronic illness, and (3) other socio-demographic characteristics, in order to provide
an understanding of existing disparities as well as to concur with, or refute, current theories.
Methods
Study population The current study used a secondary cross-sectional dataset from the Jamaica Survey of Living
Conditions (JSLC). The JSLC was provided by the Planning Institute of Jamaica (PIOJ) and the
Statistical Institute of Jamaica (STATIN) for analysis [17-19]. These two organizations are
responsible for planning, data collection and formulating policy guidelines for Jamaica. The
cross-sectional survey was conducted between May and August 2002 in the 14 parishes across
Jamaica and included 25,018 people of all ages [20]. The JSLC used stratified random
probability sampling technique to draw the original sample of respondents, with a non-response
rate of 26.2%. The sample was weighted to reflect the population.
Study instrument
The JSLC used an administered questionnaire where respondents were asked to recall detailed
information on particular activities. The questionnaire was modelled on the World Bank’s Living
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Standards Measurement Study (LSMS) household survey. The questionnaire covered
demographic variables, health, education, daily expenses, non-food consumption expenditure
and other variables. Interviewers were trained to collect the data from household members.
Statistical methods Descriptive statistics were used to provide socio-demographic characteristics of the sample. Chi-
square analyses were used to examine the association between non-metric variables. Analysis of
variance was used to test the statistical significance of a metric and non-dichotomous variable.
Logistic regression analyses examined 1) the relationship between good health status and some
socio-demographic, economic and biological variables; as well as 2) a correlation between
medical care-seeking behaviour and some socio-demographic, economic and biological
variables. The statistical package SPSS 16.0 was used for the analysis. A p-value less than 5%
(2-tailed) was used to indicate statistical significance.
The correlation matrix was examined in order to ascertain if autocorrelation and/or
multicollinearity existed between variables. Based on Cohen and Holliday [21] correlation can
be low (weak) - from 0 to 0.39; moderate – 0.4-0.69, and strong – 0.7-1.0. Any variable that had
at least moderate (r > 0.6) was re-examined in order to address multicollinearity and/or
autocorrelation between or among the independent variables [22-28]. Another approach in
addressing collinearity (r > 0.6) was to independently enter variables in the model to determine
which one should be retained during the final model construction. The method of retaining or
excluding a variable from the model was based on the variables’ contribution to the predictive
power of the model and its goodness of fit. Wald statistics were used to determine the magnitude
(or contribution) of each statistically significant variable in comparison with the others, and the
Odds Ratio (OR) for the interpreting of each significant variable.
Measures
Self-reported illness status is a dummy variable, where 1 = reporting an ailment or dysfunction or
illness in the last 4 weeks, which was the survey period; 0 if there were no self-reported ailments,
injuries or illnesses [29-31]. While self-reported ill-health is not an ideal indicator of actual
health conditions because people may under-report, it is still an accurate proxy of ill-health and
mortality [32, 33]. Health status is a binary measure where 1=good to excellent health; 0=
otherwise which is determined from “Generally, how do you feel about your health?” Answers
for this question are on a Likert scale, ranging from excellent to poor. Medical care-seeking
behaviour was taken from the question “Has a health care practitioner, healer, or pharmacist been
visited in the last 4 weeks?” with there being two options: Yes or No. Medical care-seeking
behaviour therefore was coded as a binary measure where 1=Yes and 0= otherwise. Crowding is
the total number of individuals in the household divided by the number of rooms (excluding
kitchen, verandah and bathroom).
Sex: This is a binary variable where 1= male and 0 = otherwise.
Age is a continuous variable which is the number of years alive since birth (using last birthday).
where ki represents the frequency with which an individual
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witnessed or experienced a crime, where i denotes 0, 1 and 2, in which 0 indicates not witnessing
or experiencing a crime, 1 means witnessing 1 to 2, and 2 symbolizes seeing 3 or more crimes.
Tj denotes the degree of the different typologies of crime witnessed or experienced by an
individual (where j = 1…4, which 1 = valuables stolen, 2 = attacked with or without a weapon, 3
= threatened with a gun, and 4 = sexually assaulted or raped. The summation of the frequency of
crime by the degree of the incident ranges from 0 to a maximum of 51.
Result The sample was 25,018 respondents: males, 49.3%; rural residents, 61%; semi-urban
residents, 25.6%; married, 16.2%; never married, 67.3%; divorced, 0.8%; separated, 1.2%;
widowed, 5.6%; self-reported illness, 12.5%; self-reported injury, 1.2%; health care seekers in
the last 4-week period, 63.9%; level of education primary or below, 20.9; secondary level
education, 73.1%, and the mean age of the sample was 28.8 years (SD = 22.0 years). The mean
number of people per room was 2.0 (SD = 1.4), and the mean number of crimes experienced
(including family members) was 2.1 (SD = 8.0).
Table 11.1 presents information on demographic characteristics of the sample by area of
residence for 2002. There was a significant statistical association between social hierarchy and
area of residence – χ2 = 1739.98, P < 0.0001. Poverty (i.e. poorest 20%) was substantially a rural
phenomenon (74.9%) compared to semi-urban poverty (17.2%) and urban poverty (7.9%) - χ2 =
1739.98, P < 0.0001. Almost 14% of rural residents reported having an illness in the last 4 weeks
compared to semi-urban residents (10.9%) and urban residents (10.9%) - χ2 = 36.861, P <
0.0001. However, for 2002, no significant statistical relationship existed between self-reported
diagnosed health conditions and area of residents - χ2 = 12.62, P = 0.397.
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The mean age of the sample was 28.8 years (± 22.0 years), with there being a statistical
difference between the mean ages of respondents based on their area of residence – F-statistic [2,
24991] = 7.28, P < 0.0001: the mean age of rural residents was 29.1 years (± 22.6 years); that of
semi-urban residents was 27.9 years (± 21.0) and the mean age of urban dwellers was 29.1 years
(± 21.0 years). Concurringly, the mean number of visits to health care practitioners in the last 4-
week period was 1.7 (± 1.4). There was a significant statistical difference between the mean
number of visits to health care practitioners and area of residence (F-statistic = 5.48, P = 0.004:
the mean number of visits by rural residents was 1.6 (± 1.2) compared and 2.0 (± 2.5) for urban
dwellers, but non between rural and semi-urban dwellers (1.6 ± 1.2). However, there was no
significant difference between mean medical expenditure and area of residence (mean public
health care expenditure was USD 9.05 ± USD 25.65 – F-statistic [2, 1126] = 0.577, P = 0.562;
and mean private health care expenditure was USD 24.40 ± USD 37.13 – F-statistic [2,935] =
0.577, P = 0.220).
There was a significant statistical difference between crime and victimization and area of
residence - F-statistic [2, 24958] =28.604, P < 0.0001. The mean number of crimes and incidents
of victimization experienced by people in rural residents was 1.8 ± 7.7 compared to semi-urban
residents, 2.3 ± 8.0; and urban dwellers, 2.9 ± 9.3.
Table 11.2 examines visits to health care facilities, health insurance coverage, educational
level and crime by social hierarchy.
When self-reported illness and social hierarchy was disaggregated by area of residence,
the significant statistical relationship was explained by rural areas (χ2 = 30.92, P < 0.0001) and
not semi-urban (χ2 = 8.84, P = 0.065) and urban areas (χ2 = 1.74, P = 0.789).
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Table 11.3 presents information on self-reported injury, normally go if ill/injured, why
didn’t seek care for current illness, length of illness and number of visits to health practitioner by
social hierarchy. A statistical relationship existed between each of the variables (P < 0.0001). A
statistical difference existed between the mean length of the illness among the social hierarchy –
F statistic = 2.536, P = 0.038. This difference was accounted for by the poorest 20% and the
wealthy (P = 0.049) and the poorest 20% and the wealthiest 20% (P = 0.049). Likewise the
statistical difference between the mean number of visits made to medical practitioner(s) and
social hierarchy were accounted for by the poorest 20% and wealthy (P = 0.011) and the poorest
20% and wealthiest 20%.
The prevalence of chronic illness was 104 out of every 10,000 respondents. On
disaggregating the overall prevalence of chronic illness into the different typology of conditions
it was found that 5 out of every 10,000 respondents had diabetes mellitus; 50 out of every 10,000
had hypertension; 28 per 10,000 had arthritis; and other chronic illnesses (unspecified) accounted
for 21 per 10,000.
Chronic illness was more a female phenomenon than for males- χ2 = 6.56, P = 0.013. The
prevalence rate of females with chronic illness was 144 per 10,000 compared to 62 per 10,000
for males. Furthermore, the prevalence rates of those with particular chronic illnesses by sex was
as follows: diabetes mellitus 2 per 10,000 for males and 7 per 10,000 for females; hypertension
32 per 10,000 for males and 69 per 10,000 for females; arthritis 13 per 10,000 for males and 42
per 10,000 for females and other chronic conditions, 15 per 10,000 for males and 27 per 10,000
for females. Seventy-two percent of those who indicated that they had a chronic illness sought
medical care in the last 4-week period, compared to 78.9% not suffering from a chronic illness
who sought medical attention - χ2 = 0.030, P = 0.562. Likewise no statistical association existed
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between health insurance coverage and chronic illness - χ2 = 0.048, P = 0.649. Concurringly,
there was a significant statistical association between marital status and individuals with chronic
illness - χ2 = 12.708, P = 0.013. Of those who indicated that they had chronic illness, 44.9%
were married; 29.1% were never married; 0.4% divorced; 1.2% separated and 24.4% widowed.
Multivariate analyses
Table 11.4 provides information on particular variables and their correlation (or not) with self-
reported illness. Of the 17 variables identified from the literature and available for this study, 5
emerged as being statistically significant correlates of self-reported illness of Jamaicans (i.e.
social hierarchy, medical expenditure, sex, age and income) - Model χ2 (17) =56.45, P < 0.001.
The statistically significant correlates accounted for 14.8% of the variability in self-reported
illness.
Table 11.5 examines social hierarchy and sex and their influence (or not) on self-reported
chronic illness. One sex emerged as being a statistically significant correlate of self-reported
chronic illness in Jamaica - Model χ2 (3) =6.42, P < 0.001.
Discussion
The current study revealed that 13 out of every 100 Jamaicans reported an illness in the 4-week
surveyed period. Concurringly, those in the two wealthy social hierarchies were 18% less likely
to report chronic illnesses compared to those in the two poor social hierarchies, and the former
group was 64% less likely to report an illness compared to the latter group. Males were 69% less
likely to report chronic illness compared to females, as well as 56% less likely to indicate an
illness. The prevalence rate of those with chronic illness was 104 per 10,000 respondents –
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diabetes, 5 per 10,000; hypertension, 50 per 10,000; arthritis, 28 per 10,000 and other chronic
conditions, 21 per 10,000. When the chronic illnesses were disaggregated by sex of respondents,
the prevalence rate of females with hypertension was 2.2 times more than hypertensive males;
3.2 times more than male arthritic patients, and 3.0 times more than male diabetics. Poverty was
substantially a rural phenomenon (75%), and almost 14% of rural residents indicated an illness
compared to semi-urban (11%) and urban dwellers (11%). The disparity did not cease there as
rural residents had the least percentage of people with tertiary level education, and the least per
capita consumption, which was 57.4% of consumption per capita of urban residents and 69.0%
of that consumption per capita of semi-urban people. On the contrary, those in the poorest 20%
self-reported fewer injuries (owing to work and care accidents, poisoning, and burns) than those
in the wealthiest 20%.
For centuries, using objective indices such as life expectancy, infant mortality and
general mortality, it has been well established that poverty is associated with illness, and those
with more chronic illnesses are more likely to be poor. The current study, using self-reported
illnesses, has concurred with the literature that the poor report more illnesses and are more likely
to have more chronic illness than those in the upper class. This study, however, found that there
is no significant statistical correlation between self-reported illness or chronic illness of those in
the poor social hierarchies and those in the middle class. The current research does not concur
with the literature that married people are healthier than other marital cohorts [34-38] as the
findings showed no statistical association between marital status and self-reported illness.
However, the findings revealed that almost 45% of those with chronic illnesses were married
compared to those who were never married, widowed, separated or divorced.
Lillard and Panis [39] contradicted many of the traditional findings, for instance that
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married people are healthier and report less health conditions than non-married people. They
found that healthier men are less likely to be married; and secondly, that healthier married men
enter into unions later in life and that they do postpone remarriage. Conversely, Lillard and Panis
[39] revealed that it is unhealthy men who enter marriage at an early age, which suggests that
these men do so because of health reasons [39]. This then would support the current research of
married people indicating more chronic illnesses than non-married people. Concurringly, married
people do not report more illnesses, but do report more chronic illnesses than non-married people
in this study.
An interesting finding that emerged from this study is the low statistical relationship
between self-reported illness and self-reported injury (i.e. contingency coefficient = 0.11).
Furthermore 4.4% of those who indicated that they were ill had an injury in the last 4 weeks, and
of those who had an injury, 46.2% claimed they were ill. This denotes that few people
considered illness and injury and vice versa. Illnesses therefore is in keeping with acute and
chronic health conditions, and less so with injuries caused by accidents, burns, poisoning and
other such events.
Marmot [3] asked the question “Does money matter for health? If so, why?” It is the lack
of money (i.e. insufficient money) that accounts for the inability of the poor to access (1) higher
level education; (2) greater and better, or the best, health care treatment; (3) a better physical
milieu; (4) lower levels of infant mortality; (5) better material conditions; (6) clean water and
nutrition; and (7) social position. It follows that poverty incapacitates the individual and this
extends into the future if he/she is not assisted by external sources. Does money really make a
difference in Jamaica? The answer is a resounding yes. Those in the poorest 20% spent on
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average almost 3 times less than those in the wealthiest 20%, and the second poor spent 2 times
less than those in the wealthiest 20% on medical expenditure. Concurringly, 76 out of every 100
of those in the poorest 20% normally utilize public health facilities (including hospitals)
compared to 28 out of every 100 of those in the wealthiest 20%.
Poverty therefore retards people’s health care choices, expenditure on medication, and by
extension healthy life expectancy. The current study found that 35 out of every 100 respondents
in the poorest 20% indicated that the reason why they have not visited a health care practitioner
was owing to insufficient funds, compared to 9 out of every 100 of those in the wealthiest 20%.
Furthermore, findings from the present research showed that people who spend more on medical
expenditure are 39% less likely to report an illness, suggesting that the poor are more likely to be
living with their health conditions without seeking medical care, compared to the wealthy. This
matter of insufficient financial resources hampers the healthy life expectancy of the poor, as well
as explaining the greater infant and general mortality among them than those in the upper class.
According to Grossman [40], Smith and Kington [41], there is a positive statistical association
between income and health, and income and demand for health, which further unfolds the
complexity of poverty and health. Corbett [42] argued that Edwin Chadwick, in the 1840s,
believed “that the primary cause of pauperism and misery was not poverty or rampant capitalism,
but filth.” This study is not arguing that the main cause of pauperism is ill-health, but it does
substantiate an association between poverty and illness and poverty and chronic illness. This
finding is contrary to the belief of Edwin Chadwick; insufficient money does account for some
amount of illness, and illness can lead to poverty and future constraints on capabilities, limiting
opportunities for the creation of a better life for themselves.
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If those in the poorest 20% group experienced illnesses and visited medical practitioners
more than those in the upper class, it follows that poverty explains (1) most of the prevalence of
illness, (2) the severity of the illness, and (3) more chronic illnesses. Money therefore does
matter in health, and offers an explanation of how chronic illness can result in poverty, and how
pauperism leads to increased morbidity and premature mortality. An understanding of poverty in
Jamaica as well as a comprehensive knowledge of the relationship between poverty and illness as
well as the other health inequalities, will aid physicians in understanding the reasons for the
disproportionately greater number of poor visiting them and having particular chronic illnesses.
Health is also a social phenomenon, and so physicians need training in the roles of social
determinants and their influence on health, as these are outside of the clinical laboratory, but
provide an understanding of those on the social margins of the health care system. Given that
illness is influenced by exposure to pathogens, the socio-physical milieu of the poor, coupled
with their incapacitation because of money, provides some insights into their plight. It is critical
to understand this group and where they live, as Kiefer said, and to see poverty “not as a simple
economic condition, but as a state of demoralization, where people lack all or most of the
minimum ingredients we accept as the basis of a decent life” [43] and we can also add the
justifications of their encounter with illness and particular health conditions such as tuberculosis,
HIV/AIDS, diarrhoea, respiratory tract infections, arthritis and malaria.
Another issue is nutritional deficiency, as some people hold the belief that so long as they
have something to eat, or a ‘full tummy’, it is enough to prevent illness. The image of a ‘full
tummy’ is embedded in those in the lower socioeconomic class and not the upper class. It
follows therefore that households in lower socioeconomic group find it difficult to address
material, food and opportunity deprivation within the context of a social setting to pay special
292
attention to the nutritional value in food intake. Households in low-income groups are
substantially found in rural areas in Jamaica where a ‘full tummy’ is important and not the
nutritional intake of the food groups. According to Foster [44] “…a better-off individual who is
generally healthy may be more readily able to identify when he or she is ill than a poor
individual with low caloric intake.” Within Foster’s perspective lies the underlying fact that
reported illnesses among those in the lower socioeconomic group may be understated figures, as
their image of ill-health is hampered by nutritional deficiency. Diet and nutrition are important
ingredients in good health [45], but do residents of low-income rural areas as well as low-income
urban areas know that a deficient intake of calcium, iron, magnesium, zinc, folate, vitamin A,
vitamin B6 and vitamin C is responsible for some of their illnesses? And another aspect to this
discussion is their image of health, illness and the role that these play in influencing the collected
survey data on health, health conditions and health outcome from those in the lower
socioeconomic group.
Conclusion
For centuries researchers have been using objective indices such as life expectancy, infant
mortality and the general mortality of a population or sub-population to measure health, and
these have been used to establish a statistical association with poverty. Other scholars and
institutions have found a significant statistical relationship between diagnosed illness and
poverty, but this research has established that self-reported illness and self-reported diagnosed
health conditions can be used instead of the objective indices of the past. While those people in
poor social hierarchies were more likely to report more illnesses and self-reported chronic
293
illnesses than those in the wealthy group, there is no difference between those in the poor group
and the middle class.
Those with chronic illnesses are not only more likely to be poor, they are married,
females, rural residents, less educated at the tertiary level, more likely to visit public hospitals,
most likely to have hypertension, and there is less probability that they will utilize health care
facilities than the upper class. In summary, subjective indices such as self-reported illness or self-
reported diagnosed health conditions can be used to measure health as the traditional infant
mortality, general mortality and life expectancy. Poverty indeed still continues to influence ill-
health, and those with chronic illnesses are more likely to be poor than in the upper class, but
other demographic characteristics provide more information on the poor and those with chronic
illnesses.
In summary, much investment has been made in health and this clearly has not reduced
the inequalities and disparities between and among the different social groups in Jamaica. It
means that merely mobilizing greater domestic resources for health will not address the
inequalities, as using national health aggregates do not provide a detailed understanding of the
disparities between and among groups. While poverty has declined in Jamaica since the 1990s,
the health disparity between the poor and the upper social hierarchy has continued to this day.
The information provided in this research has far-reaching implications, and can be used to guide
policies, frame interventions and provide a focus for future research in Jamaica.
The way forward
Subjective indices such as self-reported illness and self-reported chronic illness can be used to
measure ill-health and replace infant and general mortality in the study of health. The use of
national statistics does not provide a comprehensive understanding of the health disparity and
294
inequalities between and among the social groups in a society. In order to address some of the
health inequalities and disparities in society, programmes are needed that will address issues in
rural areas, gender, income inequalities, and the health disparities between public and private
health care services offered to the public. Another area which must be addressed is that of the
nutritional deficiencies between and among the social hierarchies and area of residences. A
national dietary survey is needed in order to provide evidence for policy intervention as well as
the role of identified social problems and their influence on mental health. Concurringly, future
research is needed to examine the harmful effects of mental health on the accumulation of
people’s negative life events, and their effects on the crime problem in the Caribbean. Another
issue which must be investigated is the quality of care offered to the poor from the perspective of
the individual (i.e. a survey research). This would provide pertinent information as to whether
those people who are poor perceived themselves to be receiving the worst health, and to devise a
method that will objectively assess, service and deliver to the social group in order to address
this, if it is contributing to the lower health outcomes. Researchers need to treat poverty as an
illness and not a cause of illness, which would allow for a new shift in the study of poverty, and
this thereby could provide more answers to health practitioners and policy makers.
Conflict of interest
The author has no conflict of interest to report.
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Table 11.1: Demographic characteristic of sample, 2002 Characteristic
2002 P Area of residence
Urban Semi-urban Urban n (%) n (%) n (%) Sex < 0.0001 Male 7727(50.7) 3062(47.9) 1543(46.0) Female 7524(49.3) 3337(52.1) 1814(54.0) Marital status < 0.0001 Married 2460(25.5) 1115(26.9) 475(21.0) Never married 6436(66.6) 2758(66.5) 1619(71.6) Divorced 56(0.6) 41(1.0) 26(1.2) Separated 104(1.1) 49(1.2) 32(1.4) Widowed 610(6.3) 187(4.5) 108(4.8) Self-reported diagnosed illness 0.397 Acute conditions Influenza 1(0.5) 0(0.0) 0(0.0) Diarrhoea 4(2.1) 5(8.9) 0(0.0) Respiratory 6(3.1) 2(3.6) 1(3.1) Chronic conditions Diabetes mellitus 10(5.2) 1(1.8) 1(3.1) Hypertension 82(42.9) 29(51.8) 15(46.9) Arthritis 48(25.1) 13(23.2) 8(25.0) Other 40(20.9) 6(10.7) 7(21.9) Health care-seeking behaviour 0.816 Yes 1302(63.8) 436(63.4) 228(65.3) No 740(36.2) 252(36.6) 121(34.7) Self-reported illness < 0.0001 Yes 1987(13.5) 669(10.9) 354(10.9) No 12713(86.5) 5488(89.1) 2902(89.1) Health insurance < 0.0001 Yes 1036(7.0) 1023(16.5) 612(18.7) No 13714(93.0) 5178(83.5) 2654(81.3) Social hierarchy < 0.0001 Poorest 20% 3724(24.4) 858(13.4) 393(11.7) Poor 3574(23.4) 968(15.1) 414(12.3) Middle 3169(20.8) 1217(19.0) 598(17.8) Wealthy 2774(18.2) 1427(22.3) 822(24.5) Wealthiest 20% 2017(13.2) 1929(30.1) 1130(33.7) Per capita consumption mean ± SD (in USD)
1181±1340 1771±1605 2129±2434
†USD 1.00 = Ja. $ 80.47 at the time of the survey) (2007) ††USD 1.00 = Ja. $50.97 (in 2002)
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Table 11.2. Particular variable by social hierarchy, 2002 Characteristic
Social hierarchy P Poorest 20%
Poor Middle Wealthy Wealthiest 20%
Sex 0.002 Male 2454(49.3) 2345(47.3) 2440(49.0) 2482(49.4) 2611(51.4) Female 2520(50.7) 2609(52.7) 2542(51.0) 2540(50.6) 2464(48.6)Marital status < 0.0001 Married 569(21.1) 656(22.3) 742(23.3) 860(25.4) 1223(31.7) Never married 1926(71.3) 2094(71.2) 2229(69.9) 2303(67.9) 2261(58.7) Divorced 14(0.5) 5(0.2) 16(0.5) 26(0.8) 62(1.6) Separated 30(1.1) 21(0.7) 30(0.9) 31(0.9) 73(1.9) Widowed 162(6.0) 164(5.6) 173(5.4) 172(5.1) 234(6.1)Visits to health care institutions (for last visit) Public hospitals 166(49.3) 135(38.5) 164(42.7) 175(42.1) 137(30.6) < 0.0001 Private hospitals 14(4.2) 29(8.3) 19(5.0) 40(9.7) 52(11.7) < 0.0001 Public health care centre 107(31.7) 102(29.1) 75(19.6) 64(15.5) 34(7.6) < 0.0001 Private health care centre 76(22.6) 120(34.1) 137(35.6) 176(42.2) 258(57.2) < 0.0001Health insurance ownership < 0.0001 Yes 84(1.7) 172(3.6) 270(5.6) 655(13.5) 1490(30.7) No 4745(98.3) 4651(96.4) 4574(94.4) 4204(86.5) 3370(69.3)Educational level < 0.0001 Primary and below 609(24.6) 588(22.0) 628(22.7) 604(20.1) 568(16.5) Secondary 1837(74.3) 2048(76.5) 2114(75.3) 2249(75.0) 2292(66.4) Tertiary 25(1.0) 41(1.5) 57(2.0) 146(4.9) 591(17.1)Crime and victimization index mean ± SD 2.4±10.2 1.5±4.9 2.0±7.2 2.2±8.5 2.4±8.2Age mean ± SD 25.5±22.7 26.8±22.2 28.3±21.9 29.6±21.3 33.8±20.9 < 0.0001Crowding mean ± SD 3.0±1.8 2.3±1.3 2.0±1.2 1.6±0.9 1.2±0.8 < 0.0001Total medical expenditure mean ± SD (in USD)†
15.22±28.91 21.67±37.99 22.54±42.87 33.11±70.35 45.53±79.52 < 0.0001
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†USD 1.00 = Jamaican $50.97
Table 11.3. Self-reported injury, normally go if ill/injured, why didn’t seek care for current illness, length of illness and number of visits to health practitioner by social hierarchy, 2002 Characteristic
Social hierarchy P Poorest 20%
Poor Middle Wealthy Wealthiest 20%
Self-reported injury < 0.0001 No 4811(99.1) 4815(99.1) 4801(98.9) 4806(98.7) 4797(98.2) Yes 46(0.9) 43(0.9) 54(1.1) 61(1.3) 87(1.8) Normal go it ill/injury < 0.0001 Public hospital 2252(46.4) 2004(41.3) 1786(36.8) 1449(29.7) 1049(21.5) Public health centre 1474(30.3) 1124(23.2) 854(17.6) 605(12.4) 315(6.5) Private hospital 1123(23.1) 1713(35.3) 2202(45.4) 2799(57.4) 3498(71.6) Pharmacy 2(0.0) 0(0.0) 1(0.0) 3(0.1) 3(0.1) Other 7(0.1) 8(0.2) 12(0.2) 17(0.3) 10(0.4)Why didn’t seek care for current illness
< 0.0001
Could not afford it 72(35.1) 61(26.3) 47(21.3) 23(11.2) 19(8.6) Was not ill enough 59(28.8) 92(39.7) 111(50.2) 105(51.2) 97(43.9) Use home remedy 50(24.4) 43(18.5) 35(15.8) 47(22.9) 61(27.6) Did not have the time 2(1.0) 2(0.9) 10(4.5) 6(2.9) 14(6.3) Other (unspecified) 22(10.7) 34(14.7) 18(8.1) 24(11.7) 30(13.6)Length of illness (in days) mean ± SD
11.5±10.4 10.8±10.0 10.4±10.9 9.8±9.7 9.9±9.7 0.038
Number of visits to health practitioner mean ± SD
6.1±8.8 5.5±8.6 4.9±7.7 4.6±6.3 4.8±7.7 0.007
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Table 11.4. Logistic regression: Self-reported illness by particular variables
Variable
CoefficientStd. error Wald
statistic P
Odds ratio
95.0% C.I.
