Cardiovascular risk factor levels and cardiovascular risk estimation in a rural area of India

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1 Cardiovascular risk factor levels and cardiovascular risk estimation in a rural area of India Clara Kayei Chow This thesis is submitted in full satisfaction of the requirements for the degree of Doctor of Philosophy at the University of Sydney Central Clinical School Faculty of Medicine University of Sydney April,

2 Abstract... 4 Index of Tables... 6 Index of Figures... 8 Preface Summary of Chapters Glossary notes Author s contribution Acknowledgements Statement of Authentication Papers CHAPTER 1 THE BURDEN OF CARDIOVASCULAR DISEASE IN INDIA...18 Changing patterns of disease in low and middle-income countries Cardiovascular disease in India Reasons for increasing cardiovascular disease in India Summary CHAPTER 2 WHAT IS KNOWN ABOUT CARDIOVASCULAR RISK FACTORS IN RURAL INDIA?...43 Introduction Methods Results Summary CHAPTER 3 CARDIOVASCULAR RISK FACTORS IN TWO VILLAGES IN RURAL ANDHRA PRADESH...83 Background Methods Results Discussion CHAPTER 4 DIABETES IN RURAL ANDHRA PRADESH SURVEY RESULTS OF 4,535 PEOPLE IN TWENTY VILLAGES...95 Background Methods Results Discussion CHAPTER 5 SIGNIFICANT LIPID, ADIPOSITY AND METABOLIC ABNORMALITIES AMONGST 4,535 INDIANS FROM A DEVELOPING REGION OF RURAL ANDHRA PRADESH Introduction Methods Results Discussion

3 CHAPTER 6 HIGH BLOOD PRESSURE - PREVALENCE, IDENTIFICATION, TREATMENT AND CONTROL IN RURAL ANDHRA PRADESH Introduction Methods Results Discussion CHAPTER 7 TOBACCO USE IN RURAL ANDHRA PRADESH Introduction Methods Results Discussion CHAPTER 8 GREATER ADVERSE EFFECTS OF CHOLESTEROL AND DIABETES ON ATHEROSCLEROSIS IN A SOUTH ASIAN INDIAN POPULATION COMPARED WITH CAUCASIAN AUSTRALIANS Background Methods Results Discussion CHAPTER 9 CARDIOVASCULAR RISK ESTIMATION IN A RURAL INDIAN POPULATION Introduction Methods Results Discussion CHAPTER 10 CONCLUSIONS AND IMPLICATIONS Implications of adverse cardiovascular risk levels Implications of more adverse effects of some risk factors Need for locally calibrated risk assessment tools Translation of findings into policy and practice Overall significance REFERENCES APPENDICES Appendix 1 Andhra Pradesh Rural Health Initiative (APRHI) Appendix 2 Survey Instruments used in APRHI Pilot study Appendix 3 Survey Instruments used in rural Andhra Pradesh (APRHI) Main study Appendix 4 Lab methodology and quality control in rural Andhra Pradesh (APRHI) Main Study Appendix 5 Protocol for analysis of carotid intima-media thickness Appendix 6 Protocol for Rural Andhra Pradesh Cardiovascular Prevention Study (RAPCAPS)

4 Abstract Background Cardiovascular disease is now a leading cause of death in India. Although projections indicate that the situation will worsen, there is a paucity of data on the determinants of cardiovascular disease in the country. This lack of data is particularly pronounced for rural areas where the majority of the population lives and the greatest increases in vascular disease are likely to occur. Clear insight into the determinants of vascular mortality in rural regions will be crucial for the development of appropriate disease control strategies. Methods A large scale complex sample survey was conducted in 20 rural villages broadly representative of the East and West Godavari region of Andhra Pradesh. Data on key cardiovascular risk factors were collected from a stratified random sample of 4,535 individuals aged 30 years (response rate 81%). In addition carotid intima-media thickness was measured in a random sample of adults from two villages. These data were compared with other national and international datasets to make inferences about the likely course of vascular disease in rural India. A recalibration of the Framingham risk equation was also done to develop a locally applicable vascular risk prediction tool. Results The survey found high levels of diabetes (13.2%, 95% confidence interval ), hypertension (27.0%, ), metabolic syndrome (28.6% ( ) for men and 20.4% ( ) for women), overweight/obesity (men 18.4%, women 26.3%) and smoking (men 45.2%, and women 4.8%, ). Rural Indians had higher levels of mean carotid IMT than an urban Australian comparator group with more adverse effects of total cholesterol (p for interaction=0.009) and diabetes (p=0.04) on carotid IMT in rural Indians compared to urban Australians. In addition, while increasing HDL-cholesterol was associated with decreasing carotid IMT in the Australian population the reverse was true for the Indian population (p<0.001). Using the original Framingham equation, the proportion of the population with an estimated 10-year CHD risk greater than 20% was 8.7% ( ). The corresponding - 4 -

5 estimate using an equation recalibrated with local data from Andhra Pradesh was 7.8% ( ) but if the equation was recalibrated using Indian data available in the World Health Atlas the estimates was about threefold higher 23.2% ( ). Conclusions The survey data provide definitive new information about the adverse levels of cardiovascular risk factors in this part of rural India and identify a possibly more adverse effect of some of these risk factors on the development of atherosclerosis in rural Indians. The Framingham recalibration process demonstrates the vital role of local data in the development of risk prediction tools and highlights the magnitude of the high risk population in this rural area. Taken together these data suggest that cardiovascular disease is a large and increasing problem in developing regions of rural India that requires an urgent and significant public health response. While the region studied here was more developed than many it is likely to provide a good indication of what is to come

6 Index of Tables Table 1-1 Incidence of CHD in India compared with selected studies from the United Kingdom Table 1-2 Urban and rural Indian studies of CHD prevalence defined on ECG and symptoms Table 1-3 Urban and rural Indian studies of CHD prevalence defined on ECG only Table 1-4 Standardised mortality ratios for CHD, stroke and all-cause mortality for Asian Indian and Caribbean ethnic groups compared to the adult general population of the UK, Table 1-5 Age and risk factor adjusted hazard ratios for CHD in Asian Indian males compared with other ethnic groups in Singapore, Table 1-6 Age-standardised death rates per 100,000 for CHD, stroke and all-cause mortality for Asian Indian adults compared with other ethnic groups in Canada, Table 1-7 Age-standardised death rates per 100,000 for CHD, stroke and all-cause mortality for Asian Indian adults compared to other ethnic groups in California, Table 2-1 Distribution of rural cardiovascular studies according to Indian State Table 2-2 Studies of diabetes prevalence in rural areas of India Table 2-3 Studies of mean blood pressure and/ or prevalence of hypertension in rural India Table 2-4 Studies of mean BMI and overweight/ obesity in rural India Table 2-5 Studies of mean levels and/or prevalence of lipoproteins in rural India: sampling methods and population demographics Table 2-6 Mean lipoprotein levels in rural India Table 2-7 Prevalence of abnormal lipoprotein levels in rural India Table 2-8 Smoking prevalence in men and women in rural India, Table 3-1 Population sampled and response rates in pilot study conducted in two villages in rural Andhra Pradesh, Table 3-2 Demographic characteristics of participants in two villages in rural Andhra Pradesh, Table 3-3 Mean levels of key cardiovascular risk factors overall and separately for men & women in two villages of rural Andhra Pradesh, Table 4-1 Villages sampled for rural Andhra Pradesh main survey Table 4-2 Number sampled from each village by age and sex in rural Andhra Pradesh main survey, Table 4-3 Prevalence of known diabetes, undiagnosed diabetes and impaired fasting glucose based on capillary fasting glucose levels overall and by age and sex groups, in 20 villages in rural Andhra Pradesh, India Table 4-4 Cardiovascular risk factors in groups defined by normal fasting glucose, impaired fasting glucose, undiagnosed diabetes, known diabetes in rural Andhra Pradesh, India, Table 5-1 Mean levels of lipoproteins, weight, waist, BMI and WHR by age and sex in 20 villages of rural Andhra Pradesh,

7 Table 5-2 Prevalence of desirable, borderline high and high total-cholesterol according to ATPIII guidelines in 20 villages of rural Andhra Pradesh, India Table 5-3 Prevalence of optimum, borderline high, high and very high LDL-cholesterol according to ATPIII guidelines in 20 villages of rural Andhra Pradesh, India Table 5-4 Prevalence of low, normal and high HDL-cholesterol according to ATPIII guidelines in 20 villages of rural Andhra Pradesh, India Table 5-5 Prevalence of normal, borderline high, high and very high Triglycerides according to ATPIII guidelines in 20 villages of rural Andhra Pradesh, India Table 5-6 Prevalence of abnormalities of waist, body mass index and the metabolic syndrome overall and by age and sex for usual and Asian cut offs across 20 villages in rural Andhra Pradesh, India, Table 6-1 Mean systolic and diastolic blood pressure and prevalence of high blood pressure overall and by age and sex in 20 villages of rural Andhra Pradesh, Table 6-2 Prevalence of blood pressure screening in 20 villages in rural Andhra Pradesh, Table 6-3 Awareness, Treatment and Control of high blood pressure amongst hypertensive adults in rural Andhra Pradesh, Table 7-1 Prevalence of smoking and chewing tobacco overall and by age and sex group in 20 villages in rural Andhra Pradesh, India, Table 7-2 Exposure to passive tobacco smoke amongst non-current smokers in rural Andhra Pradesh, India, Table 7-3 Prevalence of smoking by occupation, overall and by sex, rural Andhra Pradesh, Table 1 Age and sex-adjusted levels of cardiovascular risk factors in urban Australian and rural Indian participants Table 8-2 Age and sex-adjusted associations between cardiovascular risk factors and carotid IMT for Australian and Indian populations Table 9-1 Distribution of risk factors for APRHI versus Framingham cohort Table 9-2 Deaths from vascular causes in in rural Andhra Pradesh APRHI mortality surveillance Table 9-3 World Heart Atlas data for India and method used to estimate CHD mortality rate Table 9-4 β -coefficients from the 1998 Wilson Framingham categorical multivariable risk equation Table 9-5 Risk factor levels from Andhra Pradesh survey expressed according to Original Framingham equation categorical definitions Table 9-6 Mean risk by sex and age group calculated from Framingham, AP recalibrated and WHO Atlas recalibrated equations Table 9-7 Examples of variation in risk scores in men & women using calibrated and original Framingham equations

8 Index of Figures Figure 1-2 Proportional mortality due to circulatory system causes compared with other causes from the Andhra Pradesh Rural Health Initiative (APRHI) Mortality Surveillance Study of Figure 2-1 Flowchart of articles identified and selected for inclusion in review of cardiovascular risk factor studies from rural India Figure 2-2 Rate of publications produced over time on cardiovascular risk factors in rural India Figure 2-3 Map of States and Union Territories of India Figure 2-4 Prevalence of diabetes by year, region and method of diagnosis in rural India with studies from the same State related by connecting lines Figure 2-5 Prevalence of high blood pressure by year and blood pressure cut-point in rural areas of India with studies from the same State related by connecting lines Figure 2-6 Distribution of hypertension prevalence studies in rural India by minimum age of population sampled Figure 2-7 Mean systolic blood pressure reported in studies from rural India over time Figure 2-8 Prevalence of overweight/obesity by year and effect of different definitions of overweight/obesity in rural India Figure 2-9 Mean BMI reported in rural Indian studies over time Figure 2-10 Prevalence of smoking by year and definition in rural India, circle size proportional to study size Figure 3-1 The study area: East and West Godavari, Andhra Pradesh, India Figure 5-1 Distribution of risk factors amongst men and women with metabolic syndrome according to NCEP-ATPIII and Asian definitions in rural Andhra Pradesh, India, Figure 5-2 Prevalence of metabolic syndrome, abnormal waist, overweight and obesity according to Usual and Asian definitions in men and women in rural Andhra Pradesh, India, Figure 6-1 Prevalence of blood pressure screening in adults 30 years in rural Andhra Pradesh, Figure 6-2 Awareness, treatment and control of high blood pressure in rural Andhra Pradesh, Figure 6-3 Mean blood pressure by age deciles for Rural Andhra Pradesh compared to Urban India, China and NHANES Figure 7-1 Prevalence of tobacco use by sex and age group in rural Andhra Pradesh, Figure 7-2 Prevalence of current smoking by highest level of education completed, rural Andhra Pradesh, Figure 7-3 Prevalence of current smoking by income category, rural Andhra Pradesh, Figure 8-1 Mean levels of carotid intima-media thickness in urban Australian and rural Indian participants Figure 8-2 Distribution of carotid intima-media thickness in rural Indian and urban Australian participants by age group Figure 9-1 Risk categories in Men 30years using original and calibrated equations

9 Figure 9-2 Risk categories in Women 30years using original and calibrated equations Figure 9-3 Sensitivity analysis Effect of changing CHD incidence, mean risk factors and beta-coefficients on mean estimated 10 years CHD risk

10 Preface Summary of Chapters Chapter 1 is a summary overview of the available information on cardiovascular disease in India and the factors contributing to the increasing burden of cardiovascular diseases in India. Chapter 2 is a systematic review of the literature on the prevalence of key cardiovascular risk factors: diabetes, hypertension, lipids, obesity and smoking in rural India. It assesses the quality of the methodology of studies and evaluates the evidence for increasing cardiovascular risk factors. Chapter 3 describes a pilot cardiovascular survey conducted in two villages of rural Andhra Pradesh. This chapter highlights the important methodological lessons gained from conducting the pilot study. Chapter 4 describes the methods of the large scale survey of 20 villages in rural Andhra Pradesh and reports on the prevalence of diabetes and its associated risk factors. It compares the data to other data from India, other developing populations and developed populations and discusses the implications of these results. Chapter 5 reports on the levels of lipids, obesity and metabolic abnormalities from the large scale survey. It discusses these in the context of international and new Asian definitions and compares findings to other developed and developing populations. Chapter 6 reports data from the large scale survey on the prevalence, awareness, treatment and control of hypertension. It compares this to other developed and developing populations. Chapter 7 reports data from the large scale survey on the prevalence of smoking, and the characteristics which make tobacco use different in India from other populations

11 Chapter 8 describes a study of carotid intima-media thickness in participants of the pilot study from rural Andhra Pradesh. It compares the levels to that of a control urban Australian population. It also explores in detail the associations of cardiovascular risk factors with IMT to evaluate if the differences in associations may explain some of the increased risk of cardiovascular disease in South Asian populations. Chapter 9 uses data from the large scale survey and the Andhra Pradesh Mortality Surveillance project to recalibrate the Framingham risk equation. It compares risk estimates from the recalibrated equation to that from the original equation and examines the effects of recalibration to different estimates of CHD incidence. It discusses the implications of the findings for India and other developing countries. Chapter 10 summarises the main findings of the thesis, outlines the implications for policy and discusses future research directions. Glossary notes The terms Coronary Heart Disease (CHD) and Ischaemic Heart Disease (IHD) are used interchangeably. South Asian refers to persons descendent from India, Pakistan, Bangladesh, Nepal and Sri Lanka. Asian Indian refers to those from India, but in some texts is used interchangeably with South Asian. The term Indian refers to people from or descendent from India. Cerebrovascular disease (CbVD) is used interchangeably with stroke Cardiovascular diseases (CVD) refers to combined IHD/CHD and stroke APRHI refers to the Andhra Pradesh Rural Health Initiative

12 Author s contribution As is the nature of collaborative research projects, many investigators have worked on the studies analysed in this thesis. My specific role in relation to each of the pieces of work presented in this thesis is outlined below: Rural Indian studies of cardiovascular risk factors - systematic review I was the principal investigator for this work. I designed the study, conducted the literature review, analysed the data and wrote the research report. Andhra Pradesh Rural Health Initiative (APRHI) Pilot cardiovascular survey I am a principal investigator in this study. I contributed to the study design and development of the survey tools. I conducted the data analysis, and wrote the research report. Andhra Pradesh Rural Health Initiative (APRHI) - Large scale cardiovascular survey I am a principal investigator in this project and took primary responsibility for setting up this project, including submissions to ethics committees, preparation of protocols and project manuals, developing survey tools, interviewer training, training of survey field monitors, project co-ordination and liaising with other project staff, and writing of reports. I conducted the data analysis, and wrote the research report. Heart disease in Indians Study (HINDI) Assessment of carotid intima-media thickness in rural Indians and comparison to Caucasian controls I am a principal investigator in this project and took primary responsibility for setting up this project, including submissions to ethics committees, preparation of protocols and project manuals, developing the questionnaire, recruiting participants, conducting study clinics, project co-ordination and liaising with other staff, and writing of reports. I conducted the data analysis, and wrote the research report

13 Recalibration of a Framingham risk tool for rural India I am a principal investigator in this project. I designed the study, conducted the literature review, analysed the data and wrote the research report. Rural Andhra Pradesh Cardiovascular Prevention Study (RAPCAPS) I am a principal investigator in this project and took primary responsibility for setting up this project, including submissions to ethics committees, preparation of protocols and project manuals, developing the cardiovascular program, developing evaluation tools, training of health workers and physicians involved in the study, project co-ordination and liaising with other project staff, and writing of reports. I conducted the data analysis, and wrote the research report

14 Acknowledgements I would like to thank Associate Professor Bruce Neal, who supervised my doctoral studies, for his guidance, advice and support throughout my candidature. I am also grateful to my co-supervisor, Professor David Celermajer, for his encouragement to pursue doctoral studies and his support of my career development. I am particularly grateful to Dr Anushka Patel, Professor Mark Woodward, Mr Sam Colman and Dr Federica Barzi for their invaluable expert knowledge that guided me through the analysis of the Andhra Pradesh Survey data. I am also thankful to Professor Raj Bhopal who provided me with academic support during the period I was overseas in Scotland. I am also thankful to all the collaborators of the Andhra Pradesh Rural Health Initiative (APRHI) Project, Professor K Srinath Reddy, Dr K Rama Raju, Dr Krishnam Raju and Mr PK Madhav who made this research possible. I am also grateful to Dr Magnolia Cardona who oversaw much of APRHI s management and Dr C Ravi Raju, Dr S Iyengar and Mr A Sukumar who helped manage the project at the field level. I am thankful to all the Survey Interviewers, the Multipurpose Primary Healthcare Workers and the lab technicians who helped in data collection. I am also thankful to the village Community Leaders and all participants who supported the study. I am also grateful to Mr Jason Harmer who helped in the analysis of the carotid intima-media thickness data. Finally I would like to thank my family and friends, for their constant support and friendly advice through out my candidature, in particular my husband Dr Venu Chalasani and colleague Dr Rohina Joshi. I received a National Health and Medical Research Council Public Health Medical scholarship during my candidature, with additional financial support from the George Institute for International Health

15 Statement of Authentication This thesis is submitted to the University of Sydney in fulfilment of the requirement for the degree of Doctor of Philosophy. The work presented in this thesis is, to the best of my knowledge and belief, original except as acknowledged in the text. I hereby declare that I have not submitted this material, either in full or in part, for a degree at this or any other institution. Signature: Date: May 8,

16 Papers The chapters in this thesis have formed the basis of the following manuscripts: 1. From Chapter 3: Chow CK, Cardona M, Raju PK, Iyengar S, Sukumar A, Raju R, Colman S, Madhav P, Raju Rama, Celermajer D, Neal B. Cardiovascular disease and risk factors among 345 adults in rural India The Andhra Pradesh Rural Health Initiative. International Journal of Cardiology 2007; 116: From Chapter 4: Chow CK, Raju PK, Raju R, Reddy KS, Cardona M, Celermajer DS, Neal BC. The prevalence and management of diabetes in rural India. Diabetes Care, 2006; 29 (7): From Chapter 5: Chow CK, Naidu S, Raju K, Raju R, Joshi R, Sullivan D, Celermajer DS, Neal BC. Significant lipid, adiposity and metabolic abnormalities amongst 4,535 Indians from a developing region of rural Andhra Pradesh. (Accepted to Atherosclerosis, February 27, 2007). 4. From Chapter 8: Chow CK, McQuillan B, Raju PK, Iyengar S, Raju R, Neal B, Celermajer D. Greater adverse effects of cholesterol and diabetes on carotid intima-media thickness in South Asian Indians. (Under review). 5. From Chapter 9: Chow CK, Joshi R, Celermajer DS, Neal BC, Patel A. Application of re-calibrated and original Framingham risk estimates to a rural Asian Indian population and implications for primary prevention of cardiovascular disease: cross-sectional population based study. (To be submitted) And the following abstracts accepted for conferences: 1. Chow CK, McQuillan BM, Raju PK, Raju R, Neal B, Celermajer DS. High levels of subclinical atherosclerosis and cardiovascular risk factors in rural South Indian Adults. Runner up in Ralph Reader Young Investigator Finalist Session, Cardiac Society of Australia & New Zealand. August Chow CK, McQuillan BM, Raju PK, Raju R, Neal B, Celermajer DS. Rural Indians have greater subclinical atherosclerosis than urban Australians. Young Investigator Finalist. High Blood Pressure Research Council of Australia ASM

17 3. Chow CK, Joshi R, Cardona M, Raju K, Raju R, Neal B, Celermajer D. The George Institute. Department of Cardiology, Royal Prince Alfred Hospital. Field Studies in rural India demonstrate a surprisingly high prevalence of cardiovascular risk and disease. Cardiac Society of Australia & New Zealand Joshi R, Cardona M, Raju R, Sukumar A, Iyengar S, Raju CR, Raju PK, Reddy KS, Chow CK, Neal B. The George Institute. Chronic disease mortality in rural Andhra Pradesh. World Congress of Epidemiology, Bangkok, August Chow CK, Neal B. The George Institute. Prevalence of cardiovascular risk factors and disease in rural Andhra Pradesh. Oral presentation given at Cell to Society, College of Health Sciences meeting, November

18 Chapter 1 The Burden of Cardiovascular Disease in India Changing patterns of disease in low and middle-income countries The pattern of disease in many low and middle income countries is changing from a predominance of perinatal conditions, nutritional deficiencies and infectious diseases to those classified as degenerative, chronic, or non-communicable diseases - in particular cardiovascular diseases [2, 3]. In 1990, it was estimated that there were 14 million deaths from cardiovascular disease worldwide 5 million of these occurred in populations from higher income countries and 9 million occurred in populations from middle- and lowincome countries. By 2020, it is projected that there will be 25 million deaths from cardiovascular disease worldwide 6 million in populations from higher income countries and 19 million in populations from middle- and low-income countries [4]. The increase in cardiovascular disease in India is projected to be one of the greatest of any country in the world [5, 6]. Cardiovascular disease in India Deaths from cardiovascular disease, the occurrence of new cardiovascular events and measures of the prevalence of vascular disease are the indicators most widely used to evaluate cardiovascular disease burden in a population. Repeated measures of each can provide a useful indication of the progression of cardiovascular disease in a community [7]. Unfortunately while the projections for cardiovascular disease in India are alarming, there is a paucity of high quality data about cardiovascular disease in the country [8, 9]. In terms of mortality data, death registration is substantially incomplete [8, 10], information about causes of death is only available for a small proportion of all deaths registered and the reliability of the causes assigned is generally thought to be low [10-12]. For new cardiovascular events, there are no nationwide systems established to collect such data and the few studies available are limited in geographic coverage and quality [8]. Robust national information about the prevalence of cardiovascular disease is also unavailable [9], although there are a number of cross-sectional surveys that provide data

19 for selected regions [13-20]. The lack of data is particularly pronounced for rural areas which are greatly under-represented in terms of both the quantity and quality of the information available about cardiovascular diseases and the literature with regards to cardiovascular disease determinants will be reviewed in detail in Chapter 2. Mortality data from India The accuracy of cause of death data relies heavily upon the quality of the systems which have been established for recording and assigning causes to deaths. The infrastructure required for mortality surveillance is typically less available in developing countries such as India with consequent implications for the data generated [8, 10, 21]. Cause of death data in India is particularly limited by the large proportion of deaths that take place at home and the fact that most causes of death are assigned by lay people [22]. In India, it is only required that hospital or police-related deaths are medically certified and resource limitations greatly restrict medical certification outside of this setting [10]. The most recent, widely cited and comprehensive regional estimates of cardiovascular mortality for South Asia were prepared by the Global Burden of Disease Studies (GBDS) [23-25]. In 1990, it was estimated that 2266 x 10 3 deaths occurred due to cardiovascular disorders in India compared with 3175 x 10 3 in all established market economies [25]. In 2006 the GBDS reported detailed cause of death estimates for the South Asian region based on 2001 data [24]. Ischaemic heart disease (IHD) was reported to be the number one cause of death in South Asia and cerebrovascular disease (CbVD) to be the 4 th leading cause of death. The GBDS provides data in terms of proportional mortality. Proportional mortality data for cardiovascular disease (CVD) provides an indication of the relative importance of CVD within populations [26]. The GBDS estimated that 13.6% of all deaths in South Asia were due to IHD and 6.8% to CbVD. This compares to Europe and Central Asia where 29.7% of deaths are caused by IHD and 18.2% by CbVD [24]. For India, the GBDS drew on different data sources for urban and rural areas to estimate cause patterns of mortality. In urban areas this was the Medical Certificate of Cause of Death Database (MCCD) [12]. These data are not representative of the whole of India as

20 some states have not yet implemented the scheme [10]. Furthermore, in the 17 States and Union Territories that do participate, only 41.8% of deaths are medically certified [10]. It is generally the more urbanized states that are better represented [10] and this adds further uncertainty to the overall data s representativeness. The rural estimates of the GBDS were based on the Annual Survey of Causes of Death (SCD) [12]. This utilizes a verbal autopsy system to establish cause of death. Verbal autopsy (VA) methods are frequently used to establish cause of death in countries in which medical certification is incomplete and post-mortems are not done [27]. VA methods have been validated against medical certification but there is some variation in methodology across sites [27, 28]. Generally, relatives of the person who has died are interviewed regarding the symptoms and signs prior to a person s death. This together with any medical records are reviewed by a suitably qualified person who assigns a cause of death [10, 27]. The method utilized by the SCD had a number of problems. One important issue identified was that cause of death assignment was frequently done by insufficiently trained paramedical staff, meaning that a significant proportion of the causes of death were not appropriately assigned [8, 10]. For example in Andhra Pradesh, in , 38% deaths were not classifiable and a further 25% were assigned senility [8]. Data from the SCD is hence also not representative and even less likely to be accurate than urban data. While the GBDS tried to adjust for these problems identified and incorporated adjustments to improve estimates for poorly reported conditions such as HIV/AIDS and illicit drug dependence, there is considerable uncertainty about the validity of some aspects of the GBDS estimates [29]. Some recent direct measures of cause of death are available from verbal autopsy studies in India. The largest and most recent study of adult ( 25 years) mortality reports on cause of death in 48,000 urban adults and 32,000 rural adults from Tamil Nadu, India. This study reported death rates in For men 35 to 69 years death rates from cardiovascular diseases were 685 per 100,000, and for women for the corresponding age group, 428 per 100,000 [28]. While the differences in methodology limit a rigorous comparison, it is of note that age-standardised death rates from coronary heart disease

21 (CHD) in California in the United States at a similar time, , were 280 per 100,000 for men and 139 per 100,000 for women [26]. The Tamil Nadu study also found that 41% of urban male deaths and 37% of urban female deaths were due to cardiovascular diseases (ICD , ) with proportions in rural areas being 25% for men and 22% for women [30]. By contrast, in California, the proportion of death due to cardiovascular diseases was 23% for men and 20% for women [26]. Data documenting new cardiovascular events There are three studies that attempt to document the occurrence of new cardiovascular events in a population. They report an annual rate of new fatal and non-fatal CHD events of between 6 to 17 per 1000 for men and 2 to 21 per 1000 for women. The earliest study in urban Rohtak ascertained cases for the whole of Rohtak through a network of health care facilities and providers over a 12 month period, [31]. It defined events according to 1970 WHO definitions, as persons with: 1. Definite myocardial infarction (typical history and/or positive ECG and/or raised cardiac enzymes), 2. Probable myocardial infarction (typical history and/or equivocal ECG and/or minor increase in cardiac enzymes), 3. Death from CHD or 4. Sudden death. The incidence of CHD events for the population 30 years and above was 5.7/1000 for men and 2.3/1000 for women. This study could have undercounted events as all persons with new CHD events may not present to a medical facility and death registers are incomplete. It is also of note when comparing results here to other studies that the definitions did not include angina or asymptomatic CHD. The other two studies describe the occurrence of new events from cohort study populations [32, 33]. The Delhi study reports on 3-year follow-up of 4151 participants of a random-sample cross-sectional survey of adults 25 to 64 years who were free of CHD at the time of survey [32]. It is unclear from this paper what proportion of the baseline population this was, however from the paper referenced as the baseline study [20], 13,723 were initially surveyed, 6017 had ECG examinations, 814 were found to have CHD, which would leave 5203 who had ECG examinations and didn t have CHD. Of the 4151 surveyed in the 3-year follow-up, 245 had developed CHD in the 3 years (annual

22 incidence of 19.7 per 1000). 73 of 245 cases were identified clinically medical diagnosis of myocardial infarction or with angina symptoms on medical treatment; an additional 172 of 245 were identified through repeat ECG examination. This approximates to an overall annual incidence of CHD of 19.7 per 1000: 5.9 per 1000 clinically identified CHD, and 13.8 per 1000 ECG identified asymptomatic CHD. The method of case ascertainment in this study would identify more non-fatal CHD events compared with the prior study as ECG would identify asymptomatic cases. No mention is made of fatal events amongst the 4151 followed up, and it is unclear from the report whether these are included in the clinically identified events. The incidence of new events in men was similar to that in women. This is not the usual pattern, though a few studies from India have reported similar prevalence of CHD in men and women [34, 35]. The rural Gujarat study was a cohort study of 719 persons from one village. This study invited a random sample of 1460 persons selected from village population listings to participate, but only 449 persons were identified and responded. Volunteers were included to supplement the low response rate and the representativeness of the study population is very uncertain. At baseline 719 were examined, 5 with CHD were excluded and the rest followed for 5 years [33]. Over the 5 years, the annual CHD incidence rate ranged from 6.1 to 10.3 per 1000 for combined fatal and non-fatal events. This was 4.6 to 7.4/1000 for non-fatal CHD events (medical diagnosis of MI and angina), and 1.4 to 3.0 per 1000 for fatal CHD events (sudden death and CHD death). The incidence of new events in men was approximately three times that of women. This is a small study and case ascertainment did not include routine repeats of ECG in all participants as was done in the Delhi study. The authors do comment that low-income earners were less likely to participate. However, the higher rates in men compared to women is consistent with the usual gender distribution for CHD and provides some assurances of the data s validity. In summary these three incidence studies [31-33] used different case attainment methodologies and were done in different regions of India across a 15 year period. This hence makes it virtually impossible to draw conclusions about possible changing rates of CHD over time. However, all the rates reported in these studies are high when compared

23 to rates in developed countries. For example, when compared to two high quality studies from the UK, Table 1-1, rates of CHD in men and women in the Indian studies are at least double UK rates [36]. The two UK studies are from the same region of Oxford. They include fatal and non-fatal CHD events and report data age-standardised to the world population. Age-standardisation to the world population assists in making these studies comparable to the Indian studies, as the world age distribution more closely reflects the age distributions of developing countries which make up the larger proportion of the total world population. Table 1-1 Incidence of CHD in India compared with selected studies from the United Kingdom District Year Cases Method of case identification & Event criteria No. of events Age group Study size Events/ 1000 persons/ year Men Women Urban Rohtak, India Urban Delhi, India Rural Gujarat, India MI Case identified from network of health facilities including local GPs. Events include non-fatal & fatal. Non-fatal MI - Definite & probable MI confirmed with ECG/Cardiac enzymes. Fatal CHD death or sudden death MI Cases from repeat survey of persons free of CHD. Events include 'clinically defined' and asymptomatic 'ECG defined' events CHD Cohort followed annually for 5 years. Events included fatal & non-fatal. Non-fatal - medical diagnosis of MI or angina, Fatal - CHD death or sudden death Registry study. Adults residing in Rohtak (n=37,689) 73 clinical events, 172 on ECG only 18 nonfatal, 8 fatal Cohort study. Individuals with known CHD excluded. (n=4151, 3378 had repeat ECG) Cohort study. Individuals with known CHD excluded. (n=714) overall, male female ratio 3:1 Oxfordshire, UK CHD Case identified from network of health facilities. Monica criteria, non-fatal: definite MI & fatal: CHD death or sudden death. Oxfordshire, UK CHD Case identified from network of health facilities. Monica criteria, non-fatal definite MI & fatal definite MI, fatal possible MI or unclassifiable death. *Age standardised rates Registry study. Resident population in Oxfordshire Registry study. Resident population in Oxfordshire n=568, Data from Indian documenting prevalence

24 There are more studies reporting estimates of the prevalence of cardiovascular disease in India than estimates of mortality or total CHD incidence. Prevalence of CHD from these studies show rates that vary from 7 to 90 per 1000 persons for men and 11 to 92 per 1000 persons for women. A recent systematic review of the literature found 27 studies reporting CHD prevalence from India using ECG data over a 28 year period between 1974 and 2002 [9, 37]. A repeat Ovid Medline search of the literature using the same criteria identified no study presenting new data between 2002 and June There are many different measures of coronary heart disease prevalence [38]. Common methods of ascertaining prevalence include: enquiring with regards to a history of previous CHD events and/or enquiring about the presence of angina or the presence of symptoms of angina, for example with the Rose questionnaire [39]. CHD disease prevalence can also be measured by ECG examination. ECG may confirm the presence of CHD or detect the presence of CHD which is asymptomatic [40]. Minnesota coding of ECG recordings is the standard method of analysing ECG data [41, 42]. This is a complex process which assigns codes to different patterns of ECG abnormalities and requires extensive training. However despite training and careful analysis, discrepancies between coders can arise up to 15% of the time [41]. There is a large body of literature reporting on the combination of abnormalities most sensitive and specific in determining the presence of CHD [41]. Use of the same ECG definitions for defining CHD can hence make comparison possible [41, 42]. While the studies included in this review here all collected ECG data, different combinations of codes were used to define CHD. In addition some studies combined prevalence measures based on self-reported symptoms or history with ECG measures. Broadly comparable studies are grouped in Table 1-2. Prevalence of CHD was generally higher in urban compared with rural areas. Direct urban-rural comparisons were made in two large studies. In the Delhi study 5,621 urban and 1,047 rural individuals were surveyed [43]. The prevalence of CHD based on ECG criteria for urban areas was 56 per 1000 in men and 76 per 1000 in women; for rural areas this was 6 per 1000 for men and 27 per 1000 for women. The Moradabad study of 1806 urban and 1769 rural participants found similarly higher prevalence in urban areas of

25 per 1000 for men and 34 per 1000 for women; compared with rural areas of 18 per 1000 for men and 11 per 1000 for women [44]. The prevalence of CHD, at least for urban areas, seems to be somewhat similar to studies from developed countries. The prevalence of CHD based on ECG only findings reported by a large population based Belgian study of 47,358 adults from a comparable time period, was similar in Belgian men (60 per 1000) compared with men from the Delhi study, and higher compared with men from Moradabad and Jaipur; Belgian women (43 per 1000), however had a lower prevalence of CHD compared to Delhi and Jaipur women, and similar rates to Moradabad women [42]. There was little evidence to support an increase in CHD prevalence over time across any region of India. Comparison of studies to determine changing rates over time was however difficult. This was because the studies were done in differing regions of India and used different sampling methodologies. In addition, trends over time were made more difficult to assess since dates of field work were more than 5 years prior to the publication date in two studies [15, 18, 43] and in two thirds of studies, field work dates were not reported at all. There were also few studies sufficiently separated in time to enable comparison between time periods with the majority of studies done in the 1990s. Two studies conducted in Rajasthan in the same city of Jaipur in 1995 and 2002 measured urban prevalence of CHD enabling a direct comparison. The first study of a random sample of 2212 (response rate 87.9% in men, 57.3% in women) reported a prevalence of CHD based on ECG criteria only of 35 per 1000 in men and 84 per 1000 in women. The rates reported by the second study, also a random sample survey conducted in Jaipur of 1800 people (response rate 57.3% in men, 68.2% in women) reported a similar prevalence of CHD based on ECG criteria of 38 per 1000 for men and 72 per 1000 for women. The higher prevalence of CHD in women in both these studies is unusual and the sex distribution of risk factors did not seem to explain the differences. In the 1995 study, prevalence of smoking and diabetes were higher in men, whilst hypertension and obesity were comparable across genders. In the 2002 study, smoking

26 was more prevalent amongst men, diabetes and hypertension were similar between genders and obesity was more prevalent amongst women. Table 1-2 Urban and rural Indian studies of CHD prevalence defined on ECG and symptoms Author Date of publication Location Sample size Age group Cases/ 1000 persons/ year Men Women Urban Studies Gupta [45] 1975 Haryana 1407 > Gupta [14] 1995 Rajasthan 2212 > Gupta [46] 1996 Rajasthan 1415 > Singh [47] 1998 Moradabad Ramachandran [34] 1998 Madras 953 > Mohan [48] 2001 Chennai 1175 > Gupta [49] 2002 Rajasthan/ Hindus 1777 > Gupta [49] 2002 Rajasthan/ Muslims 363 > Gupta [50] 2002 Rajasthan 1123 > Rural Studies Dewan [51] 1974 Haryana 1504 > Gupta [52] 1994 Rajasthan 3133 > Wander [16] 1994 Punjab 1100 > Gupta [17] 1994 Rajasthan 1150 > Gupta [46] 1996 Rajasthan 1982 >29 28 NS Singh [53] 1997 Moradabad Gupta [35] 1997 Rajasthan 3148 > Table 1-3 Urban and rural Indian studies of CHD prevalence defined on ECG only Author Date of publication Location Sample size Age Group Cases/ 1000 persons/ year Men Women Urban Studies Chadha [19] 1992 Delhi Gopinath [15, 18] 1992 Delhi Singh [54] 1995 Moradabad 561 > Singh [53] 1997 Moradabad Rural Studies Jajoo [55] 1988 Sevagram 2433 > Gupta [13] 1996 Rajasthan 3148 > Chadha [43] 1997 Delhi Singh [44] 1997 Moradabad

27 In summary the available data suggest that the prevalence of CHD is static in India. However, the data are very poor and CHD prevalence could be increasing or decreasing and possibly quite different patterns across the country could easily go unnoticed. Insight into the impact on CHD prevalence of changes in CHD incidence and CHD mortality are also completely unknown. Synthesis There is a general consensus from the literature that the burden of cardiovascular disease in India is significant, especially in urban areas. It is the mortality studies that provide the best evidence of this [23-25, 28]. In terms of evidence for changing trends in CVD, the lack of repeat measures in the same region and the variation in methodology and definitions used in studies make it difficult to directly compare studies and evaluate change over time. There is more uncertainty about the burden of cardiovascular disease in rural areas of India because of the disproportionate lack of data for these regions. While a number of studies of urban-rural differences have demonstrated the anticipated higher levels of cardiovascular disease in urban compared to rural regions the magnitude of the burden of vascular disease in rural India is very uncertain. A greater proportion of the population lives in rural areas and even proportionately lower rates of vascular disease in rural areas would translate into very large absolute numbers of people affected. This argues for a renewed focus on the more accurate assessment and monitoring of cardiovascular diseases and other evolving diseases in rural areas. Andhra Pradesh Rural Health Initiative (APRHI) Recent information on mortality in a developing rural area In 2002, a collaborative research project APRHI (Andhra Pradesh Rural Health Initiative) was set up by partners in India and Australia (detailed in Appendix 1) to better understand the healthcare needs of rural communities in the Godavari region of Andhra Pradesh and develop appropriate interventions to improve health. The Indian partners of the project included: the Byrraju Foundation, a local not-for profit organization which

