Educational Status and Cardiovascular Risk Profile in Indians

Educational status and cardiovascular risk profile in Indians

K. Srinath Reddy*†‡, Dorairaj Prabhakaran†, Panniyammakal Jeemon†, K. R. Thankappan§, Prashant Joshi¶, Vivek Chaturvedi†, Lakshmy Ramakrishnan†, and Farooque Ahmedʈ

*Public Health Foundation of India and †Department of Cardiology, All India Institute of Medical Sciences, New Delhi 110029, India; §Achutha Menon Centre for Health Sciences and Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala 695011, India; ¶Department of Medicine, Government Medical College Nagpur, Nagpur, Maharashtra 440033, India; and ʈKhajabandanawaz Institute of Medical Sciences, Gulberga, Karnataka 585106, India

Edited by Barry R. Bloom, Harvard School of Public Health, Boston, MA, and approved July 9, 2007 (received for review February 1, 2007) The inverse graded relationship of education and risk factors of to an indirect relationship has been predicted to occur as the coronary heart disease (CHD) has been reported from Western epidemic advances. Even two decades ago, McKeigue and Sevak populations. To examine whether risk factors of CHD are predicted (12, 13) predicted that an inverse association between SES and by level of education and influenced by the level of urbanization CHD will finally emerge among South Asians, based on several studies carried out among migrant South Asians in the United ؍ in Indian industrial populations, a cross-sectional survey (n 19,973; response rate, 87.6%) was carried out among employees Kingdom during that period. Later, Bhopal et al. (14) observed and their family members in 10 medium-to-large industries in this relationship in South Asian migrants and most clearly in highly urban, urban, and periurban regions of India. Information migrant Indians. on behavioral, clinical, and biochemical risk factors of CHD was Studies in India over the past half century have revealed a obtained through standardized instruments, and educational sta- similar trend toward a progressive reversal of the social gradient tus was assessed in terms of the highest educational level attained. for CHD. Although studies conducted from the 1960s to the Data from 19,969 individuals were used for analysis. Tobacco use early 1990s suggested a direct relationship between income and and hypertension were significantly more prevalent in the low- CHD risk, studies conducted in the last decade have reported an (56.6% and 33.8%, respectively) compared with the high- inverse relationship between education and/or income with education group (12.5% and 22.7%, respectively; P < 0.001). prevalent or incident CHD (15–20). A large case-control study MEDICAL SCIENCES However, dyslipidemia prevalence was significantly higher in the conducted by us (20) revealed that the risk of developing high-education group (27.1% as compared with 16.9% in the myocardial infarction was two times higher in those with the lowest-education group; P < 0.01). When stratified by the level of lowest when compared with the highest level of education. urbanization, industrial populations located in highly urbanized Studies of CHD risk factors in Indians have revealed variable centers were observed to have an inverse graded relationship (i.e., associations with SES, reporting an inverse graded relationship higher-education groups had lower prevalence) for tobacco use, of education with tobacco consumption and hypertension, with SCIENCE hypertension, diabetes, and overweight, whereas in less-urban- no clear relationship identified for other risk factors (21, 22). SUSTAINABILITY ized locations, we found such a relationship only for tobacco use It is likely that the relationship between cardiovascular disease and hypertension. This study indicates the growing vulnerability of risk factors and SES in Indian population groups is influenced lower socioeconomic groups to CHD. Preventive strategies to by the stage of health transition. At the midpoint of health reduce major CHD risk factors should focus on effectively address- transition, an urbanized population would reveal a reversal of the ing these social disparities. social gradient (the presence of a graded inverse relationship of SES with CHD risk factors), whereas in a relatively rural coronary heart disease ͉ socioeconomic status population group, the relationship of SES and CHD risk factors would still show a direct relationship. Because different regions ecause cardiovascular disease has become the leading cause of India are at different stages of epidemiological transition, we Bof mortality worldwide, coronary heart disease (CHD) is hypothesize that (i) the relationship of CHD risk factors with now contributing to large and rising burdens of death and SES will vary depending on the level of urbanization; and (ii) the disability in many developing countries (1). The relationship of pattern of reversal in social gradient for CHD risk factors, in socioeconomic status (SES) and CHD has varied across different Indian population groups, will be different from that presently populations, when concurrently studied, and within each popu- observed in Western societies. To test these hypotheses, we lation, when studied over time (2). In populations where the analyzed data of individuals who participated in the multicenter CHD epidemic has matured over several decades, it has been sentinel surveillance for CHD risk factors in Indian industrial observed that the epidemic of CHD appears to emerge first in workers and their families (23). Of the several measures of SES, higher socioeconomic groups and declines first in the same educational attainment has been reported to be a valid and easily groups (3, 4). Studies conducted in developed countries over the measurable indicator of SES and considered suitable for social past three decades provide convincing evidence of an inverse

