J Fam Econ Iss (2010) 31:328–337
DOI 10.1007/s10834-010-9207-2
ORIGINAL PAPER
Health Divide: Economic and Demographic Factors Associated
with Self-Reported Health Among Older Malaysians
Sharifah Azizah Haron • Deanna L. Sharpe
Jariah Masud • Mohamed Abdel-Ghany
•
Published online: 9 June 2010
Ó Springer Science+Business Media, LLC 2010
Abstract Data from the 2004 Survey of Economic and
Financial Aspects of Aging in Malaysia were analyzed to
determine factors associated with self-reported health status among older Malaysians. Odds of self-reporting health
as bad versus moderate or good were higher for respondents who were in lower income quintiles, who perceived
their financial situation as bad, who were older and who
were not married. Malay, Chinese, Indian, and Bumiputra
ethnic groups had lower odds of perceiving their health to
be bad as compared with those in other ethnic groups.
Keywords Asian Health inequality Health status Older Malaysians
Introduction
Between 1990 and 2001, the life expectancy of men and
women in Malaysia lengthened by almost 3 years (68.8–
S. A. Haron J. Masud
Department of Resource Management and Consumer Studies,
Faculty of Human Ecology, Universiti Putra Malaysia,
43400 Serdang, Selangor, Malaysia
e-mail: sh.azizah@putra.upm.edu.my
J. Masud
e-mail: jariah@putra.upm.edu.my
D. L. Sharpe (&)
Personal Financial Planning Department,
University of Missouri-Columbia, 239 Stanley Hall,
Columbia, MO 65211, USA
e-mail: sharped@missouri.edu
M. Abdel-Ghany
Consumer Sciences Department, University of Alabama,
Tuscaloosa, AL 35487, USA
e-mail: mabdel-g@ches.ua.edu
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71.4) and 6 years (70.3–76.0) respectively (Abdel-Ghany
2008; Department of Statistics 2008; Economic Planning
Unit 2002). This increase in life expectancy does not
necessarily indicate an improvement in the health status of
older Malaysians, however. Although declining health is
not an inevitable consequence of aging, older individuals
constitute the majority of those with health problems
(Grundy and Sloggett 2003). Consequently, as the proportion of older persons in the Malaysian population
increases, their health status becomes a concern. As such,
identifying determinants of health status and health
inequality among older Malaysians is of interest to
researchers and policy makers.
Health status has been measured in various ways. Some
researchers have focused on objective measures of health
such as the presence of a specific disease (e.g. diabetes,
heart disease), observed levels of physical or mental
functioning, or mortality rates (i.e. Kivimaki et al. 2003).
Although objective measures provide important information about health status, they often depend on technical
assessment by a trained professional and can be narrow in
scope. Therefore, objective health measures might be less
effective as assessments of global health status, as well as
difficult to implement in survey research of broad populations. Respondents’ subjective rating of their own health
has been used as an alternative, especially in health
research in gerontology (e.g. Baron-Epel and Kaplan 2001;
Grundy and Sloggett 2003). Although the efficacy of selfreported health status has been debated, it has support in
the literature as a viable alternative to objective assessments (Baker et al. 2001; Idler and Benyamini 1997; Kim
and Lyons 2008; Meer et al. 2003; Miilunpalo et al. 1997).
Health inequality refers to differences in health status
between two or more groups that are distinguished by some
salient characteristic such as income level, gender, or
J Fam Econ Iss (2010) 31:328–337
ethnicity (Todd 1996). Marked inequalities in health status
have been described as a health divide when such
inequality aligns with differences in economic status or
social class to highlight the rift between haves and havenots (Whitehead 1987). Previous studies have found evidence that disparities in health status can originate from
differences in individual internal factors such as health
history and genetics as well as from inequalities in external
factors such as physical environment and living conditions
(i.e. Palloni 2000; Todd 1996).
To date, studies that have examined determinants of
health status have largely focused on western developed
countries such as the United States or Europe (e.g. Carlson
2004; Starfield and Shi 2002) or on developing countries
(e.g. Marmot 2005). Little attention has been given to Asia
in general or to Malaysia in particular. This study addresses
this gap in the literature, focusing on older Malaysians.