28 had already developed primary health care facilities in approximately 150 of the Godavari villages; the Care Foundation part of the network of Care hospitals, a major local health care provider; and the Centre for Chronic Disease Control (CCDC), who acted as adviser and academic partner to the project from Delhi. The George Institute for International Health was the sole Australian partner. In the absence of reliable and current information about causes of death in the Godavari region, or data from which reasonable estimates could be made, an early goal of APRHI was the establishment of a mortality reporting system with reliable ascertainment of the main causes of death in the population. The system utilised the existing Byrraju primary health care infrastructure to collect detailed information on deaths using the verbal autopsy tool. The quality of this VAT system was carefully monitored and the process continually improved and updated through careful training of interviewers and coders and a focus on ensuring that the system captured all deaths in the participating villages. In its first year of operation (2003-4) the surveillance system covered 45 villages with a population of 180,162 and identified 1354 deaths (a crude death rate of 7.5/1000). Verbal autopsy questionnaires were completed for 1329 (98%) of the deaths and a specific underlying cause of death was assigned for 1084 (82% of all deaths). Diseases of the circulatory system accounted for 34% of male deaths and 30% of female deaths. Cardiovascular diseases (heart attack and stroke) were found to be the leading cause of death in the region accounting for an estimated 28% of all deaths in males and 25% of all deaths in females [1]. Cardiovascular deaths made up a slightly larger proportion of all deaths in this area compared with estimates for the South Asian region reported by the Global Burden of Disease Study [24]. The estimates for this region were also slightly higher than those reported by the Tamil Nadu studies of cardiovascular disease in rural India but were less than those reported for urban India [26]. The data suggest that cardiovascular disease is a significant problem in this rural population and hence that it may become a serious issue for many other parts of rural India as they develop

29 Male Female 34% 30% Circulatory system (I00 - I99) Respiratory system (C00 - D48) External causes of mortality (S00 - Y98) Not classifiable Infectious & parasitic diseases (A00 - B99) Neoplasm (C00 - D48) Other Figure 1-2 Proportional mortality due to circulatory system causes compared with other causes from the Andhra Pradesh Rural Health Initiative (APRHI) Mortality Surveillance Study of Reasons for increasing cardiovascular disease in India The reasons why developing urban and rural areas of India are recording a shift to chronic disease patterns are likely to have much in common with the factors that have driven the same changes in developed countries during the previous century. Epidemiologic transition is resulting from improved maternal and child health, better nutrition, increasing life expectancy and changes in key behaviours such as cigarette smoking and physical activity [2]. There is now a large body of evidence to support the notion that a broad range of established determinants of vascular disease are causing an epidemic of vascular disease in India and a comprehensive review of these data is provided in Chapter 2. However, in addition to the impact of the established causes of cardiovascular disease it is also possible that there are some currently unexplained factors unique to South Asians that are further magnifying the vascular disease epidemic in this population group. For example, there is a strong literature supporting an increased

30 susceptibility of this South Asian population to heart disease and diabetes [56, 57]. Whilst the reasons for this are uncertain it may be substantially increasing the regional vascular disease burden. Increased susceptibility to cardiovascular disease in Asian Indian populations The first report of a possible excess of CHD in Asian Indians was made in 1960 in a study of IHD in seven racial groups in Singapore using data from 19,415 consecutive autopsies. Indians were noted to have higher rates, earlier onset and a more fulminating type of coronary disease [58]. Over the ensuing decades the hypothesis that an excess risk exists in Asian Indian populations has been supported by a growing body of evidence, the majority of which comes from studies of migrant population of South Asians. The great majority of these studies are based on migrant populations in the United Kingdom, Singapore, Canada and the United States Studies of Indian migrants to the United Kingdom There have been at least 20 studies comparing mortality and morbidity between South Asian and other populations in the United Kingdom [37, 59]. The first of these studies was done in 1975 and the most recent was reported in 2005 [60]. The overall conclusion of these studies is South Asians have an excess risk of CHD [57, 61-63]. Seven of the nine studies reporting on ethnic differences from data on deaths showed an excess risk of death from CHD in Asian Indians compared with mostly Caucasian reference populations [59, 60, 63-68]. Similarly higher rates of CHD were found in South Asian sub-groups from studies of CHD prevalence [69-71]. While risk factor levels such as diabetes [72, 73], low HDL [74], microalbuminuria [75], physical activity [76, 77], homocysteine [78] and inflammatory makers (CRP) [79] are typically worse in South Asian migrants to the UK, the magnitude of the excess risk in these South Asians that cannot be explained by existing knowledge about risk factors is difficult to quantify. The most recent study of comparative mortality provides some insight into the magnitude of excess risk observed through the use of standardized mortality ratios (SMR) for the UK population by ethnic group. This study was based on mortality data collected

31 between 1989 and 1992 for adults (20-69 years) in England and Wales and the 1991 census. SMRs were calculated by expressing as a percentage the age-standardised death rate from IHD or stroke in each ethnic group by the proportion of deaths due to IHD or stroke for the total population. Asian Indians had a 40 50% excess in IHD and CbVD mortality compared with the general population. The all-cause mortality ratio was similar to the reference population as it was balanced out by lower mortality from cancer (Table 1-4). The SMRs reported were however not adjusted for differences in risk factors levels, and hence it was not possible to determine how much of the excess risk in South Asians was due to different levels of established risk factors, different treatment patterns or other factors. In another study of case fatality in South Asians compared with white patients, the contribution of risk factors to higher case fatality in South Asians is explored. This was an observational study of AMI admissions into a district general hospital of East London [80]. South Asians had twice the admission rate compared to white patients. Most aspects of treatment were similar in the two groups, except a higher proportion of the South Asians received thrombolytic drugs (81.2% v 73.8%). After adjustment for age, sex, previous myocardial infarction, and treatment with thrombolysis or aspirin, or both, the South Asians had a poorer survival over the six months from myocardial infarction (hazard ratio 2.02 (95% confidence interval 1.14 to 3.56), P = 0.018). A substantially higher proportion were diabetic (38% v 11%, P < 0.001) and additional adjustment for diabetes removed much of their excess risk (adjusted hazard ratio 1.26 (0.68 to 2.33), P = 0.47). From this study, a higher case fatality may contribute to the higher standardised mortality ratios (SMRs) reported amongst South Asians in the UK and diabetes seems to explain much of this excess risk. Further support for these findings is provided by the Southall Study of 1,421 South Asian men and 1,515 European men aged years in London [70]. Prevalence of ischemic ECG abnormalities was higher in South Asians than in Europeans (17% versus 12%, p<0.001) with an excess of major Q waves (Minnesota codes 1-1 or 1-2) in younger South Asian men (p=0.01 for the age-ethnicity interaction). In a logistic model controlling for smoking and cholesterol, the odds ratio for major Q waves in South Asians compared with Europeans was 2.4 (95% CI, ). Adjusting

32 for glucose intolerance and hyperinsulinemia reduced this ratio to 1.5 (95% CI, ) suggesting that more adverse risk factor levels, particularly diabetes, accounted for a significant proportion of the excess risk in asymptomatic ECG-defined IHD. The consensus, however, is that variation in classic risk factors probably does not explain all the increased risk of death or high prevalence of IHD amongst South Asians [37, 59]. Exploratory investigation into other determinants of cardiovascular disease such as social isolation, psychosocial factors and healthcare inequalities also find worse profiles in South Asians [37, 81-83]. Higher IHD mortality may be due to treatments being less effective in South Asians, poorer compliance to treatment in South Asians, or poorer outcomes with the occurrence of disease in South Asians. Some support for poorer outcomes being contributory is found in a cohort study of 828 South Asian and 27,962 non-south Asian insulin-treated diabetes patients from the UK. In South Asian compared with non-south Asian diabetic patients, cardiovascular mortality (p=0.02) and all cause mortality (p=0.001) rates were higher [84]. The extent these factors may or may not explain the excess IHD observed is also uncertain [81, 82, 85, 86]. Table 1-4 Standardised mortality ratios for CHD, stroke and all-cause mortality for Asian Indian and Caribbean ethnic groups compared to the adult general population of the UK, Asian Indian Caribbean Total Population** No. of deaths SMR* 95% CI No. of deaths SMR 95% CI No. of deaths Men All causes ( ) (74-79) CHD ( ) (42-49) CbVD ( ) ( ) SMR Women All causes (97-103) (87-95) CHD ( ) (61-80) CbVD ( ) ( ) Table adapted from Wild et al BMJ 1997 [68]. *Standardised mortality ratio **This refers to all UK adults (20-69 years) and is the reference population to which Asian Indian and Caribbean death rates are compared. CHD coronary heart disease codes , CbVD cerebrovascular disease codes [87]

33 Studies of Indian migrants to Singapore Singapore is a multi-ethnic society comprising Chinese 76.5%, Malays 14.8%, and Indians 6.4%. The majority of the Indians (80%) originate from the Southern states of India (Tamil Nadu and Kerala) and Sri Lanka [88, 89]. Analysis of data from the Singapore National Death Registry collected between 1980 and 1984 found South Asians had an approximate 3-fold higher relative risk for IHD compared with Chinese [89]. No corresponding excess was identified for stroke. This study reported the proportional mortality rate (PMR), over the period , for adults years for IHD and CbVD by ethnic group. Proportional mortality data is useful in that it provides an indication of the relative importance of CHD and/or stroke as causes of death in subpopulations. It also does not use census data, thus preventing any miscalculations due to different reporting of race in death records compared with census data. Ischaemic heart disease accounted for a much larger proportion of Asian Indian deaths (males 45.3%, females 24.7%) compared with Chinese (males 16.5%, females 13.1%) and Malays (32.1%, 17.4%). The proportional mortality rates for stroke were more similar in Asian Indians (male 7.4%, female 11.6%) compared with Chinese (male 9.1%, female 13.2%) or with Malays (male 10.1%, female 15.2%). Adjusting for age and sex, Indians had a 3.8 times higher death rate from IHD compared with Chinese, and 2.0 times higher rate compared to Malays. The excess mortality from IHD in Indians over Chinese and Malays was more pronounced in younger compared with older age groups (Indian to Chinese relative risk is 12.5 in age group, and 3.0 in the agegroup). For cerebrovascular disease, there were minimal differences in mortality rates between ethnic groups and this was similar across age groups. This study additionally reports that the proportion of IHD deaths due specifically to acute myocardial infarction was much the same between sex- and ethnic- groups providing some reassurance that biases attributable to diagnostic differences between ethnic groups do not account for the findings. In summary, these figures indicate IHD is more common in Asian Indians. One possible explanation for the differences in rates may be differences in risk factor levels

34 At a similar period of time to the previous study, a high quality multistage stratified random sample survey was conducted to investigate risk factor levels across ethnic groups in Singapore. This study of 2143 persons aged 18 to 69 years (response rate 60.3%, comparable across ethnic groups) found worse age-adjusted rates of HDLcholesterol and diabetes in Indians compared to Malays and Chinese, but not lower levels of smoking, mean systolic blood pressure, total cholesterol, LDL-cholesterol and triglycerides [90]. This study was not able to be directly related to the mortality study, but suggests that some of the excess risk in IHD observed by the previous study may be due to risk factor variation. The Singapore Cardiovascular Cohort Study had similar findings to the Singapore National Death Registry study. Importantly, however, this project adjusted for classic risk factor levels [91]. This study was an amalgamation of three previous cross-sectional surveys in Singapore[92]: the Thyroid and Heart Study , the National Health Survey 1992 and the National University of Singapore (NUS) Heart Study The study used record linkage to establish outcomes for 5920 participants. The results for 2879 males without CHD at baseline were reported in 2001 with a total of personyears of follow-up (an average of nine years follow-up). In this time 125 males developed CHD with South Asians having the highest incidence of CHD. The hazard ratio for developing CHD in Asian Indians compared with Chinese was 3.0 ( ) adjusting for age; and 3.1 ( ) after adjusting for age and other classic risk factors (Table 1-5). While these data suggest that LDL-C, HDL-C, BMI, smoking, diabetes, hypertension, alcohol were not the causes of the excess risk observed, other factors related to treatment were not however included in the adjustment. Table 1-5 Age and risk factor adjusted hazard ratios for CHD in Asian Indian males compared with other ethnic groups in Singapore, 2001 HR adjusted for age* 95%CI HR adjusted for All** 95%CI Asian Indian 3.0 ( ) 3.1 ( ) Malay 1.2 ( ) 0.9 ( ) Chinese 1 1 *Hazard ratios adjusted for age **Hazard ratios adjusted for age and other risk factors (LDL-C, HDL-C, BMI, smoking, diabetes, hypertension, and alcohol)

35 Studies of Indian migrants to Canada Like Singapore, Canada is another ethnically diverse country well suited to the investigation of ethnicity and disease. In 2001 the largest two visible minority groups were the Chinese (3.5%) and South Asians (3.1%). For the period 1979 to 1993, the age-standardized death rates per 100,000 people aged 35 to 74 years were calculated using data from the Canadian death register on 1.2 million deaths and Canadian census data from 1981, 1986 and 1991 and reported by ethnic group [93]. All-cause death rates were highest in Europeans compared with Indian and Chinese groups (Table 1.6). South Asian men had a similar IHD death rate to European men and South Asian females had a significantly higher IHD death rate compared to European and Chinese women. Table 1-6 Age-standardised death rates per 100,000 for CHD, stroke and all-cause mortality for Asian Indian adults compared with other ethnic groups in Canada, Asian Indian origin Chinese origin European origin No. of deaths Death rate No. of deaths Death rate No. of deaths Death rate 95% CI 95% CI 95% CI Men All causes ( ) ( ) ( ) IHD* ( ) ( ) ( ) CbVD** ( ) ( ) ( ) Women All causes ( ) ( ) ( ) IHD ( ) ( ) ( ) CbVD ( ) ( ) ( ) *CHD Coronary heart diseases ICD , **CbVD - Cerebrovascular diseases ICD Age standardised death rate for adults aged years Table adapted from Sheth et al, CMAJ 1999 [93] Comparison of proportional mortality rates adjusts for the differences in all cause death rates. Similarly to the Singapore study, proportional mortality rates were highest amongst South Asians. Proportional mortality for IHD was 42% in South Asian men and 29% in South Asian women. This was a greater proportion compared to European men (29%) and European women (19%) and Chinese men (18%) and Chinese women (11%). Rates

36 were however not adjusted for risk factors and the extent to which differing levels of risk factors might have driven the variation in mortality was uncertain. As for the Singaporean studies, there was little variation in stroke death rates between ethnicities (Table 1-6). To establish whether the variation in cardiovascular disease rates can be explained by differences in disease risk factors and subclinical atherosclerosis, a population-based study of cardiovascular risk factors and carotid intima-media thickness in three ethnic groups in Canada was conducted. Carotid intima-media thickness (IMT) is a wellvalidated non-invasive measure of atherosclerosis. It can be assessed accurately and reproducibly, and is predictive of the risk of future cardiovascular events [94-98]. IMT has been used in population studies to quantify atherosclerotic disease burden in different ethnic groups and has been shown to be highly correlated with cardiovascular risk factors in populations of differing ethnicity [99-101]. Carotid IMT studies may also be useful in evaluating whether the relationships of risk factors with atherosclerosis differs between ethnic groups [ ]. The SHARE study in Canada recruited 985 participants by stratified random sampling from three centres, (response rate 63%). The study found a higher age and sex standardized prevalence of cardiovascular disease amongst South Asians (11%) compared with Chinese (5%) and Europeans (2%), p= Some of this variability could be due to risk factor levels since diabetes, total cholesterol, HDL-cholesterol, triglycerides and novel prothrombotic markers (homocysteine and lipoprotein (a)) were all worse in South Asians. Prevalence of cardiovascular disease reported in this study was not however reported with adjustment for these risk factors and the extent to which these risk factors explain the variation in IMT is unknown. This study also showed that increasing quartiles of carotid IMT were associated with increasing prevalence of cardiovascular disease for all ethnic groups. Interestingly however the crude means of maximum carotid IMT were slightly higher in Europeans, mean (STD) 0.75(0.16), compared to South Asians, 0.72(0.15) and Chinese, 0.69(0.16). This difference was reported to be significant after adjusting for age, sex and recruiting

37 centre. Again, however, it is unclear from the analysis presented what the effect of controlling for risk factors may have on these differences. Studies of Indian migrants to the United States In the United States, studies of cardiovascular disease amongst ethnic groups have been conducted in California. California received 26.1% of the total immigration to the US in 1987 [106] and in the 2000 census the Californian population was composed of 53.1% White non-hispanic, 26.2% Hispanic, 6.2% African American, 3.2% Chinese, 1.1% Japanese and 1.0% Asian Indian. There have been two studies of IHD and CbVD comparing death rates in South Asians to other ethnic groups living in California. The studies done in 1996 and 2004 had similar findings to each other which were also comparable to the results of the Canadian mortality study. IHD proportional mortality rates were higher in South Asians compared to all other ethnic groups. For example in the 1996 study [26], 35% of all deaths in South Asians were due to IHD compared to 23% of all deaths being attributed to IHD amongst the predominantly Caucasian general population. However, in terms of age-standardised rates, South Asian men and women had generally lower all-cause and IHD mortality rates. All-cause death rates in South Asians compared to the general population were lower (in South Asian men, 668 per 100,000 versus in the general population, 1200 per 100,000; in women 335 per 100,000 versus 700 per 100,000). IHD death rates were also slightly lower compared with the general population (in men 258 per 100,000 versus 280 per 100,000; in women 110 per 100,000 versus 139 per 100,000) (Table 1-7). Formal significance testing was not included in this report but graphical representation of the 95% confidence intervals suggested that the differences between South Asians and White groups may be significant for women. The pattern of mortality was similar for the 2004 study [107]: 34% of all deaths in South Asians were due to IHD compared to 21% of all deaths due to IHD in the general population. For age-standardised rates, South Asian men and women had lower all-cause death rates, (in men, 551 per 100,000 versus 1024 per 100,000: in women, 387 per

38 100,000 versus 657 per 100,000) and slightly lower IHD death rates compared with the general population (in men, 201 per 100,000 versus 220 per 100,000: in women 116 per 100,000: 119 per 100,000). These studies indicate that IHD accounts for a large proportion of deaths in South Asians from California. In contrast CbVD rates amongst South Asian men was about half that of the overall rate for California. Rates in women were lower, but not as much as for men (Table 1-7). Table 1-7 Age-standardised death rates per 100,000 for CHD, stroke and all-cause mortality for Asian Indian adults compared to other ethnic groups in California, South Asian Chinese White, non- Hispanic Overall No. of deaths Death rate No. of deaths Death rate No. of deaths Death rate No. of deaths Death rate Men All causes IHD* CbVD** Women All causes IHD CbVD *CHD Coronary heart diseases identified using International Classification of Diseases, 9 th revision (ICD-9), classification numbers 410 to 414 **CbVD stroke ICD-9: 431 to 438 as the primary cause of death. Age standardised death rates per 100,000 for adults years. Table adapted from Wild al [107] The more recent study reported age-standardised death rates per 100,000 for the population aged 25 to 84 years based on data from a total of 1.8 million deaths between 1990 and Compared to the previous study period (1985 to 1990) there was a decline in all-cause and IHD mortality for all ethnic groups except for Asian-Indian women, who experienced a 16% increase in all cause mortality and a 5% increase in CHD mortality. The lower age-standardised all-cause and IHD mortality rates amongst Asian Indians were likely due to a healthy migrant effect [37]. Migrant populations may be healthier than the general population as they may have to pass through health examinations prior to migrating or may be from more affluent sectors of society. The large proportion of deaths due to IHD amongst Asian Indians and the increase in CVD rates in Asian Indians whilst

39 rates have fallen in all other ethnic groups in these Californian studies is comparable with data from Singapore and Canada indicating an increased susceptibility to CVD. A crosssectional study of the offspring of South Asian migrants in the US also found high ageadjusted prevalence of CHD compared to the participants of the Framingham offspring study. These rates were not adjusted for other cardiovascular risk factors but the study noted some differences in risk factor levels such as higher levels of diabetes, lower levels of smoking and obesity and lower prevalence of hypertension in men and lower prevalence of LDL-cholesterol in women of South Asian descent compared with Framingham offspring [108]. Synthesis The consensus of opinion is that the risk of CHD is elevated amongst Asian Indians although by just how much and for exactly what reasons remains uncertain. The view is based heavily on the many influential studies that have been derived from the United Kingdom, North America and Singapore but these data are further supported by other studies of South Asian migrant populations in the United Arab Emirates (UAE) [109], South Africa [110, 111], Fiji [112] and Trinidad [113]. In each case there were higher levels of CHD reported in the South Asian population group. In many cases, this was in the context of elevated levels of cardiovascular risk factors and it remains unclear to what extent this was the reason for the observed elevations in risk. The Californian and Canadian studies both find lower or similar age-standardised allcause rates. The Californian study also finds lower age-standardised IHD rates. It is likely this is due to a healthy migrant effect. The large proportion of all deaths due to IHD in these studies is consistent with the Singaporean studies indicating Asian Indians are susceptible to IHD. The Canadian SHARE study found a higher prevalence of known cardiovascular disease in Asian Indians compared to the other ethnic groups, however did not report their findings adjusted for cardiovascular risk factors. By contrast, the levels of carotid IMT were slightly higher in the Europeans. Carotid IMT in this study was shown to be

40 associated with increasing prevalence of CHD and increasing levels of risk factors within all the different ethnic groups studied. These findings are somewhat difficult to interpret, but could indicate that for a given level of IMT thickness, Asian Indians have higher levels of coronary artery disease. There are a number of factors that may produce differences in cardiovascular disease rates between ethnic groups that do not reflect a real difference in underlying disease susceptibility. The main potential confounders in the types of studies reviewed here are differences in risk factor levels and treatment levels between South Asian Indians and the comparator groups. While the majority of studies age-standardise to overcome differences in age distribution between the migrant and comparator populations, many studies did not control for known cardiovascular risk factors. The few studies that did adjust for risk factors suggest that they may explain a significant amount of the excess risk observed in South Asians [80]. A large recent case-control study of 1732 persons with MI and 2204 controls in the South Asian countries of India, Bangladesh, Nepal and Sri Lanka compared with 10,728 cases and 12,431 controls in other countries, found that risk factor differences explained almost all of the excess risk of MI in South Asian populations [114]. In contrast, other studies have found that adjusting for risk factors makes little difference to the excess risk [91, 92]. In addition, a number of studies have identified other factors such as socio-economic status, health care access, occupation and education as possible explanators. The study of ethnic differences in disease by studying migrant groups, have some important limitations. Different routine data collection systems use different definitions to describe ethnicity with country of birth, self-reported ethnicity and observed ethnicity common variants [115]. There is evidence that each of these definitions have different sensitivity and specificity in identifying ethnic groups [116, 117]. Further complication is added in some countries by the application of different definitions of ethnicity to mortality compared to census databases. These definitional issues may lead to incomplete identification and misclassification of migrants and incorrect estimates of vascular mortality and prevalence rates might ensue. Whether these would over- or under

41 estimate differences between South Asians and other population groups is not immediately clear for every study, but in general misclassification would be anticipated to lead to under-estimation of any real differences. Misclassification in migrant studies may also occur at another level. To have sufficient power for comparison smaller migrant groups are often merged, and then compared to the majority population. Merging of groups may or may not make sense racially and/or culturally. If done inappropriately, merging ethnic groups may hide important differences between these groups. This is demonstrated by the very different levels of risk factors found in migrants to the UK from India, Pakistan and Bangladesh [71]. The evidence of an excess risk in South Asians for CbVD is much less convincing. Besides the UK studies, studies in Singapore, Canada and the US find similar rates for CbVD compared with the rest of the population. The numbers studied are however all low and the power and reliability of the data for CbVD are limited. A different pattern of variation in CbVD between South Indian and comparator populations would not be unexpected since the chief determinants of CbVD and IHD are different and the levels of risk factors such as cholesterol and blood pressure may differ markedly between ethnic groups [91, 99]. Summary The reasons for higher levels of IHD in Asian Indian populations are not entirely clear, although differences in levels of known risk factors, socio-economic differences and access to health care likely explain a significant proportion. Higher proportional IHD mortality rates in South Asians could also be explained by South Asians being less likely to die from other causes. The proportion of all deaths due to IHD amongst South Asians would be greater. This theory is termed the competing causes hypothesis [37]. Some support for this theory is seen in the lower rates of cancer deaths observed amongst South Asians in Canada, compared to European and Chinese

42 groups [93]. Higher prevalence of CHD in South Asians may also be due to reporting bias and/or greater investigation for asymptomatic disease because of perceived risks [37]. South Asians may be more susceptible for genetic reasons [37, 118, 119], there may be maternal reasons [120] or they may be prone to higher levels of certain risk factors such as diabetes or novel risk factors such as Lipoprotein (a), homocysteine, infection, poverty or stress [37, 121]. There may be a potent interaction of known causal factors specific to South Asians [37]. The combined effects of increased susceptibility and very rapid epidemiologic transition have enormous implications for the cardiovascular disease burden in India. While urban areas of the country are likely to have higher rates of disease, developing rural populations of India are likely to suffer a significant proportion of the burden of cardiovascular disease as the bulk of the population lives in rural areas. Disadvantaged populations typically suffer the brunt of cardiovascular disease [122, 123] and these populations are particularly poorly equipped to manage chronic disease [124]. Approaches which target rural populations in developing countries have the potential to prevent large numbers of cardiovascular events

43 Chapter 2 What is known about cardiovascular risk factors in rural India? Introduction The majority of reports of cardiovascular disease in India focus on the changes occurring in urban areas [6, 125]. While urban areas of developing countries are experiencing the consequences of epidemiologic transition first, changes are also occurring in rapidly developing rural areas [ ]. Furthermore, small increases in the levels of cardiovascular risk factors in rural areas where the bulk of the population lives could translate into large absolute increases in numbers of persons at risk of cardiovascular disease. Those studies that have reported urban-rural comparisons of the effects of epidemiological transition in developing countries such as India have typically showed pronounced differences between urban and rural settings although the magnitude of the urban-rural differences depends heavily upon the regions selected [9, 43, 129]. As such, to reliably track the impact of the epidemiologic transition in developing countries it is ever more important to gain representative information from rural areas. Only with regular and accurate assessments of the health burden attributable to evolving chronic conditions such as cardiovascular disease will it be possible to effectively plan health policy for rural regions. In India, as for most other developing countries, there is a lack of representative data on cardiovascular disease burden or the risk factors that are driving it in rural regions. Systematic reviews that synthesise the available literature with critical assessments of the quantity and quality of cardiovascular risk data from rural India are also largely absent [9]. The primary aim of this review was, therefore, to determine the number, size and coverage of cross-sectional surveys of cardiovascular risk factors conducted in rural areas of India and published prior to May The secondary aims of this review were to evaluate the methodology of cardiovascular surveys conducted in rural areas of India and to evaluate whether there is evidence of increasing cardiovascular risk factors levels in rural India. The key risk factors addressed were blood pressure, overweight/obesity, tobacco use, diabetes and cholesterol

44 Methods Search strategy Studies were identified by searches of OVID/ Medline and Pub Med electronic databases from 1966 to May 2006 (with no language restrictions) for studies reporting one or more key cardiovascular risk factors in a population from rural India. Searches were performed using the following search terms - rural and India/Indian combined with one of the MeSH subject headings: cardiovascular, coronary heart disease, hypertension, diabetes, lipids, tobacco or obesity. In addition the search was supplemented by searching reference lists of technical reports and publications of the World Health Organisation and by cross checking the reference lists of all identified papers. Study inclusion and exclusion criteria Studies had to fulfil the following criteria to be included in this review: 1. Set in a rural part of India, 2. Reporting data on one or more cardiovascular risk factor (blood pressure, overweight/obesity, tobacco use, diabetes and cholesterol) 3. Method of sampling that attempted to select a representative sample of a demarcated community or population. For example a systematic sample of households in a defined number of villages or a random sample of clusters or individuals, such as a random sample of households or random sample from electoral rolls or local census lists. Studies were excluded if subject selection was on the basis of disease type or the study was an in-hospital study of patients. A study reporting data in more than one paper was included only once but using data from all relevant papers combined. Data extraction A standard set of information was extracted from all eligible studies. This included the publication year, time and location of field work, number of respondents, response rate, demographics of group studied, survey sampling method, definitions used for cardiovascular risk factor measurement and mean and/ or prevalence of all key risk factors reported

45 Quality assessment The quality of manuscripts was assessed for requirements of cross-sectional epidemiological surveys [130]. The main criteria were: (1) Sampling strategy, (2) Documentation of field work time and place (3) Reporting response rates and (4) Documentation of methodology and definitions used for measuring cardiovascular risk factor levels. Statistical analysis All studies that contained information on a particular risk factor were compared with each other. Mean values and/or proportions of risk factors were extracted from the papers or occasionally calculated from data presented in papers if the exact information required was not reported. For example, mean BMI could be calculated from average weight and height. Prevalence figures are rounded to the nearest integer and expressed per 1000 persons. Means are rounded to 3 significant figures or 1 decimal place. For blood pressure, means are rounded to whole numbers. Measures of uncertainty were generally not reported or not reported in a systematic way such that meaningful comparison was not possible. Study sample size was routinely reported and provides some rough measure of uncertainty

46 360 potentially relevant abstracts identified and screened for retrieval 200 articles excluded after title and abstract review 160 articles reviewed for more detailed evaluation (including cross references) Reasons for exclusion (75) Reviews with no new data (15) Urban populations (36) Longitudinal studies with no baseline data (4) Unclear as to rural/urban or data combined (7) Letter insufficient information (4) Elderly >60 yrs only (5) Patients/ selected on specific disease criteria (3) Unable to obtain manuscript (1) 85 manuscripts included Figure 2-1 Flowchart of articles identified and selected for inclusion in review of cardiovascular risk factor studies from rural India Results Studies identified and availability of data Of the 360 articles identified, 85 met the inclusion criteria. Figure 2-1 shows details of study selection. Studies were published in both national journals (n=36) and international journals (n=49). Numbers of publications increased over time and then reached a plateau in the 1990s (Figure 2-2). A number of studies spawned a multitude of papers and when this was accounted for the 85 papers found in this search described 58 separate studies throughout rural India. One rural Rajasthan dataset published first in 1994 provided information for 9 papers [13, 17, 35, 46, 52, ]

47 25 20 Number of publications Year published Figure 2-2 Rate of publications produced over time on cardiovascular risk factors in rural India Geographic coverage of rural India by identified studies India is subdivided into twenty-eight states and seven union territories. The population of these states range from 540,000 to 166 million and the proportion of each that are urbanised range from 9.8% in Himachal Pradesh to 49.8% in Goa [135, 136]. 15 of the 28 Indian states are represented in the published surveys (Table 2-1). Generally States with higher GSDP (Gross State Domestic Product) had more studies, while many States with lower GSDP rank had no studies (Table 2-1). Studies were also not well distributed across the population. For example, while a similar total population reside in the Northern states as the combined Southern and Central states, of the 58 studies: 35 occurred in North India, 7 in central India and 12 in South India. There were only 3 National studies reporting on single risk factors, one on diabetes and the other two on tobacco use [137, 138]

48 Table 2-1 Distribution of rural cardiovascular studies according to Indian State Map ref.* State Papers Studies Population** Rural (%) GSDP Rank 27 Uttar Pradesh ,197,921 79% 2 15 Maharashtra ,878,627 58% 1 4 Bihar ~ ~ 82,998,509 90% West Bengal ,176,197 72% 4 1 Andhra Pradesh ,210,007 73% 3 24 Tamil Nadu ,405,679 56% 5 14 Madhya Pradesh ~ ~ 60,348,023 74% 9 22 Rajasthan ,507,188 77% 8 12 Karnataka ~ ~ 52,850,562 66% 7 7 Gujarat ,671,017 63% 6 20 Orissa ,804,660 85% Kerala ,841,374 74% Jharkhand ~ ~ 26,945,829 78% 16 3 Assam ,655,528 87% Punjab ,358,999 66% 11 8 Haryana ,144,564 71% 13 5 Chhattisgarh ~ ~ 20,833,803 80% 18 G Delhi ,850,507 7% Jammu and Kashmir ,143,700 75% Uttaranchal ~ ~ 8,489,349 74% 21 9 Himachal Pradesh 5 5 6,077,900 90% Tripura ~ ~ 3,199,203 83% Meghalaya 1 1 2,318,822 80% Manipur 1 ~ ~ 2,166,788 73% Nagaland ~ ~ 1,990,036 83% 27 6 Goa ~ ~ 1,347,668 50% 22 2 Arunachal Pradesh ~ ~ 1,097,968 79% 29 F Pondicherry ~ ~ 974,345 33% 25 B Chandigarh ~ ~ 900,635 10% Mizoram ~ ~ 888,573 50% Sikkim ~ ~ 540,851 89% 31 A Andaman and Nicobar Islands ~ ~ 356,152 67% 32 C Dadra and Nagar Haveli ~ ~ 220,490 77% 33 D Daman and Diu ~ ~ 158,204 64% 34 E Lakshadweep ~ ~ 60,650 56% 35 National studies 5 3 Studies in multiple states 1 1 Total studies *Map reference refers to Figure 2-3: States labelled with number, Union territories with letter **Population Census 2001 Gross State Domestic Product estimated for the year 2004 by Ministry of Statistics and Programme Implementation of Indian Rupees [135, 136]

49 Figure 2-3 Map of States and Union Territories of India Numbers refer to Indian States and letters to Union Territories; see Table 2-1 for key [136]

50 Methods used for surveys The sampling methodology used for the majority of the studies was a simple systematic household sampling technique. For example, all adults in every second house were enrolled in this type of survey. Other studies utilized census lists as a sampling frame while others still employed a range of more complex sampling techniques. The methodological characteristics of the surveys are described in more detail in each section below. However key indicators of the success of the various sampling strategies were reported by only some studies with response rates documented by only 56% of studies (ranging from 60% to 100%) and the year that the fieldwork was done being stated in only 62% of studies. Prevalence of key cardiovascular risk factor levels in rural areas of India Diabetes Number and characteristics of studies Diabetes prevalence was reported by 16 studies. The size of these studies ranged from 467 to 19,754. The majority of studies were studies of villages sampled from within a given State. The largest study was the PODIS (Prevalence of Diabetes in India study) conducted between 1999 and This was a multi-centre study done in 49 urban and 59 rural sites across India. The selection of the 108 centres for this study was done by an independent polling agency, and was designed to represent a composite picture of the vast, diverse and populous country. In each centre, one or more study areas were chosen randomly and all adults 25 years and over were invited. In centres with a larger population and wider geographic spread, more study areas were selected, with larger towns and cities having up to 8 areas selected. Further detail regarding the geographic distribution of study centres or the random selection process is not given in any of this study s publications [137, 138, 142]. The rural results of the study are described in detail below. Most other studies used simple sampling methods and all included far fewer sites: either systematic sampling of all persons above a certain age in all households in a given geographic area [16, 52, 128, ], all individuals from voters lists [35], or a

51 systematic sample of all households [43, 51, ]. A few described random sample techniques, for example random sampling of households [ ] or blocks of households [53]. The sampling method was uncertain in one study [150]. Only 9 studies documented the date of actual field work. In two of these studies the publication date was approximately 10 years after the conduct of field work [43, 143]. Response rates were reported in 12 of the studies and ranged from 67 to 100%. The method and definition of diabetes was reported in 15 of the studies (Table 2-2). Diabetes was diagnosed by blood testing in recent years (6 studies) and according to urine testing (4 studies) and via selfreport (5 studies) prior to this. Blood testing was for the majority by a single test of fasting capillary blood from finger-prick collections, with just 3 studies [53, 138, 145] doing post-glucose challenge testing as well (Table 2-2). Prevalence of diabetes Diabetes prevalence ranged from 0.1% to 6.3% and trended up over time (Figure 2-4). The method of diagnosis of diabetes is an important confounder in any comparison of diabetes rates between populations. This is highlighted in Figure 2-4, showing diabetes prevalence and the changes in diabetes diagnostic methods used over time. The average age of the participants in the majority of studies reviewed here were similar and hence did not appear to be a major confounder of diabetes rates (Table 2-2). Diabetes prevalence was generally comparable between men and women, though some of the earlier studies reported higher rates in men compared with women [150, 152] (Table 2-2). The PODIS study measured fasting glucose using the One Touch Glucose meters (Lifescan, Johnson & Johnson, Mumbai, Maharashtra) calibrated to give plasma glucose equivalents and quality checks were done in every 15 th person with a glucose peroxidise lab method (Pearson correlation 0.89, 95% CI ). In total 41,270 subjects were studied, (response rate 88.3%) 21,516 from urban and 19,754 from rural areas, (male to female ratio approximately 1). The standardised prevalence of diabetes using ADA1997 criteria [153] in the rural population was 1.9%(95%CI ) compared with urban areas of 4.6%(95%CI ). In 10,617 urban and 7,746 rural participants, oral glucose

52 tolerance testing was done, and in this group the prevalence of diabetes, according to WHO 1999 criteria [154], was 5.9% for urban areas and 2.7% for rural areas. A further 6.3% urban and 3.7% rural participants had impaired glucose tolerance. Table 2-2 Studies of diabetes prevalence in rural areas of India Author Year published N Male (%) Age group Mean Age Diabetes definition Cases per 1000 persons M F ALL Tripathy BB % >10 ~ urine glucose Rao KS % > urine glucose Dewan BD % urine glucose Patel JC ~ >10 ~ Unclear ~ ~ 38 Srivastava RN % urine glucose Rao PV ~ > self-reported ~ ~ 29 Ramachandran A % > hr PPG>11.1mmol/L Kutty VR ~ ~ self-reported ~ ~ 35 Gupta* % > self-reported Patandin % hr PPG >11.1mmol/L Wander GS % self-reported ~ ~ 47 Chadha SL % ~ self-reported & on medications Singh RB* % FG>7.7mmol/L and PPG>11.1mmol/L Kutty VR % FPG>7.7mmol/L/ PPG>11.1mmol/L/ selfreport on meds Sadikot SM % FPG 7.0mmol/L ~ ~ 19 Sadikot SM % FG 7.0mmol/L &/or 2hr PPG 12.2mmol/L Ramachandran A % > hr PPG >11.1mmol/L *combined papers, ~ Not reported, PPG - post-prandial glucose measurement, FPG - fasting plasma glucose, FG - fasting glucose, Sadikot included twice as have reported rates using fasting glucose only criteria and in a sub-sample performed OGTT

53 70 60 Method of diabetes diagnosis Urine testing Blood testing Self-report TN Prevalence per Orissa AP Gujarat TN Punjab Kerala AP TN UP Kerala National 1 20 National 2 10 UP 0 Haryana Delhi Rajasthan Year Figure 2-4 Prevalence of diabetes by year, region and method of diagnosis in rural India with studies from the same State related by connecting lines Labelled according to location of study. AP Andhra Pradesh, UP Uttar Pradesh, TN Tamil Nadu. National 1 prevalence by WHO definition, National 2 prevalence by ADA definition Commentary on diabetes studies There is a marked variation in the prevalence of diabetes between the many studies reporting data. There is some evidence of a general upward trend in prevalence over time that is also observed in the few States that have done surveys on multiple occasions. While a general upward trend would fit with the likely progression of the epidemiologic transition in rural areas of India whether this is the correct interpretation of the data remains substantively uncertain. The sampling methods utilised in the majority of studies should have yielded prevalence rates broadly representative of most of the areas studied although this cannot be

54 confirmed for some and the sample may be significantly biased in a few. The lack of information about field work dates and the possible discrepancy between field work and publication dates (two studies published approximately 10 years after fieldwork [43, 143]) means that reported prevalence may reflect a very different time period to that assumed here. Some studies report very high response rates (98% [51] and 100% [148]) which are probably implausible raising questions regarding how these rates were calculated and the quality control processes that were in place. For example, in one study s methods section it states that all individuals were invited from consecutive households, but that the response rate was calculated as the number attended divided by the number of voters registered in that village [52]. In addition to the variation in method and quality of sampling, differences in the agegroups studied, diabetes definitions and the measurement tools used make direct comparisons of prevalence rates uncertain. For example studies with a larger number of young subjects would be anticipated to report lower overall prevalence because diabetes is so strongly age dependent. Likewise, studies in which diagnosis of diabetes was solely based on urine testing or self-report are likely to significantly under-estimate diabetes prevalence compared to studies in which blood testing of fasting glucose level or glucose tolerance testing were utilised. In one study as few as 19% of people with diabetes on blood testing were aware that they had diabetes [138] and issues associated with the validity of self-report and urine testing are the likely explanations for the very low prevalence reported in the 3 studies lying along the X-axis in Figure 2-4. While these limitations make it difficult to be confident in the comparison of individual estimates against each other, there does seem to be an overall upward trend in the prevalence of diabetes and the States with repeat measurements all show greater rates in later years. The 3 studies reported from Tamil Nadu are a good example. Two of these studies were conducted by the same researchers and have been directly compared [128]. Since diabetes rates were measured using the same methodology and were standardised to each other to remove the effects of changing population demographics, the findings are likely to be robust with an almost three-fold increase in diabetes recorded from the first

