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16
Markers and Drivers:
Cardiovascular Health of
Middle-Aged and Older Indians1
Jinkook Lee, P. Arokiasamy, Amitabh Chandra,
Peifeng Hu, Jenny Liu, and Kevin Feeney
W
ith a population of more than 1.2 billion (Census of India, 2011),
India is the second most populous country in the world. In the
past decade, the country has witnessed accelerated economic
growth, emerging as the world’s fourth largest economy in purchasing
power parity terms (World Bank, 2010). Together with economic develop-
ment, the country is undergoing a demographic transition: The popula -
tion is aging rapidly. Currently, the 65+ population in India is roughly
60 million people, accounting for 5% of the population (United Nations
Population Division, 2009). By 2050, the 65+ population is projected to
climb to more than 13%, or approximately 227 million people. Economic
development and population aging have contributed to an emerging
trend of noncommunicable diseases, such as cardiovascular diseases
and obesity, previously thought to be a concern mostly for affluent or
developed countries (Mahal, Karan, and Engelgau, 2009). According to
the World Health Organization (World Health Organization, 2009), age-
standardized cardiovascular disease mortality among adults 60 years and
older was 1,978 per 100,000 persons in India, compared to 800 per 100,000
in the United States.
1 An earlier version of this paper was presented at the International Conference on Policy
Research and Data Needs to Meet the Challenges and Opportunities of Population Aging
in Asia, organized by the National Academy of Sciences and the Indian National Academy
of Science. We thank Drs. David Bloom, James P. Smith, Lisa Berkman, and two anonymous
reviewers for their comments and suggestions. This project is funded by NIA/NIH (R21
AG032572-01).
387
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388 AGING IN ASIA
Researchers have documented a strong inverse relation between
health and socioeconomic status (SES) in developed countries, such as
the United States (Banks et al., 2006; Smith, 2004). However, this relation -
ship is not well established in developing countries like India (Zimmer
and Amornsirisomboon, 2001). Further, recent literature suggests that the
direction of association between cardiac health and SES in such develop -
ing countries may be opposite of what is observed in the developed world;
that is, higher SES is associated with increased risk of poor cardiac health
(Reddy, 2002; Reddy et al., 2007). As regional and national economies in
India continue to expand, the consumption basket of many individuals is
changing, leading to dietary changes and increased obesity that pose risks
to cardiac health (Subramanian and Smith, 2006). This phenomenon has
been documented in other developing countries, such as Brazil, China,
and Russia, as well as south Asian countries such as India, Sri Lanka, and
Thailand (Monteiro et al., 2004; World Health Organization, 2002).
From recently collected data in the Longitudinal Aging Study in
India (LASI) pilot study, we examine SES gradients in cardiovascular
health of older Indians across four states using both self-reports and
health markers measured at the time of the interview. Self-reports of
diagnosed medical conditions are tied to access to healthcare services
and, therefore, can mask undiagnosed conditions (Lee and Smith, 2011;
Smith, 2007a, 2007b). In countries like India where access to healthcare
is limited, the prevalence of undiagnosed conditions is expected to be
greater than in developed countries. The use of biomarkers enables us
to study health outcomes without self-report biases that may be dif -
ferentially associated with SES and access to health services. These bio -
marker measures may also provide additional insights into true disease
prevalence as well as the extent of undiagnosis and good management
of chronic diseases in India.
METHODS
Data
The study sample is drawn from the pilot survey of LASI. LASI is
designed to be a panel survey representing persons at least 45 years of age
in India and their spouses. The pilot study was fielded in four states: Kar-
nataka, Kerala, Punjab, and Rajasthan. These four states were chosen to
capture not only regional variations, but also socioeconomic and cultural
differences. Punjab is an example of a relatively economically developed
state located in the north, while Rajasthan, also in the north, is relatively
poor. The southern state of Kerala, which is known for its relatively
efficient healthcare system and high literacy rate (Shetty and Pakkala,
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LEE, AROKIASAMY, CHANDRA, HU, LIU, and FEENEY
2010), is included as a harbinger of how other Indian states may develop.
Karnataka, located in the south, is used as a reference state.
Data were collected from 1,683 individuals during October through
December of 2010. Primary sampling units (PSUs) were stratified across
urban and rural districts within each of the four states to capture a variety
of socioeconomic conditions. LASI randomly sampled 1,546 households
from these stratified PSUs, and among them, households with a member
at least 45 years old were interviewed. The household response rate was
88.6%. All age-eligible household members and their spouses regardless
of age were asked to be interviewed. The individual response rate was
91.7%, and the response rate for the biomarker component of the survey
was 82.5%. We restrict the analysis in this paper to 1,451 respondents who
are at least 45 years of age; spouses under age 45 are excluded.
Although the pilot round of LASI only surveyed four states, the
overall demographic characteristics of our sample are congruent with
the population characteristics of India. However, at the state level, a
comparison of sample characteristics of respondents reveals a somewhat
greater representation of uneducated individuals in Rajasthan and lesser
representation of married individuals, women, and elderly in Karnataka
(for more details, see Arokiasamy et al. in Chapter 3 of this volume).