Lower Upper Injury -0.20 0.32 0.40 0.53 0.82 0.44 1.52 Health care-seeking 0.57 0.43 1.81 0.18 1.78 0.77 4.09 Middle -0.80 0.51 2.49 0.12 0.45 0.17 1.21 Two Wealthy quintiles -1.03 0.51 4.02 0.04 0.36 0.13 0.98 †Two poor quintiles 1.00 Logged medical expenditure -0.49 0.14 12.00 0.00 0.61 0.47 0.81
Durable goods 0.01 0.07 0.01 0.91 1.01 0.88 1.16 Separated, divorced or widowed 0.27 0.64 0.18 0.67 1.31 0.38 4.57
Married 0.08 0.42 0.03 0.86 1.08 0.47 2.47†Never married 1.00 Physical environment -0.43 0.33 1.74 0.19 0.65 0.34 1.23 Semi-urban -0.01 0.37 0.00 0.99 0.99 0.48 2.07 Urban 0.96 0.77 1.58 0.21 2.62 0.59 11.72†Rural 1.00 Secondary -0.33 0.44 0.55 0.46 0.72 0.31 1.71 Tertiary -0.90 0.87 1.07 0.30 0.41 0.08 2.23†Primary or below 1.00 Sex 0.81 0.32 6.54 0.01 0.44 0.24 0.83 Crowding -0.15 0.16 0.88 0.35 0.86 0.63 1.18 Age 0.03 0.01 5.51 0.02 1.03 1.01 1.05 Total expenditure 0.00 0.00 3.54 0.06 1.00 1.00 1.00
Model χ2 =56.45, P < 0.001 -2 Log likelihood = 368.58 Nagelkerke R2 =0.148 Hosmer and Lemeshow goodness of fit χ2= 6.53, P = 0.59 Overall correct classification =97.1% Correct classification of cases of self-rated illness =100.0% Correct classification of cases of not self-rated ill =54.9% †Reference group
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Table 11.5. Logistic regression: Self-reported chronic illness by some variable
Variable Coefficient Std. error Wald
statistic P
Odds ratio
95.0% C.I.
Lower Upper Middle -0.34 0.66 0.26 0.61 0.72 0.20 2.62 Two wealthy quintiles -0.33 0.58 0.31 0.58
0.72 0.23 2.26
†Two poor quintiles 1.00 Sex -1.16 0.49 5.75 0.02
0.31 0.12 0.81
Model χ2 =6.42, P < 0.001 -2 Log likelihood = 368.58 Nagelkerke R2 =0.06 Hosmer and Lemeshow goodness of fit χ2= 1.34, P = 0.854 Overall correct classification =93.2% Correct classification of cases of self-rated illness =100.0% Correct classification of cases of not self-rated ill =49.9% †Reference group
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Chapter 12
Variations in social determinants of health using an adolescence population: By different measurements, dichotomization and non-dichotomization of health
Paul A. Bourne
On examining health literature, no study emerged that evaluated whether the social determinants vary across measurement, dichotomization, non-dichotomization and aged cohorts. With the absence of research on the aforementioned areas, it can be extrapolated that social determinants of health are constant across measurement, dichotomization and non-dichotomization, and this assumption is embedded in health planning. This paper seeks to elucidate (1) whether social determinants of health vary across measurement of health status (ie self-rated health status or self-reported antithesis of disease) or the cut-off (dichotomization) and/or the non-cut-off of health status (non-dichotomization), (2) examine the similarities between social determinants found in the literature and that of using an adolescence population, (3) whether particular demographic characteristic as well as illness and health status vary by area of residence of respondents, (4) the health status of the adolescence population, (5) typology of health conditions that they experience, and (6) evaluate the antithesis of illness (disease) and self-rated health. The current study extracted a sample of 1, 394 respondents aged 10 to 19 years old from the 2007 Jamaica Survey of Living Conditions (JSLC). The present subsample represents 20.6% of the 2007 national cross-sectional sample (n = 6,783). Multivariate logistic and ordinal logistic regression analyses were used to examine the association between many independent variables and a single dependent variable. In this study, health was measured using (1) self-rated health status or (2) the antithesis of illness (not reporting a health condition). The dichotomization of each denotes the use of two groups, and non-dichotomization means that self-rated health status was used in its Likert scale form (i.e. very good; good; moderate; poor and very poor). Antithesis of illness is a better measure than self-reported health status in determining social determinants because of its explanatory power (53%) compared to those that used the self-rated health status (at most 38%). There were noticeable variations in social determinants of health among the dichotomized, non-dichotomized health and antithesis of illness. Social determinants of health vary across the measurement and dichotomization and non-dichotomization of health status. The findings provide insights into the social determinants and health, and recommend that we guard against a choiced approach without examining the studied population in question.
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Introduction
Adolescents aged 10 to 19 years are among the most studied groups in regard health issues in the
Caribbean, particularly sexuality and reproductive health matters [1-4]. Apart of the rationales
for the high frequency of studies on those in the adolescence years are owing to the prevalence of
HIV/AIDS, unwanted pregnancy, inconsistent condom usage, mortality arising from the
HIV/AIDS virus, and other risky sexual behaviour. With one half of those who are infected with
the HIV/AIDS virus being under 25 years old [1], this provides a justification for the importance
of researching this aged cohort. Statistics revealed that the HIV virus is the 3rd leading cause of
mortality among Jamaicans aged 10-19 years old (3.4 per 100,000, for 1999 to 2002) [5], and
again this provides a validation for the prevalence of studies on this cohort. Outside of the
Caribbean, sexuality and reproductive health matters among adolescents are well studied [6-11],
suggesting that those issues are national, regional and international.
While sexuality and reproductive health matters are critical to the health status of people
[1], reproductive health problems as well as sexuality form a part of the general health status.
Health is more that the ‘antithesis of diseases’ [12] or reproductive health problems as it extends
to social, psychological or physical wellbeing and not merely the antithesis of diseases [13].
Bourne opined that despite the broadened definition of health as offered by the WHO [14],
illness is still widely studied in the Caribbean, particularly among medical researchers and/or
scholars. A search of the West Indian Medical Journal for the last one half decade (2005-2010), a
Caribbean scholarly journal, revealed that the majority of the studies have been on different
variations of illness, and antithesis of diseases instead of the broadened construct of health.
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Outside of the West Indian Medical Journal, few Caribbean studies have sought to
examine the health status of adolescents [15-18] but even fewer published research were found
that examine quality of life of those in the adolescence years [19]. Even though quality of life is
a good measure of general health status, international studies exploring quality of life and 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
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.
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Statistics revealed that one in every five Jamaican is aged 10-19 years old [39], which
means this is a substantial population and because of its influence of future labour supply it is of
great value. Although Pan American Health Organization (PAHO) [5] stated that adolescents
enjoy good health, and only about 2% of morality in 2003, which was equally the case for
adolescents in the Americas, this information does not indicate distancing examination from their
health status. The current work, therefore, will bridge the gap in the literature by evaluating
social determinants of health among those in the adolescence years across varying measurement
of health. Using data for 2007 Jamaica Survey of Living Conditions (2007 JSLC), this paper
seeks to elucidate (1) whether social determinants of health vary across measurement of health
status (ie self-rated health status or self-reported antithesis of disease) or the cut-off
(dichotomization) and/or the non-cut-off of health status (non-dichotomization), (2) are there
similarities between social determinants found in the literature and that of using an adolescence
population, (3) whether particular demographic characteristic as well as illness and health status
vary by area of residence of respondents, (4) what is the health status of the adolescence
population, (5) typology of health conditions that they experience, and (6) evaluate the antithesis
of illness (disease) and self-rated health.
Methods and measure
Data
The current study extracted a sample of 1, 394 respondents aged 10 to 19 years old from the
2007 Jamaica Survey of Living Conditions (JSLC). The inclusion/exclusion criterion for this
study is aged 10 to 19 years old. The present subsample represents 20.6% of the 2007 national
cross-sectional sample (n = 6,783). The JSLC is an annual and nationally representative cross-
sectional survey that collects information on consumption, education, health status, health
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conditions, health care utilization, health insurance coverage, non-food consumption
expenditure, housing conditions, inventory of durable goods, social assistance, demographic
characteristics and other issues [40]. The information is from the civilian and 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.
The survey was drawn using stratified random sampling. This design was a two-stage
stratified random sampling design where there was a Primary Sampling Unit (PSU) and a
selection of dwellings from the primary units. The PSU is an Enumeration District (ED), which
constitutes a minimum of 100 residences in rural areas and 150 in urban areas. An ED is an
independent geographic unit that shares a common boundary. The country was grouped into
strata of equal size based on dwellings (EDs). Based on the PSUs, a listing of all the dwellings
was made, and this became the sampling frame from which a Master Sample of dwellings was
compiled, which in turn provided the sampling frame for the labour force. One third of the
Labour Force Survey (LFS) was selected for the JSLC.
Overall, the response rate for the 2007 JSLC was 73.8%. Over 1994 households of
individuals nationwide are included in the entire database of all ages [40]. A total of 620
households were interviewed from urban areas, 439 from other towns and 935 from rural areas.
This sample represents 6,783 non-institutionalized civilians living in Jamaica at the time of the
survey. The JSLC used complex sampling design, and it is also weighted to reflect the
population of Jamaica. This study utilized the data set of the 2007 JSLC to conduct our work
[42].
Measure
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Age is a continuous variable which is the number of years alive since birth (using last birthday)
Adolescence population is described as the population aged 10 to 19 years old [23]
Self-reported illness (or self-reported dysfunction): The question was asked: “Is this a diagnosed
recurring illness?” The answering options are: Yes, Cold; Yes, Diarrhoea; Yes, Asthma; Yes,
Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No. For the antithesis of disease
(illness) a binary variable was created, where 1= not reported a health condition (no to each
illness) and 0 = otherwise (absence of reporting an illness). The use of two groups for 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
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The non-dichotomization of self-rated health status means that the measure remained in its Likert
scale form (i.e. very good; good; moderate; poor and very poor health status).
Social class (hierarchy): This variable was measured based on income quintile: The upper classes
were those in the wealthy quintiles (quintiles 4 and 5); middle class was quintile 3 and poor those
in lower quintiles (quintiles 1 and 2).
Family income is measure using total expenditure of the household as reported by the head.
Statistical analysis
Statistical analyses were performed using the Statistical Packages for the Social Sciences v 16.0
(SPSS Inc; Chicago, IL, USA) for Windows. Descriptive statistics such as mean, standard
deviation (SD), frequency and percentage were used to analyze the socio-demographic
characteristics of the sample. Chi-square was used to examine the association between 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 12.3 and 12.4).
Models
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The use of multivariate analysis to study health status and subjective wellbeing (i.e. self-reported
health) is well established in the literature [36-38]. Previous works have examined the
dichotomization of health status in order to establish whether a particular measurement of health
status is different from others [43-45]. The current study will employ multivariate analyses to
examine health by different dichotomization and statistical tools to determine if the social
determinants remain the same. The use of this approach is better than bivariate analyses as many
variables can be tested simultaneously for their impact (if any) on a dependent variable.
Scholars like Grossman [33], Smith & Kingston [34], Hambleton et al. [37], Bourne
[46], Kashdan [47], Yi & Vaupel [48], and the World Health Organization pilot work a 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
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determine any variability in social determinants based on the measurement of health status.
Model (1) is the antithesis of disease, non-dichotomization of self-reported health (antithesis of
disease); Model (2) is the non-dichotomization of self-rated health status (ie using the 5-item
Likert scale as a continuous variable), and Models (3-6) are the different dichotomized self-rated
health status (ie. 3= very poor-to-poor; 4=good, 5=moderate-to-very good 6=good-to-very good).
All the models were tested with the same set of social determinants of health, with the only
variability being the measurement of health status (self-rated health status), cut-off of health
(dichotomization) and/or non-dichotomization of self-rated health status.
HA=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, εi) (1)
where HA (i.e. self-rated antithesis of diseases) is a function of age of respondents, Ai; sex
of individual i, Gi; area of residence, ARi; current self-reported illness of individual i, It;
logged duration of time that individual i was unable to carry out normal activities (or
length of illness), lnDi; Education level of individual i, EDi; union status of person i, USi;
social class of person i, Si; health insurance coverage of person i, HIi; logged family
income, lnY; crowding of individual i, CRi; logged medical expenditure of individual i in
time period t, lnMCt; social assistance of individual i, SAi; and an error term (ie. residual
error).
Note that length of illness was removed from the model as it had 93.5% of the cases were
missing as well as union status which had 58.2%.
HND=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, εi) (2)
Where HND denotes the non-dichotomization of self-rated health status.
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Note that length of illness was removed from the model as it had 93.5% of the cases were
missing as well as union status which had 58.2%.
HD1=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, εi) (3)
Where HD1 is very poor-to-poor self-rated dichotomized health status.
Note that length of illness was removed from the model as it had 93.5% of the cases were
missing as well as union status which had 58.2%.
HD2=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, εi) (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, εi) (5)
Where HD3 is very moderate-to-very good self-rated dichotomized health status.
Note that length of illness was removed from the model as it had 93.5% of the cases were
missing as well as union status which had 58.2%.
HD1-4=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, εi) (6)
Where HD4 is good-to-excellent self-rated dichotomized health status.
Note that length of illness was removed from the model as it had 93.5% of the cases were
missing as well as union status which had 58.2%.
Results
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Demographic characteristics of studied population
Table 12.1 presents information on demographic characteristic of the sampled population.
Of the population (n = 1,394), 43.9% has primary or below primary level education, 53.1%
secondary level and 3.0% had tertiary level education.
Table 12.2 presents information on the particular demographic characteristic as well as
health status and self-reported illness of respondents by area of residence.
Table 12.3 depicts information of variables which explain the antithesis of illness among
the adolescence population.
Table 12.4 shows the different dichotomizations of self-rated health status and non-
dichotomized self-rated health status, and the various social determinants which explain each.
Table 12.5 examines associations between self-rated health status and antithesis of illness
(or disease).
Limitations of study
This study was extracted from a cross-sectional survey dataset (Jamaica Survey of Living
Conditions, 2007). Using a nationally representative cross-sectional survey dataset, this research
extracted 1394 adolescent Jamaicans which denote that the work can be used to generalize about
the adolescent population in Jamaica at the time in question (2007). However, it cannot be used
to make predictions, forecast, and establish trends or causality about the studied population.
Discussion
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The current work showed that while the majority of Jamaican adolescents have at least
self-rated good health status (92 out of every 100); some indicated at most moderate self-rated
health status. Even though only 1.4% of the sample mentioned that they have very poor-to-poor
health status, 6.5% indicated that they experienced a health condition in the last 30 days. Of
those who reported a health condition, 5.3% were diagnosed with chronic illness (diabetes
mellitus, 3.9%; hypertension, 1.3%). Although 2.4 times more adolescent in rural areas are in the
lower class compared with those in urban areas, rural adolescents have a greater good health
status compared to their urban counterparts, but this was the reverse for rural and periurban
adolescents. Another important finding was that there is no statistical association between health
conditions and area of residence, but urban and periurban adolescents were more likely to have
health insurance coverage compared to those in rural areas.
In Jamaica, the adolescence population’s health status is comparable to those in the
United States [23], suggesting that inspite of the socioeconomic disparities between the two
nations and among its peoples, the self-reported health status among adolescent Jamaicans is
good. The high health status of those in the adolescence population in Jamaica speaks good of
the inter dynamics within the countries, but does not imply that they are the same across the two
nations or can it be interpreted that the quality of life of Jamaicans is the same as those in the
United States. Simply put, the adolescence population in Jamaica is experiencing a good health
status although HIV/AIDS, unwanted pregnancies, and inconsistent condom usage are high in
this cohort [1-5].
While the aforementioned results about good health status of Jamaican adolescents
concurs with PAHO’s work in 2003 [5] and others [17], which has continued into 2007, the
current paper provides more information on health matters of adolescents aged 10-19 years than
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that offered by PAHO. An adolescent in Jamaica who seeks medical attention is 100% less likely
to report an illness, and those who indicated at least good self-rated health status was 13 times
more likely not to report an illness. Continuing, adolescents in the upper class are 15 times more
likely to report very poor-to-poor health status compared to those in the lower class. And that
those who indicated very poor-to-poor health status are more likely to seek medical care (10
times), live in crowded household and less likely to spend more on consumption and 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
Jamaica, we can add value to the existing literature on health status as well as the social
determinants of health.
Grossman introduced the use of econometric analysis in the examination of health in the
1970s to establish determinants of self-rated health [33], which has spiraled a revolution in this
regard since that time. Using data for the world’s population, he identified particular social
determinants of health that was later expanded upon by Smith and Kington [34]. Since the earlier
pioneers’ work on social determinants of health [33, 34], the WHO joined the discourse in 2000s
[27] as well as Marmot [26], Kelly et al. [28]; Marmot and Wilkinson [29]; Solar and Irwin [30];
Graham [31]; Pettigrew et al. [32], Bourne [35], Bourne [36], Hambleton et al. [37] and Bourne
and Shearer [38], but none of them evaluated whether there was variability in the determinants of
health depending on the measurement and/or dichotomization of health.
The variability in social determinants of health was established by Bourne and Shearer
[38] in a study between men in the poor and the wealthy social strata in a Caribbean nation, but
the literature at large has not recognized the variances in social determinants based on the
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dichotomization and non-dichotomization self-rated health status, and measurement of heath
(using antithesis of illness and self-rated health status). Such a gap in the literature cannot be
allowed to persist as it assumes that social determinants are consistent over the measurement of
health.
Bourne [43] like Manor et al. [44] and Finnas et al. [45] have dichotomized self-reported
health status and cautioned future scholars about how the dichotomization can be best done.
According to Bourne [43] “The current study found that dichotomi[z]ing poor health status is
acceptable assuming that poor health excludes moderate health status, and that it should remain
as is and ordinal logistic be used instead of binary logistic regression” [43, p.310], and others
warned against the large dichotomization of self-rated health status [44,45]. Because self-rated
health status is a Likert scale variable, ranging from very poor to very good health status, many
researchers arbitrarily dichotomized it, but the cut-off is not that simple as was noted by Bourne
[43], Manor et al. [44] and Finnas et al. [45].
From data on Jamaicans, Bourne’s work revealed that the cut-off in the dichotomization
of self-rated health status should be best done without moderate health when dichotomizing for
poor health status [43]. All the scholars agreed that narrowed cut-offs are preferable in the
dichotomization of self-rated health status, but only a few variables were used (marital status,
age, social class, area of residence and self-reported illness) [43-45]. Bourne postulated that “By
categorising an ordinal measure (i.e., self-reported health) into a dichotomous one, this means
that some of the original data will be lost in the process.” [43, p.295]. Using many more
variables, the present work highlighted that some social determinants of health are lost as a result
of the dichotomization process. Simply put, the social determinants of health are not consistent
across the dichotomization process which concurs with the literature.
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While we concur with other scholars that by dichotomizing self-rated health status some
social determinants are lost in the process [43-45], we will not argue with those who opined that
self-rated health status should remain a Likert scale measure [52, 53]. The evidence is in that
more social determinants in the non-dichotomized self-rated health do not give a greater
explanatory power; instead this model had the least explanation. This indicates that more is not
necessarily better, and such information must be taken into account in a decision to cut-off at a
particular point. The fact that more social determinants of health emerged when health was 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
preferable to dichotomizing it. A narrower dichotomization of self-rated health status,
particularly very poor-to-poor health, as well as moderate-to-very good health status yielded
greater explanations than non-dichotomizing health status.
This study used both the antithesis of illness and self-rated health status to measure, and
evaluates the social determinants of health, and assess whether antithesis of illness is still a better
measure of health than self-rated health status. A comparison of the social determinants based
on the measurement of health revealed that for the Jamaican adolescence population, antithesis
of illness is a better measure than self-reported health status in determining social determinants
because of its explanatory power (53%) compared to those that used the self-rated health status
(explanatory power at most 38%). On the other hand, the antithesis of illness had fewer social
determinants compared with those in self-rated health status, suggesting that more social
determinants of health should not be preferred to fewer because the latter measure had a greatest
explanation. Like dichotomizing self-rated health status, variation also exists among
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dichotomization of health and antithesis of illness. Thus, it appears that the antithesis of illness
may provide a better measure for the social determinants of health than self-rated health status.
Diener [50, 51] had postulated that self-reported health status can be effectively applied
to evaluate health status instead of objective health status measurement (morbidity, life
expectancy, mortality), and Bourne [46] found a strong statistical association between 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
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
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status, this justifies why the former had a greater explanation when the social determinants of
health were examined than that of self-rated health status. But, where were their differences in
the variables used in one measure compared with the others?
In fact, all the variables used in this study were social determinants that were identified in
the literature [26-38], and many of them were not significant for the adolescence population of
this research. It can be extrapolated from the current work that social determinants of health for a
population are not the same for a sub-population, in particular adolescence population. Thus,
when the WHO [27] and affiliated scholars [26, 28-32] forwarded social determinants of health,
prior to that some scholars like Grossman [33] and Smith and Kington [34] had already social
determinants of health of a population. However, none of them stipulated that there are
disparities and variations in these based on the dichotomization, non-dichotomization, 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.
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This paper concurs with the literature that there are variations in some social
determinants of health status across measurement, dichotomization and non-dichotomization of
health. However, the present work went further than the current literature and found that
particular dichotomization of health had stronger explanatory power, and disparity in
determinants. As such, the variations in social determinants of health vary across the
dichotomization and measurement of health as this paper showed that more social factors do not
translate into greater explanatory power; and that stronger explanation does not denotes more
social determinants. And the social determinants of health had the greatest explanatory power
used antithesis of illness to measure health.