55 study done in 1989 (2.2% with diabetes) to the second study conducted 14 years later (6.3% with diabetes) [128].The prevalence of diabetes in the earlier Tamil Nadu study may be lower than a contemporaneous study in Tamil Nadu [148] as the study area chosen in the former was selected to be representative of low-income rural area [128]. Blood pressure Number and characteristics of studies Thirty four studies provided information about mean systolic/diastolic blood pressure or prevalence of hypertension. 29 of 34 studies report prevalence of hypertension, and 5 studies reported mean blood pressure only. The sample size of these studies varied from 149 to All studies were done within a single State and there were no studies of a nationally representative sample. Simple systematic sampling methods were used in the majority of studies. For example all adults in a specified age-group invited from all households or systematic selection of households in a demarcated area [16, 17, 43, 51, 55, 132, 143, 144, ]. Some of the studies described random sample methods [53, 151, ], and in a few insufficient information was provided to classify sampling methods [ ]. Approximately half (13) of the studies documented the date of field work and response rates were reported by two thirds (19) and ranged from 60 to 100%. About one third of studies did not document details of the blood pressure measurement equipment used and a similar proportion did not state the number of blood pressure measurements used for the analyses. In the 29 studies reporting prevalence, the majority documented the systolic and diastolic blood pressure cut-offs used to determine high blood pressure (Table 2-3 and Figure 2-5). One study stated diagnosis was by a cardiologist, but did not give a blood pressure cut-off [160], in one study no definition was stated [144] and in one study a diastolic blood pressure cut-off only was stated [159] (Table 2-3)

56 Prevalence of hypertension The prevalence of hypertension ranged from 0.2% to 60.8% and trended up over time (Figure 2-5). The increasing trend over time was also observed within a range of different age-groups (Figure 2.6). This increasing trend was clearer in States with repeat crosssectional studies at different points in time such as Uttar Pradesh [143, 160, 162, 178] (Figure 2-6). Average systolic blood pressure also trended up over time (Figure 2-7). A systolic blood pressure cut-off of 160mmHg was used in the majority of earlier studies and 140mmHg for more recent studies, (Figure 2-6). The prevalence of high blood pressure was similar in men and women (Table 2-3). Two studies both conducted in Assam stand out for the very high prevalence of hypertension reported [165, 166]. The authors of these studies comment that local reports existed suggesting high blood pressure was particularly prevalent in these areas and diets in Assam were very high in salt. In both these studies blood pressure was measured with a mercury manometer and defined as high if it fit the criteria SBP 140 and/or DBP 90 and/or taking blood pressure lowering medication. Subjects in the first study [166], published in 2002, were a random sample of tea garden workers 30 years or more (response rate 98%, n=1015) and each had two measures of blood pressure taken while sitting. The overall prevalence of hypertension was 60.8% and associated with age, alcohol consumption and salt intake. The larger study published in 2004[165] used a multi-stage sampling method in which 5 districts were selected from 23 in Assam State. Within each district 5 villages were selected at random and subjects in each village were invited to participate from a systematic sample of households. A total of 3180 (1441 men and 1739 women) participated with response rates ranging from 80 90% across districts. Blood pressure was measured 3 times in each person, and if high confirmed with a repeat of 3 measurements the next day. This study reported the prevalence of hypertension to be 33.3%. It is unclear why the tea garden workers had nearly double the prevalence of hypertension compared to the second Assam study despite being of a similar age group

57 225 SBP cut at 160 mmhg SBP cut at 140 mmhg Other definition Rajasthan 175 Uttar Pradesh Kerala Prevalence per Maharasthra Gujarat Rajasthan AP Rajasthan Haryana Punjab Rajasthan Tamil Nadu Uttar Pradesh 150/95 Haryana 25 Haryana AP Delhi UP Rajasthan Himachal Pradesh Uttar Pradesh Mahar. Mahar. Maharashtra Haryana UP Orissa Figure 2-5 Prevalence of high blood pressure by year and blood pressure cut-point in rural areas of India with studies from the same State related by connecting lines Note: Sample size of studies is proportional to circle size. Two recent studies from Assam that are outliers and have been omitted here. Other definition included diastolic blood pressure >90mmHg for Rajasthan study, >150/95 for Uttar Pradesh study and cut-points were not defined for the Gujarat and other UP study. Year

58 Not given Assam 450 Prevalence per Kerala Rajasthan Punjab Tamil Nadu Uttar Pradesh Assam 50 Haryana Haryana UP AP Orissa Gujarat Maharasthra Rajasthan Haryana Rajasthan AP Delhi Himachal Pradesh UP Mahar. Rajasthan Mahar. Uttar Pradesh Haryana Uttar Pradesh Year Figure 2-6 Distribution of hypertension prevalence studies in rural India by minimum age of population sampled Note: Sample size of studies is proportional to circle size

59 Table 2-3 Studies of mean blood pressure and/ or prevalence of hypertension in rural India Author Year State Year± N Male Age Age Response Definition used for hypertension Cases/1000 SBP % group mean rate M F All All Dewan BD 1974 Haryana ~ % % 160/95 ~ ~ 2 ~ Gupta SP 1977 Haryana ~ % % SBP 160 &/or DBP Srivastava RN 1977 Uttar Pradesh ~ % > % NA ~ ~ ~ 112 Rao RS 1981 Andhra Pradesh ~ % ~ 160/95 &/or on meds* ~ Dasgupta DJ 1982 Himachal Pradesh % % NA ~ ~ ~ 111 Wasir HS 1983 Haryana ~ % 20 ~ ~ 140/ ~ Baldwa VS 1984 Rajasthan ~ % % 140/ Dash SC 1986 Orissa % ~ 140/ ~ Puri DS 1986 Himachal Pradesh ~ % ~ 96% SBP 160 and/or DBP ~ Patel JC 1986 Gujarat ~ >10 ~ 67% NA ~ ~ 78 ~ Srivastava RN 1987 Uttar Pradesh % % SBP 160 and/or DBP Hussain SA 1988 Rajasthan % ~ ~ ~ SBP 140 and/or DBP ~ Jajoo UN 1988 Maharashtra ~ % > % SBP 160 and/or DBP ~ Majumder PP 1990 West Bengal % > ~ NA ~ ~ ~ 106 Kumar P 1991 Rajasthan ~ % ~ DBP Jajoo UN 1993 Maharashtra ~ % 20 ~ 95% WHO ~ Joshi PP 1993 Maharashtra ~ % > ~ SBP 160 and/or DBP Kutty R 1993 Kerala ~ ~ 90% SBP 160 &/or DBP 95 &/or on meds* ~ ~ 161 ~ Agarwal AK 1994 Uttar Pradesh % > ~ diagnosis by cardiologist ~ Gilberts EC 1994 Tamil Nadu ~ % % SBP 160 and/or DBP 95 ~ ~ Gupta R 1994 Rajasthan ~ % 25 ~ ~ SBP 140 and/or DBP ~ Wander GS 1994 Punjab ~ % % SBP 140 and/or DBP 90 ~ ~ 145 ~ Gupta R 1994 Rajasthan ~ % > % SBP 140 &/or DBP 90 &/or on meds* Singh RB Uttar Pradesh ~ % ~ >150/95 ~ ~ Goel NK 1996 Uttar Pradesh ~ % 30 ~ 92% SBP 160 and/or DBP ~

60 Chadha SL 1997 Delhi % ~ ~ SBP>160 &/or DBP>90 &/or on meds* ~ Singh RB 1997 Uttar Pradesh ~ % % SBP 140 &/or DBP Malhotra P 1999 Haryana % % SBP>160 &/or DBP>90 &/or on meds* Nirmala A 2001 Andhra Pradesh % ~ SBP 140 and/or DBP Kusuma YS 2001 Andhra Pradesh % > ~ ~ ~ ~ ~ 123 Lubree HG 2002 Maharashtra ~ % ~ 140/90 20 NA Hazarika NC 2002 Assam ~ % % SBP 140 &/or DBP 90 &/or on meds* Hazarika NC 2004 Assam ~ % % SBP 140 &/or DBP 90 &/or on meds* Sidhu S 2004 Punjab % ~ ~ ~ ~ ~ 127 *meds = medication, Year of publication, ±year field work (~) - Data not reported

61 Average SBP (mmhg) Year Figure 2-7 Mean systolic blood pressure reported in studies from rural India over time Commentary on blood pressure studies Blood pressure is the most common risk factor measured in rural cardiovascular surveys. As for the diabetes studies, comparison of these studies is limited by differences in population sampling, blood pressure measurement methodology and definitions used. Despite this, the data suggest an increasing prevalence of hypertension in rural areas of India that is consistent across different age groups and comparable to trends reported from urban areas [179]. The differences in blood pressure cut-offs used for defining high blood pressure is an important confounder of direct comparisons between all of the studies although the prevalence of high blood pressure appeared to trend up within the groups of studies that used similar definitions. The corresponding trend of increasing average systolic blood pressure over time is re-assuring although strong correlation of prevalence of hypertension and mean blood pressure levels would be anticipated in those studies that reported both measures. As with the diabetes studies, the sampling methods utilised in the majority of these studies should yield prevalence rates representative of the areas selected for study. Also, as for the diabetes studies there may be biases hidden by very incomplete reporting of study sampling

62 methods, response rates and field work dates. In addition response rates reported by many of these studies were also very high again raising uncertainty about the method by which they were calculated or the validity with which they were reported. The considerable variation in the prevalence of high blood pressure between States may be due to methodological differences between the studies or may represent real differences in the prevalence of hypertension. Likewise the finding of increasing prevalence of high blood pressure over time may be real or a consequence of the timing of surveys in States with high and low prevalence of hypertension. In Haryana [51, 158] and Rajasthan [132, 174] repeat studies within each State, using similar definitions to define high blood pressure show an increase over time. By contrast, repeat studies in Maharashtra [55, 180] show falling levels of high blood pressure over time. Real differences in prevalence of high blood pressure between States would be anticipated on the basis of environmental, dietary and socioeconomic differences [ ] but the ability to relate such changes to the data for rural India reported here is limited by the nature of the information reported. Obesity Number and characteristics of studies Prevalence of obesity or mean BMI was reported for 28 different studies. 16 of 28 studies reported prevalence of overweight or obesity with the rest reporting average BMI only. The majority of studies were single State studies and their sample sizes ranged from There was one nationally representative study: mean BMI and the prevalence of obesity was reported for ever-married women years from the National Family Health Survey (NFHS-2) [184]. Many of the studies included here overlapped with the diabetes studies and as such sampling methods were similar. Simple systematic sampling methods were most common, [16, 43, 51, 52, 55, 128, 143, , 158, 160, 161, 165, 185, 186], a few studies reported random sampling methods [150, 151, 168, 171] and a few reported more complex multi-level sampling methods utilising systematic and random selection of villages and then households

63 [173, 184, ]. Insufficient information was available to determine sampling methodology in one study [191]. In addition, similarly to the studies reporting diabetes prevalence, only about half the studies documented field work dates and half of studies documented response rates. All studies reporting prevalence of overweight and obesity documented definitions but these varied considerably between studies. A cut-off of 25kg/m2 was most frequently used. The value of the cut-off used to define overweight/ obesity did not seem to have a relationship with time (Figure 2-8). Prevalence of overweight and obesity The prevalence of overweight and obesity ranged from 1.6% to 22.3% and rates trended up over time (Figure 2-8). Difference in BMI cut-off used for defining overweight/obesity did not appear to significantly impact on this trend (Figure 2-8). Mean BMI ranged from 17.2 to 24.5 kg/m 2 and similarly trended up over time (Table 2-9). The majority of studies were conducted in populations of a similar age range, and differences in the age groups sampled did not appear to impact upon the observed changes in the prevalence of overweight and/or obesity over time. In those studies that showed a sex difference, obesity tended to be greater in women (Table 2-4). The two directly comparable studies of diabetes prevalence done in Tamil Nadu also made high quality measurements of obesity. In these studies mean BMI increased over the 14 year period (1988 to 2004) from 17.6 kg/m 2 to 20.7 kg/m 2 in men and 18.7 kg/m 2 to 21.5 kg/m 2 in women. The prevalence of overweight and obesity was reported to be 1.9% in 1988 compared to 17.1% in This study also measured waist circumference which increased from71.4cm to 79.9cm in men and 68.7cm to 76.4cm in women [128] over the same time period. The highest mean BMI and the highest prevalence of obesity were reported by two studies of women done in Punjab. The first study reported in 2004 on 503 women from rural Punjab aged 20 years or over. These women had an overall calculated mean BMI of 24.5kg/m 2 [177]. This ranged from 21.5kg/m 2 in the year age-group to 28.2kg/m 2 in the year

64 age group. Sampling in this study was in two stages, random selection of study site, and age/sex/location stratified sampling of individuals and hence probably resulted in a representative selection of the population. The group also had typical rural demographics with the majority engaged in physical labour and 85% being illiterate. The response rate was however not documented. The second Punjab study reported a prevalence of overweight/obesity of 22.3% from a relatively young group (20 45 years) of 800 rural Punjab women [188]. In contrast to the previous study however, this group were sampled from upper middle class groups of rural landowners BMI 25 BMI 27 BMI 30 BMI 25women, 27 men Punjab Prevalence per Delhi Punjab Tamil Nadu Uttar Pradesh Kashmir Rajasthan Uttar Pradesh 50 Maharashtra Kerala Rajasthan Uttar Pradesh Assam Tamil Nadu Haryana NFHS-2* Year Figure 2-8 Prevalence of overweight/obesity by year and effect of different definitions of overweight/obesity in rural India Note: Sample size proportional to circle size * NFHS-rural women only sampled in this national study and overweight defined as BMI

65 BMI (kilograms/metres squared) Year Figure 2-9 Mean BMI reported in rural Indian studies over time The largest study which provided data was the Second National Family Health Survey (NFHS-2). NFHS-2 is a large-scale, multi-round survey conducted in a representative sample of households throughout India [114]. The NFHS is a collaborative project of the International Institute for Population Sciences (IIPS), Mumbai, India; ORC Macro, Calverton, Maryland, USA and the East-West Centre, Honolulu, Hawaii, USA. The first National Family Health Survey (NFHS-1) was conducted in The main objective of the survey was to collect reliable and up-to-date information on fertility, family planning, mortality, and maternal and child health. The second National Family Health Survey (NFHS- 2), was conducted in , the principal objective of NFHS-2 were the same as NHFS-1 [184]. However, in addition, NFHS-2 collected more detailed information on women s health and included measurement of height and weight in eligible women (ever married, years). NFHS-2 also measured the extent to which households in India use cooking salt that has been fortified with iodine and tobacco use (discussed in later section on tobacco). NFHS-2 collected data on 91,196 households and 89,199 ever-married women using a complex sampling scheme designed to collect representative data from urban and rural areas of the 26 states of India. Results were reported weighted to 2001 census India figures. The majority of the data reported for this study were national estimates (urban and rural data combined). This study reported a national mean BMI for women years of 20.3kg/m

66 and ranged from 19.2kg/m 2 to 23.7kg/m 2 across 26 states. The national prevalence of overweight (BMI 25kg/m 2 ) was 10.6% and ranged from 3.7% to 33.8% and obesity (BMI 30kg/m 2 ) was 2.2%. The States of Orissa and Bihar had the lowest mean BMI/ prevalence of overweight/obesity. Delhi and Punjab reported the highest mean BMI/ prevalence of overweight/obesity. For rural India, 56,556 women were assessed and the mean BMI reported for rural women was 19.6 with 5.9% with a BMI 25kg/m 2 and 0.9% with a BMI 30kg/m 2. Information regarding the inter-state variation of the rural results was not reported

67 Table 2-4 Studies of mean BMI and overweight/ obesity in rural India Author Year** State Field work N Age Mean Response Average BMI Definition Prevalence - Cases/1000 group Age Rate M F All Overweight/ obese M F All Rao KS 1972 Andhra Pradesh ~ 2006 > NA nil ~ ~ ~ Dewan BD 1974 Haryana ~ % nil ~ ~ ~ Gupta SP 1977 Haryana ~ % nil ~ ~ ~ Srivastava RN 1987 Uttar Pradesh % nil ~ ~ ~ Jajoo UN 1988 Maharashtra ~ 2433 > % ~ ~ ~ BMI> Ramachandran A 1992 Tamil Nadu > % BMI>25(F), >27(M) Kutty R 1993 Kerala ~ 90% ~ ~ ~ BMI>27 ~ ~ 58 Agarwal AK 1994 Uttar Pradesh > ~ ~ ~ ~ BMI 28.6(F), 30(M) ~ ~ 157 Gupta R 1994 Rajasthan ~ 3148 > % BMI Patandin S 1994 Tamil Nadu % ~ ~ 19.6 nil ~ ~ ~ Wander GS 1994 Punjab ~ % ~ ~ ~ BMI>25(F), >27(M) ~ ~ 166 Gupta R 1994 Rajasthan ~ ~ 75% ~ ~ ~ BMI Singh RB 1995 Uttar Pradesh ~ % BMI>25 ~ ~ 95 Chadha 1997 Delhi ~ ~ ~ ~ ~ BMI> Singh RB 1997 Uttar Pradesh % BMI Malhotra P 1999 Haryana % nil ~ ~ ~ NFHS National ~ ~ ~ BMI 25 ~ Zargar AH 2000 Kashmir ~ 4993 > % BMI>25(F), >27(M) ~ ~ ~ Zargar AH 2000 Kashmir ~ 2723 >40 ~ ~ ~ ~ 23.0 *BMI 25(F) 27(M) Kusuma YS 2001 Andhra Pradesh > ~ nil ~ ~ ~ Khongsdier R 2002 Meghalaya ~ ~ 19.9 ~ 19.9 not measured ~ ~ ~ Kutty VR 2002 Kerala % ~ ~ 22.3 BMI>29.9 ~ ~ ~ Lubree HG 2002 Maharashtra ~ ~ 21.0 ~ 21.0 nil ~ ~ ~ Reddy KS 2002 Haryana ~ ~ ~ ~ BMI Hazarika NC 2004 Assam ~ % ~ ~ ~ BMI 25 ~ ~ 69 Ramachandran A 2004 Tamil Nadu > % BMI> Sidhu S 2004 Punjab ~ ~ nil ~ ~ ~ Sidhu S 2005 Punjab ~ ~ ~ ~ ~ BMI>25 ~ * Rural & urban measurements combined as rural not given separately, ** publication date, (~) data not available

68 Commentary on studies of overweight/ obesity Overweight and obesity is a major problem in high-income countries [ ], while many lower-income countries are still experiencing the health consequences of malnutrition and under-weight [195]. Overweight and obesity is, as expected, far less common in rural India than in high-income countries, however evidence from rural studies reviewed here already finds high rates of overweight and obesity in some selected rural areas of India [177, 188]. This suggests that changing lifestyles in rural areas are causing changes in body weight and it is likely that this will continue to progress with social and economic development in these areas. Comparison of the studies included here is limited by very similar problems to those noted for the studies of diabetes and hypertension. In addition, however, the majority of the reports of overweight/obesity are from recent years and this significantly impacts on the ability to detect trends over time. Differences in definitions used to define overweight and obesity and differences in study localities were again an important confounder of comparisons between studies over time and while numbers were small, comparison of studies using similar definitions did suggest upward trends in the prevalence of overweight/obesity in rural areas during the last few decades (Figure 2-8). The findings were also supported by data from the studies reporting mean BMI, which showed a progressive increase over time (Figure 2-9). Two studies separated in time but conducted in the same state (Tamil Nadu) that used the same cut-off in BMI noted a marked increase [128]. These data suggest very rapid changes in body weight over a 14 year period and are highly consistent with the dramatic increase in diabetes prevalence also documented in Tamil Nadu [128]. In developed populations obesity typically occurs more frequently among the poor [196]. The opposite is usually true in less developed societies although in some households undergoing nutritional transition underweight may coexist with obesity [ ]. Under-nutrition and over-nutrition are epidemics of the impoverished and the affluent in

69 India, respectively [195]. Many of the rural studies presented here report little detail of socio-economic status of the population studied or the specific area surveyed. There does however seem to be some broad differences between States. Participants of the Tamil Nadu study reporting the lowest prevalence of obesity (1.9%) were described as a lowincome rural area [147]. In contrast, the study reporting the highest prevalence of obesity (22.3%) was in Punjab and rural participants of this study were described to be predominantly land-owners [191]. The state of Punjab has also reported very high levels of adolescent and childhood obesity [188]. The NFHS-2 data sheds more light on this issue. While it was a study confined to married women between the ages of years, it reported estimates by state and for some socio-economic parameters. The mean estimated BMI in rural areas was 19.6kg/m 2 compared with 22.1kg/m 2 in urban areas. Consistent with the data above, Punjab reported one of the highest prevalence of obesity at 30.2% and Tamil Nadu reported a prevalence of overweight/obesity of 14.7%. Prevalence of overweight/obesity was lowest amongst the illiterate (5.1%) compared with those who had completed education of high school or above (26.0%). Other important determinants of obesity in developing countries are falling levels of physical activity which have been shown to correlate with increasing income and increasing BMI [198]. There is some evidence to suggest that these lifestyle factors contribute to the inter-state variation in overweight and obesity, particularly in Punjab compared with other states [200, 201] and this likely explains the findings of very high levels of overweight and obesity in Punjab [177, 191]

70 Lipids Number and characteristics of studies Few rural Indian studies have collected venous blood samples, and the studies that have done so are all relatively recent. The earliest reported data on blood lipid levels in rural India are from In total 6 studies report some data on abnormal lipids [16, 43, 44, 131, 168, 202] with 5 of these studies also documenting information on mean lipid levels [43, 44, 131, 168, 202]. Samples sizes ranged from 149 to1769 and in 3 of the 6 studies participants were a random sub-sample of a larger group studied [43, 131, 202]. Only one of the studies documented the field work date and the starting year of field work was 13 years prior to publication of the results [43]. Documentation of lab methods was generally poor. Three studies did not document any lab methods [16, 43, 202] and the others reported only basic information such as standard enzymatic methods were used or other non-specific information [168, 203]. Similarly, details of blood storage, time between phlebotomy and centrifuge/testing, equipment used and quality control methods, were not systematically reported. Definitions used for abnormal lipids also varied greatly as illustrated in Table 2-5. Table 2-5 Studies of mean levels and/or prevalence of lipoproteins in rural India: sampling methods and population demographics Author Publication State Fasting Age N Male Response Sampling date sample range (%) rate Sharma S 1990 Himachal Pradesh ~ % ~ unclear Wander GS 1994 Punjab ~ % 68% systematic sample Gupta R 1994 Rajasthan Yes % 91% (M), 59% (F) random sub-sample of 10% of total study population Singh RB 1997 Uttar Pradesh Yes % 91% random sample Chadha SL 1997 Delhi Yes % ~ systematic sample Lubree HG 2002 Maharashtra ~ % ~ random sample (~) Data not available Prevalence of abnormal lipids The earliest study recorded the highest mean cholesterol, and the lowest mean cholesterol was reported by a study done in 2002 (Table 2-7). By contrast, in studies reporting the prevalence of abnormal lipids there appears to be a trend upward over time (Table 2-6)

71 Mean levels of HDL-cholesterol were generally low across studies ranging from 1 to 1.3mmol/L. In studies reporting mean levels of lipoproteins in men and women separately [43, 44, 131, 202] mean levels of total cholesterol, LDL-cholesterol, triglycerides and HDL were similar in men and women in all except 1 study [202]. That study was of 296 individuals from the high altitude Himalayan region of Himachal Pradesh where total cholesterol and HDL-cholesterol were reported as significantly higher in men. This study also found that the sub-group living at the highest altitude had the highest HDL-cholesterol [202]. Two studies reported mean levels of lipoproteins by age group. Age correlated significantly with increasing total cholesterol, LDL-cholesterol, triglycerides and HDL-cholesterol in the 1994 Rajasthan study of 300 individuals [131]. Similar trends were also observed across age-groups in the Delhi study of 359 individuals [43]. Table 2-6 Mean lipoprotein levels in rural India Author Year BMI (ALL) TC (M) TC (F) TC (ALL) LDL (M) LDL (F) LDL (ALL) TG (M) TG (F) TG (ALL) Sharma S 1990 ~ Gupta R Singh RB Chadha SL 1997 ~ Lubree HG ~ 3.8 ~ ~ ~ 0.9 ~ ~ 1.0 * Year, (~) data not available, units in mmol/l HDL (M) HDL (F) HDL (ALL)

72 Table 2-7 Prevalence of abnormal lipoprotein levels in rural India Author Year* State Age N Mean Male High total cholesterol: cases/1000 Range age % Definition M F All Wander GS 1994 Punjab % 6.2mmol/L ~ ~ 71 Gupta R 1994 Rajasthan % 6.2mmol/L Singh RB 1997 Uttar Pradesh % >5.18mmol/L Chadha SL 1997* Delhi ~ 35.6% >4.9mmol/L Lubree HG 2002 Maharashtra % 5.2mmol/L 34 ~ 34 High LDL cholesterol: cases/1000 Definition M F All Wander GS 1994 Punjab % ~ ~ ~ ~ Gupta R 1994 Rajasthan % 4.1mmol/L Singh RB 1997 Uttar Pradesh % >3.21mmol/L Chadha SL 1997* Delhi ~ 35.6% >2.9mmol/L Lubree HG 2002 Maharashtra % ~ ~ ~ ~ Low HDL cholesterol: cases/1000 Definition M F All Wander GS 1994 Punjab % ~ ~ ~ ~ Gupta R 1994 Rajasthan % <0.9mmol/L Singh RB 1997 Uttar Pradesh % <0.9mmol/L Chadha SL 1997* Delhi ~ 35.6% <0.9mmol/L Lubree HG 2002 Maharashtra % 0.9mmol/L 446 ~ 446 High triglycerides: cases/1000 Definition M F All Wander GS 1994 Punjab % ~ ~ ~ ~ Gupta R 1994 Rajasthan % 5.6mmol/L Singh RB 1997 Uttar Pradesh % >2.08mmol/L Chadha SL 1997* Delhi NA 35.6% >1.4mmol/L Lubree HG 2002 Maharashtra % 1.7mmol/L 61 ~ 61 * Publication year, (~) data not available Discussion Total cholesterol and cholesterol sub-fractions are important indicators of cardiovascular risk, yet there is very little information on these markers from studies conducted in rural India. The majority of the studies that have reported information are small, though two individual studies published in 1994 [16] and 1997 [44] have reasonable sample sizes (greater than 1000). The different and completely non-comparable methods across studies, however, make any estimate of what is occurring more generally in rural India difficult to assess. There are insufficient data to assess trends over time or differences across States with most studies being recent and no States having repeat studies done at different timepoints

73 The data that are available show levels of cholesterol and sub-fractions in men and women and across age-groups that are compatible with observations in other populations [204]. A pattern of HDL-cholesterol rising in parallel with total cholesterol has been observed in populations with increasing saturated fat intake and high carbohydrate diets [205, 206] and this may explain the higher total cholesterol and HDL-cholesterol amongst men compared with women living in the high altitude areas of Himachal Pradesh [202]. Greater levels of physical activity are, however, also known to increase HDL-cholesterol and this is another plausible explanation for the higher-hdl-cholesterol levels in men in this region [207]. The main reason for this lack of data from rural areas of India is likely the relative inaccessibility of these areas. Infrastructure to conduct laboratory testing in rural India is poor, and frequent power cuts in rural areas make setting up temporary labs difficult. In the diabetes studies this was mainly overcome with the use of point of care devices. New devices allowing immediate measurement of lipoproteins may be useful in the rural developing country setting. It is very difficult to draw any conclusions about lipid levels in rural India with the few studies done, the variation in methods utilised and the variation in definitions employed to define abnormal levels. Lipoproteins are key predictors for cardiovascular disease, and vital in measuring and tracking the burden of cardiovascular disease in rural areas of India. New large-scale serial studies are urgently required

74 Smoking and Tobacco use Number and characteristics of studies Eighteen studies report on the prevalence of smoking and/or tobacco use in rural India. Study sample sizes ranged from 301 to 6840 for single State studies with one large study of 4 centres (N=33,304) [139] and two large national studies (n= 273,267 [141] and n~157,800 [140]). The definitions of smoking varied substantially (Table 2-8) and were not defined at all in 3 studies [16, 202, 208]. Two thirds of studies did not report the year of fieldwork. The sampling methods of the two national studies were multistage and described in detail below. The most recently reported multi-centre study employed a two-stage stratified sampling method - the urban or rural village as the 1 st stage unit and the household the second stage unit. No further details are provided in this study with regards to whether selection was random, how many 1 st stage or 2 nd stage units were sampled and what parameters stratification was based on. The smaller studies mainly utilised systematic sampling methods, e.g. all adults over a certain age in a defined area such as a single or small group of villages [16, 43, 52, 55, 159, 161, 165, 186, 208]. A few studies reported randomly selected participants from a defined area [53, 151, 170] and in three studies sampling methods were unclear or insufficient information was reported to classify the method used [176, 202, 209]. Prevalence of tobacco use Smoking was common in most rural areas studied. Two studies conducted in the Himalayan regions of Himachal Pradesh reported very low rates of smoking but neither reported the definition used to define smoking (Table 2-8) [202, 208]. Smoking was much more common amongst men with rates in men up to 3 to 4 times that in women. There was no convincing trend of increasing or decreasing smoking rates over time (Figure 2-10). Two recent National studies stand out due to their very large sample size. Both these studies were conducted to collect general information about the household and were not done specifically to determine the prevalence of smoking. The overall smoking rates

75 reported by both are similar for rural areas at17.6% [141]and 17.8%[140], compared to 12.3% and 11.1% respectively for urban areas. The earlier study was the National Sample Survey (NSS) [141], a national household survey conducted in multiple rural and urban centres across the 35 States and Union Territories of India from June This survey used a stratified two-stage design to achieve a representative sample of household from rural and urban areas. In the first stage, villages from rural areas and blocks from urban areas were randomly selected from lists (7663 rural villages and 4991 urban blocks). Households were than randomly selected from each village/block. In total 120,942 households containing 471,143 persons over age 10 were included in the study. The survey was conducted by direct questionnaire to the head of the household who was asked to recall information about all household members. Data was collected in 4 timeperiods throughout the year to reduce seasonal bias. The detailed study design should have ensured a representative national sample but response rate was not reported. Poor response could importantly bias findings. The study was specifically designed to assess morbidity and private health expenditure. In addition respondents were asked if they regularly consumed biri/ cigar/ cigarette/ hukka or tobacco. Aside from the overall figure for prevalence of smoking in rural areas, other results of this survey were only reported for the combined urban and rural sample. For the combined urban-rural population, smoking prevalence was higher in men than in women (29.6% versus 2.2%) with a similar though less extreme pattern observed for chewing tobacco (20.2% versus 7.4%). Smoking prevalence (17.6% versus 12.3%) and chewing tobacco use (15.9% versus 8.4%) was higher in rural areas than in urban areas. Smoking rates increased with age to peak in the fifth decade (40 49 yrs) in men, (50.3%) and then decreased. In women smoking rates peaked in the eighth decade (70 79), (6.3%). Use of chewing tobacco in both genders increased with age and was most common in the seventh decade (60 69 years) - men 35.0% and women 19.6%. Respondents with household incomes below the poverty line (adjusted odds ratio: 1.5, ) and with no formal education (OR: 1.7, ) were more likely to be regular tobacco smokers. The second large study reported on tobacco use using data from the National Family Health Survey-2 (NFHS-2) conducted between across the 26 states of India (~1%

76 population in union territories were not represented) [140]. This survey, similar to the earlier national survey employed a complex two-stage stratified sampling method with sampling of villages and urban blocks and then random selection of households from within each. The sample was stratified according to a number of variables in each stage including regions, sub-regions, village size, and percentage of males in the nonagricultural sector, percentage of scheduled castes and scheduled tribes and female literacy. The sample size was large enough to provide reliable estimates of prevalence of tobacco consumption for each of the 26 states. Data on tobacco consumption was collected from household informants in 91,196 households and 315, 598 individuals aged 15 years and older. Two questions were asked: does he/she (1) chew pan masala or tobacco or (2) smoke tobacco. For rural areas, this study found higher rates of smoking in men compared with women (32.5% versus 3.0%), higher rates of chewing pan masala or tobacco in men (31.1% versus 13.3%) and higher rates of smoking or chewing tobacco in men (50.9% versus 15.5%). Variations in customs and culture as well as other societal influences are manifest in the wide variation in smoking rates across different areas of India. The NFHS-2 reported individual rates by State and found the prevalence of smoking amongst men ranged from 13.3% in Maharashtra, to 59.4% in Mizoram, and chewing tobacco from 7.7% in Goa, to 60.2% in Mizoram. Chewing tobacco was relatively more common in the Central, Eastern, Western (except Goa) and North Eastern states (except Meghalaya) compared to the Northern and Southern states. However in the Northern states, the prevalence of smoking tobacco is high, except in Punjab where tobacco rates are amongst the lowest as the majority of its population are of Sikh religion, which prohibits tobacco consumption

77 W Bengal Prevalence per Delhi W Bengal (Current only) Kerala Maharashtra (Current only) Haryana Rajasthan National (NSS) (Current only) Maharashtra (Current only) Punjab Rajasthan Himachal Pradesh Himachal Pradesh National (NFHS-2) Uttar Pradesh Multi-centre Kerala Assam (Current only) Year Figure 2-10 Prevalence of smoking by year and definition in rural India, circle size proportional to study size

78 Table 2-8 Smoking prevalence in men and women in rural India, Author Year* State Field work Age N Age Male Definition Prevalence, cases/ 1000 Range Mean % M F Jajoo UN 1988 Maharashtra ~ > % Current smoking/ beedi Mukherjee BN 1988 West Bengal ~ ~ 49% Current smoker Sharma S 1990 Himachal Pradesh ~ % Nil 0 0 Majumder PP 1990 West Bengal 1986 > % Any tobacco use Kumar P 1991 Rajasthan ~ % Self-reported smoker ~ ~ Kutty VR 1993 Kerala ~ ~ Self-reported smoker ~ ~ Joshi PP 1993 Maharashtra ~ > % Current smoker smoked 1 beedi/ day Wander GS 1994 Punjab ~ % Nil ~ ~ Gupta R 1994 Rajasthan ~ > % Any tobacco current/ past use Kaushal SS 1995 Himachal Pradesh ~ % Nil 78 2 Singh RB 1997 Uttar Pradesh ~ % Any tobacco current/past use Chadha SL 1997 Delhi ~ 36% Self-reported cig/bidis 10/day, chewed >2/day Malhotra P 1999 Haryana % Any tobacco current/past use Kutty VR 2002 Kerala ~ Current smoker Hazarika NC 2004 Assam ~ % Occasional or regular smoker ~ ~ Rani M 2003 National > ~ 51% Smokes tobacco Neufeld KJ 2005 National > ~ ~ Tobacco smoking (biri/ beedi/ cigar/ cigarette/ hukka) ~ ~ Jindal SK 2006 Multicentre ~ > ~ 52% Ever smoking * Publication year, estimated as half of total national sample, (~) = not reported

79 Commentary on studies of lipids Smoking is very common in rural India and particularly so amongst men. The data show no clear time trends but smoking was generally higher in groups with poorer education, with lower incomes and amongst manual workers compared with other occupations. Data on smoking in rural India is more current and data collection more systematic compared with studies of other cardiovascular risk factors in rural India. Large and recent national surveys provide robust estimates of smoking prevalence across rural India with the main limitation being that data collection was indirect with one household member reporting on smoking for all household members and this could possibly result in under-reporting. Smoking rates vary greatly across states. The two Himalayan studies (Himachal Pradesh) reported the lowest rates of smoking (38.6%) although the rates reported were not much lower than rates in Maharashtra reported from the NFHS-2. The low smoking rates in the Himalayan studies may be attributed to socio-economic factors, difference in traditions/ religion or related to limited tobacco supply and high tobacco cost in the villages studied. One of the difficulties in comparing smoking rates in India is the difference in types of smoked tobacco. Instead of cigarettes, tobacco is commonly smoked in a hand-rolled form in India. One of the reasons hand-rolled tobacco is so common in rural areas is that it is cheap, 1 U.S. Dollar =45 Indian Rupees buys 30 cigarettes or 180 beedis/ chuttas (personal communication 26/8/2006 from Dr. Arun Kumar, CCDC, India). The most common type of non-cigarette smoked tobacco is the beedi. This is manufactured and has an outer cover of dried beedi leaf with tobacco leaf cuttings inside. It has no filter and is tied together with a piece of thread and a company sticker (Personal communication 25/8/2006 from Dr. Arun Kumar, CCDC, India). Smoking customs vary across India and for example in rural Andhra Pradesh, the chutta is common; this is similar to beedi, but is wrapped in a different type of leaf, made locally and usually contains more tobacco. In some areas of rural Andhra Pradesh there is a custom of smoking chutta in the reverse (that is burning end in mouth)

80 While cigarettes usually contain a standard amount of tobacco or nicotine, there is no standard for hand-rolled tobacco in India. These differences make it difficult to quantify tobacco exposure and difficult to track increasing or decreasing tobacco use across India. These differences may also confound estimates of the impact of smoking on tobaccorelated morbidity and mortality. Including details of types of smoked tobacco would be useful, or use of another standardised measure such as nicotine or salivary cotinine levels. However most studies do not include any of these measures and in many it is unclear whether tobacco use included cigarette smoking and/or hand rolled tobacco smoking. Smokeless tobacco or chewing tobacco is also commonly used in India. The chewing of betel quid a mixture of the leaf of the Piper betle vine, aqueous calcium hydroxide paste (slaked lime), pieces of areca nut (supari) and frequently some spices - is a popular habit in South Asia believed to data back some thousands of years. Tobacco introduced into South Asia in the 1600s became a new ingredient in betel quid (pan) which is the most commonly used form of smokeless tobacco [210]. Chewing tobacco can be chewed, sucked or applied to the gums, may be processed or unprocessed and varies in ingredient quality, quantity and spices added [210]. The effects of chewing tobacco are probably different to the effects of smoking and as such it is probably not correct to group any tobacco use together as has been done by some studies (Table 2-8). Smokeless tobacco is well documented to be associated with oral and oesophageal cancers [210] but its relationship with cardiovascular disease has been less well documented. Recent analysis of the INTERHEART study, a case-control study of 27,089 participants in 52 countries found beedi smokers to have an increased risk of non-fatal AMI (2.89, ) but smokers who also chewed tobacco to have an even higher risk (4.09, ) [211]

81 Summary Cardiovascular risk factor data from rural India is generally sparse. Literature searches identified only 85 publications reporting information from 58 cross-sectional risk factor surveys done over a 40-year period. The publication rate increased over time but reached a plateau in the 1990s. Approximately half of the States of India have had at least one study done but only a few have had comprehensive measurement of all key cardiovascular risk factors. Only three national studies include representative areas from rural India and all focus on the single risk factors of diabetes and tobacco use [137, 138]. This seriously limits the ability to rigorously compare risk factor levels for different geographic areas of India. An important weakness of many publications was the omission of methodological detail. About half to two-thirds of studies neglected to report response rates, dates of field work, sampling strategies and definitions used. Studies including blood testing also omitted methodological information about sample management and assays. The lack of such information greatly limits the ability to interpret the data collected and to make comparisons between studies. Plots of prevalence for blood pressure, diabetes and obesity over time appear to show upward trends. Though there are a number of confounders restricting the ability to estimate the actual rate of change over time accurately, such upward trends are in line with changing patterns of mortality in India and would fit with a progressive epidemiological transition in rural areas [3, 6]. There is considerable diversity in risk factor levels between States and this is likely driven primarily by the very varied levels of socio-economic development that they have reached. This diversity argues for the importance of individual regions collecting data to monitor risk factor transition although national studies with standardised and robust sampling strategies that provide nationally representative data would be ideal

