Abstract

Background To investigate the prevalence, screening and knowledge of cardiovascular risk factors (CVRFs) by socio-economic position (SEP) in rural India.

Methods An age- and sex-stratified random sample of 4535 adults was recruited from rural Andhra Pradesh and a questionnaire was administered to assess prevalence, screening and knowledge of CVRFs and record recent attempts to modify behaviour. Education, income and occupation were used to measure SEP.

Results Lower fruit intake and higher tobacco and alcohol use were found in those with lower SEP. Overweight, physical inactivity, diabetes, hypertension, family history of cardiovascular disease (CVD) and previous CVD (men only) were greater in higher SEP participants. Lower SEP participants had less blood pressure, glucose or cholesterol screening and less knowledge of nine CVRFs. Regardless of SEP, participants knowledgeable of the harms of a CVRF were more likely to have attempted to modify behaviour. For example, knowledge of benefits of smoking cessation was associated with an increased odds ratio (OR) for attempting to quit: in educated participants—OR 3.67, 95% confidence interval (CI) 2.10–6.42; in participants with no education—OR 3.98, 95% CI 2.27–6.97.

Conclusions Some biological CVRFs were worse in higher SEP participants while some behavioural risk factors were worse in lower SEP participants. Lower SEP participants had less CVRF screening and knowledge of CVRFs. Those with knowledge of CVRFs were more likely to make healthy behavioural changes. Our findings suggest equipping rural Indians with knowledge about CVRFs may ameliorate projected future increases in CVD.

Introduction

When cardiovascular disease (CVD) was first recognized as a major cause of death during the 20th century in ‘developed’ regions like Europe and the USA, it was found to be most common among the affluent sections of society. Over time, prevalence transitioned down the social scale to become higher among people of lower socio-economic position (SEP).1 In ‘developing’ world populations, those of higher socio-economic position are currently thought to be at highest risk of CVD.2 However, there is some evidence that a transition, similar to developed regions, is already occurring,3,4 as recent studies from urban India indicate that cardiovascular risk factors (CVRFs) such as tobacco use, obesity and hypertension is increasing in those of lower SEP.5–8

In rural India, where ∼70% of India's population live, recent studies indicate that CVDs are now the leading cause of death9,10 and CVRF levels are higher than previously assumed in some regions.11–13 Health care is generally worse in rural compared with urban India, and studies of other health conditions have shown that the poorest in rural areas are worst off.14 A shift in CVRFs to the more disadvantaged in rural India would therefore present a major health care challenge. One recent study of 1983 rural individuals reported that CVRFs such as smoking were higher in the most socio-economically disadvantaged.15

In high-income countries, higher SEP is generally associated with better knowledge of health risks.16 As knowledge about unhealthy behaviours is one of the precursors to positive behavioural modification,17,18 if those of lowest socio-economic position in rural India are less aware of potential risks, they may be more susceptible to adverse lifestyle behaviours and subsequent CVD.

The objective of this analysis was, in a rural Indian population, to examine the socio-economic distribution of: (i) CVRFs (smoking, diabetes, hypertension, overweight, physical inactivity, low fruit intake, alcohol use, established and family histories of CVD); (ii) rates of screening for risk factors [blood pressure (BP), blood glucose and cholesterol]; and (iii) knowledge of key behaviours that influence cardiovascular health (lose weight, quit smoking, increase exercise, eat more fish, drink less alcohol, reduce fat in meals, reduce salt in meals, eat more fresh fruit, eat more green leafy vegetables). The analysis then sought to explore the hypothesis that greater knowledge of key behaviours which influence cardiovascular health was associated with protective lifestyle behaviour regardless of SEP.

Methods

The study was conducted as part of the Andhra Pradesh Rural Health Initiative (APHRI) whose members and details are listed in acknowledgements. The study was approved by the Ethics Committees of the CARE Hospital, Hyderabad in India and the University of Sydney in Australia. All participants provided informed consent and the study was performed in accordance with the Declaration of Helsinki and subsequent amendments.

Recruitment of study population

This study was conducted in 2005 in 20 villages in the rural East and West Godavari regions of Andhra Pradesh, the fifth largest state of India, having 73% of its total population (of approximately 76 million) residing in rural areas. The proportion living in rural areas is similar to other states of India and the Godavari regions have a predominantly agrarian economy with few factories. The major town in the area, Bhimavaram, has a train station that connects it to Hyderabad, though taking ∼10 h travel time. The 20 villages were stratified by population size, distance from the rural centre and region to obtain a representative sample of villages of the area. The villages were all participating in a rural development initiative run by the Byrraju Foundation, a local non-governmental organization and collaborator in APRHI. They were sampled from a list of 88 villages from the East and West Godavari regions for which the community leaders of the villages had consented to participate and the Byrraju Foundation had complete population listings. Six villages were from East Godavari, 14 villages from West Godavari and the mean distance from a large town was ∼20 km. The survey sample for each village was a stratified random sample of roughly equal number of individuals from eight groups defined by age (30–39, 40–49, 50–59, >60 years) and sex. A roughly equal total sample was drawn from each of the 20 villages. Using this method of sampling and weighted survey data analysis enabled precise estimation of overall CVRF levels, and by age–sex group for the population living in the 20 villages. A simple random sample would not have allowed calculation of precise estimates for older age groups because the bulk of the population of India is <40 years of age19 and thus would have resulted in fewer individuals from older age groups. The average household size was four, and as a sample of 200 individuals was sampled from each village, the chances of two or more individuals being randomly sampled from a household was low. Of the 6985 individuals invited, 5627 were still living in the villages, of whom 4535 (81%) agreed to take part and provided informed consent.20

Study variables

For each individual who consented to participate, trained study staff administered a structured questionnaire (see Supplementary Appendix 1 available as Supplementary data at IJE online)20 and a fasting finger-prick glucose test using B-Braun USV meters (Melsungen, Germany). All individuals had a brief physical examination that included two sitting measurements of BP using an Omron M2 manual inflation monitor and measurements of weight, height, waist and hip circumference with participants wearing light clothing without shoes. A team of five health workers were assigned to taking measurements (BP, waist, height and weight). The first measurement was taken after a period of at least 5 min of rest. A fasting venous blood sample was sought from every fourth individual rendering a sub-sample of 1086 participants from which the lipid profile was measured. Frozen samples were transferred to a central internationally accredited laboratory in the CARE hospital, Hyderabad, India, where analyses were performed using a Beckman Coulter Synchron Cx9 Clinical system ALX analyser. The coronary heart disease (CHD) risk for each participant was calculated using a locally recalibrated Framingham equation for the sub-sample of participants in whom lipids were measured.21

Information on three socio-economic variables (education, income and occupation) was collected by self-report. For each participant the highest level of education completed was recorded from the categories: higher education (diploma/technical/university); secondary school (to grade 12); primary school (to grade 6) or no formal schooling. Household income was recorded in Indian rupees for an average month. For seasonal workers, interviewers were trained to assist in calculating an average monthly income based on the last 12 months income. Participants were asked their current occupation and interviewers categorized this based on the standard Indian classification system22 with the assistance of a prompt card (see Supplementary Appendix 2 available as Supplementary data at IJE online) into the following groups: skilled manual workers, unskilled manual workers, owner of business or farm, office worker/non-professional, professional, housewife or unemployed/retired. We then defined ‘Educated’ as those who had attended primary school, secondary school or higher education and ‘No education’ as those reporting no formal schooling. In analysing occupation, we excluded housewife as we hypothesized that this category would not adequately describe SEP based on previous literature,5 and unemployed/retired as the way the question was administered did not allow these two categories to be differentiated. Less than 10% of the study population were unemployed/retired. Thus, we also dichotomized occupation into ‘skilled occupation’, defined as those with an occupation of skilled manual worker or owner of business/farmer office worker/non-professional or professional and ‘unskilled occupation’ as those who were unskilled manual worker, as again approximately half the sample were unskilled, and the remaining categories of skilled manual workers, owner of business or farm and office worker/non-professional were relatively homogenous in terms of literacy. Income was examined by thirds (high: ≥2000 rupees; medium: 1200–1999 rupees; poor: 0–1199 rupees).

Information on behavioural CVRFs was ascertained through a questionnaire (see Supplementary Appendix 1 available as Supplementary data at IJE online). Current smokers were defined as those that smoked regularly on most days for at least a year, and ex-smokers as those who had stopped smoking for at least 1 year. Low fruit intake was defined as fruit consumed on one day or less a week. Physical inactivity was defined as a report of ‘almost none’ in response to a question about physical activity during working hours and outside of work. Alcohol use was defined as those who reported at least one drink on one or more days of the week.