Specifically, this study uses data from the 2004 Survey of
Economic and Financial Aspects of Aging in Malaysia to
examine the factors associated with self-reported health
status of Malaysians aged 55–70. Age 55 is considered
normal retirement age in Malaysia, so this age grouping
captures the early years of retirement (Masud et al. 2008).
Understanding determinants of health variation in later
life is important because older individuals’ health status
largely determines their ability to remain independent and
autonomous (Arber and Ginn 1993). Also, identification of
factors associated with health disparities can point to
potential ways to narrow the gap in health status between
certain groups. Older Malaysians are of particular interest
because government statistics indicate that this group has
the highest incidence of poverty in the country (22%)
(Eight Malaysia Plan 2001). A significant link between
poverty and poor health outcomes is widely supported in
the literature (Smith 1999). In addition, older Malaysians
are a heterogeneous group that differ considerably in religious belief and practice, cultural background and socioeconomic status; all factors which may affect health-related
behavior.
Literature Review
Health Status
The health history of older persons as well as their access
to satisfactory medical care influences their current health
status (Palloni 2000). Health history chronicles the influence that biological, environmental, behavioral, lifestyle
and psychological factors have on a person’s health and
well-being (Palloni 2000). For example, risk of contracting
serious illness can depend on such things as early childhood exposure to health risks (e.g. quality of prenatal care),
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lifetime behavioral risk profiles (e.g. habits of smoking or
exercising), or past use of health inputs (e.g. taking a
vitamin supplement) (Palloni 2000).
Health status can be assessed objectively or subjectively. Objective indicators of health status include physician reports and assessment of physical capacity (e.g.
mobility and muscle strength), motor performance and
psychological functioning. Subjective measures utilize
perception and self-ratings of one’s own general health to
indicate one’s sense of well-being (Grundy and Sloggett
2003). Baron-Epel and Kaplan (2001) proposed that older
individuals form their perception of their own health by
comparing their current health status with: (1) their own
health at other times, (2) health status of other people of the
same age, and (3) health status of other people that have the
same illness. They noted that the last two comparisons
could help explain the positive health perceptions that older
persons often have even when experiencing serious or
debilitating illness.
The most commonly used subjective health measure is a
single item that asks study participants to rate their present
health status using a five-point Likert-type scale that ranges
from ‘‘very good’’ or ‘‘excellent’’, ‘‘good,’’ ‘‘fair,’’ ‘‘poor,’’
to ‘‘very poor’’ (i.e. Grundy and Sloggett 2003; Ren 1997;
Sargent-Cox et al. 2008). To capture a broader view of
survey respondent health, researchers have used various
alternatives to a single scale measure (Mete 2004). Some
have used several scales. For example, Sargent-Cox et al.
(2008) used three scales. Respondents used the first scale to
rate their current overall health status. Using the second
scale, respondents compared their current health status to
that of others their age. With the third scale, respondents
compared their current health status to their own past
health status. Taking another approach, some researchers
have asked respondents to rate general and specific aspects
of their health. For instance, in a study of health inequality,
Grundy and Sloggett (2003) asked respondents for selfreport of presence and duration of long-standing illness
along with a self-report of general health. Still another
approach has been to use both objective and subjective
health assessments. For example, Rautio et al. (2005)
considered physician report of respondent’s blood pressure,
ECG performance, sensory and motor performance and
psychological functioning along with respondent selfreport of health.
Although the reliability and validity of self-reported
health measures has been debated, there is evidence across
a number of studies that such measures offer a reliable
assessment of actual health status (Baker et al. 2001; Idler
and Benyamini 1997; Kim and Lyons 2008; Meer et al.
2003). Zimmer et al. (2005) found support for using a
general self-assessment of health to predict mortality. They
argue, similar to Baron-Epel and Kaplan (2001), that self-
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assessment of health status reflects not only current health
status, but a lifetime of experience that allows comparison
of current health status to one’s past health status and to the
health status of others.
Relationship Between Socioeconomic Characteristics
and Health
Prior research has identified significant relationships
between health status and education, economic resources,
age, gender, ethnicity and, marital status (Adler and
Newman 2002; Hayward et al. 2000). Higher levels of
education attainment have been consistently associated
with better health status across various health measures
(Baron-Epel and Kaplan 2001; Murrell and Meeks 2002;
Masud et al. 2006). Education level reflects the quantity
and quality of resources that were available early in life
(Murrell and Meeks 2002) and influences subsequent
occupation, financial and social resources, and better selfcare through middle age and older adulthood, which could
contribute to better health in later life.