While these differences may largely be due to the small sample size of
the pilot study, the representatives of our findings should be interpreted
with such caveats.
Measures
Hypertension
A binary variable indicating self-reported diagnosis of hypertension is
created based on the following question: “Has any health professional
ever told you that you have high blood pressure or hypertension?” As
part of the biomarker module, LASI field investigators measured blood
pressure, recording three readings each of systolic and diastolic, using
an Omron 712c digital reader. We create a binary variable for measured
hypertension based on the mean value of the second and third readings
and classify respondents as hypertensive if they have systolic blood pres-
sure of at least 140 mm Hg or diastolic blood pressure of at least 90 mm
Hg. Because blood pressure tends to stabilize after sitting and resting, the
first reading is excluded. For one respondent who had only two measure-
ments for both systolic and diastolic pressure, we calculated the mean of
these two readings. The comparison between diagnosed and measured
hypertension is critical in differentiating those who are diagnosed and
manage their blood pressure well from those who are diagnosed but fail
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390 AGING IN ASIA
to manage blood pressure, as well as differentiating undiagnosed from
diagnosed among those who have high blood pressure readings.
Based on self-reported and measured hypertension, we define total
hypertension as having ever been diagnosed by a health professional or
hypertensive based on blood pressure readings at the time of the inter-
view. Among the hypertensive (defined as total hypertension), we also
define a measure of undiagnosed hypertension counting respondents who
report not having ever been diagnosed with hypertension, but who have
high blood pressure based on the field measurements. We then define a
measure of good management of blood pressure to represent respondents
who report having been diagnosed with hypertension but manage to have
low blood pressure based on the field measurements.
Obesity
The LASI biomarker module also included anthropometric measures,
such as weight, height, and waist and hip circumferences. Based on these
measures, we calculate body mass index (BMI) as weight in kilograms
divided by height in meters squared and a waist-to-hip ratio (WHR). We
create a categorical indicator for obesity if a respondent has a BMI of at
least 30 kg/m2, for overweight if BMI is between 25 and 29.9, and for
underweight if BMI is less than 18.5.
Health behaviors
Smoking is constructed as a series of categorical variables for current
smokers, former smokers, and those who have never smoked. Here, “smok-
ing” refers to both cigarettes and any sort of chewing tobacco. Drinking is
represented by a binary variable indicating whether or not the respondent
currently drinks any alcohol. For vigorous physical activities,2 we construct
a categorical variable that indicates the frequency of vigorous physical
activities: everyday, sometimes (referring to more than once a week, once a
week, or one to three times a month), and never or almost never.
LASI also asked whether or not a respondent has ever visited a pri -
vate doctor with an Bachelor of Medicine and Bachelor of Surgery (MBBS)
degree in his/her lifetime. Respondents’ self-report of diagnosis by a
health professional is only possible given access to health services, which
2 The question reads, “We would like to know the type and amount of physical activity
involved in your daily life. How often do you take part in sports or activities that are vig -
orous, such as running or jogging, swimming, going to a health center or gym, cycling, or
digging with a spade or shovel, heavy lifting, chopping, farm work, fast bicycling, cycling
with loads: everyday, more than once a week, once a week, one to three times a month, or
hardly ever or never?”
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LEE, AROKIASAMY, CHANDRA, HU, LIU, and FEENEY
is often determined by socioeconomic standing rather than need. For
example, those with higher SES have better access to healthcare and may
also be more aware of or more likely to be diagnosed with cardiovascular
diseases. We choose to control for having seen a private doctor with an
MBBS degree as most respondents who self-reported being diagnosed
with a condition reported being diagnosed by a private MBBS doctor.
However, this variable provides only limited information about health -
care utilization, not being able to differentiate the extent of healthcare
utilization or the use of different healthcare providers. While the current
paper is bound by data available from the pilot survey, the baseline instru-
ment of LASI will collect more detailed information about healthcare
utilization, addressing this issue.
Socioeconomic status
We use education, per capita household consumption, and caste affili-
ation as SES measures. In developed countries, education has been found
to be the strongest measure of SES in relation to health (Smith, 2007a,
2007b), influencing it through multiple pathways, including health behav-
iors and access to healthcare (Lee, 2011). We categorize education into
three groups: no schooling, primary or middle school education, and high
school or more schooling based on a respondent’s self-reported highest
level of attainment.
Caste is our second measure of socioeconomic standing. Respondents
self-report as members of scheduled castes, scheduled tribes, other back -
ward class, and all “others” including “no caste.” Scheduled castes and
scheduled tribes are particularly disadvantaged due to a historical legacy
of inequality; scheduled tribes often represent more geographically iso -
lated, ethnic minority populations while scheduled castes can generally
be characterized as socially segregated by traditional Hindu society, often
excluded from education, public spaces (such as wells for drinking water
and temples), and most other aspects of civil life in India (Subramanian et
al., 2008). Many respondents are considered by the Government of India
to be a member of an OBC (other backward class). While less marginalized
and stigmatized than scheduled castes or tribes, these individuals also
face barriers to economic and educational opportunities (Subramanian et
al., 2008). Even though much has been done to improve the standing of
scheduled tribes and scheduled castes, some of these efforts are relatively
recent given the age of our respondents.