relationship between SES and CHD (5–9). Additionally, the This paper is part of a special series on Sustainable Health. See the related editorial on page lowest socioeconomic group is reported to have increased prev- 15969 and accompanying articles on pages 16038, 16044, and 16194. alence of subclinical CHD compared with those in the highest Author contributions: K.S.R. and D.P. designed research; P. Jeemon, K.R.T., P. Joshi, V.C., socioeconomic group (10, 11). However, when multiple coun- L.R., and F.A. performed research; K.S.R., D.P., and P. Jeemon analyzed data; and K.S.R., tries are compared, the relationship is quite variable, depending D.P., and P. Jeemon wrote the paper. on the level of health transition in each country. It has been The authors declare no conﬂict of interest. suggested that studies of CHD risk factors in heterogeneous This article is a PNAS Direct Submission. populations of developing countries may help us understand the Abbreviations: CHD, coronary heart disease; SES, socioeconomic status; ES, educational multifactorial nature of CHD causation (2). status. In India, a large developing country, the relationship of SES ‡To whom correspondence should be addressed. E-mail: [email protected]. to CHD has not been clear, although an evolution from a direct © 2007 by The National Academy of Sciences of the USA

www.pnas.org͞cgi͞doi͞10.1073͞pnas.0700933104 PNAS ͉ October 9, 2007 ͉ vol. 104 ͉ no. 41 ͉ 16263–16268 Downloaded by guest on September 30, 2021 Table 1. General characteristics of study population Men Women

ES I ES II ES III ES IV ES I ES II ES III ES IV (n ϭ 1,611) (n ϭ 2,607) (n ϭ 5,820) (n ϭ 1,859) (n ϭ 960) (n ϭ 1,635) (n ϭ 2,832) (n ϭ 2,645)

Age group, % 20–29 25.7 27.7 23.0 17.2 39.0 40.6 27.6 15.0 30–39 26.9 22.2 19.8 22.5 30.3 25.8 27.0 20.2 40–49 27.2 26.9 31.9 22.9 22.6 26.5 30.9 28.8 50–59 19.1 22.0 22.9 21.0 7.5 6.7 11.2 21.0 60–69 1.1 1.2 2.4 16.3 0.6 0.5 3.2 14.9 Age, mean years Ϯ SD 38.6 Ϯ 10.9 38.9 Ϯ 11.5 40.7 Ϯ 11.2 44.0 Ϯ 13.3 34.3 Ϯ 9.7 34.0 Ϯ 10.3 37.8 Ϯ 11.1 44.1 Ϯ 12.7 Occupation, % High end 84.7 60.1 19.5 1.7 67.8 52.1 14.6 0.5 Middle end 14.9 39.1 74.4 35.4 12.0 19.4 35.8 8.4 Low end/ 0.4 0.8 6.1 62.9 20.2 28.5 49.6 91.1 Unemployed