Conclusion
In summary, the general health status of the adolescence population in Jamaica is good, but 7 in
every 100 have reported an illness of which some had chronic conditions (diabetes mellitus,
3.9% and hypertension, 1.3%), and those who classified as being in the wealthy class were more
likely to report very poor-to-poor health status compared with those in the lower class. Another
important finding was that rural adolescents had a greater health status than urban adolescents,
but periurban adolescents had the greatest health status.
Outside of the aforementioned good health news, the social determinants of self-rated
health status vary across the measurement of and dichotomization and non-dichotomization of
health and the population used. This work provides invaluable insights into how social
determinants should be examined, modify the general social determinants of health offered by
the World Health Organization and some associated scholars. By varying the measurement,
dichotomization and non-dichotomization of health, this work provide some justification as to
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whether a particular dichotomization of health is better or non-dichotomization is preferable to
dichotomization.
This researcher will not join the group of scholars who are purporting for the 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.
Disclosures
The author reports no conflict of interest with this work.
Disclaimer
The researcher would like to note that while this study used secondary data from the 2007 Jamaica Survey of Living Conditions (JSLC), none of the errors in this paper should be ascribed to the Planning Institute of Jamaica and/or the Statistical Institute of Jamaica, but to the researcher.
Acknowledgement The author thank the Data Bank in Sir Arthur Lewis Institute of Social and Economic Studies, the University of the West Indies, Mona, Jamaica for making the dataset (2007 Jamaica Survey of Living Conditions, JSLC) available for use in this study.
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Table 12.1: Demographic characteristic of studied population, n = 1394 Characteristic n 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 12.2: 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 12.3: Logistic regression: Variables of antithesis of illness among adolescence population, n = 1,280 Characteristic OR CI (95%) Age 1.1 1.0 - 1.3 Health care-seeking (1=yes) 0.0 0.0 - 0.01* Health insurance coverage (1=yes) 1.0 0.4 - 2.5 Primary education (reference group) 1.0 Secondary 1.8 0.9 - 3.7 Tertiary 1.9 0.3 - 15.1 lnMedical 0.8 0.1 - 5.0 Male 1.4 0.7 - 2.6 Social assistance from government 1.6 0.6 - 4.4 Logged family income 0.8 0.3 - 1.8 Rural area (reference group) Urban 1.6 0.7 - 3.8 Periurban 1.2 0.5 - 2.9 Poor-to-Very poor health status (reference group) 1.0 Moderate-to-Very good health status 0.3 0.03 - 2.1 Good-to-Very good health status 12.6 6.0 - 26.3* Lower class (reference group) Middle class 1.6 0.5 - 5.2 Upper 0.8 0.2 - 3.1 Crowding 0.9 0.8 - 1.1 Model χ2, P 287.08, < 0.0001 -2 LL 327.56 R2 0.53 Hosmer and Lemeshow χ2 = 4.40, P = 0.82 OR denotes odds ratio, CI (95%) means 95% confidence interval and *P < 0.05
Table 12.4: 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-ver
good OR CI (95%) OR CI (95%) OR CI (95%) OR 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 –Age 1.0 0.9 – 1.2 0.9 0.9 – 1.1 1.0 0.8 – 1.2 0.9 0.9 –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 –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 –Primary education (reference group) 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 –Tertiary 0.0 0 – 0.0 0.4 0.1 – 1.0 5E+007 0.0 - 0.4 0.2 –Logged Medical expenditure 1.6 0.7 – 3.6 0.6 0.4 – 1.2 0.7 0.4 –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 –Lower class (reference group) 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 –Upper 14.9 1.9 – 118.3 * 0.7 0.3 – 1.4 0.1 0.01 – 0.5* 0.7 0.3 –Rural area (reference group) 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 – Periurban 0.0 0.0 - 0.0 3.3 1.3 – 8.2* 2E+0007 3.3 1.53–Male 0.9 0.3 – 2.3 1.5 1.0 – 2.4 1.1 0.4– 3.0 1.4 0.9 –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 – Crowding 1.6 1.3 – 2.0* 0.9 0.8 – 1.0* 0.6 0.5 – 0.8* 0.9 0.8 – 0Model χ2, P 59.66, < 0.0001 113.11, < 0.0001 30.37, < 0.0001 113.11, <0.0-2 LL 146.38 588.76 175.67 58R2 0.38 0.20 0.31 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 =
OR denotes odds ratio; *P < 0.05
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Table 12.5: 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 13
Childhood Health in Jamaica: changing patterns in health
conditions of children 0-14 years
Paul Andrew Bourne
The new thrust by WHO is healthy life expectancy. Therefore, health must be more than morbidity. It is within this framework that a study on childhood health in Jamaica is of vital importance. This study 1) expands the health literature in Jamaica and by extension the Caribbean, 2) will aid public health practitioners with research findings upon which they are able to further improve the quality of life of children, 3) investigates the age at with children in Jamaica become influenced by particular chronic diseases and 4) assesses the subjective wellbeing of children. The current study extracted a sample of 8,373 and 2,104 children 0-14 years from two surveys collected jointly by the Planning Institute of Jamaica and the Statistics Institute of Jamaica for 2002 and 2007 respectively. A self-administered questionnaire was used to collect the data. Ninety-one percent of children in Jamaica, for 2007, reported good health. The number of children who had diarrhoea fell by 84.2% in 2007 over 2002, and a similar reduction was observed for those with asthma (42.1% in 2002 and 19.7% in 2007). Another critical finding was that 1.2% of children, in 2007, had diabetes mellitus compared to none in 2002. Public health now has an epidemiological profile of health conditions of children and the demographic shifts which are occurring and this can be used for effective management and planning of the new health reality of the Jamaican child.
INTRODUCTION
One of the measures of child health and the health status of the general populace is infant
mortality or mortality, which is well studied in Jamaica and the wider Caribbean [1-11]. The
simple rationale for the use of mortality in evaluating health status is owing to its ease in which it
can be used to precisely measure its outcome unlike other indicators such as quality of life,
subjective wellbeing, happiness or life satisfaction [12-22]. Another reason for the use of infant
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mortality in the measurement of health is because of the strong inverse significant correlation
between it and/or general mortality and life expectancy [23,24]. There is no denial therefore that
infant mortality and/or mortality in general play a critical role in determining health outcomes.
Although life expectancy emerged from mortality, the former only speak to length of life and not
the quality of those lived years. An individual can live for 40 years or even 100 years, of which
all those years were lived in severe morbidity. It is owing to aforementioned rationale why the
World Health Organization (WHO) developed a mathematical technique which discount the life
expectancy by the years spent in disability or morbidity [25]. The WHO therefore emphasized
healthy life expectancy and not life expectancy. Health therefore must be more than morbidity as
it expands to quality of life.
Within the broadest definition of health conceptualized by the WHO in the 1940s [26], is
social, psychological and physical wellbeing and not the mere absence of diseases suggesting
that health is more than living to the quality of those lived years. Health has been expanded to
mean much more than the absence of diseases to include measures of healthy life expectancy,
happiness, utility, personal preference, and self-reported quality of life [12-22]. Simply put,
wellbeing is subjectively what is ‘good’ for each person [26]. It is sometimes connected with
good health. Crisp [26] offered an explanation for this, when he said that “When discussing the
notion of what makes life good for the individual living that life, it is preferable to use the term
‘wellbeing’ instead of ‘happiness”, which explains the rationale for this project utilizing the term
wellbeing and not good health.
The issue of wellbeing is embodied in three theories – (1) Hedonism, (2) Desire, and (3)
Objective List. Using ‘evaluative hedonism’, wellbeing constitutes the greatest balance of
332
pleasure over pain [26, 27]. With this theorizing, wellbeing is just personal pleasantness, which
represents that more pleasantries an individual receives, he/she will be better off. The very
construct of this methodology is the primary reason for a criticism of its approach (i.e.
‘experience machine’), which gave rise to other theories. Crisp [26] using the work of Thomas
Carlyle described the hedonistic structure of utilitarianism as the ‘philosophy of swine’, because
this concept assumes that all pleasure is on par. He summarized this adequately by saying that
“… whether they [are] the lowest animal pleasures of sex or the highest of aesthetic
appreciation” [26].
The desire approach, on the other hand, is on a continuum of experienced desires. This
is popularized by welfare economics. As economists see wellbeing as constituting satisfaction of
preference or desires [26, 27], which makes for the ranking of preferences and its assessment by
way of money. People are made better off, if their current desires are fulfilled. Despite this
theory’s strengths, it has a fundamental shortcoming, the issue of addiction. This forwarded by
the possible addictive nature of consuming ‘hard drugs’ because of the summative pleasure it
gives to the recipient.
Objective list theory: This approach in measuring wellbeing list items not merely
because of pleasurable experiences nor on ‘desire-satisfaction’ but that every good thing should
be included such as knowledge and-or friendship. It is a concept influenced by Aristotle, and
“developed by Thomas Hurka as perfectionism” [26]. According to this approach, the
constituent of wellbeing is an environment of perfecting human nature. What goes on an
‘objective list’ is based on reflective judgement or intuition of a person. A criticism of this
technique is elitism. Since an assumption of this approach is that, certain things are good for
333
people. Crisp [26] provided an excellent rationale for this limitation, when he said that “…even
if those people will not enjoy them, and do not even want them”.
In Arthaud-day et al work [28], applying structural modeling, subjective well was found
to constitute “(1) cognitive evaluations of one's life (i.e., life satisfaction or happiness); (2)
positive affect; and (3) negative affect.” Subjective wellbeing therefore is the individual’s own
viewpoint. If an individual feels his/her life is going well, then we need to accept this as the
person’s reality. One of drawbacks to this measurement is, it is not summative, and it lacks
generalizability.
In keeping therefore with the broad definition of health forwarded by the WHO, any
study of health must go beyond mortality. A comprehensive search of health literature in the
Caribbean in particular found no research that 1) using national cross-sectional survey(s)
examined health status of children, 2) investigated the changing pattern of morbidity which
affect children ages 0-14 years, 3) investigated whether health status (ie. subjective wellbeing)
and self-reported morbidities (ie health conditions) are correlated, and if they are good measure
for each other, 4) investigated whether from among the health conditions, chronic diseases and
the time they begin to affect children as well as the 5) demographic characteristics of health
conditions affecting children. The current study will examine the aforementioned issues as health
literature in the region on child health must expand beyond infant mortality. The objectives of
the study are to 1) expand the health literature in Jamaica and by extension the Caribbean, 2)
understand the status of child health outside of mortality, 3) aid public health practitioners with
research upon which they are able to further improve the quality of life of children by adding
quality to their lived years, 4) investigate the age at with children in Jamaica become influenced
334
by chronic disease, it typology and 5) evaluate the subjective wellbeing of children as is done for
the general populace and elderly [30-37].
The current study used two cross-sectional surveys which were conducted jointly by the
Planning Institute of Jamaica and the Statistical Institute of Jamaica (for 2002 and 2007) that
collect data on Jamaicans. A subsample of 8,373 and 2,104 children 0-14 years was extracted
from a sample of 25,018 and 6,783 respondents for 2002 and 2007 respectively. The survey was
a national probability sample of Jamaica, and it was weighted to reflect the populace and sub-
populations. The response rate for each survey was in excess of 72%. Descriptive statistics, such
as mean, standard deviation (SD), frequency and percentage were used to analyze the socio-
demographic characteristics of the sample. Chi-square was used to examine the association
between non-metric variables, and Analysis of Variance (ANOVA) was used to test the
relationships between metric and non-dichotomous categorical variables whereas independent
sample t-test was used to examine a statistical correlation between a metric variable and a
dichotomous categorical variable. The level of significance used in this research was 5% (ie 95%
confidence interval).
335
METHODS AND MATERIALS
The current study extracted a sample of 8,373 and 2,104 children 0-14 years from two surveys
collected jointly by the Planning Institute of Jamaica and the Statistics Institute of Jamaica for
2002 and 2007 respectively.[38,39] The method of selecting the sample from each survey was
solely based on an individual being less than or equal to 14 years. The survey (Jamaica Survey of
Living Condition) began in 1989 to collect data from Jamaicans in order to assess policies of the
government. Since 1989, yearly the JSLC adds a new module in order to examine that
phenomenon which is critical within the nation. In 2002, the foci were on 1) social safety net and
2) crime and victimization; and for 2007, there was no focus. The sample for the earlier survey
was 25,018 respondents and for the latter, it was 6,783 respondents.
The survey was drawn using stratified random sampling. This design was a two-stage
stratified random sampling design where there was a Primary Sampling Unit (PSU) and a
selection of dwellings from the primary units. The PSU is an Enumeration District (ED), which
constitutes a minimum of 100 residence in rural areas and 150 in urban areas. An ED is an
independent geographic unit that shares a common boundary. This means that the country was
grouped into strata of equal size based on dwellings (EDs). Based on the PSUs, a listing of all the
dwellings was made, and this became the sampling frame from which a Master Sample of
dwelling was compiled, which in turn provided the sampling frame for the labour force. One
third of the Labour Force Survey (ie LFS) was selected for the JSLC. [40, 41] The sample was
weighted to reflect the population of the nation.
The JSLC 2007 [40] was conducted May and August of that year; while the JSLC 2002
was administered between July and October of that year. The researchers chose this survey based
336
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
Social class: This variable was measured based on the income quintiles: The upper classes were
those in the wealthy quintiles (quintiles 4 and 5); middle class was quintile 3 and poor those in
lower quintiles (quintiles 1 and 2).
Health care-seeking behaviour. This is a dichotomous variable which came from the question
“Has a doctor, nurse, pharmacist, midwife, healer or any other health practitioner been visited?”
with the option (yes or no).
Age is a continuous variable in years.
Child. A person who has celebrated less than or equal to 14 years.
337
Health conditions (ie. self-reported illness or self-reported dysfunction): The question was asked:
“Is this a diagnosed recurring illness?” The answering options are: Yes, Cold; Yes, Diarrhoea;
Yes, Asthma; Yes, Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No.
Self-rated health status: “How is your health in general?” And the options were very good; good;
fair; poor and very poor.
Statistical Analysis
Descriptive statistics, such as mean, standard deviation (SD), frequency and percentage were
used to analyze the socio-demographic characteristics of the sample. Chi-square was used to
examine the association between non-metric variables, and Analysis of Variance (ANOVA) was
used to test the relationships between metric and non-dichotomous categorical variables whereas
independent sample t-test was used to examine a statistical correlation between a metric variable
and a dichotomous categorical variable. The level of significance used in this research was 5%
(ie 95% confidence interval).
RESULT
For this study there were two samples (8,373 from 2002 data survey and 2,104 from the 2007
survey). In 2002, the sample was 50.7% males and 49.3% females compared to 51.3% males and
48.7% females for 2007. The mean age for the sample in 2002 was 7.2 years (SD = 4.2 years)
and 7.3 years (SD = 4.3 years) for 2007. The proportion of the sample in particular social class
(using population income quintile) was relative the same across the two years. The number of
days recorded as suffering from illness fell by 2 days in 2007 over 2002 (median number of days
experiencing ill-health). In 2002, 9.4% of the sample reported an illness/injury in the 4-week
period of the survey and this increased by 34.0% (to 12.6%). The percent of the sample that 338
visited health care practitioners marginally increase from 56.7%, in 2002, to 58.6% in 2007.
Concurrently, 9.3% of sample was covered by health insurance (ie total private in 2002) and this
increased by 62.4% and a part of this was accounted for by a 5.1% having public health
insurance coverage. In 2002, 62.6% of the sample dwelled in rural areas, 25.1% in semi-urban
areas and 12.3% in urban areas compared to a shift which was noticed in 2007 as 53.2% resided
in rural areas and 20.2% in semi-urban areas with 26.6% lived in urban zones (Table 13.1).
The general health status of children in Jamaica, for 2007, was good (91.3%) compared
to 6.7% fair and 2.0% poor.
Interestingly, in the current study, a shift in health condition was noticed in 2007 over
2002. The number of children who had diarrhoea fell by 84.2% in 2007 over 2002, and a similar
reduction was observed for those with asthma (42.1% in 2002 and 19.7% in 2007). Another
critical finding was that 1.2% of children, in 2007, had diabetes mellitus compared to none in
2002. On the contrary, 37.5% of children, in 2007, had cold which increased from none in 2002
(Table 13.1).
A cross-tabulation between health conditions and sex of respondents, revealed that no
significant statistical correlation existed between the two variables and that this was for both
years: For 2002 - χ2 (df = 2) = 0.232, p> 0.05; and for 2007 - χ2 (df = 5) = 8.915, p> 0.5 (Table
13.2). In spite of the aforementioned, the new diabetic cases were accounted for by females (for
2007).
In 2002, no significant statistical relationship existed between diagnosed health
conditions and area of residents (χ2 (df = 4) = 1.301, p > 0.05). On the other hand, a statistical
correlation was observed for 2007 between the aforementioned variables. Furthermore, more 339
children in semi-urban areas had cold than those who dwelled in other areas. On the contrary,
diabetic cases were found in urban areas and none in other geographical zones. The findings
revealed also that more rural children had asthma and more urban children had unspecified
health conditions (Table 13.3).
Table 13.4 revealed that no significant association was found between diagnosed health
condition and social class (ie population income quintile). However, the diabetic cases were
spread among the lower class (poorest 20%, 1.9%; and poor, 1.8%) and the upper class (wealthy,
2.0%).
The examination of diagnosed health conditions by mean age of respondents revealed
that a significant relationship existed between the two aforementioned variables in 2007, F
statistic = 4.875, p < 0.001; but none in 2002 - F statistic = 3.334, p > 0.05. In 2007, the mean
age of a child with diabetes mellitus was 12.33 years (SD = 2.1 yrs), 95% CI = 7.16 – 17.5
(Table 13.5). However the mean age a child with diarrhoea lower than a child and other health
conditions.
The first time in the history of the Jamaica Survey of Living Conditions (JSLC) that
health status and self-reported health condition was collected together was in 2007. Hence, the
current study will cross-tabulate both in order to determine whether a significant correlation
exist between them and what is the strength of a relationship if one does exist. Based on Table
13.6 a weak significant statistical association exist between health status and self-reported
health condition - χ2 (df = 2) = 174.512, p < 0.0001, cc= 0.282. On further examination of the
findings, it was observed that no child was classified has having very good health status.
Ninety-four percent of sample who had no health condition reported good health compared to
70% of those who had at least one health condition. Of those who had at least one health
340
condition, 9.4% of them reported poor health status compared to 1% who had no health
condition (Table 13.6).
Using independent sample t-test, in 2002, the current study found that there was a
significant difference between the mean age of those who sought and not seek medical care –
t3.425 , p < 0.001. The mean age of those who do not seek medical care higher, 6.2 years (SD =
4.1), compared to those who seek care, 5.2 years (SD = 4.2 years). However, there was no
difference in 2007: seek care – mean age 5.2 years (SD = 4.1 years) and not seek care – mean
age 5.8 years (SD = 4.2 years).
On examination as to whether a significant statistical correlation existed between health
care-seeking behaviour and sex of respondents, none was found in each year – p > 0.05 (Table
13.7).
DISCUSSION
It is established in epidemiology that diseases in childhood do influence poor health in adulthood
[42], suggesting the value of child health to health status over the life course. Another
importance to the study of health status is its contribution to all typology of development as
human capital is critical to socio-economic and political systems. In Jamaica, the Statistical
Institute of Jamaica [42] estimated that for 2007, there was 28.3% of the nation’s population was
less than 14 years. Simply put, there are 45 children for every 100 working age (ages 15-64
years) Jamaican; and to omitted the health status of this cohort is to substantially neglect a
critical sector of the population. The current study found that 2 in every 100 children had poor
health status; and that weak significant statistical correlation existed between health status and
self-reported health conditions. This therefore concurs and contradicts another study that found
341
statistical association between health conditions and health status [36]. Hambleton et al. [36],
examining data for elderly Barbadians, found that self-reported health conditions accounted for
most of the variability in health status (ie. current diseases accounted for 33.5% out of R2 =
38.3%).
This takes the study in the direct of current diseases (ie health conditions) of children in
Jamaica. This study revealed 34% increase in cases of self-reported diseases in Jamaican
children. Only 13 in 100 children in Jamaica, in 2007, had a least one health condition. These
conditions include cold, diarrhoea, asthma, diabetes mellitus and other unspecified diseases. In
2007, 20 in every 100 children had asthma, 5 out of every 100 diarrhoea cases, 38 in every 100
had cold and 21 in every 100 unspecified conditions. Of the different typology of chronic
dysfunctions, 12 in every 1,000 reported diabetes mellitus and no cases were found of
hypertension and arthritis. Given the breadth of the unspecified category, this could include
cancers, HIV/AIDS and other communicable or non-communicable diseases. In spite of this
uncertainty, what emerged from the current research is the change in pattern of health conditions
of children between 2002 and 2007. A study conducted by Walker [43] found that growth
retardation in children influence blood pressure, obesity, and other chronic health conditions, and
that some 5-6% of children in Trinidad and Tobago, and Jamaica are classified in this group.
Walker also found that these children are more likely to experience more episodes of diarrhaea,
fever and other morbidities.
This research revealed that number of cases of asthma, diarrhoea and unspecified
conditions fell accompanied with a corresponding rise in cold and diabetes mellitus. Interestingly
to note is that the 1.2% of child population that were diagnosed diabetic patients represents 2.3%
342
of the female population. The diabetic cases were not only females, but urban residents. Of those
with diabetes, 1.9% was in the poorest 20%, 1.8% poor and 2.0% of the wealthy social class.
Continuing, the mean age of female diabetic children was 12.3 years; and this indicates the year
age in which diabetes mellitus begin to affect females in Jamaica. The aforementioned finding
explains the disproportionate number of females to males in the general population that have
diabetes -14% females to 7.7% males [40]. Although no cases of hypertension was reported in
this study, it is established that diabetes mellitus is correlated with hypertension.
Diabetes Mellitus is not the only challenge faced by patients, but McCarthy [44] argues
that between 30 to 60% of diabetics also suffer from depression, which is a psychiatric illness.
Diabetes mellitus does influence the health status of children and follows them across the life
course. It affects lifestyle choice, functional capacity, and like McCarthy said the psychological
state of people. This health condition also affects other disease. Morrison [45] opined that
diabetes mellitus and hypertension have now become two problems for Jamaicans and in the
wider Caribbean. This situation was equally collaborated by Callender [46] who found that there
was a positive association between diabetic and hypertensive patients - 50% of individuals with
diabetes had a history of hypertension [46]. Children with diabetes mellitus therefore are highly
likely to develop hypertension in the future, and so children in Jamaica in the future will have
twin chronic conditions. This envelope further shifts in health conditions of children in Jamaica;
Morrison alluded to a transitory shift from infectious communicable diseases to chronic non-
communicable diseases as a rationale for the longevity of the Anglophone Caribbean populace
and this does not mitigates against lowered healthy life expectancy of the sexes in particular
females who live 6 years more than males [34,42].
343
Diabetes mellitus and any other typology of chronic diseases do more than affect healthy
life expectancy; they are directly correlated with mortality. Statistics from the Statistical Institute
of Jamaica [42] is the leading cause of deaths in female Jamaicans. The reality of changing
pattern of health conditions from communicable to non-communicable and the fact that this is
accounted with urban poor and wealthy, indicate that public health policies are needed to address
this currently and in the future. Another important fact that embedded in the current study is the
early age in which females are having chronic disease, and this indicates the length of time with
which they will life with this non-curable disease or likeliness of mortality.
A study on morbidity and mortality patterns in the Caribbean established that the
transition in morbidity is not atypical to Jamaica [47], and that the leading cause of mortality in
region is similar to developed nations. WHO [48] opined that 80% of chronic illnesses were in
low and middle income countries, indicating the preponderance of chronic illness in regions such
as the Caribbean as well as the fact that chronic illnesses are also a part of the landscape of
industrialized nations. With the changing pattern of morbidity of children in Jamaica, this will
support modifications in lifestyle behaviour which must begin from children to the populace.