As a final measure of SES, we use per capita household consumption.
This measure is preferred to income as past studies reveal that consump-
tion is a better indicator of economic status in low-income and rural set-
tings (Strauss et al., 2010). Consumption is measured at the household
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392 AGING IN ASIA
level, constructed from a sequence of questions that asks about expenses
incurred over the previous year in the following categories: food (pur-
chased, home-grown, and meals eaten out), household utilities (e.g., vehi -
cle or home repairs, communications, fuel), fees (taxes, loan repayments,
insurance premiums), purchases of durable goods (including clothing),
education and health expenditures, discretionary spending items (alcohol
and tobacco, entertainment, holiday celebrations, and charitable dona-
tions), transit costs, and remittances. The household consumption burden
is calculated according to the OECD equivalence scale that differentially
weights household members: the household head (1), each additional
adult (0.5), and each child (0.3). Total household yearly consumption is
then divided by the OECD equivalent household consumption burden
to obtain a per capita measure. LASI provides imputed data for missing
values using a hot deck method, and we control for imputed consump-
tion in the models to adjust for any systematic bias due to missing data
for some components of household consumption. We operationalized this
variable as dummy tertile indicators in our analysis. Consumption is more
strongly correlated with education than caste. Individuals with at least a
high school education have more than two times greater per capita con-
sumption than those without schooling: an average of 53,472 Rupees per
capita for those with no schooling, 68,750 for those with primary or middle
schooling, and 122,058 for those with high school or more. The differences
across castes are less pronounced. Members of scheduled castes and tribes
consume less per capita (45,188 and 59,785, respectively) than those of
other backward classes and all others (81,403 and 73,800, respectively).
Demographics
We include categorical variables for age (45–54, 55–64, 65–74, and 75
and older) and a dummy indicator for gender.
Analysis
To account for sampling design and non-responses, means and per-
centages in the descriptive statistics are weighted with individual sample
weights designed to be representative within each state. Additionally,
we apply an all-state representative weight when pooling individuals
across states to look at the sample as a whole. All analyses account for
the clustered sample design, which was stratified on state, district, and
urban-rural residency.
First, we examine interstate differences in descriptive sample char-
acteristics and socioeconomic status and report a design-corrected Chi-
square test (Stata Corporation, 2009).
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LEE, AROKIASAMY, CHANDRA, HU, LIU, and FEENEY
Second, we examine interstate variations in the prevalence of our
cardiovascular health outcomes (i.e., self-reported, measured, total hyper-
tension) and risk factors such as obesity and health behaviors; we again
report the design-corrected Chi-square and F-statistics. We compare self-
reported with measured hypertension and examine interstate variations
in undiagnosed and well-managed blood pressure among the hyperten-
sive (based on total hypertension).
Third, we test the bivariate association between our outcome of inter-
est (i.e., self-reported, measured, and total hypertension as well as per-
centage of undiagnosed and well managed among the hypertensive) and
the demographic, geographic, and socioeconomic risk factors in a pooled
sample accounting for stratified, cluster sample design.
We then estimate logistic multivariate models to investigate whether
interstate variations and SES gradients hold after accounting for other risk
factors, such as obesity and health behaviors. We formally test changes in
the odds ratios for interstate and socioeconomic covariates after control -
ling for obesity and health behaviors. As all our dependent variables are
binary variables, we run logistic models and report the odd ratios and
confidence interval. Robust standard errors of the regression coefficients
are computed to correct for heteroskedasticity.
Of particular interests are obesity and its relationship with socio-
economic status, as it may explain the SES gradients in hypertension we
observe. Thus, we estimate multinomial logistic models to estimate body
mass index with normal weight as a reference category and ordinary
least squares to estimate WHR. We investigate whether SES gradients
and state variations in obesity hold after accounting for health behaviors.
We formally test the difference in coefficients in states and SES and report
F-statistics. All multivariate models are unweighted.