ES, educational status. ES I, graduates plus; ES II, above secondary school and up to graduation; ES III, above primary level up to secondary school; ES IV, no formal education and up to primary level.

ranking across many populations at different stages of develop- postgraduates. The remaining 21.2% were either college grad- ment (2). The present study reports the associations of educa- uates or had studied beyond the secondary-school level. tional status with different CHD risk factors in several Indian The age group and occupational status of the study group industrial population groups at different levels of urbanization. across different educational group are presented in Table 1. The low-educational-status group was significantly older compared Results with the high-educational-status group. As expected, the job Demographic Data. A total of 19,973 individuals consented to position occupied by the individuals was commensurate with participate in our study in the age group of 20–69 years. The their level of education. response rate was 87.6%. Data from 19,969 individuals were used for analysis, because the database did not capture the Prevalence of CHD Risk Factors Stratified by Education. The mean educational status of four individuals. The general characteris- levels of major CHD risk factors across various educational tics of the study population are published elsewhere (23). The groups are given in Table 2. The prevalence and prevalence ratio mean age in our study population was 39.8 Ϯ 11.9 years. A third with 95% confidence interval (across educational group) of of the study population (32.8%) were individuals occupying categorical variables are given separately for men and women in high-end executive positions. Fourteen percent of the study Table 3. Tobacco use (P Ͻ 0.001) and hypertension prevalence group were occupying low-end jobs (mainly manual workers), (P ϭ 0.05) showed a significant inverse relationship with edu- and less than one-tenth (8%) of the individuals were unem- cation status in men, even after adjustment for age and occu- ployed. The remaining had jobs that were intermediate between pation. Similarly, tobacco use (P Ͻ 0.001), hypertension (P Ͻ executive positions and manual workers. More than one-fifth 0.001), diabetes (P Ͻ 0.01), and metabolic syndrome (P ϭ 0.04) (22.6%) of the study group were uneducated or had education were inversely related to education level among women. The only up to primary school. The majority of the individuals inverse relationship of educational status and leisure-time phys- (43.3%) had education above primary school and up to second- ical activity was also observed both in men and women (P Ͻ ary school. A small proportion (12.9%) of the study group were 0.001). However, in men, although diabetes did not show any

(Table 2. Mean levels of CHD risk factors stratiﬁed by education and gender (mean ؎ SD Men Women

ES I ES II ES III ES IV P value ES I ES II ES III ES IV P value (n ϭ 1,611) (n ϭ 2,607) (n ϭ 5,820) (n ϭ 1,859) for trend* (n ϭ 960) (n ϭ 1,635) (n ϭ 2,832) (n ϭ 2,645 for trend*

BMI, kg/m2 24.1 Ϯ 3.4 23.7 Ϯ 3.4 23.4 Ϯ 3.6 19.8 Ϯ 3.6 Ͻ0.001 24.2 Ϯ 4.2 23.9 Ϯ 4.3 24.5 Ϯ 4.6 21.3 Ϯ 5.0 Ͻ0.001 WC, cm 87.6 Ϯ 9.5 86.6 Ϯ 9.7 86.4 Ϯ 9.8 76.2 Ϯ 10.8 Ͻ0.001 80.2 Ϯ 10.9 80.1 Ϯ 11.3 82.8 Ϯ 11.0 76.1 Ϯ 12.5 Ͻ0.001 DBP, mmHg 79.4 Ϯ 9.5 79.7 Ϯ 10.3 79.1 Ϯ 10.9 78.9 Ϯ 11.8 0.06 76.0 Ϯ 10.0 76.7 Ϯ 10.2 78.8 Ϯ 10.4 78.9 Ϯ 11.7 0.03 SBP, mmHg 125.2 Ϯ 13.4 126.6 Ϯ 15.0 126.8 Ϯ 16.4 129.6 Ϯ 18.9 0.02 117.9 Ϯ 11.5 119.6 Ϯ 15.0 122.2 Ϯ 15.7 128.1 Ϯ 20.2 0.02 Fasting plasma 95.1 Ϯ 22.3 95.7 Ϯ 27.0 97.6 Ϯ 29.8 93.1 Ϯ 29.6 0.18 90.6 Ϯ 20.1 90.8 Ϯ 25.1 94.5 Ϯ 32.6 93.3 Ϯ 31.3 0.06 glucose, mg/dl Serum 139.3 Ϯ 75.6 139.9 Ϯ 78.2 141.1 Ϯ 79.1 112.4 Ϯ 75.5 0.60 109.3 Ϯ 56.1 109.9 Ϯ 51.3 113.6 Ϯ 58.5 120.9 Ϯ 74.7 Ͻ0.001 tryglycerides, mg/dl Total 179.6 Ϯ 35.2 180.8 Ϯ 39.7 180.2 Ϯ 40.5 152.0 Ϯ 38.2 0.06 177.4 Ϯ 40.6 177.2 Ϯ 37.6 178.6 Ϯ 40.3 169.6 Ϯ 41.2 Ͻ0.001 cholesterol, mg/dl