Although there is no statistical difference between the 3 area of residents and health
conditions, the fact that the chronic dysfunctions were found in urban areas denote that public
health policies must begin in earnest in those places. There is another situation that must be
explored here and that is response of health services, and the management of care for those who
are affected by chronic illnesses. It should be noted that 57 out of every 100 children were taken
for medical care which speaks to the high proportion of children despite being ill who were not
taken to traditional medical facilities. A part of the rationale for this non-medical care seeking
behaviour of children is adults’ definition of health and the cultural perspective of health. 344
Generally, health in Jamaica is defined as the absence of illness which although is
negative and narrow in scope speaks to people’s perspective on the matter. Interestingly in this
discourse is not only the narrowed definition of health, but that severity in health conditions is
substantially what drives medical care-seeking and not on the onset of illness or preventative
care. This goes to the crux of why only 57 out of every 100 children who are ill would be taken
to health care practitioners as their families are less likely to taken then for conditions such as the
cold, but also provide an explanation for the low medical care seeking behaviour for the general
populace.
Statistics revealed that for the last 2 decades (1988-2007), there were 4 times (years) in
which males sought more medical care than females – 1991 (48.5% males to 47.4% females);
1995(59.0% males to 58.9% females): 1997 (60.0% males to 59.3% females) and 2006 (71.7%
males to 68.8% females) [30, 41, 40], which speaks to some embedded culturalization for this
health care-seeking disparity in nation. While this is not atypical to Jamaica [49-51], that fact
that the current study revealed that there was no significant statistical difference between male
and female children being taken for medical care, the disparity that exist in the general populace
begin in young adulthood. This is the period in which identify formulation begins in adolescents
and when males begin to imitate the practices of adult men. The adolescent male therefore will
seek less medical care because his adult counter believes that this is weak, feminine and reduces
his machoism.
One anthropologist in seeking to explain the practices of Caribbean men used social
learning theory to examine the lifestyle practices of boys [52]. Chevannes [52] argued that the
young imitate the roles of society members through role modeling of what constitute acceptable
and good roles which is supported by reinforcement. The young male is a subset of the society, 345
and if men are less likely to seek health care because of a cultural perspective that they form of
ill-health which goes to the crux of their manhood and possibly seeks to threaten it, young males
as soon as they are somewhat responsible for their choices will do more of the same as their
mentors. This gender role of sexes and health disparity which results after childhood is not
limited to Jamaica or the Caribbean but a study carried out by Ali and de Muynck [53] found that
street children in Pakistan had a similar gender stereotype about health, health care and medical
care seeking-behaviour. Using a descriptive cross-sectional study carried out during September
and October 2000 of 40 school-aged street children (8-14 years), they found boys were reluctant
to seek medical care except when there is severity of ill-health, it threatens their economic
livelihood or there is a perceived reduction in functional capacity. The reason being that mild
ailment is not severe enough to barr them from physical functioning and within the context of the
general population that men ought to be tough, this means that they are okay; and so some
morbidity are not for-hospital, which was so the case in Nairobi slums [54]. This again justifies
why some children in Jamaica are not taken to health practitioners as there is a perception that
some illness requires home remedy.
Statistics revealed that 56.0% of children (ages 0-4) who were not taken for medical
treatment despite having an illness was because home remedies were used, figure was 32.8% for
those 5-9 years and 25.6% for those 10-19 years [40]. Inaffordability accounted for 33%, 32.5%
and 35.9% of those ages 0-4 years, 5-9 years and 10-19 years respectively who were not brought
to health care practitioner even though they were ill.
CONCLUSION
The general health status of children in Jamaica is good; but this mitigate against the relatively
346
low age with which females are reported to have had diabetes mellitus and the changing pattern
of health conditions which have occurred since the 2002. Public health now has an
epidemiological profile of health conditions of children and the demographic shifts which are
occurring and this can be used for effective management and planning of the new health reality
of the Jamaican child. With the removal of health care user fees for children ages 0-18 years
from the health care landscape of Jamaica (since May 28, 2007), the transition to chronic cases in
this cohort means that health care expenditure in the future will rise as we seek to care for those
patients over there life course. It is critical that future research examine the composition of
unspecified health conditions as this constitutes a significant percentage of diseases in 2007
unlike 2002.
Conflict of interest
There is no conflict of interest to report
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Table 13.1. Sociodemographic characteristic of sample
Variable 2002 2007 N= 8373 N=2104 Sex Male 50.7 51.3 Female 49.3 48.7 Health care-seeking behaviour Yes 56.7 58.6 No 43.3 41.4 Health insurance coverage Yes 9.3 15.1 No 90.7 84.9 Area of residence Rural 62.6 53.2 Semi-urban 25.1 20.2 Urban 12.3 26.6 Self-reported illness Yes 9.4 12.6 No 90.6 87.4 Diagnosed Health conditions Cold - 37.5 Diarrhoea 31.6 5.0 Asthma 42.1 19.7 Diabetes mellitus (ie diabetes) - 1.2 Hypertension - - Arthritis - - Other 26.3 20.8 Not - 17.0 Population Income quintile Poorest 20% 26.0 26.0 Poor 22.9 22.6 Middle 20.3 19.5 Wealthy 18.0 18.9 Wealthiest 20% 12.8 13.0 Age Mean (SD) 7.2 yrs (4.2 yrs) 7.3 yrs (4.3 yrs) Length of illness Median 7 days 5.0 days Number of visits to health practitioner(s) median 1.0 1.0 Crowding mean (SD) 2.5 persons (1.5
persons) 5.5 persons (2.3
persons)
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Table 13.2. Diagnosed health conditions by Sex, 2002 and 2007 Variable 20021 20072
Male
Female
Male
Female Diagnosed Health conditions
Cold - 35.7 39.2
Diarrhoea 27.3 37.5 3.1 6.9
Asthma 45.5 37.5 21.7 17.7
Diabetes - 0.0 2.3
Hypertension - - -
Arthritis - - -
Other 27.3 25.0 19.4 22.3
No - - 20.2 11.5
1χ2 (df = 2) = 0.232, p> 0.05 2χ2 (df = 5) = 8.915, p> 0.5
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Table 13.3. Diagnosed health conditions by area of residence
Variable 20021 20072
Rural
Semi-urban
Urban
Rural
Semi-urban
Urban Diagnosed Health conditions
Cold - - - 27.0 56.5 36.0
Diarrhoea 33.3 40.0 0.0 - 2.2 8.0
Asthma 41.7 40.0 50.0 25.4 15.2 18.7
Diabetes - - - - - 2.3
Hypertension - - - - - -
Arthritis - - - - - -
Other 25.0 20.0 50.0 20.6 13.0 23.3
No - - - 27.0 13.0 12.0
1χ2 (df = 4) = 1.301, p > 0.05 2χ2 (df = 10) = 25.079, p = 0.005, cc = 0.297
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Table 13.4. Diagnosed health conditions by Population income quintile
Variable 20021 20072
Poorest 20%
Poor
Middle
Wealthy
Wealthiest 20%
Poorest 20%
Poor
Middle
Wealthy
Wealthiest 20%
Diagnosed Health conditions
Cold - - 16.7 14.3 50.0 35.8 37.5 44.3 36.7 30.0
Diarrhoea 75.0 - 66.7 57.1 0.0 3.8 12.5 4.9 2.0 0.0
Asthma 0.0 - - - - 22.6 17.9 18.0 14.3 27.5
Diabetes - - - - - 1.9 1.8 0.0 2.0 0.0
Hypertension - - - - - - - - - -
Arthritis - - - - - - - - - -
Other 25.0 - 1.0 28.6 50.0 28.3 19.6 16.4 20.4 20.0
No - - - - 7.5 10.7 16.4 24.5 22.5
1χ2 (df = 6) = 8.105, p > 0.05 2χ2 (df = 20) = 25.079, p > 0.05
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Table 13.5. Mean Age of respondent who has a particular health condition
Variable 20021 20072
Mean age (SD)
95% CI
Mean age (SD)
95% CI Diagnosed Health conditions
Cold - - 4.4 yrs (4.0 yrs) 3.55 – 5.15
Diarrhoea 1.5 yrs (1.5yrs) - 0.09 -3.09 3.5 yrs (2.8 yrs) 1.93 – 5.15
Asthma 5.0 yrs (3.0 yrs) 2.51-7.49 6.5 yrs (3.5 yrs) 5.51 – 7.47
Diabetes - - 12.33 yrs (2.1 yrs) 7.16 – 17.5
Hypertension - - - -
Arthritis - - - -
Other 5.4 yrs (3.8 yrs) 0.62 – 10.18 6.0 yrs (4.5 yrs) 4.82 – 7.26
No - - 5.8 yrs (4.3) 4.46 – 7.20
1F statistic = 3.334, p > 0.05 2F statistic = 4.875, p < 0.001
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Table 13.6. Health status by self-reported illness Variable 20021 20072
Self-reported illness Self-reported illness
None
(in %)
At least one
(in %)
None
(in %)
At least one
(in %) Health status
Very good - - - -
Good - - 94.3 70.2
Fair - - 4.7 20.4
Poor - - 1.0 9.4
1In 2002, health status data were not collected. This took place the first time in 2007 2χ2 (df = 2) = 174.512, p < 0.0001, cc= 0.282
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Table 13.7. Health (or medical) care-seeking behaviour by sex
Variable 20021 20072
Sex Sex
Male
Female
Male
Female Health care-seeking behaviour
Sought care 42.2 44.5 40.8 42.0
Did not seek care 57.8 55.5 59.2 58.0
1χ2 (df = 1) = 0.419, p > 0.05 2χ2 (df = 1) = 0.040, p > 0.05
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Chapter 14 The uninsured ill in a developing nation
Paul Andrew Bourne
Empirical studies have used a piecemeal approach to the examination of health, health care-seeking, uninsured people and the health status of those who are chronically ill, but no study emerged in an extensive literature search, on the developing nations, and in particular Latin America and the Caribbean, that has investigated health and health care-seeking behaviour among uninsured ill people in a single research. The current study aims to narrow this divide by investigating health, self-reported diagnosed health conditions, and health care-seeking behaviour among uninsured ill Jamaicans, and to model factors which account for their moderate-to-very good health status as well as health care-seeking behaviour. The current study utilises cross-sectional survey data on Jamaicans which was collected in 2007. The survey is a modification of the World Bank’s Living Standard Household Survey. This work extracted a sample of 736 respondents who indicated that they were ill and uninsured from a sample of 6,783 respondents. Logistic regression analyses examined 1) the relationship between moderate-to-very good health status and some socio-demographic, economic and biological variables; as well as 2) a correlation between medical care-seeking behaviour and some socio-demographic, economic and biological variables. Sixty out of every 100 uninsured ill Jamaicans were females; 43 out of every 100 were poor; 59 out of every 100 uninsured ill persons dwelled in rural areas; 1 of every 2 utilised public health care facilities, two-thirds had chronic health conditions, and 22 out of every 100 reported at least poor health. Moderate-to-very good health status was correlated with age (OR = 0.97, 95% CI = 0.95-0.98); male (OR = 0.60, 95% CI = 0.37-0.97); middle class (OR = 0.45, 95% CI = 0.21-0.95); logged income (OR = 2.87, 95% CI = 1.50-5.49); area of residence (Other Town – OR = 2.33, 95^% CI = 1.19-4.54; Urban – OR = 2.01, 95% CI = 1.11-3.62), and health care-seeking behaviour (OR = 0.45, 95% CI = 0.27-0.74). Sixty-one of every 100 uninsured respondents with ill health sought medical care. Medical care-seeking behaviour was significantly related to chronic illness (OR = 2.25, 95%CI = 1.31-3.88); age (OR = 1.03, 95%CI = 1.01-1.04); crowding (OR = 1.12, 1.01-1.24); income (OR = 1.00, 95% CI = 1.00-1.00); and married people (OR = 0.48, 95% CI = 0.28-0.82). Uninsured ill Jamaicans who resided in rural areas had the lowest moderate-to-very good health status, but there was no difference in health care-seeking behaviour based on the geographical location of residence. Despite the fact that there is health insurance coverage available for those who are chronically ill and elderly in Jamaica, there are still many such people who are without health insurance coverage. The task of public health specialists and policy makers is to fashion public education and interventions that will address many of the realities which emerged in this research.
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Introduction
In all cultures, people desire good health and long life. Ill-health, therefore, is a challenge to the
aim of healthy life expectancy, and is the rationale for investments in health options such as
exercise, diet, nutrition, science and technology, medical consultation and/or health care
utilisation. All living organisms will experience ill-health as well as good health over their life
courses; and when ill-health threatens the quality and length of life, it becomes the justification
for humans’ willingness to rectify, address and possibly postpone illnesses. Ill-health (i.e. illness,
sickness or ailment) threatens existence, productivity, development, the individual and the wider
society, and because of that humans demand the best health care options. Demand for health care
must be paid for by (1) a combination of health insurance coverage and out-of-pocket payment,
(2) the state, (3) out-of-pocket payments or (4) relatives, associates and/or family members. Ill-
health can be a burden to the individual, family, community and the nation, and it is a probability
against which people and the society seek to protect themselves. All illnesses require some
typology of treatment, and while this does not necessarily have to be a traditional medical
practitioner, curing illness means that the individual must forego consuming something in order
to restore his/her good health.
Some illnesses such as the common cold may not require a trained medical practitioner to
cure, but often the individual will be required to spend money on over-the-counter medications,
use a home remedy or utilise non-traditional healers in the quest to restore his/her former healthy
state. There are other illnesses such as diabetes mellitus, heart disease, kidney problems,
hypertension, HIV/AIDS, sexually transmitted infections, and other chronic and non-
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communicable diseases, which require the attention of traditional medical experts to address
their cure.
The traditional medical practitioners require payment in the form of cash and/or health
insurance coverage. Because individuals desire to restore their health, they are expected to
provide payment for health care, which for particular health conditions can be exorbitantly high.
It is this reality which may result in premature mortality if the state does not provide health care
coverage for those who are economically challenged and/or vulnerable. The World Health
Organization (WHO) [1] opined that 80% of chronic illnesses were in low and middle income
countries, suggesting that illness interfaces with poverty. The WHO continued that 60% of
global mortality was caused by chronic illness, and this should be understood within the context
that four-fifths of chronic dysfunctions are in low-to-middle income countries [1]. It also
postulated that “In reality, low and middle income countries are at the centre of both old and new
public health challenges” [1]. Embedded in the realities outlined by the WHO are the incapacity
of the poor, the association between poverty and illness, between poverty and premature
mortality, poverty and human suffering, and poverty and future retardation of economic growth,
and the fact that health insurance provides some cushion against this, for the individual and for
society. Other studies have equally found that there is a significant statistical relationship
between poverty and illness [2-4] and poverty and chronic illness, [5] which means that illness
can make the vulnerable less likely to survive and the wealthy become poor.
The high risk of mortality in developing countries is owing to food insecurity, low water
quality and low sanitation coupled with inadequate access to material resources. Poverty makes
it an insurmountable hurdle for poor people to effectively address illness unless health care
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services are free. Hence, those in the lower socioeconomic class will be expected to have poorer
health, as they are crippled by their material deprivation and low health options. The WHO
captures this aptly “... People who are already poor are the most likely to suffer financially from
chronic diseases, which often deepen poverty and damage long term economic prospects”. [1]
Among the challenges for people living in poverty is access to health insurance coverage. Such a
possibility means that the burden of health care is an out-of-pocket payment that cannot be
provided by the poor, and this will eliminate life in the process. Cass et al. [6] found that infant
mortality in Peru for those in the poorest quintile (i.e. poorest 20%) was almost 5 times more
than for those in the wealthiest quintile (i.e. wealthiest 20%). This indicates the extent of the
health challenge of the poor, and the role that the lack of health insurance and income play in the
demise of individuals and even their children.
Another research paper revealed that life expectancy between the poorest 20% and the
wealthiest 20% was 6.3 years, and this figure rose to 14.3 years for disability-free life
expectancy, [7] suggesting that access and lack of access to resources explain health and healthy
life expectancy in and among the social classes in a society. Grossman [8] found a positive
correlation between income and health status, indicating that money makes a difference in
health, health care-seeking behaviour, physical milieu and health care coverage. Smith and
Kington, [9] on the other hand, went further than Grossman when they postulated that money
buys health. This viewpoint is somewhat deceptive, as money provides access to good physical
milieu, the best health care options, nutrition, dietary choices and health information which are
not readily available to the poor, but it does not buy health. Health is not a commodity for sale,
and so it cannot be purchased, but money allows for access to better health choices and by
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extension can change health outcomes. Those issues could be the intent of Smith and Kington,
when they say that money buys health, and they further exemplify the challenges if an individual
does not have access to it.
Material deprivation is such that the poor will be far from concerned with health
insurance coverage, proper diet and nutrition, health care choices, but more with survivability.
This denotes that they will be living on the margins of survivability and the decision to purchase
health insurance will be the opportunity cost of food, clothing, shelter, minimal education and
health options. Within the context of material and widespread health deprivation for those in the
lower socioeconomic strata, the state must play a role in aiding improvements in the healthy life
expectancy of those therein. It is through this avenue that public health must act in order to fulfill
the aim of the state in improving the quality of life of all residents in the nation.
Public health uses information from within and outside the society to improve the health
and quality of people’s lives, and this requires continuous research findings. According to the
WHO, “In Jamaica 59% of people with chronic diseases experience financial difficulties because
of their illness...” Hence, poverty and illness, poverty and chronic illness, and poverty and low
access to material resources are well established in research literature, but a dearth of
information existed in Latin America and the Caribbean, and in particular Jamaica, on the sick
and uninsured. Can we assume that they are all poor people, and use this to plan for them in a
developing nation? An extensive review of the literature in developing nations, and in particular
Latin America and the Caribbean, did not produce a single study that has examined health, and
health care-seeking behaviour among uninsured ill people. The current study aims to narrow this
divide by investigating health, self-reported diagnosed health conditions and health care-seeking
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behaviour, at the same time examining who are the unhealthy and uninsured, and modelling
factors which account for the moderate-to-very good health status of uninsured ill Jamaicans, in
order to provide public health specialists with pertinent information that can be used to address
some of the challenges within the society.
Methods and material
Data
The current study utilised the latest cross-sectional survey data in Jamaica to examine health,
self-reported diagnosed health conditions and health care-seeking behaviour, and to model
factors which account for the moderate-to-very good health status of unhealthy and uninsured
Jamaicans. The Jamaica Survey of Living Conditions (JSLC) began collecting data from
Jamaicans in 1988 and the latest dataset available is for 2007. The JSLC is a modification of the
World Bank’s Living Standard Household Survey [10, 11]. This work extracted a sample of 736
respondents who indicated that they were ill and not insured, from a sample of 6,783 respondents
[12]. The cross-sectional survey was conducted between May and August 2002 in the
14 parishes across Jamaica, and included 6,783 respondents of all ages. The JSLC used a
stratified random probability sampling technique to draw the original sample of respondents,
with a non-response rate of 26.2%. The sample was weighted to reflect the population.
The design was a two-stage stratified random sampling design where there was a Primary
Sampling Unit (PSU) and a selection of dwellings from the primary units. The PSU is an
Enumeration District (ED), which constitutes of a minimum of 100 dwellings in rural areas and
150 in urban areas. An ED is an independent geographical unit that shares a common boundary.
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This means that the country was grouped into strata of equal size based on dwellings (EDs).
Pursuant to the PSUs, a listing of all the dwellings was made, and this became the sampling
frame from which a Master Sample of dwellings was compiled, which in turn provided the
sampling frame for the labour force. One third of the 2007 Labour Force Survey (i.e. LFS) was
selected for the survey.
Study instrument
The JSLC used an administered questionnaire where respondents were asked to recall detailed
information on particular activities. The questionnaire was modelled on the World Bank’s Living
Standards Measurement Study (LSMS) household survey. The questionnaire covered
demographic variables, health, education, daily expenses, non-food consumption expenditure,
and other variables. Interviewers were trained to collect the data from household members.
Statistical methods Descriptive statistics were used to provide socio-demographic characteristics of the sample. Chi-
square analyses were used to examine the association between non-metric variables. Analysis of
variance was used to test the statistical significance of a metric and non-dichotomous variable.
Logistic regression analyses examined 1) the relationship between good health status and some
socio-demographic, economic and biological variables; as well as 2) a correlation between
medical care-seeking behaviour and some socio-demographic, economic and biological
variables. The statistical package SPSS 16.0 was used for the analysis. A p-value less than 5%
(2-tailed) was used to indicate statistical significance.
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The correlation matrix was examined in order to ascertain if autocorrelation and/or
multicollinearity existed between variables. Based on Cohen and Holliday [13] correlation can
be low (weak) - from 0 to 0.39; moderate – 0.4-0.69, and strong – 0.7-1.0. Any variable that had
at least moderate (r > 0.6) was re-examined in order to address multicollinearity and/or
autocorrelation between or among the independent variables [14-16]. Another approach in
addressing collinearity (r > 0.6) was to independently enter variables in the model to determine
which one should be retained during the final model construction. The method for retaining or
excluding a variable from the model was based on its contribution to the predictive power of the
model and its goodness of fit [17]. Wald statistics were used to determine the magnitude (or
contribution) of each statistically significant variable in comparison with the others, and the
Odds Ratio (OR) for the interpreting of each significant variable.
Measurement
Health status is a binary measure where 1= moderate-to-very good health; 0= otherwise which is
determined from “Generally, how do you feel about your health”? Answers to this question were
analyzed on a Likert scale ranging from excellent to poor. Medical care-seeking behaviour was
taken from the question ‘Has a health care practitioner, healer, or pharmacist been visited in the
last 4 weeks?’ with there being two options: Yes or No. Medical care-seeking behaviour
therefore was coded as a binary measure where 1=Yes and 0= otherwise. Crowding is the total
number of individuals in the household divided by the number of rooms (excluding kitchen,
verandah and bathroom). Sex: This is a binary variable where 1= male and 0= otherwise. Age is
a continuous variable which is the number of years alive since birth (using last birthday). Age
group is a non-binary measure: children (aged less than 15 years); young adults (ages 15 to 30
years); other-aged adults (ages 31 to 59 years); young elderly (ages 60 to 74 years); old elderly 366
(ages 75 to 84 years) and oldest elderly (ages 85 years and older). Social hierarchy: This variable
was measured based on income quintile: The upper classes were those in the wealthy quintiles
(quintiles 4 and 5); middle class was quintile 3 and the poor were those in the lower quintiles
(quintiles 1 and 2).
Chronic illnesses: These are ailments or diseases that are prolonged, not likely to be resolved
spontaneously, and are infrequently cured.
Inequity denotes differences that are unnecessary and avoidable, but are also thought to be unfair
and unjust, and these are adjudged based on the context of the customs operating in the society in
general.
Equity in health means (1) equal access to care for equal needs, (2) equal access to utilisation for
equal needs, and (3) equal quality of care for all in the society.
Inequalities in health mean patterns of socioeconomic disparities in health outcome which are
systematic, avoidable and important within a country.
Model
The multivariate model used in this study is in keeping with wanting to capture the multi-
dimensional concept of health and the health care-seeking behaviour of uninsured ill people.
Utilising logistic regression on secondary cross-sectional data, the present study modelled
moderate-to-very good health status and the health care-seeking behaviour of uninsured ill
Jamaicans. Using a p-value of less than 0.05 to indicate statistical significance, each model
reflects only those variables that are statistically significant.
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Health Model
Hit = f(Ait, Xi, SSit, lnYit, ARit, HSBit, εit) ………………………………. [1]
Health Care-seeking Behaviour Model
Hit = f(Ait, CIit, Hit, lnYit, CRit, MSit, εit) ………………………………. [2]
Where Hti is current moderate-to-very good health status of uninsured ill person i in time
period t; Ai is age (in years) of person i in time period t; Xi is gender of person i; SSit is social
class of person i in time period t; lnYit is logged income of person i in time period t; ARit is area
of residence in time period time t; HSBit is health care-seeking behaviour in time period t; CRi is
crowding in the household of person i in time period t; CIit is chronic illness of person i in time
period t; MSit is marital status of person i in time period t; εit is residual error of person i - in time
period t.
