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Note: Tables and figures
of the article can be access and seen in the PDF file.
Introduction
Poverty which
incapacitates an individual (Sen, 1979) and accounts for
some typology of chronic illnesses (WHO, 2005) has
drastically fallen in Jamaica from 19.9% in 1997 to 9.9%
in 2007. Despite the significant reduction in national
poverty in Jamaica, in 2007, 15.3% of rural residents
were living in poverty compared to 6.2% of urban and
4.0% of semi-urban Jamaicans (Planning Institute of
Jamaica and Statistical Institute of Jamaica, 2008).
Globally statistics on poverty for 2007 revealed that
5.3% of Jamaicans were in the poorest 20% compared to
10.6% for Japan and 5.4% for the United States (UNDP,
2007). Concomitantly, since the 1900s poverty has been
reducing in the world and in particular the Caribbean
(Ahmed and Wiesmann, 2007; UNDP, 2006, 2007; World Bank,
2007), but it should be noted that this is synonymous
with increased chronic conditions.
Statistics from the WHO (2005) revealed that 80% of
deaths due to chronic diseases occurred in low and
middle income countries and in the next decade, these
will increase by 17%, suggesting that the burden of
illnesses will erode the health expenditure of poor
individuals, families, communities and the developing
nations in which they reside. Poverty is not only
associated with low education (Oxaal, 1997; Younger,
2002), poor milieu, low choices and worse health
(Marmot, 2002; WHO, 2005), but it is the equally
correlated with the depletion of valuable human
capital. When poverty is coupled with social exclusion,
it increases the risk of more chronic diseases and which
can result in complications and premature deaths.
Poverty
constitutes the poor and poorest, and through extensive
examination of the Caribbean literature in particular
Jamaica, the latter group is absent from the discourse
as to what explains their health status. Using health
indicators such as child mortality, life expectancy and
under-nutrition for Jamaicans, it may appear that there
is no need to examine the poorest health status as those
indicators are highly comparable to many developing
nations. In Jamaica, statistics revealed that in 1997,
11.0% of the poorest reported illness in the four-week
period of the survey and 2-decades later, the figure
increased by 35% (to 15.0). In addition to the
aforementioned, there is no information on what
determines current good health status of this cohort.
The 20th lowest income categorization (or
poorest) in Jamaica (ie those who received 20 percent of
the income) has been over
looked in health statistics discourse. Within the
perspective that 80% of chronic diseases are in
low-to-middle income countries, this is sufficient
reason to examine health status and medical care seeking
behaviour of the poorest 20% as this will aid in the
planning process.
The
poverty discourse cannot be left to income inequality.
Income inequality in Jamaica is vast; according to
Ventura (2004), 20 percent of the population accounts
for 50 percent of the national consumption. While it is
undeniably the case that income mal-distribution and
deprivation account for health conditions, singly
examining those phenomena do not account for the
rationale of predictors of health status of the poorest
in any geographic area in the Caribbean or in Jamaica.
The
current study will bridge the gap in the literature, by
examining the socio-economic and medical characteristics
of the 20th lowest income categorization in
Jamaica. In addition, another objective is to examine
variables that are correlated with the current health
status of the poorest 20%. The model will provide
socio-economic and biological correlates of current good
health status; their contribution to the overall model
and assist in understanding estimators of the health
status of those in the poorest 20 percent categorization
in Jamaica.
Methods
Sample and respondents
Data from the Jamaica
Survey of Livings Conditions (JSLC) for 2007
commissioned by the Planning Institute of Jamaica and
the Statistical Institute of Jamaica were used to
provide the analyses for this study. These two
organizations are responsible for planning, data
collection and policy guideline for Jamaica, and have
been conducting the JSLC annually since 1989. The
cross-sectional survey was conducted between May and
August 2007 from the 14 parishes across Jamaica and
included 6,782 people of all ages. The sample for this
study was 1,343 respondents who are classified as the
poorest 20 percent in Jamaica (or the poorest).