82 In rural Andhra Pradesh, there is no recent comprehensive information on cardiovascular risk factor levels. The APRHI mortality surveillance found a high proportion of deaths due to cardiovascular causes in the Godavari regions which suggests that epidemiologic transition is well progressed in these areas [1]. Current high quality evidence about cardiovascular risk factors is needed to design and implement appropriate intervention strategies appropriate for this developing area of rural India

83 Chapter 3 Cardiovascular risk factors in two villages in rural Andhra Pradesh Background Biomedical surveys done in rural areas of India face a number of logistic and methodological challenges and require careful planning. The Andhra Pradesh Rural Health Initiative (APRHI) has been developed to discover new information that will improve the health of Indians living in rural areas. The wider initiative includes components addressing mortality surveillance (summarised in Chapter 1) as well as treatment and prevention strategies with a particular focus on non-communicable conditions. The study area encompasses approximately 150 villages in the East and West Godavari regions, which flank the Godavari River and are situated on the East coast of Andhra Pradesh (Figure 3-1). The main access to this area is by train from Hyderabad (capital of Andhra Pradesh) to Bhimavarum. The journey takes approximately 10 hours. The Godavari region already has better access to health care than many parts of rural India, as the Byrraju Foundation has established a network of primary health care facilities in approximately 150 villages in this region and introduced a variety of programs to improve health and education. For non-communicable diseases the Foundation has introduced a hypertension control program and at the time of this study was starting to roll out a diabetes screening program. To aid in the planning of a large-scale survey of cardiovascular risk factors in villages in this area, a pilot study was first conducted in two villages. The aim of this study was to test the use of all survey instruments, evaluate sampling methods and response rates and investigate the key determinants and levels of cardiovascular disease among adult Indians living in two villages participating in APRHI. 83

84 Hyderabad The Godavari Region INDIA ANDHRA PRADESH Figure 3-1 The study area: East and West Godavari, Andhra Pradesh, India Methods This survey was conducted in June 2004 as a collaborative project between partners in India and Australia. 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 written informed consent and the study was conducted in line with the Declaration of Helsinki and subsequent amendments. The sample design Two villages from the 137 villages participating in the Byrraju Foundation Rural Development Program in 2004 were identified for this study. One less developed village 84

85 (Rajupalem, about 3 hrs drive from Bhimavarum, the main town in the area), smaller in size with a younger population and lower average income and one more developed village (Palakoderu, about 20 minutes drive from Bhimavarum) with a larger, older population and higher average income. A random sample stratified by age and gender was selected from each village. This was done using census lists compiled by the Byrraju Foundation in The registered population of each village aged 20 years and above was divided into ten strata defined by age (20-34, 35-44, 45-54, 55-64, 65+) and gender. The same fixed number of individuals were then randomly sampled from each stratum and invited to attend the study (Table 3-1). Data collection and measurements For each individual that consented to participate, trained study staff administered a structured questionnaire, performed a brief physical examination and collected a fasting blood sample. The questionnaire (Appendix 2) was developed from other validated questionnaires [184, 212, 213] and other technical publications using expert advice from a range of sources [214, 215]. The questionnaire sought information on sociodemographic variables (including education level, household income and occupation), cardiovascular risk factors, current treatments and knowledge and attitudes towards cardiovascular disease. The questions on knowledge and attitudes were administered by interviewers reading out Do you think these sorts of actions might prevent disease? followed by a list of options to which Yes or No responses were sought. No was recorded if the participant was unsure. The entire questionnaire was translated into the local language of Telugu with check back-translation and resolution of discrepancies. The Telugu questionnaire was then tested between interviewers employed from a local college in Bhimavarum for the survey. Through training any uncertainties raised about the meaning of questions were addressed such that it complied with the original questionnaire definitions and a uniform approach was applied. The examination included two measurements of blood pressure (measured using an Omron M2 manual inflation 85

86 blood pressure monitor), measurement of body weight, height, waist and hip circumference with participants wearing clothes without shoes. Venous blood samples for biochemical analysis were obtained after an 8-hour overnight fast. Samples were stored immediately over ice and transferred to the study laboratory in Bhimavarum within 4 hours of collection. All analyses were performed using a Hitachi Boeringer Mannheim 902 Automatic analyser and Elecys Quality control and standardization was achieved through the analysis of internal and external quality assurance materials provided by the Royal College of Pathologists Australia quality control program run concurrently with study bloods. Definitions High blood pressure was defined as mean systolic blood pressure 140mmHg, and/or mean diastolic blood pressure 90mmHg, and/or treatment with blood pressure-lowering medication [216]. Cholesterol and sub-fractions were classified according to ATP III Guidelines as high total cholesterol 6.2mmol/L (240mg/dL), high LDL-cholesterol 4.1mmol/L (160mg/dL) and low HDL-cholesterol <1.0mmol/L (40mg/dL) [217]. Diabetes was defined as fasting plasma glucose 7.0mmol/L (126 mg/dl) [218, 219] or a previous diagnosis of diabetes. Overweight was defined as body mass index (BMI) 25kg/m2 but <30kg/m2 and obesity as BMI 30 kg/m2 [220]. Sedentary lifestyle was defined as answering almost none to level of physical activity during and after working hours. Cardiovascular disease was defined as a previous medical diagnosis of heart attack or stroke or a positive Rose Angina questionnaire [221, 222]. Statistical analysis Weighted estimates (with confidence intervals) of mean, or percentage risk factor levels in the overall population aged 20 years and over and among age and sex sub-groups were calculated. Comparisons of risk factor levels between population sub-groups were performed using independent t-tests for continuous variables and chi-squared tests for categorical variables and proportions. All analyses accounted for the survey design. There were ten strata (defined by the five age groups and gender in each village) and survey weights were calculated using local census data and equalled the ratio of the 86

87 population to sample size for each combination of age, sex and village [223]. All analyses were done using STATA 8.0. Results Recruitment and response rates A total of 600 individuals were invited to participate in the study and 345 (58%) were located, agreed to participate, gave informed consent and presented for interview. Among the 345 participants the data from the questionnaire, physical examination and blood tests were all more than 99% complete. Between 10 and 24 individuals were sampled in each of the 10 age-, sex- groups in each village. The response rate was higher in Palakoderu than Rajupalem (62% versus 53% p=0.03) and among females than males (62% versus 53% p=0.03) with some variation between different age groups (Table 3-1). Demographic characteristics The average age of the adult population in these two villages was 41 (range 20 to 90 years) and this was the same for men and women (Table 3-2). 50% were male and 79% were married. The mean number in each household was 4.2 (range 1-18) people and the average combined household income was 25,454 rupees (about US$580) per year. 87

88 Table 3-1 Population sampled and response rates in pilot study conducted in two villages in rural Andhra Pradesh, 2004 Rajupalem Palakoderu Age-group Men Registered population Invited Sampled Response (%) Registered population Invited Sampled Response % % % % % % % % % % Women % % % % % % % % % % % % % % Overall % % 88

89 Table 3-2 Demographic characteristics of participants in two villages in rural Andhra Pradesh, 2004 Mean age (range) 41 (20-90) Proportion female, % 50 (45 55) Average household size (range) 4.2 (1-18) Married, % 79 (74 84) Illiterate, % 54 (48 60) Finances Average annual household income, Rupees (inter quartile range) 25,454 (10,000 30,000) Average annual spend on health, Rupees (inter quartile range) 4,701 ( ) Highest level of education completed, % No formal schooling 54 (48 60) Primary school 31 (25 37) Secondary school 1.3 (-3 6) Graduate or post graduate studies 2.1 (1 4) Currently studying 3 (0 6) Employment, % Unemployed or retired 5 (2 8) Housewife 25 (21 29) Skilled manual worker 16 (11 21) Unskilled manual worker 39 (33 45) Owner of business or office worker 10 (6 14) Professional 0.3 (0 1) Mean or percentage with 95% confidence interval in brackets unless other index indicated Levels of major vascular risk factors The mean levels of major vascular risk factors are shown in Table 3-3 for the overall population of the two villages and separately for men and women. Total cholesterol and 89

90 LDL-cholesterol were both higher in women (both p<0.02) but there were no differences in other lipid parameters, glucose or blood pressure between sexes (all p>0.1). Less women than men reported a sedentary lifestyle (p<0.001) but BMI was similar between gender groups (p=0.1). The anticipated lower WHR was observed in women (p<0.001). Current smoking and passive smoking were both significantly higher amongst males (both p<0.001). Chewing tobacco was used by only 3.3% ( ) of adults 20 years and above in the two villages. Prevalence and treatment of selected cardiovascular disease states The prevalence of hypertension was 20.3% ( ) and according to ATPIII defined criteria 87.2% ( ) of the population had low HDL-cholesterol with only 12.3% ( ) having either high total cholesterol or a high LDL-cholesterol. Diabetes was identified in 3.7% ( ) of the population with 2.6% ( ) of the population aware they had diabetes prior to the survey. The prevalence of overweight (BMI>25) was 16.9% ( ) and obesity (BMI 30) 4.4% ( ). A medical diagnosis of cardiovascular disease (previous heart attack, stroke or angina) was reported by 2.5% ( ) and a further 1.1% ( ) had Rose questionnaire definite angina. Treatment and prevention of cardiovascular disease An estimated 58% (53-64) of the population reported having their blood pressure checked in the prior 12 months and 14.3% ( ) of the village population were receiving blood pressure lowering medication. 6.7% ( ) reported a cholesterol check in the same period and 2.9% ( ) were using cholesterol-lowering therapy. To the question asking whether the following behaviour might prevent disease, 93% ( ) of the population replied yes to weight loss, 89% ( ) yes to smoking cessation, 88% ( ) yes to exercise, 89% ( ) yes to lowering alcohol consumption and 95% ( ) yes to lowering fat intake. 90

91 Table 3-3 Mean levels of key cardiovascular risk factors overall and separately for men & women in two villages of rural Andhra Pradesh, 2004 ALL Male Female Mean 95% CI* Mean 95%CI Mean 95% CI Systolic Blood pressure (mmhg) 116 ( ) 117 ( ) 114 ( ) Diastolic Blood Pressure (mmhg) 73 (72-75) 73 (71-75) 73 (72-75) Total Cholesterol (mmol/l) 4.6 ( ) 4.5 ( ) 4.8 ( ) LDL Cholesterol (mmol/l) 3.2 ( ) 3.1 ( ) 3.3 ( ) HDL Cholesterol (mmol/l) 0.8 ( ) 0.8 ( ) 0.8 ( ) Triglycerides (mmol/l) 1.3 ( ) 1.4 ( ) 1.2 ( ) Glucose (mmol/l) 4.2 ( ) 4.2 ( ) 4.2 ( ) Body Mass Index (kg/m 2 ) 21.2 ( ) 20.8 ( ) 21.6 ( ) Waist Hip Ratio 0.84 ( ) 0.89 ( ) 0.79 ( ) Weight (Kilograms) 53.0 ( ) 56.2 ( ) 49.9 ( ) Height (centimetres) 157 ( ) 164 ( ) 150 ( ) Waist (centimetres) 76.2 (75-78) 78.4 ( ) 74.2 ( ) Hip (centimetres) 90.6 (89-92) 87.8 ( ) 93.3 ( ) Sedentary lifestyle (%) 11.3 ( ) 20 ( ) 2.5 ( ) Current smoking (%) 19.9 ( ) 36.7 ( ) 3.3 ( ) Passive smoking (%) 48.8 ( ) 60.3 ( ) 37.5 ( ) 95% confidence interval Discussion This survey demonstrates that it is eminently feasible to conduct a high quality survey of cardiovascular risk factors using the survey tools, population sampling methods and analysis methods used in this two-village pilot study. In addition, the findings here indicate that the determinants of cardiovascular disease in these two villages of rural Andhra Pradesh are highly prevalent and require further investigation. The risk factor levels identified in these villages are above those reported from previous studies conducted in rural areas [13, 224, 225] although still below those typically observed in urban parts of India [14] or in Western countries. This together with APRHI mortality data (summarised in Chapter 1) indicating that heart attack and stroke are now leading causes of death in these villages [226] justify the need for further information on 91

92 cardiovascular disease determinants in this region of rural Andhra Pradesh. Information from a large-scale survey of the region would provide more definitive and detailed information on the determinants of CVD in this region, which would facilitate development of appropriate interventions for CVD prevention for this region. Most of the survey findings reported here are highly plausible and broadly consistent with the economic status of the area. There are, however, some aspects of the findings that may have been influenced by specific local anomalies. First, the higher than anticipated [227, 228] proportion of the population reporting having had their blood pressure checked is almost certainly a consequence of a locally implemented hypertension detection and treatment program. Likewise high levels of knowledge relating to vascular diseases may be due to that hypertension program or other low-level health promotion activities already initiated in these villages. The reported prevalence of cardiovascular disease is probably an underestimate of the true prevalence since while health care in these villages is better than many areas of rural India only a small proportion of the population would have been exposed to an examination or investigation process sufficient to reliably exclude conditions such as angina. The very high proportion of participants meeting an ATPIII criterion for dyslipidaemia is also probably misleading. Low HDL-cholesterol levels in the context of concurrent low total and LDL-cholesterol levels may or may not indicate a high level of lipid-related risk. This is an issue that has been noted in other such studies [229] and highlights the need for either different reference ranges for different populations or else a greater focus on indicators of risk such as cholesterol ratios [230]. The finding of low HDL-cholesterol may also have been magnified by use of the indirect, precipitation method used in the local Bhimavarum hospital laboratory and in future surveys may require updating to the newer more robust direct method to quantify HDL to ensure the measures are comparable to international standards. The population sampling and weighting method utilized in this study is well-established and should provide reliable estimates of risk factor levels and disease prevalence [223]. 92

93 This methodology ensures maximally precise, reliable and generalisable results are obtained from the data available. An important limitation of this study was that not all invited participants were able to attend our survey and despite the weighting process it is possible that this may have impacted on the reliability of some of our results. Non-response of invited participants was in part a consequence of the census data being outdated (a number of selected participants had migrated) and in part because some members of the population were unable to attend due to work or other prior commitments. Since very limited information about non-responders is available it is difficult to predict the impact on the results. In future surveys, updating of the population census data, earlier and repeated invitation of participants, flexibility of appointment days and timing of the survey for a non-harvest, non-planting agricultural season would likely significantly increase the response rate. Our relatively small sample size has also limited our ability to estimate precise means and frequencies of the various risk factors in different age and sex groups. In part this limitation was offset by the stratified sampling method that maximized sample size efficiency, hence allowing us to give accurate overall estimates. While some differences between sexes were detected, a study of larger size may identify significant differences between age and sex groups and between villages with different characteristics. Unexpectedly high rates of coronary heart disease have been reported in migrant South Asian populations around the world [59, 92, 99] and this has raised concern that coronary heart disease and its risk factors may become a particularly serious health problem in India as the country moves through the epidemiological transition [2]. There is already good evidence of increasing levels of risk factors [231, 232] in urban areas and there is some data indicating corresponding increases in levels of coronary heart disease in urban regions [35, 203]. Clear evidence that this same pattern is occurring in rural areas is not available and the future of coronary heart disease in rural India, whilst concerning, remains to be seen [9]. 93

94 With epidemiological transition progressing rapidly in many developing countries the evaluation of chronic diseases in resource poor rural settings and the development of appropriate health system responses is a public health priority. The study reported here clearly demonstrates the feasibility of obtaining such data. This survey was done in just a few weeks and the total cost for the survey was just a few thousand dollars including all local salaries, all assays, transport, accommodation and subsistence and rental of all necessary equipment. Larger surveys that could provide more precise and reliable disease and risk factor estimates in different developing regions are eminently feasible and affordable and would provide valuable additional insights. 94

95 Chapter 4 Diabetes in rural Andhra Pradesh Survey results of 4,535 people in twenty villages Background Diabetes is a large and growing global health problem. Diabetes is becoming more prevalent with large increments in case numbers expected around the world due to population ageing, urbanisation, increasing obesity and physical inactivity [233, 234]. South Asians appear to be particularly susceptible to diabetes with high rates of diabetes reported in migrant South Asian populations [90, 235] and urban India [231]. Diabetes increases the risks of cardiovascular disease and mortality [ ] and a number of migrant studies suggest that South Asian Indians may be at particular risk from the complications of diabetes [239]. Reliable statistics about diabetes prevalence, management and associated risk factors have the potential to importantly influence the prevention of cardiovascular disease in India. While there is some information about diabetes in urban areas of the country [231, 240], few data have been reported from rural regions where over 70% of the population lives (see Chapter 2). In this chapter comprehensive new information about the prevalence and management of diabetes and its associations with cardiovascular risk factors is reported from a largescale survey conducted in rural Andhra Pradesh in The methods of this main study were based on the experiences and results of the pilot study described in the previous chapter. Methods The large-scale survey was conducted by the Andhra Pradesh Rural Health Initiative (APRHI) whose members and details are listed in Appendix 1. 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 conducted in line with the Declaration of Helsinki and subsequent amendments. 95

96 Survey design and sampling method Participants were an age and sex stratified sample of adults over the age of 30 years resident in 20 villages in the East and West Godavari regions of rural Andhra Pradesh. The 20 villages were all participating in a broad-based rural development initiative run by the Byrraju Foundation, a local non-governmental organisation and collaborator to APRHI. The villages 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. The two pilot villages were not included the list. The villages were selected to be broadly representative of these two districts on the basis of population size, district and distance from a large town. Six villages were from East Godavari and 14 villages from West Godavari and the mean distance from a large town was about 20kms (Table 4-1). At the commencement of the survey the most recent population lists available were those collected by the Byrraju Foundation in These electronic listings included age, sex and contact details of all individuals living in the 20 villages and thus enabled biostatisticians at the George Institute for International Health in Australia to generate a stratified random sample of individuals over the age of 30 years from 8 groups defined by age (30-39, 40-49, 50-59, 60+) and sex. The goal of sampling was to recruit roughly equal numbers into each of these 8 groups and roughly equal numbers from the 20 villages. Based on knowledge of the reliability of the listings and the response rate from the pilot survey, a random list of 400 individuals was generated from the population lists of each of the 20 villages. The first 300 were invited from each village, if 200 accepted the invitation, only 300 were invited. If less than 200 of the sample accepted the invitation, additional individuals were invited from the random list to ensure an adequate sample from each village. Using this method of sampling and weighted survey data analysis enabled precise estimation of key risk factor levels overall and by age-sex group for the population living in the 20 villages. A simple random sample would not allow calculation of such precise estimates for older age groups. This is because the bulk of the population of India is less than 40 years of age [241], and a simple random sample would hence select fewer individuals from older age groups. The average population of each of 96

97 the twenty villages was 3,754 (range 1,384 to 7,882), and about half the population of the villages were aged 30 years or over. 97

98 Table 4-1 Villages sampled for rural Andhra Pradesh main survey Village District Distance Registered Invited Contact possible Sampled Response population ALLAVARAM East Godavari % CHERUKUWADA West Godavari % CHINCHINADA West Godavari % GARAGAPARRU West Godavari % GODI East Godavari % GOLLALAKODERU West Godavari % GOTERU West Godavari % JALLIKAKINADA West Godavari % KASIPADU West Godavari % KOPALLE West Godavari % KORUKOLLU West Godavari % LOLLA East Godavari % NEDUNURU East Godavari % PODURU West Godavari % POLAMURU West Godavari % POTHUMARRU West Godavari % UNIKILI West Godavari % VENDRA West Godavari % YEDURLANKA East Godavari % YENUGUPALLI East Godavari % Total % Distance in kilometres from a major rural centre (Bhimavarum or Amlapuram) Registered population = Number on Byrraju population list for that village Invited = Number randomly sample from registered population and invitation attempted Contact Possible = Invited minus all deaths and permanent migrations 98

99 Table 4-2 Number sampled from each village by age and sex in rural Andhra Pradesh main survey, 2005 Village Male Females Overall Total Total ALLAVARAM CHERUKUWADA CHINCHINADA GARAGAPARRU GODI GOLLALAKODERU GOTERU JALLIKAKINADA KASIPADU KOPALLE KORUKOLLU LOLLA NEDUNURU PODURU POLAMURU POTHUMARRU UNIKILI VENDRA YEDURLANKA YENUGUPALLI Total

100 Data collection and measurements Survey work was conducted in February and March For each participant, trained study staff administered a structured questionnaire (Appendix 3) [ , 242], performed a brief physical examination and did a fasting finger-prick measurement of capillary blood sugar. For a quarter of individuals randomly selected from each age and sex group, a fasting venous blood sample was also drawn. Participants were asked to fast overnight and if their reported fasting time was less than eight hours on the day of testing they were asked to return the next day. Participants who did not return or did not return fasting were excluded from analyses. All participants with abnormal blood results were referred to the local physician as their results became available. The examination included two measurements of blood pressure using an Omron M2 manual inflation monitor and measurements of body weight, height, waist and hip were made with participants wearing light clothing without shoes. Fasting capillary plasma glucose was measured using B-Braun (Germany) U.S.V. meters. Venous blood samples were immediately refrigerated in a generator-powered portable refrigerator and then transferred over ice to the field laboratory within four hours of collection for separation, glucose analysis, and freezing of the remaining sample at minus twenty degrees Celsius. Glucose assay was performed via the glucose oxidase peroxidase method using an ALFA Biotech PLD-951 semi-automated analyser. Frozen samples were transferred to a central internationally accredited laboratory in the CARE hospital, Hyderabad, India where creatinine, cholesterol and sub-fraction analyses were performed using a Beckman Coulter Synchron Cx9 Clinical system ALX analyser. Concurrent with the laboratory s usual quality assurance program, a RCPA (Royal College of Pathologists Australia) process was also run (Quality Control results for repeatability and reliability included in Appendix 4). Definitions Diabetes mellitus was defined according to the American Diabetes Association (ADA) criteria as a fasting capillary plasma glucose value 7.0 mmol (126mg/dL) [218, 219] or 100

101 a previous physician diagnosis of diabetes (but not a previous diagnosis of gestational diabetes). Diabetes was further sub-classified as known diabetes if the participant had been informed previously of their condition by a medical practitioner or was currently taking oral hypoglycaemic or insulin therapy and undiagnosed diabetes if the diagnosis was made on the basis of the assay performed for this study. Impaired fasting glucose (IFG) was defined as a fasting plasma glucose value of mmol/l ( mg/dL), in the absence of a previous diagnosis of diabetes. All other participants were defined as having normal fasting glucose. The analyses presented are based on capillary blood glucose measurements unless otherwise stated. Hypertension was defined as mean systolic blood pressure 140mmHg, and/or mean diastolic blood pressure 90mmHg, and/or treatment with blood pressure-lowering medication [216]. Prior vascular disease was defined as medical history of previous heart attack, stroke or angina. Statistical analysis Statistical analyses were carried out using STATA 8.0. Analysis methods were based on that of other large scale surveys [243, 244]. All mean risk factors levels and measures of prevalence reported are weighted estimates for the total adult population 30 years and above resident in the 20 sampled villages of the Godavari region. Overall and stratumspecific estimates of risk factor levels are reported. Weights used in analyses were the population to sample size ratios for each combination of age, sex and village. These weights adjust results for the unequal probabilities of selection consequent upon the stratified sampling technique and took into account non-response. Separate weights were used for the 4535 individuals interviewed and the 1085 individuals with venous blood samples. At the time of analysis, the Byrraju Foundation completed a second household census, which had commenced in This more recent local census provided the most up to date population data for the 20 villages and hence was used in the calculation of weights. The age & sex distribution of the population had not changed greatly compared with the 2002 population, with the average ratio of population weights calculated to be 0.98 and ranging from 0.89 to 1.07 across the eight age & sex strata. 101

102 Means and proportions are presented with standard errors of the mean (SEM) or with 95% confidence intervals. Risk factor levels were compared between groups using independent t-tests for continuous variables and chi-square tests for proportions. All tests accounted for the sampling method and weighting and p<0.05 was considered to indicate a result unlikely to have arisen by chance. Associations of established cardiovascular risk factors with fasting blood sugar and diabetes were investigated using general linear models and logistic regression models. Results Of the 6985 individuals invited to participate in the survey from the random lists drawn from the Byrraju 2002 population listings, 5627 were still living in the villages at the time of the study and were invited to participate agreed to take part and provided informed consent (Table 4-1, 4-2). This equated to a response rate amongst current residents of 80.6%, ranging from 70.7% to 89.2% across the 20 villages. The age and sex distribution of those declining to participate was similar to that of the participants. The response rate was 78.4% in men and 82.4% in women and ranged from 72.5% to 82.1% across age groups in men and from 78.6% to 84.7% in women. Data from the questionnaire and physical examination were more than 99% complete. Capillary blood samples were available for 97% and the 3% of participants without these samples were excluded from all analyses. The population was entirely rural with 54.1% (95% CI, ) of the population being unskilled manual labourers working mainly in agriculture and aquaculture. 47.3% ( ) were literate and the mean monthly income per household was US$50.8 ( ). Mean fasting plasma glucose levels The estimated overall fasting capillary glucose levels for rural Indian adults aged 30 years and over was 5.8mmol/L ( ). In men the value was 5.9mmol/L ( ) 102

103 compared with women 5.7mmol/L ( ), p=0.07. Using the measurements made on venous blood samples the overall estimated mean glucose was 5.6mmol/L ( ) with correspondingly lower estimates for both men (5.5, ) and women (5.6, ). While venous and finger prick capillary glucose plasma results were closely correlated (Pearson s correlation coefficient 0.87), venous glucose measurements were on average 0.2mmol/l ( ) lower than finger-prick capillary plasma measurements. Prevalence of diabetes and impaired fasting glucose Based on the capillary glucose measurements the estimated overall prevalence of diabetes in this rural Indian population aged 30 years or above was 13.2% ( ). This was made up of known diabetes 6.4% ( ) and newly diagnosed diabetes 6.8% ( ). Overall prevalence of diabetes was slightly higher in males 14.6% ( ) compared with females 12.2% ( ), p=0.03. An estimated additional 15.5% ( ) had impaired fasting glucose levels (Table 4-3). Analyses based upon the 1,070 individuals with fasting venous blood samples made using identical glucose cut-offs defined 10.9% ( ) as diabetic. This was made up of known diabetes 7.4% ( ) and newly diagnosed diabetes 3.5% ( ). Impaired fasting glucose levels were recorded in an additional 8.3% ( ). Association of diabetes and impaired fasting glucose with cardiovascular risk factors In general, the levels of cardiovascular risk factors worsened across the four population groups defined as normal glucose tolerance, impaired fasting glucose tolerance, undiagnosed diabetes and known diabetes with progressively increasing age higher measures of adiposity, higher blood pressures and less favourable lipid profiles (Table 2). In age- and sex- adjusted linear regression analysis done separately for each risk factor, increasing fasting blood sugar was significantly associated with higher levels of systolic blood pressure, waist circumference, weight, BMI, waist to hip ratio, triglycerides, total cholesterol and a personal history of previous vascular disease (all p<0.05); with lower levels of physical activity and a family history of diabetes (all p<0.001); but was not 103

104 associated with current smoking, alcohol intake or a family history of cardiovascular disease (all p>0.05). Use of preventive treatment by patients with known diabetes Of those with known diabetes, 42.4% ( ) had high blood pressure (blood pressure 140/90), 12.5% had a high total cholesterol (total cholesterol 6.2mmol/L) and 17.6% ( ) had a previous diagnosis of vascular disease (Table 4-3). With regards to preventive treatment, 66.9% ( ) of known diabetics were taking oral hypoglycaemic therapy, 3.1% ( ) were receiving insulin, 46.2% ( ) were taking blood pressure-lowering agents, 10.2% ( ) were taking anti-platelet therapy and 4.3% ( ) were taking cholesterol-lowering therapy. 104

105 Table 4-3 Prevalence of known diabetes, undiagnosed diabetes and impaired fasting glucose based on capillary fasting glucose levels overall and by age and sex groups, in 20 villages in rural Andhra Pradesh, India 2005 Overall By age group % CI % CI % CI % CI % CI ALL Known 6.4 ( ) 2.4 ( ) 5.6 ( ) 9.1 ( ) 11.5 ( ) Undiagnosed 6.8 ( ) 2.8 ( ) 7.9 ( ) 10.0 ( ) 9.0 ( ) IFG* 15.5 ( ) 14.0 ( ) 17.3 ( ) 17.1 ( ) 14.5 ( ) MALE Known 6.8 ( ) 2.6 ( ) 6.3 ( ) 10.1 ( ) 11.5 ( ) Undiagnosed 7.5 ( ) 3.1 ( ) 8.0 ( ) 11.5 ( ) 10.7 ( ) IFG 16.6 ( ) 16.4 ( ) 18.1 ( ) 18.7 ( ) 13.4 ( ) FEMALE Known 6.0 ( ) 2.2 ( ) 4.8 ( ) 8.1 ( ) 11.6 ( ) Undiagnosed 6.0 ( ) 2.5 ( ) 7.7 ( ) 8.5 ( ) 7.5 ( ) IFG 14.3 ( ) 11.6 ( ) 16.3 ( ) 15.4 ( ) 15.5 ( ) *IFG = impaired fasting glucose 95% confidence interval 105

106 Table 4-4 Cardiovascular risk factors in groups defined by normal fasting glucose, impaired fasting glucose, undiagnosed diabetes, known diabetes in rural Andhra Pradesh, India, 2005 Normal Fasting Glucose Impaired Fasting Glucose Undiagnosed Diabetes Known Diabetes Mean age (years) 45.7 ( ) 47.5 ( ) 51.1 ( ) 54.0 ( ) Male (%) 48.4 ( ) 53.8 ( ) 55.9 ( ) 53.5 ( ) Mean BMI (Kg/m 2 ) 21.5 ( ) 22.5 ( ) 23.8 ( ) 24.2 ( ) Waist to Hip ratio 0.86 ( ) 0.88 ( ) 0.90 ( ) 0.91 ( ) Mean SBP (mmhg) 121 ( ) 127 ( ) 130 ( ) 135 ( ) Mean DBP (mmhg) 76 (75-76) 79 (78-80) 80 (79-82) 80 (79-81) Mean glucose (mmol/l) 5.10 ( ) 6.46 ( ) 8.52 ( ) 9.00 ( ) Mean TC (mmol/l) 4.64 ( ) 4.87 ( ) 4.94 ( ) 5.12 ( ) Mean LDL (mg/dl) 2.94 ( ) 3.02 ( ) 3.10 ( ) 3.09 ( ) Mean HDL (mmol/l) 1.16 ( ) 1.18 ( ) 1.14 ( ) 1.16 ( ) Mean TG (mmol/l) 1.24 ( ) 1.63 ( ) 1.85 ( ) 2.28 ( ) Mean creatinine (μmol/l) 86 (84-87) 86 (82-89) 89 (86-92) 96 (87-104) BMI 25 (%) 18.6 ( ) 26.7 ( ) 39.0 ( ) 39.5 ( ) Known HT or BP 140/90 (%) 21.7 ( ) 33.4 ( ) 41.6 ( ) 57.4 ( ) TC>6.2mmol/L (%) 6.2 ( ) 8.3 ( ) 10.1 ( ) 12.5 ( ) Drinker of alcohol (%) 15.7 ( ) 19.1 ( ) 16.0 ( ) 13.5 ( ) Current smoker (%) 24.1 ( ) 25.0 ( ) 29.8 ( ) 23.4 ( ) Previous vascular event (%) 5.5 ( ) 7.4 ( ) 6.3 ( ) 17.6 ( ) Family history CVD (%) 14.2 ( ) 15.9 ( ) 17.7 ( ) 21.2 ( ) Family history diabetes ± (%) 9.7 ( ) 11.6 ( ) 13.1 ( ) 35.2 ( ) Means and proportions followed by confidence intervals in brackets *Systolic blood pressure/ diastolic blood pressure; total cholesterol; Family history of CVD defined as history of heart attack or stroke in a first degree relative 60 years; ± Family history of diabetes defined as history of diabetes in a first degree relative of any age 106

107 Discussion This study shows that diabetes and impaired fasting glucose are very common health problems in this adult population of rural India. Because both conditions are associated with adverse levels of cardiovascular risk factors and increased risks of cardiovascular disease the health implications are substantial. The prevalence of diabetes and impaired fasting glucose estimated here is comparable (and for some age groups higher) than that reported for populations in established market economies such as the U.S.A. [245], Australia [246] and Europe [247]. In addition, the prevalence estimates are greater than those reported for urban and rural regions of China and Thailand [232, 248, 249] and comparable to those reported for Pacific Island populations [250]. This is despite mean BMI levels in the rural Indian population studied here (mean BMI 22kg/m 2 ) being very much lower than the levels reported in the Tongan study (32 kg/m 2 ) and lower than those reported for rural Chinese (23kg/m 2 ) and Thai populations (24kg/m 2 ). The estimated prevalence of diabetes in this rural area of India also approaches that reported by largescale studies conducted in urban India which have reported amongst the highest prevalence of diabetes in any population in the world [231, 232]. This study provides important new evidence about diabetes in rural India where there is currently a paucity of data. Despite more than 70% of the population living in rural areas, [135] only five large-scale studies (n>1000) have reported the prevalence of diabetes based on glucose testing in rural areas during the last 20 years. The first study was done in 1993 in a group of villages in Sriperumbudur 40 miles from Madras [147]. That study reported a prevalence of diabetes of 2.4% amongst adults aged 20 years and over based on fasting and post-glucose load finger-prick blood sugar levels. The participants in this study were a particularly low-income group (with mean BMI of only 18.2 kg/m 2 ) and this likely explains the low prevalence of diabetes. The second study done in Uttar Pradesh, North India in 1997 [251] amongst 1769 persons between 25 and 64 years defined diabetes according to the presence of both fasting glucose >7.7mmol/L and a postprandial 2 hours after 75g of oral glucose >11.2mmol/L. They reported an overall prevalence of diabetes of 2.9% in that population with a mean BMI of 21.2 kg/m 2. The 107

108 third study was conducted in four rural areas of Kerala during 2000 [145] and reported the prevalence of diabetes in the age group as ranging from 3.6% to 10.1% across the different regions included. This study used screening with random blood sugars followed by diagnostic oral glucose tolerance tests in those with a random blood sugar level greater than 6.1mmol/L (110mg/dL). The fourth study also in Tamil Nadu reported in 2004 [128] by the same group reporting the 1993 study in the same state, studied 1213 individuals in a separate rural area from their previous study and reported a prevalence of diabetes of 6.3% using a post-meal glucose cut-off level >11.1mmol/L. The fifth and most recent study done between 1999 and 2002 was the Prevalence of Diabetes in India Study (PODIS) [137, 138]. The rural component of this study included 59 centres and involved adults aged 25 years or over. All rural participants had fasting finger-prick samples and the prevalence of diabetes was reported as only 1.9%. The corresponding estimate for the urban areas included in PODIS was 4.6% which is also much lower than the prevalence reported for other recent studies done in urban Indian populations [231, 232]. It seems likely that the variation in the prevalence estimates for diabetes between studies can be ascribed to the different stages of development of the study populations, the different diagnostic strategies used and the different times at which the studies were done. Other studies relying on self report are likely to have substantially under-estimated the true prevalence of diabetes. One such study also done in rural Andhra Pradesh was done in Eluru and four surrounding villages in rural Andhra Pradesh reported diabetes prevalence of 6.1% based on self-report among the population aged 40 years and above[252]. The region of rural Andhra Pradesh studied in this project is more affluent and better developed than other rural areas of India and nationwide the rural prevalence of diabetes is almost certainly lower than that reported here. However, the East and West Godavari districts are not particularly remarkable in terms of their economic advancement and probably provide a good indication of the economic development and associated health changes that will be seen in much of rural India over the next few decades. Since growth 108

109 in diabetes prevalence in most countries appears to have been largely dependent upon factors such as aging of the population, increasing levels of obesity, falling levels of physical activity and greater consumption of energy rich foods it seems likely that diabetes prevalence throughout rural India will progressively rise. In addition, in the case of India this may be further exacerbated by a genetically mediated susceptibility to the effects of causal exposures on the risk of developing diabetes [253, 254]. Approximately half of all individuals with diabetes identified by this survey were not aware of their diagnosis prior to their participation. This rate of diagnosis is comparable to reports from other developing countries [248, 249] but is less than reported in Western countries [245, 246]. Cardiovascular mortality rates in undiagnosed diabetics are approximately double that of the general population and approach that of known diabetics [ ]. Since there are a number of proven and low cost strategies for the prevention of complications from diabetes that are already being utilized in the Indian population, strategies that enhanced the identification of undiagnosed diabetes would likely produce significant health benefits. The large sample size of the study ensured that the overall estimates of risk factor levels were fairly precise. Likewise the careful sampling procedures, good response rates, largely complete data and sophisticated statistical weighting process should have minimized biases. While even the low rates of non-participation observed here may have introduced biases it is unlikely that any bias would be so large in magnitude as to alter the chief findings of this survey. Some reassurance about the reliability of the study findings is also provided by their broad comparability to other relevant studies reporting diabetes prevalence and the association of diabetes with established disease correlates. Blood glucose measurements in this study were based on assays of fasting capillary glucose. Finger-prick glucose assays were used since they were the most practical solution for the remote study environment where the spinning, separating, refrigerating and early assay of venous samples was difficult. This decision appears justified on the basis of the well correlated but systematically lower levels of blood glucose obtained 109

110 from the venous samples compared to the capillary samples. Reliance on venous samples in this setting would likely have significantly underestimated the true prevalence of diabetes and impaired fasting glucose. Fasting measurements were made rather than measurements after a glucose load on the basis of prior work suggesting that fasting assays are less affected by environmental heat and have lower overall variability [ ]. Had oral glucose tolerance tests (OGTT) also been performed it would likely have further increased the number of individuals that met the definition of diabetes [232, 261] and our prevalence estimates may therefore still be conservative. In conclusion, this survey demonstrates that diabetes now has huge implications for health in rural as well as urban India. New evidence about effective diabetes control strategies suited for implementation in resource poor settings are urgently required. While the widespread implementation of interventions proven to prevent diabetes would be ideal, the systematic use of low cost diagnostic tools and the prescription of low cost preventive drug therapies also have much to offer in the immediate term. 110

111 Chapter 5 Significant lipid, adiposity and metabolic abnormalities amongst 4,535 Indians from a developing region of rural Andhra Pradesh Introduction Markedly abnormal levels of cardiovascular risk factors and high rates of cardiovascular disease have been observed in both local and migrant populations of South Asian Indians [50, 59, 92, 99, 262]. Lipids, obesity and metabolic factors appear to be key determinants of the burden of cardiovascular disease amongst the mainly urban populations that have been studied to date [34, 70, 239]. As noted in the preceding chapter, recent data suggest that developing rural areas of India may be following the pattern of urban India, with high rates of diabetes and a substantial proportion of mortality from ischaemic heart disease and stroke [1, 263]. There are, however, scanty data about the levels of lipids, obesity and metabolic syndrome in rural parts of India (where approximately 70% of the population resides) and the likely current and future role of these risk factors in rural India is thus uncertain. In this chapter, data on lipids, obesity and metabolic syndrome are reported from the main survey and the significance of this data discussed with reference to available rural and urban data from India. Methods The methods used for this survey have been reported in detail in the preceding chapter relating to diabetes. Unless specified, the same processes were utilised here and the methods section for this chapter includes only new information specific to the outcomes addressed in this chapter. 111