BMI, body mass index; DBP, diastolic blood pressure; SBP, systolic blood pressure; WC, waist circumference. *Adjusted for age and occupation.

16264 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.0700933104 Reddy et al. Downloaded by guest on September 30, 2021 trend of increasing prevalence with educational status, the metabolic syndrome was high among those with a lower level of 0.04 0.04 value 0.01 education, although not reaching statistical significance levels. In Ͻ 0.001 Ͻ 0.001 Ͻ 0.001 Ͻ 0.001 P

for trend* contrast, we observed a direct graded relationship of dyslipidemia prevalence and level of education in both men (P ϭ 0.009) and women (P ϭ 0.04).

Knowledge and Awareness of CHD Risk Factors Stratified by Educa- 2,645) 19.3 39.4 34.7 22.9 11.2 16.1 42.1 ES IV

ϭ tion. Treatment-seeking behavior and data on optimum man- ( n 0.5 (0.3–0.8) 1.6 (1.2–2.0) 2.9 (2.4–3.5) 0.2 (0.1–0.3) 2.8 (1.8–4.4) 0.3 (0.2–0.4) 8.2 (6.4–9.9) agement of risk factors are given in Table 4. Despite the high prevalence of hypertension, significantly fewer men in the low- compared with the high-educational group sought treatment (P ϭ 0.01). However, this relationship was exactly the opposite among women, because significantly more individuals in the 2,832) 9.8 2.7 20.9 40.8 23.8 41.5 21.2 low-educational group sought treatment for hypertension com- ES III ϭ Women pared with the high-educational group (P ϭ 0.05). Optimal ( n 0.8 (0.6–1.1) 1.7 (1.3–2.1) 1.7 (1.4–2.1) 1.0 (0.8–1.3) 2.4 (1.5–3.8) 0.6 (0.5–0.8) 1.1 (0.76–1.4) control of blood pressure (Յ140/90 mmHg; 1 Hg ϭ 133 Pa) was significantly lower in the low-educational groups. Although there was no graded relationship for awareness of diabetes both among men and women, optimal control of diabetes was low

1,635) across all four educational groups. 4.8 1.6 ES II 17.0 30.7 18.4 37.4 22.5 ϭ ( n 0.7 (0.5–1.0) 1.1 (0.8–1.4) 1.2 (1.0–1.5) 1.1 (0.6–1.8) 0.9 (0.8–1.2) 0.9 (0.7–1.1) 1.1 (0.8–1.3) Level of Urbanization, Educational Class, and Prevalence of Risk Factors. An interesting relationship was observed when a comparison was made based on the location of industries (Table 5). In highly urbanized locations, we observed a reversal of the 960) 1 1 1 1 1 1 1 4.2 ES I ϭ 29.0 15.3 social gradient (the inverse relationship of the prevalence of risk