Information on biological CVRFs was ascertained through questionnaire, physical examination and blood testing. Hypertension was defined according to Joint National Committee on Prevention, Detection, Evaluation and Treatment of High BP guidelines23 as mean systolic BP ≥140 mmHg, and/or mean diastolic BP ≥90 mmHg, or known hypertension (diagnosed by a doctor) on BP lowering prescription medication. Overweight was defined as a body mass index (BMI) of ≥25 and underweight as a BMI of <18.5.24 Diabetes was defined as fasting blood glucose ≥7 mmol/l (≥126 mg/dl)25 or on self-report.

Established CVD was defined as self-reported doctor-diagnosed heart attack, stroke or angina and family history of CVD as a first degree relative having had a heart attack or stroke (according to the knowledge of the study participant) before the age of 60 years.

In addition, all participants were asked whether they had had their BP, blood glucose or cholesterol checked in the last 12 months. Knowledge of nine key behaviours (lose weight, quit smoking, increase exercise, eat more fish, drink less alcohol, reduce fat in meals, reduce salt in meals, eat more fresh fruit, eat more green leafy vegetables) that reduce the risk of CVD was assessed in the questionnaire by asking participants to respond ‘yes’, ‘no’ or ‘unsure’ as to whether a change in each behaviour could prevent a heart attack or stroke. We analysed ‘no’ and ‘unsure’ together. Participants were also asked if they had taken action to beneficially modify any of these nine behaviours in the previous 12 months, as ‘yes’ or ‘no’.

Statistical analysis

  • (i)

    Prevalence of CVRFs, screening of risk factors and knowledge about key behaviours that reduce the risk of CVD, by SEP.

We analysed, in turn, prevalence of risk factors, screening of risk factors and knowledge about key behaviours that reduce the risk of CVD, by the three markers of SEP (education, occupation and income). Data are expressed as percentages for categorical variables and means and standard deviations (SDs) for continuous measures, which were all normally distributed. Differences between socio-economic groups were tested using chi-squared tests for categorical variables or t-tests for continuous variables and t-tests for trend were used to examine patterns across income categories. Levels of risk factor, screening and knowledge are presented unadjusted; however, comparisons across socio-economic groups are all adjusted for age and sex.

  • (ii)

    Influence of SEP and knowledge of key behaviours that reduce the risk of CVD on attempt to modify behaviour.

For further analyses, we chose educational level as the SEP variable, as there was considerable co-linearity between the three socio-economic variables and educational level is a widely used measure of SEP. The association between educational level and an attempt to modify a CVRF in the last 12 months was examined in logistic regression models adjusted for age and sex, in those with that specific risk factor. The association between knowledge about a risk factor and an attempt to modify that risk factor in the past 12 months was examined in logistic regression models adjusted for age and sex, in those with that specific risk factor. These analyses were carried out for smoking cessation, weight loss, increase in exercise, eating more fruit and drinking less alcohol. Effect sizes were measured as odds ratios (ORs), with 95% confidence intervals (CIs).

  • (iii) Influence of both SEP and knowledge on attempt to modify behaviour.

A new indicator variable comprising both educational level and knowledge about a CVRF was established—those with no formal education and no knowledge was the reference group, with three other groups representing those with no formal education but with knowledge, those who were educated but having no knowledge, and those who were educated with knowledge. In age- and sex-adjusted logistic regression models, we examined this variable against the outcome of an attempt to modify either smoking (in current smokers) or weight (in those who were overweight) in the last 12 months. These were plotted alongside the two-level variables (educational level, knowledge) examined in section ii. Thus, our three logistic regression models adjusted for age and sex were:

  • association between educational level and an attempt to modify that risk factor in the past 12 months;

  • association between knowledge about a risk factor and an attempt to modify that risk factor in the past 12 months; and

  • association between educational level/knowledge and an attempt to modify that risk factor in the past 12 months.

All statistical analyses were carried out using STATA 11.0.

Ethical approval

The study was approved by the Ethics Committees of the CARE Hospital, Hyderabad in India, and the University of Sydney in Australia.

Results

Among the 4535 participants, the mean age was 49.4 (SD 13.6), and 51.4% were women. Table 1 describes the socio-economic characteristics of the study sample by sex. Men were more likely to be literate than women, and, concordantly, more likely to have had some formal education. Unskilled manual labourers made up 68.1% of men and 37.4% of women. Data fields were >99% complete for all descriptive and socio-economic variables.

Table 1

Characteristics of study sample from rural Andhra Pradesh, India (n = 4535)

Characteristic Men N (%) Women N (%) 
Mean age (SD) 50.7 (13.6) 48.1 (13.5) 
Illiterate, n (%) 1164 (52.8) 921 (39.5) 
Highest level of education completed 
    No formal schooling 895 (40.6) 1255 (53.9) 
    Primary school 750 (34.0) 806 (34.6) 
    Secondary school 447 (20.3) 241 (10.4) 
    Graduate or post- graduate studies 114 (5.2) 27 (1.2) 
Employment   
    Unemployed or retired 266 (12.1) 153 (6.6) 
    Housewife 5 (0.2) 1216 (52.2) 
    Skilled manual worker 148 (6.7) 51 (2.2) 
    Unskilled manual worker 1501 (68.0) 872 (37.4) 
    Owner of business/ non-professional 175 (7.9) 21 (0.9) 
    Professional 72 (3.3) 12 (0.5) 
Characteristic Men N (%) Women N (%) 
Mean age (SD) 50.7 (13.6) 48.1 (13.5) 
Illiterate, n (%) 1164 (52.8) 921 (39.5) 
Highest level of education completed 
    No formal schooling 895 (40.6) 1255 (53.9) 
    Primary school 750 (34.0) 806 (34.6) 
    Secondary school 447 (20.3) 241 (10.4) 
    Graduate or post- graduate studies 114 (5.2) 27 (1.2) 
Employment   
    Unemployed or retired 266 (12.1) 153 (6.6) 
    Housewife 5 (0.2) 1216 (52.2) 
    Skilled manual worker 148 (6.7) 51 (2.2) 
    Unskilled manual worker 1501 (68.0) 872 (37.4) 
    Owner of business/ non-professional 175 (7.9) 21 (0.9) 
    Professional 72 (3.3) 12 (0.5) 

Disease risk factors by SEP

Those with no education were more likely to be current smokers compared with those with some education, in both men and women (Table 2). Among the participants, 57.7% of men with no formal education smoked compared with 39.5% of educated men and while the pattern was similar in women, fewer of them smoked. A higher educational level in men was associated with a higher prevalence of diabetes, hypertension, overweight, physical inactivity, family history of CVD, established history of CVD, higher fruit intake and lower alcohol intake. In women, a higher educational level was associated with a higher prevalence of overweight, physical inactivity, family history of CVD, higher fruit intake and lower alcohol intake. Women were overall much less likely to drink alcohol than men. BMI and waist circumference were greater in those of a higher educational level for both sexes. The patterns were broadly similar when occupation was used as a marker of SEP. Patterns by income revealed that poorer men smoked more, but had less diabetes, less overweight, were more physically active and were less likely to have a family or established history of CVD compared with richer men. Similar patterns were seen in women for diabetes, overweight and BMI, suggesting socio-economic differences in dietary patterns, but not for smoking with its low prevalence in women across all social groups.