The
JSLC used stratified random probability sampling
technique to drawn the original sample of respondents,
with a non-response rate of 26.2%. The JSLC survey was
based on a complex design with multiple stratifications
to ensure that it represents the population; marital
status; area of residence; and social class. The sample
was weighted to reflect the population.
The
instrument used by the JSLC was an administered
questionnaire where respondents are asked to recall
detailed information on particular activities. The
questionnaire was modeled from the World Bank’s Living
Standards Measurement Study (LSMS) household survey.
There are some modifications to the LSMS, as JSLC is
more focused on policy impacts. The questionnaire covers
demographic variables, health, immunization of children
0–59 months, education, daily expenses, non-food
consumption expenditure, housing conditions, inventory
of durable goods and social assistance. Interviewers
were trained to collect data from household members. The
sample for this study was 1,343 respondents who are
classified as receiving 20th percentile of
the income in Jamaica (or the poorest 20%).
Statistical Analysis
Data was stored, retrieved
and processed using SPSS for windows 16.0 and a 5
percent level of significance was used to test
significance (ie 95% confidence interval). Descriptive
statistics were used to provide background information
on the sample; chi-square and F-statistic were used for
bivariate analyses and logistic regression was performed
to determine the factors for the model. Using logistic
regression, the forward stepwise technique was used to
estimate the association coefficient of each significant
independent variable on the dependent variable. Odds
Ratio (OR) was used to interpret each significant
variable as well as the association coefficient.
The
predictive power of the model was tested using the
Omnibus Test of Model and Hosmer & Lemeshow (2000) to
examine goodness of fit. The association matrix was
examined in order to ascertain whether auto-correlation
(or multicollinearity) existed between variables. Based
on Cohen & Holliday (1982) association can be low (weak)
- from 0 to 0.39; moderate – 0.4-0.69, and strong –
0.7-1.0 (Cohen, 1988; Cohen, et al., 2003). This was
used to exclude (or allow) a variable in the model. In
addition, variables were excluded from the model if they
had in excess of 20% of the cases missing. Marital
status was omitted from being tested in the model as it
had 40% of non-responses.
Models
Multivariate analyses have
been used in the past to model health status (Grossman,
1972; Smith and Kington 1997; Hambleton et al. 2005;
Bourne, 2008a, 2008b; Bourne and McGrowder, 2009;
Bourne, 2009), and this approach is in keeping with the
social determinants which has been emphasized by the
World Health Organization (2008) and others (Solar &
Irwin, 2005; Graham, 2004; Marmot, 2003; Kelly et al.,
2007). The use of multivariate analysis captures more
variables, and so this study modified the works of
aforementioned scholars. Importantly, a fundamental
difference of the current work and that of Grossman;
Smith and Kington; Hambleton et al; Bourne, and Bourne
and McGrowder is that it is cohort-specific (ie it
focused on those in the 20th income
quintile). The proposed model that this research seeks
to evaluate is displayed (Eqn 1):
Ht = f(Ai,
Ii, EDi, HIi,ARi,
X, HHi, Ci,εi)
1
The
variables identified in Eqn [1] were based on the
literature. Using the principle of parsimony, only those
explanatory variables that are statistically significant
(p < 0.05) were used in the final model to
predict good health status of poorest (i.e. those who
received 20th percentile of the income) in
Jamaica. Hence, the predictive model of the current work
is (Eqn 2).
Ht = f(Ai,
Ii, X, Ci, εi)
2
Current
good health status of the poorest Jamaicans, Ht,
is a function of 4 explanatory variables: where Ht
is current good health status of person i, if good or
above; Xi is the gender of person i, 1 if
male, 0 if female; age of respondent i, Ai;
per capita consumption expenditure of person i, Ci;
and illness (1 if person I has one or more illness, 0 if
no).