( n factors to the level of education) for hypertension, diabetes, tobacco use, and overweight. Although the difference in prev- MEDICAL SCIENCES alence of tobacco use, hypertension, and diabetes were marked (P value for trend Ͻ0.01 for all three risk factors), the differences for overweight were modest (P value for trend ϭ 0.02). On the value 0.009 21.1 0.06 0.05 0.08 Ͻ 0.001 39.3 Ͻ 0.001Ͻ 0.001 1.2 33.3 P contrary in periurban locations, we observed a clear reversal of for trend* social gradient for tobacco use (P value for trend Ͻ0.001) and hypertension (P value for trend ϭ 0.03) with a direct relationship SCIENCE

(higher prevalence among those with higher levels of education) SUSTAINABILITY for diabetes (P value for trend Ͻ0.001) abdominal obesity (P value for trend Ͻ0.001) and dyslipidemia (P value for trend ϭ 1,859) 7.6 9.1 12.4 24.9 32.6 77.3 13.2 ES IV

ϭ 0.02). ( n 0.5 (0.3–0.6) 1.3 (1.1–1.7) 1.3 (1.1–1.4) 0.9 (0.6–1.4) 0.2 (0.1–0.3) 6.5 (5.2–8.1)

0.2 (0.18–0.25) Discussion The relationship of SES to CHD has been observed to change as the epidemic evolves (3, 4). Initially, the urban, affluent, and educated sections of a population (early adopters) use their

5,820) higher disposable incomes to experiment with risk-prone behav- Men 32.1 20.6 28.6 13.3 30.4 40.2 34.7 ES III ϭ iors and, therefore, are at a greater risk of CHD. Later, as the ( n 1.3 (1.1–1.5) 1.2 (0.9–1.3) 1.1 (0.9–1.2) 1.6 (1.3–2.1) 0.8 (0.7–0.9) 1.9 (1.6–2.2) 0.7 (0.6–0.8) mediators of risk (tobacco, unhealthy foods, and automated transport) become widely available for mass consumption, all social classes are affected. In the advanced phase of the CHD epidemic, the urban, educated, and affluent sections acquire

2,607) health information, adopt healthy behaviors, and access health ES II 29.1 20.9 29.9 10.4 33.1 26.5 40.0 ϭ care more efficiently. As CHD rates decline in that group, risk 1 (0.8–1.2) ( n 1.2 (0.9–1.4) 1.1 (0.9–1.3) 1.2 (0.9–1.6) 0.8 (0.7–0.9) 1.3 (1.1–1.5) 0.9 (0.8–1.1) factors and disease burdens of CHD become higher in the less-educated and low-income groups and finally even in rural populations. Different CHD risk factors are likely to experience reversal of the social gradient at different times as health 1,611) 1 1 1 1 1 1 1 transition advances. The associations observed in our cross- 8.4 ES I 29.6 27.2 37.0 19.8 41.6 ϭ sectional study need to be interpreted in the context of that

( n evolutionary profile of the CHD epidemic. We observed reversal of the social gradient for tobacco use and hypertension across the whole population. When stratified by level of urbanization, in industrial populations located at highly urbanized centers, we observed reversal of social gradi- ents for tobacco use, hypertension, diabetes, and overweight, whereas in less-urbanized locations, we found such a reversal only for tobacco use and hypertension. Prevalence of diabetes and abdominal obesity was directly associated with educational activity Dyslipidemia Metabolic syndrome 19.2 Hypertension Diabetes Overweight Tobacco use Leisure-time physical Table 3. Prevalence and prevalence ratio with 95% CI of coronary risk factors stratiﬁed by gender and education *Adjusted for age and occupation. status in the less-urbanized locations. Awareness and treatment

Reddy et al. PNAS ͉ October 9, 2007 ͉ vol. 104 ͉ no. 41 ͉ 16265 Downloaded by guest on September 30, 2021 Table 5. CHD risk factor prevalence across educational group based on level of urbanization value 0.05 0.05 0.01 0.06