Considerable attention has been given in the literature to
the empirical relationship between income and health status (i.e. Nummela et al. 2007; Smith 1998). There is evidence of a reciprocal relationship. Considering health as a
determinant of economic resources, Smith and Kington
(1997) found that older individuals with excellent health
had 2.5 times more household income and five times more
wealth than older individuals with poor health. In a similar
vein, Kim and Lyons (2008) found that medical expenditures made by older individuals in poor health placed a
significant drain on financial resources. Some studies (e.g.
Wilkinson 1996; Ullah 1990) have indicated that the subjective experience of financial strains is more closely
related to health status than it is to the actual level of
income. Conversely, economic resources can be viewed as
a determinant of health status. For example, poverty can
influence health outcomes through several mechanisms
including detrimental early life conditions, inadequate use
of or access to medical care, injurious health behaviors,
environmental exposure to toxins or poor working conditions (Williams and Collins 1995).
Functional disability is associated with advanced age
(e.g. Ng et al. 2006). Rampal et al. (2008) concluded that
the prevalence of hypertension increased with age in both
men and women. Nummela et al. (2007) found that those
aged 52–56 in both rural and urban areas reported the best
self-rated health, as compared with their older counterparts.
Gender differences in health status of older individuals
have been found. In a study of the problems of older
Malaysians, Masud et al. (2006) noted that although more
than half of all the respondents rated their health as good,
the older men in the study were more positive about their
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health than the older women. Arber and Cooper (1999), on
the other hand, found that self-assessment health measures
showed minimal gender differences until age 80.
Ethnicity represents differences in biological, demographic, and social environment as well as psychological
and behavioral characteristics that contribute to one’s
health (Lillie-Blanton and Laveist 1996). Hamid et al.
(2006) noted among Malaysian elderly, the Chinese had the
longest lifespan followed by Malays and Indians. Indians
had lower life expectancy than the national average for
males of 70 years (Hamid et al. 2006). Lee et al. (2009)
noted that, in Malaysia, Malays and Indians fared worse
than other ethnic groups in hospital admissions and mortality for congestive heart failure. They concluded that
racial differences might exist in the rate of disease progression or response to drug therapy. Or, socio-cultural
beliefs and knowledge levels might differ, which would
affect treatment compliance and, eventual outcome. Culture may also affect use of various forms of self-care. For
example, Aziz and Tey (2008) found that the odds of
Malay using herbal medicine were 1.35 times higher than
Chinese and 5.81 times higher than Indians.
Marital status is closely associated with health condition. As compared with the married, the unmarried tend to
have higher age-adjusted mortality rates from all causes of
death, use more health services (Verbrugge 1979), have a
higher level of psychological distress (Wertlief et al.
1984), and assess their global health and well-being as
relatively worse than the married (Mauldon 1990). Ren
(1997) noted that, as compared with the married, the
separated, divorced, and cohabiting were all more likely to
report poor health (2.23 times, 1.31 times, and 1.35 times,
respectively). But, Bos and Bos (2007) found Brazilian
widows were 20% more likely than married women to
report better health.
Methods
Data
The data used in this study were from the 2004 Economic
and Financial Aspects of Aging in Malaysia funded by the
Intensified Research Priority Area (IRPA), Ministry of
Science, Technology and Innovation, the government of
Malaysia. A multistage random sampling approach was
used. First, the total number of older persons in different
age categories in each mukim (county/territorial division)
within each state in Peninsular Malaysia was obtained from
the Department of Statistics, Malaysia. From this sample,
6% of the mukims with the highest proportion of older
individuals were selected. A total of 3,000 older persons
between ages 55 and 70 were systematically selected from
J Fam Econ Iss (2010) 31:328–337
these mukims to participate in the survey. Of these, 2,327
responded, for a response rate of 78%. One person per
household was surveyed. In married couples, the typical
respondent was the husband.
The data were collected through personal interviews
conducted by trained enumerators. Questionnaires were
developed in four languages: Malay, English, Mandarin
(Chinese) and Tamil to facilitate interviews with the different ethnic groups in Malaysia. Data were obtained from
1,296 Malay (51% male, 49% female); 523 Chinese (52%
male, 48% female), 162 Indian (38% male, 62% female)
and 346 other (51% male, 49% female).