Measurement of variable
Selected variables from
the JSLC were chosen to represent dependent and
independent variables for this study. Measurement of
dependent and independent variables used in this
research are explained below.
Dependent variable
Self-rated health status:
is measured using people’s self-rate of their overall
health status (Kahneman, & Riis, 2005), which ranges
from excellent to poor health status. The question that
was asked in survey was “How is your health in general?”
And the options were very good; good; fair; poor and
very poor. For the purpose of the model in this study,
self-rated health was coded as a binary variable (1=
good and fair 0 = Otherwise) (Finnas, et al., 2008;
Helasoja, et al., 2006; Molarius et al., 2006; Leinsalu,
2002; Idler, & Benjamin, 1997; Idler & Kasl, 1995)
Independent variables
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 (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: “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. A
binary variable was later created from this construct
(1= yes, 0 = otherwise) in order to be applied in the
logistic regression.
Medical
care-seeking behaviour was taken from the question ‘Has
a health care practitioner, or pharmacist being visited
in the last 4 weeks?’ with there being two options Yes
or No. Medical care-seeking behaviour therefore was
coded as a binary measure where 1= Yes and 0 =
otherwise.
Crowding is
the total number of individuals in the household divided
by the number of rooms (excluding kitchen, verandah and
bathroom). Age is a continuous
variable in years.
Sex.
This is a binary variable where 1= male and 0 =
otherwise.
Results
Socio-demographic
characteristic of the sample
The sample was 1,343
respondents: 671 males and 672 females. Majority of the
sample did not have health insurance coverage (93.2%, n
= 1,201) compared to 5.6% (n = 72) with public coverage
and 1.2% private (n = 12). Fifty-eight percent of the
sample answered the marital status question (n = 773).
Of those who indicated a marital status, seventy-three
percent were never married, 19.8% married, 5.6% widowed,
1.2% legally separated and 0.5% legally divorced.
Approximately 41% (n = 546) of the sample was children,
24.3% (n = 326) young adults; 22.8% (306) other
aged-adults; and 12.3% (n = 165) elderly (8.1% young
elderly; 3.4% old elderly and 0.8% oldest elderly). For
those who answered the education question, the response
rate was 97.9% (n = 1,315). A substantial percentage of
the valid sample (n = 1,315) had at most basic schooling
(71.5%) compared to 17.5% primary or preparatory; 10.6%
secondary and 0.4% tertiary. Only 14.7% (n = 194) of
the respondents indicated that they had an illness. Of
those who reported having an illness, 93.8% indicated
that this was diagnosed by a medical practitioner. The
self-reported diagnosed illnesses were asthma, 11.9%,
n=23; hypertension, 24.2%, n = 47; arthritis, 7.7%,
n=15; diabetes mellitus, 10.8%, n = 21; influenza,
13.4%, n=26; diarrhoea, 1.5%, n = 3 and unspecified
condition, 24.2%, n=47. When the respondents were asked
about their health status, 96.8% responded (n = 1,300).
The self-rated health status responses were 32.9%
indicated very good; 46.4% good; 13.0% fair; 6.6% poor
and 1.1% reported very poor. When the respondents were
asked ‘has a doctor, nurse, pharmacist, midwife, healer
or other health practitioner been visited? 14.3% of the
sample responded (n=192). Marginally more of those who
responded to having visited medical practitioner in the
4-week period of the survey indicated “yes” (54.7%);
53.5% revealed that they purchased the prescribed
medication and 30.8% indicated that they did not buy the
prescribed medicines. The median amount spent on medical
care was US $3.72 (1 US$ = Ja. 80.47); 25th
percentile spent US $1.24; the 50th
percentile spent US $3.72 and the 75th
percentile used US $12.43. In addition, the mean per
capita consumption per day for the sample was US $1.80
(SD = US $0.48).