112 Data collection and measurements specific to lipids, adiposity and metabolic abnormalities Fasting total cholesterol and cholesterol sub-fractions were measured on venous blood samples drawn from every fourth individual at a central internationally accredited laboratory in the CARE hospital, Hyderabad, India. Assays were done using a Beckman Coulter Synchron Cx9 Clinical system ALX. Total cholesterol and triglycerides were determined using CHOD-PAP and GPO-POD methods respectively (Beckman Synchron Cx System reagents, Fullerton, U.S.). HDL cholesterol was quantified by the Catalase CHOD-PAP procedure, direct assay (Randox reagents, Antrim, UK). LDL cholesterol was measured by Detergent Technology CHOD-PAD direct assay (Daichi, Tokyo, Japan). In addition to this laboratory s own quality control processes an external quality assurance program was instituted using quality control materials provided by the Royal College of Pathologists Australia (Appendix 4). Definitions Abnormalities of total cholesterol, high density lipoprotein cholesterol, low density lipoprotein (LDL) cholesterol and triglycerides were defined according to National Cholesterol Education Program Expert Panel Adult Treatment Panel (NCEP-ATP III) guidelines and reported in Tables 2-5 [264]. Overweight and obesity were defined according to conventional World Health Organisation cut points of body mass index (BMI) of 25 kg/m 2 and 30 kg/m 2 respectively, as well as by cut points recommended by the WHO for Asian populations of 23kg/m 2 and 27.5 kg/m 2 [220]. Likewise, abnormalities of waist were categorised, both according to NCEP-ATP III guidelines and suggested lower cut points for Asians (men >90cm, women >80cm) [266]. The metabolic syndrome was defined according to the 2005 updated NCEP-ATP III guidelines on the basis of the presence of three or more of abdominal obesity, elevated triglycerides ( 1.7mmol/L or 150mg/dL), low HDL cholesterol (men<1.0mmol/l or 40mg/dL, women<1.3mmol/l or 50mg/dL), high blood pressure 130/ 85mmHg or raised fasting glucose ( 5.6mmol/L or 100mg/dL), and drug treatment for abnormalities of cholesterol, sub-fractions, blood pressure or glucose [266]. A second Asian definition of the metabolic syndrome was also used, with abdominal obesity defined by the lower cut 112

113 points (men >90cm, women >80cm) as recommended by the updated NCEP-ATP III guidelines and the International Diabetes Federation (IDF) guidelines [266, 267]. Prior cardiovascular disease was defined as reporting a medical diagnosis of previous heart attack, stroke or angina. Results The response rate and general population demographics have been reported in detail in the preceding chapter relating to diabetes. This chapter includes only new information specific to the outcomes addressed in this chapter. The prevalence of cardiovascular disease (previous medical diagnosis of heart attack, angina or stroke) was 6.6% ( ) overall, 7.1% ( ) for men and 6.0% ( ) in women. Lipid levels Mean levels of total cholesterol, LDL-cholesterol, HDL-cholesterol and triglycerides were 4.6, 2.9, 1.1 and 1.4mmol/L respectively (Table 5-1). Total cholesterol, LDL cholesterol and HDL cholesterol levels were higher in women (all p<0.001) but triglycerides levels were higher in men (p=0.02). Total cholesterol, LDL cholesterol and triglycerides increased with age (all p 0.005) but no association of age with HDL cholesterol levels was detected (p=0.3). High total cholesterol was recorded in 7.2% and high LDL cholesterol in 7.3%, with high total cholesterol more common in women than men (P=0.03) but no significant difference between sexes detected for LDL-cholesterol (p=0.08) (Table 5-2 and 5-3). The prevalence of low HDL cholesterol was 30.5% and the prevalence of high triglycerides was 11.0%, with both abnormalities more common in men than women (both p 0.002) (Table 5-4 and 5-5). The prevalence of lipid abnormalities by age mirrored the patterns seen for mean lipid levels. 113

114 Table 5-1 Mean levels of lipoproteins, weight, waist, BMI and WHR by age and sex in 20 villages of rural Andhra Pradesh, 2005 Total Cholesterol LDL-cholesterol HDL-cholesterol Triglycerides Weight Waist BMI WHR Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Overall Men Men, All Women Women, All Means and standard errors Total cholesterol, LDL-cholesterol, HDL cholesterol, triglycerides - units in mmol/l; Weight in Kg; Waist in cm; BMI = body mass index in kg/m 2 ; WHR = Waist hip ratio 114

115 Table 5-2 Prevalence of desirable, borderline high and high total-cholesterol according to ATPIII guidelines in 20 villages of rural Andhra Pradesh, India 2005 Desirable Borderline high High % SE % SE % SE Overall 69.8% 1.6% 23.0% 1.5% 7.2% 0.9% % 2.9% 17.5% 2.7% 3.2% 1.2% % 3.4% 24.6% 3.1% 9.0% 2.0% % 3.8% 27.3% 3.6% 10.5% 2.1% % 3.0% 26.4% 2.8% 8.8% 1.8% Men Men, total 73.5% 2.3% 21.1% 2.2% 5.4% 1.1% % 4.3% 16.2% 4.1% 4.2% 1.9% % 4.8% 24.7% 4.5% 6.5% 2.3% % 5.5% 30.2% 5.3% 7.7% 2.7% % 3.5% 17.5% 3.3% 4.2% 1.7% Women Women, total 66.1% 2.3% 24.9% 2.1% 9.0% 1.3% % 3.7% 18.7% 3.6% 2.3% 1.3% % 4.8% 24.5% 4.2% 11.8% 3.3% % 5.3% 24.3% 4.7% 13.5% 3.4% % 4.6% 34.8% 4.4% 13.1% 3.1% Desirable is TC<5.2mmol/L (200mg/dL) Borderline high is TC 5.2mmol/L (200mg/dL) and <6.2mmol/L (240mg/dL) High is TC 6.2mmol/L (240mg/dL) Table 5-3 Prevalence of optimum, borderline high, high and very high LDL-cholesterol according to ATPIII guidelines in 20 villages of rural Andhra Pradesh, India 2005 Optimum Borderline high High Very high % SE % SE % SE % SE Overall 69.7% 1.7% 23.0% 1.5% 5.8% 0.8% 1.5% 0.5% % 2.9% 16.8% 2.5% 4.1% 1.5% 0.9% 0.7% % 3.5% 27.1% 3.3% 5.5% 1.4% 1.9% 1.2% % 3.8% 26.3% 3.5% 9.3% 2.2% 2.4% 0.9% % 3.0% 25.2% 2.8% 6.0% 1.5% 1.6% 0.8% Men Men, total 72.3% 2.4% 22.0% 2.2% 4.7% 1.2% 0.9% 0.5% % 4.3% 14.8% 3.4% 5.1% 2.7% 1.6% 1.3% % 5.0% 27.4% 4.9% 3.8% 1.8% 0.2% 0.2% % 5.5% 27.7% 5.3% 9.6% 3.3% 1.3% 0.9% % 3.8% 22.3% 3.7% 1.5% 0.8% 0.6% 0.6% Women Women, total 67.1% 2.3% 23.9% 2.1% 6.8% 1.1% 2.2% 0.8% % 3.9% 18.8% 3.7% 3.1% 1.5% 0.2% 0.2% % 4.9% 26.8% 4.5% 7.4% 2.2% 3.7% 2.6% % 5.3% 24.9% 4.6% 9.0% 2.9% 3.7% 1.6% % 4.5% 27.9% 4.1% 10.3% 2.8% 2.5% 1.3% Optimum is LDL<3.4mmol/L (130mg/dL) Borderline high is LDL 3.4mmol/L (130mg/dL) and <4.1mmol/L (160mg/dL) High is LDL 4.1mmol/L (160mg/dL) and <4.9mmol/L (190mg/dL) Very high is LDL 4.9mmol/L (190mg/dL) 115

116 Table 5-4 Prevalence of low, normal and high HDL-cholesterol according to ATPIII guidelines in 20 villages of rural Andhra Pradesh, India 2005 Low Normal High % SE % SE % SE Overall 30.5% 1.7% 61.8% 1.8% 7.8% 1.0% % 3.3% 57.6% 3.5% 8.1% 2.1% % 3.2% 65.6% 3.4% 7.3% 1.8% % 3.4% 66.7% 3.5% 4.2% 1.2% % 2.9% 60.2% 3.1% 10.5% 1.9% Men Men, total 38.9% 2.6% 54.1% 2.6% 7.0% 1.5% % 5.1% 48.2% 5.2% 8.3% 3.3% % 4.9% 58.4% 5.1% 7.6% 2.7% % 5.5% 54.4% 5.6% 4.8% 1.9% % 4.2% 57.9% 4.3% 5.9% 2.1% Women Women, total 21.8% 2.0% 69.6% 2.3% 8.6% 1.3% % 4.1% 67.0% 4.4% 7.9% 2.5% % 3.9% 73.7% 4.3% 7.0% 2.2% % 3.5% 79.5% 3.8% 3.5% 1.5% % 3.9% 62.3% 4.5% 14.8% 3.2% Low is HDL<1.0mmol/L (40mg/dL) Normal is HDL 1.0mmol/L (40mg/dL) and <1.6mmol/L (60mg/dL) High is HDL 1.6mmol/L (60mg/dL) Table 5-5 Prevalence of normal, borderline high, high and very high Triglycerides according to ATPIII guidelines in 20 villages of rural Andhra Pradesh, India 2005 Normal Borderline high High Very high % SE % SE % SE % SE Overall 75.8% 1.5% 13.1% 1.2% 9.8% 1.0% 1.2% 0.4% % 2.5% 8.1% 1.9% 8.2% 1.8% 0.5% 0.5% % 3.3% 16.0% 2.9% 10.2% 2.1% 1.1% 0.7% % 3.5% 18.6% 3.0% 8.8% 1.9% 1.2% 1.2% % 2.9% 13.3% 2.1% 12.7% 2.1% 2.3% 0.9% Men Men, total 71.4% 2.4% 14.2% 1.8% 12.6% 1.7% 1.5% 0.6% % 4.4% 10.6% 3.0% 14.4% 3.5% 1.1% 1.1% % 5.1% 19.8% 4.5% 13.3% 3.5% 2.0% 1.3% % 4.6% 20.1% 4.3% 5.0% 1.8% 0.0% 0.0% % 3.8% 8.3% 2.2% 15.0% 3.3% 2.9% 1.2% Women Women, total 80.3% 1.9% 11.9% 1.6% 7.0% 1.1% 0.8% 0.5% % 2.5% 5.6% 2.2% 2.1% 1.2% 0.0% 0.0% % 3.9% 11.6% 3.6% 6.7% 2.1% 0.0% 0.0% % 5.3% 17.1% 4.2% 12.8% 3.4% 2.5% 2.5% % 4.2% 18.0% 3.5% 10.6% 2.8% 1.8% 1.3% Normal is triglycerides <1.7mmol/L (150mg/dL) Borderline high triglycerides 1.7mmol/L (150mg/dL) and <2.3mmol/L (200mg/dL) High is triglycerides 2.3mmol/L (200mg/dL) and <5.7mmol/L (500mg/dL) Very high is triglycerides 5.7mmol/L (500mg/dL) 116

117 Adiposity The mean weight of the population was 54.4kg; the mean waist circumference was 78.1cm, the mean waist: hip ratio (WHR) was 0.87 and the mean BMI was 21.9kg/m2 (Table 5-1). Weight, waist circumference and waist: hip ratios were all greater in men (all p<0.001) but mean BMI was greater in women (p<0.001). Waist and WHR both increased with age (both p 0.001), weight decreased with age (p<0.001) and no association was found between age and BMI (p=0.6). The proportion with a waist measurement above NCEP ATPIII recommended levels was 7.6%, the proportion with a BMI 25 kg/m2 was 22.3%and the proportion with a BMI 30 kg/m2 was 4.2 % (Table 5-6). Metabolic syndrome The prevalence of NCEP-ATPIII defined metabolic syndrome in adults 30 years and over was 24.6% with a greater prevalence in men, 28.6% than women 20.4% (p=0.03) and an increasing prevalence with age (p<0.001) (Table 6). Amongst men with metabolic syndrome, defining risk factors in order of most common to least common were an abnormal fasting glucose, an elevated blood pressure, a low HDL cholesterol level, high triglycerides, and abdominal obesity. Amongst women with metabolic syndrome, defining risk factors in order of most common to least common were an abnormal fasting glucose, an elevated blood pressure, high triglycerides, a low HDL cholesterol level, and abdominal obesity. This order changed if the Asian definition was used (Figure 5-1). 117

118 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Men ATPIII Men Asian Women ATPIII Women Asian Blood pressure Triglycerides HDL cholesterol Waist Fasting blood glucose Figure 5-1 Distribution of risk factors amongst men and women with metabolic syndrome according to NCEP-ATPIII and Asian definitions in rural Andhra Pradesh, India, 2005 Impact of usual versus Asian cut offs Using the suggested lower Asian cut-offs for waist abnormality [268] increased the proportion with an abnormal waist measurement to 26.0% overall (19.7% for men and 32.4% for women). Using recommended lower Asian cut-offs for BMI [265] increased the proportion overweight to 36.8% (32.4% for men, 41.4% for women), and the proportion obese to 10.3% (7.0% for men, 13.6% for women) (Figure 5-2, Table 5-6). If the suggested lower cut offs for waist circumference abnormality were used in an Asian definition for metabolic syndrome, the overall proportion with the syndrome increased to 30.2% (33.6% for men and 26.6% for women). In addition, the proportion with an abnormal waist circumference rose markedly and the proportion with each of the other abnormalities fell. 118

119 Metabolic syndrome Obese Usual definition Asian definition' Overweight Abnormal waist Men % population with condition Metabolic syndrome Obese Overweight Women Abnormal waist % population with condition Figure 5-2 Prevalence of metabolic syndrome, abnormal waist, overweight and obesity according to Usual and Asian definitions in men and women in rural Andhra Pradesh, India,

120 Table 5-6 Prevalence of abnormalities of waist, body mass index and the metabolic syndrome overall and by age and sex for usual and Asian cut offs across 20 villages in rural Andhra Pradesh, India, 2005 Abnormal waist Overweight Obese Metabolic syndrome Usual definition Asian definition Usual definition Asian definition Usual definition Asian definition Usual definition* Asian definition Waist=102/88cm Waist=90/80cm BMI 25 BMI 23 BMI 30 BMI 27.5 Waist 102/88cm Waist 90/80cm % SE % SE % SE % SE % SE % SE % SE % SE Overall 7.6% 0.4% 26.0% 0.7% 22.3% 0.7% 36.8% 0.8% 4.2% 0.3% 10.3% 0.5% 24.6% 1.5% 30.2% 1.6% % 0.6% 17.5% 1.2% 18.6% 1.3% 33.2% 1.6% 2.8% 0.5% 7.1% 0.8% 9.3% 2.0% 12.1% 2.2% % 1.0% 31.6% 1.6% 26.9% 1.5% 42.7% 1.7% 5.6% 0.8% 12.9% 1.2% 28.1% 3.3% 38.0% 3.6% % 0.9% 33.5% 1.7% 26.8% 1.6% 42.0% 1.8% 4.7% 0.7% 12.8% 1.2% 32.0% 3.7% 36.5% 3.8% % 1.0% 27.5% 1.4% 19.6% 1.2% 32.2% 1.5% 4.6% 0.7% 10.3% 1.0% 38.7% 3.1% 44.5% 3.2% Men Men, All 3.0% 0.4% 19.7% 1.0% 18.4% 1.0% 32.4% 1.2% 2.5% 0.4% 7.0% 0.6% 28.6% 2.2% 33.6% 2.3% % 0.6% 11.0% 1.6% 14.3% 1.8% 28.4% 2.3% 1.9% 0.7% 3.7% 1.0% 12.0% 3.2% 14.8% 3.4% % 1.1% 24.7% 2.2% 22.2% 2.1% 37.6% 2.4% 3.7% 1.0% 10.8% 1.6% 35.5% 5.1% 43.3% 5.2% % 0.8% 29.5% 2.4% 24.8% 2.3% 38.7% 2.6% 2.1% 0.7% 10.1% 1.6% 31.7% 5.1% 36.3% 5.3% % 0.7% 20.0% 1.7% 15.6% 1.5% 27.4% 1.9% 2.4% 0.6% 5.3% 0.9% 44.1% 4.4% 49.6% 4.4% Women Women,All 12.2% 0.8% 32.4% 1.1% 26.3% 1.0% 41.4% 1.2% 6.0% 0.5% 13.6% 0.8% 20.4% 1.9% 26.6% 2.1% % 1.1% 23.9% 1.9% 22.8% 1.9% 37.9% 2.1% 3.6% 0.7% 10.5% 1.4% 6.5% 2.4% 9.4% 2.8% % 1.8% 39.4% 2.3% 32.4% 2.2% 48.5% 2.4% 7.7% 1.3% 15.4% 1.7% 19.6% 4.0% 32.0% 4.9% % 1.7% 37.7% 2.5% 28.8% 2.3% 45.5% 2.6% 7.4% 1.3% 15.5% 1.8% 32.3% 5.3% 36.8% 5.4% % 1.7% 34.6% 2.2% 23.4% 1.9% 36.7% 2.2% 6.7% 1.2% 15.0% 1.7% 33.8% 4.3% 39.8% 4.5% Percentage and standard errors * Usual definition of the metabolic syndrome according to 2005 NCEP ATPIII criteria - presence of three or more of abdominal obesity males>102cm, females >88cm, elevated triglycerides ( 150mg/dL), low HDL cholesterol (men < 40mg/dL, women < 50mg/dL), high blood pressure 130/ 85mmHg or raised fasting glucose ( 100mg/dL), drug treatment for abnormalities of cholesterol, sub-fractions, blood pressure or glucose was counted as abnormal [266]. Asian definition of the metabolic syndrome was above except with abdominal obesity defined by the lower cut points (men >90cm, women >80cm). 120

121 Discussion This survey shows that a substantial proportion of the rural population of the East and West Godavari regions of Andhra Pradesh has dyslipidaemia, abnormal levels of adiposity and the metabolic syndrome. If suggested lower Asian cut points for metabolic and weight abnormalities are used, the magnitude of the problem is even greater than that suggested by the usual definitions. In conjunction with the effects of a previously reported high prevalence of diabetes [263], these abnormalities are likely to be an important driver of the high rates of cardiovascular mortality previously reported for this population [1]. While the findings for this region may not necessarily be indicative of the situation for rural India as a whole, they do provide an important early warning of the direction that metabolic risk factors and cardiovascular disease are likely to take as rural regions of India develop. Over the coming decades economic and social transformation in India will continue apace and many rural areas are likely to develop comparable levels of risk factors to those observed here. Corresponding substantial increases in cardiovascular diseases will likely ensue, with major implications for population health and rural health service provision. There is rather little other information about population levels of lipids, adiposity and metabolic syndrome in rural India. Only six prior studies provide data about lipids from representative samples of a rural population and all are from the North [16, 43, 131, 168, 202, 203]. Just two of these studies, one from Punjab [16] and the other from Uttar Pradesh [203] included a large sample size, although the validity and practicality of comparisons between those data and ours is somewhat limited by differences in the methodologies used and the summary measures reported. In rural Punjab the proportion of adults aged 30 years and over with abnormal total cholesterol levels was similar to that observed here although the proportion reported overweight was somewhat lower. In Uttar Pradesh mean total and LDL-cholesterol levels were lower, mean HDL cholesterol levels were higher and triglyceride levels were comparable to those observed in Andhra Pradesh although mean BMI was again somewhat lower. 121

122 Compounding the paucity of cross-sectional data describing levels of lipids, adiposity and metabolic syndrome in rural India is an almost complete absence of data about the temporal changes that are occurring in cardiovascular risk factors in these regions. As discussed in Chapter 2, some data suggest that hypertension and diabetes may be increasing in prevalence [128, 269] but information about other risk factors is unavailable. Even the direction of trends in the prevalence of cardiovascular disease remains somewhat uncertain. While epidemiological models project large increases in cardiovascular disease in rural areas [3, 234], a recent review of the literature identified no upward trend in the prevalence of coronary heart disease, although the project was significantly limited by the small quantity and limited comparability of the data available [9]. Information about cardiovascular disease in urban centres in India is more comprehensive and recent reports show generally worse levels of lipids, adiposity, and metabolic syndrome than in rural regions. For example, in the Jaipur Heart Watch 3 survey, adults twenty years and over selected from the Punjabi Bhatia community had a raised total cholesterol in about one third, overweight in almost two thirds and NCEP-ATP III defined metabolic syndrome in more than forty percent [270]. In the preceding Jaipur Heart Watch 2 survey done some 2 years earlier the corresponding levels of those metabolic risk factors recorded were also higher than those reported by the few studies from rural areas but not as elevated as the levels seen in the most recent survey [50]. Very high levels of total cholesterol have also been reported from an urban population in Thiruvananthapuram in the South [271] although cholesterol levels in the Chennai Urban Population Study were not different from those observed in this study of rural Andhra Pradesh [48]. The prevalence of metabolic syndrome observed in rural Andhra Pradesh was high and above that recently reported by studies of representative samples of the Hong Kong and Taiwanese populations and only slightly less than that reported for urban and rural Thailand combined [230]. The proportion of those with the metabolic syndrome with abnormal fasting glucose levels was particularly high in rural Andhra Pradesh, a finding 122

123 that is consistent with observations of disproportionately high rates of glucose intolerance and diabetes in a number of South Indian populations [71, 90, 99, 272]. The rates of metabolic syndrome observed here are, however, low in comparison to the very high prevalence reported for some urban parts of India. In urban Chennai, for example more than forty percent of adults aged 20 to 75 years met the criteria for metabolic syndrome [262], a prevalence beyond that reported for the United States [230, 244] and comparable to the very high rates observed amongst Aboriginal communities in Canada [273]. Substantial variation in what constitutes the metabolic syndrome in different populations has been noted previously [230], raising uncertainty about the validity of current definitions of adiposity and metabolic syndrome for Asian populations [274]. Proposed new cut points for abnormality of BMI [265] and waist [268] have been suggested for Asian populations and their application significantly increases the prevalence of measures of adiposity if applied to this population from rural Andhra Pradesh. Incorporating the Asian waist cut off into the definition of metabolic syndrome both increases the prevalence by about one third and shifts the pattern of abnormalities somewhat towards that of other countries [230] - the proportion with an abnormal waist measurement increases and the proportions with each of the other abnormalities all slightly decrease. As for the diabetes results the study design, large sample size, good response rates and sophisticated statistical weighting process should have minimized systematic errors. In addition the use of an internationally standardised laboratory and quality control should provide reassurance about the validity of lipid assays for international comparisons, as should the broad comparability of the results to other studies done in urban and rural India [16, 203]. In conclusion, two main points arise from these data. First, dyslipidaemia, adiposity and metabolic syndrome are common in this rural Indian population and are more common if proposed lower Asian cut offs are used. The levels of risk factors observed here suggest that rural areas of India will follow the trends seen in urban India and the West, with very significant implications for rural health. The second main point is that the pattern of 123

124 abnormalities observed amongst those meeting criteria for the metabolic syndrome was once again different to that reported in other populations [230]. The heterogeneity of metabolic abnormalities observed between populations with metabolic syndrome in different countries provides further support for the argument that metabolic syndrome is a definitional clustering of risk factors rather than a specific disease entity. For most countries, but particularly developing countries with limited health care resources, it is not clear that routine assessment of metabolic syndrome will add much to other absolute risk-based approaches to the treatment and prevention of cardiovascular disease. 124

125 Chapter 6 High blood pressure - prevalence, identification, treatment and control in rural Andhra Pradesh Introduction Blood pressure is an important predictor of cardiovascular risk and disease [ ]. Multiple cross-sectional and longitudinal studies have shown the continuous relationship of increasing blood pressure levels with other indicators of cardiovascular risk and incidence of cardiovascular events [275, 278]. In addition numerous intervention studies have shown the efficacy of blood pressure lowering in reducing morbidity and mortality from cardiovascular disease [ ]. The identification and treatment of high blood pressure is simple and low cost [283, 284]. In many developing countries hypertension programs have been a common first strategy in the prevention of cardiovascular disease [227]. This approach is advocated by the WHO CVD- Risk Management Package for low and medium-resource settings [285] in which blood pressure measurement is the first step to assessing cardiovascular risk. In the study area, the Byrraju Foundation has previously introduced a blood pressure program in This program has involved a house to house checking of blood pressure by a Foundation employed health worker, referral of individuals found to have high blood pressure to a Foundation employed primary care physician and free treatment with beta blockers or diuretics if repeat blood pressures are high. In this chapter the mean levels and prevalence of blood pressure are reported together with the proportions of the population screened for high blood pressure and the proportions of the population aware, treated and with their high blood pressure controlled. Methods The design and analysis of survey results have been discussed in previous chapters (see Chapter 3). Results reported here were obtained from all 4,535 subjects participating in 125

126 the survey and have been weighted, as in previous chapters, such that they are representative of the adult population 30 years and above in the 20 study villages. Means and proportions are reported with 95% confidence intervals, unless otherwise stated. Data collection and methods specific to hypertension All participants had two sitting measurements of blood pressure taken by a trained and experienced nurse measured at least 5 minutes apart using an Omron M2 with manual inflation and electronic display. Definitions Hypertension or high blood pressure was defined as mean systolic blood pressure 140mmHg, and/or mean diastolic blood pressure 90mmHg, or known hypertension (diagnosed by a doctor) on blood pressure lowering prescription medication [216]. Awareness of hypertension was defined as self-report of a prior diagnosis of hypertension by a health care professional, among the population defined as having hypertension. Treatment of hypertension was defined as use of a prescription medication for management of high BP at the time of the interview. Control of hypertension was defined as pharmacological treatment of hypertension associated with an average systolic BP < 140 mmhg and an average diastolic blood pressure < 90 mmhg [286]. Results Mean levels and prevalence of high blood pressure The overall mean systolic blood pressure was 123mmHg, which was lower in females compared with males (p<0.001) (Table 6-1). The overall mean diastolic blood pressure was 77mmHg and females had a marginally lower value (p<0.06). Overall 27.0% ( ) had high blood pressure according to current definitions, and this was similar between males and females (p=0.6). Systolic and diastolic blood pressure increased with age (p<0.001) (Table 6-1) and similar age trends were seen when results were analysed in terms of a diagnosis of hypertension. 126

127 Table 6-1 Mean systolic and diastolic blood pressure and prevalence of high blood pressure overall and by age and sex in 20 villages of rural Andhra Pradesh, 2005 Systolic Diastolic Prevalence* mmhg 95%CI mmhg 95%CI % 95%CI Overall 123 ( ) 77 (76-77) 27.0% ( ) ( ) 74 (74-75) 9.4% ( ) ( ) 78 (78-79) 24.8% ( ) ( ) 79 (78-80) 36.4% ( ) ( ) 77 (77-78) 50.0% ( ) Male 125 ( ) 77 (77-78) 26.6% ( ) ( ) 74 (73-75) 9.9% ( ) ( ) 79 (78-80) 25.9% ( ) ( ) 79 (78-81) 37.3% ( ) ( ) 78 (77-79) 46.1% ( ) Women 122 ( ) 76 (76-77) 27.5% ( ) ( ) 74 (74-75) 9.0% ( ) ( ) 78 (77-79) 23.6% ( ) ( ) 78 (77-79) 35.5% ( ) ( ) 77 (76-78) 53.5% ( ) *Hypertension defined as per JNCVII -SBP 140 or DBP 90 or known HT on BP lowering treatment Screening of blood pressure Overall 65.8% ( ) of adults 30 years and above recalled having had their blood pressure checked at least once in their lifetime. This was more frequent amongst women compared with men (p<0.001) and increased with age (p<0.001). In addition 48.5% of adults 30 years and above recalled that they had received a blood pressure check in the last 12 months and 27.2% had their blood pressure checked by staff of the Byrraju Foundation. (Table 6-2, Figure 6-1) Overall Women Male BP checked by Byrraju Foundation BP checked in last 12 months BP ever checked % 20% 40% 60% 80% 100% Figure 6-1 Prevalence of blood pressure screening in adults 30 years in rural Andhra Pradesh,

128 Table 6-2 Prevalence of blood pressure screening in 20 villages in rural Andhra Pradesh, 2005 BP ever checked BP checked in last year BP checked by Byrraju Foundation % CI % CI % CI Overall 65.8% ( ) 48.5% ( ) 27.2% ( ) % ( ) 35.0% ( ) 16.2% ( ) % ( ) 45.3% ( ) 22.9% ( ) % ( ) 56.7% ( ) 33.9% ( ) % ( ) 67.3% ( ) 44.2% ( ) Male 58.3% ( ) 42.3% ( ) 20.4% ( ) Women 73.5% ( ) 54.9% ( ) 34.1% ( ) Percentage and 95% confidence intervals Awareness, treatment and control of high blood pressure in rural Andhra Pradesh Of the total population defined as with hypertension, approximately half, 45.6% ( ) were aware of their diagnosis of hypertension. 41.8% ( ) of all hypertensives were on prescribed blood pressure lowering therapy and 14.9% ( ) of all hypertensives had their blood pressure controlled (Table 6-3). Women were more likely than men to be aware of hypertension, receiving prescribed blood pressure lowering medication and achieving blood pressure control (all p<0.001). Increasing age was also associated with higher levels of awareness, treatment and control (all p 0.02). (Table 6-3, Figure 6-2). Just over half (55.3%, ) of patients with controlled blood pressure were receiving treatment from the Byrraju Foundation. In multivariate models predicting blood pressure control and adjusted for BP lowering, only lower age (p=0.001) and female sex (p=0.03) were significant predictors of blood pressure control. 128

129 Table 6-3 Awareness, Treatment and Control of high blood pressure amongst hypertensive adults in rural Andhra Pradesh, 2005 Overall Men Women % SE % SE % SE Awareness 45.6% 1.5% 37.5% 2.1% 53.6% 2.2% Treatment Treatment (Among those aware) 83.6% 1.8% 83.0% 2.9% 84.0% 2.2% Treatment (Among all with hypertension) 41.8% 1.5% 34.9% 2.1% 48.6% 2.2% Treatment from BF (Among all with hypertension) 23.1% 1.2% 18.2% 1.6% 28.0% 1.9% Control Control (Among those treated) 35.6% 2.2% 31.2% 3.2% 38.8% 3.0% Control (Among all with hypertension) 14.9% 1.1% 10.9% 1.3% 18.9% 1.7% Control (Among hypertensive diabetics) 26.0% 3.6% 21.5% 4.7% 30.8% 5.4% <130/85 mm Hg (Among hypertensive diabetics) 16.2% 3.0% 11.8% 3.7% 20.8% 4.7% Control (Among hypertensive with previous vascular disease) 22.1% 3.5% 17.2% 4.4% 27.6% 5.5% Overall Male Women Controlled Treated Aware 0% 20% 40% 60% 80% 100% Figure 6-2 Awareness, treatment and control of high blood pressure in rural Andhra Pradesh,

130 Discussion Blood pressure levels in rural Andhra Pradesh were generally lower than that reported for similar age groups in urban India and urban China [269, ] and similar to levels reported in Thailand [243]. They were however similar or higher compared to levels recently reported in the United States and other established market economies [244, 289], where blood pressure levels have fallen compared to previous years [125]. The proportions of this rural Indian population aware of their high blood pressure, on prescribed blood pressure-lowering treatment and achieving desired levels of blood pressure control were low compared to developed countries [290] Age deciles Rural India Urban India Urban China NHANES Figure 6-3 Mean blood pressure by age deciles for Rural Andhra Pradesh compared to Urban India, China and NHANES Urban India & China data from WHO Global InfoBase [287] In the United States results from NHANES reported a comparable 28.7% of participants with hypertension but 68.9% were aware of their hypertension, 58.4% of those were treated and 31.0% were controlled with therapy [291]. Very similar trends in awareness, treatment and control were reported from the 1994 England household survey (63% were aware of their diagnosis, 50% were receiving treatment and 30% were 130

131 controlled) although differences in definitions reflect the different times at which the studies were done [292]. The levels of high blood pressure identification, treatment and control in this area of rural India are however similar or marginally higher than that reported in urban areas of India. In urban Kerala, a study of 314 urban middle aged subjects reported in 2003 identified 29% as hypertensives aware of their diagnosis, with 29% of those being on blood pressure lowering treatment and approximately 10% achieving control [293]. Very similar values for identification, treatment and control were observed in a sample of 357 elderly individuals (mean age 70) from urban and rural communities in Kerala (prevalence of hypertension 51.7% of which 44.9% were aware, 42.7% were receiving treatment and 11.4% were controlled) [294]. Likewise, the Hypertension Study Group reported results in 2001 for 1203 elderly individuals (mean age 70) sampled from 3 sites in India and 2 sites in Bangladesh. Approximately 60% of those sampled were from urban areas and the overall prevalence of hypertension was 65% reflecting the high average age. However of those with hypertension, 45% were aware of their condition, 40% were taking anti-hypertensive medications and 10% were controlled according to JNC VI criteria [295]. In comparison to data available from other developing countries, this area of rural Andhra Pradesh has somewhat higher rates of identification, treatment and control [290]. The InterAsia China study reported 27.2% of the Chinese adult population years with hypertension and amongst those 44.7% were aware of their high blood pressure, 28.2% were taking antihypertensive medications and 8.1% were achieving blood pressure control [286]. The Inter ASIA China study also noted considerable geographic variation in the levels, treatment and control of hypertension in China although an urban-rural difference was not observed. The reasons for geographic variation are unclear though possible explanations include variations in dietary salt or alcohol intake and differences in access to healthcare [288]. 131

132 The Byrraju Foundation high blood pressure program has likely raised the levels of awareness, treatment and control in this area. While the effectiveness of Hypertension programs such as this one is difficult to evaluate it is clear that the program had impacted upon significant numbers of individuals and that this had lead to treatment in a proportion of cases. There clearly remain, however, major barriers to the identification, treatment and control of high blood pressure levels in rural India. Whether individual risk factor targeted programs such as that established in this community should be pursued further is not clear. Absolute cardiovascular risk-based approaches to cardiovascular management are increasingly the norm [296, 297] and would almost certainly be of even greater value in resource poor settings such as this [296]. The development and evaluation of such programs for rapidly developing low and middle income countries is an urgent public health requirement. 132

133 Chapter 7 Tobacco use in rural Andhra Pradesh Introduction Smoking is a well established risk factor for cardiovascular morbidity and mortality [ ] and risk has been shown to fall with cessation of smoking [301, 302]. Likewise passive smoking has also been shown to be associated with coronary heart disease and is increasingly recognised as a public health issue [303]. The relationship between smokeless tobacco and cardiovascular disease is less well established, [304, 305] although one recent large case-control study has shown chewing tobacco to be associated with an increased risk of myocardial infarction with an additive effect to the risk conferred by smoking alone [211]. In India and much of South Asia the role of tobacco in cardiovascular disease has been more difficult to elucidate because tobacco is consumed in such a variety of forms. Broadly these can be classified into three main groups: standard cigarettes, hand-rolled cigars and smokeless or chewed tobacco. In the geographic area included in this study hand-rolled cigars called beedis or chuttas are common although use of chewing tobacco is relatively limited. The rates of tobacco smoking in industrial and urban India are known to be high [50, 306, 307] but in contrast to other risk factors where levels are usually better in rural areas, tobacco use may be more prevalent outside the cities [43, 141, 308]. In this chapter the prevalence of smoking in the adult population and specific population sub-groups at elevated cardiovascular risk are explored for adults in the East and West Godavari regions of rural Andhra Pradesh. Methods The survey methods have been described previously. The results reported here were obtained from all 4,535 subjects participating in the survey and have been weighted, as in previous chapters, such that they are representative of the adult population 30 years and 133

134 older in the 20 study villages. Means and proportions are reported with 95% confidence intervals, unless otherwise stated. Data collection and methods specific to measurement of tobacco use All participants were asked if they had ever smoked regular. If they replied in the affirmative, they were asked if they were currently smoking. Current smokers were asked how many years they had smoked for, what form of tobacco they smoked (cigarettes or hand-rolled cigar) and how many of each they smoked. Hand-rolled cigar referred to other smoked tobacco e.g. beedi and chutta. A separate question was asked of all participants regarding the use of chewing tobacco. To evaluate passive smoking, all participants were asked how many people in their household smoked and how many hours/ minutes of smoke they were exposed to per day at work or at home. Definitions An ever smoker was defined as a person who had smoked tobacco any time in their life for at least 12 months continuously. A current smoker was defined as an ever smoker who had been smoking any time in the last 1 month. Passive smoking was defined as smelling of smoke for 1 or more hours/ week on the basis of other studies that have used similar definitions and recent work that show an effect on health of this level of passive smoking [309, 310]. Results Smoking prevalence by age and sex Overall 32.4% ( ) of the population stated they had smoked regularly at some point in their lives and 25.2% ( ) of the population reported they were currently smoking. Smoking was much more common in men (55.7%, ) compared with women (8.7%, ) and more common with increasing age (Table 7-1, Figure 7-1). 134

135 Use of chewing tobacco was much less common overall (3.0%, ) and more common amongst men, but there was no association with age (p=0.4) (Table 7-1). 100% 100% 90% 90% 80% 80% 70% 70% 60% 60% 50% 50% 40% 40% 30% 30% 20% 20% 10% 10% 0% 0% Males Ever smoked Current Chew tobacco Females 60+ Figure 7-1 Prevalence of tobacco use by sex and age group in rural Andhra Pradesh, 2005 The hand-rolled cigar was the most common form of tobacco smoked. Amongst current smokers 69.9% used the cigar only, 26.7% cigarettes only, and 3.5% smoked both. Cigarette smoking was confined to men, with no women reporting cigarette smoking. Heavy smoking was not common. Only 33% of male current smokers smoked 10 or more cigarettes/ hand-rolled cigars per day and for females this was less than 1%. The median number of cigarettes/ hand-rolled cigars smoked was 4 (inter-quartile range 2 to 7). Cigarette smokers consumed a median of 10 cigarettes/ day (inter-quartile range 7 to 13) and cigar users a median of 3 cigars/day (inter-quartile range 1.5 to 4.5). Current smokers had smoked for a median of 25 years (inter-quartile range 15 to 40). 135

136 Table 7-1 Prevalence of smoking and chewing tobacco overall and by age and sex group in 20 villages in rural Andhra Pradesh, India, 2005 Age group Overall % 95%CI % 95%CI % 95%CI 95%CI % 95%CI Ever smoked All 32.4% ( ) 22.5% ( ) 33.9% ( ) 37.4% ( ) 42.6% ( ) Men 55.7% ( ) 41.9% ( ) 57.3% ( ) 62.1% ( ) 70.8% ( ) Women 8.7% ( ) 3.1% ( ) 7.1% ( ) 11.7% ( ) 16.4% ( ) Current smoker All 25.2% ( ) 19.1% ( ) 28.0% ( ) 29.0% ( ) 28.9% ( ) Men 45.2% ( ) 36.7% ( ) 48.9% ( ) 50.4% ( ) 50.4% ( ) Women 4.8% ( ) 1.5% ( ) 4.2% ( ) 6.8% ( ) 9.0% ( ) Past smoker All 7.2% ( ) 3.4% ( ) 5.9% ( ) 8.4% ( ) 13.7% ( ) Men 10.4% ( ) 5.2% ( ) 8.5% ( ) 11.7% ( ) 20.4% ( ) Women 3.9% ( ) 1.6% ( ) 2.9% ( ) 4.9% ( ) 7.4% ( ) Chew tobacco All 3.0% ( ) 2.9% ( ) 2.2% ( ) 3.9% ( ) 3.5% ( ) Men 5.0% ( ) 5.2% ( ) 3.8% ( ) 6.2% ( ) 5.0% ( ) Women 1.0% ( ) 0.5% ( ) 0.3% ( ) 1.4% ( ) 2.1% ( ) Passive smoking In 46.3% ( ) of households there were one or more smokers. Amongst non-current smokers, 27.6% reported 1 or more hours/week of passive tobacco smoke exposure. Exposure was more common amongst women non-current smokers (31.4%, ) compared with men (21.1%, ). Exposure was more common at home: 19.0% ( ) of non-current smokers reported 1 or more hours/week exposure at home compared to 15.0% of non-current smokers ( ) who reported 1 or more hours/week exposure at work. Men non-current smokers were more likely to be exposed at work and women non-current smokers were more likely to be exposed at home (Table 7-2). Table 7-2 Exposure to passive tobacco smoke amongst non-current smokers in rural Andhra Pradesh, India, 2005 Overall Men Women Exposure at work/home for 1 or more hours/week 27.6% ( ) 21.1% ( ) 31.4% ( ) Exposure at work for 1 or more hours/week 15.0% ( ) 17.9% ( ) 13.2% ( ) Exposure at home for 1 or more hours/week 19.0% ( ) 8.1% ( ) 25.4% ( ) 136