Conceptual Framework
Health status pertains to individual characteristics (e.g.
genetic disorders), as well as to private and social investment in health capital (Grossman 1972; Pincus et al. 1999).
The latter is especially important when considering the
health status of Malaysians who were born before or near
Malaysia’s independence in 1957. At that time, agriculture
was the primary base of the Malaysian economy, educational opportunity was limited, and money income for most
households was quite low (United Nations Development
Programme 2007). The rapid transformation of the
Malaysian economy to an industrial and information base
has had little direct effect on the economic circumstances
of Malaysians age 50 and older, due largely to their low
levels of education and limited job skills (United Nations
Development Programme 2007).
Based on economic theory and prior research, human
capital, economic, and demographic variables are used to
explain differences in self-reported health status of
Malaysians age 55–70. Of particular interest in this study is
ascertaining whether there is evidence of a health divide,
that is, a significant difference in health status between
those with relatively higher or lower resource levels.
Empirical Model
The model dependent variable was self-reported health
status; categories were ‘‘bad,’’ ‘‘moderate,’’ or ‘‘good.’’
Since the response variable was comprised of multiple
categories, multinomial logit was used to estimate the
effect of the independent variables on the log odds of
reporting a good, moderate or bad health condition.
The logistic model was specified as:
Log½P=1 P ¼ b0 þ b1 E þ b2 Y þ b3 D þ e
where P was the probability that a respondent reported
moderate or bad health (good health was the reference
category). E represented human capital, Y represented
level of economic resources, and D represented a set of
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demographic variables that included age, gender, ethnicity
and marital status.
Variable Measurement
Dependent variable coding was based on survey respondents’ answer to the question: ‘‘How would you rate your
present state of health?’’ Bad health was coded 1 if a
respondent indicated ‘‘bad,’’ 0 otherwise. Similarly, moderate health was coded 1 if a respondent indicated ‘‘moderate,’’ 0 otherwise. Good health was the reference
category.
Education was used as a proxy for human capital. Since
this birth cohort would have had low levels of education, it
was measured by a dummy variable set equal to 1 if the
respondent had a primary education or less, 0 otherwise.
Malaysia follows the British education system. Primary
school, for children age 7–12, consists of standard one
through standard six and corresponds to first to sixth grade
in the American education system.
Objective and subjective measures of income were used
to proxy economic resources. The objective measure was
respondent report of own income measured as annual
amount of Malaysian ringgits (RM) (at time of this study
1 RM = $3.60 US) received from salary or wages, profit
from business, pension, rental income, transfers from sons,
transfers from daughters, transfers from grandchildren or
other relatives, agricultural sales, dividends, bonuses,
annuities, or other sources. Reported income was recoded
into income quintiles to indicate respondent’s relative
position within the income distribution of the sample.
The subjective measure of financial status was respondent answer to the question: ‘‘How would you rate your
present financial situation?’’ Possible responses were
‘‘bad,’’ ‘‘moderate,’’ and ‘‘good (reference category).’’
Dichotomous variables were created for bad and moderate,
each set equal to 1 for those giving that response, 0
otherwise. The subjective assessment of income status was
included in this study to provide a perspective of income
adequacy from the respondent’s point of view.
The demographic variables in this study were age,
gender, ethnicity, and marital status. Age was a continuous
variable. Given the relatively longer life spans of women,
gender was coded 1 if female, 0 otherwise. Malaysia has
several distinct ethnic groups, each with their own history,
culture, dominant religion, and social customs. Ethnicity
was coded as a series of dummy variables. Malay was
coded 1 if Malay, 0 otherwise. Chinese was coded 1 if
Chinese, 0 otherwise, Indian was coded 1 if Indian, 0
otherwise. Sabah and Sarawak natives were coded as
Bumiputra (1 if yes, 0 otherwise). The name ‘‘Bumiputra’’
means ‘‘the son of soil,’’ referring to the original inhabitants of the country prior to the colonization era. Other
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ethnic groups, which would be any ethnicity other than
those specified, were the reference category. Given
potential for a relatively large number of widows among
the older women in the sample, marital status was coded 1
if not married, 0 otherwise.
Findings and Discussion
Sample Characteristics
The socio-demographic characteristics of the sample are
reported in Table 1. As expected, slightly over a third of
respondents (34.6%) had no formal education. Close to half
(46.6%) had only a primary education. Somewhat less than
20% had completed secondary education, whereas only
1.2% has completed tertiary education.