Table 2

Risk factor distribution by education, occupation and income

 Educationa
 
Occupationb
 
Incomec
 
 
 Educated No education P-value Skilled Unskilled P-value High Middle Low P-trend 
Men           
    N 1311 895 – 434 1501  912 717 577  
    Mean age (SD) 50.2 (13.6) 51.5 (13.5) 0.022 47.4 (12.0) 48.7 (12.5) 0.058 50.8 (13.2) 47.5 (12.6) 54.6 (14.5) <0.001 
    Current smoker 518 (39.5) 516 (57.7) <0.001 148 (34.1) 770 (51.3) <0.001 381 (41.8) 355 (49.5) 298 (51.7) <0.001 
    Diabetes 255 (20.1) 106 (12.4) <0.001 102 (24.1) 186 (12.9) <0.001 186 (21.2) 84 (12.2) 91 (16.2) <0.001 
    Hypertension 417 (31.8) 250 (27.9) 0.005 149 (34.3) 371 (24.7) <0.001 294 (32.2) 169 (23.4) 204 (35.4) 0.352 
    Overweight 310 (23.7) 95 (10.6) <0.001 152 (35.0) 203 (13.5) <0.001 231 (25.3) 105 (14.6) 69 (12.0) <0.001 
    Underweight 269 (20.6) 300 (33.5) <0.001 53 (12.2) 435 (29.0) <0.001 159 (17.5) 197 (27.5) 213 (36.9) <0.001 
    Physical inactivity 435 (33.2) 208 (23.2) <0.001 201 (46.3) 185 (12.3) <0.001 311 (34.1) 153 (21.4) 176 (30.5) <0.001 
    Low fruit intake 491 (37.4) 501 (55.6) <0.001 135 (31.1) 717 (47.8) <0.001 328 (36.0) 348 (48.5) 316 (54.8) <0.001 
    Family history of CVD 226 (17.2) 114 (12.7) 0.006 94 (21.7) 210 (14.0) <0.001 157 (17.2) 106 (14.8) 77 (13.3) 0.065 
    Prior history of CVD 126 (9.6) 56 (6.3) 0.002 34 (7.8) 95 (6.3) 0.151 95 (10.4) 41 (5.7) 46 (8.0) 0.005 
    Alcohol use 334 (25.5) 329 (36.8) <0.001 103 (23.7) 523 (34.8) <0.001 240 (26.3) 246 (34.3) 177 (30.7) 0.005 
    SBP (mmHg mean, SD) 127 (20) 126 (21) 0.013 127 (19) 124 (20) 0.001 127 (19) 123 (19) 130 (23) 0.414 
    BMI (mean, SD) 22.2 (3.9) 20.6 (3.4) <0.001 23.5 (4.0) 21.0 (3.5) <0.001 22.5 (3.9) 21.1 (3.7) 20.5 (3.4) <0.001 
    Waist (cm mean, SD) 82.7 (11.5) 78.0 (10.3) <0.001 86.5 (11.2) 79.0 (10.5) <0.001 83.6 (11.6) 79.4 (10.5) 78.1 (10.7) <0.001 
    N 328 204  109 370  223 170 139  
    Total cholesterol (mmol, SD) 4.6 (1.2) 4.4 (1.1) 0.171 4.7 (1.2) 4.5 (1.2) 0.094 4.4 (1.3) 4.5 (1.2) 4.4 (1.3) 0.019 
    LDL-cholesterol (mmol, SD) 2.8 (0.9) 2.7 (0.9) 0.166 2.9 (0.9) 2.8 (0.8) 0.065 2.9 (0.9) 2.7 (0.8) 2.7 (0.9) 0.012 
    HDL-cholesterol (mmol, SD) 1.1 (0.3) 1.2 (0.3) 0.002 1.0 (0.3) 1.1 (0.3) 0.024 1.1 (0.3) 1.1 (0.3) 1.1 (0.4) 0.567 
    Triglycerides (mmol, SD) 1.6 (1.4) 1.3 (1.0) 0.009 1.7 (1.1) 1.5 (1.3) 0.057 1.6 (1.1) 1.5 (1.6) 1.4 (1.1) 0.058 
    Framingham risk score (%) 13.1 11.9 0.014 11.7 10.7 0.044 13.6 9.6 14.9 0.384 
    Framingham risk category           
        High (>20% CHD risk in 10 years) 66 (20.2) 33 (16.3) 0.730 18 (16.5) 52 (14.1) 0.101 48 (21.6) 17 (10.0) 34 (24.6) 0.286 
        Medium (10–20% CHD risk) 74 (22.6) 61 (30.1) 27 (24.8) 91 (24.7) 60 (27.0) 35 (20.6) 40 (28.9) 
       Low (<10% CHD risk) 187 (57.2) 109 (53.7) 64 (58.7) 226 (61.3) 114 (51.4) 118 (69.4) (46.4) 
Women           
    N 1074 1255 – 88 872 – 945 550 834 – 
    Mean age (SD) 45.9 (13.0) 49.9 (13.7) <0.001 42.6 (11.5) 43.2 (11.1) 0.644 46.8 (12.8) 45.4 (13.5) 51.3 (13.6) <0.001 
    Current smoker 13 (1.2) 107 (8.5) <0.001 1 (1.1) 62 (7.1) 0.059 40 (4.2) 21 (3.8) 59 (7.1) 0.101 
    Diabetes 138 (13.1) 156 (12.8) 0.258 11 (12.9) 65 (7.7) 0.075 138 (15.0) 57 (10.7) 99 (12.1) 0.005 
    Hypertension 313 (29.1) 373 (29.7) 0.009 15 (7.1) 149 (17.1) 0.932 269 (28.5) 151 (27.5) 266 (31.9) 0.165 
    Overweight 354 (33.0) 255 (20.3) <0.001 27 (30.7) 147 (16.9) 0.002 300 (31.8) 138 (25.1) 171 (20.5) <0.001 
    Underweight 189 (17.6) 386 (30.8) <0.001 12 (13.6) 268 (30.7) 0.001 163 (17.3) 138 (25.1) 274 (32.9) <0.001 
    Physical inactivity 662 (61.6) 619 (49.3) <0.001 45 (51.4) 106 (12.2) <0.001 541 (57.3) 277 (50.4) 453 (54.3) <0.001 
    Low fruit intake 466 (43.4) 712 (56.7) <0.001 44 (50.0) 483 (55.4) 0.344 410 (43.4) 274 (49.8) 494 (59.2) <0.001 
    Family history of CVD 209 (19.5) 117 (9.3) <0.001 17 (19.3) 95 (10.9) 0.021 141 (14.9) 69 (12.6) 116 (13.9) 0.456 
    Prior history of CVD 70 (6.5) 75 (6.0) 0.227 6 (6.8) 36 (4.1) 0.236 48 (5.1) 34 (6.2) 63 (7.6) 0.128 
    Alcohol use 5 (0.5) 22 (1.8) 0.014 2 (2.3) 20 (2.3) 0.997 13 (1.4) 5 (0.9) 9 (1.1) 0.350 
    SBP (mmHg mean,SD) 122 (20) 124 (22) 0.095 119 (21) 116 (19) 0.057 122 (20) 120 (21) 125 (24) 0.257 
    BMI (mean, SD) 23.3 (4.5) 21.6 (4.5) <0.001 23.3 (3.8) 21.3 (4.0) <0.001 23.2 (4.6) 22.2 (4.5) 21.5 (4.5) <0.001 
    Waist (cm mean, SD) 76.9 (10.6) 74.9 (11.1) <0.001 76.2 (9.9) 73.3 (10.4) 0.009 77.5 (10.6) 75.2 (10.9) 74.4 (11.0) <0.001 
    N 248 306  18 199  238 132 184  
    Total cholesterol (mmol, SD) 4.9 (1.3) 4.9 (1.1) 0.522 5.5 (1.3) 4.8 (1.0) 0.011 5.0 (1.1) 5.0 (1.1) 4.7 (1.2) 0.033 
    LDL-cholesterol (mmol, SD) 3.1 (0.9) 3.1 (0.8) 0.817 3.5 (1.0) 3.1 (0.8) 0.052 3.2 (0.8) 3.2 (0.9) 3.0 (0.9) 0.022 
    HDL-cholesterol (mmol, SD) 1.2 (0.3) 1.2 (0.3) 0.713 1.3 (0.2) 1.2 (0.3) 0.179 1.2 (0.3) 1.2 (0.3) 1.2 (0.3) 0.263 
    Triglycerides (mmol, SD) 1.4 (0.9) 1.3 (0.9) 0.191 1.7 (2.4) 1.2 (0.6) 0.018 1.4 (1.1) 1.4 (0.7) 1.3 (0.7) 0.180 
    Framingham risk score (%) 4.2 5.0 0.955 3.6 3.2 0.391 4.3 4.2 5.3 0.279 
    Framingham risk category           
        High (>20% CHD risk in 10 years) 3 (1.2) 4 (1.3) 0.783 2 (1.0) 0.291 3 (1.3) 4 (2.2) 0.128 
        Medium (10–20% CHD risk) 22 (9.0) 36 (11.8) 2 (11.8) 10 (5.1) 19 (8.2) 14 (10.6) 25 (13.6) 
        Low (<10% CHD risk) 220 (89.8) 264 (86.8) 15 (88.2) 185 (93.9) 211 (90.6) 118 (89.4) 155 (84.2) 
 Educationa
 