Results
Sample characteristics
Table 16-1 shows the characteristics of our sample. Significant inter-
state variations reflect patterns in economic development and popula-
tion growth. While women’s representation in the survey does not vary
significantly across states, there is an uneven age distribution. Kerala
and Rajasthan have greater proportions of elderly; about one-third of the
Kerala and Rajasthan populations are aged 65 and older, compared to
Karnataka and Punjab where 19% and 25% of respondents, respectively,
are of the same age group. Most of our sample are members of an OBC
or some “other/none” caste category. However, scheduled tribes and
schedule castes are disproportionately represented across states: 35% of
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394 AGING IN ASIA
TABLE 16-1 Sample Characteristics: Age 45 and Older
Unweighted N
All states Karnataka Kerala Punjab
All 1,451 315 413 365
Gender men 706 150 184 188
women 745 165 229 177
Rural 1,040 206 289 259
Age 45–54 638 156 153 175
55–64 413 100 129 100
65–74 256 44 81 53
75+ 144 15 50 37
Caste scheduled caste 242 53 31 123
scheduled tribe 152 27 0 0
other backward class 510 188 177 43
other/none 546 47 205 199
Education none 665 135 30 220
primary/middle school 513 123 249 97
high school or more 272 57 133 48
Per capita median
consumption mean
(Rps ) sd
at bottom tertile 483 55 139 86
at middle 469 114 125 135
at top tertile 480 136 143 144
total 1,432 305 407 365
NOTES: Consumption tertile calculated on an all-India basis. The cutoff for the middle tertile
is 31,672 Indian Rupees, and the cutoff for the top tertile is 57,796 Indian Rupees. The cutoff
values, means, medians, and standard deviations are reported with income top-coded at the
95% percentile after imputation. * denotes p < 0.05; ** p < 0.01; *** p < 0.001.
SOURCE: Data from Longitudinal Aging Study in India (LASI) Pilot Wave.
the Rajasthan sample identifies as a scheduled tribe, while the highest
proportion of scheduled castes, 33%, is found in Punjab. Punjab also has
the higher proportion of respondents who do not belong to a scheduled
caste, tribe, or OBC.
The two northern states have relatively lower educational attain -
ment. In Rajasthan, 79% of respondents report having no schooling of
any kind, and nearly 60% in Punjab are similarly uneducated. In Kerala,
much higher rates of educational attainment are observed—only 7%
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395
LEE, AROKIASAMY, CHANDRA, HU, LIU, and FEENEY
Weighted %
Rajasthan All states Karnataka Kerala Punjab Rajasthan F-stat
358
184 48.69% 47.59% 44.87% 51.43% 51.46% 2.04
174 51.31% 52.41% 55.13% 48.57% 48.54%
286 72.91% 64.33% 75.26% 69.91% 80.77% 9.10 ***
154 44.30% 49.46% 37.10% 48.06% 43.06% 2.29 *
84 28.35% 31.77% 31.10% 27.38% 23.39%
78 17.84% 14.01% 19.51% 14.43% 21.77%
42 9.51% 4.76% 12.29% 10.13% 11.78%
35 14.49% 16.67% 7.04% 33.48% 9.85% 12.63 ***
125 13.87% 8.57% 0.00% 0.00% 35.40%
102 39.29% 59.79% 42.93% 11.75% 28.32%
95 32.34% 14.98% 50.04% 54.77% 26.43%
280 48.04% 42.59% 7.28% 60.12% 78.65% 37.02 ***
44 34.06% 38.99% 61.13% 26.67% 12.18%
34 17.90% 18.41% 31.59% 13.21% 9.17%
41,993 55,250 42,387 48,093 28,091
55,696 72,431 58,929 58,934 35,979
45,103 52,510 45,328 38,811 28,284
203 34.99% 17.92% 33.41% 23.58% 57.30% 7.35 ***
95 32.37% 37.30% 31.01% 36.97% 26.71%
57 32.64% 44.78% 35.58% 39.45% 15.99%
355 100.00% 100.00% 100.00% 100.00% 100.00%
report receiving no schooling, and close to one-third of the sample has
received some high school education. These socioeconomic differences
across states persist when we examine other measures of economic
well-being, such as household per capita consumption. Karnataka has
the highest amount of per capita consumption, and Rajasthan has the
lowest amount: 57% of respondents in Rajasthan fall into the bottom
tertile of consumption compared to 18% of respondents in Karnataka
and 24% in Punjab.
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396 AGING IN ASIA
Interstate variations in health markers
Table 16-2 presents the distribution of self-reports of diagnosed, mea-
sured, and total hypertension across the four states. Prevalence of self-
reports of diagnosed hypertension differs significantly across states. Kerala
has the highest prevalence of self-reported diagnosed hypertension, while
Rajasthan has the lowest (33% versus 6%). Interstate variations are also
observed in measured blood pressure readings by the interviewer, but
TABLE 16-2 Interstate Variations in Health Markers
Unweighted N
Health Markers All Karnataka Kerala Punjab
Hypertension diagnosed 274 46 134 73
measured 544 101 131 167
total 661 118 201 192
Among hypertensive undiagnosed 408 78 74 125
good management 118 17 71 25
Measured BMI BMI < 18.5 304 84 50 38
18.5 < BMI < 25.0 669 147 223 144
25 < BMI < 30 249 47 82 97
30 < BMI 82 16 20 33
Measured WHR mean for men
Sd for men
mean for women
Sd for women
non missing WHR 1,282 281 361 300
Self-reported smoking current smoker 219 66 82 14
Former smoker 69 12 45 2
never smoked 1,158 237 283 349
Self-reported drinking current drinker 135 33 50 33
not a drinker 1,308 281 360 332
Self-reported vigorous everyday 296 70 94 49
physical activity 1+ per week 93 13 27 33
once a week 59 7 13 32
1–3 per month 36 9 7 7
hardly or never 962 216 269 244
Healthcare utilization ever visited an MBBS 856 222 293 227
NOTE: * denotes p < 0.05; ** p < 0.01; *** p < 0.001.