About 31% of the older individuals in the sample were
in the bottom 40% of income quintile. A large percentage
of the study respondents (30.1%) clustered within the
middle-income quintile, whereas about 19% of study
respondents belonged to the top income quintile. Interestingly, despite the large percentage with relatively low
income levels, only 12% of survey respondents perceived
their financial situation to be ‘‘bad.’’ The remainder of the
respondents either rated their financial situation as moderate (46%) or good (42%).
The mean age of the respondents in the sample was
63.47 years old. The sample had almost equal proportion
of males (50.6%) and females (49.4%). At 55%, Malays
Table 1 Socio demographic background and perception of health status
All sample (n = 2,327)
Freq/mean
%/SD
Health perceptions
Chi-square/F-value
Bad (n = 400)
Moderate (n = 837)
Good (n = 1,090)
Freq/mean
Freq/mean
Freq/mean
%/SD
%/SD
%/SD
Highest education obtained
No formal education
806
34.6
206
25.6
308
38.2
292
36.2
1,085
46.6
157
14.5
387
35.7
541
49.9
408
17.6
34
8.3
137
33.6
237
58.1
28
1.2
3
10.7
5
17.9
20
71.4
Quintile 1
468
20.1
117
25.0
167
35.7
184
39.3
Quintile 2
267
11.5
57
22.3
94
40.6
116
37.0
Quintile 3
700
30.1
145
19.5
284
37.6
271
42.9
Quintile 4
460
19.8
55
12.0
173
37.6
232
50.4
Quintile 5
432
18.6
26
6.0
119
27.5
287
66.4
279
1,061
12.0
45.7
118
191
42.3
18.0
106
486
38.0
45.8
55
384
19.7
36.2
980
42.2
88
9.0
242
24.7
650
66.3
64.87
5.623
Primary education
Secondary education
Tertiary education
97.291**
Quintile
128.946**
Perception of financial situation
Bad
Moderate
Good
Mean age
63.47
5.729
63.95
5.702
62.59
5.647
353.929**
F = 28.525**
Sex
Male
1,178
50.6
174
14.8
400
34
604
51.3
Female
1,149
49.4
226
19.7
437
38
486
42.3
20.812**
Ethnicity
Malay
1,296
55.7
214
16.5
488
37.7
594
45.8
Chinese
523
22.5
74
14.1
183
35.0
266
50.9
Indians
162
7.0
20
12.3
56
34.6
86
53.1
Bumiputra
337
14.5
88
26.2
107
31.8
142
42.2
9
0.4
4
44.5
3
33.4
2
22.3
Other ethnic groups
33.995**
Marital status
Not married
Married
Divorced/separated
** p \ 0.01
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47
2.0
15
31.9
12
25.5
20
42.6
1,532
748
65.8
32.1
212
173
13.8
23.1
522
303
34.1
40.5
798
272
52.1
36.4
65.247**
J Fam Econ Iss (2010) 31:328–337
represented the largest ethnic group, followed by Chinese
(22.5%), Bumiputra (14.5%), Indians (7.0%) and others
(0.4%). These figures correspond to national population
data for older persons aged 50 and above stratified by
ethnic group—47% Malay, 35% Chinese, 9% Bumiputra,
7% Indian and 0.87% other ethnic groups (Department of
Statistics 2008). Most sample respondents were married
(65.8%). Interestingly, almost a third of respondents
(32.1%) reported being divorced or separated, of which
most were women.
Self-Reported Health
Table 1 presents respondent’s health perception by their
socio demographic background. About half (47%) of the
respondents assessed their health as good, 36% rated their
health as moderate and 17% rated their health as bad. The
percentage distribution of older individuals who perceive
their health as good was higher for those with higher
levels of education. Specifically, 71% of older individuals
with tertiary education assessed their health as ‘‘good’’ as
compared with 58, 49.9 and 36.2% of those with secondary, primary and those with no formal education,
respectively. The converse was true for those who perceived their health as moderate. In this situation, those
without formal education formed the highest percentage
of those who judged their health condition to be
moderate.
A similar distribution pattern is observed between
income quintile and health perception. In general, the
percentage of those rating their health as ‘‘good’’ was relatively higher for those in the higher income quintiles.