Occupationb
 
Incomec
 
 
 Educated No education P-value Skilled Unskilled P-value High Middle Low P-trend 
Men           
    N 1311 895 – 434 1501  912 717 577  
    Mean age (SD) 50.2 (13.6) 51.5 (13.5) 0.022 47.4 (12.0) 48.7 (12.5) 0.058 50.8 (13.2) 47.5 (12.6) 54.6 (14.5) <0.001 
    Current smoker 518 (39.5) 516 (57.7) <0.001 148 (34.1) 770 (51.3) <0.001 381 (41.8) 355 (49.5) 298 (51.7) <0.001 
    Diabetes 255 (20.1) 106 (12.4) <0.001 102 (24.1) 186 (12.9) <0.001 186 (21.2) 84 (12.2) 91 (16.2) <0.001 
    Hypertension 417 (31.8) 250 (27.9) 0.005 149 (34.3) 371 (24.7) <0.001 294 (32.2) 169 (23.4) 204 (35.4) 0.352 
    Overweight 310 (23.7) 95 (10.6) <0.001 152 (35.0) 203 (13.5) <0.001 231 (25.3) 105 (14.6) 69 (12.0) <0.001 
    Underweight 269 (20.6) 300 (33.5) <0.001 53 (12.2) 435 (29.0) <0.001 159 (17.5) 197 (27.5) 213 (36.9) <0.001 
    Physical inactivity 435 (33.2) 208 (23.2) <0.001 201 (46.3) 185 (12.3) <0.001 311 (34.1) 153 (21.4) 176 (30.5) <0.001 
    Low fruit intake 491 (37.4) 501 (55.6) <0.001 135 (31.1) 717 (47.8) <0.001 328 (36.0) 348 (48.5) 316 (54.8) <0.001 
    Family history of CVD 226 (17.2) 114 (12.7) 0.006 94 (21.7) 210 (14.0) <0.001 157 (17.2) 106 (14.8) 77 (13.3) 0.065 
    Prior history of CVD 126 (9.6) 56 (6.3) 0.002 34 (7.8) 95 (6.3) 0.151 95 (10.4) 41 (5.7) 46 (8.0) 0.005 
    Alcohol use 334 (25.5) 329 (36.8) <0.001 103 (23.7) 523 (34.8) <0.001 240 (26.3) 246 (34.3) 177 (30.7) 0.005 
    SBP (mmHg mean, SD) 127 (20) 126 (21) 0.013 127 (19) 124 (20) 0.001 127 (19) 123 (19) 130 (23) 0.414 
    BMI (mean, SD) 22.2 (3.9) 20.6 (3.4) <0.001 23.5 (4.0) 21.0 (3.5) <0.001 22.5 (3.9) 21.1 (3.7) 20.5 (3.4) <0.001 
    Waist (cm mean, SD) 82.7 (11.5) 78.0 (10.3) <0.001 86.5 (11.2) 79.0 (10.5) <0.001 83.6 (11.6) 79.4 (10.5) 78.1 (10.7) <0.001 
    N 328 204  109 370  223 170 139  
    Total cholesterol (mmol, SD) 4.6 (1.2) 4.4 (1.1) 0.171 4.7 (1.2) 4.5 (1.2) 0.094 4.4 (1.3) 4.5 (1.2) 4.4 (1.3) 0.019 
    LDL-cholesterol (mmol, SD) 2.8 (0.9) 2.7 (0.9) 0.166 2.9 (0.9) 2.8 (0.8) 0.065 2.9 (0.9) 2.7 (0.8) 2.7 (0.9) 0.012 
    HDL-cholesterol (mmol, SD) 1.1 (0.3) 1.2 (0.3) 0.002 1.0 (0.3) 1.1 (0.3) 0.024 1.1 (0.3) 1.1 (0.3) 1.1 (0.4) 0.567 
    Triglycerides (mmol, SD) 1.6 (1.4) 1.3 (1.0) 0.009 1.7 (1.1) 1.5 (1.3) 0.057 1.6 (1.1) 1.5 (1.6) 1.4 (1.1) 0.058 
    Framingham risk score (%) 13.1 11.9 0.014 11.7 10.7 0.044 13.6 9.6 14.9 0.384 
    Framingham risk category           
        High (>20% CHD risk in 10 years) 66 (20.2) 33 (16.3) 0.730 18 (16.5) 52 (14.1) 0.101 48 (21.6) 17 (10.0) 34 (24.6) 0.286 
        Medium (10–20% CHD risk) 74 (22.6) 61 (30.1) 27 (24.8) 91 (24.7) 60 (27.0) 35 (20.6) 40 (28.9) 
       Low (<10% CHD risk) 187 (57.2) 109 (53.7) 64 (58.7) 226 (61.3) 114 (51.4) 118 (69.4) (46.4) 
Women           
    N 1074 1255 – 88 872 – 945 550 834 – 
    Mean age (SD) 45.9 (13.0) 49.9 (13.7) <0.001 42.6 (11.5) 43.2 (11.1) 0.644 46.8 (12.8) 45.4 (13.5) 51.3 (13.6) <0.001 
    Current smoker 13 (1.2) 107 (8.5) <0.001 1 (1.1) 62 (7.1) 0.059 40 (4.2) 21 (3.8) 59 (7.1) 0.101 
    Diabetes 138 (13.1) 156 (12.8) 0.258 11 (12.9) 65 (7.7) 0.075 138 (15.0) 57 (10.7) 99 (12.1) 0.005 
    Hypertension 313 (29.1) 373 (29.7) 0.009 15 (7.1) 149 (17.1) 0.932 269 (28.5) 151 (27.5) 266 (31.9) 0.165 
    Overweight 354 (33.0) 255 (20.3) <0.001 27 (30.7) 147 (16.9) 0.002 300 (31.8) 138 (25.1) 171 (20.5) <0.001 
    Underweight 189 (17.6) 386 (30.8) <0.001 12 (13.6) 268 (30.7) 0.001 163 (17.3) 138 (25.1) 274 (32.9) <0.001 
    Physical inactivity 662 (61.6) 619 (49.3) <0.001 45 (51.4) 106 (12.2) <0.001 541 (57.3) 277 (50.4) 453 (54.3) <0.001 
    Low fruit intake 466 (43.4) 712 (56.7) <0.001 44 (50.0) 483 (55.4) 0.344 410 (43.4) 274 (49.8) 494 (59.2) <0.001 
    Family history of CVD 209 (19.5) 117 (9.3) <0.001 17 (19.3) 95 (10.9) 0.021 141 (14.9) 69 (12.6) 116 (13.9) 0.456 
    Prior history of CVD 70 (6.5) 75 (6.0) 0.227 6 (6.8) 36 (4.1) 0.236 48 (5.1) 34 (6.2) 63 (7.6) 0.128 
    Alcohol use 5 (0.5) 22 (1.8) 0.014 2 (2.3) 20 (2.3) 0.997 13 (1.4) 5 (0.9) 9 (1.1) 0.350 
    SBP (mmHg mean,SD) 122 (20) 124 (22) 0.095 119 (21) 116 (19) 0.057 122 (20) 120 (21) 125 (24) 0.257 
    BMI (mean, SD) 23.3 (4.5) 21.6 (4.5) <0.001 23.3 (3.8) 21.3 (4.0) <0.001 23.2 (4.6) 22.2 (4.5) 21.5 (4.5) <0.001 
    Waist (cm mean, SD) 76.9 (10.6) 74.9 (11.1) <0.001 76.2 (9.9) 73.3 (10.4) 0.009 77.5 (10.6) 75.2 (10.9) 74.4 (11.0) <0.001 
    N 248 306  18 199  238 132 184  
    Total cholesterol (mmol, SD) 4.9 (1.3) 4.9 (1.1) 0.522 5.5 (1.3) 4.8 (1.0) 0.011 5.0 (1.1) 5.0 (1.1) 4.7 (1.2) 0.033 
    LDL-cholesterol (mmol, SD) 3.1 (0.9) 3.1 (0.8) 0.817 3.5 (1.0) 3.1 (0.8) 0.052 3.2 (0.8) 3.2 (0.9) 3.0 (0.9) 0.022 
    HDL-cholesterol (mmol, SD) 1.2 (0.3) 1.2 (0.3) 0.713 1.3 (0.2) 1.2 (0.3) 0.179 1.2 (0.3) 1.2 (0.3) 1.2 (0.3) 0.263 
    Triglycerides (mmol, SD) 1.4 (0.9) 1.3 (0.9) 0.191 1.7 (2.4) 1.2 (0.6) 0.018 1.4 (1.1) 1.4 (0.7) 1.3 (0.7) 0.180 
    Framingham risk score (%) 4.2 5.0 0.955 3.6 3.2 0.391 4.3 4.2 5.3 0.279 
    Framingham risk category           
        High (>20% CHD risk in 10 years) 3 (1.2) 4 (1.3) 0.783 2 (1.0) 0.291 3 (1.3) 4 (2.2) 0.128 
        Medium (10–20% CHD risk) 22 (9.0) 36 (11.8) 2 (11.8) 10 (5.1) 19 (8.2) 14 (10.6) 25 (13.6) 
        Low (<10% CHD risk) 220 (89.8) 264 (86.8) 15 (88.2) 185 (93.9) 211 (90.6) 118 (89.4) 155 (84.2) 

P-values are age-adjusted; values are numbers, and those in parentheses are percentages unless otherwise stated.

aWe defined some education as those who had attended any level of education from primary school, through secondary school up to higher education (e.g. Diploma/Technical/University studies).

bWe defined a skilled occupation as a skilled manual worker or owner of business/farm or office worker/non-professional or professional. Unskilled was defined as those who came under the unskilled manual worker section in the question, which accounted for 68.1% of men and 37.4% of women. We excluded housewife and unemployed/retired.

cWe analysed income by thirds (high: ≥2000 rupees; medium: 1200–1999 rupees; poor: 0–1199 rupees).