137 Knowledge of the harms of tobacco and socio-economic factors associated with tobacco use Only 58.0% ( ) of male current smokers and 28.1% ( ) of female current smokers answered yes when asked whether cessation of smoking would prevent heart disease. Smoking prevalence was inversely related to the highest level of education achieved (Figure 7-2). Prevalence of current smoking also trended down with increasing household income (Figure 7-3). Lower rates of current smoking were found in professional and office worker groups compared with unskilled and skilled manual workers (Table 7-2). Higher education Secondary school Primary school No formal schooling Females Males 0% 20% 40% 60% 80% 100% Figure 7-2 Prevalence of current smoking by highest level of education completed, rural Andhra Pradesh,

138 60% Males Females Prevalence of current smoking 50% 40% 30% 20% 10% Household income (Rs/month) 1 < >3000 0% Income category Figure 7-3 Prevalence of current smoking by income category, rural Andhra Pradesh, 2005 Table 7-3 Prevalence of smoking by occupation, overall and by sex, rural Andhra Pradesh, 2005 Overall Male Females % SE % SE % SE Unemployed 31.5% 2.5% 42.4% 3.4% 12.4% 3.0% Unskilled manual worker 34.2% 1.1% 49.2% 1.5% 7.0% 1.0% Skilled manual worker 30.1% 3.8% 41.5% 4.8% 0 0 Owner of business or farm 33.0% 3.9% 36.4% 4.3% 5.5% 5.4% Office worker/ nonprofessional 22.4% 5.5% 25.1% 6.0% 0 0 Professional 12.0% 5.0% 13.2% 5.5% 0 0 Housewife 2.9% 0.5% % 0.5% Discussion Smoking was shown to be very prevalent in this rural area of Andhra Pradesh and to be much more common amongst men than women. Smoking rates were also higher in the socio-economically disadvantaged and awareness of smoking as a risk factor for cardiovascular disease was low. The patterns seen here are similar to that seen in other parts of rural India and common to the developing world [53, 141, 311]. Tobacco use in low and middle income countries is predicted to contribute to an increasing share of the global burden of disease and already in 1995 an estimated 82% of the world s 1.1 billion smokers lived in low- or middle-income countries [312]. Tobacco is hence an important 138

139 target for public health intervention for India and particularly rural areas of India where rates are reported to be higher than urban rates [184]. While higher rates of smoking have previously been reported in rural compared with urban areas of India, the rates of smoking in this community in rural Andhra Pradesh are much greater than the rural smoking rates reported in two recent national surveys discussed in detail in Chapter 2 [140, 141]. The National Sample Survey (NSS) conducted in reported mean smoking rates of 17.6% in rural areas and the National Family Health Survey-2 (NFHS-2) conducted in reported rates of 32.5% in rural men and 3.0% in rural women. The NHFS-2 also reported results by state average smoking rates in urban and rural Andhra Pradesh were 35.3% in men and 4.2% in women providing considerable reassurance of the validity of the findings in our study. By contrast to smoking, the use of chewing tobacco in this study was less than national estimates. In the NSS, 15.9% of rural and 8.4% of urban subjects used chewing tobacco and for the NFHS-2 chewing tobacco was used by 31.1% of rural men and 13.3% of rural women. The rates of chewing tobacco in Andhra Pradesh in the NFHS-2 were lower than the national average at 10.7% for men and 9.9% for women and once again this finding correlates well with the data collected here. A greater mean rate of smoking would be anticipated in the APRHI study because unlike the two national surveys the APRHI study did not include young adults amongst whom smoking rates were lower. In addition it appears that the indirect method of data collection used in the national studies (whereby one family member reported the smoking habits of all others) may have resulted in significant under-reporting. In another large study (the Mumbai cohort study) conducted at a similar time as the NFHS-2 but using direct data collection methods the documented smoking rates were about 10% greater [313]. While there is evidence of falling smoking rates in high-income countries, tobacco consumption is probably increasing in low- and middle-income countries [314]. There is 139

140 some evidence for this in parts of India. For example in Andhra Pradesh, the rates of smoking found in this study (45.2% in males, 4.8 % in women) are higher than previous reports from Andhra Pradesh from the 1998 NFHS [140] (35.4% in men, 4.2% in women) and the 1969 oral cancer study in the Srikakulam district, north of the Godavari district (18.2% in males, 2.4% in females) [315]. While the sampling methodologies, age groups and definitions used in these studies were not directly comparable; they give some indication of possible changing trends in Andhra Pradesh. A similar pattern has been observed in Maharashtra. Smoking amongst men in the Poona district of Maharashtra, near Mumbai, was uncommon at 8% in 1972 [316] and has risen to 23.6% in the 1992 Mumbai study [313]. While the rate for the state was documented as 13.3% in the 1998 NFHS-2 study this is likely an under-estimate consequent upon the household reporting method used and not an indication of falling smoking rates in Maharashtra. In our study, rates of tobacco smoking were generally higher in groups with poorer education, lower incomes and amongst manual workers compared to those with other occupations. These findings are comparable to those of the NFHS and NSS. Multivariate logistic regression for tobacco smoking in NFHS found lower educational level and lower household income to be the strongest predictors of smoking. They also showed that socially disadvantaged groups (scheduled castes/ tribes and other backward castes) were more likely to smoke compared to forward castes and showed that Muslim men were more likely to smoke than Hindu men (there were no differences for women). Likewise in the NSS, respondents belonging to scheduled castes and tribes were 1.4 times more likely to be regular smokers, and respondents with no formal education were 1.7 times more likely to be regular smokers [141]. The urban Mumbai study also found similar relationships for education and occupation. Unskilled workers were 1.7 times more likely to be current tobacco users and this was so for both beedi and smokeless tobacco smokers users [317]. While smoking and use of tobacco was common in our study only 33% smoked more than 10 cigarettes/ beedi per day. This is low compared with reports from China where 56% of male smokers smoked 20 or more cigarettes per day [318]. However, similar low 140

141 rates of heavy smoking were found in the urban Mumbai study where the median number of cigarettes smoked by male cigarette smokers was 5, and by male beedi smokers was 12 [313]. Hand-rolled cigar smoking was twice as common as conventional cigarette use in the study area. The likely reason for this is the lower cost (1 U.S. Dollar = 45 Indian Rupees buys 30 cigarettes or 180 beedis/ chuttas personal communication 26/8/2006 from Dr. Arun Kumar, CCDC, India). This form of tobacco is also the only form of smoked tobacco used by women in this area because smoking of beedis and chuttas is socially accepted for women, but smoking of cigarettes is not. Passive smoking was also common in this area with more than a quarter of non-smokers exposed to regular passive smoke. There is now considerable data that passive smoking has an adverse effect on the health of non-smokers [ ]. The magnitude of this risk is less clear and probably has been hampered by difficultly in accurate measurement of passive smoke exposure [322, 323], however recent reviews suggest a relative risk increase of 30% for cardiovascular disease [321]. Interventions that legislate to restrict smoking in public places reduce passive smoke exposure and have been associated with prevention of cardiovascular mortality [324] and decrease in hospital admissions for acute myocardial infarction [325]. The effects of such interventions also seem to be faster than one would expect if there was a simple dose-response relationship [326, 327]. A comprehensive public health response is necessary to achieve tobacco control but is an expensive undertaking and likely a major challenge for poorly resourced India [6, 328, 329]. Percentages of former smokers are a good measure of smoking cessation at a population level [139]. In high-income countries, percentages of former smokers have increased over the past two to three decades, and today about 30% of the male population are former smokers [330]. By contrast, in recent years, percentages of male former smokers have been recorded at only 10% in Vietnam [331], 5% in India [313] and 2% in China [332]. In this study 7.2% were past smokers and knowledge of the risks of smoking were also 141

142 low. It hence seems likely that interventions targeting smoking cessation have not yet permeated to these areas. Population level interventions, such as tax on tobacco, legislation restricting tobacco advertising or use in public places, have been the most effective in reducing tobacco use in Western countries and would be cost-effective for developing countries [333]. Governmental changes in policy are required in India to support these strategies alongside prevention programs. Targeting smoking cessation strategies to high-risk sub-groups of the population may also be cost-effective [334]. While the types of tobacco used in India compared to the West are different, tobacco smoking is already a leading cause of cardiovascular morbidity and mortality in India [335, 336]. The 5-year follow-up results of the Mumbai Cohort found higher mortality rates amongst smokers compared with non-users of tobacco with an age-adjusted relative risk of 1.39 for cigarette smokers and 1.78 for beedi smokers. Smokeless tobacco was also associated with an increased risk of all-cause mortality (age-adjusted relative risk of 1.22 for men and 1.35 for women) [337]. Reanalysis of this study with 5.5 years of follow-up confirmed these findings and found the relative risk of cardiovascular death was 1.54 ( ) amongst smokers and 1.32 ( ) amongst smokeless tobacco users [336]. Another case control study of 309 men with myocardial infarction compared to 618 age matched controls found currents smokers of cigarettes or beedis had 4.7 ( ) the risk of never smokers [338]. More recent analysis of the INTERHEART study, a case-control study of 27,089 participants in 52 countries confirms these findings. Beedi smoking (OR 2.89, ), cigarette smoking (2.95, ) and smokeless tobacco use (OR 2.23, ) were all associated with an increased risk of non-fatal AMI. In addition smokers who also chewed tobacco compounded their risk (OR 4.09, ) [211]. It is generally thought that the burden of tobacco related diseases has not been completely realised in developing countries with the full impact of smoking in a population taking many decades to appear. China has recently reported increasing tobacco related mortality [339, 340] and it is likely that similar trends will be seen in India. In this region of Andhra Pradesh smoking rates are high and quit rates are low. The identification of 142

143 effective and appropriate smoking intervention strategies for India, and in particular rural India, is a public health priority. 143

144 Chapter 8 Greater Adverse Effects of Cholesterol and Diabetes on atherosclerosis in a South Asian Indian population compared with Caucasian Australians Background There is substantial data from the United Kingdom, Canada and Singapore [59, 68, 89, 93] showing that populations of South Asian Indian background have markedly higher mortality from coronary heart disease (CHD) than is seen in other population groups. Although it is acknowledged that levels of known cardiovascular risk factors vary between these groups they may not fully explain the differences in CHD rates [90]. One possible reason for this is that there are ethnic differences in susceptibility to vascular risk factors, as well as differences in mean risk factor levels. Carotid intima-media thickness (IMT) is a well-validated measure of atherosclerosis that is predictive of the risk of future cardiovascular events [94-98]. Carotid IMT has been used to quantify atherosclerotic disease burden in different ethnic groups [99, 100] but previous analyses have largely restricted themselves to comparing mean levels of carotid IMT between populations. In this study we sought to compare the mean levels of carotid IMT between populations in the usual way. In addition, however, we also compared the associations of cardiovascular risk factors with carotid IMT between populations to identify any differences in the susceptibility of the two populations to the risk factors. Methods This study involved the comparison of measures of carotid intima-media thickness made in a population of urban Australians in 1999 [341] with comparable measures made in a population of rural South Indians studied in The India-based component of the project was done as part of the Andhra Pradesh Rural Health Initiative (APRHI) and was approved by the Ethics Committees of the CARE Hospital, Hyderabad in India and the University of Sydney in Australia. The Perth-based component of the work was approved by the Ethics Committee of the University of Western Australia. All participants 144

145 provided informed consent and the study was conducted in line with the Declaration of Helsinki and subsequent amendments. Study populations Rural India - A random population sample stratified by age and gender was invited from two villages in the Godavari district of rural Andhra Pradesh. The main industry of this district is farming. The two villages selected were typical of this area and had census lists compiled by a local non-governmental organisation in The total population of village 1 was 2858, it was 20km from a rural town centre, whilst the total population of villages 2 was 6194, 11km from a rural town centre. Of the 6018 adults in the combined villages, 600 were invited to participate in the survey [342]. The registered population of each village aged 20 years and above was divided into ten strata defined by age (20-34, 35-44, 45-54, 55-64, 65+) and gender. A similar fixed number of individuals within each stratum were selected at random and invited to attend for study. Data collection occurred over two visits, 4 months apart in Of the 600 individuals invited, 345 (58%) were located, agreed to participate and gave informed consent and presented for the 1 st visit interview. This group was studied for the pilot study and hence detailed results of the whole group are reported and discussed in Chapter 3. Of this 345, 303 (88%) presented to the second visit 4 months later for carotid IMT measurements. The mean age of participants was similar to that of non-participants (p=0.7) as were the proportions of poor or very poor subjects (p=0.4) and the proportion literate (p=0.09). However, a larger percentage of participants compared to non-participants were women (55% versus 46%, p=0.03). Urban Australia - Detailed descriptions of the protocol and sampling strategy as well as main results from the Australian study have been published previously [341]. In brief, this study was conducted in Perth, the 4th largest city of Australia (population 1.4 million in 2001) [343]. Subjects were original participants in the 1989 Australian National Heart Foundation (NHF) Perth Risk Factor Prevalence Survey. This was an age and sex stratified random electoral roll survey of 2000 people between the ages of 20 and 70 years of age from the Perth metropolitan area. Record matching done in May

146 established a current address for 1807 living subjects. All were invited to attend study clinics between June 1995 and December 1996, and 1111 subjects (61% of those eligible) agreed to participate. The age-adjusted prevalence of risk factors in this study population was similar to that reported for the entire 1989 cohort [344]. Most Perth CUDAS (Carotid Ultrasound Disease Assessment Study) subjects were Australian-born (61%), with three quarters of the overseas born subjects being from the UK or Europe and 6% from Asia, mainly South East Asia [345]. Data collection and measurements Data collection and measurement techniques were broadly comparable for rural India and urban Australia with specially trained study staff administering a structured questionnaire, performing a brief physical examination and collecting a fasting venous blood sample in both studies. Socio-demographic information and information on medical history and cardiovascular risk factors were recorded on the basis of self-report. Examination included two sitting measurements of blood pressure made using an Omron M2 manual inflation blood pressure monitor in rural India and a mercury column manometer in urban Australia. Measurement of body weight, height, waist and hip circumference were done with participants wearing clothes without shoes. Venous blood samples for biochemical analysis were obtained after an 8-hour overnight fast. In rural India the samples were stored immediately over ice and transferred to the study laboratory in Bhimavarum within 4 hours of collection where analyses were performed using a Hitachi Boeringer Mannheim 902 Automatic analyser and Elecys In urban Australia analyses were performed using a Hitachi 747 auto-analyser. Quality control and standardization in rural India and urban Australia was similarly achieved through the analysis of internal and external quality assurance materials provided by the Royal College of Pathologists Australia quality control program, run concurrently with study bloods. Definitions Diabetes was defined as fasting plasma glucose 7.0 mmol/l [218] or a previous diagnosis of diabetes. A current smoker was defined as a person who was smoking 5 or more days in a week. In rural India this included the smoking of cigarette, beedi or cigar, but not chewing tobacco. Body mass index (BMI) was calculated as the weight over the 146

147 squared height and waist-hip ratio (WHR) was the waist circumference divided by hip circumference. Carotid ultrasound measurements All participants underwent carotid B-mode ultrasound to measure IMT of the left and right common carotid arteries using a high-resolution transducer. In rural India scans were performed with a Terason digital ultrasound system (5-10MHz transducer, Version Teratech, Burlington MA, USA), and in urban Australia scans were performed using an Interspec (7.5 MHz transducer, Apogee) CX200 ultrasound machine [341]. In rural India a single operator captured all images as digital video files. On return to Australia files were converted to bitmap files and analysed. In Perth all images were captured on video by a single operator who similarly converted these to bitmap files in their local lab for analysis. Observers from the Perth Study and the Rural Indian study were both trained in the same methods of analysis, as described by us previously [346], and were blinded to the subject s identity and risk factor profiles while analysing the images. All scans were performed according to similar standard scanning protocols [346, 347], using images of the far wall of the common carotid arteries. Participants were placed in a supine position with the head rotated approximately 45 degrees to the opposite side of the body to that imaged. The jugular vein and carotid artery were initially imaged transversely with the jugular vein stacked above the carotid artery. The transducer was then rotated 90 degrees around the central line of the stacked jugular vein carotid artery to obtain a longitudinal image. The common carotid artery was interrogated from all three angles (anterolateral, lateral or posterolateral) for all subjects. The distal common carotid segment was free of major plaque (defined as >50% lumen narrowing) in all cases. When an optimal image was obtained it was stored for offline analysis. From these moving images, images were frozen in systole, digitised and measured from both sites using a computerised edgedetection system, previously described and validated [346]. 147

148 In all studies the reader selected images from the far wall judged to have the thickest intima-media complex. By placing a cursor over the straight portion of the far war of the common carotid artery 10 mm from the common carotid artery bulb the computer would output the measurement of mean and maximum IMT. At least three frames were analysed from each of the left and right arteries, a minimum of six per individual, and an average calculated from the mean measurements to give an overall mean IMT for each subject. To check for comparability of analysis methods and results, recordings from a random sample of 32 urban Australian subjects were re-read by the Indian group observer who was blind to the initial measurement made by the Australian group observer. Interobserver comparisons showed a mean (SD) inter-observer difference of /- 0.06mm (range to 0.12mm) with an intra-class correlation coefficient of r=0.87 (95% CI ). This is comparable with that published in other studies [99, 345, 346, 348]. Statistical analyses Throughout, mean levels or proportions are shown with 95% confidence intervals for the 1414 participants with carotid IMT measurements (303 rural India, 1111 urban Australia). Quantitative cardiovascular risk factors and carotid IMT were adjusted by age and sex using a generalised linear model and least squares means were compared between rural India and urban Australia populations. Comparisons of IMT were also made adjusting for systolic blood pressure, total cholesterol, HDL-cholesterol, BMI, diabetes, current smoking and ethnicity. Further sensitivity analyses were done by adding or replacing total cholesterol with LDL-cholesterol, HDL-cholesterol with triglycerides, systolic blood pressure with diastolic pressure, BMI with waist, waist-hip ratio or weight, current smoking with former smoking and diabetes with glucose level. For comparisons of risk factor levels between populations, proportions and means standardised to the age and sex distribution of the combined study population were compared with p-values for differences between ethnic groups calculated using logistic models with a population term. Comparisons of the associations of risk factors with carotid IMT between populations were investigated using generalized linear models incorporating populationrisk factor interaction terms. Distributions of all variables were examined for normality 148

149 and carotid IMT was log-transformed for all analyses. Standard model checking procedures were applied [7] and all analyses were done using STATA 8.0. Results Participant characteristics The average age of the rural Indian study participants was 51 (range 20 to 90) years and the urban Australian participants 53 (range 27 to 77) years. The proportions of males were 45% and 50% respectively. The age and sex adjusted levels of cardiovascular risk factors were different with worse levels of blood pressure, total cholesterol, LDLcholesterol, fasting plasma glucose, body mass index, waist and weight in urban Australia compared to rural India and only smoking, triglycerides, waist hip ratio, HDL: total cholesterol ratio and high density lipoprotein levels less favourable in rural India (Table 8-1). 149

150 8-1 Table 1 Age and sex-adjusted levels of cardiovascular risk factors in urban Australian and rural Indian participants Urban Australians Rural Indians Parameters Mean 95% CI Mean 95% CI *P-value Systolic blood pressure (mmhg) 128 ( ) 122 ( ) < Diastolic blood pressure (mmhg) 80.2 ( ) 75.2 ( ) < Total cholesterol (mmol/l) 5.6 ( ) 4.9 ( ) < HDL-cholesterol (mmol/l) 1.3 ( ) 0.9 ( ) < LDL-cholesterol (mmol/l) 3.6 ( ) 3.4 ( ) < HDL/Total cholesterol ratio 0.25 ( ) 0.18 ( ) < Triglycerides (mmol/l) 1.3 ( ) 1.5 ( ) < Glucose (mmol/l) 5.7 ( ) 4.4 ( ) < Waist (cm) 84.5 ( ) 80.1 ( ) < Hip (cm) ( ) 92.1 ( ) < Waist: hip ratio 0.83 ( ) 0.86 ( ) < Weight (kg) 74.9 ( ) 53.7 ( ) < Height (cm) 169 ( ) 156 ( ) < Body mass index (kg/m 2 ) 26.1 ( ) 21.7 ( ) < Smoking (%) 15.1 ( ) 23.1 ( ) Diabetes (%) 5.6 ( ) 4.8 ( ) The Australian cohort is the reference population Data are expressed as mean values or proportions with 95%confidence intervals (CI) *P-value for difference between age and sex adjusted means in two populations studied 150

151 Carotid Intima-Media Thickness The age and sex adjusted mean carotid IMT was significantly greater amongst rural Indians (0.74, ) compared to urban Australians (0.69, ) (P<0.001) (Figure 1). After adjusting for systolic blood pressure, total cholesterol, HDL cholesterol, triglycerides, diabetes, current smoking and body mass index the levels in rural Indians (0.75, ) and urban Australians (0.69, ) were largely unchanged and still highly significantly different (p<0.001) (Figure 1). The results were similar with addition or substitution of diastolic blood pressure with systolic blood pressure, triglycerides with HDL-cholesterol, fasting plasma glucose with diabetes, current smoking with former smoking and if different measures of adiposity (waist, waist-hip ratio or weight) were used in addition or instead of BMI. In addition, mean carotid IMT increased with age in both rural Indians and urban Australians and was of comparable or greater thickness in rural Indians in each age group (Figure 2). Figure 8-1 Mean levels of carotid intima-media thickness in urban Australian and rural Indian participants The analyses adjusted by all risk factors included the covariates of age, sex, systolic blood pressure, total, HDL-cholesterol, triglycerides, diabetes, current smoking and body mass index. Error bars represent 95% confidence intervals. 151

152 Figure 8-2 Distribution of carotid intima-media thickness in rural Indian and urban Australian participants by age group Error bars represent standard error of mean. Cardiovascular risk factors and associations with carotid IMT In urban Australians, carotid IMT was associated with total cholesterol, systolic blood pressure, diastolic blood pressure, triglycerides, LDL-cholesterol, HDL-cholesterol, waist, waist-hip ratio and body mass index (all p<0.004) but was not associated with current smoking, glucose or diabetes (all p>0.05) (Table 2). For rural Indians, significant associations of carotid IMT were observed for all these risk factors (all p<0.02) except diastolic blood pressure (p=0.4) and current smoking (p=0.6). The association of carotid IMT with diabetes (p=0.04) and the association of carotid IMT with total cholesterol (p=0.009) were significantly steeper in rural Indians compared with urban Australians (Table 3). For HDL-cholesterol the association of carotid IMT was significantly inverse 152

153 in urban Australians (p<0.001), significantly positive in rural Indians (P<0.001) and highly significantly different between the two populations (p<0.001). There were no differences in the associations of the HDL: total cholesterol ratio with carotid IMT between the rural Indian and urban Australian populations (p=0.32). 153

154 Table 8-2 Age and sex-adjusted associations between cardiovascular risk factors and carotid IMT for Australian and Indian populations Urban Australian Rural Indian Parameters β SE Lower Upper P- value β SE Lower Upper P-value P- Homogeneity* Diabetes Total cholesterol (mmol/l) < Systolic BP (mmhg) < < Diastolic BP (mmhg) Triglycerides (mmol/l) LDL-cholesterol (mmol/l) < HDL-cholesterol (mmol/l) < <0.001 <0.001 HDL/Total cholesterol ratio < Glucose (mmol/l) Waist (cm) < < Waist: hip ratio < < BMI (kg/m 2 ) < < Current smoker *P-homogeneity refers to comparison of the associations between each risk factor with IMT, in the Urban Australian versus Rural Indian population studied. 154

155 Discussion Our study has shown that carotid IMT, a measure of the burden of atherosclerotic disease that has been shown to be predictive of the risk of subsequent vascular events, was higher in rural Indians compared with that in urban Australians. The apparently greater thickness of the carotid intima-media observed in the rural Indian compared to urban Australian population studied here is both surprising and concerning. These differences in carotid IMT between populations could not be explained by differences in the levels of measured cardiovascular risk factors in the two groups. In addition, there was evidence that diabetes, total cholesterol and HDL-cholesterol had effects on carotid IMT that were more adverse amongst rural Indians than urban Australians. These findings suggest that the vasculature of rural Indians may respond differently to some key cardiovascular risk factors and that these differences might, in part at least, explain the higher levels of coronary heart disease previously observed in some migrant Indian populations [65, 91, 99]. Other possible contributing factors to the higher levels of subclinical atherosclerosis seen in rural Indians might include differences in diet, physical activity or socio-economic variables, the effects of inflammation, genetic polymorphisms and/ or the effects of lower birth weight/ intrauterine growth retardation. The observed differences in body size and the higher waist/hip ratio are consistent with lower birth weights [349, 350] in the rural Indian group, suggested recently as an important risk factor for arterial wall thickening [351] and cardiovascular risk [ ]. In addition there is some evidence that increased risk may be mediated by the effects of certain risk factors not measured here, which are particularly common in South Asians such as inflammatory mediators and homocysteine [78, 79, 355]. Carotid IMT is a well established indicator of atherosclerosis and has been shown to correlate with traditional cardiovascular risk factors, the presence of coronary and carotid atherosclerosis and the risk of future myocardial infarction or stroke [94, 97, ]. It has also been used in population studies to compare levels of sub-clinical atherosclerosis between different population groups [99-101]. The difference in carotid IMT observed 155

156 between the two populations included in this study (0.06mm) is broadly similar to previously documented differences between children with and without familial hypercholesterolemia (0.03mm) [361], differences between young adults with and without metabolic syndrome (0.04mm) [362] and differences between smokers and nonsmokers (0.09mm) [363]. The magnitude of carotid IMT difference is also directly comparable to that observed between active and placebo treated patients in a large-scale, 4 year trial of statin-based cholesterol lowering (0.06mm) [364] that resulted in an approximate one quarter reduction in major vascular events. In our study, both crude and risk factor-adjusted carotid IMT was significantly greater in the rural Indian adults, consistent with a greater burden of atherosclerosis in the rural Indian population. By contrast, Anand et al have recently reported slightly lower levels of carotid IMT (unadjusted for risk factor levels) in South Asian Indian migrants compared with locals of European descent, in a study performed in Canada [11]. In that study, however, the South Asian migrants had a significantly higher prevalence of almost all forms of cardiovascular disease measured. It is likely that these data reflect both an underlying susceptibility of the Indian population to atherosclerosis as well as the important and well documented effects of migration on cardiovascular event rates [104, 365]. It is also likely that the greater homogeneity of our non-migrant population, from a geographically restricted area of rural Andhra Pradesh, might have enhanced our ability to identify differences between the rural Indian and Caucasian adults, in terms of subclinical atherosclerosis or possible future cardiovascular risk [71]. Other investigators have utilised IMT measurements to investigate the effects of environment and ethnic origin on the burden of atherosclerosis. For example, Woo et al compared mean carotid IMT in rural Chinese, urban Chinese and urban Caucasian subjects, finding the more expected differences between groups (after adjusting for risk factors); IMT was lowest in rural Chinese, significantly higher in urban Chinese and highest in urban Caucasians [100]. Another recent study in the UK compared 202 Caucasian participants with 89 of African/Caribbean ethnicity, finding that age and sex adjusted carotid IMT was higher in the UK African/Caribbean group compared with the 156

157 Caucasian group, and this was similar after adjustment for conventional risk factors [101]. These data support the concept of increased atherosclerosis risk with migration from developing to Western environments, as well as raising the possibility of real differences in pre-migration risk between different ethnic groups (such as Chinese versus Indian subjects). The main limitation of our work is that the Australian and Indian populations were studied by different teams of people at different time-points. The studies did use fundamentally similar methodologies with comparable ultrasound probe frequencies, image acquirement protocols and training, and the same computerised method for IMT analysis. In addition, the magnitude of the inter-observer difference in mean carotid IMT readings estimated from the sample of Australian images re-measured by the reader of the Indian images was 0.04mm, similar to that reported by other multicentre studies of carotid IMT [99, 345, 346, 348]. This inter-observer difference could account for much of the mean difference in IMT observed between the rural Indian and urban Australian populations studied here but even comparable levels of IMT in the two populations would be surprising. Moreover, systematic differences in measurement of this type would not be expected to confound the observed differences between populations in the strengths of the associations of carotid IMT with risk factors. While it is also possible that sub-optimal response rates in the two studies could have biased the estimated difference in mean IMT between populations, it is unlikely that selection biases could account for the corresponding differences in the strengths of the associations of carotid IMT with risk factors. On balance, therefore, while there remains some uncertainty about the estimated magnitude of differences in mean levels of carotid IMT in this study, the evidence of a greater adverse impact of diabetes, total cholesterol and HDL-cholesterol on the vasculature of rural Indians is probably quite robust. Finally, differences in unmeasured risk factors, such as diet, exercise and novel cardiovascular risk markers such as genetic polymorphisms or inflammatory markers might also potentially influence the differences observed in our study. 157

158 In conclusion, this study suggests that mean carotid IMT among this rural Indian population is at least as great as the mean IMT observed in the comparator population from urban Australia, suggesting that atherosclerosis is occurring at an accelerated pace in the rural Indian population. It appears that one cause of this may be more serious adverse effects of diabetes and total cholesterol, and an adverse rather than protective effect of HDL-cholesterol in the Indian population. While the finding for HDLcholesterol is completely novel and requires confirmation, the other results presented here are quite plausible explanations for the many prior observations of disproportionately severe vascular disease in migrant South Indian populations. Perhaps the greatest significance of this study, however, is that it provides direct evidence that accelerated vascular disease is occurring in rural areas of India, as has been previously documented in migrant Indian populations in developed countries. This portends a critical new health issue for rural India where some 700 million people live, and the epidemiologic transition has already progressed such that vascular diseases are now the leading cause of death in many such regions [25, 226, 366]. If atherosclerosis is indeed accelerated in these populations, then only a very rapid and comprehensive public health response will avert an epidemic of premature heart attack and stroke in these communities. 158

159 Chapter 9 Cardiovascular risk estimation in a rural Indian population Introduction Cardiovascular risk tools are widely used to guide clinical management and Framingham risk equations are the basis of many of these tools [ ]. These equations were developed from a US population with a high background risk of cardiovascular disease and have been found to systematically over-estimate risk in cohorts from South East Asia, Europe and other cohorts from the United States [ ]. To address this issue, local risk prediction tools have been developed from local cohort studies. A good example is the SCORE tool for European populations [376]. Another approach to tailoring risk tools is to recalibrate the Framingham equation using local data about mean risk factor levels and incidence of cardiovascular events. This method has been validated in a number of countries and provides better risk prediction than direct use of the original Framingham equation [370, 377]. This method assumes the nature and strength of the association between each risk factor included in the model and the risk of a cardiovascular event is similar between populations, and simply recalibrates the risk for different mean levels of risk factors and different background incidence of cardiovascular disease. In settings such as India where cohort data are unavailable but risk factor surveys have been done, this is a practical approach to risk prediction. In this chapter, CHD incidence data estimated from mortality surveillance in rural Andhra Pradesh, and mean risk factor levels calculated from the large-scale survey done in this area are used to recalibrate a published Framingham equation. This recalibrated equation should enable more accurate calculation of an individual s cardiovascular risk for this area of rural Andhra Pradesh. We calculate the cardiovascular risk for each of the 1079 individuals with complete risk factor profiles using the recalibrated equation and compare the results to that obtained using the published Framingham equation. In addition we recalibrate the published equation again with CHD incidence estimated for 159

160 India from the World Heart Atlas and compare the results to that from the other equations. Issues regarding this approach to risk estimation in India are also discussed. Methods To develop a tool suitable for risk prediction in this rural Indian population, the Framingham risk function was recalibrated using the method described by Wilson et al [369]. This method assumes that the nature and magnitude of the association between each risk factor and coronary heart disease (CHD) (i.e. the relative risk) is similar in different populations, but replaces the mean risk factor levels and average survival (CHD-free event rate) of the Framingham model with those of the new population. The Framingham study risk function The Framingham function was derived from the Framingham cohort of 2489 men and 2856 women 30 to 74 years of age at the time of their Framingham Heart Examination in 1971 to Participants attended either the 11 th examination of the original Framingham cohort or the initial examination of the Framingham Offspring Study. Study participants were followed up over 12 years for the development of CHD (angina pectoris, recognised and unrecognised myocardial infarction, coronary insufficiency and coronary heart disease death). At initial examination, the mean age+/-sd was 48.6+/-11.7 for men and 49.8+/-12.0 for women. Risk factor levels reported in Wilson s paper are in Table 9.1 alongside the corresponding values from the rural Andhra Pradesh survey. The 10 year CHD-free survival rates reported for the Framingham participant s were for men and for women [369]. Risk factor data from Andhra Pradesh The mean levels & prevalence of risk factors used for recalibration were obtained from the large-scale survey conducted in 2005 in rural Andhra Pradesh. Survey methods have been described in detail in Chapter 3 and the results have been presented in the chapters following that. For the recalibration, data for the 1079 participants with complete data on age, sex, total cholesterol, HDL-cholesterol, blood pressure, smoking status and blood sugar for estimation of diabetes prevalence, were used and weighted (as previously 160

161 described) to obtain mean risk factor level estimates for the adult population 30 years and above living in the 20 survey villages (Table 9.3). Table 9-1 Distribution of risk factors for APRHI versus Framingham cohort APRHI men Framingham men APRHI women Framingham women Age (years) Total cholesterol (mmol/l) HDL-cholesterol (mmol/l) Diabetes (%) Smoking (%) Normal BP (%) High-normal BP (%) Stage I hypertension (%) Stage II hypertension (%) Adapted from data in Wilson 1998 [369] Estimates of CHD-free survival rate Reliable national or sub-national data about coronary heart disease or cardiovascular (CHD & stroke) event rates do not exist in India. To obtain locally relevant estimates for 10-year CHD-free survival rates two sources of data were used and their effects on recalibration compared. APRHI: First, the CHD incidence rate for adult Indians was estimated using data from the APRHI Mortality Surveillance conducted in the same area of rural Andhra Pradesh [1]. The APRHI mortality surveillance was discussed in Chapter 1. In brief, the surveillance commenced in 2003 in 45 villages (total population 180,162) and in its 1 st year recorded data on 1354 deaths for which verbal autopsies were completed for 98%. The crude death rate in the 1 st year of data collection was 7.5/1000. Diseases of the circulatory system were the leading causes of mortality (32%) with similar proportions of vascular deaths attributed to CHD and stroke. The principal findings for this study are in Table 9.2 and the incidence of CHD estimated using this data was 0.23% per year for men, and 0.11% per year for women. Since the APRHI mortality surveillance system provided data for CHD mortality alone, this estimate assumed a ratio of fatal to non-fatal CHD of 1:2. The estimated CHD incidence was then used to calculate the 10-year survival rate (1-10 year cumulative incidence). The 10-year CHD-free survival rate was estimated from the APRHI data to be for men and for women. 161

162 Table 9-2 Deaths from vascular causes in in rural Andhra Pradesh APRHI mortality surveillance All Deaths CHD* Stroke Other heart Sudden death Population in 45 villages All years Males years Females years Other heart includes heart failure and valvular disease WHO Atlas: The second source of data used to estimate 10 year CHD-free survival rate was from data published by the 2004 WHO / CDC Atlas of Heart Disease and Stroke [378]. The Atlas provided data for CHD mortality for the whole of India (Table 9-3). As with the APRHI data, the CHD incidence was calculated from the CHD mortality data to be 0.44% per year for men and 0.22% for women in India. As the Atlas provides CHD mortality data for the whole population only, these CHD incidence estimates for men and women were made assuming the ratio of male: female deaths was 2:1, that all CHD deaths were among adults aged 30 to 74 years, and that 45% of the total population of India is between years of age [1]. The steps taken to reach these estimates are detailed in Table 9-3. The 10-year CHD-free survival rate estimated from the WHO-Atlas data was 0.87 for men and 0.93 for women. Table 9-3 World Heart Atlas data for India and method used to estimate CHD mortality rate Group Method for calculating IHD Deaths Deaths from CHD Deaths from Stroke Population denominator Deaths from Rheumatic disease Method for calculating population denominator CHD mortality rate All World Atlas data Total pop. India IHD Adult deaths* Adult pop. = 0.45** X total pop. Males Male IHD deaths = Male pop. = /3 X IHD deaths X Adult Females Female IHD deaths = 1/3 X IHD deaths pop Female pop. = 0.5 X Adult pop. Data extracted from World Heart Atlas 2002 highlighted in bold *Assuming all IHD deaths occur in population years **Factor calculated from age distribution of Andhra Pradesh population 1049,549, % 465,569, % 232,784, % 232,784, % 162

163 Analysis For the 1079 participants in the Andhra Pradesh survey with complete risk factor data, the 10-year CHD risk probabilities were calculated in three ways. The first probability used the Framingham original equation published by Wilson and colleagues [369]; the second used a recalibration of the Wilson equation using mean risk factor levels from the rural Andhra Pradesh survey and CHD-free survival estimates from the APRHI mortality surveillance [1]; and the third used mean risk factor levels from the rural Andhra Pradesh survey but CHD-free survival estimates from the 2004 WHO / CDC Atlas of Heart Disease and Stroke [378]. Statistical analyses were carried out using STATA 8.0. Recalibration The base equation used here was derived from the original Framingham cohort data described above and published by Wilson et al [369]. The method by which this equation is recalibrated and individual probabilities calculated is shown here. Step 1: In the recalibrated equation the original β-coefficients from the Wilson equation were used (Table 9.4) and risk factor levels from the Andhra Pradesh survey were substituted into the original Framingham equation in a function termed G for general population. This function has been calibrated to the designated general population, in this case the population of rural Andhra Pradesh. Risk factor levels have to be in categorical format and expressed using the same cut-offs as used by Wilson as tabulated in Table 9.5. These are substituted into the equation G where P equals the proportion with each category (or mean value, for age) in the general population. G is calculated for both men and women. G men = ( x mean age) ( x P(TC<160)) + (0.0 x P(TC )) + ( x P(TC )) + ( x P(TC )) + ( x P(TC 280) + ( x P( HDL<35)) + ( x P(HDL 35-44)) + (0.0 x P(HDL 45-49)) ( x P( HDL 50-59)) ( x P( HDL 60)) ( x P(BP optimal)) + (0.0 x P(BP normal)) + ( x P( BP high normal)) + ( x P(BP stage I hypertension) )+ ( x P(BP stage II hypertension)) + ( x P(diabetes 163

164 present)) + (0.0 x P(diabetes not present)) + ( P(smoker)) + (0.0 x P( not smoker)) G women is calculated in the same way using the equation for women with the appropriate β-coefficients. Using the Andhra Pradesh survey data, G AP men = and G AP women = In comparison, the published Framingham data reports a GFramingham men = and G Framingham women = are published [369]. Step 2: For each individual, a function we will term I is computed. I is calculated for each individual whose risk is to be calculated. In this function, the number 1 is inserted if the risk factor criteria is met by the individual and otherwise replaced with zero. The calculation of I for a male and I for a female is shown here. In the calculation for I, TC refers to total cholesterol, HDL to HDL-cholesterol and BP to blood pressure. I male = ( x age) [if TC<160] [if TC ] [if TC ] [if TC ] [if TC 280] [if HDL<35] [if HDL 35-44] +0.0 [if HDL 45-49] [if HDL 50-59] [if HDL 60] [if BP optimal] [if BP normal] [if BP high normal] [if BP stage I hypertension] [if BP stage II hypertension] [if diabetes present] + [if diabetes not present] [if smoker] I female = ( x age) - ( x age 2 ) [if TC<160] + 0[if TC ] [if TC ] [if TC ] [if TC 280] [if HDL<35] [if HDL 35-44] [if HDL 45-49]) + 0[if HDL 50-59] [if HDL 60] [if BP optimal] + 0[if BP normal] - ( [if BP high normal] [if BP stage I hypertension] [if BP stage II hypertension] [if diabetes present] [if smoker] Step 3: For each individual, the results obtained from the two steps above were then combined. 164

