Aging in Asia Findings from New and Emerging Data Initiatives (2012) / Chapter Skim
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16 Markers and Drivers: Cardiovascular Health of Middle-Aged and Older Indians--Jinkook Lee, P. Arokiasamy, Amitabh Chandra, Peifeng Hu, Jenny Liu, and Kevin Feeney
16 Markers and Drivers: Cardiovascular Health of Middle-Aged and Older Indians--Jinkook Lee, P. Arokiasamy, Amitabh Chandra, Peifeng Hu, Jenny Liu, and Kevin Feeney
Pages 387-414
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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)
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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)
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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.
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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.
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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)
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First, we examine interstate differences in descriptive sample characteristics and socioeconomic status and report a design-corrected Chisquare test (Stata Corporation, 2009)
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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.
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Pilot Wave. the Rajasthan sample identifies as a scheduled tribe, while the highest proportion of scheduled castes, 33%, is found in Punjab.
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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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. 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: *
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. Further investigation of those who are hypertensive illuminates interstate variations in undiagnosis and good management.
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Significant interstate variations are also observed for obesity mea sures, such as BMI and WHR. In Punjab, the percentage of the sample with BMIs over 30 (11%)
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More than 90% of all scheduled tribe members were undiagnosed compared to just 54% among those with no scheduled caste or tribe affiliation. Scheduled tribes and scheduled castes were also the least likely to be managing their hypertension; those respondents with no tribe or caste affiliation were more than five times more likely than scheduled tribes to be managing their blood pressure.
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displays the highest prevalence of undiagnosed hypertension, contributing to a steeper age gradient in the prevalence of diagnosed hypertension than that of total hypertension. Do interstate variations and SES gradients in diagnosed, measured, and total hypertension persist after controlling for obesity and health behavior?
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We estimate interstate variations and SES gradi ents in these health outcomes, controlling for covariates, including age, gender, rural/urban residency, obesity measures (i.e., BMI and WHR) , and health behaviors.
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SOURCE: Data from Longitudinal Aging Study in India (LASI) Pilot Wave.
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We also find significant education gradients in all three measures of hypertension. Respondents who have completed some schooling are twice more likely to have hypertension than those without any schooling
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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 interstate variations and SES gradients still persist after controlling for obesity.
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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.
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Table 16-7 displays the results of our multinomial logistic regression for BMI. We find persistent interstate variations in BMI even after controlling for other covariates.
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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.
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Hypertension, accounting 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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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 hypertension prevalence, but obesity and health behavior do not account for all of the interstate variations and SES gradients.
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Once we control for education, per capita household consumption and caste are no longer significantly associated 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.
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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. Furthermore, due to small sample size, we cannot further examine SES gradients within states.
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* 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 *
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Longitudinal Aging Study in India, Pilot Wave (2011)
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. Cardiovascular diseases in the developing countries: Dimension, determinants, dynamics and directions for public health action.
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