These findings are consistent with previous research that
confirmed a positive relationship between income level and
health status (Nummela et al. 2007; Smith and Kington
1997).
Interestingly, the perception of older respondents
regarding their health aligned with their perception of their
financial situation. Specifically, the highest percentage of
those who perceived their financial situation as bad also
judged their health as bad (42.3%). Similarly, the highest
percentage of those who perceived their financial situation
as moderate or good, also perceived that their health as
either moderate (45.8%) or good (66.3%), respectively.
Such findings correspond with Kim and Lyons’ (2008)
work that suggested financial stain is a consequence of
poor health.
The mean age of the sample that judged their health as
good or moderate (62.59 and 63.95, respectively) was
smaller than the mean age of the sample that judged their
health as bad (64.87 years old). This finding supports the
idea that health condition and hence self-rating of health,
decline with age.
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Findings indicated gender differences in health perception exist. A slightly higher percentage of male (51%) as
compared with female (42.3%) perceived their health as
good. Conversely, a relatively higher percentage of females
(19.7%) as compared with males (14.8%) judged their
health status as bad. These findings are consistent with that
of Masud et al. (2006) who found that older men were more
positive about their health as compared with women. Also,
women were living longer but had more health problems
than men.
Respondents perceiving their health as ‘‘good’’ comprised the highest percentages across all ethnic groups. The
percentage of Indians who rated their health as good is the
highest of all ethnic groups (53%), followed by Chinese
(51%) and Malay (46%). The percentage of respondents
that rated their health as moderate was the highest among
Malays (37.7%), followed by Chinese (35%) and Indian
(34.6%). Bumiputra and other ethnic groups formed the
highest percentage (26.6%) among those who rated their
health as bad followed by the Malays (16.5%).
An interesting pattern emerged when health perception
was analyzed across marital status. The highest percentage
(32%) of those who perceived their health as bad were
among those who never married; the divorced and separated formed the highest percentage among those who
perceived their health as moderate, while the majority of
older individuals who assessed their health as good (52%)
were currently married. These findings were consistent
with Ren (1997) who found that older individuals that were
divorced, separated or cohabiting reported having relatively poorer health as compared with those who were
married. Ren (1997) cautioned, however, that such perceptions may depend more on the quality of marriage than
on marital status alone.
Factors Influencing Health Perception
Table 2 summarizes the result of the multivariate logit
analysis. To facilitate understanding, discussion of results
for those who rated their health as ‘‘bad’’ or ‘‘moderate’’ is
presented in separate sections:
1. Older individuals who rate their health as ‘‘bad’’
Among those who rated their health as ‘‘bad’’ the following
variables were found significant: income quintile, financial
perception, age, ethnicity, and marital status. The odds for
those who were in the lowest income quintile was 2.90
times as high as the odds for those who were in the highest
income quintile to rate their health as bad instead of good.
The odds for those who were in the second lowest income
quintile was 2.36 times as high as the odds for those who
were in the highest income quintile to rate their health as
bad instead of good. The odds for those who were in the
third income quintile was 3.11 times as high as the odds for
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Table 2 Result of multivariate logit on perception of health
Variable
Respondent perception of own health
Bad
b
Moderate
Std Error
Wald
Sig level
Exp (b)
b
Std Error
Wald
Sig level
Exp (b)
Education: (Ref C secondary)
Primary education or less
Income quintile (Ref = Q5)
Q1
0.253
0.216
1.361
1.066
0.268
15.777
Q2
0.859
0.289
8.811
Q3
1.135
0.252
20.214
Q4
0.586
0.268
4.778
0.243
1.287
-0.034
0.138
0.060
0.806
0.967
1.332
7.13E-05
2.903
0.287
0.179
2.568
0.109
0.003
2.359
0.190
0.197
0.935
0.334
1.210
6.93E-06
3.111
0.560
0.157
12.691
0.000
1.750
0.029
1.797
0.348
0.160
4.734
0.030
1.41
Perception of financial standing (Ref = good)
Bad
2.504
0.207
146.809
8.6E-34
12.233
1.568
0.187
70.494
4.62E-17
4.797
Moderate
1.273
0.149
72.767
1.46E-17
3.574
1.235
0.105
139.487
3.45E-32
3.439
Age
0.051
0.012
19.330
1.1E-05
1.053
0.037
0.009
17.170
3.42E-05
1.038
-0.141
0.144
0.956
0.328
0.869
-0.184
0.109
2.859
0.091
0.832
Malay
-2.435
0.973
6.259
0.012
0.088
-1.074
0.950
1.278
0.258
0.342
Chinese
-2.572
0.978
6.919
0.009
0.076
-1.195
0.952
1.577
0.209
0.303
Indian
-2.987
1.006
8.826
0.003
0.050
-1.303
0.964
1.825
0.177
0.272
Bumiputra
-2.078
Marital status: (Ref = married)
0.982
4.477
0.034
0.125
-1.219
0.958
1.618
0.203
0.296
Sex: (Ref = male)
Female
Ethnicity: (Ref = other)
Not married
0.467
0.151
9.524
0.002
1.594
Intercept
-3.923
1.214
10.448
0.0012
–
-2 Log likelihood
3996.2
those who were in the highest income quintile to rate their
health as bad instead of good. The odds for those who were
in the second highest income quintile was 1.80 times as
high as the odds for those who were in the highest income
quintile to rate their health as bad instead of good.