LDL: low-density lipoprotein; HDL: high-density lipoprotein.

In the sub-sample of 1086 participants with blood assays, a higher educational level or occupation in men was associated with no difference in low-density lipoprotein-cholesterol, higher triglycerides and lower high-density lipoprotein-cholesterol concentrations. When considering Framingham CHD risk, men of higher SEP (in terms of education level and occupation) had higher risk than those of lower SEP.

Screening of risk factors by SEP

Participants of lower SEP had lower rates of BP, blood glucose and cholesterol screening (Table 3), and this was consistent for men and women. Rates of BP and glucose screening were generally high in this study sample as the Byrraju Foundation had recently conducted BP and diabetes screening programs in these villages.

Table 3

Prevalence of self-reported BP, blood glucose and cholesterol checks by education, occupation and income

 Education
 
Occupation
 
Income
 
 
 Educated No education P-value Skilled Unskilled P-value High Middle Low P-trend 
Men           
    N 1311 895 – 434 1501 – 912 717 577 – 
    BP checked ever 848 (64.7) 530 (59.2) 0.009 289 (66.6) 857 (57.1) <0.001 616 (67.5) 420 (58.6) 342 (59.3) <0.001 
    BGL checked ever 559 (42.6) 327 (36.5) 0.004 206 (47.5) 518 (34.5) <0.001 422 (46.3) 259 (36.1) 205 (35.5) <0.001 
    Cholesterol check ever 127 (9.7) 17 (1.9) <0.001 54 (12.4) 63 (4.2) <0.001 90 (9.9) 35 (4.9) 19 (3.3) <0.001 
Women           
    N 1074 1255 – 88 872 – 945 550 834 – 
    BP checked ever 864 (80.5) 884 (70.5) <0.001 66 (75.0) 552 (63.3) 0.029 739 (78.3) 399 (72.6) 610 (73.1) 0.011 
    BGL checked ever 436 (40.6) 430 (34.3) 0.002 31 (35.2) 222 (25.5) 0.047 394 (41.7) 189 (34.4) 283 (33.9) 0.001 
    Cholesterol check ever 67 (6.2) 36 (2.9) <0.001 4 (4.6) 21 (2.4) 0.230 58 (6.1) 24 (4.4) 21 (2.5) <0.001 
 Education
 
Occupation
 
Income
 
 
 Educated No education P-value Skilled Unskilled P-value High Middle Low P-trend 
Men           
    N 1311 895 – 434 1501 – 912 717 577 – 
    BP checked ever 848 (64.7) 530 (59.2) 0.009 289 (66.6) 857 (57.1) <0.001 616 (67.5) 420 (58.6) 342 (59.3) <0.001 
    BGL checked ever 559 (42.6) 327 (36.5) 0.004 206 (47.5) 518 (34.5) <0.001 422 (46.3) 259 (36.1) 205 (35.5) <0.001 
    Cholesterol check ever 127 (9.7) 17 (1.9) <0.001 54 (12.4) 63 (4.2) <0.001 90 (9.9) 35 (4.9) 19 (3.3) <0.001 
Women           
    N 1074 1255 – 88 872 – 945 550 834 – 
    BP checked ever 864 (80.5) 884 (70.5) <0.001 66 (75.0) 552 (63.3) 0.029 739 (78.3) 399 (72.6) 610 (73.1) 0.011 
    BGL checked ever 436 (40.6) 430 (34.3) 0.002 31 (35.2) 222 (25.5) 0.047 394 (41.7) 189 (34.4) 283 (33.9) 0.001 
    Cholesterol check ever 67 (6.2) 36 (2.9) <0.001 4 (4.6) 21 (2.4) 0.230 58 (6.1) 24 (4.4) 21 (2.5) <0.001 

P-values are age-adjusted; values are numbers, and those in parentheses are percentages.

BSL: blood glucose level.

Knowledge about key behaviours that reduce the risk of CVD by SEP

Individuals of lower SEP had poorer knowledge of the lifestyle factors that influence cardiovascular risk. They were less likely to know that weight loss, smoking cessation, exercise, eating fish, less alcohol, reduced fat, reduced salt and more fruit and vegetables in the diet may prevent heart attack or stroke (Table 4). Again, we observed this pattern in both men and women, though overall levels of knowledge were lower in women than men.

Table 4

Knowledge about key behaviours that reduce the risk of CVD: those who correctly answered whether a given action would prevent heart attack or stroke

 Education
 
Occupation
 
Income
 
 
 Educated No education P-value Skilled Unskilled P-value High Middle Low P-trend 
Men           
    N 1311 895 – 434 1501 – 912 717 577 – 
    Lose weight 772 (58.9) 349 (39.9) <0.001 284 (65.4) 717 (47.8) <0.001 547 (60.0) 356 (49.7) 218 (37.8) <0.001 
    Quit smoking 892 (68.0) 437 (48.8) <0.001 310 (71.4) 870 (58.0) <0.001 630 (69.1) 418 (58.3) 281 (48.7) <0.001 
    Increase exercise 779 (59.4) 308 (34.4) <0.001 289 (66.6) 673 (44.84) <0.001 525 (57.6) 339 (47.3) 223 (38.7) <0.001 
    Eat more fish 398 (30.4) 182 (20.3) <0.001 143 (33.0) 367 (24.5) <0.001 304 (33.3) 173 (24.1) 103 (17.9) <0.001 
    Drink less alcohol 725 (55.3) 395 (44.1) <0.001 256 (59.0) 749 (49.9) 0.001 532 (58.3) 340 (47.4) 248 (43.0) <0.001 
    Reduce fat in meals 878 (67.0) 390 (45.6) <0.001 316 (72.8) 809 (53.9) <0.001 603 (66.1) 404 (56.4) 261 (45.2) <0.001 
    Reduce salt in meals 808 (61.6) 405 (45.3) <0.001 287 (66.1) 781 (52.0) <0.001 561 (61.5) 383 (53.4) 269 (46.6) <0.001 
    Eat more fresh fruit 841 (64.2) 441 (49.3) <0.001 286 (65.9) 852 (56.8) 0.001 591 (64.8) 414 (56.7) 277 (48.0) <0.001 
    Eat more green leafy vegetables 909 (69.3) 478 (53.4) <0.001 317 (73.0) 916 (61.0) <0.001 644 (70.6) 450 (62.8) 293 (50.8) <0.001 
Women           
    N 1074 1255 – 88 872 – 945 550 834 – 
    Lose weight 569 (53.0) 407 (32.4) <0.001 46 (52.3) 287 (32.9) <0.001 476 (50.4) 236 (42.9) 264 (31.7) <0.001 
    Quit smoking 594 (55.3) 491 (39.0) <0.001 48 (54.6) 355 (40.7) 0.014 533 (56.4) 246 (44.7) 306 (36.7) <0.001 
    Increase exercise 534 (49.7) 329 (26.2) <0.001 44 (50.0) 249 (28.6) <0.001 435 (46.0) 194 (35.3) 234 (28.1) <0.001 
    Eat more fish 261 (24.3) 201 (16.0) <0.001 22 (25.0) 124 (14.2) 0.009 235 (24.9) 107 (19.5) 120 (14.4) <0.001 
    Drink less alcohol 490 (45.6) 386 (30.8) <0.001 42 (47.7) 283 (32.5) 0.005 431 (45.6) 205 (37.3) 240 (28.8) <0.001 
    Reduce fat in meals 689 (64.2) 504 (40.2) <0.001 61 (69.3) 350 (40.1) <0.001 568 (60.1) 290 (52.7) 335 (40.2) <0.001 
    Reduce salt in meals 651 (60.6) 566 (45.1) <0.001 54 (61.4) 369 (42.3) 0.001 572 (60.5) 287 (52.2) 358 (42.9) <0.001 
    Eat more fresh fruit 669 (62.3) 607 (48.4) <0.001 59 (67.1) 418 (47.9) 0.001 586 (62.0) 314 (57.1) 376 (45.1) <0.001 
    Eat more green leafy vegetables 728 (67.8) 639 (50.9) <0.001 63 (71.6) 447 (51.3) <0.001 624 (66.0) 337 (61.3) 406 (48.7) <0.001 
 Education
 