SOURCE: Data from Longitudinal Aging Study in India (LASI) Pilot Wave.
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LEE, AROKIASAMY, CHANDRA, HU, LIU, and FEENEY
much more modestly, ranging from 35% and 36% in Kerala and Karnataka
to 52.5% in Punjab. Once accounting for both self-reports of diagnosed
hypertension and measured hypertension based on blood pressure read-
ings, the interstate variations in total hypertension are even more modest:
The prevalence of total hypertension is the highest in Punjab (60%) and
the lowest in Karnataka (42%).
Further investigation of those who are hypertensive illuminates inter-
state variations in undiagnosis and good management. Rajasthan has the
Weighted %
Rajasthan All Karnataka Kerala Punjab Rajasthan F-stat
21 16.96% 14.66% 33.33% 20.03% 5.75% 16.66 ***
145 40.54% 35.64% 34.83% 52.50% 44.82% 5.50 **
150 48.51% 41.60% 53.66% 60.40% 46.46% 4.40 **
131 64.59% 66.14% 36.63% 65.03% 87.60% 17.43 ***
5 16.16% 14.34% 34.54% 13.07% 3.23% 13.44 ***
132 26.74% 28.33% 13.42% 12.16% 41.08% 11.27 ***
155 51.20% 50.02% 59.78% 46.11% 47.95%
23 16.47% 16.13% 21.63% 31.16% 7.00%
13 5.59% 5.53% 5.18% 10.57% 3.96%
0.960 0.996 0.970 0.966 0.922 4.94 **
0.145 0.200 0.079 0.111 0.121
0.925 0.921 0.957 0.945 0.897 4.78 **
0.154 0.231 0.080 0.099 0.102
340
57 78.38% 20.84% 20.22% 3.85% 16.08% 11.39 ***
10 16.88% 3.79% 10.94% 0.54% 2.82%
289 4.74% 75.37% 68.83% 95.61% 81.09%
19 9.12% 10.47% 12.32% 9.06% 5.43% 3.18 *
335 90.88% 89.53% 87.68% 90.94% 94.57%
83 21.56% 22.17% 23.06% 13.34% 23.35% 2.86 **
20 5.86% 4.07% 6.59% 9.09% 5.68%
7 3.26% 2.22% 3.23% 8.82% 1.94%
13 2.68% 2.86% 1.65% 1.89% 3.63%
233 66.63% 68.68% 65.47% 66.87% 65.39%
114 57.48% 70.45% 72.16% 62.15% 31.88% 22.17 ***
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404 AGING IN ASIA
(total hypertension). It is also interesting to note that education gradients
are more pronounced when we examined diagnosed hypertension than
the prevalence based on measured or total hypertension. However, per
capita consumption and caste are no longer significantly associated with
hypertension once we control for other covariates.
In addition, and consistent with bivariate findings, we find signifi-
cant gender difference in self-reports of hypertension diagnosis, but no
gender difference in measured or total hypertension. We find significant
age gradients across all measures, reflecting a well-documented associa -
tion with cardiovascular health. We also find that overweight is a signifi -
cant determinant of self-reported and total hypertension, but not statisti -
cally significant for measured hypertension. Smoking in the past is also
found to be a significant risk factor for diagnosed hypertension, but not
for measured or total hypertension. Although counterintuitive, we find
that physical exercise every day is positively associated with measured
and total hypertension. Notably, we find significant associations between
healthcare utilization (i.e., having ever visited an MBBS doctor) and hav -
ing been diagnosed with hypertension. Those who have ever visited an
MBBS doctor are 1.6 times more likely to answer affirmatively than those
who have never visited an MBBS doctor.
Do obesity and health behaviors explain interstate variations and SES
gradients in total hypertension? We further investigate whether obesity
and health behaviors may explain some of the interstate differences and
the SES gradients in our measure of total hypertension, and the results
are presented in Table 16-5. Obesity significantly reduced the interstate
variations, as well as the education gradients, though we stress that inter-
state variations and SES gradients still persist after controlling for obesity.
That is, obesity explains some of the interstate variations and education
gradients, but not all of the variances. Accounting for health behaviors,
however, does not additionally reduce the socioeconomic gradient or
geographic differences we observe.