Table 1 showed bivariate relationships between
variables. There was a statistical association between
the purchase of medication and area of residence Table
1; (p < 0.05). Continuing, urban poor were the mostly
likely to purchase the prescribed medication (66.3%)
compared to rural poor (62.9%) and semi-urban poor
(61.0% ; p < 0.05. The least mean amount spent for daily
consumption per person was by rural respondents (US
$1.77 ± US $0.48) compared to urban (US $1.91 ± US
$0.48) and US $2.07 ± US $0.48 by semi-urban respondents
(p< 0.05). Furthermore, the findings revealed a
significant statistical difference between the mean
number of persons per room in the different areas of
residence: households in urban areas have significantly
more persons per room (7 ± 4) compared to rural areas (6
± 3) and semi-urban households (5 ± 2). However, there
was no significant statistical difference in the amount
spent on medical care by the area of residence (p >
0.05; Table 1).
Based
on Table 2, there was a statistical association between
self-rated health status and self-reported illness (χ2
(df = 4) = 265.716,
p < 0.001). Only 7.9% of those who revealed that they
had at least one illness indicated very good health
status compared to 37.3% of those who did not report an
ailment. Twenty-four percent of those with at least one
illness reported poor health status compared to 3.5% of
those who did not indicate a dysfunction. Furthermore,
there was a negative statistical association between
self-rated health status and self-reported, with the
association being also a moderate one (contingency
coefficient = 0.413 or 41.3%).
Fifty-five percent of respondents indicated that they
sought medical, and there was no significant statistical
difference between medical care-seeking behaviour and
gender of respondents (p = 0.250): 49.3% of males and
57.9% of females.
Figure
1 displayed the percentage of sample that sought medical
care by particular self-reported diagnosed recurring
illnesses. Of those who had asthma 59.1% sought medical
care; 61.9% of those with diabetes mellitus; 56.5% of
those with hypertension; 40% of those with arthritis and
50% of those with unspecified conditions.
When
the respondents were asked ‘Why did they not seek care?’
the reasons included could not afford it (33%); 35%
reported that they were not ‘ill’ enough, 12% used home
remedy1% indicated that they did not have the time and
19% did not specify (Figure 2).
Table 3
showed a cross-tabulation between self-reported
illnesses and age group of respondents. Based on Table
3, there was a statistical association between
self-reported illness and age group (p < 0.001). Young
adults were the least likely to report an illness
(7.3%); and children were more likely to report an
illness than young adults. The findings revealed that as
people become older, they were more likely to report an
illness. However, the old-elderly reported more ailments
than the other elderly. In fact the old-elderly who
reported are the most likely ones to indicate having a
dysfunction: sixty-two percent of old elderly reported
an illness compared to 46% of oldest-elderly, 32% of
young elderly.
Table 4
displayed a cross tabulation between self-reported
diagnosed recurring illnesses and age group of
respondents. The cross tabulation between self-reported
diagnosed recurring illnesses and age group revealed a
statistical association (p < 0.001). The findings
revealed that as the sample becomes older, the typology
of recurring illnesses change from influenza, diarrhoea
and asthma to diabetes mellitus, hypertension and
arthritis. Forty-nine percent of elderly had
hypertension compared to 28% of other aged-adults and
this was similar for arthritis (8% of other aged-adults
and 17% of elderly). Although no children reported
having hypertension and arthritis, approximately 2% had
recurring diabetes mellitus. Diabetes mellitus and
hypertension were most prevalence amongst other adults,
and arthritis among elderly (Table 4).
Multivariate Analysis
Table 5 displayed selected
independent and dependent variables. Using multiple
logistic regression technique, four variables emerged as
statistically significant predictors of good health
status in this sample (Table 5): age (OR = 0.956, 95% CI
= 0.945 – 0.968); illness (OR = 0.125, 95% CI = 0.085 –
0.185); gender (OR = 1.543, 95% CI = 1.107 – 2.151) and
per capita consumption (OR = 1.152, 95% CI = 0.741 –
1.790).