The odds for those who rated their financial situation as
bad, to rate their health as good instead of bad was 12.23
times as high as the odds for those who rated their finances
as good. The odds for those who rated their finances as
moderate, to rate their health as good instead of bad, was
3.57 times as high as the odds for those who rated their
finances as good.
For age, the odds of rating health as bad instead of good
increased by 1.05 times with 1 year increase in age. As for
ethnicity, the odds for Malays is 0.09 times as high as the
odds for those who were classified as others to rate their
health as bad instead of good. The odds for the Chinese was
0.08 times as high as the odds for those who were classified
as others to perceive their health as bad instead of good.
The odds for Indians was 0.05 times as high as the odds for
those who were classified as others to perceive their health
to be bad instead of good. The odds for ‘‘other Bumiputra’’
is 0.12 times as high as the same odds for those who were
123
0.300
0.120
6.270
0.012
1.350
-2.499
1.097
5.188
0.023
–
classified as others to perceive their health to be bad instead
of good. The odds for those who were not married was 1.59
times as high as the odds for those who were married to
rate their health as bad instead of good.
2. Older individuals who rated their health as ‘‘moderate’’ The odds for those who were in the third income
quintile was 1.75 times as high as the odds for those who
were in the highest income quintile to rate their health as
moderate instead of good. The odds for those who were in
the second highest income quintile was 1.42 times as high
as the odds for those who were in the highest income
quintile to rate their health to be moderate instead of
good.
The odds for those who rated their financial situation to
be bad, to rate their health to be moderate instead of good,
was 4.80 times as high as the odds for those who rated their
finances as good. The odds for those who rated their
finances to be moderate, to rate their health moderate
instead of good is 3.44 times as high as the odds for those
who rated their finances as good.
The odds of rating health as moderate instead of good
increased by 1.04 times with a 1-year increase in age. The
odds for those who were not married was 1.35 times as
J Fam Econ Iss (2010) 31:328–337
high as the odds for those who were married to rate their
health as moderate instead of good.
Conclusion
This study used data from the 2004 Survey of Economic
and Financial Aspects of Aging in Malaysia to examine the
factors associated with self-reported health status of
Malaysians aged 55–70. The study provides insights into
the circumstances of a population group that has received
little attention in prior literature on health inequality.
Study findings indicate that there is a health divide
among older Malaysians. Income appears to be the main
dividing factor, although age, ethnicity, and marital status
also play a role. Those who were in the lower income
quintiles and those who perceived their financial standing
to be bad were significantly more likely to also rate their
health as bad. Odds of rating health as bad were also
greater for older individuals, those of ‘‘other’’ race, and
single individuals. Interestingly, when other factors were
controlled, neither education nor gender had a significant
influence on self-reported health status.
Results of this study have several implications. First, the
presence of different levels of health among older Malaysians indicates that members of this group do not fare
equally well. Consequently, attention must be given to
potential reasons for differential health status among this
population group. Second, study findings imply that the
health status of older Malaysians cannot be attributed to the
aging process alone. The influence of economic and social
factors on health status must be considered as well. Those
interested in improving the health status and consequent
quality of life of older Malaysians should note that, of the
factors found significantly related to health level in this
study, income is the key factor that would be amenable to
change via public policy. Given the significant link
between poverty and poor health indicated in the literature
(Smith 1999), exploring means of improving the financial
status of low income older Malaysians would appear to be
an important policy goal. Finally, education and gender
were not significant factors in explaining health differences
among older Malaysians. This finding is contrary to prior
research, suggesting that this birth cohort may have some
unique characteristics. Indeed, we know this is the case.