P P value for for trend ES I ES II ES III ES IV trend*

Tobacco use, % Highly urban 11.7 14.6 26.0 22.2 0.006 Urban 9.6 15.1 21.9 27.1 Ͻ0.001 4.8 4.1 41.3 84.9 Periurban 20.9 26.6 42.5 76.6 Ͻ0.001 3.2 (2.3–4.3) 0.9 (0.7–0.1) 0.3 (0.1–0.4) 0.3 (0.1–0.6) Hypertension, % Highly urban 23.2 26.1 31.6 37.6 Ͻ0.001 Urban 23.0 25.5 26.8 40.5 0.09 Periurban 20.7 23.3 26.5 30.5 0.03 Diabetes, % 9.5 7.0 Ͻ Women

37.2 85.7 Highly urban 7.1 9.6 15.1 16.7 0.001 Urban 8.2 6.0 8.9 14.3 0.01 1.9 (1.3–2.5) 0.5 (0.3–0.7) 0.9 (0.7–1.1) 0.5 (0.4–0.9) Periurban 4.1 6.0 4.4 3.0 Ͻ0.001 Overweight (BMI Ն 25), % Highly urban 40.7 40.3 44.2 44.2 0.02 Urban 35.5 30.4 32.3 37.0 0.6 Periurban 32.1 26.4 13.0 12.9 0.4 9.0 12.3 32.9 78.4 Abdominal obesity, % 1.2 (0.8–1.8) 0.8 (0.7–0.9) 0.7 (0.6–1.0)

0.7 (0.5–0.8)) Highly urban 35.7 36.6 44.0 45.0 0.2 Urban 34.5 31.4 37.1 43.0 0.2 Periurban 33.5 29.5 16.4 3.8 Ͻ0.001 Dyslipidemia, % 1 1 1 1 Highly urban 26.7 26.9 31.4 34.3 0.3 Urban 30.7 23.7 30.6 31.2 0.3 Periurban 18.8 13.6 9.7 4.5 0.002

*Adjusted for age and occupation. value 0.01 32.9 0.22 90.9 0.05 13.0 Ͻ 0.001 16.3 P for trend ES I ES II ES III ES IV status both for diabetes and hypertension were low across all categories. Thus, we have demonstrated a high and largely untreated burden of CHD risk factors in a relatively young population. These burdens vary, depending on the levels of 5.4 4.0 26.4 76.9 urbanization and education. 0.4 (0.3–0.6) 0.4 (0.3–0.6) 0.5 (0.1–2.6) 0.3 (0.1–0.5) Educational level and CHD risk factors have an inverse relationship in several populations (24–27). This is demonstrated by the higher burden of CHD among the less educated in several populations (28, 29). However, in countries such as India, which are experiencing rapid epidemiological transition, the results Men 8.7 10.0 30.9 85.6 have been varied. In case-control studies from large tertiary- level hospitals, a higher risk of myocardial infarction has been 0.7 (0.6–0.8) 0.6 (0.3–0.9) 0.6 (0.4–0.7) 0.6 (0.1–2.7) reported by us and others among the poor and less educated (19, 20). However, to the best of our knowledge, there are no reports evaluating this relationship in secondary and primary care centers, which are located predominantly in less-urbanized rural settings. With regard to CHD risk factors (which indirectly 10.7 10.8 33.9 76.9 predict the burden of CHD), the results have been heteroge- 0.7 (0.6–0.8) 0.7 (0.4–0.9) 0.6 (0.5–0.9) 0.4 (0.1–1.9) neous. Although smoking and hypertension have been consis- tently reported to be more prevalent in those who are poor and less educated, data are sparse and inconclusive with regard to 1 1 1 1