SES gradients in obesity
We first present the bivariate association between SES and two obesity
measures, BMI and WHR, in Table 16-6. We observe significant association
with each measure of SES: caste, education, and consumption for obe-
sity. Scheduled tribes had the largest percentage underweight (54%), while
respondents who were not of a scheduled tribe or caste had the highest
prevalence of obesity at 7% for OBC and respondents with other or no
caste. Additionally, about one-quarter of other or no-caste respondents were
overweight, so that 35% of respondents in this group were overweight or
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TABLE 16-5 Do Obesity and Health Behaviors Explain Interstate Variations and SES Gradients in Total
Hypertension? Results from Logistic Regression Models
Model A Model B Model C A vs. B B vs. C
State Punjab 3.082 *** 2.670 *** 2.895 *** 4.74 ** 1.59
Rajasthan 1.511 1.519 1.716
Kerala 1.232 1.175 1.292
SES caste scheduled caste 0.882 0.936 0.967 2.38 0.13
scheduled tribe 1.623 1.815 1.785
OBC 1.163 1.189 1.185
education primary/middle 2.196 *** 2.080 ** 1.985 ** 4.72 * 1.98
high school or more 2.234 ** 1.978 ** 2.132 **
consumption mid 1.315 1.287 1.313 2.72 0.08
high 1.039 0.950 0.955
NOTES: Model A includes only covariates in the table, as well as rural, female, and age. Model B includes all covariates of Model A plus obesity
measures; Model C includes all covariates in Model B plus those for health behaviors. Table presents odds ratios for the three models and the
F-statistics when testing coefficients across models. * denotes p < 0.05; ** p < 0.01; *** p < 0.001.
SOURCE: Data from Longitudinal Aging Study in India (LASI) Pilot Wave.
405
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406 AGING IN ASIA
TABLE 16-6 SES Gradients in Obesity
BMI
% Underweight % Normal % Overweight
N 1,304 304 669 249
All 100.00% 26.74 51.20 16.47
Caste scheduled caste 30.67 53.81 10.22
scheduled tribe 53.56 42.12 3.58
OBC 23.49 52.49 17.51
other/none 16.49 52.67 24.03
Education none 38.03 49.19 9.31
primary/middle 20.64 52.57 19.69
high school or 8.27 53.92 29.43
more
Per capita low 38.54 48.30 11.19
consumption mid 26.76 51.56 14.63
tertiles high 13.65 53.90 24.66
NOTE: * denotes p < 0.05; ** p < 0.01; *** p < 0.001.
SOURCE: Data from Longitudinal Aging Study in India (LASI) Pilot Wave.
obese. Education and per capita consumption showed a similar gradient.
Those without education or in the bottom expenditure tertile had the high-
est percentage of respondents underweight (38–39%), while those with
some high school education or in the top tertile for per capita consumption
were about 32–38% overweight or obese. Across both men and women, we
see smaller waist-to-hip (WTH) ratios for consumption, but the association
between WTH ratio and caste and education are only significant for men.
Do interstate variations and SES gradients in obesity persist after
controlling for health behavior? Table 16-7 displays the results of our
multinomial logistic regression for BMI. We find persistent interstate vari-
ations in BMI even after controlling for other covariates. The residents of
Punjab are less likely to be underweight and more likely to be overweight
and obese than the residents of Karnataka. The residents of Kerala are less
likely to be underweight than those in Karnataka but no more likely to be
overweight or obese.
Similarly, we find that higher SES as measured by education also
increased the odds of being overweight or obese and decreased the odds of
being underweight. Consumption also increased the odds of being obese
and decreased the odds of being underweight, but did not show significant
association with the odds of being overweight. Caste affiliation, another
measure of SES, also showed significant association with obesity.
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LEE, AROKIASAMY, CHANDRA, HU, LIU, and FEENEY
WHR
% Obese Chi-sq * Men F-stat Women F-stat
82 626 656
5.59 0.961 0.923
5.31 8.67 *** 0.964 4.34** 0.923 2.69
0.74 0.905 0.887
6.50 0.981 0.931
6.82 0.963 0.930
3.47 16.02 *** 0.932 4.62* 0.914 1.37
7.10 0.980 0.934
8.38 0.982 0.935
1.98 10.84 *** 0.939 3.46* 0.924 6.41**
7.05 0.957 0.902
7.80 0.989 0.948
We also find that respondents aged 75 and older significantly
increased the odds of being underweight compared to normal BMI.
Women also increased the odds of being overweight and obese. Among
health behaviors, currently smoking showed significant relationships
with BMI. Current smoking increased the multinomial odds of being
underweight and decreased the odds of being overweight compared
to respondents in a healthy BMI range. Table 16-8 shows that health
behaviors did not account for any interstate variations or SES gradients.
Table 16-9 presents the results of OLS regression of WHR. Once we
control for basic demographic characteristics, interstate variations and
SES gradients in WHR are no longer statistically significant.