Results
Table 14.1 presents information on the demographic characteristics of the sample. The sample
was 736 respondents (i.e. 10.85% of the initial survey) who indicated that they were both sick
and uninsured, and of which 40.5% were males. Concurringly, of the sample 95.4% had at most
primary level education and 0.8% had tertiary level education. Children constituted 28.7% of the
sample; young adults, 10.2%; other adults, 31.3%; young-old, 16.4%; old-old, 10.5%; and
oldest-old, 3.0%. The median age was 42.0 years (range = 0 – 99 years). The median total annual
expenditure was USD 5,689.89 (range = USD 261.56 – 32,780.78; US$ 1.00 = J$ 80.47 - at the
time of the survey). The number of visits made to medical practitioner(s) was 1.4 ± 1.0), while 368
the amount of time spent in private care facilities was 3.0 ± 2.8 compared to 5.2 ± 5.0 for public
care facilities). The mean cost of public medical care was USD 4.44 ± USD 16.14 compared to
USD 13.64 ± USD 28.22 for private medical expenditure.
Of those who utilised public health care facilities, 22.9% of them purchased the
prescribed medication compared to 78.8% who visited private health care facilities.
Table 14.2 highlights information on health care-seeking behaviour, health care
utilisation, self-reported illness and area of residence by social hierarchy. Based on Table 14.2,
there were significant statistical associations between (1) health care-seeking behaviour and
social hierarchy; (2) public health care centre utilisation and social hierarchy, and (3) private
health care centre utilisation and social hierarchy.
Table 14.3 highlights information on monthly food expenditure, per capita consumption,
length of illness, number of visits made to health practitioners, medical expenditure and self-
reported diagnosed illness by area of residence. Based on Table 14.3, there were significant
statistical associations between (1) monthly food expenditure and area of residence and (2) per
capita consumption and area of residence – P < 0.05. However, there were no significant
statistical relationships between the other variables and area of residence – P > 0.05.
There was a statistical association between health care-seeking behaviour and age group
of respondents – χ2 = 11.1, P = 0.048. As uninsured ill people become older, they are more likely
to seek medical care: Children, 54.8%; old-adults, 54.8%; other-age adults, 64.0; young-old,
63.3%; old-old, 73.3%; and oldest old, 66.7%.
There was a statistical relationship between having chronic illness and being the
household head – χ2 = 63.3, P < 0.0001. Almost 55% of those with chronic illnesses were
369
household heads, compared to 22.4% who did not have chronic illness but were household
heads.
A significant statistical association existed between sex and having chronic illness - χ2 =
4.7, P < 0.031. More females had chronic illness (69.8%) than males (61.7%).
There was a significant statistical association between health status and typology of
illnesses (i.e. acute and chronic conditions) - χ2 = 62.3, P < 0.0001. Thirty-seven percent of
those with chronic illnesses reported at least poor health status compared to 12.2% of those with
acute conditions. On the other hand, 61.1% of those with acute conditions reported at least good
health status compared to 31.3% of those with chronic conditions.
A statistical difference was found between the mean income of those in the different
social hierarchies – F statistic = 277.50, P < 0.0001. The mean income for those in the poorest
20% was USD 666.07 ± 175.40 followed by the second poor, USD 1,090.68 ± 132.14; middle
class, USD 1,489.69 ± 169.07; second wealthy, USD 2,131.55 ± 254.49 and the wealthiest 20%,
USD 4,201.39 ± 235.26.
Multivariate analysis
Table 14.5 shows variables which are correlated (or not) with the moderate-to-very good
health status of uninsured ill respondents. Seven variables emerged as significantly associated
with moderate-to-very good health status – Model χ2 = 83.70, P < 0.001, -2 Log likelihood =
482.9 – and they accounted for 23% of the variability in health status. The model is a good fit
for the data - Hosmer and Lemeshow goodness of fit χ2= 3.72, P = 0.88.
Table 14.6 presents information on variables and self-reported health care seeking
behaviour of uninsured respondents. Six variables emerged as significant statistical correlates of
self-reported health care-seeking behaviour - Model χ2 = 47.9, P < 0.001, -2 Log likelihood =
370
486.1. The model is a good fit for the data - Hosmer and Lemeshow goodness of fit χ2= 8.11, P =
0.62.
Discussion
The current research used a sample of respondents who indicated both experiencing ill-health
and having no health insurance coverage. Of the sample of respondents (i.e. n = 736), 60 out of
every 100 were females, 43 out of every 100 were poor, 35 out of every 100 were in the upper
social class, 59 out of every 100 dwelled in rural areas, 3 out of every 100 had been injured
during the last 4 weeks, 61 out of every 100 sought medical care, 50 out of every 100 utilised
public health care, two-thirds reported being diagnosed with a chronic illness, 31 out of every
100 were elderly, and 29 out of every 100 were children. Those in the lower socioeconomic
class were more likely to dwell in rural areas. Those in the poorest 20% were more likely to use
public health centres, and the wealthiest 20% were more likely to utilise private health care
centres. Fifty-four percent of those in the poorest 20% sought medical care in the last 4 weeks
compared to 72% of those in the wealthiest 20%. Concurringly, of the sample, 78.4% indicated
at least fair health status. Moderate-to-very good health status was explained by age, sex, social
class, income, area of residence and health care-seeking behaviour. Rural residents had the least
moderate-to-very good health status among uninsured ill Jamaicans. People who dwelled in
Other Towns were 2.3 times more likely to indicate moderate-to-very good health compared to
those in rural areas, and those in urban areas were 2.0 times more likely to claim moderate-to-
very good health status. Those who indicated having a chronic illness were 37% less likely to
report moderate-to-very good health. In addition, the present sample represents 70% of those
who indicated having an illness in Jamaica for 2007.
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Statistics from the Planning Institute of Jamaica and the Statistical Institute of Jamaica
[10] showed that 15.5% of Jamaicans reported ill-health in 2007. Within the context of the
current findings and that of PIOJ and STATIN, it computes that 71% of those who were
experiencing illness were without health insurance coverage. Given that 50% of those who
claimed to be experiencing ill-health utilised the public health care system and the fact that two-
thirds of the illnesses were chronic conditions (3 females for every 2 males were uninsured and
ill, and 6 out of every 10 uninsured ill people were of the dependent age cohort - less than 15
years or 60+ years), the public health care sector in Jamaica needs to recognize the impending
challenges of uninsured unhealthy people.
Van Agt et al. [5] found that the chronically ill were more likely to be poor, a statement
with which this study concurs. In this paper, 43.2% of the chronically ill were poor (25.2% of
poorest 20%) compared to 35.2% of the upper class (15.3% of the wealthiest 20%). This study
went further than Van Agt et al.’s work, as the chronically ill were more likely to be elderly
(42.5% of the chronically ill were 60+ years), to seek more medical care, were more likely to
utilise public health facilities, more likely to live in rural areas (59.1%), more likely to be
household heads (54.8%) and more likely to be females (63%). Clearly the poor are highly
vulnerable to chronic illness [1, 5] and material deprivation [4], which accounts for more of them
not having health insurance coverage while suffering from ill-health. Hence, those who are
uninsured and ill must interface with chronic health conditions as well as income deprivation.
Income is well established in the health literature as being associated with health [4, 8, 9],
and this explains the fact that those in the lower socioeconomic class have poorer health than
those in the upper class [18, 19]. This paper found that uninsured ill people with more income
are 2.9 times more likely to report moderate-to-very good health status, and they are also more
372
likely to seek medical care. The challenge for those in the lower class is more than lower health
status; it is also being deprived of the health care that they need. Statistics revealed that poverty
in Jamaica is substantially a rural phenomenon (prevalence of poverty in rural areas, 15.3%;
semi-urban poverty, 4.0%; urban poverty 6.2%) [10]. This study highlights that those who are ill
and uninsured are likely to dwell in rural zones, explaining how financial deprivation accounts
for lower ownership of health insurance coverage, the worst health being found among those in
rural areas compared to city dwellers. Using per capita consumption to measure income in this
study, it was revealed that urban residents had 1.7 times more income than rural residents, and
that semi-urban residents had 1.3 times more income than rural dwellers, suggesting that the
health disparities between the geographical dwellers is explained by this income inequity. It is
therefore this access to more income that accommodates the greater health status of the urban
and semi-urban respondents, compared to the rural dwellers, and it highlights a real need to
correct income inequality among the socioeconomic groups in the nation. A study by Stronks et
al. [20] found an interrelationship between income, health and employment status, which further
argues for greater health for urban and semi-urban dwellers, as rural residents are more likely to
be seasonally employed, self-employed or have low-income employment.
While income is related to better health status, which is also the case among uninsured ill
people, concurring with the literature on a population [8, 9, 20], the great health disparity
between the different social classes is more related to income than place of residence. Such a
finding provides clarification for a study done by Vila et al. [21] which stated that great health
disparities in the city of Milwaukee were associated with area of residence by different social
hierarchy. Income has a greater influence on better health than area of residence, and it even
correlates with health care-seeking behaviour among the uninsured ill, unlike area of residence.
373
Money matters in the health of uninsured Jamaicans as well as the general populace, as it offers a
better explanation for peoples’ choices, accounting for the greater health of those who are able to
choose, than their place of residence. Lack of access to money, therefore, in any geographical
locality, explains health and material deprivation. Hence, it is not the fact of being in a rural area
that accounts for poor health, but material and other deprivations are greater in rural areas, a
factor which provides an understanding for the massive health disparity between them and city
residents.
Poverty is associated with premature mortality, and the current research provides some
explanation for this established fact. This paper is on uninsured ill Jamaicans, and the findings
highlighted that 54% of those in the poorest 20% visited a health care practitioner, 58% of the
poor compared to 65% of the second wealthy and 72% of the wealthiest 20%. While the affluent
class has access to material and other resources to address health concerns, the poor are not as
privileged as the upper class. This research found that 70.1% of those in the poorest 20% had at
least one chronic health condition, the second poor, 61.2%; the second wealthy, 72.7% and the
wealthiest 20%, 68.7%, which means that non-utilisation of medical care is likely to lead to
complications and possible premature mortality. The WHO had stated that 60% of global
mortality is caused by chronic illness, but clearly poverty, non-treatment of chronic illnesses and
cultural practices are all a part of the rationale for mortality, and not merely the condition.
Although those who suffer from chronic conditions in Jamaica are able to access public
health insurance which can reduce out-of-pocket payments for treatment and medication, clearly
the culture prevents some people from accessing this facility. This work showed that a large
percentage of uninsured ill people dwelled in rural areas, where poverty was 2.5 times more than
urban poverty and 3.8% more than semi-urban poverty, arguing for the role of the culture in
374
preventing them from accessing assistance from the state. With this preponderance of
unwillingness on the part of poor and rural residents to access health insurance, accompanied by
their low demand for health services compared to the wealthy, the inference is that many of them
will seek health services based only on severity of illness. Chronic illnesses are such that non-
medical practitioners should not interpret when conditions are serious and warrant health care
assistance. It is this culture underpinning that accounts for the premature mortality and not the
poverty or illness, as those with chronic health conditions in Jamaica are able to access public
health care despite their reluctance to access public health insurance coverage. With not having
health insurance coverage, poverty and illness are likely to become a burden to individuals and
family, and when those social agents are unable to assist with the costing of medical treatment, it
will then become the responsibility of the state.
This paper did not examine nutrition and health, but a study by Khetarpal and Kochar
[22] found a statistical relationship between nutrition and health in rural women, which offers
some explanation for the great health disparities in geographical areas of residence. Another
study by Foster [23] on low-income rural areas concurs with Khetarpal and Kochar [22] that
nutrition accounts for health or ill-health, as the body requires particular nutrients. It can be
extrapolated from the aforementioned studies, to that of the current one, that great disparities in
health status among the different geographical areas in Jamaica can be explained by the
nutritional intake (or lack of intake) based on where people dwell in this nation. There is a
question which must be addressed in order to provide some explanation for the seemingly low
nutritional intake of rural uninsured residents: Are rural residents less likely to intake the
required nutrients compared to residents in other geographical areas in Jamaica? The answer is
clearly yes as more of the uninsured ill Jamaicans are poor, and this means that they will be less
375
concerned about the required nutrient intake than food consumption and mere survivability.
Poverty is therefore more a factor in insurance, illness, lower health status and health care-
seeking behaviour than the geographical area of residence, but what about the general health
status of the uninsured ill, and is it lower than that of the population of Jamaica?
Almost 78 out of every 100 uninsured ill Jamaicans claimed to have at least good health
status. A study by Bourne [24] found that 82 in every 100 Jamaicans reported at least good
health status, which is greater than that for the uninsured ill people. Furthermore, 3.3 times more
Jamaicans indicated very good health compared to the uninsured ill Jamaicans. The health
disparities were not only between the good and very good health status of Jamaicans and
uninsured ill Jamaicans, but were also evident for poor health status. Comparatively, 4.4 times
more uninsured ill Jamaicans claimed at least poor health as compared to the general population
(i.e. 4.9%), and 3 times more uninsured chronically ill Jamaicans reported at least poor health
status compared to those with acute health conditions. The current study concurs with (1) Reed
and Tu’s work [25] that uninsured chronically ill people in America reported lower health status
(or worse health) and (2) Bourne and McGrowder [26] which stated that 25.3% of chronically ill
Jamaicans reported at least poor health. Reed and Tu went on to state that the majority of
uninsured people with chronic illnesses delay health care utilisation owing to cost, which
explains an aspect of this study, that although 43.2% of the uninsured ill people were living in
poverty (i.e. poorest 20% and second poor income quintile), 39% did not seek medical care.
Faced with poverty, no health insurance coverage and chronic illness, uninsured ill
Jamaicans are highly likely to face all kinds of life challenges such as material deprivation,
dietary and nutritional deficiencies, high risk of health complications, high out-of-pocket medical
bills, disruptions in family life, future vulnerabilities and premature mortality. When this burden
376
becomes untenable for the individual, family and wider community, it will then become the
responsibility of the state [27]. This justifies the need to expand public health insurance to
protect the poor, the chronically ill and the vulnerable in a society [28], as chronic illness can
erode the economic livelihood of an individual and therefore delay needed health care [29]. One
study stated that uninsured households are one illness away from financial catastrophe [30],
indicating that if a household was already in poverty this will become the burden of the state or
may lead to premature mortality, as the individual will be unable to access needed health care
owing to his/her inability to afford medical care. This implies that poverty encapsulates
powerlessness, physical weakness, illness, chronic illness, premature mortality, lack of
productive assets, emotional distress, constricted freedom and future impoverishment due to the
aforementioned conditions, if they are not addressed by policy makers.
While impoverishment in urban areas is highly visible in the form of squalor, dilapidated
edifices, zinc fencing, improper sanitation, squatting and violence, rural poverty is less easily
identifiable and may be overlooked by the naked eye. Clearly, using health disparities between
area of residence and the socioeconomic strata, rural poverty in Jamaica is showing signs of
depleting the human capital more than urban poverty. According to Harpham and Reichenheim
[31], on the disaggregating of rural and urban health indicators, the latter ‘appear’ to have better
health status. This study dispels the notion of ‘appearance’ and goes to the reality of the health
differential using self-reported health among urban, semi-urban and rural uninsured ill
Jamaicans. The discipline of public health cannot only use external findings to carry out its
mandate, or divorce itself from the realities which emerge from the current study; poverty is
destroying the human capabilities and resilience of the Jamaican people and more so in the case
of rural uninsured ill people. Because poverty is strongly associated with illness, and illness can
377
result in poverty [32-34], those who are presently uninsured, ill and poor are highly vulnerable to
ill-health and premature mortality, which argues for an immediate health campaign to address the
challenges among the socioeconomic strata and area of residence, as these were not alleviated
with the introduction of the National Health Fund – NHF [35].
The NHF is a statutory company which was established by the NHF Act (2003) with a
Chairman and Board of Management appointed by the Minister of Health. It was established in
2003 to provide direct assistance to patients with chronic conditions, to purchase drugs and fund
support to private and public companies for approved projects [35]. The NHF is a social health
insurance which is geared towards alleviating out-of-pocket payments for medication for those
who suffer from chronic illnesses. Fourteen chronic illnesses are covered by the NHF, with
respect to pharmaceutical benefits in direct assistance to ill individuals. The chronic health
conditions that are covered by the NHF are hypertension, diabetes mellitus, breast cancer,
prostate cancer, glaucoma, arthritis, asthma, high cholesterol, rheumatic heart disease, major
depression, epilepsy, psychosis, ischemia and vascular diseases. The NHF became operational in
August 2003, and has undoubtedly aided many chronically ill, non-poor and poor Jamaicans.
With all the investment, the NHF has not failed to have a major coverage of chronically ill
respondents using the Fund. The individuals are mostly rural residents, poor, under 60 years of
age, and female. Such a reality speaks to the administrative and operational failure of the NHF
to improve the lives of its intended population owing to the centralization of its operations in
Kingston, which is an urban area in Jamaica. The verdict is in, that merely instituting an agency
to carry out a particular task (which is to distribute benefits evenly across the socioeconomic
strata, area of residence and sex) will not provide solutions to the inequalities and inequities in
health between the particular groups in Jamaica. This study concurs with one in Finland [36]
378
showing that the poor are more vulnerable to illnesses, and research conducted in the United
Kingdom [37] found that those in the lower socioeconomic stratum were more likely to die
prematurely than those in the upper income groups. Embedded in those findings is the fact that
any equitable distribution of NHF benefits to those in the different socioeconomic strata will
show further unfairness and injustices in the health outcomes which already exist, owing to
income inequalities.
Conclusion
Two-thirds of uninsured ill Jamaicans are chronically ill. The uninsured ill are mostly within the
dependent age cohort (children and elderly), they are female and are rural respondents who are
generally poor people. With one half of the uninsured ill respondents utilising the public health
care system, and only 2 in every 10 of them purchasing medications, there are serious future
challenges for public health in Jamaica. There is an inverse relationship between the health status
of uninsured ill Jamaicans and those in socioeconomic strata. The findings of this study highlight
the likely challenge of the state in assisting uninsured ill Jamaicans. Despite the fact that health
insurance coverage is freely accessible to those who are chronically ill in Jamaica, there are still
many such people who are without health insurance coverage, and some are not even seeking
medical care. Another reality which emerged from this paper is that although health care
utilisation is free in Jamaica for children 18 years and younger, 45 out of every 100 of those
uninsured and ill did not seek medical care, emphasizing people’s interpretation of illnesses that
require medical attention, and how this retards health care demand. The task of public health
specialists and policy makers, therefore, is to fashion public education and intervention
programmes that will address many of the realities which emerged in this research. The great
health disparity between the lower socioeconomic strata and those in the upper strata, as well as
379
those who reside in rural areas, cannot be left to resolve itself, as clearly it has not happened in
the past and the situation cannot be allowed to continue indefinitely in the future.
The Way Forward
The variations in health status and health care-seeking behaviour within and between the
socioeconomic strata who are uninsured ill people, clearly present information that reveals public
health concerns, and highlights many challenges which are still unresolved in Jamaica. The
current study did not examine the emotional distress and mortality patterns of uninsured ill
respondents, and this should be the subject of some future study, as it would provide needed
information about these individuals. Despite the investments in health, the health sector and
poverty alleviation programmes in Latin America and the Caribbean, there is still a need to study
the heterogeneity in health outcome between the socioeconomic strata and area of residence, as
health disparity between and within countries is still great and not in keeping with health
inequality eradication in the region. Another unresolved issue stemming from the present
research is how much of the cognitive dimension explains the health differential between the
socioeconomic strata and the area of residence. In order to understand how to address policy
intervention and health education programmes for people in Jamaica, studies need to examine the
breadth and scope of cognitive dimensions in explaining health inequalities. This will allow
public health technocrats to understand why 70.3% of those who were ill in Jamaica in 2007 did
not have health insurance, and some of the chronically ill people, despite having access to public
health insurance, did not possess such insurance, and did not seek medical care. A critical issue
which needs to be addressed in the future is the structure of the National Health Fund (the NHF
is accessible to, and provides public health insurance coverage for, those experiencing chronic
illnesses). Barrett and Lalta [32] wrote that “The National Health Fund dealt with these issues by
380
treating the non-poor and the poor as part of the same target beneficiary. Survey data and health
officials indicate that the poor suffer as much from chronic diseases as the rich, but are less likely
to seek treatment, or are only able to pay for part of their prescription drugs by reducing out-of-
pocket payment …” This study is 4 years after the operational establishment of the NHF, and
new findings are coming in, which show that the NHF cannot treat different socioeconomic
strata in the same way, neither can it deal equitably with those who reside in different
geographical areas. The health disparities will not be addressed by merely offering equal
benefits to all within the context of the current findings, as these will only perpetuate health
inequalities and inequities. The NHF therefore needs to be restructured in order to provide
definitions based on socioeconomic class and area of residence, so as to effectively alleviate
some of the challenges which emerged from this research.
Conflict of interest
The author has no conflict of interest to report.
Disclaimer
The researcher would like to note that while this study used secondary data from the Jamaica Survey of Living Conditions, 2007, none of the errors that are within this paper should be ascribed to the Planning Institute of Jamaica or the Statistical Institute of Jamaica as they are not theirs, but are instead owing to the researcher.
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Table 14.1. Demographic characteristic of sample, n=736 Characteristic n % Sex Male 298 40.5 Female 438 59.5Marital status Married 161 30.8 Never married 276 52.8 Divorced 14 2.7 Separated 10 1.9 Widowed 62 11.9Social hierarchy Poorest 20% 170 23.1 Second poor 146 19.8 Middle 165 22.4 Second wealthy 142 19.3 Wealthiest 20% 111 15.4Area of residence Urban 176 23.9 Semi-urban 128 17.4 Rural 432 58.7Injury in last 4-weeks Yes 23 3.1 No 712 96.9Self-reported diagnosed illness Acute conditions Influenza 124 18.6 Diarrhoea 26 3.9 Asthma 73 10.9 Chronic conditions Diabetes mellitus 69 10.3 Hypertension 147 22.0 Arthritis 40 6.8 Other 189 28.3Health care-seeking behaviour Yes 446 61.4 No 280 38.6Health care utilization Public hospital (yes) 146 29.9 Private hospital (yes) 27 5.5 Public health care centres (yes) 96 19.6 Private health care centres (yes) 212 43.4 Other (yes) 8 1.6Purchased medication Yes 411 58.6
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Table 14.2. Health care-seeking behaviour, health care utilization, self-reported illness and area of residence by social hierarchy Characteristic
Social hierarchy
P Poorest
20% Second
poor Middle Second
wealthy Wealthiest
20% n (%) n (%) n (%) n (%) n (%)
Health care-seeking behaviour
0.046
Yes 90(54.2) 86(59.7) 98(60.1) 91(65.0) 81(71.7) No 76(45.8) 58(40.3) 65(39.9) 49(35.0) 32(28.3) Health care utilization
Public hospitals 0.337 Yes 32(37.2) 30(35.3) 35(35.7) 30(33.7) 19(23.5) No 54(62.8) 55(64.7) 63(64.3) 59(66.3) 62(76.5) Private hospitals 0.451 Yes 5(5.7) 5(5.9) 3(3.1) 6(6.7) 8(9.9) No 83(94.3) 80(94.1) 95(96.9) 83(93.3) 73(90.1) Public health care centres
0.016
Yes 29(33.3) 21(24.7) 21(21.4) 15(17.0) 10(12.3) No 59(67.0) 64(75.3) 77(78.6) 73(83.0) 71(87.7) Private health care centres
0.001
Yes 28(31.5) 35(41.2) 49(50.0) 52(58.4) 48(59.3) No 61(68.5) 50(58.8) 49(50.0) 37(41.6) 33(40.7) Self-reported diagnosed illness
0.200
Acute conditions Influenza 24(15.0) 25(19.1) 34(22.7) 26(20.3) 15(15.2) Diarrhoea 3(1.9) 9(6.9) 7(4.7) 3(2.3) 4(4.0) Asthma 21(13.1) 17(13.0) 17(11.3) 6(4.7) 12(12.1) Chronic conditions Diabetes mellitus 15(9.4) 15(11.5) 9(6.0) 15(11.7) 15(15.2) Hypertension 38(23.8) 23(17.6) 37(24.7) 27(21.1) 22(22.2) Arthritis 15(9.4) 8(6.1) 7(4.7) 6(4.7) 4(4.0) Other 44(27.5) 34(26.0) 39(26.0) 45(35.2) 27(27.3) Area of residence <0.0001 Urban 19(11.2) 21(14.4) 35(21.2) 42(29.6) 59(52.2) Semi-urban 16(9.4) 25(17.1) 30(18.2) 36(25.4) 21(18.6) Rural 135(79.4) 100(68.5) 100(60.6) 64(45.1) 33(29.2) Length of illness (i.e. in days) mean± SD
10.6±11.6 12.9±22.7 11.1±15.9 31.5±116.3 14.9±21.8 0.006
386
387
Table 14.3. Monthly food expenditure, per capita consumption, length of illness, number of visits made to health practitioner, medical expenditure and self-reported diagnosed illness by area of residence Characteristic
Area of residence P Urban Semi-urban Rural
n (%) n (%) n (%) †Monthly food expenditure mean ± standard deviation
280.71±192.00 277.45±162.97 237.07±145.59 0.002
Per capita consumption mean ± standard deviation
2425.23±1992.18
1923.62±1241.60
1441.30±1179.85
< 0.0001
Length of illness in day mean ± standard deviation
9.5±19.1 13.5±23.0 17.7±65.4 0.256
Number of visits made to health care practitioner in last 4-weeks mean ± standard deviation
1.4±0.7 1.4±1.3 1.4±1.0 0.927
†Medical expenditure Public mean ± standard deviation
3.47±7.07 4.72±16.51 4.78±18.65 0.787
Private mean ± standard deviation
13.58±13.21 15.38±15.60 13.14±35.37 0.851
Self-reported diagnosed illness
0.162
Acute conditions Influenza 19(12.3) 34(28.8) 17(17.9) Diarrhoea 3(1.9) 4(3.4) 19(4.8) Asthma 21(13.6) 9(7.6) 43(10.9) Chronic conditions Diabetes mellitus 16(10.4) 13(11.0) 40(10.1) Hypertension 37(24.0) 24(20.3) 86(21.7) Arthritis 10(6.5) 6(5.1) 24(6.1) Other 48(31.2) 28(23.7) 113(28.5)†Quoted in USD (USD 1.00 = Ja. $ 80.47 at the time of the survey)
Table 14.4. Self-reported diagnosed health conditions of uninsured ill respondents by age cohort Characteristic
Age cohort P Children Young adults Other-aged
adults Young old Old-old Oldest-old
n (%) n (%) n (%) n (%) n (%) n (%) Self-reported diagnosed illness
< 0.0001
Acute conditions Influenza 83(45.6) 10(15.6) 19(9.1) 6(5.1) 6(8.1) 0(0.0) Diarrhoea 13(7.1) 2(3.1) 6(2.9) 2(1.7) 2(2.7) 1(4.5) Asthma 42(23.1) 11(17.2) 13(6.2) 4(3.4) 2(2.7) 1(4.5) Chronic conditions Diabetes mellitus 1(0.5) 2(3.1) 32(15.3) 21(17.9) 10(13.5) 3(13.6) Hypertension 0(0.0) 4(6.3) 55(26.3) 41(35.0) 36(48.6) 11(50.0) Arthritis 0(0.0) 0(0.0) 12(5.7) 18(15.4) 9(12.2) 1(4.5) Other 43(23.6) 35(54.7) 72(34.4) 25(21.4) 9(12.2) 5(22.7)
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Table 14.5: Logistic regression: Variables of moderate-to-very good health status of uninsured ill respondents
Variable
Coefficient
Std. Error
Wald statistic Odds ratio
95.0% C.I.