The
model (good health status) had statistically predictive
power [χ2 (df = 10) =
354.269, p < 0.001];
Hosmer and Lemeshow goodness of fit χ2=
6.086, P = 0.638,
and correctly classify 85.4% of the sample
(correctly classified 96.1% of those who had good health
status and 45.3% of those who had poor health status).
The model (ie. independent variables) can explain 38% (Nagelkerke
R2) of the variability in good health status
of sample. The logistic regression model can be written
as: Log (probability of good health
status/probability of poor health status) = 2.075 –
0.045 (Age) – 2.077 (Illness) + 0.434 (Male) + 0.000
(per capita consumption).
Having
established those variables that are correlated with
good health status of the sample, forward stepwise
multiple logistic regression technique was used to
determine the correlation coefficient of each
significant variable. Table 6 displayed the significant
statistical correlates of good health status, and their
correlation coefficient. Of the thirty-eight percentage
points of the independent variables that can be used to
explain the dependent variable (ie good health status),
illness accounted for 22.8%; age 13.2%, consumption 1.4%
and gender 0.6% (Table 6).
Discussion
Infant mortality and life
expectancy traditionally have been utilized to measure
health status of a population; but this does not
comprehensively explain the influence of poverty on an
individual, family, community, or nation. Marmot (2002)
argued that it is ignorant to perceive that there is no
significant statistical association between poverty and
health as poverty accounts for low quality housing, lack
of sanitation, malnutrition, overcrowding, high infant
mortality, chronic illnesses, material deprivation and
lack of quality medical care. All of these increase the
probability of lower standard of living and life
expectancy. There is a paradox with poverty, infant
mortality and life expectancy as infant mortality in
Jamaica for 2007 was 17 per 1000 (UNDP, 2007) while the
life expectancy was 72 years and only 5.3% of population
was in the poorest 20%. Jamaica’s life expectancy is
high incomparable with that of the many developed
nations such as United States (77.4 years) and that 5.4%
of population in the United States were classified as in
the poorest 20%, yet Jamaica is a developing country and
the former is a developing nation. Economic indicators
for each nation are vastly different; suggesting that
studies in the developed world should not be widely used
to formulate policies nor guide public health practices
in the Caribbean or other developing nations like
Jamaica.
In the
current study, 8 out of every 10 respondents in the
poorest 20% of the Jamaican population indicated at
least good health status which is similar to rural
Jamaicans (8 out of every 10; Bourne and McGrowder,
2009) and higher than that of the Jamaicans who sought
medical care (5 out of every 10; Bourne, 2009b). Good
health status does not mean that people are not
experiencing a dysfunction. This study revealed that 15
out of every 100 of the poorest 20% of the Jamaican
reported an illness, which is same for the population of
Jamaicans (Planning Institute of Jamaica and Statistical
Institute of Jamaica, 2008). There is a statistical
significant association between health status and
illness of the poorest 20%, and that 36 out of every 100
respondents who reported an illness indicated at least
good health compared to 28 out of every 100 of
respondents with poor health status. Furthermore,
the general health status of Jamaicans across the
different social standings is high and offers minimal
difference. According to a cross-sectional probability
survey of 1,338 Jamaicans, Powell, Bourne and Waller
(2007) found that those in the lower class indicated
that their ‘state of health’ was 5.9 out of 10 compared
to 6.5 for those in the upper class and 6.6 for those
classified in the middle class. While Powell et al.’s
work did not deconstruct the health conditions of the
different social classes; this research offers
information about this issue for those classified in the
poorest 20% of the Jamaican population.