165 That is, for each individual, the function A was calculated, where A = I G. Then for each individual, the function B is calculated, where B = e A Step4: Finally, for each individual, P 10 the 10- year probability of fatal or nonfatal CHD) was calculated where P 10 = 1 [S(t) B ]. In this final calculation S(t) is the CHD-free survival rate. For the Framingham calibrated probability, the Framingham baseline CHD- free survival rate at 10 years are published for men ( ) and for women ( ) [369]. For the recalibrated probability, the corresponding values for India have been derived from external sources as detailed above: S(t) Andhra Pradesh equal (men), (women) and S(t) India equals (men), (women). Sensitivity Analyses Sensitivity analyses were performed by varying key assumptions. The equation was recalibrated for CHD-free survival rates only, keeping the mean risk factors levels as in the original published equation [369]. Assumptions used for estimating CHD rates were varied including changing the ratio of fatal to non-fatal CHD events and for Andhra Pradesh data the effects of inclusion of sudden deaths in total CHD deaths were also examined. In addition the beta coefficients for diabetes and total cholesterol were increased by 10%, based on the IMT analysis data presented in Chapter 8 as well as other data indicating that Asian Indians may be more susceptible to the effects of these risk factors [253]. 165

166 Table 9-4 β -coefficients from the 1998 Wilson Framingham categorical multivariable risk equation Variable Men Women Age, years Age squared TC, mg/dl < Referent Referent HDL-C, mg/dl < Referent Referent Blood pressure Optimal Normal Referent Referent High normal Stage I hypertension Stage II-IV hypertension Diabetes Smoker

167 Table 9-5 Risk factor levels from Andhra Pradesh survey expressed according to Original Framingham equation categorical definitions. Men Women Total cholesterol % SE % SE <160 mg/dl 32.9% 2.5% 22.6% 2.1% mg/dl 41.4% 2.6% 44.5% 2.5% mg/dl 20.4% 2.1% 23.3% 2.1% mg/dl 4.3% 0.9% 7.5% 1.2% 280 mg/dl 1.1% 0.6% 2.1% 0.7% HDL-cholesterol % SE % SE <35 mg/dl 20.6% 2.1% 10.4% 1.6% mg/dl 43.1% 2.6% 35.0% 2.4% mg/dl 16.5% 1.9% 20.5% 2.0% mg/dl 13.2% 1.7% 25.7% 2.3% 60 mg/dl 6.6% 1.4% 8.3% 1.3% Blood pressure % SE % SE Optimal 46.8% 2.5% 48.2% 2.4% Normal 16.7% 1.9% 14.4% 1.8% High-normal 12.4% 1.6% 15.7% 1.8% Stage I hypertension 16.6% 1.8% 14.5% 1.6% Stage II hypertension 7.5% 1.3% 7.2% 1.3% % SE % SE *Diabetes 10.7% 1.5% 9.8% 1.0% Current smoker 38.7% 2.5% 4.4% 0.9% Age, mean (SE) *Diabetes defined according to Framingham original definition of fasting glucose>140mg/dl or known diabetes 167

168 Results The recalibrated Framingham function based on data from Andhra Pradesh produced similar results to the original Framingham results. The mean 10-year probability of CHD for adults >30 years was 10.4% ( ) for men & 5.3% ( ) for women using the original Framingham calculation, and 10.7% ( ) for men and 4.2% ( ) for women using the Andhra Pradesh recalibrated function. However, in the recalibration using World Heart Atlas data, the recalibrated equation calculated a higher risk with a mean risk in males of 18.9% ( ) and females of 8.2% ( ). Using the original Framingham the proportion of the population with a 10-year CHD risk of >20% was 8.7% ( ), with the Andhra Pradesh recalibrated function this was 7.8% ( ), and with the World Health Atlas India recalibrated, this was 23.2% ( ). The proportion men and women in rural Andhra Pradesh with a risk probability >20%, between 10 20% and <10% using the different equations is illustrated for men in Figure 9-1 and women in Figure % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Original Framingham Calibrated to APRHI Calibrated to WHO >20% 10-20% <10% Figure 9-1 Risk categories in Men 30years using original and calibrated equations 168

169 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Original Framingham Calibrated to APRHI Calibrated to WHO <10% 10-20% <20% Figure 9-2 Risk categories in Women 30years using original and calibrated equations Mean 10-year probability of CHD using the Andhra Pradesh recalibrated function ranged from 0.6% for females in the age group years, to 24.9% for males 60 years. In contrast, mean 10-year probability of CHD using the equation based on WHO Atlas data ranged from 1.2% for females in the age group years to 41.4% for males 60 years (Table 9.5). Table 9-6 Mean risk by sex and age group calculated from Framingham, AP recalibrated and WHO Atlas recalibrated equations Framingham original Calibrated to AP survival Calibrated to WHO Atlas survival % 95%CI % 95%CI % 95%CI Men 10.4% ( ) 10.7% ( ) 18.9% ( ) % ( ) 3.3% ( ) 6.5% ( ) % ( ) 7.3% ( ) 13.9% ( ) % ( ) 13.0% ( ) 23.8% ( ) % ( ) 24.9% ( ) 41.4% ( ) Women 5.3% ( ) 4.2% ( ) 8.2% ( ) % ( ) 0.6% ( ) 1.2% ( ) % ( ) 2.7% ( ) 5.5% ( ) % ( ) 6.5% ( ) 12.9% ( ) % ( ) 9.5% ( ) 18.3% ( ) Examples of variation in calculated probability of CHD using the different equations for individuals with identical levels of risk factors are shown (Table 9.6). 169

170 Table 9-7 Examples of variation in risk scores in men & women using calibrated and original Framingham equations Age SBP DBP TC HDL Current smoker Diabetes Framingham original Calibrated to AP survival Calibrated to WHO Atlas survival Mr. X Yes Yes 33.6% 34.5% 57.0% Mr. X Yes No 13.5% 13.9% 25.9% Mr. X Yes Yes 6.5% 6.7% 12.9% Mr. X Yes No 4.3% 4.4% 8.6% Mrs. X Yes Yes 20.8% 16.8% 31.5% Mrs. X Yes No 12.0% 9.6% 18.8% Mrs. X Yes Yes 1.5% 1.2% 2.4% Mrs. X Yes No 0.8% 0.6% 1.3% In the sensitivity analyses the effect of changing assumptions underlying calculation of the mean 10-year probabilities of CHD for adults >30 years were examined. The mean probability obtained from the APRHI recalibrated equation, 10.7% ( ), increased to 12.6% ( ) if the definition of CHD death included deaths defined as sudden. Changing the ratio assumed for CHD fatal to non-fatal events from 1:2 to 1:1 decreased the mean probability calculated to 8.7% ( ). Recalibration with substitution of CHD-free survival rate only into the equation and no change in the original mean risk factor levels decreased the mean probability to 7.5% ( ). Increasing β-coefficients for both total cholesterol and diabetes by 10% did not significantly change mean probabilities, 10.8% ( ) (Figure 9-3). 170

171 20% Mean estimated 10 year risk of CHD (%) 18% 16% 14% 12% 10% 8% 6% 4% 2% Male Female 0% Original Framingham 2. APRHI recalibrated Framingham 3. WHO Atlas recalibrated Framingham 4. APRHI recalibration including sudden death 5. APRHI recalibration with Fatal : Non-fatal F 1:1 6. APRHI recalibration with Fatal : Non-fatal 1: APRHI recalibration of event - free survival only 8. APRHI recalibration with increase in TC & DM - beta coefficients Figure 9-3 Sensitivity analysis Effect of changing CHD incidence, mean risk factors and betacoefficients on mean estimated 10 years CHD risk Discussion In the examples presented here, 10-year CHD risks were similar calculated using the original Framingham equation compared with the Andhra Pradesh calibrated equation but higher using the WHO-Atlas equation. This pattern is different to other parts of Asia where probabilities calculated using the original Framingham equation generally overestimate probabilities calculated using recalibrated equations [370, 379]. However this finding is likely to be plausible in this population for a number of reasons. Studies of 171

172 South Asian Indians living overseas have repeatedly shown higher rates of CHD, and the excess risk cannot be fully explained by the pattern of traditional risk factors [90, 380]. The ETHRISK tool, which estimates the 10-year risk of CHD and CVD in seven British black and minority ethnic groups, demonstrates this [381]. This tool is a recalibrated Framingham risk tool which used mean risk factor measures from UK health survey data, in which sampling was enriched for ethnic minorities, and survival rate estimates calculated by adjusting known UK CHD/CVD incidence rates by ethnic group to UK CHD/CVD prevalence ratios. It found South Asian men to have the highest 10-year risk of CHD and CVD for the same given levels of risk factors [381]. Such findings are also consistent with the results presented here of higher carotid IMT levels (a measure correlated with future CHD events), in South Asians compared with Caucasians despite generally lower mean levels of risk factors (Chapter 8). It is also likely that the Framingham population recruited in the 1970s is more similar to this Indian population, in terms of treatment rates and higher smoking rates which have fallen in the US since the 1970s [382]. The third method used here recalibrated the Framingham equation using survival estimates from the 2004 WHO / CDC Atlas of Heart Disease [378]. This resulted in nearly a doubling of 10-year CHD risk. This illustration of the sensitivity of recalibration to CHD rates emphasizes the extreme importance of using accurate data relevant to the population. High quality data on cardiovascular death and events from representative populations in India is very limited [9]. The WHO/CDC Atlas is a summary document of Heart disease morbidity and mortality from the Global Burden of Diseases Studies (personal communication from Dr. Collin Mathers) and was, at the time of this study, probably the best nationally representative data on cardiovascular mortality for India. However the databases from which these estimates are made are importantly incomplete [11, 29] and their limitations described in more detail in Chapter 1. An important limitation of this study is that the recalibrated equations could not be tested against a separate Indian population cohort to test if the equations were able to accurately predict future events. We are unable to do this because there are no data that has been 172

173 collected prospectively on CV events from an Andhra Pradesh cohort and the few data collected from cohorts from other parts of India have not collected the necessary baseline data required by the equations for calculation of CHD risk and are probably not demographically comparable cohorts as discussed in detail in Chapter 1 [31-33]. Recalibration of Framingham risk tools assume the nature and strength of the association between each risk factor included in the model and the risk of a cardiovascular event is similar between populations. From the observational findings in Chapter 8 of stronger risk factor IMT associations in South Asians compared with Caucasians, it follows that there may be stronger associations of total cholesterol and diabetes with incidence of CHD. While it is difficult to estimate what magnitude of difference in strengths of associations may exist, the case studies above explored the effects of increasing the ß- coefficients for total cholesterol and diabetes. Increasing the ß-coefficients for both total cholesterol and diabetes resulted in only a small non-significant increase in cardiovascular risk estimates (population mean estimate increased from 10.7% to 10.8%) suggesting that the overall impact of any such effects may not be large. It is difficult to qualify this any further and for this cohort study data may have utility. Sensitivity analyses also demonstrated the amount of variation in estimated risk when population mean risk levels were left as those from the original Framingham cohort to be relatively minor compared with the impact of changing CHD rates (Figure 9-2, scenario 7). While cohort study data has been traditionally used to develop and validate cardiovascular risk prediction tools, such studies are expensive and take a number of years to collect sufficient data. In addition, in developing countries, risk factor levels and CHD rates are rapidly changing, and as such cohort study data may be quickly outdated. Moreover in vast countries such as India and China, differences in risk factor levels and CHD rates are likely to vary substantially across regions. As shown here, recalibration is very sensitive to CHD rates, and hence it is quite unlikely that a single recalibrated equation would be relevant for all of India. For these reasons, waiting for a cohort study is unlikely to contribute significantly to the development of clinical risk prediction tools for use in the immediate future in developing countries such as India. Resources are 173

174 probably better used to build up existing systems of surveillance to provide accurate regional measures of risk factor levels and mortality/event rates. Locally relevant absolute risk identification tools may play an important role in identifying those at higher overall risk of cardiovascular disease in low and middleincome countries. The growing concerns regarding increasing non-communicable disease in developing countries have led to some preventative initiatives. Single risk factor programs such as hypertension or diabetes programs are a common first start in developing countries [294, ] and have also been initiated in this region as described in Chapter 6. While single-risk factor programs have the advantage of simplicity, they may result in omission of treatment to a large proportion of high-risk individuals and treatment of a large number of low risk individuals. Programs that target those at high absolute risk of cardiovascular disease are more likely to be efficient in terms of cost per event avoided [386, 387]. The cost of investigations and the complexity of absolute risk identification tools may limit their utility in developing countries. Further work is required to determine low-cost methods of delivering this care. Some work has been done on low-information risk tools which require less information to estimate risk [379]. Novel technologies such as computerised decision support systems may also play a role here [388]. The clinicalversus cost-effectiveness of simple versus more complex methods of clinical risk stratification need to be addressed to better utilise the limited resources in these countries. In summary, the recalibration of Framingham risk tools are a practical approach to estimation of cardiovascular risk in countries such as India, but care needs to be taken to ensure that relevant representative data is used to achieve accurate recalibration. The utility of tools that identify those at high risk in developing countries is the greater costbenefit achieved by treating a smaller group of high-risk people for relatively greater gain. The use of risk tools to screen and identify high-risk persons for treatment has been effectively carried out in a number of developed countries. On the basis of the evidence presented here it is clear that risk assessment tools can be developed for resource poor 174

175 settings and it would follow that risk based models of care could be implemented. Future research is, however, required to develop comprehensive evidence about methods of delivering preventative therapies in the very different clinical settings present in most developing countries. 175

176 Chapter 10 Conclusions and implications India is undergoing rapid epidemiologic transition with new evidence indicating that rural areas are now following the lead of the cities. High quality evidence about cardiovascular risk factors, a full understanding of the factors driving the epidemic of cardiovascular disease and locally applicable risk assessment tools are fundamental to the design and implementation of intervention strategies appropriate for developing rural areas. The data presented in this thesis confirms that the acquisition of such information is practical and highly feasible. Furthermore, the findings suggest that the development of clinical and public health strategies for the prevention of cardiovascular diseases in resource poor rural settings should be both practical and worthwhile. Implications of adverse cardiovascular risk levels The data from the large-scale survey identified high levels of cardiovascular risk factors in the study population drawn from rural Andhra Pradesh. Diabetes rates were higher than in a number of Western countries, a third of the population were overweight or had abdominal obesity and nearly half of all males smoked. In addition, awareness of risk was poor with approximately half of those with diabetes or high blood pressure undiagnosed. For those that were aware, treatment and control of risk factor levels was poor and there was limited knowledge regarding the adverse effects of risk factors such as smoking. Data from the APRHI mortality surveillance system had previously provided the project sponsor, the Byrraju Foundation, with important new insight into the main causes of death in the villages they are supporting [1]. That project had also clearly demonstrated the feasibility of incorporating a reliable system of mortality surveillance into the primary health care infrastructure [10]. The results of this cardiovascular survey now go a step further and provide the Byrraju Foundation with information about the chief determinants of these deaths. This information can now be used by the Foundation in its efforts to develop effective interventions for the prevention of the most common causes of death, heart attack and stroke. 176

177 The information provided by this survey also has significant implications for health care planning in many other parts of rural India and the developing world. The villages surveyed here were more developed than the average and had progressed further down the path towards Western diseases than most in India. However, while this region currently is not representative of rural India as a whole, it does provide an important and ominous sign of things to come. The next decade will see many more parts of rural India reach comparable stages of development and soaring rates of cardiovascular diseases will almost certainly ensue unless immediate and effective risk factor control strategies and treatment programs are put in place. Implications of more adverse effects of some risk factors The possibility that there are more adverse effects of some established risk factors on cardiovascular disease in South Asians has potentially important implications for cardiovascular disease in the populations of the Asian subcontinent. While the conclusions of the many studies that have addressed this question remain somewhat at odds [80, 91], the new data presented here provide a novel insight into possibly more adverse effects of cholesterol and diabetes and a possible adverse rather than protective effect of HDL-cholesterol. The greater harm produced by any greater impact of diabetes may be particularly relevant at present. Diabetes was highly prevalent in the rural population studied, is known to be particularly common in other South Asian communities and likely contributes significantly to the burden of vascular disease in these populations [34, 70, 99]. Even risks that were only moderately proportionally greater amongst South Asians might have significant absolute effects because so many South Asians are affected by conditions such as diabetes. With continuing development the impact of possibly more adverse effects of cholesterol and a harmful effect of HDLcholesterol would also be expected to become substantial. While cholesterol levels were not particularly abnormal in this rural population they would be expected to deteriorate significantly as development and epidemiological transition in the villages progressed. In conjunction with a likely shift towards a greater proportion of vascular disease being 177

178 attributable to coronary heart disease (rather than stroke) as epidemiological transition progresses, the impact of more adverse effects of lipids would be further magnified. All evaluations of the comparative risk of vascular disease in South Asian compared to other populations has its limitations and while there is good evidence of an excess vascular risk in many South Asian groups, there is much uncertainty about whether that risk is, or is not, explicable on the basis of effects of known risk factors [37, 59]. In many studies there is a clear possibility that the increased risk might be attributable to effects of known risk factors but in others this is harder to postulate [80, 90]. In the latter case, more adverse effects of established risk factors remain a plausible explanation, although the precise mechanism by which such effects might operate remains uncertain [253]. Further and more robust evidence about the comparative effects of risk factors in South Asian and other populations is likely to become available over the coming decade. Large-scale randomised trials of interventions that modify factors such as cholesterol, blood pressure and blood glucose levels will provide a specific opportunity to address questions about the interactions of ethnicity and the main determinants of vascular disease [389]. In the meantime, the new data presented in this thesis provides a further argument for the more aggressive monitoring and treatment of cardiovascular risk factors amongst individuals from South Asia. If there are greater harmful effects of some risk factors then there would likely be corresponding greater benefits from lowering of cholesterol and controlling diabetes in these populations. But even in the absence of such effects the high rates of CHD in South Asian populations warrant the more intensive investigation and management of this patient group. Need for locally calibrated risk assessment tools Recalibration of the Framingham risk equation is a method widely used for populations in which the large-scale and long-term follow-up data required to develop a de novo equation are absent [ ]. Furthermore, validation of the recalibration process shows it to provide good estimates of cardiovascular risk within such populations [ ]. 178

179 Recalibration of the Framingham equation based on the local mortality and risk factor prevalence data available for this rural population produced markedly different results from a recalibration based on national data for India. Specifically, the proportion of the rural population with an estimated 10-year CHD risk of 20% or greater was 7.8% based on the local calibration and 23.2% based on the calibration using national data. These analyses served to underscore the sensitivity of these equations to CHD incidence and risk factor prevalence in the population of interest and demonstrate the feasibility of developing absolute risk tools for India. However, in a country as diverse as India, where disease patterns and risk factor levels vary so markedly no one single calibration of the Framingham risk equation will suffice. To enable multiple versions it will be necessary to have reliable data to input into the recalibration process from multiple and representative sites around the country. Cardiovascular prevention strategies based on estimation of absolute risk have much to offer developing countries such as India and the collection of data to enable the necessary tools to be developed would be very worthwhile. In particular, the identification and treatment of those at highest risk has significant benefits in terms of cost-effectiveness [387]. Even in Western countries, where there are now absolute risk based guidelines encouraging primary, secondary and tertiary health care practitioners to use this approach, it is the more efficient allocation of resources that is the key driver of the change [386]. In developing countries such as India this consideration becomes doubly important because available resources are so few and access to them is so limited. In the case of rural Andhra Pradesh it was the estimate of vascular disease incidence that was the key determinant of the estimated level of cardiovascular risk in the population. Differences in risk factor levels and possible differences in the associations of risk factors with outcomes had lesser effects on estimated risk. While direct validation of the tool developed here is not currently possible, it is more likely than not that a tool making use of current local data will provide a more accurate prediction of vascular risk in that area than any other method presently available. This is confirmed by a series of projects that 179

180 illustrate the relative value of these and other methods of risk estimation in both developed and developing countries [370, 371]. Finally, this project clearly demonstrates that collection of the data required for the development of locally applicable risk equations is both possible and practicable, even in rural India. Low cost cardiovascular risk tools developed using the type of data collected here could efficiently direct the limited financial resources for cardiovascular prevention in different regions of India. Such data and equations could be delivered in the short term but it is important to note that periodic review and updates will be required as epidemiologic transition progresses across the country. Translation of findings into policy and practice Evidence to define the optimal systems for delivery of cardiovascular disease prevention programs is lacking in India as it is for most low-income countries. In terms of intervention strategies, there are in broad terms several possible approaches to the problem but one over-riding consideration is clear: India must not try to emulate the chronic disease care mechanisms of high-income countries. The introduction in India of Western, physician-driven programs could provide a high cost solution for a wealthy few but it will do little to contain the epidemic of chronic disease threatening the poor rural majority. Novel low-tech, low-cost and high-value solutions that focus on widely applicable interventions must be the priority. Both population level and targeted cardiovascular preventative programs have been advocated by the WHO for low and middle income countries such as India [285, 390]. These fall into three main approaches. Firstly, there are classical public health strategies that seek to reduce the causes of disease in the entire population [386]. Legislated restriction of tobacco advertising is one example that has produced significant reductions in the proportion of the population that smokes in many countries [333]. Other proposed strategies relate to reducing salt consumption, decreasing fat intake and increasing physical exercise. 180

181 Historically, the second main strategy for disease prevention has been a medical one [386], focused on the control of individual risk factors such as blood pressure or cholesterol. The problem that has arisen with this second approach is that the proportion of the population for whom treatment is recommended has grown ever larger. On the basis of the data collected in rural Andhra Pradesh it is clear that a program targeting high blood pressure would require treatment of a very large number of individuals. A large proportion of these hypertensive individuals would be at relatively low risk of cardiovascular disease and the drug therapy prescribed to them would be a rather inefficient use of the scarce resources available. The third option is the risk-based approach[386]. Like the second, it is a medical model. It does not, however, focus prevention activities on people with an elevated level of a single risk factor. Instead people with elevated levels of multiple risk factors are targeted using a tool such as the recalibrated Framingham equation developed using the data collected here. Because the individuals identified for treatment are at much greater risk than individuals with isolated risk-factor abnormalities this approach offers greater efficiency [296]. More heart attacks and strokes are prevented by treating fewer people at lower cost. The main impediments to this approach are the novelty of the method (which post-dates the training of most practicing physicians) and the need for locally applicable tools to enable the identification of those at high risk. Population wide interventions to control the causes of vascular disease may be potentially highly cost-effective for India but require the active participation of the government, which may be difficult to achieve. Strategies that restricted tobacco use, reduced consumption of salt/fat or increased physical activity would undoubtedly have widespread and long-term benefits for India. The reality of programs that require government led, legislative implementation, suggest that the impact of such initiatives would not be rapid. In the interim interventions that improve knowledge and awareness of risk factors is probably a tangible first step. Such knowledge could be imparted by non-physician health workers or through village level health promotion campaigns. 181

182 Strategies that target individual risk factors are already common in much of India, though not widely implemented in rural areas. While these strategies may seem easy in terms of set up, they will be inefficient for a population as large and poor as that of India. For India, a strategy that resulted in an extra 0.5% of the population being put on medical therapy would mean an extra 5 million individuals treated at potentially very substantial cost. Clearly any such increases need to be very carefully thought through from both health and economic angles. The risk-based approach is likely to be the most practical, affordable and effective for India in the immediate term. Chronic disease programs could be piggy-backed onto India s already well-established system of primary health care. India s primary health networks staffed by trained non-physician health workers already provide immunization programs, perinatal care and have been effective in rolling out programs such as the as the Revised National Tuberculosis Control Program and the National Blindness Control Program [10]. If these systems could be reoriented towards chronic diseases, they could be a low-cost method of rolling out risk-based programs to identify and manage those at risk of CVD. A high-risk approach that may be amenable to delivery by non-physician primary health care workers is secondary prevention. One quarter to one third of all strokes and heart attacks occur in people who have survived a previous such event. Such individuals are easily identified by simple questioning, are at very high risk and have much to gain from preventive treatment. It is quite plausible that non-physician health workers could identify and treat these high-risk people. Importantly the cost of access to care would be much lower than would be possible with a traditional physician-based system. On this basis, we have used the data collected here to design and evaluate risk based and population-wide interventions. This will be done using a cluster randomised trial to be conducted through the Byrraju Foundation primary health care centres in 44 villages in rural Andhra Pradesh. The risk based part of the program will seek to identify those in the population with very high risk of CVD and ensure they are on appropriate risk- 182

183 controlling treatments. The population-wide intervention will seek to increase knowledge and awareness of key behavioural determinants of vascular disease. This study has received funding through IC Health and the Future Forum and the protocol is attached in Appendix 6. Overall significance Delaying deaths in developing countries from childhood to older age is clearly a major health advance but with it comes the legacy of a huge burden of chronic disease. In countries like India where surveillance systems are limited the rapid evolution of the causes of the national disease burden can easily go undocumented. The data presented here provide new information about a developing rural region of India and a clear indication of changes in the determinants of vascular disease that are likely to affect much of rural India over the next few decades. The work also demonstrates the feasibility of establishing high quality data collection systems in poor rural areas and provides a model that might guide future efforts to document the progression of key aspects of the epidemiological transition in rural India and indeed in developing rural regions of other countries. Ultimately data such as those presented here should enable a more evidencebased approach to health policy in developing rural regions. Failure to adequately track and respond to the epidemic of cardiovascular diseases in developing countries like India will have major implications. Conditions such as diabetes, high blood pressure and cholesterol are more often undiagnosed and untreated in developing regions and complications occur years earlier than in Western countries. A much larger proportion of the population is afflicted at an economically-productive, younger age and the social and fiscal consequences are enormous. By 2015 it is anticipated that cardiovascular diseases alone will trim more than 1% from the gross domestic product of India [391]. The work here and the information to be provided by the ongoing cluster randomised trial provide an important new plank on which future strategies for clinical practice and health policy in India can be based. 183

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224 Appendices Appendix 1 Andhra Pradesh Rural Health Initiative (APRHI) The APRHI Collaboration works to improve health status, prevent and manage noncommunicable disease, prevent premature death, and enhance access to health services for the people of rural Andhra Pradesh. We aim to achieve this through the design, implementation and evaluation of affordable and sustainable interventions that can be incorporated in the existing primary health care infrastructure of rural areas. ( The partners of the Collaboration include: Byrraju Foundation, Hyderabad, India ( CARE Foundation, Hyderabad, India Centre for Chronic Disease Control, New Delhi, India The George Institute For International Health ( School of Population Health, University of Queensland, Brisbane, Australia ( 224

225 Appendix 2 Survey Instruments used in APRHI Pilot study 225

226 Andhra Pradesh Rural health Initiative 2004 Study ID SURVEY INSTRUMENT INSTRUCTIONS Mark pre-coded answers with an X or a Try to obtain an answer for all the questions with large bold numbers (e.g. 21). Obtain answers for the other questions with numbers and letters as required (e.g. 21a) When asking questions, read out to the participant the text written in bold Follow the instructions in brackets For any answer that requires you to write words, use BLOCK LETTERS If the participant is uncertain about a question record the best answer possible. Try not to leave any question blank In general, if the participant is unsure, mark the No answer If you are still in any doubt about how to do the survey consult the manual of operations 1 / / Date of interview (dd/mm/yyyy) 2 hr: min Time survey started. 3 Sex (write M or F) Thank you for agreeing to take part in this survey to find out the main causes of illness in rural Andhra Pradesh and how people use health services. Most of the questions I ask will be about you and a few will ask about your household. At the end we will do a brief physical examination. Each part of the survey will be explained in more detail as the interview progresses. Feel free to ask for clarification on any part of the interview that seems unclear or confusing. We will start with a few general questions: 4 How many years old are you? 5 1 Married/living together 2 Widowed 3 Divorced / separated 4 Never married 5 Unknown What is your current marital status? (Read out and mark one box only) 226

227 6 1 No formal schooling 2 Primary school 3 Secondary school 4 Graduate studies 5 Post-graduate studies 7 1 Unemployed/retired 2 Housewife 3 Skilled manual worker 4 Unskilled manual worker 5 Owner of business or farm 6 Office worker/nonprofessional 7 Professional 8 Student 8 Y N What is the highest level of education that you have completed? (Read out and mark one box only) Which of these best describes your usual main occupation? (Read out and mark one box only) Can you read and write? I now have a few questions about health services that you may have used and how much the services cost 9 1 Government PHC 2 NGO PHC 3 Private hospital 4 Government hospital 5 Health centre 6 Other 10 1 RMP doctor 2 MBBS doctor 3 Traditional healer 4 Pharmacist 5 MPHW 6 Other 7 None Where would you usually go for health care? (Read out and mark one box only) If other please specify Who is your usual health care provider? (Read out and mark one box only) If other please specify 11 About how many times have you seen this health care provider in the last 12 months? (If the participant is unsure, please provide the best estimate. Put zero if none) 11a If ZERO Why have you not seen your health care provider in the last 12 months? (Read out all options and ask for answer Yes or No to each) 227

228 1. Y N I have been well and not needed to see anyone (Go straight to question 14) 2. Y N I was too unwell to travel 3. Y N It was too far to go 4. Y N There was no transport 5. Y N It was too expensive 6. Y N I had a problem getting an appointment 7. Y N The health care providers skills are inadequate 8. Y N I was too lazy 9. Y N I didn t think they could help 10. Y N Other Please specify 11b IF ONE OR MORE TIMES Which of these reasons best describes why you last saw this health care provider? (Read options to participant and mark one only) 1 For a check-up 2 For a test 3 For medication 4 For a chronic illness 5 For an acute illness 6 For an injury 7 For advice 8 Other If other please specify 12 Days In the last 12 months how many days work/school did you miss because of illness (not including injury)? (write 999 if permanently disabled) 13 1 Excellent 2 Very good 3 Good 4 Fair 5 Poor 14 1 Excellent 2 Very good 3 Good 4 Fair 5 Poor Which of these best describes your quality of life? (Read out and mark one box only) Which of these best describes your health? (Read out and mark one box only) 228

229 I now want to ask you some questions about your household. When answering these questions think about all the people that usually live with you 15 How many people live in your household including yourself? 15a 1 Single member 2 Nuclear family 3 Broken nuclear family 4 Supplemented nuclear 5 Joint family What type of household do you live in? (Mark one response that best describes the household) 16 Rs What is the combined annual income of everyone in your household including yourself? (Give best estimate if unsure) 17 Rs In the last year how much do you think your household spent on your health care? Include the costs of any consultations, tests, medicines, procedures and travel? (Give best estimate if unsure) 18 Rs How much of this money do you think was spent on medications? 19 1 Not at all 2 Only a minor impact, we can afford the fees and medicines 3 Moderate impact, we have to borrow money/sell something to cover health costs 4 Serious impact, my family is broke because of health bills / can t meet basic needs now 5 Refused to answer / wouldn t know Does paying for health care usually have a substantial impact on the finances of your household? (Read out options and mark one box only) I would now like to ask now about your medical history and treatments you may have received 20 Have you ever been told by a doctor that you suffer from any of these diseases and have you ever been treated for any of these diseases? (Read out all options and ask for answer Yes or No to each) 229

230 1. Anaemia 2. Asthma, bronchitis or emphysema 3. Chronic back pain 4. Cancer 5. Tooth or gum disease 6. Epilepsy 7. Malnutrition 8. Depression 9. Schizophrenia 10. Angina 11. Heart attack 12. Heart failure 13. Hole in the heart 14. Heart valve disease 15. Rheumatic heart disease 16. Stroke 17. Thyroid disease 18. Diabetes (when not pregnant) 19. Cataract 20. Tuberculosis 21. HIV or AIDS 22. Pneumonia 23. Chronic lung disease 24. Malaria 25. Gastroenteritis 26. Stomach ulcer 27. Hepatitis 28. Other please specify 29. Other please specify 30. Other please specify 31. Other please specify 32. Other please specify 21 Y N Ever told suffered Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Have you taken any medications in the past 12 months? If NO go to question 22 Y Y Y Y Y N N N N N If YES - ever treated Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y N 230

231 21a If YES in the last year what types of medications have you used? (Ask the patient about the reasons they took the medications and write on the RIGHT column what you think the participant took. If the participant has brought medicines review them and record as appropriate. Record traditional and Western medications. Check the prompt card to identify the medicine group (1-9) for each medication and write your final group number on the LEFT column). Medicine Group Medicine name as stated by patient (include herbal & western) from prompt card b 1. Y N Drug store too far away 2. Y N Too expensive 3. Y N Forgot instructions 4. Y N Side effects 5. Y N Didn t work 6. Y N Felt better 7. Y N Other Have any of these reasons stopped you taking your medications? (Read out all options and ask for answer Yes or No to each) If other please specify The next set of questions are about heart and circulatory diseases. In the past 12 months: 22 Y N Have you had your blood pressure checked? 23 Y N Have you been advised to lower your blood pressure? If YES what has been recommended to you? (Read out all options and ask for answer Yes or No to each) 231

232 23a 1. Y N Traditional medication 2. Y N Western medication 3. Y N Reducing salt in your diet 4. Y N Losing weight 5. Y N Taking more exercise 6. Y N Decreasing alcohol intake 7. Y N Other Please specify 24 Y N Have you had your cholesterol checked using a special blood test? (If unknown, mark No) 25 Y N Have you been advised to lower your cholesterol? If YES what has been recommended to you? (Read out all options and ask for answer Yes or No to each) 25a 1. Y N Traditional medication 2. Y N Western medication 3. Y N Reducing fat in your meals 4. Y N Losing weight 5. Y N Doing more exercise 6. Y N Other Please specify 26 Y N 27 Y N 28 Y N 29 Y N 29a 29b 29c 29d Have you ever had a blood test for diabetes? (If unknown, mark No) Have you ever had a urine test for diabetes? (If unknown, mark No) Do you currently use chewing tobacco? Do you currently smoke? If YES what do you smoke most days? (Read out all options and ask for answer Yes or No to each) Y N Cigarette Y N Beedi Y N Cigar How many do you usually smoke each day? 29e How many years have you smoked for? 30 Y N Did you used to smoke (but have stopped now)? If YES how long ago did you last smoke regularly? (If stopped less than one year put number of months) 30a years or months 232

233 31 How many people in your household smoke (include yourself if you are a current smoker)? 32 On average, how many hours a day can you smell tobacco smoke at home (either your own or others)? 33 On average, how many hours a day can you smell tobacco smoke in a workplace? (If home is the workplace record zero here) 34 1 Lots (eg lifting heavy weights, construction work, labourers, running) 2 Medium (eg bike riding, rickshaw drivers, carrying buckets of water or loads of laundry to and from well multiple times a day, walking long distances up and down hills) 3 Light activity (e.g. walking on the level, standing all day working at a shop, housework such as cooking, cleaning in the house) 4 Almost none (e.g. seated at a desk, driving a car, watching TV, reading, resting) 35 1 Lots (eg lifting heavy weights, construction work, labourers, running) 2 Medium (eg bike riding, rickshaw drivers, carrying buckets of water or loads of laundry to and from well multiple times a day, walking long distances up and down hills) 3 Light activity (e.g. walking on the level, standing all day working at a shop, housework such as cooking, cleaning in the house) 4 Almost none (e.g. seated at a desk, driving a car, watching TV, reading, resting) On average, how much physical activity do you do each day during working hours? On average, how much physical activity do you do each day after working hours? 36 Y N Have you ever been told by a doctor that you have had a heart attack or a stroke? 37 Y N Have you ever experienced the sudden onset of difficulty in talking, weakness of an arm and/or a leg on one side of your body or numbness on one side of your body? If YES ask the following question 37a Y N Do you still have these symptoms now? 38 38a Y N Have you ever had a severe chest pain across the front of your chest lasting for half an hour or more? If YES, where did you get this severe pain? (Show the participant the picture and mark X in the places described) 233

234 39 Y N Have you ever had any pain or discomfort or any pressure or heaviness in your chest? If YES ask the following questions 39a Y N Is the pain reproduced by walking on the level, up a hill or when hurrying? 40 Y N Do you get more breathless than people your own age when you go walking either on the level or up a hill? 41 Y N Do you ever have to stop walking because of breathlessness? 42 Y N Do you ever get pain in your calf muscles while walking on the level, when walking uphill or when hurrying? I now want to ask you some questions about preventing diseases Y N Stroke 2. Y N High blood pressure 3. Y N Heart attack 4. Y N Diabetes 5. Y N Diarrhoea 6. Y N Overweight 7. Y N Epilepsy 8. Y N Poisoning 9. Y N Depression 10. Y N Alcoholism 11. Y N Tooth and gum disease Which of these conditions do you think can be prevented? (Read out all options and ask for answer Yes or No to each. If the participant is unsure ask them for their best guess) 234

235 44 1. Y N Lose weight 2. Y N Quit smoking 3. Y N Increase exercise 4. Y N Eat less fish 5. Y N Drink less alcohol 6. Y N Reduce fat in meals 7. Y N Eat more pickles Y N Lose weight 2. Y N Quitted smoking 3. Y N Increased exercise 4. Y N Drink less alcohol 5. Y N Eat more fish 6. Y N Reduce fat in meals 7. Y N Eat less pickles Do think these sorts of actions might prevent disease? (Read out all options and ask for answer Yes or No to each- If the participant is unsure, mark No) In the past 12 months have you done any of these things to safeguard your health? (Read out all options and ask for answer Yes or No to each) Y N Health care provider 2. Y N School 3. Y N Family and/or friends 4. Y N Radio 5. Y N Newspapers 6. Y N Television 7. Y N Other Which of these are the main places where you have learned information about health? (Read out all options and ask for answer Yes or No to each) If other please specify 47 Y N Have you been injured once or more in the last year? This means have you suffered an accident, a poisoning, or another type of injury. For the purpose of this survey, an injury is defined as an incident that caused you to miss work or school, visit a health facility, or limit you daily activities for at least one day. If YES, complete the supplementary injury questionnaire AFTER you finish this main questionnaire I would now like to know about your general health and wellbeing. In the past month have you generally 48 Y N Been able to concentrate on whatever you re doing? 49 Y N Lost much sleep over worry? 50 Y N Felt that you are playing a useful part in things? 51 Y N Felt capable of making decisions about things? 235

236 52 Y N Felt constantly under strain? 53 Y N Felt you could overcome your difficulties? 54 Y N Been able to enjoy your normal day-to-day activities? 55 Y N Been able to face up to your problems? 56 Y N Been feeling unhappy and depressed? 57 Y N Been losing confidence in yourself? 58 Y N Been feeling reasonably happy, all things considered? 59 Y N You became unemployed and were seeking work unsuccessfully for more than one month Y N You had major financial difficulties Y N You had a serious problem with a close friend, neighbour or relative Y N You experienced divorce or marital separation (or broke off a steady relationship) Y N A close friend or another relative died Y N Your parent, child or spouse died Y N You experienced armed conflict, terrorism, natural or technological disaster Y N You had problems with the police and a court appearance Y N Something you valued was lost or stolen Y N Some other major event (specify) During the last 12 months, did you experience any stressful events in your life such as..? (Read out list and seek answer Yes or No to every question) These next few questions are about alcohol drinking Never (Go to Injury questionnaire if relevant. If not, go to question 70 and send respondent to examination room) 2 Monthly or less often 3 Two to four times in a month 4 Two to three times a week 5 Four or more times a week Thinking of the last month or 2 drinks 2 3 or 4 drinks 3 5 or 6 drinks 4 7 or 8 drinks 5 10 drinks or more How often do you have a drink containing alcohol? How many drinks do you have on a typical day when you are drinking? (Record for standard drinks) 236