Age -0.033 0.008 18.605 0.967*** 0.95 - 0.98 Average Medical Expenditure 0.000 0.000 1.668
1.00 1.00 - 1.00
Male -0.511 0.244 4.374
0.60* 0.37 - 0.97
Middle Class -0.807 0.387 4.345
0.45* 0.21 - 0.95
Upper class -1.029 0.553 3.465 0.36 0.12 - 1.06†Lower class 1.00 Married 0.140 0.278 0.253
1.15 0.67 - 1.98
Divorced, separated or widowed -0.421 0.349 1.455
0.66 0.33 - 1.30
†Never married 1.00 Logged Income 1.053 0.332 10.063
2.87** 1.50 - 5.49
Urban area 0.696 0.300 5.365
2.01* 1.11 - 3.62
Other town 0.844 0.342 6.092 2.33* 1.19 - 4.54†Rural area 1.00 Head household 0.218 0.250 0.761
1.24 0.76 - 2.03
Dummy health care-seekers -0.803 0.255 9.882
0.45** 0.27 - 0.74
Chronic illness -0.456 0.351 1.696
0.63* 0.32 - 0.86
Model χ2 (12) = 83.70, P < 0.001 -2 Log likelihood = 482.96 Nagelkerke R2 = 0.23 Hosmer and Lemeshow goodness of fit χ2= 3.72, P = 0.88 Overall correct classification = 75.1% Correct classification of cases of self-rated moderate-to-very good health status = 93.4% Correct classification of cases of not self-rated not moderate-to-very good health status = 26.5% †Reference group *** P < 0.0001, **P < 01, *P < 0.05
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Table 14.6: Logistic regression: Variables of self-reported health care-seekers of uninsured ill respondents
Variable Coefficient
Std. Error
Wald statistic Odds ratio 95% CI
Chronic illness 0.812 0.277 8.609 2.25** 1.31 - 3.88 Age 0.024 0.008 9.593
1.03** 1.01 - 1.04
Moderate-to-very good health -0.857 0.281 9.274
0.42** 0.24 - 0.74
Secondary education 1.117 0.762 2.148
3.06 0.69 - 13.60
Tertiary education 1.278 1.222 1.094
3.59 0.33 - 39.42
†Primary and below education 1.00
Male -0.358 0.244 2.154
0.70 0.43 - 1.13
Crowding 0.114 0.053 4.694
1.12* 1.01 -1.24
Logged income 0.000 0.000 4.138
1.00* 1.00 - 1.00
Length of illness 0.000 0.002 0.013
1.00 1.00 - 1.00
Married -0.733 0.274 7.181
0.48** 0.28 - 0.82
Divorced, separated, or widowed -0.692 0.384 3.248 0.50 0.24 - 1.06
†Never married 1.00 Urban area 0.171 0.286 0.359
1.19 0.68 - 2.08
Other town -0.336 0.302 1.238 0.72 0.41 - 1.29†Rural area 1.00
Model χ2 (13) = 47.85, P < 0.001 -2 Log likelihood = 486.1 Nagelkerke R2 = 0.15 Hosmer and Lemeshow goodness of fit χ2= 8.11, P = 0.62 Overall correct classification = 69.0% Correct classification of cases of self-reported health care-seekers = 89.4% Correct classification of cases of self-reported health care-nonseekers = 32.2% †Reference group *** P < 0.0001, **P < 01, *P < 0.05
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Chapter 15 Determinants of self-rated private health insurance coverage in
Jamaica
Paul A. Bourne & Maureen D. Kerr-Campbell The purpose of the current study was to model the health insurance coverage of Jamaicans; and to identify the determinants, strength and predictive power of the model in order to aid clinicians and other health practitioners in understanding those who have health insurance coverage. This study utilized secondary data taken from the dataset of the Jamaica Survey of Living Conditions which was collected between July and October 2002. It was a nationally representative stratified random sample survey of 25,018 respondents, with 50.7% females and 49.3% males. The data was collected by way of a self-administered questionnaire. The non-response rate for the survey was 29.7% with 20.5% not responding to particular questions, 9.0% not participating in the survey and another 0.2% being rejected due to data cleaning. The current research extracted 16,118 people 15 years and older from the survey sample of 25,018 respondents in order to model the determinants of private health insurance coverage in Jamaica. Data were stored, retrieved and analyzed using SPSS for Windows 15.0. A p-value of less than 0.05 was used to establish statistical significance. Descriptive analysis was used to provide baseline information on the sample, and cross-tabulations were used to examine some non-metric variables. Logistic regression was used to identify, determine and establish those factors that influence private health insurance coverage in Jamaica. This study found that approximately 12% of Jamaicans had private health insurance coverage, of which the least health insurance was owned by rural residents (7.5%). Using logistic regression, the findings revealed that twelve variables emerged as statistically significant determinants of health insurance coverage in this sample. These variables are social standing (two wealthiest quintile: OR=1.68, 95% CI=1.23-2.30), income (OR=1.00, 95%CI=1.00-1.00), durable goods (OR=1.16, 95%CI=1.12-1.19), marital status (married: OR=1.97, 95%CI=1.61-2.42), area of residence (Peri-urban: OR=1.45, 95%CI=1.199-1.75; urban: OR=1.83, 95%CI=1.40-2.40), education (secondary: OR=1.57, 95%CI=1.20-2.06; tertiary: OR=9.03, 95%CI=6.47-12.59), social support (OR=0.64, 95%CI=0.53-0.76), crowding (OR=1.14, 95%CI=1.02-1.28), psychological conditions (negative affective: OR=0.97, 95%CI=0.94-1.00; positive affective: OR=1.11, 95%CI=1.06-1.16), number of males in household (OR=0.85, 95%CI=0.77-0.93), living arrangements (OR=0.62, 95%CI=0.41-0.92) and retirement benefits (OR=1.55, 95%CI=1.03-2.35). This study highlighted the need to address preventative care for the wealthiest, rural residents and the fact that social support is crucial to health care, as well as the fact that medical care costs are borne by the extended family and other social groups in which the individual is (or was) a member, which explains the low demand for health insurance in Jamaica. Private health care in Jamaica is substantially determined by affordability and education rather than illness, and it is a poor measure of the health care-seeking behaviour of Jamaicans.
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1. INTRODUCTION
Literature on private health insurance or health insurance in the Caribbean, and in particular
Jamaica, has been substantially on (i) population density – i.e. coverage, (ii) coverage offerings,
(iii) cost of care – i.e. health economics, and (iv) acceptance (or lack of) by health service
providers of certain insurance coverage. Having extensively perused the literature review on
private health insurance and health care reform in Jamaica, it is obvious that no study has been
conducted identifying the different factors that explain health insurance coverage in this nation.
The individual utilization pattern of health insurance coverage is highly associated over time
with older adults [1, 2] as they prepare for the degeneration of the body; but, what else do we
know about those who have private health insurance in Jamaica? Do insurers attract healthy
patients, and are high risk individuals more likely to become insured as against their low risk (i.e.
less health conditions) counterparts? Health insurance is a constituent of health seeking
behaviour, suggesting that it is equally important in any study of health, quality of life, and
wellbeing. In this study the researchers will critically examine factors that can be used to predict
private health insurance coverage by using a logistic regression technique to explain the
independent effect; and in the process the researchers will investigate the lives of respondents in
order to understand those who reported having private health insurance coverage.
Instead of providing an elaborate and extensive description of ‘health insurance’, we will
give a simplified meaning of this construct. Health insurance is protection against medical costs
owing to the possibility of injuries, dysfunctions and other happenings that hinder the body from
performing at some functional standard. In keeping with this definition, a health insurance
policy is the contract that is signed by an insurer (i.e. insurance provider) and an individual or a
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group, in which the insurer agrees to pay a specific sum (i.e. a premium). Hence, the
population’s health service is partially dependent on health insurance coverage or the welfare
system of the state. Jamaica does not have a public health insurance system, but one for the
elderly and those who have particular chronic health conditions, such as diabetes mellitus,
hypertension, cancer or a combination. In September 2001, the Cabinet of Jamaica accepted and
approved a proposal for the establishment of a National Health Fund (NHF) that would assists
patients as well as the elderly in Jamaicans. The individual benefits of the NHF (i.e. public health
insurance options) for the elderly and for those with particular chronic health conditions was
officially commenced in 2003 (i.e. August 1, 2003), and so there are only data on private health
insurance coverage from 1988-2002. Despite the fact that Jamaica has instituted a free health-
care service delivery programme for its child population (below 18 years in 2006), the quality of
care which is relatively good is still surrounded by a certain socio-psychological milieu as well
as inequality in health care offerings in the private versus the public sector. This explains the
rationale why some people seek private health care and by extension private health insurance
coverage [3] to meet the impending higher medical cost of care [1, 4-7] and a particular quality
of service – environment, customer service and length of service. The current study will be
examined within the theoretical framework used by Franc, Perronnin, & Pierre. [8]
1.2 Theoretical Framework
A South African Health Inequalities Survey (SANHIS) carried out in 1994 of 3,489 women ages
16 to 64 years was used to model the determinants of health insurance coverage. Kirigia et al.
[8] sought to model health insurance demand among South African women. They used binary
logistic regression analyses to estimate health insurance coverage among the sample and various
determinants of health insurance coverage. Health insurance coverage of the sample was
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determined by socio-demographic characteristics, health rating, environment rating, bad health
choices (i.e. smoking and alcohol consumption), and contraceptives. These were embodied in
the mathematical formula, (Eq. (1) :
Pij= (α + β1Health rating + β2Environment rating + β3 Residence + β4 Income + β5 Education + β6Age + β7 Age squared + β8 Race + β9 Household size + β10 Occupation + β11 Employment + β12Smoking + β13 Alcohol use + β14Contraceptive use = β15Marital status + εi ……………Eq. (1)
where Pij = 1 if individual I owns insurance (j=1) and equal otherwise (j=0); α is
intercept terms; (β’s) are the estimated coefficients; and εi is the stochastic error term.
The conceptual framework of Kirigia et al.’s work [8] was on two risks of health care.
They believed that these risks are (1) the risk of becoming ill, with the associated loss in quality
of life, cost of medical care, loss of productive times, more serious cases, mortality, and (2) the
risk of total or incomplete or delayed recovery [8]. This denotes that a person’s decision to buy
health insurance would be based on differentials between the level of expected utility of the
insurance and the expected utility without insurance. It is this binary nature dependent variable
and the desire to determine the effect of particular independent variables that justified the binary
logistic regression technique.
Eq. (1) allows for the estimation of the individual probability of having or not having
health insurance by some explanatory variables. Kirigia et al. [8] did not stipulate whether
health insurance was public or private coverage, and this was addressed in another research
paper. Using the same principle of econometric analysis as Kirigia et al, a group of researchers
used a single multiple regression equation that identified explanatory variables and the powers of
particular factors that can be used to determine determinants of those who have private health
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insurance [9]. This captures a standard utility theory model of a demand for private health
insurance coverage, Eq. (2):
Y = β0+ β1P+β2I+β3Z ………………………………..…………………………..Eq (2)
where the standard utility theory is expressed in the quantity demanded of health insurance,
Y, can be written as a function of the user price of health insurance, P, income, I, and a vector of
other factors, Z or (with time subscripts suppressed); and β1 and β2 represent, respectively, the price
and income elasticity of the demand for private health insurance.
Like Kirigia et al., [8] self-rated private health insurance coverage is a binary variable (1=
yes and 0= otherwise), which denotes that a logistic regression model will be used to estimate the
determinants and determine their impact on the dependent variable, as was done by Ahking,
Giaccotto, and Santerre [9] - Eq. (3). Instead of having a vector factor which envelopes
individual characteristics, this research isolates those factors including income, unlike Eqs. (1)
and (2), and added more variables such as psychological conditions, living arrangements and
social support.
HIi = ƒ(Yi, HCi, Eni, MSi, ARi , Ei, SSi, Oi, Pi, Gi, NPi, PPi, Mi, Fi , Di, EWi , Ai, Ri, YPi, Pmci, LLi, CRi,)……………………………………………………………………………………………..…..(3)
where Eq (3) is Private Health Insurance coverage, HIi, is a function of Yi is average
current income per person in household i; HCi is health conditions of person i; Eni is physical
environment of person i; MSi is marital status of person i; ARi is area of residence of person i; Ei
is educational level of person i; SSi is social support of person i; Oi is average occupancy per
person i; Pi is property ownership of person i; Gi is gender per person i; NPi is negative affective
psychological conditions per person i; PPi is positive affective psychological conditions per
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person i; Mi is number of males per household per person i; Fi is number of females per
household per person i; Di is the number of children per household per person i; EWi is durable
goods; Ai is age of person i; Ri is retirement benefits of person i; YPi is social standing of person
i; Pmci is cost of medical care of person i, LLi is living arrangements of person i; and CRi is
crowding.
The current study found the following determinants of private health insurance of
Jamaica (Eq (4)):
HIi = ƒ (Yi, ARi, MSi, SSi, Ei, ∑(NPi, PPi), Mi , EWi, Ri, YPi,LLi,CRi,)..........................(4)
where Eq (4) is Private Health Insurance Coverage, HIi, is a function of Yi is average
current income per person in household i; HCi is health conditions of person i; ARi is area of
residence of person i; MSi is marital status of person i; SSi is social support of person i; Gi is
gender per person i; Ei is educational level of person i; NPi is negative affective psychological
conditions per person i; PPi is positive affective psychological conditions per person i; EWi is
durable goods of person i; Di is the number of children per household per person i; Ri is
retirement benefits of person i, YPi is social standing of person i, LLi is living arrangements and
CRi is crowding.
2. MATERIALS AND METHODS 2.1 Method This study utilized secondary data taken from the dataset of the Jamaica Survey of Living
Conditions which was collected between July and October 2002. It was a nationally
representative stratified random sample survey of 25,018 respondents, with 50.7% females
(N=12,675) and 49.3% males (N=12,332). The data was collected by way of an administered
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questionnaire. The non-response rate for the survey was 29.7% with 20.5% not responding to
particular questions, 9.0% not participating in the survey and another 0.2% being rejected due to
data cleaning. The current research extracted a sub-sample of 16,118 people 15 years and older
from the survey sample of 25,018 respondents in order to model the determinants of private
health insurance coverage in Jamaica.
The rationale for the use of the 2002 data set instead of the 2007 is primarily because of
the sample population. In 2002, the institutions that were principally responsible for the data
collection used 10% of the national population to gather pertinent data on the labour force, and
this was for the Survey of Living Conditions. It represents the largest data collected on the
Jamaican population, and data was also collected on crime and victimization and the
environment, these being included for the first time, and omitted in subsequent surveys. Given
the nature of crime, violence and victimization in the nation, we opted to use a survey that had
crime and the environment as among data collected. Another condition for the selection of this
dataset was the fact that it was a large population, as against other years when the population was
less than 3,000. Within the context of a non-response rate that ranges from 10 to 30 per cent, a
larger rather than a smaller sample size coupled with some pertinent variables was preferred to a
smaller sample size without the two critical aforementioned variables. Data were stored,
retrieved and analyzed using SPSS for Windows 15.0. A p-value of less than 0.05 was used to
establish statistical significance. Descriptive analysis will be done on the sampled population in
order to provide background information on the respondents; and the enter method of logistic
regression will be used to establish the determinants of self-reported private health insurance in
Jamaica. Using the principle of parsimony, the final model will consist of only those statistically
significant variables. Where multicollinearity existed (r > 0.7), variables were independently
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entered into the model to aid in determining which one should be retained during the final model
construction (i.e. the decision therefore was based on the variable’s contribution to the predictive
power of the model and the goodness of fit).
2.2 Measure Health conditions: The summation of reported ailments, injuries or illnesses in the last four
weeks, which was the survey period; where higher values denote greater health conditions; it
ranges from 0 to 4 conditions. Health status is a dummy variable, where 1 (good health) = not
reporting an ailment or dysfunction or illness in the last four weeks, which was the survey
period; 0 (poor health) if there were no self-reported ailments, injuries or illnesses. While self-
reported ill-health is not an ideal indicator of actual health conditions as people may under-report
their health condition, it is still an accurate proxy of ill-health and mortality. Household
crowding: This is the average number of persons living in a room. Physical Environment: This
is the number of responses from people who indicated suffering landsides; property damage due
to rains, flooding or soil erosion. Psychological conditions are the psychological state of an
individual, sub-divided into positive and negative affective psychological conditions.18-19
Positive affective psychological condition signifies the number of responses with regard to being
hopeful and optimistic about the future and life generally. Negative affective psychological
condition means number of responses from a person on having lost a breadwinner and/or family
member, loss of property, being made redundant, or failing to meet household and other
obligations.
Income is proxied by total individual expenditure in USD. The rate was USD1=Ja. $50.97 in
2002 at the survey period. Average income (i.e. per person per household) is total expenditure
divided by the number of persons in the household. Age: The number of years lived, which is
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also referred to age at last birthday. This is a continuous variable, ranging from 15 to 99 years.
Age group is classified into three categories. These are young adults (ages 15 to 30 years),
middle aged adults (ages 31 to 59 years) and the elderly (ages 60+ years). Retirement benefits
were measured by those who received retirement income. Private Health Insurance Coverage:
This is a dummy variable, where 1 denotes self-reported ownership of private health insurance
coverage and 0 is otherwise.
Durable goods: This variable is the summation of the self-reported durable goods owned by an
individual excluding houses, buildings and property.
where Di ranges from 1 to 28, where higher values denote greater
ownership of durable goods.
Living arrangements are a dummy variable where, 1=living alone, 0= living with family
members or relative.
Social support (or network) denotes different social networks with which the individual has been
or is involved (1= membership of and/or visits to civic organizations or having friends that visit
one’s home or with whom one is able to network, 0=otherwise).
Crime:
where Ki represents the frequency with which an individual has witnessed or experienced
a crime, where i denotes 0, 1 and 2, in which 0 indicates not witnessing or experiencing a crime,
1 means witnessing 1 to 2, and 2 symbolizes seeing 3 or more crimes. Ti denotes the degree of
the different typologies of crime witnessed or experienced by an individual ( where j=1 …4,
where 1=valuables stolen, 2=attacked with or without a weapon, 3= threatened with a gun, and
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4= sexually assaulted or raped. The summation of the frequency of crime by the degree of the
incident ranges from 0 and a maximum of 51.
Social standing is proxied by per capita population quintile (from poorest-to-wealthiest)
3. RESULTS
3.1 DEMOGRAPHIC CHARACTERISTICS OF SAMPLE
The sample was 16,619 respondents (i.e. 48.6% males and 51.4% females; with 39.2% young
adults, 42.7% middle aged adults and 18.1% elderly). Some 25.8% of the sample resided in peri-
urban areas; 60.2% in rural zones; 14.0% were from urban areas; 16.8% were below the poverty
line (i.e. poorest 20%); while 18.2% were just above the poverty line compared to 21.2% in the
wealthy quintile and 24.1% in the wealthiest 20%. Of the sample, 97.6% responded to the health
status question. Of those who responded to the health status question, 80.6% indicated at least
good health and 19.4% poor health. Ninety-seven percentage points of the sample (n=16,118)
responded to the health insurance coverage question, of that 11.9% revealed having health
insurance coverage.
Based on Table 15.1, poverty is substantially a rural phenomenon. The findings revealed
that 21.2% of rural residents were below the poverty line (i.e. poorest 20%) compared to 10.7%
of peri-urban dwellers and 9.5% of urban settlers. Health insurance was greatest among urban
residents: Some 20.8% of urban dwellers had health insurance compared to 17.6% for peri-urban
settlers and 7.5% of rural residents. A significant statistical difference was found between area of
residence and crime, and income in this sample.
Peri-urban residents spent the most statistically on medical care (USD39.16 ± USD85.77,
95%CI: USD31.39-USD46.94) compared to urban (USD30.25± USD61.47, 95%CI: USD22.66-
USD37.83) and rural residents (USD29.33±USD54.15, 95%CI: USD26.58-USD32.06) (Table
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15.1)
On examination of the cross tabulation between good health status and social standing, a
statistical correlation was found (P= 0.001) (Table 15.2). Table 15.2 showed that the worst health
was reported by those in the wealthiest quintile (21.8%), the poorest (19.9%), the poor (18.6%)
and so on.
There is a positive statistical correlation between ageing and self-reported poor health (or
health conditions) of Jamaicans (P =0.001) (Table 15.3). Further examination of Table 15.6
revealed that 10.3% of young adults reported poor health compared to 17.4% of middle aged
adults and 43.6% of the elderly.
3.2 Multivariate Analysis
Table 15.4 presents information on the variables which are correlated (or non-correlated) with
private health insurance coverage in Jamaica of people 15 years and older. Using logistic
regression, twelve variables emerged as statistically significant determinants of health insurance
coverage in this sample (Table 15.7). These variables are social standing (two wealthiest
quintiles: OR=1.68, 95% CI=1.23-2.30), income (OR=1.00, 95%CI=1.00-1.00), durable goods
(OR=1.16, 95%CI=1.12-1.19), marital status (married: OR=1.97, 95%CI=1.61-2.42), area of
residence (Peri-urban: OR=1.45, 95%CI=1.199-1.749; urban: OR=1.831, 95%CI=1.395-2.402),
education (secondary: OR=1.57, 95%CI=1.20-2.06; tertiary: OR=9.03, 95%CI=6.47-12.59),
social support (OR=0.64, 95%CI=0.53-0.76), crowding (OR=1.14, 95%CI=1.02-1.28),
psychological conditions (negative affective: OR=0.97, 95%CI=0.94-1.00; positive affective:
OR=1.11, 95%CI=1.06-1.16), number of males in household (OR=0.85, 95%CI=0.77-0.93),
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living arrangements (OR=0.62, 95%CI=0.41-0.92) and retirement benefits (OR=1.55,
95%CI=1.03-2.35).