The WHO
(2005) declared that chronic illnesses are associated
with poverty, and this study concurs with the current
findings and a study by McCally et al. (1998). The
findings of the current research showed that 24 out of
every 100 of the poorest 20% had hypertension, 11 out of
every 100 diabetes mellitus, 8 out of every 100
arthritis and 24 out of every 100 unspecified conditions
and 13 out of every 100 influenza. Comparatively, 22
out of every 100 of the Jamaican population had
hypertension, 9 out of every 100 had arthritis, 12 out
of every 100 diabetes mellitus and 9 out of every 100
Jamaicans had asthma. The high rates for hypertension
and diabetes mellitus for the poorest 20% are reflecting
their lifestyle practices. The inadequacy to afford the
proper nutrients and food are responsible for those
numbers; but these will be difficult to change as these
people would be less likely to afford not only the
correct foods but seek adequate medical care. Of the 45
out of every 100 respondents who did not seek medical
care, 33 out of every 100 was because of
in-affordability and 35 out of every 100 were due to
‘not ill enough’. The issue of not being ill enough
speaks to the poorest 20% unwillingness not only to seek
medical care for all illnesses but their perception
about severity of illness and that being use to measure
and indicate when medical treatment should be sought.
The number of people seeking medical care in Jamaica for
2007 was 66 out of every 100, which is 11% more than
that for those in the poorest 20%. The poorest 20% are
also not seeking medical care, but only 53 out of every
100 purchased the prescribed medication compared to 66
out of every 100 of the general population (Statistical
Institute of Jamaica, 2008). The poorest 20% of
Jamaicans spent a mean of US$3.72 on medical care which
is 2.1 times more than their average per person
consumption per day, and their medical expenditure is
7.4 times less than that for the population (US $27.58).
The
capacity of this group to recover from their current
socio-economic status will be difficult with assistance
from government and other social networks as 89 out of
every 100 of the poorest 20% had at most primary level
education. The severity of this social reality can be
further understood within the context that 65% of this
group is less than 31 years and 41% less than 15 years.
Although since 2007 user fee for medical services have
been reduced for Jamaicans 18 years and younger, this
does not take away the difficulty of the group to seek
health care, and nutrients deficiency. Only 10 out of
every 100 children were ill and out of every 100 for
young adults, which means that the issues for this group
is not curative care but is preventative care and the
high cost for the society for curative care for this
cohort when they become old (ages 60 years and beyond).
This research revealed that 49 out of every 100 elderly
in the poorest 20% reported hypertension which is 1.3
times more than that for the population 65 years and
beyond and 1.8 times more than that for the general
population, suggesting the cost of curative care for the
elderly poorest 20% will be higher for the nation. It
is not only the elderly poorest 20% that has greater
risk of particular pathogens in Jamaican than the
general elderly population or the general population,
but this spread across the poorest 20% cohort.
A study
by Hambleton et al. (2005) on elderly Barbadians found
that current disease indicators (health conditions)
accounted for 33.6% of the explanation of health status
out of total explanation of 38.2% (ie R2),
indicating power of ill estimators. While the current
study found that current disease indicators accounted
for 22.8% of the explained variation in health status,
this represented 60% of the variability compared to 52%
in Hambleton et al’s work. In Jamaica, with inflation
having increased by 194.7% in 2007 over 2006 coupled
with the global economic downturn, this not only speak
of the economic challenges of the poorest 20% but also
reinforced the economic burden of this cohort on the
national budget. Nugent (2008) noted that between 0.02
to 6.77% of GDP in a country is estimated to be spent on
chronic illnesses, and in United States the figure is
5.0% of GDP. He continued that the treatment costs of
diabetes mellitus in developing countries are estimated
to be 9% of the global total.
Infectious diseases continue to be among the leading
cause of premature mortality in adults in the developing
countries which emphasize the choices that are made by
poor in order for livelihood. This includes the poorest
20% in Jamaica who have not sought medical care although
they indicated that they have particular chronic
illness. It should be noted that 60 out of every 100
arthritic poorest 20% did not seek care, 44 out of every
100 hypertensive and 38 out of every 100 diabetic which
are causes of premature mortality and economic burden of
futuristic care and the challenges for public health in
Jamaica. In an article published by CAJANUS, the
prevalence rate of diabetes mellitus affecting Jamaicans
is higher than in North American and “many European
countries” (Callender 2000). Diabetes Mellitus is not
the only challenge faced by patients, but McCarthy
(2000) argued that between 30 to 60% of diabetics also
suffer from depression, which is a psychiatric illness.