DISCUSSION
Our analysis examines several markers and potential drivers of car-
diovascular health of middle-aged and older adults in India using data
from representative samples of four states: Karnataka, Kerala, Punjab, and
Rajasthan. Using both self-reported and measured health outcomes, we
find that there are significant socioeconomic and interstate variations in
the prevalence of hypertension. Notably, such variations are more evident
in self-reports of hypertension diagnosis than measured hypertension,
suggesting self-report bias associated with the access to healthcare. Based
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408 AGING IN ASIA
TABLE 16-7 Multinomial Logistic Regression Results of Obesity: BMI
Underweight
Reference = Normal RRR CI *
Demo gender female 0.798 0.564 1.129
age 55–64 0.895 0.618 1.296
65–74 1.307 0.855 1.997
75+ 2.100 1.271 3.472 **
rural 1.409 0.959 2.070
State Punjab 0.439 0.260 0.742 **
Rajasthan 0.855 0.540 1.355
Kerala 0.420 0.258 0.686 **
SES caste scheduled caste 1.221 0.757 1.969
scheduled tribe 1.816 1.060 3.111 *
OBC 1.187 0.794 1.776
education primary/middle 0.935 0.618 1.413
high or more 0.456 0.243 0.857 *
consumption mid 0.797 0.557 1.140
high 0.451 0.294 0.690 ***
Health Behaviors quit smoking 0.939 0.459 1.921
currently smoking 1.710 1.104 2.649 *
currently drinks 0.962 0.548 1.688
some exercise 0.901 0.561 1.447
daily exercise 0.830 0.566 1.217
MBBS visit 0.741 0.530 1.036
N 1,278
Wald chi2 7,089.82 ***
NOTE: * denotes p < 0.05; ** p < 0.01; *** p < 0.001.
SOURCE: Data from Longitudinal Aging Study in India (LASI) Pilot Wave.
on blood pressure readings, our estimate of hypertension prevalence
of Indians aged 55–64 (43%) are comparable to those in the same age
group in the United States (40%) and United Kingdom (39%) (Banks et
al., 2006). Our results are in line with other previous studies in India.
Gupta (2004) observes significant interstate variations, ranging from 4.5%
in rural Haryana to 44–45% in urban Mumbai. Hypertension, account-
ing for both self-reported and directly assessed blood pressure readings
taken during the interview, is estimated to affect 49% of Indians aged 45
and older and exhibits similar interstate variation, ranging from 42% in
Karnataka to 60% in Punjab.
Changing lifestyle factors have been cited as a contributing cause
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LEE, AROKIASAMY, CHANDRA, HU, LIU, and FEENEY
Overweight Obese
RRR CI RRR CI
1.672 1.156 2.419 ** 5.889 2.896 11.972 ***
1.018 0.701 1.477 1.913 1.076 3.399 *
0.960 0.609 1.514 2.226 1.132 4.379 *
0.756 0.397 1.439 0.396 0.088 1.785
0.782 0.553 1.106 0.710 0.431 1.170
2.678 1.627 4.407 *** 4.234 1.800 9.963 **
0.725 0.385 1.363 2.083 0.855 5.077
0.915 0.561 1.493 0.669 0.309 1.446
0.550 0.331 0.913 * 0.895 0.389 2.058
0.496 0.176 1.392 0.000 0.000 0.000 ***
0.893 0.619 1.288 1.220 0.658 2.261
1.999 1.285 3.110 ** 3.137 1.585 6.208 **
2.532 1.506 4.259 *** 3.266 1.506 7.084 **
1.005 0.651 1.550 2.442 1.145 5.211 *
1.353 0.885 2.069 2.675 1.233 5.804 *
0.967 0.470 1.991 1.551 0.426 5.648
0.494 0.257 0.950 * 0.635 0.206 1.951
1.313 0.676 2.551 2.635 0.932 7.454
0.844 0.514 1.386 0.972 0.436 2.167
0.705 0.461 1.080 0.914 0.443 1.882
1.052 0.748 1.480 0.844 0.491 1.449
of these trends. For example, obesity is particularly prevalent in Punjab
compared to other states. We found the supporting evidence that obesity
explains some of the interstate variations and SES gradients in hyperten-
sion prevalence, but obesity and health behavior do not account for all
of the interstate variations and SES gradients. After controlling for these
lifestyle factors, we find that interstate variations and SES gradients in
hypertension persist. Identifying what contributes to such interstate varia-
tions and SES gradients calls for further research.
The results of our analyses also suggest significant interstate varia -
tions in diagnosis and management of such diseases and the role that the
healthcare system plays. Respondents in Kerala had significantly lower
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410 AGING IN ASIA
TABLE 16-8 Do Health Behaviors Explain Interstate Variations
and SES Gradients in Obesity? Results from Multinomial Logistic
Regression Models
Model A (Demo)
Underweight Overweight Obese
State Punjab 0.403 ** 2.861 *** 4.359 ***
Rajasthan 0.866 0.707 2.038
Kerala 0.421 *** 0.863 0.676
Caste scheduled caste 1.219 0.534 * 0.890
scheduled tribe 1.870 * 0.492 0.005 ***
OBC 1.171 0.839 1.187
Education primary/middle 0.901 2.003 ** 3.075 **
high school or more 0.411 ** 2.765 *** 3.291 **
Consumption mid 0.792 0.997 2.361 *
high 0.451 ** 1.369 2.630 *
NOTE: * denotes p < 0.05; ** p < 0.01; *** p < 0.001.
SOURCE: Data from Longitudinal Aging Study in India (LASI) Pilot Wave.
likelihoods of undiagnosed hypertension than all other states. Coupled
with the highest percentage of respondents having ever seen a licensed
private doctor and a high proportion of respondents diagnosed with
hypertension keeping their blood pressure under control, the develop -
ment of the health infrastructure may play a critical role in shaping the
course of disease management as such chronic conditions become more
prevalent. In fact, having ever seen a licensed doctor was significantly
related to self-reported diagnoses of hypertension.