Respondents in this study witnessed a remarkable and
relatively rapid transformation of the Malaysian economy
from agriculture to industry and technology. Their low
levels of education and training limited their opportunity to
participate in the gains from that change, however, a
problem not uncommon to developing economies (Fuess
and Hou 2009). Further research is needed to determine
whether the economic development that Malaysia has
335
experienced since independence in 1957 will serve to
widen or narrow this health divide for future generations.
The relatively low levels of economic resources of the
current older generation suggest that they will need financial support during their remaining years (Rubin and
White-Means 2009; Sheng and Killian 2009; Ulker 2009),
whether from family, community, or government sources.
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Author Biographies
Sharifah Azizah Haron, Ph.D., IFPÒ is an Associate Professor in
the Department of Resource Management and Consumer Studies,
Faculty of Human Ecology at the University of Putra Malaysia,
specializing in Consumer and Family Economics. Her research
focuses on consumer behavior, empowerment, wellbeing and policy,
as well as household economic well-being and poverty. She has
served on the Malaysian national review committee for the Consumer Protection Act and as a member of the National Council of
Consumer Advisory. She is currently involved in the formulation of
Consumer Master Plan of Malaysia. She serves on the editorial board
of the Malaysian Journal of Consumer and Family Economics.
Deanna L. Sharpe, Ph.D., CFPÒ is an Associate Professor in the
Personal Financial Planning Department at the University of
Missouri. Her teaching and research center on factors affecting later
life economic well-being, including labor supply, family resource
management, financial planning, consumer expenditure patterns,
retirement savings behavior, and financial planner/client relationships.
She has won five Outstanding Paper awards. She is a past board
member of the American Council on Consumer Interests and the
Association of Financial Counseling and Planning Education. She has
J Fam Econ Iss (2010) 31:328–337
served on the CERTIFIED FINANCIAL PLANNERTM exam review team, the
Certified Financial Planner Board of Standards Education Task Force,
and has been an invited participant to the U.S. Department of the
Treasury National Symposium on Financial Education Research. She
serves on the editorial boards of the Journal of Family and Economic
Issues, the Journal of Financial Planning and Counseling, and the
Pertanika Journal of Social Sciences and Humanities.
Jariah Masud, Ph.D. is a Professor Emeritus in Management and
Family Economics, specializing in gender issues and poverty of the
aged. She is currently a Senior Research Fellow at the Institute of
Gerontology, University of Putra Malaysia. Her research focuses on
family economics and financial well-being of the elderly, women’s
role in the economy, gender and development. She has been actively
involved in poverty, consumer and gender-related training, consultancy and advisory at the national and international level, and in the
formulation of national policies such as the National Policy of the
Aged, Consumer Master Plan of Malaysia and National Family
Policy. She serves on the editorial board of the Malaysian Journal of
Consumer and Family Economics, Malaysian Journal of Family
Studies, and Malaysian Journal of Consumers and Journal of Islamic
Education.
337
Mohamed Abdel-Ghany, Ph.D. is a Professor Emeritus in the
Consumer Sciences Department at the University of Alabama. He is
serving a third consecutive term as Associate Editor of the Family and
Consumer Sciences Research Journal. He has published more than
115 refereed articles in several journals and conference proceedings.
He is a Distinguished Fellow of the American Council on Consumer
Interests, and Distinguished Research Fellow of International Society
for Quality of Life Studies. He is the 1998 recipient of the Family
Economics Research Award (American Association of Family and
Consumer Sciences). He served as a President of the American
Council on Consumer Interests, the Asian Consumer and Family
Economics Association, and Vice-President of Professional Affairs
for International Society for Quality-of-Life Studies. He has served as
a member of the Editorial Board of Journal of Consumer Affairs,
Journal of Consumer Studies and Home Economics, Journal of
Family and Economic Issues, International Journal of Consumer
Studies, Malaysian Journal of Consumer and Family Studies, and the
Family Economics and Nutrition Review.
123