Occupation
 
Income
 
 
 Educated No education P-value Skilled Unskilled P-value High Middle Low P-trend 
Men           
    N 1311 895 – 434 1501 – 912 717 577 – 
    Lose weight 772 (58.9) 349 (39.9) <0.001 284 (65.4) 717 (47.8) <0.001 547 (60.0) 356 (49.7) 218 (37.8) <0.001 
    Quit smoking 892 (68.0) 437 (48.8) <0.001 310 (71.4) 870 (58.0) <0.001 630 (69.1) 418 (58.3) 281 (48.7) <0.001 
    Increase exercise 779 (59.4) 308 (34.4) <0.001 289 (66.6) 673 (44.84) <0.001 525 (57.6) 339 (47.3) 223 (38.7) <0.001 
    Eat more fish 398 (30.4) 182 (20.3) <0.001 143 (33.0) 367 (24.5) <0.001 304 (33.3) 173 (24.1) 103 (17.9) <0.001 
    Drink less alcohol 725 (55.3) 395 (44.1) <0.001 256 (59.0) 749 (49.9) 0.001 532 (58.3) 340 (47.4) 248 (43.0) <0.001 
    Reduce fat in meals 878 (67.0) 390 (45.6) <0.001 316 (72.8) 809 (53.9) <0.001 603 (66.1) 404 (56.4) 261 (45.2) <0.001 
    Reduce salt in meals 808 (61.6) 405 (45.3) <0.001 287 (66.1) 781 (52.0) <0.001 561 (61.5) 383 (53.4) 269 (46.6) <0.001 
    Eat more fresh fruit 841 (64.2) 441 (49.3) <0.001 286 (65.9) 852 (56.8) 0.001 591 (64.8) 414 (56.7) 277 (48.0) <0.001 
    Eat more green leafy vegetables 909 (69.3) 478 (53.4) <0.001 317 (73.0) 916 (61.0) <0.001 644 (70.6) 450 (62.8) 293 (50.8) <0.001 
Women           
    N 1074 1255 – 88 872 – 945 550 834 – 
    Lose weight 569 (53.0) 407 (32.4) <0.001 46 (52.3) 287 (32.9) <0.001 476 (50.4) 236 (42.9) 264 (31.7) <0.001 
    Quit smoking 594 (55.3) 491 (39.0) <0.001 48 (54.6) 355 (40.7) 0.014 533 (56.4) 246 (44.7) 306 (36.7) <0.001 
    Increase exercise 534 (49.7) 329 (26.2) <0.001 44 (50.0) 249 (28.6) <0.001 435 (46.0) 194 (35.3) 234 (28.1) <0.001 
    Eat more fish 261 (24.3) 201 (16.0) <0.001 22 (25.0) 124 (14.2) 0.009 235 (24.9) 107 (19.5) 120 (14.4) <0.001 
    Drink less alcohol 490 (45.6) 386 (30.8) <0.001 42 (47.7) 283 (32.5) 0.005 431 (45.6) 205 (37.3) 240 (28.8) <0.001 
    Reduce fat in meals 689 (64.2) 504 (40.2) <0.001 61 (69.3) 350 (40.1) <0.001 568 (60.1) 290 (52.7) 335 (40.2) <0.001 
    Reduce salt in meals 651 (60.6) 566 (45.1) <0.001 54 (61.4) 369 (42.3) 0.001 572 (60.5) 287 (52.2) 358 (42.9) <0.001 
    Eat more fresh fruit 669 (62.3) 607 (48.4) <0.001 59 (67.1) 418 (47.9) 0.001 586 (62.0) 314 (57.1) 376 (45.1) <0.001 
    Eat more green leafy vegetables 728 (67.8) 639 (50.9) <0.001 63 (71.6) 447 (51.3) <0.001 624 (66.0) 337 (61.3) 406 (48.7) <0.001 

P-values adjusted for age; values are numbers, and those in parentheses are percentages.

Influence of SEP and knowledge of key behaviours that reduce the risk of CVD on attempt to modify behaviour

In age- and sex-adjusted models, and in those with the risk factor, those who were educated were: (i) more likely to attempt to lose weight (OR: 2.57; 95% CI 1.85–3.57 in those with BMI ≥ 25); (ii) more likely to attempt to increase physical activity (OR: 3.66; 95% CI 2.67–5.01 in those who were physically inactive); (iii) more likely to attempt to increase fruit intake (OR: 1.28; 95% CI 1.02–1.61 in those on less fruit); and (iv) more likely to attempt to decrease their alcohol intake (OR: 1.40; 95% CI 1.00–1.94 in those who drank alcohol)—in the past 12 months compared with those with no education. Educational level was not, however, a significant predictor of an attempt to quit smoking (OR: 1.26; 95% CI 0.88–1.80 in current smokers).

In age- and sex-adjusted models, and in those with the risk factor, those that had specific knowledge of the benefit of modifying that risk factor were: (i) more likely to attempt to lose weight (OR: 4.62; 95% CI 3.30–6.47); (ii) more likely to attempt to increase physical activity (OR: 6.08; 95% CI 4.50–8.22); (iii) more likely to attempt to increase fruit intake (OR: 4.14; 95% CI 3.20–5.36); (iv) more likely to attempt to decrease their alcohol intake (OR: 1.65; 95% CI 1.18–2.30); and (v) more likely to attempt to quit smoking (OR: 3.13; 95% CI 2.09–4.71)—compared with those without knowledge.

Influence of both SEP and knowledge on the beneficial modification of behaviour

Knowledge of the benefit of weight loss, in those who were overweight, was associated with an increased odds of attempting to lose weight in both those with no formal education (OR: 3.43; 95% CI 1.89–6.23) and in participants who were educated [(OR: 7.68; 95% CI 4.58–12.89), Figure 1]. Similarly, knowledge of the benefits of smoking cessation, in current smokers, was associated with increased odds of attempting to quit smoking in both those with no formal education (OR: 3.98; 95% CI 2.27–6.97) and in participants who were educated [(OR: 3.67; 95% CI 2.10–6.42), Figure 2].

Figure 1

ORs (95% CIs) for attempt to lose weight by SEP and knowledge, in those with BMI ≥ 25. OR adjusted for age and sex. SEP, no education vs eduated, knowledge of link between excess weight and future heart attack/stroke

Figure 1

ORs (95% CIs) for attempt to lose weight by SEP and knowledge, in those with BMI ≥ 25. OR adjusted for age and sex. SEP, no education vs eduated, knowledge of link between excess weight and future heart attack/stroke

Figure 2

ORs (95% CIs) for attempt to quit smoking by SEP and knowledge, in current smokers. OR adjusted for age and sex. SEP, no education vs eduated, knowledge of link between excess weight and future heart attack/stroke

Figure 2

ORs (95% CIs) for attempt to quit smoking by SEP and knowledge, in current smokers. OR adjusted for age and sex. SEP, no education vs eduated, knowledge of link between excess weight and future heart attack/stroke

Discussion

This study finds that in this rural Indian population undergoing epidemiological transition, CVRF levels were variably associated with SEP. Those of lower SEP had higher rates of behavioural risk factors, including smoking, alcohol and lower fruit intake, while those of higher SEP had higher levels of biological risk factors, including hypertension (men only), diabetes and overweight. Individuals of higher SEP were more likely to have had CVRF screening and were better informed about CVRFs. Both education and knowledge of CVRFs were associated with positive attempts to change behaviours. Importantly, knowledge of CVRFs remained associated with positive behavioural change in the socio-economically disadvantaged.

Comparison with previous research

The socio-economic distribution of CVRF profiles here is consistent with findings from another recent study of rural individuals,15 an older study of three villages in rural Rajasthan,26 and findings among those of lower SEP in an urban setting.27 We, however, sought to go beyond simply describing the socio-economic distribution of risk factor prevalence. In a representative sample of individuals from this rural region, we demonstrate socio-economic inequities in the knowledge of behaviours that might pertain to future CVD and socio-economic inequities in receipt of CVRF screening. We also analysed rarely available data from these rural settings of the socio-economic distribution of attempts at behavioural modification.