ES I ES II ES III ES IV other CHD risk factors (21, 22). Our study, which collected data from several parts of India using standardized methods, fills these lacunae and demonstrates the importance of targeting those with lower levels of education when planning CHD risk-factor prevention programs. The effects of urbanization of CHD have been well studied in Western populations (30). Urbanization leads to lifestyle changes, resulting in an increased consumption of energy-rich foods, a decrease in energy expenditure (through less physical activity), and loss of social support that is available for rural societies, all of which lead to higher rates of obesity, raised population mean levels of blood pressure, serum cholesterol, and blood glucose, and a decrease in insulin sensitivity (31). Al- Table 4. Management of elevated CHD risk factors and educational status (%, OR; 95% CI) Hypertension under control, % 14.1 Diabetes under control, % 13.6 Hypertension on treatment, % 42.9 Diabetics on treatment, % 80.4 though urbanization has been a relatively uniform process in

16266 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.0700933104 Reddy et al. Downloaded by guest on September 30, 2021 prosperous Western economies, it occurs in an unplanned and Sampling. All of the employees and their family members be- sometimes chaotic manner, with the establishment of large tween the ages of 20 and 69 were eligible to be included in the slums, in India and other developing counties. Even then, an survey. At each participating center, detailed data were obtained ascending gradient for CHD risk factors has been observed in a from randomly selected employees and their eligible family comparison of rural, urban slum and nonslum dwelling popula- members (n ϭ 2,000 at each center). Further, from this group, tions in India (32). As in Western populations, tobacco con- we chose 1,000 individuals per center by stratified random sumption has been the first CHD risk factor to demonstrate a subsampling for biochemical analysis. reversal of the social gradient. Although the reversal of the social gradient for hypertension has occurred along with or has fol- Study Variables. The study involved collection of data related to lowed the reversal of the social gradient for obesity in Western the demographic profile, individual characteristics related to populations (33), it has occurred even in relatively nonobese major risk factors of CHD, past medical history, clinical and population groups in India. This is exemplified in our results by anthropometric profile, and biochemical parameters. the low body mass index and waist circumference observed among the less educated, who nevertheless have higher levels of Quality Control Measures. To ensure the accuracy, completeness, blood pressure and hypertension. Although the causes of this and comparability of blood pressure and anthropometric mea- early reversal for hypertension have yet to be ascertained, diets surements and of interviewee responses across the 10 study sites, high in salt and low in fruit and vegetables may be responsible. several quality-control measures were included in the study Given that Indians have a high propensity to develop metabolic protocol. More details are available in the methodology paper abnormalities with lower levels of body weight and abdominal published earlier (23). Briefly, the study followed a common obesity (also evident in our results), the higher burden of CHD study protocol and a manual of operation. Common measure- risk factors among the urban poor is a cause for major concern, ment techniques by trained study staff using structured pretested and urgent and concerted prevention policies are necessary to proformae were used in all participating centers. Ten percent of reduce this burden. the biochemical samples were reanalyzed at the central coordinating center laboratory. The analysis of the results of this 10% Employees and their family members in all participating Ͻ industries were eligible for on- or off-site health care (worksite sample from all participating sites yielded 5% coefficient of dispensaries, reimbursement of medical expenses, or medical variation between the central coordinating laboratory results insurance). Despite this, levels of awareness and control of CHD and the individual laboratory results.

risk factors, such as hypertension and diabetes, were low. Even MEDICAL SCIENCES Definitions. Educational status and occupation. Educational status was with such low overall levels of awareness and risk factor control, assessed in terms of highest educational level and was stratified the higher-ducation groups had better treatment-seeking behav- into four categories. The current primary occupation was taken iors and risk-factor management. The reasons for this could be as the employment status of the individual. The industries were many. For example, lack of education could adversely influence classified as highly urban, urban, and periurban/rural based on health-seeking behaviors or access to health care. In addition, the location of the industry.