The model [Eqn (4)] had statistically significant predictive power (model χ2 = 1604.389,
P=0.001; Hosmer and Lemeshow goodness of fit χ2= 5.280, P = 0.727), and correctly classified
91.3% of the sample (Correct classification of cases of reported health insurance coverage
=32.0% and correct classification of cases with no insurance coverage = 98.3%).
4. DISCUSSION
This study found that health insurance coverage is influenced by social standing, durable goods,
income, marital status, area of residence, education, social support, crowding, psychological
conditions, retirement benefits, living arrangements and the number of males in the household,
and that those with good health are more likely to purchase health insurance than those with poor
health. Continuing, rural residents, elderly and poorest, are the least likely to purchase health
insurance coverage in Jamaica.
In the literature, it is well documented that the majority of uninsured workers in South
Dakota were either employed or self-employed [6]. The poor, elderly and many rural residents
are more likely to be employed on a seasonal basis in the informal sector, and these occupations
and employment types do not have private health insurance, suggesting a further rationale for
why unemployed people within a particular socio-economic status would be less likely to be
holders of health insurance coverage. In this study, it was revealed that more uninsured
Jamaicans were poor, elderly and from rural zones, and these were the ones most likely to be
unemployed in Jamaica. The current study was not able to validate the direct claim of
employability of the uninsured, but the elderly can indirectly validate the literature that more
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unemployed people do not have health insurance. In addition to the aforementioned fact, another
finding was that poor health is associated with low income, owing to the difficulties it creates
with accessing crucial health care [6].
This research disagrees with the literature that the poor have lower health statuses,
suggesting that they have more health-related conditions than the wealthy. The rich engage in
highly involved particular lifestyle practices that expose them to health hazards, and this is not
equally comparable to the poor environment of the poor, justifying why they reported the least
health status. Pacione [10] has shown that the quality of the physical environment affects the
quality of life (or health or wellbeing) of people, but that lifestyle behavioural practices play a
significant role in determining one’s health [11] like the physical milieu. [12,13] Moreover, the
high cost of health care is a deterrent for the poor to have health insurance coverage; [6] and we
concur with the literature as we found a positive statistical association between self-rated health
insurance coverage and income. However, in this study we have refined the income variable, as
there is a ceiling to income and its relation with the purchase of health coverage in Jamaica. The
current work has revealed that those in the wealthy-to-wealthiest quintiles were twice as likely to
purchase health insurance coverage as the poor-to-poorest people. Within the context that those
in the wealthiest quintile purchased the most health insurance and indicated the lowest health
status, it can be inferred that the purchase of health insurance is in keeping with their life style
and the perceived role of income in buying good health, as against preventative behaviour.
Health insurance coverage is an elderly phenomenon, [6] and this work does not concur
with the literature. The argument put forward is that younger people are healthier, and so do not
see the need to invest in health coverage, as the risk of becoming ill is low, hence the willingness
to engage in risky behaviour compared to their older counterparts, [6] suggesting that the
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futuristic end for health insurance coverage becomes even more critical after 30 years when more
people will have families, as well as the fact that the purchase of health insurance may
materialize owing to futuristic changes in the economic circumstances of the individual.
There is a statistical relationship between socioeconomic conditions and the health status
of Barbadians, which is not the case in Jamaica. A study by Hambleton et al., [11] of elderly
Barbadians revealed that 5.2% of the variation in reported health status was explained by the
traditional determinants of health. Furthermore, when this was controlled for current experiences,
the percentage fell to 3.2% (a drop of 2%). When the current set of socioeconomic conditions
was used, they accounted for some 4.1% of the variation in health status, while 7.1% were due to
lifestyle practices compared to 33.5% which were as a result of current diseases. [11] Despite
this fact, it is obvious from the data that there are other indicators which explain health status;
people do not necessarily pay attention to this fact although they may have more income or
access to more economic resources. This explains the rationale for more health conditions being
reported by the wealthiest as well as the group that purchased the most health insurance, where
the thinking is that money can buy health.
A study published in the Caribbean Food and Nutrition Institute on the elderly in the
Caribbean found that 70% of individuals who were patients within different typologies of health
services were senior citizens. [14, 15, 16] Among the many issues that the research reported on
are the five major causes of morbidity and mortality, taken from the Caribbean Epidemiology
Centre, which are of paramount importance to this discussion, and their influence on the elderly -
cerebrovascular, cardiovascular, neoplasm, diabetes, hypertension and acute respiratory infection
- and these dysfunctions are highly costly to treat. It should be noted that many of these
dysfunctions are owing to lifestyle behaviour. Hence, the purchase of private health insurance
404
coverage by these people when they become old and approach retirement is in keeping with the
cost of health care and the high likelihood of becoming ill.
Eldemire, [17] on the other hand, opined that the elderly are not as sick as some people
are making them out to be – “The majority of Jamaican older persons are physically and
mentally well and living in family units” [17]; but the fact is they are preparing for the
eventuality of health conditions owing to the principle of the degeneration of the body with the
onset of old age. Eldemire is somewhat right. The current study found that for every 1 young
adult who reported poor health, there were approximately 2 middle aged adults and 4 elderly
persons. Simply put, there were elderly people with poorer health than other age cohorts; but of
the elderly, more of them indicated good health status (56.4%). The mere fact of living longer
(life expectancy post retirement is at least 15 years), suggests that the aged population will
require more for medical care if they become ill. [18] With ageing the issue is not if they become
ill but when. A group of scholars found that there is a direct association between ageing and
health conditions, [19] a concept with which this study concurs. And this provides the
explanation for the purchase of private health insurance more than other age cohorts, because
they are at a different stage from other age cohorts in a population.
Health conditions are crucial to the purchase of primary health insurance coverage, and
this is highlighted by ageing. Eldemire’s works [17, 18] have shown that ageing in an individual
does not translate to high physical impairments, but that with ageing come particular changes in
the profile of dysfunctions – Alzheimer’s disease, dementia, cerebrovascular, cardiovascular,
neoplasm, diabetes, hypertension and acute respiratory infection. [21] A study conducted by
Costa [22], using secondary data drawn from the records of the Union Army (UA) pension
programme that covered some 85% of all UA, shows that there is an association between chronic
405
conditions and functional limitation – which includes difficulty walking and bending, blindness
in at least one eye and deafness [22]. Among the significant findings is – (i) the predictability
between congestive heart failure in men and functional limitation (i.e. walking and bending).
Although Costa’s study was on men, this applies equally to women, as biological ageing reduces
physical functioning, and so any chronic ailment will only further add to the difficulties of
movement of the aged, be it man or woman. One study has contradicted the works of Eldemire,
and it showed that a large percentage of the elderly suffer from at least one health condition.
Women are more involved in health seeking behaviour, compared to their male
counterparts, [20] irrespective of the age factor, and this is owing to the cultural background in
which they live. Unlike women, across the world men have a reluctance to ‘seek health-care’
compared to their female counterparts. It follows in truth that women have bought themselves
additional years in their younger years, and it is a practice that they continue throughout their
lifetime which makes the gap in age differential what it is – approximately a 4-year differential
in Jamaica. In keeping with the preventative care approach to health care, it would be expected
that women would purchase more health insurance coverage than them, but this is not the case in
Jamaica as gender was not a predictor of health status. However, the more men in a household,
the less an individual will purchase health insurance coverage.
The Planning Institute of Jamaica in collaboration with the Statistical Institute of Jamaica
has shown that while the general health status is commendable, chronic illnesses are undoubtedly
eroding the quality of life enjoyed by people who are 65 years and older [23, 24]. The JSLC
report reveals that the prevalence of recurrent (chronic) diseases is highest among individuals 65
years and over. [23] The findings show that in 2000, the prevalence of self-reported illness/injury
for people aged 65 years and over was 41.7%, for those 60 to 64 years it was 27.6% compared to
406
19.8% for children less than five years old. However, the prevalence of self-reported
illness/injury for those 50 to 59 years was 18.8%. Some 36.6% of individuals 65 years and over
reported injuries/illnesses in 2002 which is a 5.6% reduction in self-reported prevalence of
illnesses/injuries over 2000, but the self-reported prevalence of illness/injuries rose by 25.8% to
62.4% in 2004. [25, 26] It should be noted here that this increase in self-reported cases of
injuries/ailments does not represent an increase in the incidence of cases, as according to the
JSLC for 2004,the proportion of recurring/chronic cases fell from 49.2% in 2002 to 38.2% in
2004 [26]. In addition, the PIOJ and STATIN [23] in (JSLC 2000) opined that individuals 60-64
years of age were 1.5 times more likely to report an injury than children less than five years of
age, and the figure was even higher for those 64 years of age and older (2.5 times more). In this
paper, the findings concurred with the literature that health conditions are significantly greater;
but other issues account for them not demanding more health insurance coverage than middle
age adults. This is reinforced in the findings that showed that people who received retirement
benefits were approximately twice as likely to purchase health insurance coverage as those who
did not receive any retirement benefits. Embedded in this finding is the fact that health insurance
is a matter of affordability and education, and not illness, which justifies why rural residents had
the lowest health insurance coverage, yet still the poorest 20% good health status was greater
than that of those in the wealthiest 20%. Statistics revealed that poverty in 2007 for the nation
was 9.9%, and rural poverty was 15.3% compared to 4% in peri-urban and 6.2% in urban areas
[27], accounting for the lowest private health insurance coverage in that group.
5. CONCLUSION
In summary, married Jamaicans are more likely to purchase health insurance coverage compared
to those who were never married, with urban residents being more likely to purchase health
407
insurance than rural dwellers. An individual who has attained tertiary level education was more
likely to purchase health insurance than one with at most primary level education, and those who
lived alone were less likely to purchase health insurance coverage than those who dwelled with
relatives or family members. Moreover the wealthiest were more likely to purchase health
insurance, but were less healthy, and this indicates that income does not buy good health.
Therefore, this study highlighted the need to address preventative care for the wealthiest, and the
fact that social support is crucial to health care, along with the fact that medical care costs are
borne by the extended family and other social groups in which the individual is (or was) a
member, which explains the low demand for health insurance in Jamaica.
Conflict of interest
The authors have no conflict of interest to report.
Disclaimer
The researchers would like to note that while this study used secondary data from the Jamaica Survey of Living Conditions, none of the errors in this paper should be ascribed to the Planning Institute of Jamaica or the Statistical Institute of Jamaica, but to the researchers.
Acknowledgement The author would like to take this opportunity to thank the Data Bank in Sir Arthur Lewis Institute of Social and Economic Studies, the University of the West Indies, Mona, Jamaica for making the dataset (ie Jamaica Survey of Living Conditions, 2002) available accommodated the current study.
408
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elderly. Journal of Health Economics, 16,543-562. 2. Liu, T., and Chen, C. (2002) An analysis of private health insurance purchasing decisions
with national health insurance in Taiwan. Social Science & Medicine, 55,755-774. 3. Dong, H., Kouyate, B., Cairns, J., Mugisha, F., Sauerborn, R. (2003) Willingness-to-pay
for community-based insurance in Burkina Faso. Health Economics, 12,849-862. 4. Carrin, G. (2003) Social health insurance in developing countries: A continuing
challenge. International Social Security Review, 55, 57-69. 5. Thomasson, M.A. (2006) Racial differences in health coverage and medical expenditure
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South Dakota: Final Report of the State Planning Grant Program. OA: South Dakota Dept. of Health.
7. Varghese, R,K,, Friedman, C., Ahmed, F., Franks, A.L., Manning, M., and Seeff, L.C. (2005) Does health insurance coverage of Office Visits Influence Colorectal Cancer Testing. Cancer Epidemiology Biomarkers & Prevention 14,744-747.
8. Kirigia, J.M., Sambo, L.G., Nganda, B., Mwabu, G.M., Chatora, R., and Mwase, T. (2005) Determinants of health insurance ownership among South African women. BMC Health Services Research, 5, 1-17.
9. Ahking, F.W, Giaccotto, C., and Santerre R. (2009) The aggregate demand for private health insurance coverage in the U.S. Journal of Risk and Insurance, The American Risk and Insurance Association, 76, 133-157
10. Pacione, M. (2003) Urban environmental quality of human wellbeing–a social geographical perspective. Landscape and Urban Planning, 65, 19-30.
11. Hambleton, I.R., Clarke, K., Broome, H.L., Fraser, H.S., Brathwaite, F., and Hennis, A.J. (2005) Historical and current determinants of self-rated health status among elderly persons in Barbados. Rev Panam Salud Publica 2005, 17:342-353.
12. P. Bourne Determinants of well-being of the Jamaican Elderly. M.S. thesis, The University of the West Indies, Mona Campus, Jamaica.
13. Bourne, P. (2007) Using the biopsychosocial model to evaluate the wellbeing of the Jamaican elderly. West Indian Medical Journal, 56, (suppl 3), 39-40.
14. CAJANUS. (1999) Health of the Elderly. Caribbean Food and Nutrition Institute Quarterly 32,217-240.
15. CAJANUS. (1999) Focus on the elderly. Caribbean Food and Nutrition Institute Quarterly, 32,179-240.
16. Anthony, B.J. (1999) Nutritional Assessment of the elderly. Caribbean Food and Nutrition Institute Quarterly, 32:201-216.
17. Eldemire, D. (1995) A situational analysis of the Jamaican elderly, 1992. The Planning Institute of Jamaica, Kingston.
18. Eldemire, D. (1997) The Jamaican elderly: A socioeconomic perspective & policy implications. Social and Economic Studies, 46, 175-193.
19. Zimmer, Z., Martin, L.G., and Lin, H-S. (2003) Determinants of old-age mortality in Taiwan. (http://www.popcouncil.org/pdfs/wp/181.pdf)
20. Rice, P.L. (1998) Health psychology. Brooks/Cole Publishing, CA. 21. Eldemire, D. (1996) Level of Mental Impairment in the Jamaican Elderly and the Issues
409
of Screening Levels, Caregiving, Support Systems, Carepersons, and Female Burden. Molecular and Chemical Neuropathology, 28, E1-E5.
22. Costa DL. Chronic diseases rates and declines in functional limitation. Demography 2002, 39:119-138.
23. Planning Institute of Jamaica, (PIOJ) and Statistical Institute of Jamaica, (STATIN). (2001) Jamaica Survey of Living Conditions 2000. PIOJ and STATIN, Kingston.
24. Planning Institute of Jamaica, (PIOJ) and Statistical Institute of Jamaica, (STATIN). (1998) Jamaica Survey of Living Conditions 1997. PIOJ and STATIN, Kingston.
25. Planning Institute of Jamaica, (PIOJ) and Statistical Institute of Jamaica, (STATIN). (2003) Jamaica Survey of Living Conditions 2002. PIOJ and STATIN, Kingston.
26. Planning Institute of Jamaica, (PIOJ) and Statistical Institute of Jamaica, (STATIN). (2005) Jamaica Survey of Living Conditions 2004. PIOJ and STATIN, Kingston.
27. Planning Institute of Jamaica, (PIOJ) and Statistical Institute of Jamaica, (STATIN). (2008) Jamaica Survey of Living Conditions 2007. PIOJ and STATIN, Kingston.
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Table 15.1: Demographic characteristic of sample by area of residence
Rural Peri-urban Urban P
% (n) % (n) % (n)
Age group 0.001 Young adults 38.3 (3833) 41.0 (1760) 39.7 (923) Middle age adults 41.6 (4160) 44.2 (1895) 44.6 (1039) Elderly 20.1 (2010) 14.8 (634) 15.7 (365) Health insurance coverage 0.001 Yes 7.5 (722) 17.4 (726) 20.8 (471) No 92.5 (8969) 82.6 (3442) 79.2 (1788) Gender 0.001 Male 50.4 (5041) 46.8 (2006) 44.3 (1031) Female 49.6 (4962) 53.2 (2283) 55.7 (1296) Per capita income quintile 0.001 1=Poorest 20% 21.2 (2118) 10.7 (458) 9.5 (222) 2 22.0 (2196) 13.3 (572) 11.2 (261) 3 20.8 (2085) 18.7 (800) 16.7 (388) 4 19.8 (1978) 22.7 (972) 24.3 (565) 5=Wealthiest 20% 16.2 (1625) 34.7 (1487) 38.3 (891) Marital status 0.001 Married 25.5 (2460) 26.9 (1115) 21.0 (475) Never married 66.6 (6433) 66.4 (2755) 71.6 (1619) Divorced 0.6 (56) 1.0 (41) 1.2 (26) Separated 1.1 (104) 1.2 (49) 1.4 (32) Widowed 6.3 (610) 4.5 (187) 4.8 (108) Crowding mean (SD) 1.77 ± 1.24 1.75 ± 1.28 1.72 ± 1.18 0.216 Crime index 1.74 ± 7.37 2.34 ± 8.08 2.83 ± 9.30 0.001 Medical expenditure1 mean (SD) $29.33±$54.15 $39.16 ± $85.77 $30.25± $61.47 0.012 Income2 mean (SD) $5496.12 ± $4860.97 $7534.74 ± $5544.26 $8779.26 ±$10568.69 0.001 1Medical Expenditure is expressed in USD: 1USD= JA$50.97 for the period 2002 2Income is expressed in USD: 1USD= JA$50.97 for the period 2002
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Table 15.2: Good health status by social standing (Per capita population quintile)
Good health status
Social standing (Per Capita Population Quintile)
Total
1=Poorest
2
3
4
5=Wealthiest
Poor
19.9
18.6
17.9
18.4
21.8
19.4
Good 80.1 81.4 82.1 81.6 78.2 80.6
Total
2738
2975
3208
3413
3883
16217
χ2(4) = 23.273, P= 0.001, contingency coefficient = 0.038
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Table 15.3: Good health status by age group
Good health status
Age group
Total
Young age(15 to 30 years)
Middle age (31 to 59 years)
Elderly (60+
years)
Poor
10.3 17.4
43.6 19.4
Good
89.7 82.6
56.4 80.6 Total
6283 6973
2961 16217
χ2(2) = 1458.12, P= 0.001, contingency coefficient = 0.287
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Table 15.4: Logistic regression: Private health insurance coverage by some variables
P Odds Ratio 95.0% C.I.
Lower Upper Age 0.443 1.00 0.99 1.00 Middle quintile 0.174 1.24 0.91 1.71 Two wealthiest quintiles 0.001 1.68 1.23 2.30 †Poorest 20%-to-poor
Household Head
0.2131.00 1.80
0.71
4.55
Logged medical expenditure 0.671 1.01 0.95 1.08 Average income 0.009 1.00 1.00 1.00 Durable goods 0.000 1.16 1.12 1.19 Separated or Divorced 0.608 0.90 0.61 1.33 Married 0.000 1.97 1.61 2.42 †Never married
Peri-urban
0.0001.00 1.45
1.10
1.75
Urban 0.000 1.83 1.40 2.40 †Rural area
Environment
0.1161.00 0.85
0.70
1.04
House tenure - rented 0.999 0.00 0.00 House tenure - owned 0.950 1.04 0.27 4.03 House tenure – squatted*
Secondary
0.0011.00 1.57
1.20
2.06
Tertiary 0.000 9.03 6.47 12.59 †Primary and below
Social support
0.0001.00 0.64
0.53
0.76
Sex 0.722 1.03 0.86 1.24 Crowding 0.018 1.14 1.02 1.28 Crime index 0.652 1.00 0.99 1.01 Land ownership 0.665 0.96 0.79 1.16 Negative affective 0.034 0.97 0.94 1.00 Positive affective 0.000 1.11 1.06 1.16 Number of males in house 0.001 0.85 0.77 0.93 Number of females in house 0.622 0.98 0.89 1.07 Number of children in house 0.438 0.97 0.90 1.05 Living arrangement 0.017 0.62 0.41 0.92 Retirement benefits (1=yes) 0.038 1.55 1.03 2.35 Poor health status 0.309 0.94 0.83 1.06
-2Log Likelihood= 3982.175 Nagelkerke R Square= 0.359 Model χ2(8)= 1604.389, P-value=0.001 Hosmer and Lemeshow χ2= 5.280, P=0.727 Overall correct classification = 91.3%: Correct classification of cases of reported health insurance coverage =32.0%; Correct classification of cases with no health insurance coverage =98.3% †Reference group
414
Chapter 16 Difference in social determinants of health between men in the
poor and the wealthy social strata in a Caribbean nation
Paul A. Bourne & Denise Eldemire-Shearer
Studies that have examined social determinants of health have made their investigations on the population, but none have reviewed them from the perspective of particular social hierarchies. The study examined the factors determining the self-reported health of men of different socioeconomic status, by using models derived through econometric analyses. The study used a sample of 6,474 respondents: 2,704 from the two poor quintiles and 3,770 from the two wealthy quintiles. The survey used a random stratified probability sampling technique and involved the use of self-administered questionnaires. Multiple logistic regression technique was used to identify variables which are associated with health conditions of men in the two social hierarchies. The findings revealed that the self-reported health of men in the two wealthiest quintiles were substantially influenced by private health insurance coverage (Odds Ratio (OR) = 32.9, 95%CI: 20.64, 52.45) and age of respondents (OR = 1.03, 95%CI: 1.02, 1.04) This was similar for men in the two poorest income quintiles; private health insurance coverage (OR = 16.97, 95%CI: 10.18, 28.27) and age (OR=1.05, 95%CI: 1.03, 1.06). Negative affective psychological conditions, consumption and medical expenditure affected the self-reported health of those in the two wealthiest quintiles, while positive affective, secondary levels of education and living alone influenced those in the two poorest quintiles. This research serves as a foundation for further work relating to the determinants of self-reported health conditions, inequity across socio-economic strata for men, and how patient care should be addressed.
INTRODUCTION
In recent years the World Health Organization (WHO) has increasingly drawn attention to the
importance of the relationship between health and social conditions in determining the health of
individuals and populations [1]. Social determinants (conditions, in which people are born, live,
grow, work and age as well as the health system available to them) produce inequalities in
health, and need to be considered in health development. Addressing social determinants and
health policy now forms the basis for political action both nationally and internationally [2].
415
Human poverty is defined as more than income poverty; it is the denial of choices and
opportunities for living a tolerable life [3]. Poverty as described above in the Caribbean has been
predominantly a rural phenomenon; however, rising levels of urban poverty have also been seen.
In 1996 the World Bank estimated 38% of the total population (or 25% including Haiti) in the
Caribbean, or more than seven million people, to be poor [4]. One study found that rural poverty
in Argentina, Barbados, Boliva, Brazil, Colombia, Jamaica, Suriname, Trinidad and Tobago, and
Uruguay was at least twice more than urban poverty [5]. According to the Jamaica Survey of
Living Conditions (JSLC), in 2003, the poverty rate stood at 19.1%, and in 2007 it fell to 9.9%
[6]. The JSLC for 2001 [6] indicates that the wealthiest 20% of the population accounted for
45.9% of national consumption, while the poorest 20% accounted for only 6.1% of national
consumption.
On average, the wealthiest 10% of the population consumed approximately 12.5 times
more than the poorest 10% [6]. This is a mean per capita annual consumption expenditure of
US$ 3,963.53 compared to US$ 314.48. Gafar found that in some Latin American and Caribbean
countries, between 2 to 8 percentage of income is estimated to be received by those in the
poorest 20% compared to between 42 and 58% that is received by those in the wealthiest 20%
[5] which indicates that income inequalities are vast between the poor and the wealthy within
those societies, and does account for some of the health disparities between the social
hierarchies.
According to the WHO’s definition, health is not merely the absence of disease but the
highest possible state of physical, social and mental wellbeing. At both a societal and individual
level, the aim is to extend healthy life expectancy, as well as productivity and quality of life at
older ages for as long as possible [7]. Understanding how the social determinants influence
416
health and social wellbeing is an area of considerable research interest. That the unequal
distribution of variables such as income, unemployment and education produce health
inequalities, has been documented [8-10]. Studies have established a statistical relationship
between health status and poverty [11-13], between standard of living and health conditions,
health status owing to a particular natural disaster [14,15], and income and health [16]. It is
recognized that more information is needed at the social level, and that knowledge needs to be
translated into action [17].