We find significant SES gradients in hypertension—particularly with
education—suggesting that those individuals with higher SES are at
increased risk for hypertension when compared to those lower on the
socioeconomic ladder. Education remains significant even after adjust -
ing for obesity and health behaviors. Once we control for education, per
capita household consumption and caste are no longer significantly asso-
ciated with hypertension, suggesting that the historical disadvantages
associated with caste membership as well as differences in consumption
levels are predominantly mediated by education.
Our analyses also illustrate that individuals at the lowest SES are the
most vulnerable to undiagnosed hypertension. This result is not surpris -
ing given that these individuals may also be less likely to be diagnosed
due to more limited access to healthcare services. We also find that among
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411
LEE, AROKIASAMY, CHANDRA, HU, LIU, and FEENEY
Model B (Demo + health behavior) Model A vs. B
Underweight Overweight Obese Underweight Overweight Obese
0.439 ** 2.678 *** 4.234 ** 2.41 3.73 0.45
0.855 0.725 2.083
0.420 ** 0.915 0.669
1.221 0.550 * 0.895 1.00 5.93 2.02
1.816 * 0.496 0.006 ***
1.187 0.893 1.220
0.935 1.999 ** 3.137 ** 4.34 5.53 0.68
0.456 * 2.532 *** 3.266 **
0.797 1.005 2.442 * 0.05 0.63 1.18
0.451 *** 1.353 2.675 *
those who are hypertensive, the more educated are more likely to keep
their blood pressure under control. This finding is consistent with what
has been found in other studies (Reddy et al., 2007).
The results of this study focus on the increasingly complex dynamic
between health and its socioeconomic determinants, though it is not with-
out limitations. Given the cross-sectional design of the LASI pilot survey,
we cannot speak to causality of lower SES influencing health outcomes
and highlight our findings only in the context of associations. Further-
more, due to small sample size, we cannot further examine SES gradients
within states. We also do not have individual-level consumption data and
acknowledge the limitation of our healthcare utilization measure.
Particularly in India, access to healthcare is closely tied to the
same determinants of health outcomes, such as SES, caste, gender, and
geography (Balarajan, Selvaraj, and Subramanian, 2011; De Costa et al.,
2009). Furthermore, one of the most striking features of healthcare in India
is its heterogeneity, ranging from the best possible evidence-based care
to health-threatening practices by unqualified care providers (Banerjee,
Deaton, and Duflo, 2003; Das and Hammer, 2007; Ramaraj and Alpert,
2008). Therefore, further research attention and analyses of how access
to and quality of healthcare influences health outcomes are needed to
deepen our understanding of the relationship between health and SES.
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412 AGING IN ASIA
TABLE 16-9 OLS Regression Results of WHR
Estimates SE *
Demo gender female –0.032 0.009 **
age 55–64 0.001 0.012
65–74 0.002 0.012
75+ 0.004 0.015
rural 0.000 0.012
State Punjab –0.001 0.022
Rajasthan –0.032 0.022
Kerala 0.001 0.019
SES caste scheduled caste –0.004 0.014
scheduled tribe –0.022 0.017
OBC 0.004 0.009
education primary/middle 0.004 0.012
high school or more 0.004 0.014
consumption mid –0.008 0.010
high 0.014 0.013
Health Behaviors quit smoking –0.003 0.016
currently smoking –0.008 0.013
currently drinks 0.032 0.014 *
some exercise –0.022 0.011 *
daily exercise –0.016 0.011
MBBS visit 0.011 0.009
N 1,255
F-stat 4.24 ***
NOTE: * denotes p < 0.05; ** p < 0.01; *** p < 0.001.
SOURCE: Data from Longitudinal Aging Study in India (LASI) Pilot Wave.
CONCLUSIONS AND IMPLICATIONS
Our study contributes to a better understanding of the associations
between higher socioeconomic status and increased risk of hypertension.
Data from the pilot study of the Longitudinal Aging Study in India show
two-fold increases in the risk of these conditions for individuals of older
ages, those who have higher education, and those who are overweight.
Our comparison between self-reports and directly assessed measures
of hypertension reiterates the significance of bias associated with self-
reported medical conditions. The prevalence estimates based on a doctor’s
diagnosis will seriously underestimate the true disease prevalence. As
access to healthcare services increases, the prevalence of undiagnosed
diseases will decline, but such declines will reach the socioeconomically
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LEE, AROKIASAMY, CHANDRA, HU, LIU, and FEENEY
disadvantaged group last. These findings are consistent with the inter-
pretation that a rapid epidemiological transition in India is taking place
due to changes in diet and lifestyle (Popkin et al. 2001; Yusuf et al., 2001)
associated with economic development. Balancing economic growth with
population health, perhaps through strengthening the healthcare system,
should be considered in tandem to tackle the rapidly changing etiology
of noncommunicable diseases in India.
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