Importance of the study findings

The fact that smokers with knowledge of the adverse effects of smoking were more likely to have attempted quitting, regardless of SEP, suggests that interventions which improve knowledge of health risks may have substantial potential to improve health behaviours. In these settings, interventions which improve knowledge of health risks are highly practicable and may be delivered through similar mechanisms as other health education programmes, such as on HIV and sex education.28 They also may have particular benefits for those of lower SEP and potential in ameliorating social inequities. While community education interventions have limited efficacy in developed world settings,29 they have not been tested in developing country settings where general educational levels are very low.30 While the lower screening rates of CVRFs for those of lower SEP may simply reflect the lower prevalence of these risk factors in these individuals and may be appropriate, strategies are needed to ensure access in accordance with need. Of note, under-nutrition (as evidenced by lower BMI and lower fruit intake) remains a problem of those of lower SEP,31 and was evident in the current cohort despite residing in a highly fertile region. Also, those of lower SEP consumed more alcohol,32 perhaps due to increased availability and social stresses. Thus, any policy that seeks to equitably improve the cardiovascular health of all in rural India needs to take into account the specific challenges of tailoring educational messages about cardiovascular risk and behaviour change in a generation in which undernutrition still exists. It should be remembered that the term higher SEP as used in this paper is a relative term, and that this part of India remains relatively poor, with a median income in our study of 2248 Indian rupees a month ($51).

Strengths and limitations

This study used relatively simple indicators of SEP; more complex measures have been used in India by others, using data covering quality of housing, ownership of land and durable goods.33 However, our findings were consistent across these simple measures of education, income and occupation, providing reassurance about their likely validity. The dichotomization of educational level (approximately half the sample, 40.6% of men and 53.9% of women, had no formal schooling) reflects the notion that an important marker of socio-economic status in such rural settings is access to any formal education at all at an early age, particularly for girls.34,35 We acknowledge that income is harder to measure than either education or occupation in such settings due to the informal economy36 and that asset ownership may be of more utility.37 The stronger effects with education than with income and occupation may indicate that education is specifically protective. While higher income may improve access to healthier diets and better medical care, it may afford individuals unhealthier lifestyle choices; education, however, potentially equips individuals to make better lifestyle choices whilst also being associated with greater material wealth.38

Some measures of risk factors were on the basis of self-report. While many of these (e.g. smoking, medical diagnosis of CVD) have been validated,39,40 the sensitivity of self-reported behavioural change is lower.41 That such self-reported measures are themselves biased in terms of socio-economic profile has been largely refuted,42 but few validated tools are available in these rural Indian settings for the assessment of knowledge—others have used similar questioning techniques to us.43

We acknowledge that the cross-sectional design prevents full determination of cause and effect. Asking participants about their knowledge of the benefits of key behaviours and then whether they have attempted to modify the behaviour could lead to reporting biased answers and our findings should be validated in prospective studies and randomized controlled trials. The strengths of the study were its representative sampling methodology, high response rates, comprehensive measures of CVRFs and screening, knowledge and behaviours and largely complete data acquisition. By using the unique resources of the Byrraju Foundation, we were able to get complete population listings that were not available elsewhere, and to hence conduct such a complete study. Though the main drive of the project was to determine socio-economic distribution of risk factors, we acknowledge that overall screening rates for BP and blood glucose level were higher than might be expected for rural India due to the previous work by the Byrraju Foundation, but we present results that still demonstrate a socio-economic distribution. Explaining this would require further work into access to health care, which we have not examined in this paper and was not the remit of the study design.

Conclusion

The study has shown that those of lower SEP had more adverse levels of the behavioural risk factors of smoking, alcohol and low fruit intake, while those of higher SEP had more adverse levels of hypertension, diabetes and overweight. However those of lower SEP had poorer knowledge of CVRFs and were less likely to have received risk factor screening and are therefore particularly ill-prepared to manage the consequences of a projected CVD epidemic. The finding that those with knowledge of CVRFs were more likely to make healthy behavioural changes regardless of their education level may indicate a potential important benefit of specific education on preventive behaviours. Such CVRF education could be built into CVD prevention programs in this and other rural Indian communities. The efficacy and cost-effectiveness of such community-level programs would be worthy of further evaluation using adequately powered randomized and controlled study designs.

Supplementary Data

Supplementary Data are available at IJE online.

Funding

This work was supported by the Byrraju Foundation and the Initiative for Cardiovascular Health Research in Developing Countries. M.J.Z. was supported by the UCL-Gonville and Caius Berkeley Research Travel Fellowship and a UK National Institute for Health Research Lectureship. C.K.C. was supported by a National Health and Medical Research Council (NHMRC) (512119) (Australia)/National Heart Foundation (Australia) co-funded fellowship and a Sydney Medical School Foundation Chapman Fellowship. B.N. was supported by an Australian Research Council Future Fellowship. S.J. is funded by an NHMRC Career Development Award (457117). G.S.H. is funded by a New South Wales Office for Science and Medical Research, Life Sciences Research Award. Andhra Pradesh Rural Health Initiative has been developed as collaboration between four partners, The Byrraju Foundation in Hyderabad, India, The Centre for Chronic Disease Control in Delhi, India, The Care Foundation in Hyderabad, India and The George Institute for Global Health in Sydney, Australia. This research was carried out independently of funders and sponsors.

Acknowledgements

We thank the chairpersons, village committees and communities of the 20 villages visited in the survey for their tremendous support of the project. We would like to acknowledge the support of our field monitors Drs S. Iyengar, Magnolia Cardona, G. Ramakrishna, Uma Snehi and Ravi Raju, and Mr A. Sukumar and the survey interviewers from the Roda Mistry College of Social Work and Research Center, Osmania University, Hyderabad. M.J.Z. conceived the ideas for this report in consultation with C.K.C. M.J.Z. developed the analytical plan, and carried out all the analyses. C.K.C. designed the database, ran the study and was involved in the conception of ideas, analytical plan, first draft, discussion and interpretation. B.N., K.R. and A.P. were involved in the original planning and running of the study and in the discussion and interpretation. S.J. and G.S.H were involved in the discussion and interpretation. All authors participated in the discussion and interpretation of the final results and contributed to the final paper. M.J.Z. is the guarantor of this paper. He has checked the references for accuracy and completeness. This material has not been published previously in a substantively similar form and has been carefully edited by M.J.Z., a native English speaker with an excellent mastery of the English language.

Conflict of interest: All authors declare that the answer to the questions on your competing interest form are all no and therefore have nothing to declare. All authors declare no support from any organization for the submitted work other than the funding grant; no financial relationships with any organisations that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work.

KEY MESSAGES

  • In rural Andhra Pradesh, India, those of lower SEP had higher rates of behavioural risk factors—smoking, alcohol and lower fruit intake—whereas those of higher SEP had higher levels of biological risk factors—hypertension (men only), diabetes and overweight.

  • Individuals of higher SEP were more likely to have received CVRF screening and had better knowledge of the risk factors for CVD.

  • Knowledge of the harms of a risk factor was associated with positive attempts to modify that risk factor, regardless of SEP.

  • While there are limitations to the analyses, our findings suggest equipping rural Indians with information about CVD risk may decrease inequities and may ameliorate potential increases in CVD that are projected in coming years.