Poverty
is considered to be the greatest cause of health
inequalities between affluent and poor countries (WHO
1998); but this study has shown that the poorest 20% of
Jamaicans are substantially been affected by not only
poverty, low education and material deprivation but also
include health conditions, and their low responses to
preventative as well as curative medical care. Hence,
the reality for the poorest 20% of Jamaicans is likely
to be catastrophic in the future and will account for
high mortality and economic burden for the society
(Nugent, 2008). This is confirmed by a study conducted
by McCally et al. (1998) which found that mortality
rates for those in the lower class higher than that for
the other social classes (Marmot 1994; Marmot et al.
1984; Marmot et al. 1991). Another study presented to
the United Nations by a Caribbean scholar cited that
poverty is correlated with risky sexual behaviour
(Bernard, 2003) furthering exposure to disease causing
pathogens and accounting for some of the HIV/AIDS cases
in the Caribbean in particular Jamaica.
Consumption was found to be positively correlated with
good health status for the poorest 20% which concurs
with many other studies (Marmot, 2002; McCally et al.
1998; Bourne and McGrowder 2009; Smith and Kington,
1997; Grossman, 1972). With the poorest 20% being
incapacitated by economic and material deprivation,
another critical aspect to this study is what is
embedded in their consumption pattern. Their consumption
pattern will constitute of mostly innutritious items
such as fatty foods and starches, which add to the
reasons for the higher hypertension in this cohort than
that for the population of Jamaica. Another aspect to
this issue is the barrier to health care that the lack
of income affords the poorest 20% from purchasing
prescribed medication and an explanation for lowered
visits to medical practitioners for preventative
check-ups.
Among
the social determinants of health status of the poorest
20% of the Jamaicans is gender. The findings indicate
that men have a greater health status than women. They
are 1.5 times likely to report a greater health status
than females, suggesting that latter group will be
experiencing greater socio-economic hardships. Females
have a high propensity than males to contract particular
conditions such as depression, osteoporosis and
osteoarthritis (WHO, 2005; Herzog, 1989). A study that
was conducted by Schoen et al. (1998) on a group of
adolescents reveals something different from that which
was reported by WHO. They found that males are more
likely than females to feel stressed ‘overwhelmed’ or
‘depressed’, and they attributed this to limitedness of
men’s social networks. Other research have agreed with
Schoen et al that men in general tend to be more
stressed and less healthy than females, and further
argued that men can use denial, distraction, alcoholism
and other social strategies to conceal their illness or
disabilities (Friedman, 1991; Kopp et al. 1998; Weidner
and Collins, 1993; Sutkin and Good, 1987). Males,
nevertheless, are more likely to have heart diseases,
gout and hypertension than women. World Health
Organization attributes this biomedical condition to
difference between the genders based on hormonal
differentiations, social networks and support, and
cultural and lifestyle practices of the sexes, this was
concurred by Courtenay et al. (2002).
Conclusion
The thrust to reducing
poverty in developing countries in particular Jamaica
must be coupled with lifestyle behavioural modification
programmes for the poorest 20% along with
multi-dimensional approach to health, perception of
health and treatment among this cohort. While the
economic costs of treatment of chronic diseases are
high, public health practitioners and governments cannot
allow the poorest 20% to become ill before retarding all
possibilities of futuristic delays in the seeking of
medical care outside of curative measures.
Acknowledgement
The author would like 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 (Jamaica Survey of Living Conditions,
2002) available for use in this study. In addition, he
would like to extend gratitude to Mrs. Cynthia Francis,
Librarian in the Documentation Centre, Department of
Community Health and Psychiatry, University of the West
Indies, Mona, for her contribution in sourcing materials
for the literature review.
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