these findings could be a result of the emphasis on curative SCIENCE Current tobacco use. Current tobacco use was defined as use of any clinical care rather than preventive programs in participating form of tobacco products in the previous 30 days. SUSTAINABILITY industries. It is in the interest of sustainable health of all Physical activity level. Physical activity levels were assessed by using population groups in India that policies and program for CHD a structured questionnaire. We obtained data on occupation- prevention are designed to protect persons in all SES groups and related physical activity in a semiquantitative way. Occupational- are delivered with special attention to the needs of the less- related physical activity levels were classified into four catego- educated and more vulnerable groups. ries: very light (walking, having a job involving desk work, and watching television), light (standing all day working and house- Limitations. We have discussed some of the limitations of our work such as cooking and cleaning in the house), moderate work elsewhere (23). Briefly, the study population was mainly (gardening, agricultural work, walking long distances up and composed of industrial employees and may not be representative down hills, and climbing Ͼ20 steps in a day), and heavy (lifting of the general population. Although the prevalence of risk heavy weights, a job involving labor, and running). Leisure-time factors could be different in the general population, we expect physical activity was assessed by the number of minutes of a similar social gradient in risk factors across different educa- activity as well as the type of activity. In addition, we obtained tional groups, as suggested by small and localized community data on physical activity expended toward travel to work. We studies (22). However, the results of this large study, which has also obtained data on sedentariness (amount of time spent a good mix of different types of industries in India, can be watching television, working with the computer, and reading). generalized to the working population in the organized sector, Hypertension. Hypertension (stages I and II combined) was de- which employs Ͼ30 million people. fined as either a systolic blood pressure Ն140 mmHg and/or a diastolic blood pressure Ն90 mmHg and/or being on drug Methods. The detailed methods of the study are described else- treatment for hypertension (34). where (23). We provide a brief description of the methods below. Diabetes. Diabetes was defined as a fasting blood glucose value Ն126 mg/dl and/or being on treatment for diabetes (35). Im- Methods paired fasting glucose was defined as a fasting blood glucose Objective. The objective of the paper was to examine whether value 110–126 mg/dl and not being on any drug therapy CHD risk factors are predicted by level of education. Metabolic syndrome (MS). The definition of MS was based on National Cholesterol Education Program Adult Treatment Study Setting. This was a cross-sectional study conducted in 10 Panel III criteria (36). industries across India. Ten medium-to-large industries (defined Dyslipidemia. Dyslipidemia was defined as fasting total cholesterol as industries employing 1,500–5,000 people) in the organized to high-density cholesterol (high-density lipoprotein) ratio Ͼ5. sector were selected from different sites spread across India, from both public and private sectors, based on their willingness Statistical Analysis. The analysis focused on assessing both the to participate in the study and proximity to an academic medical direction and magnitude of the relationship of educational level and institution. CHD risk factors. Differences in means of CHD risk factors were

Reddy et al. PNAS ͉ October 9, 2007 ͉ vol. 104 ͉ no. 41 ͉ 16267 Downloaded by guest on September 30, 2021 compared across the four educational groups, using analysis of emphasis on socially deprived groups. The differences in risk variance, after adjusting for differences in age and occupation. For factor levels observed in this study may contribute to social categorical variables, logistic regression was used to calculate the disparities in morbidity and mortality because of CHD, espe- risk ratio and 95% confidence interval of risk ratio, after adjustment cially because awareness and treatment are at very low levels. for age and occupation. The level of statistical significance was set Strategies to reduce major CHD risk factors should focus on at a P value of Ͻ0.05 without adjustment for multiple comparisons. socially disadvantaged groups. The data were analyzed by using the Statistical Package for Social Sciences Version 13 (SPSS, Inc., Chicago, IL). We acknowledge the financial support provided by the Ministry of Conclusions Health, the Government of India, and the World Health Organization. The current study stresses the scale and seriousness of the We also acknowledge the infrastructural support provided by the emerging challenge of CHD risk factors in India, with particular participating industries.

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