References

1
Marmot
MG
Adelstein
AM
Robinson
N
Rose
GA
Changing social-class distribution of heart disease
Br Med J
 , 
1978
, vol. 
2
 (pg. 
1109
-
12
)
2
Sobal
J
Stunkard
AJ
Socioeconomic status and obesity: a review of the literature
Psychol Bull
 , 
1989
, vol. 
105
 (pg. 
260
-
75
)
3
Marmot
MG
Rose
G
Shipley
M
Hamilton
PJ
Employment grade and coronary heart disease in British civil servants
J Epidemiol Commun Health
 , 
1978
, vol. 
32
 (pg. 
244
-
49
)
4
Registrar General
Occupational Mortality 1970–72: Decennial Supplement
 
London
HMSO1978
5
Sorensen
G
Gupta
PC
Pednekar
MS
Social Disparities in Tobacco Use in Mumbai, India: the roles of occupation, education, and gender
Am J Public Health
 , 
2005
, vol. 
95
 (pg. 
1003
-
1008
)
6
Monteiro
CA
Moura
EC
Conde
WL
Popkin
BM
Socioeconomic status and obesity in adult populations of developing countries: a review
Bull World Health Organ
 , 
2004
, vol. 
82
 (pg. 
940
-
46
)
7
Reddy
KS
Prabhakaran
D
Jeemon
P
, et al.  . 
Educational status and cardiovascular risk profile in Indians
Proc Natl Acad Sci USA
 , 
2007
, vol. 
104
 (pg. 
16263
-
68
)
8
Gupta
R
Kaul
V
Agrawal
A
Guptha
S
Gupta
VP
Cardiovascular risk according to educational status in India
Prev Med
 , 
2010
, vol. 
51
 (pg. 
408
-
11
)
9
Joshi
R
Cardona
M
Iyengar
S
, et al.  . 
Chronic diseases now a leading cause of death in rural India—mortality data from the Andhra Pradesh Rural Health Initiative
Int J Epidemiol
 , 
2006
, vol. 
35
 (pg. 
1522
-
29
)
10
Gajalakshmi
V
Peto
R
Verbal autopsy of 80,000 adult deaths in Tamilnadu, South India
BMC Public Health
 , 
2004
, vol. 
4
 pg. 
47
 
11
Ramachandran
A
Snehalatha
C
Baskar
AD
, et al.  . 
Temporal changes in prevalence of diabetes and impaired glucose tolerance associated with lifestyle transition occurring in the rural population in India
Diabetologia
 , 
2004
, vol. 
47
 (pg. 
860
-
65
)
12
Chow
CK
Naidu
S
Raju
K
, et al.  . 
Significant lipid, adiposity and metabolic abnormalities amongst 4535 Indians from a developing region of rural Andhra Pradesh
Atherosclerosis
 , 
2008
, vol. 
196
 (pg. 
943
-
52
)
13
Chow
CK
Raju
PK
Raju
R
, et al.  . 
The prevalence and management of diabetes in rural India
Diabetes Care
 , 
2006
, vol. 
29
 (pg. 
1717
-
18
)
14
Kesterton
A
Cleland
J
Sloggett
A
Ronsmans
C
Institutional delivery in rural India: the relative importance of accessibility and economic status
BioMed Central Pregnancy and Childbirth
 , 
2010
, vol. 
10
 pg. 
30
 
15
Kinra
S
Bowen
LJ
Lyngdoh
T
, et al.  . 
Sociodemographic patterning of non-communicable disease risk factors in rural India: a cross sectional study
BMJ
 , 
2010
, vol. 
341
 pg. 
c4974
 
16
Siahpush
M
McNeill
A
Hammond
D
Fong
GT
Socioeconomic and country variations in knowledge of health risks of tobacco smoking and toxic constituents of smoke: results from the 2002 International Tobacco Control (ITC) Four Country Survey
Tob Control
 , 
2006
, vol. 
3
 
15 Suppl
(pg. 
iii65
-
70
)
17
Janz
NK
Becker
MH
The Health Belief Model: a decade later
Health Educ Q
 , 
1984
, vol. 
11
 (pg. 
1
-
47
)
18
Jepson
RG
Harris
FM
Platt
S
Tannahill
C
The effectiveness of interventions to change six health behaviours: a review of reviews
BioMed Central Public Health
 , 
2010
, vol. 
10
 pg. 
538
 
19
Census of India
Provisional Population Totals
2001
20
Chow
CK
Cardiovascular risk factor levels and cardiovascular risk estimation in a rural area of India [PhD]
2007
Sydney
University of Sydney
21
D'Agostino
RB
Russell
MW
Huse
DM
, et al.  . 
Primary and subsequent coronary risk appraisal: new results from the Framingham study
Am Heart J
 , 
2000
, vol. 
139
 (pg. 
272
-
81
)
22
National Classification of Occupations, NCO Divisions
Directorate General of Employment and Training
2004
New Delhi, India
Ministry of Labour
23
Chobanian
AV
Bakris
GL
Black
HR
, et al.  . 
Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure
Hypertension
 , 
2003
, vol. 
42
 (pg. 
1206
-
52
)
24
Report of a WHO Expert Committee
Physical status: the use and interpretation of anthropometry. WHO Technical Report Series 854
 , 
1995
Geneva
World Health Organization
25
World Health Organization/International Diabetes Foundation
Definition And Diagnosis Of Diabetes Mellitus And Intermediate Hyperglycaemia
 , 
2006
Geneva
WHO
26
Gupta
R
Gupta
VP
Ahluwalia
NS
Educational status, coronary heart disease, and coronary risk factor prevalence in a rural population of India
BMJ
 , 
1994
, vol. 
309
 (pg. 
1332
-
36
)
27
Gupta
R
Kaul
V
Agrawal
A
Guptha
S
Gupta
VP
Cardiovascular risk according to educational status in India
Prev Med
 , 
2010
, vol. 
51
 (pg. 
408
-
11
)
28
Kirby
DB
Laris
BA
Rolleri
LA
Sex and HIV education programs: their impact on sexual behaviors of young people throughout the world
J Adolesc Health
 , 
2007
, vol. 
40
 (pg. 
206
-
17
)
29
Secker-Walker
R
Gnich
W
Platt
S
Lancaster
T
Community interventions for reducing smoking among adults
Cochrane Database Syst Rev
 , 
2002
pg. 
CD001745
 
30
Singhal
N
Misra
A
Shah
P
Gulati
S
Effects of controlled school-based multi-component model of nutrition and lifestyle interventions on behavior modification, anthropometry and metabolic risk profile of urban Asian Indian adolescents in North India
Eur J Clin Nutr
 , 
2010
, vol. 
64
 (pg. 
364
-
73
)
31
Reddy
KS
Yusuf
S
Emerging epidemic of cardiovascular disease in developing countries
Circulation
 , 
1998
, vol. 
97
 (pg. 
596
-
601
)
32
Costanzo
S
Di Castelnuovo
A
Donati
MB
Iacoviello
L
de Gaetano
G
Alcohol Consumption and mortality in patients with cardiovascular disease: a meta-analysis
J Am College Cardiol
 , 
2010
, vol. 
55
 (pg. 
1339
-
47
)
33
International Institute for Population Sciences (IIPS) and ORCMacro
National Family Health Survey (NFHS-2)
 
Mumbai
IIPS
 
1998–99. 2000
34
Kingdon
GG
The progress of school education in India
Oxford Rev Economic Policy
 , 
2007
, vol. 
23
 (pg. 
168
-
95
)
35
Agardh
E
Allebeck
P
Hallqvist
J
Moradi
T
Sidorchuk
A
Type 2 diabetes incidence and socio-economic position: a systematic review and meta-analysis
Int J Epidemiol
 , 
2011
, vol. 
40
 (pg. 
804
-
18
)
36
Howe
LD
Hargreaves
JR
Huttly
SR
Issues in the construction of wealth indices for the measurement of socio-economic position in low-income countries
Emerg Themes Epidemiol
 , 
2008
, vol. 
5
 pg. 
3
 
37
Filmer
D
Pritchett
LH
Estimating wealth effects without expenditure data–or tears: an application to educational enrollments in states of India
Demography
 , 
2001
, vol. 
38
 (pg. 
115
-
32
)
38
Monteiro
CA
Conde
WL
Popkin
BM
Independent effects of income and education on the risk of obesity in the Brazilian adult population
J Nutr
 , 
2001
, vol. 
131
 (pg. 
881S
-
6S
)
39
Patrick
DL
Cheadle
A
Thompson
DC
Diehr
P
Koepsell
T
Kinne
S
The validity of self-reported smoking: a review and meta-analysis
Am J Public Health
 , 
1994
, vol. 
84
 (pg. 
1086
-
93
)
40
Lampe
F
Mary
W
Lucy
TL
Peter
HW
Shah
E
Validity of a Self-reported History of Doctor-diagnosed Angina
J Clin Epidemiol
 , 
1999
, vol. 
52
 (pg. 
73
-
81
)
41
Bowlin
SJ
Morrill
BD
Nafziger
AN
Lewis
C
Pearson
TA
Reliability and changes in validity of self-reported cardiovascular disease risk factors using dual response: the behavioral risk factor survey
J Clin Epidemiol
 , 
1996
, vol. 
49
 (pg. 
511
-
17
)
42
Macintyre
S
Der
G
Norrie
J
Are there socioeconomic differences in responses to a commonly used self report measure of chronic illness?
Int J Epidemiol
 , 
2005
, vol. 
34
 (pg. 
1284
-
90
)
43
Saeed
O
Gupta
V
Dhawan
N
, et al.  . 
Knowledge of modifiable risk factors of Coronary Atherosclerotic Heart Disease (CASHD) among a sample in India
BMC Int Health Human Rights
 , 
2009
, vol. 
9
 pg. 
2