People with lower socioeconomic status have worse health in all adult age groups, including
older ages [18]. Age has been identified as an important social determinant of health. Among
adults, reduced capacity to generate income and the growing risk of illness increase the
vulnerability of the elderly to poverty; regardless of their original economic status in developing
and industrialized countries [19].
Gender is equally as important a social determinant of health. Men are experiencing
poverty. It is important to understand the factors influencing self-reported health. Many studies
that have examined those in the poor and wealthy income groups have used a piecemeal
approach, and in the Caribbean this is also the case. Studies that have examined social
determinants of health [1, 2, 8-17] have made their investigations in the population, but have not
reviewed them from the perspective of particular social hierarchies within a nation, in order to
establish if the factors are the same, and if not, what the disparities are. It is within this
framework that the present study examined factors determining self-reported health among men
in the two poorest and the two richest quintiles in Jamaica, in order to provide public health
specialists and policy makers with research findings on these cohorts.
417
MATERIALS & METHODS
The current study extracted a sample of 6,474 men; (2,704 from the two poorest quintiles and
3,770 from the two wealthiest quintiles) from the dataset of the Jamaica Survey of Living
Conditions (JSLC). The inclusion/exclusion criteria were (1) being males, and (2) being
classified in the poor or wealthy social strata. The survey (JSLC) was a nationally representative
probability sample in which self-administered questionnaires were used to collect data from the
populace [20]. The information is from the civilian and non-institutionalized population of
Jamaica. It is a modification of the World Bank’s Living Standards Measurement Study (LSMS)
household survey.
The survey was drawn using stratified random sampling. The design was a two-stage
stratified random sampling design where there was a Primary Sampling Unit (PSU) and a
selection of dwellings from the primary units. The PSU is an Enumeration District (ED), which
constitutes a minimum of 100 residences in rural areas and 150 in urban areas. The sample was
weighted to reflect the population of the nation. The non-response rate for the survey was 27.7%.
Measurements
Self-reported health conditions: This is a dummy variable, where 1 = self-reported ailment,
injury or illness in the last four weeks, which was the survey period, 0 = otherwise. Thus, self-
reported health is a binary variable, where 1 = not reporting an illness, and 0 = reporting an
ailment.
Living arrangement: This is a dummy variable, where 1 = living alone, and 0 = otherwise,
, where represents each person in the household, and r is the number of
rooms excluding kitchen, bathroom and verandah.
418
Age: This is a continuous variable, ranging from 15 to 99 years.
Psychological conditions are the psychological state of an individual, and this is subdivided into
positive and negative affective psychological conditions. Positive affective psychological
condition is the number of responses with regard to being hopeful and optimistic about the future
and life generally. Negative affective psychological condition is the number of responses from a
person on having lost a breadwinner and/or family member, having lost property, having been
made redundant or failing to meet household and other obligations.
Natural disaster: This is the number of responses from people who indicated suffering landslides;
property damage due to rains, flooding and soil erosion.
where ki represents the frequency with which an individual
witnessed or experienced a crime, where i denote 0, 1 and 2, in
which 0 indicates not witnessing or experiencing a crime, 1 means witnessing 1 to 2, and 2
symbolizes seeing 3 or more crimes. Tj denotes the degree of the different typologies of crime
witnessed or experienced by an individual (where j = 1…4, which 1 = valuables stolen, 2 =
attacked with or without a weapon, 3 = threatened with a gun, and 4 = sexually assaulted or
raped. The summation of the frequency of crime by the degree of the incident ranges from 0 to a
maximum of 51.
Consumption: The total sum which is spent by an individual on durable and non-durable good
during a 12-month period.
Statistical analysis
419
Statistical analyses were performed using Statistical Packages for the Social Sciences (SPSS)
16.0 software for Windows (SPSS Inc, Chicago IL). Descriptive statistics were used to provide
basic information on the sampled population. Logistic regression analyses were used to establish
the model to ascertain parameters, and determine the strength of each statistically significant
variable (P < 0.05). The predictive power of the model was tested using the Omnibus Test of
Model and Hosmer and Lemeshow [23] was used to examine goodness of fit of the model. The
correlation matrix was examined in order to ascertain whether autocorrelation (or multi-
collinearity) existed between variables. Cohen and Holliday [24] stated that correlation can be
low/weak (0 to 0.39); moderate (0.4-0.69), or strong (0.7-1.0). This was used to assist in the
exclusion (or retention) of a variable in the model. In support of this, where collinearity existed (r
> 0.7), variables were entered independently into the model to assist in determining which one
should be retained during the final model construction. The decision to retain (or exclude) was
based on the variables’ contribution to the predictive power of the model and its goodness of fit.
To derive accurate tests of statistical significance, we used SUDDAN statistical software
(Research Triangle Institute, Research Triangle Park, NC), and this adjusted for the survey’s
complex sampling design.
Analytic model
The multivariate model used in this study to examine the sub-sample is a modification of that of
Grossman [21] and Smith & Kington [22] which captures the multi-dimensional concept of
health status and conditions. The present study further refined the two aforementioned works and
in the process added some new factors, such as psychological conditions, crowding, house
tenure, and the number of people in the household. Using econometric analysis the study sought
to model the self-reported health of men in the two wealthiest and poorest quintiles from a
420
general set of social determinants identified in the literature, as seen in the equation below
(Equation [1]).
Hi = ƒ(Li,Ri,lnC,Eni,ARi,SSi,CRi,( ),lnEi,HHi,Ai,HIi,Mi,Fi,MRi,EDi,lnMEi) ………[1]
Hi is a function of the 17 variables. Li is living alone of person i, 1 if living alone, 0 if not living
alone; Ri is retirement income of person i, 1 if receiving private and/or government pension, 0 if
otherwise; LnC is the average consumption expenditure of person i, in dollars; En is the natural
disaster, 1 if in the lived milieu there has been flooding, soil erosion, landslide, 0 if not; ARi is
the area of residence, other towns, KMA with the reference group being rural areas; SSi is social
support, 1 if yes, 0 if no; CR is crowding in the household of person i; lnEi is the average total
expenditure of the person i in dollars, which is the proxy for income; HHi is household head of
person, 1 if yes, 0 if no; Ai is age of person i, in years; HIi is health insurance coverage, 1 if
person has a health insurance policy, 0 if otherwise; M is number of males in household of
person i; F is number of females in household of person i; MRi is marital status of person i; EDi
is educational level of person i; lnMEi is medical expenditure of person i; NPi is
the summation of all negative affective psychological conditions and PP is the summation of all
positive affective psychological conditions.
The final model consisted of only those variables which are statistically significant (P <
0.05). Equation [2] represents those factors that explain the health conditions of those in the
poorest 20% and equation [3] denotes variables which are correlated with the health conditions
of those in the wealthiest 20%:
Hi = ƒ(Li, ,PPi, Ai,HIi,EDi) ………….……………………………………….[2]
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Hi = ƒ(lnCi, NPi, Ai,HIi,lnMEi) ………………………..……………..………[3]
RESULTS
Characteristics of sample
There are diverse dissimilarities between the demographic characteristics of men in the two
poorest quintiles and those in the two wealthiest quintiles. The average consumption per head for
the poor was US$301.79 (SD = US$96.16), which represented 22.1% of the average
consumption expenditure per head of those in the two wealthiest quintiles. Similarly, the
crowding for men in the two wealthiest quintiles was 1 person (SD = 0.798 person) compared to
2.3 persons (SD = 1.4 persons) for those in the two poorest quintiles. Furthermore, 4.6 times
more men in the two wealthiest quintiles resided alone, compared to those in the poorest
quintiles. There was a remarkable difference in the level of tertiary education of the two sampled
groups, as for every 1 man in the two poorest quintiles with tertiary level education there were 88
men in the two wealthiest quintiles. In addition to the aforementioned differences, there are 4
times more men in the two wealthiest quintiles who are receiving retirement income compared to
those men in the two poorest quintiles (Table 16.1). Moreover, those in the two wealthiest
quintiles are more vulnerable to crime (2.5 ± 8.5; Range = 88, 0) compared to those in the
poorest quintiles (1.7 ± 7.3; Range = 88, 0).
The disparity was narrower for self-reported health conditions, as for every 100 men in
the two poorest quintiles who indicated a health condition there were 109 men in the two
wealthiest quintiles.
Multivariate Analysis
Predicting the health conditions of men in the two poorest quintiles
422
In the investigation of the factors which predict the health conditions of men in the two poorest
quintiles, it was found that the data was a good fit for the model as 89.1% (n = 1,755) of the data
were correctly classified; 98.5% of those who indicated no health condition were correctly
classified, with 28.7% reporting that they had at least one dysfunction (Table 16.2). Moreover,
the 5 factors accounted for 30.6% of the variability in health conditions of this group: -2 log
likelihood =1195.541; Nagelkerke R2 = 0.306; χ2 (21) = 360.02, p < 0.001.
Predicting health conditions of men in the two wealthiest quintiles
In investigating the self-reported health of men in the two wealthiest quintiles, it was found that
the data was a good fit for the model, as 87.6% (n = 2,533) were correctly classified; 99.0% (n =
2,396) of those who indicated no health condition were correctly classified, with 29.0% (n = 76)
of those mentioning that they had at least one dysfunction (Table 16.3). Of the 17 variables that
the researchers tested, only 5 were statistically significant.
DISCUSSION
This study makes an important contribution to understanding self-reported health in
Jamaican men in two ways. It provides both an econometric model which can be used on sub-
samples of data sets for routine data collection, and it identifies the variables involved in
determining the self-reported health of the poorest and wealthiest Jamaican groups. The study is
timely, given the increasing recognition of the contribution of social determinants to health [1].
The findings of this study suggest that age, average consumption, private health insurance
coverage, level of education, whether or not the person lived alone, medical expenditure and
positive or negative affective psychological conditions were determinants of the self-reported
423
health of the wealthiest and poorest men in Jamaica. Age, health insurance and psychological
conditions are common to both groups, while consumption and medical expenditure are
significant for the wealthiest, and education and living arrangements for the poorest quintiles.
These findings are contrary to those of other studies [21, 22], and therefore contribute to the local
understanding of the relationship between self-rated health status and the socio-economic status
of men in Jamaica.
Age was the second most significant predictor of self-reported health for both groups.
The Jamaican Healthy Lifestyle Survey Report 2000 [25] noted a prevalence of hypertension of
19.9% among males, which increased with age in both rural and urban populations and in both
sexes. The most common chronic diseases identified among elderly males and females were
hypertension, arthritis, diabetes, cardiovascular arrest, stroke and cancer. Patients in the 60-and-
over age groups accounted for 37.2% and 41.1% respectively, of new hypertensive and diabetic
cases [26]. Diabetes is one of the leading causes of morbidity and mortality among persons aged
65 and older [27].
Having health insurance was a predictor for both groups of quintiles. Access to services
also depends on the capacity to pay, which can exclude men in the poorest quintile and who
might have lived all their lives in poverty [28]. The health problems of older men often
necessitate prolonged medication and treatment. The high cost of consultations, diagnostic
services and particularly medicines are among the most formidable barriers to appropriate and
timely care. Deprivation earlier in the life cycle, in terms of education and paid employment,
means that older men in the two poorest quintiles are less likely than their counterparts in the two
wealthiest quintiles to be literate, to have participated in the formal labour force, or to receive
retirement pensions or benefits, such as health insurance coverage. Even when they do receive a
424
retirement pension, this is likely to be lower than that of their wealthier counterparts because of
the lower average wages that they earned when employed. Thus, many lack the means to meet
their needs [28].
In this study 4.8% of men in the two poorest quintiles possessed medical insurance,
compared with 3.3% of men in the two wealthiest quintiles, and this was lower than the 7.6%
reported in a previous study [29]. This finding suggests that the cost of health care is the
individual’s responsibility and for the poorer quintiles emphasizes the reliance on public
services.
Being in fair or poor health, or having a chronic health condition, is strongly associated
with being underinsured. Compared to those in better health, individuals who rate their health as
fair or poor are almost three times as likely to be underinsured (19% versus 7%). While this is
true regardless of residence, rural non-adjacent residents in poorer health have the highest
underinsured rate [30]. Studies have also shown that the lack of health insurance coverage is a
significant barrier to treatment, and rural areas have disproportionate populations of uninsured
and underinsured [31, 32]. As a result of a large percentage of rural men being employed in
small businesses or being self-employed, they are more likely to be uninsured. Bennett and
colleagues [33] postulated that rural residents were more likely to be uninsured than urban
residents (17.8% versus 15.3%), and that rural respondents were more likely than urban
counterparts to report having deferred health care because of cost (15.1% versus 13.1%). This
study supports the findings of other studies.
The current study found that a positive affective psychological condition was a predictor
of self-reported health for those in the two poorest quintiles, while a negative affective condition
425
was a predictive factor for those in the two wealthiest quintiles. This means that the more a
wealthy individual experiences negative affective conditions, he/she is 1.074 times (or 7.4%)
more likely to report health conditions, suggesting that increased negative conditions result in
more hypertension, diabetes mellitus and other types of illnesses. Positive affective
psychological conditions, on the other hand, were inversely correlated with health conditions for
those in the two poorest quintiles. There, those in the two poorest quintiles who experienced
more positive conditions were 8.3% less likely to report health conditions. Embedded in this
finding is the role negative and positive affective conditions play in determining the health
conditions of different sub-groups in the Jamaican population.
Psychological wellbeing is dependent on a host of factors, including genetic traits, social
support systems, personality types, and the presence of positive and negative psychological
constructs such as happiness, optimism, morale, depression, anxiety, self-esteem, self-efficacy,
and vigour. Psychological wellbeing is particularly important for the prevention or management
of cardiovascular disease, but it also has important implications for the prevention and
management of other chronic diseases such as diabetes, osteoporosis, hypertension, obesity,
cancer and depression [34], which have been identified as significant in the Jamaican population.
People’s cognitive responses to ordinary and extraordinary situational events in life are
associated with a different typology of wellbeing [35]. It is found that happier people are more
optimistic, and as such they conceptualize life’s experiences in a positive manner. A study by
Diener and colleagues [36] found that self-reported wellbeing (personal happiness) of the
wealthy-affluent (those earning in excess of US 10 million annually) was marginally more than
that of the lower wealthy, suggesting that high incomes do not increase happiness by the same
proportion. The distinction between the importance of the positive and negative affective
426
conditions of the poor and rich respectively, underlines the importance of the state of mind in
perceived health. According to Harris and colleagues [37] and Kashdan [25], negative
psychological conditions affect subjective wellbeing in a negative manner (i.e. guilt, fear, anger,
disgust), and positive factors influence self-reported wellbeing in a direct way. This concurs with
findings in studies conducted by Fromson [38] as well as by other scholars [39, 40].
Furthermore, the poor may become more optimistic, even with a decline in their health status.
Thus the poor remain hopeful irrespective of their health conditions. The rich, on the other hand,
report that a negative affective psychological condition, such as the loss of a family member, is
associated with their decline in health.
Education was another of the five predictors of self-reported health for those in poor
quintiles. For every eighty-eight men in the two wealthiest quintiles attaining a tertiary level of
education, there was only one man in the two poorest quintiles. Education is closely associated
with an individual’s health status, and high average educational levels are closely associated with
higher average life expectancy [41]. Furthermore, educational attainment is linked to many
aspects of a person’s wellbeing. Research has shown that higher levels of education usually
translate into better health status, higher incomes, and consequently higher standards of living
[42] and better cognitive functioning in older age [43]. Men with less education and who are
poorer are more likely to experience earlier onset of disease, loss of functioning, and physical
impairment [44]. Hayward and colleagues [45] reported onset of diseases and death 5–10 years
earlier for persons with lower socioeconomic status. The average number of biological risk
factors indicating physiological dysregulation is also higher for poorer people and people with
less education [46]. In addition, education significantly affects how effectively people utilize
health care. Education further affects health because well-educated people may be more aware of
427
the benefits and disadvantages of certain types of behaviours associated with personal health
[47].
Importantly, marital status did not appear to be a proxy for who a person lives with, as it
was not a significant determinant of self-reported health conditions. Smith and Waitzman’s work
[48] noted that men’s gains from marriage were greater than those of women [49]. Smith and
Waitzman [48] offered the explanation that wives dissuaded their husbands from particular risky
behaviours, such as the use of alcohol and drugs, and would ensure that they maintain a strict
medical regimen coupled with proper eating habits [50,51] which accounts for them having
greater wellbeing than their non-married counterparts. Surprisingly, more men in the two
wealthiest quintiles lived alone. Older men are likely to live alone and be unconnected to any
family unit because of irresponsible patterns of sexual behaviour and parenting or unstable
relations during their younger years [52].
The wealthiest in the society experience better health, due to their knowledge of health
risks and their access to the resources necessary to avoid such risks, and to treat health conditions
[53, 54]. But with increasing wealth and development there has been an increase in chronic
diseases, as lifestyle changes have had a negative impact [55, 56]. This study found that there
was a large gap between the consumption of the groups, with the poorest only consuming 22% of
the proportional consumption of the wealthiest.
Among the demographic correlates of health is the cost of medical care [1, 2, 21, 22, 57,
58]. The current study concurs with the literature that the cost of medical care is associated with
health status; but this is only for wealthy Jamaicans. Medical care expenditure was not associated
with self-reported health for the poor to poorest in Jamaica.
428
Conclusion
The key finding which emerged from this is that social determinants of health are not always the
same across different social hierarchies. The similarities in social determinants across the two
social strata are age of respondents, health insurance coverage, and negative affective
psychological conditions. Educational levels and living arrangements are not associated with
health for men in the upper social strata, and consumption and medical expenditure are not for
those in the lower social strata. This study adds to the literature by showing that social
determinants of health are not the same in a particular cohort, or between different social strata.
Conflict of interest
The authors have no conflict of interest to report.
Acknowledgement
The researchers would like to note that while this study used secondary data from the Jamaica Survey of Living Conditions, none of the errors in this paper should be ascribed to the Planning Institute of Jamaica or the Statistical Institute of Jamaica, but to the researchers.
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Table 16.1: Demographic characteristics of sample Two Poorest quintiles Two Wealthiest quintile N=2,704 % N=3,770 % Educational attainment Primary and below 551 23.4 603 18.2 Secondary & post-sec. 1787 75.8 2,414 73.0 Tertiary 18 0.1 291 8.8 Marital Status Married 593 22.8 1,058 29.0 Never married 1902 73.1 2,370 65.0 Divorced 7 0.3 49 1.3 Separated 17 0.7 51 1.4 Widowed 83 3.2 116 3.2 Household Head No 94 3.5 1,505 40.0 Yes 2610 96.5 2,261 60.0 Age group Youth (15 – 25yrs) 973 36.0 1015 26.9 Older adults (26 -59 yrs) 1214 44.9 2135 56.6 Elderly (60+ yrs) 517 19.1 620 16.4 Self-reported health conditions None 2229 84.2 3,038 82.7 At least one 418 15.8 637 17.3 Receiving retirement income No 2625 97.7 3,426 91.0 Yes 63 2.3 339 9.0 Living Arrangement With family 2532 93.6 2,673 70.9 Alone 172 6.4 1,095 29.1 Ownership of private health insurance No 2508 95.2 3,462 96.6 Yes 127 4.8 118 3.3 †Average annual Consumption US $301.79 (SD=US $96.16) US$1,326.50(SD=US $1,054.97) Crowding mean (SD) 2.3 persons (1.4 persons) 1 person (0.798 person) Crime Index mean(SD) 1.7(7.3); Range=88, 0 2.5(8.5); Range=88,0 †1US$ = Ja. $50.97 (in 2002)
Table 16.2: Logistic regression: Health conditions of men in the two poorest quintiles by some explanatory variables Explanatory Variables
β Coefficient Odds Ratio
CI (95%)
Retirement income 0.166 1.18 0.52 -2.68 Age 0.044 1.05 1.03 - 1.06*** Household head -0.746 0.47 0.15 - 1.50 Log averaged consumption -0.033 0.97 0.54 - 1.73
Separated/Div/Widowed -0.123 0.88 0.48 - 1.64 Married -0.179 0.84 0.56 - 1.25 †Single
Other Towns -0.237
1.00
0.79 0.50 - 1.26 Urban areas -0.359 0.70 0.38 - 1.30 †Rural area
Health Insurance 2.831
1.00
17.0 10.18 - 28.27*** Natural disaster 0.032 1.03 0.75 - 1.41
Secondary & post secondary 0.599 1.82 1.24 - 2.68** Tertiary -0.931 0.39 0.04 - 4.23 †Primary and below
Living arrangement 0.328
1.00
1.39 1.02 - 1.88* Crowding -0.072 0.93 0.80 - 1.08 Negative affective 0.007 1.01 0.96 - 1.06 Positive affective -0.087 0.92 0.86 - 0.98** Logged medical expenditure 0.038 1.04 0.93 - 1.16 Crime index 0.014 1.01 1.00 - 1.03 Number of males per
household 0.009 1.01 0.84 - 1.21 Number of females per
household 0.043 1.04 0.87 - 1.26-2 Log likelihood =1195.541 Nagelkerke R Square = 0.306 Model χ2 (21) = 360.02, P < 0.001 Overall correct classification = 89.1% Correct classification of cases of no health conditions = 98.5% Correct classification of cases with al least one dysfunction =28.7% †Reference group *P < 0.05, **P < 0.01, ***P < 0.001
434
435
Table 16.3: Logistic regression: Health conditions of men in the two wealthiest quintiles by some explanatory variables
Explanatory Variables β Coefficient
Odds Ratio
CI (95%)
Retirement income 0.375 1.46 0.73 - 2.91 Age 0.032 1.03 1.02 - 1.04*** Household head 0.396 1.49 0.46 - 4.85 Log average annual
consumption 0.632 1.88 1.27 - 2.80**
Separated/Div/Widowed -0.227 0.80
0.49 - 1.29
Married -0.178 0.84 0.62 - 1.13 †Single
Other towns -0.124
1.00
0.88
0.68 - 1.15 Urban -0.188 0.83 0.59 - 1.16 †Rural area
Health insurance 3.494
1.00
32.90
20.64 - 52.45*** Natural disaster -0.142 0.87 0.67 - 1.13
Secondary & post-secondary 0.081 1.08
0.79 - 1.49 Tertiary -0.243 0.78 0.46 - 1.32 †Primary and below
Living arrangement -0.139
1.00
0.87
0.69 - 1.10
Crowding -0.030 0.97
0.81 - 1.17 Negative affective 0.071 1.07 1.04 - 1.11*** Positive affective -0.019 0.98 0.93 - 1.04 Logged medical expenditure 0.086 1.09 1.00 - 1.19* Crime index 0.007 1.01 1.00 - 1.02 Number of males in
household 0.157 1.17 0.98 - 1.40
Number of females in household 0.185 1.20 0.99 - 1.47
-2 Log likelihood = 2054.45 Nagelkerke R Square = 0.280 Model χ2(21) = 522.79, P < 0.001 Overall correct classification = 87.6%, Correct classification of cases of no health conditions = 99.0% Correct classification of cases with al least one dysfunction =29.0% †Reference group *P < 0.05, **P < 0.01, ***P < 0.001
Health Insurance
&
Health
By Paul Andrew Bourne
Health insurance is established as an indicator of health care-seeking behaviour. Despite this
reality, no study existed in Jamaica that examines those factors that determine private health
insurance coverage. This study bridges the gap in the literature as it seeks to determine correlates
of private health insurance coverage. The aim of this study is to understand those who possess
Health insurance coverage in Jamaica so as to aid public health policy formulation.
In 2007, statistics revealed that 21 out of every 100 Jamaicans had health insurance coverage and
66 out of every 100 sought medical care, indicating that most of the people who utilized medical
care services did not use health coverage. Within the context of the global economic downturn,
increased job redundancies and prices of commodities, the uninsured will be asked to pay more
for medical care. Apart from the increased odds of not utilizing health care services, little is
known about the uninsured in Latin American and the Caribbean, and in particular Jamaica.
Married Jamaicans are more likely to purchase health insurance coverage compared to those who
were never married, with urban residents being more likely to purchase health insurance than
rural dwellers. An individual who has attained tertiary level education was more likely to
purchase health insurance than one with at most primary level education, and those who lived
alone were less likely to purchase health insurance coverage than those who dwelled with
relatives or family members. Moreover the wealthiest were more likely to purchase health
insurance, but were less healthy, and this indicates that income does not buy good health.