237 62 1 Never 2 Less than monthly 3 Monthly 4 Weekly 5 Daily or almost daily Thinking of the past 12 months 63 1 Never 2 Less than monthly 3 Monthly 4 Weekly 5 Daily or almost daily 64 1 Never 2 Less than monthly 3 Monthly 4 Weekly 5 Daily or almost daily 65 1 Never 2 Less than monthly 3 Monthly 4 Weekly 5 Daily or almost daily 66 1 Never 2 Less than monthly 3 Monthly 4 Weekly 5 Daily or almost daily 67 1 Never 2 Less than monthly 3 Monthly 4 Weekly 5 Daily or almost daily 68 1 Never 2 Yes but not in the last year 3 Yes, in the last 12 months 69 1 Never 2 Yes but not in the last year 3 Yes, in the last 12 months How often do you have six or more drinks on one occasion? (Record for standard drinks) How often during the last 12 months have you found that you were not able to stop drinking once you had started? How often during the last 12 months have you failed to do what was normally expected from you because of drinking? How often during the last 12 months have you needed a first drink in the morning to steady your nerves or to get rid of a hang over after a heavy drinking session? How often during the last 12 months have you had a feeling of remorse or guilt after drinking? How often during the last 12 months have you been unable to remember what happened the night before because you had been drinking? Have you or some one else been injured as a result of your drinking? Has a relative or friend or a doctor or other health worker been concerned about your drinking or suggested you cut down? 237

238 Remember to administer the Supplementary Injury Questionnaire if relevant. NOTE It is very important that if you have identified any possible new physical or psychiatric complaints during the course of the interview you must discuss these with your supervisor and arrange appropriate care for the participant. Thank you very much for you time. You now need to go to the examination room. Sign off for questionnaire 70 hs: min Time interview finished Interviewer s Name Interviewer s Signature Documentation of Physical Examination 73 Height in centimetres 74 Weight in kilograms 75 Waist circumference in centimetres 76 Hip circumference in centimetres 77 Heart rate in beats per minute 78 / 79 / 80 Y N Blood sample taken Blood pressure in millimetres of mercury systolic/diastolic (I) Blood pressure in millimetres of mercury systolic/diastolic (II) 81 Y N Splitting of blood sample required (check split sample list) 81a 82 82a 82b Blood Glucose Protein If yes write dummy number used here Urine sample dipstick results (write number of + here) 83 Y N IMT scan required? (check IMT list) 238

239 84 Y N ECG required? (check ECG list) Sign off for physical examination 85 hs: min Time interview finished MPHWs Name Signature Interviewer s Name Interviewer s Signature 239

240 Appendix 3 Survey Instruments used in rural Andhra Pradesh (APRHI) Main study Main Questionnaire Consent Patient information sheet Prompt cards 240

241 THANK YOU FOR AGREEING TO TAKE PART IN THIS SURVEY TO FIND OUT ABOUT THE RISKS OF HEART ATTACK, STROKE AND OTHER CHRONIC DISEASES FOR RESIDENTS OF RURAL ANDHRA PRADESH. MOST OF THE QUESTIONS I WILL ASK WILL BE ABOUT YOU, AND A FEW WILL BE ABOUT YOUR HOUSEHOLD. AT THE END, THERE WILL BE A BRIEF PHYSICAL EXAMINATION. 1. How old are you? 2. Can you read and write? 1. Yes 2. No 3. What is the highest level of education that you have completed? MARK ONE BOX ONLY. 1. No formal schooling 2. Primary school 3. Secondary school 4. Higher education (eg. Diploma/Technical/University studies) 4. Which of these best describes your usual main occupation? READ OUT AND MARK ONE BOX ONLY. 1. Unemployed/ retired 2. Housewife 3. Skilled manual worker 4. Unskilled manual worker 5. Owner of business or farm 6. Office worker/ non professional 7. Professional 8. Student 5. How many people live in your HOUSEHOLD? 6. What is the combined MONTHLY income of everyone in your HOUSEHOLD, including yourself? Rs 7. In an average MONTH, how much do you think your HOUSEHOLD spends on i. Medication (including prescription tablets and other medicines) Rs ii. Other health care (including doctor consultations, tests, procedures and travel) Rs 241

242 8. Which of these best describes your quality of life? MARK ONE BOX 1. Excellent 2. Very Good 3. Good 4. Fair 5. Poor 9. Which of these best describes your health? MARK ONE BOX 1. Excellent 2. Very Good 3. Good 4. Fair 5. Poor 242

243 I WILL NOW ASK YOU A FEW QUESTIONS ABOUT WHAT YOUR DOCTOR HAS TOLD YOU ABOUT YOUR HEALTH 10. Have you ever been told by a doctor that you have had any of the following? 1 Heart attack Yes No 2 Angina Yes No 3 Stroke Yes No 4 Peripheral vascular disease (disease of the arteries) Yes No 5 Diabetes (sugar) Yes No 6 Hypertension (high blood pressure) Yes No 7 High cholesterol Yes No 11. Have you ever been told by a doctor that you have any of the following chronic medical problems? 1 Heart failure Yes No 2 Rheumatic heart disease or valve disease Yes No 3 Anaemia Yes No 4 Tooth or gum disease Yes No 5 Cataract Yes No 6 Thyroid disease Yes No 7 Depression Yes No 8 Chronic lung disease (NOT asthma) Yes No 9 Asthma Yes No 10 Pneumonia Yes No 11 Tuberculosis Yes No 12 Malaria Yes No 13 HIV/AIDs Yes No 14 Cancer Yes No 15 Epilepsy Yes No 16 Stomach ulcer Yes No 17 Malnutrition Yes No 243

244 I AM NOW GOING TO ASK YOU ABOUT FACTORS RELATED TO YOUR FAMILY AND LIFESTYLE 12. Have any of your close relatives (mother, father, brothers, sisters) had a heart attack before the age of 60 years? 1. Yes 2. No 13. Have any of your close relatives (mother, father, brothers, sisters) had a stroke before the age of 60 years? 1. Yes 2. No 14. Have any of your close relatives (mother, father, brothers, sister) been diagnosed with diabetes? 1. Yes 2. No 15. On average, how much physical activity do you do each day during working hours? MARK ONE BOX 1. Lots Eg. Lifting heavy weights, construction work, labourers, running 2. Medium Eg. Bike riding, rickshaw drivers, carrying buckets of water or loads of laundry to and from wells multiple times a day, walking long distances up and down hills 3. Light Eg. Walking on the level, standing all day working at a shop, housework such as cooking, cleaning in the house 4. Almost none Eg. Seated at a desk, driving a car, watching television, reading, resting 16. On average, how much physical activity do you do each day after working hours? MARK ONE BOX 1. Lots Eg. Lifting heavy weights, construction work, labourers, running 2. Medium Eg. Bike riding, rickshaw drivers, carrying buckets of water or loads of laundry to and from wells multiple times a day, walking long distances up and down hills 3. Light Eg. Walking on the level, standing all day working at a shop, housework such as cooking, cleaning in the house 4. Almost none Eg. Seated at a desk, driving a car, watching television, reading, resting 244

245 17. Have you ever smoked regularly? (i.e. on most days for at least a year) 1. Yes 2. No Do you currently smoke? 1. Yes 2. No 1. How many years have you smoked for? 2. How many of each do you smoke per day? Cigarettes Cigars 18. Do you use chewing tobacco regularly? (i.e. on most days for at least a year) 1. Yes 2. No How many people smoke in your household? (include yourself if you are a current smoker) On average, how many hours a day can you smell tobacco smoke at home (either your own or others) On average, how many hours a day can you smell tobacco smoke at work (either your own or others) On average, hours minutes hours minutes i. How many days in a week do you have a drink containing alcohol? days ii. How much do you drink on a typical day when you are drinking? bottle(s) of Standard drinks iii. How often do you have more than six alcoholic drinks on one occasion? 1. Never 2. Less than monthly 3. Monthly 4. Weekly 5. Daily or almost daily 245

246 23. On average, how many days in a week do you eat fruit? days 24. On average, how many days in a week do you eat green leafy days vegetables? I AM NOW GOING TO ASK YOU ABOUT TREATMENTS YOU RECEIVE: 25. No Yes Have you ever had your BLOOD PRESSURE checked? Yes No Was your BP checked by the Byrraju Foundation? Yes No Was your BP checked in the last 12 months? Yes No Are you taking BP- lowering tablets? Yes No Are these tablets from the Byrraju foundation? 26. No Yes Have you ever had your BLOOD SUGAR checked? Yes No Was your sugar checked by the Byrraju Foundation? Yes No Was your sugar checked in the last 12 months? Yes No Are you taking sugar-lowering tablets? Yes No Are these tablets from the Byrraju foundation? 27. No Yes Have you ever had your CHOLESTEROL checked? Yes No Was your cholesterol checked in the last 12 months? Yes No Are you taking cholesterol lowering tablets? 28. Yes No Do you take aspirin? 246

247 29. Are you taking any medications regularly? 1. Yes 2. No A. What medications are you taking? (Please write the exact name of the medication from the medicine packet or a doctor s script.) code 247

248 I AM NOW GOING TO ASK YOU SOME QUESTIONS ABOUT HEALTH SYMPTOMS: 30. Have you ever had any pain or discomfort or any pressure or a feeling of heaviness in your chest? 1. Yes 2. No 1. Do you get it when you walk uphill or hurry? Yes No 2. Do you get it when you walk at an ordinary pace on the level? Yes No 3. What do you do if you get it while you are walking? 4. If you stand still what happens to it? Stop or slow down Carry on Relieved 6. How soon? 7. Will you show me where it was? MARK THE AREA(S) ON THE DIAGRAM Not relieved 10 minutes or less More than 10 minutes 8. Have you ever had a severe chest pain across the front of your chest lasting for 30 minutes or more? Yes No 31. Do you get more breathless than people your own age when you go walking either on the level or up a hill? 1. Yes 2. No 248

249 I AM NOW GOING TO ASK YOU SOME QUESTIONS ABOUT DISEASE PREVENTION 32. Which of the following actions may prevent a person getting a heart attack or stroke? 1. Lose weight Yes No Unsure 2. Quit smoking Yes No Unsure 3. Increase exercise Yes No Unsure 4. Eat more fish Yes No Unsure 5. Drink less alcohol Yes No Unsure 6. Reduce fat in meals Yes No Unsure 7. Reduce salt in meals Yes No Unsure 8. Eat more fresh fruit Yes No Unsure 9. Eat more green leafy vegetables Yes No Unsure 33. In the last 12 months, have you done any of the following to improve your health? 1. Lose weight Yes No Unsure 2. Quit smoking Yes No Unsure 3. Increase exercise Yes No Unsure 4. Eat more fish Yes No Unsure 5. Drink less alcohol Yes No Unsure 6. Reduce fat in meals Yes No Unsure 7. Reduce salt in meals Yes No Unsure 8. Eat more fresh fruit Yes No Unsure 9. Eat more green leafy vegetables Yes No Unsure 34. During the past 12 months have you been hurt in any way which required you to seek medical attention, or to stay away from work or school for at least one day? This includes you being hurt by an object or by a person (eg pushed or hit), or from a fall, road accident, burn, electric shock, poisoning, animal bite or drowning. Include injuries that needed medical attention but you did not receive treatment due to cost. 1. Yes 2. No If YES, complete the supplementary injury questionnaire AFTER you finish the main questionnaire. THANK YOU VERY MUCH FOR YOUR TIME, YOU NOW NEED TO GO TO THE EXAMINATION ROOM 35. Interviewer Sign off Name (BLOCK LETTERS) Signature 249

250 DOCUMENTATION OF PHYSICAL EXAMINATION 36. Height in centimetres 37. Weight in kilograms 38. Waist circumference in centimetres 39. Hip circumference in centimetres 40. Blood pressure in millimetres of mercury and heart rate Time BP taken Systolic Diastolic Heart rate I II 41. Fasting for > 6 hours Yes No 42. Blood sample taken Yes No 43. Splitting sample Yes No 44. Urine dipstick Yes No Result Blood Glucose Protein 45. ECG Yes No 46. Researcher - sign off for physical examination Researcher s name Signature Date signed / / 250

251 Participant Consent Form Page 1 of 1 Please give one copy to the participant and keep one copy for the Investigator ANDHRA PRADESH CARDIOVASCULAR HEALTH STUDY Participant: Mr/Mrs/Miss/Ms (first and last name). Address (House number, Street, Village) Unique ID By signing this form, I give my free and informed consent to being interviewed for this study and to having some basic physical measurements made. I also consent to giving a blood sample for laboratory testing for the purposes specified in the Patient Information Sheet I also consent to giving a urine sample for testing for the purposes specified in the Patient Information Sheet I have been given full information about the study and I understand its nature, purpose, and duration as well as the procedures involved including any known or expected inconvenience. I have had a chance to ask questions about the survey and the tests and all of my questions have been answered to my satisfaction. My medical data are strictly confidential and I authorize only persons involved in the research and my health care providers to look at them. I understand that I am free to withdraw from the study at any given time. I have been given a copy of this consent form to keep. By signing this form I have not given up my legal rights. I understand that I am entirely free to decide whether or not to take part in this study, and if I choose not to take part, this will not affect the health care I am currently receiving. I am also free to discontinue my involvement with the study at any time. Printed name of participant: Signature of participant: Date: Printed name of person obtaining consent: Signature of interviewer: Date: 251

252 Participant Information Sheet Page 1 of 2 One copy for the participant and the second one for the researchers ANDHRA PRADESH CARDIOVASCULAR HEALTH STUDY Introduction You are being invited to take part in a study of cardiovascular health in your village. We plan to survey several thousand adult residents of rural Andhra Pradesh aged 30 years or over in the next few months. You have been selected at random from the census list held by the Byrraju Foundation. This is an independent study organised by the Byrraju Satyanarayan Raju Foundation (Hyderabad), the George Institute for International Health (Sydney) in Australia, the Centre for Chronic Disease Control (New Delhi) and the CARE Hospital (Hyderabad) in India. Before you decide whether to participate, it is important for you to understand why the research is being done and what is involved. Please take time to read the following information carefully and discuss it with friends or relatives if you wish. You are entirely free to decide whether or not to take part in this study, and if you choose not to take part, this will not affect your health care in any way. You are also free to discontinue your involvement with the study at any time. What the study hopes to achieve This study aims to find out whether a new way of providing treatment to prevent stroke and heart attack is effective in villages like yours. The best way for us to find out this information is by asking a large number of local residents like you to tell us about their health and any treatments they are taking. We plan to do this in about 40 villages and 8,000 people now and again in the same villages in about a year. You may or may not be asked to participate a second time. Over the next year we will also be training the health care workers in about half the villages in some new ways to try and help you and your fellow villagers decrease their risks of stroke and heart attack. We do not know the best way to do this at present and this study will provide important new information about this. Once we know the new methods are successful we will also train the health care workers in the remaining villages. In this way, the information collected will be used to help the Byrraju Foundation and other health care providers in your community plan the most appropriate health services. What the study involves If you agree to take part, an interviewer will ask you a series of questions about your health and any treatments you are on. The questions will mainly be related to your risks of heart disease and stroke because these are important causes of death and disability in your community for which effective health care systems are not well established. The interview will take about 10 minutes and will be done in your village. Following this, the interviewer will measure your blood pressure, height, weight and hip/waist circumference. We will also ask your permission for a laboratory technician to take a blood sample and a urine sample. You will be free to choose to agree to this or not. What will we do with the information? Your answers to the questions and your test results will be used by authorised researchers to find out how well cardiovascular diseases are managed in your community and whether the new ways of training your health care workers can improve things. Each persons laboratory results will be given to the Byrraju Foundation doctor in your area and he will notify you if you require any treatment. 252

253 Participant information Sheet Page 2 of 2 ANDHRA PRADESH CARDIOVASCULAR HEALTH STUDY Confidentiality All information you provide will be treated confidentially. To be able to locate you we have obtained your contact details from the Byrraju Foundation census records. However, personal identifying information and data on any medical ailments will not be released to others except to yourself and your doctor. The information provided to the researchers in Bhimavaram, Hyderabad or Sydney (Australia) will not include any personal identifiers, only a unique study number. Our reports of the data will include aggregated information in a way that will not identify any individual person. That is, we will report something like: 10% of the village population older than 40 years suffers from high blood pressure. Organization and data storage The data collected in the study will be stored for at least 15 years by the Byrraju Foundation because this is required by international research regulations. Paper copies of forms will be stored in locked cabinets and computer files will be password protected. Need to contact the researchers? Should you have any questions at any point now or later on, please do not hesitate to talk to your doctor or the multipurpose health worker. The researchers who coordinate the study from Sydney can be contacted on telephone: or Fax: or mcardona@thegeorgeinstitute.org and those in Bhimavaram are also available on telephone: If you agree to take part in the study please do the following: Read carefully this Information Sheet and the Consent Form Sign two copies of the Consent Form Keep this Participant Information Sheet and one of the signed Consent Forms Attend the appointment for survey and tests on the date arranged Bring the list of medications you currently take (or bring the script or the packages) Do you have any complaints? The Research Ethics Committee of CARE Hospital and the Human Ethics Committee of the University of Sydney have given approval for the conduct of this project. Any person with concerns or complaints about the conduct of a research study can contact the Ethics Committee at CARE Hospital in Hyderabad on or the Manager of Ethics & Biosafety Administration at Sydney University in Australia on Thank you for your help in this important project 253

254 PROMPT CARD 1 - MEDICATION LIST These lower blood pressure ACE inhibitor 1 Captopril Enalapril Fosinopril Lisinopril Perindopril Ramipril Trandolapril Candesartan Irbesartan Losartan (Tozaar) Valsartan Beta blocker 2 Atenolol (Aten, Atecard, Betacard) Bisoprolol Carvidilol Esmolol Labetalol Metoprolol (Betaloc) Propranolol Nebivolol Calcium antagonist 3 Amlodipine (Amlocor, Stamlo) Diltiazem (Dilzem) Felodipine Nifedipine Verapamil Diuretic 4 Acetazolamide Amiloride Chlorthalidone Hydrochlorothiazide (Bepesol) Indapamide Frusemide Sprionolactone Torasemide These lower cholesterol Statin 5 Atorvastatin (Aztor) Lovastatin Pravastatin Rosuvastatin Simvastatin These are used to thin the blood Aspirin 6 Aspirin (Ecosprin, Loprin) Clopidogrel 7 Clopidogrel (Cloflow) Warfarin 8 Warfarin 254

255 Oral hypoglycaemics Insulin injections Pain killers Vitamin supplements Other Nitrates Other BP lowering Other cholesterol lowering These are used to treat diabetes sugar 9 Acarbose Chlorpropamide Glibenclamide (Daonil, Diasol, Euglucon) Gliclazide Glimepiride Glipizide (Glinase) Metformin (Diamet, Glycomet) Pioglitazone Rosiglitazone Troglitazone Repaglinide Nateglinide Tolbutamide G-met (Gliclazide + Metformin) Glucored (Glibenclamide + Metformin) 10 Insulin Actrapid Mixtard Other medications 11 Eg for back pain (DO NOT INCLUDE ASPIRIN) 12 Includes pills, injections, powders or liquid supplements 13 Any other medication not included in listings above 14 Glyceryl Trinitrate Nicorandil Isosorbide dinitrate (Sorbitrate) Isosorbide mononitrate (Monosprin) 15 Clonidine Hydralazine Prazosin Reserpine Terazosin 16 Benzafibrate Ezetemibe Fenofibrate Gemfibrozil Policosanol 255

256 PROMPT CARD 2 - SUPPLEMENTARY MEDICATION LIST Antibiotics Lung medications Gastrointestinal medications Sedatives Metals & minerals Common other - 13 codes 13 Amoxycillin Ciprofloxacin Ofloxacin Metronidazole Ofloxacillin Penicillin 13 Beclamethasone (Aerocort) Brancodex Etophylline (Deriphyllin) Prednisone Salbutamol (Asthallin) Theophylline 13 Famotidine (Famtac, Famocid, Famodin, Topcid) Antacid Digene Oracid Gelusil Omeprazole (Omag, Omax, Omez, Ometab) Pantoprozole (Pantocid, Zolex) Ranitidine (Antac, Zinetac, Lintac, Lantac) 13 Amitryptilline Diazepam (Valium) Alprazolam (Alpram, Anixinel) 13 Iron (Ferrous, Ferum) Zincovit Other 13 Ayuvedic Cyproheptadine (Periactin) Digoxin (Lanoxin) Pheniramine(Avil) Phenytoin (Epilan) Thyroxine (Electroxin) Common Vitamins 12 codes Vitamins 12 Vitamin A, B, C, B complex (Becomfin, Becosules) Folic Acid Pyrodoxine Common Painkillers 11 codes Painkillers 11 Ibuprofen (Brufen) Diclofenac Paracetamol Nimsulide(Nise) Naproxen Piroxicam Rofecoxib Valcecoxib (Valoflam) 256

257 PROMPT CARD 3 - OCCUPATION CATEGORIES and examples Check the list and examples below before making a decision on what category to allocate the occupation. 1. Unemployed/Retired: Unemployed: Persons out of a job for more than six months due to lack of opportunity (not disability). Retired is a person who worked most of his/her life and has now officially decided to remain home or do unpaid community work 2. Housewife: A woman engaged in household chores and not employed for work outside the house 3. Skilled manual worker: Factory worker, mechanic, plumber, electrician, painter, welder, machine operator, carpenter, dressmaker, shoemaker, bricklayer, metal worker (all types), technician, mason, hair dresser, photographer, game warden 4. Unskilled manual worker: domestic servant, sweeper, shoe shiner, fisherman, attendant, cattle drover, waiter, barmaid, gardener, grass cutter, labourer, cook, driver, labourers and farm owners if they work their land, persons engaged in aquaculture activities or animal rearing. 5. Owner of business or farm: Factory owner, farm owner who doesn t work their own land, shop owner who does not work in their own shop, landlord. 6. Office worker/ non-professional: Messenger, typist, telephone operator, secretary, storekeeper, cashier, clerk, government civil servant, insurance agent, government fulltime officer, bank teller, computer operator, medical assistant, administrative manager, Police, guard, security officer, prison officer, sales person, army personnel, community development officer, non professional musician, inspector, supervisor. 7. Professional: qualified librarian, veterinary surgeon, magistrate, churchman, priest, nurse, pharmacist, teacher, lawyer, doctor, accountant, engineer, etc 8. Student: Person engaged in full time study or who studies most days and is not engaged in paid work 257

258 PROMPT CARD 4 - ALCOHOL AND SMOKING CARD Alcohol: Alcohol type Volume Number of standard drinks Beer-large bottle mls 2 Beer small bottle 300mls 1 Whiskey/brandy 60mls 1 Wine 120mls 1 Arrak 1dram 100mls 1 Kallu 700 mls 3.5 Kallu 1 sachet- 200mls 1 Kapu Sara 1 bottle 720mls 12 Kapu Sara 1 quarter 180mls 3 Sara/ Gudamba 1 bottle 720mls 12 Sara/ Gudamba 1 drink 60mls 1 Time Definition Examples Never Less often than once in >6 months Once to twice in a year Less than monthly Between once in 2 3 or 4 times a year months to less than 6 months Monthly Less than once in a week to once in a 1 to 2 times in a month month Weekly 1-3 days in a week 1 to 2 times in a Daily or almost daily 4 or more days in a week. week 4 to 7 days a week Time Less than 12 times in a year 12 to 24 times in a year 24 to 52 times in a year More than 52 times a year, therefore more than once per week Definition number Examples of days 0 Drink at festivals 6 times a year 0.5 Drink once a month Drink once a fortnight 1 3 times a week Put number of days Smoking: 258

259 Term Definition number of days regularly Any time in their life for at least 12 months continuously currently Any time in the last 1 month Term cigarettes cigar Definition number of days Cigarette that is tobacco wrapped in paper sold commercially Other tobacco that is wrapped in a leaf, made by individuals egs. beedi, chutta etc 259

260 Appendix 4 Lab methodology and quality control in rural Andhra Pradesh (APRHI) Main Study Biochemistry protocol Lab methods Quality control 260

261 Biochemistry Protocol for Bhimavarum Hospital Overview The RAPCAPS Baseline is a 20 village, 4000 person survey which will run from February 14 to March 24, 2005 with approximately 4 villages to be surveyed per week by 2 survey teams. Each village will have full blood collection in 50 of 200 people selected and 2 of these people will require split samples. Tests to be ordered o Formal Blood sugar o Total Cholesterol o LDL o HDL o Triglycerides o Creatinine Each village survey will occur over 2 days ( with a 3 rd day in some villages for chasing up participants that have not presented or checking up for missing data) In each village the 50 people for FULL blood collection will be invited on the1st day of the survey. If the target of 50 people is not reached in either village, we will either try and invite more people that day, or invite people for the next day. Blood collection will sometimes occur in 2 villages simultaneously. Hence two teams of technicians will be required to efficiently do blood collection and transportation of these to the laboratory. All blood tubes should be labeled with labels that have been prepared prior to the survey. The split samples will have special labels with a ID number ONLY and no other details. These samples must be run together with all the bloods being tested and results should be issued together with stickers prepared prior to the survey Bloods, especially those for blood sugar measurement have to be centrifuged and the serum separated from RBCs within 4 hours of collection (otherwise metabolism by RBCs will cause underestimation of true Blood sugar level) Serum needs to be stored at -20 degrees, it can be stored for up to 3 months at -20, but needs to be transferred and stored at -70degrees for longer duration The procedure for each village will follow the same protocol as set out below QC in the lab will follow own protocols as well as RCPA program protocol through time of survey Protocol for full blood biochemistry in each village Check sticky label corresponds to patients name, study ID, sex, age. Put sticky label on each tube to be used to collect blood. 3 sticky labels have been supplied per patient. Collect blood from fasting individuals in villages o 1 x yellow top serum tube filled to maximum o 1 x grey top fluoride tube 261

262 The first 2 people you collect blood from in the village will require split samples. That is you need to collect one extra serum tube of blood from these 2 people. This extra tube will get a specific sticker with a different study ID number from the ID number that the rest of that person s samples are labeled with. This tube of split sample serum must be handled in exactly the same manner as the other samples you will run. All samples are to be cooled immediately after collection. (In study fridge/ eski or over ice) When bloods from all 50 people in the village are collected, transport immediately back to Bhimavarum hospital (time is very important, the shorter the better) Field Lab All bloods need to be centrifuged immediately GLUCOSE Serum then needs to be separated off the fluoride tube and the put in the autoanalyser for measurement of blood sugar. The print out from this result needs to be kept and the result needs to be documented in the lab log book. OTHER TESTS The serum then needs to be separated from the clot tube and put into the storage tubes. Half into one tube and half into the other tube. O Serum pipetted off O Half of serum put into storage tube A, half of serum put into storage tube B, therefore each patient will have 2 storage tubes contained at least 1 millilitre of serum in each tube. O All milliliter tubes labeled with Study ID, barcode (sticker resistant to freezing) O All storage tubes put in 2 racks, rack should contain all bloods from same village ie 50 tubes. o One rack with all serum from the same village should be stored in fridge till transfer to CARE hospital, the other rack should be stored in the Byrraju Foundation -20 Freezer. Analysing samples in CARE Study ID number put into autoanalyser Tests entered (as above) o Formal Blood sugar (To be done in the field not in CARE) o Total Cholesterol o LDL o HDL o Triglycerides o Creatinine Results entered directly into excel file 262

263 Excel file ed to field monitor at the end of each day. Print out of results to be done Results copied into registration book All Printouts need to be kept and given directly to Field Monitor. Results for participants Results in Excel file will be used by Byrraju Foundation to be given to local Byrraju Physicians to be given to participants All results to kept at CARE for at least 1 year after survey Quality Control (QC) 1000 Blood samples will be run from 20 villages On each of days that study bloods will be run one sample of RCPA external QC material will be run. These are labeled with numbers 1 to 16. This number and the date needs to be entered into the autoanalyser before processing of these tests. The tests that need to be run on QC materials are the same as the rest of the study, that is (blood sugar not required by our study in CARE lab), total cholesterol, HDL, LDL, Triglycerides, Creatinine QC material should be run with study bloods eg, before or after study bloods are run, this is important because the same reagents that are used with study bloods should be used for the QC materials. QC materials will be labeled with the day and date they should be run (to be discussed with CARE lab coordinator, days may vary due to varying days of arrival of blood from field) QC materials have to be made up (ie distilled water added as per instruction) on the day they are used. Record needs to be made of QC material results and placed with the results of the bloods either at the start or end of the print out which is given to the study coordinator. For further information contact Dr. Clara Chow Cardiology Research Fellow The George Institute for International Health Royal Prince Alfred Hospital Missendon Road, Camperdown NSW Australia 2050 Cchow@thegeorgeinstitute.org 263

264 Lab methodology at the CARE biochemistry laboratory Chemistry parameter Glucose (done at Bhimavaram) Creatinine Lipid profile a. Cholesterol b. Triglycerides c. HDL-Chol. d. LDL-Chol. Instrument Methodology Company from which reagent obtained ALFA Biotech PLD-951 Semiautomated analyzer Beckman Coulter Synchron Cx9 Clinical system ALX Beckman Coulter Synchron Cx9 Clinical system ALX Beckman Coulter Synchron Cx9 Clinical system ALX Beckman Coulter Synchron Cx9 Clinical system ALX Beckman Coulter Synchron Cx9 Clinical system ALX QC material used GOD-POD Merck Biorad level 1 & level 2 Kinetic Jaffee Beckman Biorad/level 1 & level 2 CHOD PAP GPO Direct- Detergent & Enzymatic Detergent technology Beckman Beckman Randox Accurex Daiichi Biorad, Randox, Lipid controls Randox Lipid controls Note : Cholesterol subfractions were not done either by Friedewald/Delong formulae. Direct measurement of both HDL & LDL were done as mentioned above. 264

265 Quality control RCPA program sample aliquot: Tested result compared to target results for creatine, total cholesterol, HDL-cholesterol and Triglycerides Creatinine mmol/l TC mmol/l HDL mmol/l TG mmol/l RCCAP RCCAP RCCAP RCCAP RCCAP RCCAP RCCAP RCCAP Bhimavarum lab QC program sample aliquots: Repeat testing and comparison with target results Creatinine mmol/l TC mmol/l HDL mmol/l TG mmol/l EQAS EQAS EQAS EQAS

266 Ratio of split sample 1 to split sample 2 for Creatinine, total cholesterol, LDLcholesterol and HDL-cholesterol In at least 2 individuals per village, the blood samples obtained from these individuals were divided into two tubes and both tested. By repeat testing samples, the lab is tested for repeatability. A ratio of 1 indicates perfect agreement between the two samples tested. Cr1/Cr2 Distribution Ratio of Cr1 to Cr TC1/ TC2 Distribution Ratio of TC1 to TC

267 Distribution of LDL1/LDL2 2.5 Ratio of LDL1 to LDL Distribution of HDL1/ HDL2 1.4 Ratio of HDL1 to HDL

268 Appendix 5 Protocol for analysis of carotid intima-media thickness Carotid IMT analysis for Terason images in the HINDI study Image capture Follow standard techniques of image capture to optimize view of intima-media complex (see IMT measurement and analysis for HINDI October 2004) Save at least 3 digital film strips of ultrasound (equivalent to 6 seconds in total) File conversion for analysis Convert digital video images (Terason ULT files) to bitmap files for analysis, either live or off-line after patient examination Bitmap to be captured to co-incide with R wave if ECG available (preferable) or coincide with ventricular systole which should be the time when the artery is at its maximal luminal diameter. Aim to make 6 digital bmp image files per artery, a minimum of 2 are required to define the artery as measurable Ideal images for analysis should have - A clearly visible intima border of the far wall of the common carotid artery just proximal to the common carotid artery bulb. The proximal end of the CCA bulb visible to the left of the image The image should be horizontal, that is the artery should be aligned parallel to the top and bottom borders of the image Exclude images that are blurred, mis-timed or have for other reasons poor image quality where possible Analysis of bitmaps Use carotid IMT digital edge-detection software developed in-house and validated (Adams et al) Open bitmap file with analysis program Calibrate image within program to 5 mm or 10 mm Select section for analysis, section should be in the horizontal section proximal to the carotid bulb and should start approximately 1 cm from the bulb. If this is not possible because of poor image quality select an area further to the right of your image, this should occur infrequently if image quality is good If this section contains a plaque (area of localized thickening >1.5mm), analyse further to the right of the image o Note : Plaque is a focal area of thickening >1.5mm and is often calcified and irregular. Plaque usually occurs within the carotid bulb and internal and external arteries. They are less frequent in the CCA. During analysis, technicians should try and avoid sections of focal plaque for the purpose of attaining accurate carotid intima media measurements. Within the selected area, place cursor and depress mouse button in the artery media. Repeat this selecting another point of similar grey-scale. The program then detects the intima and back of media at the points of most rapid change of Page 268 of 291

269 pixel value on either side of the media. Sometimes different areas within the carotid intima complex need to be selected to allow the program to detect the edges.the program uses a statistical algorithm to automatically reject points that appear to be erroneous, but allows the observer to modify this selection if necessary, by including rejected points or excluding selected points (otherwise it does not permit the observer to modify the selection) Once detected, check that detection is accurate. If you think more points need to be selected slide the right indicator up or down to increase selection or decrease point selection as required. Exlude any mistakenly identified points (ie clearly wrong on visual inspection) eg points in the lumen or outside the vessel wall which can occur if there is image artifact. Once this is done, press enter and the results will appear. The mean IMT is calculated from the non-rejected points, converting the value in pixels to millimetres, with a calibration factor derived from digitised calibration marks recorded on the original image A typical analysis would be made on 150 to 200 points per 10mm horizontally, with 15 to 20 pixels representing 1mm of intimal thickness, and takes 5 minutes per image Repeat this process for up to 6 bitmap image files Then take the median two mean IMT measurements and average this to achieve the result for this artery If you are unable to collect 6 images, a minimum of two is required for the artery to be measurable Images that are poor quality for example blurry, incorrectly timed should be excluded from analysis Preferably images with at least 10 points per milimetre, minimum total of 50 points. Variability of measurements A certain amount of variability will exist in IMT measurements. To minimise this the median two measurements of the 6 measurements are taken. At least 2 measurements that are less than 10% different are taken from each artery and averaged to estimate the IMT of the far wall of that artery. There is also intra and inter reporter variability. To measure this variability a random selection of 10% of scans need to be analysed by the same reporter at a time interval after the first analysis. The reporter needs to be blinded from their own previous results. 10% of scans also need to be analysed by a different reporter blinded for the results of the 1 st reporter. From these results a coefficient of variation can be calculated. Page 269 of 291

270 Appendix 6 Protocol for Rural Andhra Pradesh Cardiovascular Prevention Study (RAPCAPS) Protocol Algorithm Timetable for health promotion events Consent Evaluation questionnaire Page 270 of 291

271 A factorial, cluster-randomised trial of two low-cost cardiovascular prevention strategies developed for rural Andhra Pradesh the Rural Andhra Pradesh Cardiovascular Prevention Study (RAPCAPS) PROTOCOL (18 th Februrary, 2007) Contact Details: Bruce Neal The George Institute for International Health University of Sydney PO Box M201 Missenden Road Sydney, NSW Australia Tel: Fax: bneal@george.org.au Page 271 of 291

272 BACKGROUND Burden of cardiovascular disease in developing countries Over the past few decades, many middle- and low-income countries worldwide have experienced profound changes in the burden of ill health affecting their populations [1]. In most of south Asia chronic disease has replaced nutritional, maternal, perinatal and infectious conditions as the leading cause of death and life years lost to ill health [1]. The growth in cardiovascular disease has been particularly substantial. In 1990, there were about 14 million deaths from cardiovascular disease worldwide 5 million of these occurred in populations from higher income countries and 9 million occurred in populations from middle- and low-income countries. By 2020, it is projected that there will be 25 million deaths from cardiovascular disease worldwide 6 million in populations from higher income countries and 19 million in populations from middleand low-income countries [2]. This change has been driven by two main factors. First, declining infant and child mortality has lead to rapid demographic changes resulting in large increases in the numbers of individuals surviving until middle and older age, when chronic diseases become manifest [3]. Second, lifestyle changes, such as increasing tobacco use, increasing fat and calorie consumption and decreasing exercise, have increased the rates of chronic disease [4]. Moreover, people from south Asia may be more susceptible to these risk factors with particularly high rates of cardiovascular disease reported in many expatriate populations of south Asians [5, 6]. Cardiovascular disease in India In 2005 it was estimated that the annual toll from cardiovascular disease in India was already 3.7 million (29%) deaths and 32 million (11%) disability-adjusted life years [7]. Mortality and morbidity from cardiovascular diseases is expected to rise even further with falling childhood and communicable disease mortality, rapid industrialisation and changing lifestyles across India. The victims of cardiovascular disease in India are younger, with 52% of cardiovascular deaths in India occurring below the age of 70 compared with just 23% in more developed countries in Europe [7]. There is also a substantial health burden caused by non-fatal cardiovascular disease in India. Information about the prevalence of cardiovascular disease is less widely available than information about fatal events. However, it is estimated that in urban India about 10% of adults aged 35 years or over are living with cardiovascular disease [8]. Data about the prevalence of cardiovascular disease in rural regions is more limited. While it is likely to be substantially less than in urban areas [3], levels in more developed rural areas are almost certainly increasing. In addition, because very large numbers of individuals live in rural areas, even lower rates would make a significant contribution to the national cardiovascular disease burden. The Andhra Pradesh burden of disease study is one of a number of projects that has identified cardiovascular disease as a major concern in rural regions of India [9, 10]. Page 272 of 291

273 Cardiovascular care in rural areas Rural populations are usually poorer than urban populations and typically have greatly inferior access to health care facilities providing care for chronic disease when compared to urban populations. This is true in higher-income countries such as Australia [11, 12] but even more so in the rural regions of developing countries. There are multitude reasons for the limited access of rural populations to adequate health care but key issues are the ability of the populace to pay, the absence of health care facilities and a lack of evidence about what are effective strategies for the control of cardiovascular diseases in resource poor settings. In many developing rural regions, the usual health care provider for most of the population is a non-physician health worker (who has usually have done 1-2 years of health training following secondary school) whose activities are currently focussed on maternal and child health. Many rural areas and a large proportion of the rural population would have little or no access to health services targeting chronic diseases. Work that has lead to this project - the Andhra Pradesh Rural Health Initiative The Byrraju Satyanarayan Raju Foundation (BSRF) in Hyderabad is a not for profit organization who s mission is to build progressive, self reliant, rural communities by bridging gaps in areas of health, environment, sanitation and education. In the area of health BSRF has built and is operating primary healthcare centre-based infrastructure in 150 villages in rural Andhra Pradesh. The BSRF has worked with the CARE Foundation in Hyderabad, the Centre for Chronic Disease Control (CCDC) in New Delhi and the George Institute for International Health in Australia in the development of its health programs. These groups established the Andhra Pradesh Rural Health Initiative (APRHI) in January 2003 and added to the group representatives from the University of Queensland, Australia in The aim of this Collaboration is to design, implement and evaluate affordable and sustainable evidence-based strategies for the improvement of health outcomes in the rural populations of Andhra Pradesh serviced by the Byrraju Foundation s primary healthcare infrastructure. The collaboration is currently working on two main programs of work: mortality surveillance and disease and risk factor assessment. Implementation and assessment of cardiovascular disease prevention programs is now planned. 1. Mortality surveillance under the leadership of the BSRF, the Collaboration established a mortality surveillance system in 45 villages to identify the main causes of death in the region. Some 1,400 deaths occurred in these villages each year and results show that cardiovascular diseases are the leading cause of mortality responsible for between 30 and 40% of all deaths. The second leading cause of death is deliberate and unintentional injuries, the third infectious and parasitic conditions and the fourth cancers. Page 273 of 291

274 2. Disease and risk factor assessment following the establishment of the mortality surveillance program and under the leadership of the BSRF, the Collaboration did a health survey of 4,535 adults in 20 of the participating villages. The goals of this survey were: (1) to document the non-fatal disease burden in the communities; (2) to discover the likely causes of fatal and non-fatal diseases; (3) to quantify population understanding of key aspects of chronic disease and (4) to document access to and use of health care facilities. In regard to cardiovascular disease it was found that about 7% of adults aged 30 years and above reported suffering from some form of cardiovascular disease: about 4% had been diagnosed by a doctor with angina, 2% with a stroke, and 1% with a heart attack. Regarding associated risk factors, 23% had hypertension, 13% had diabetes, 25% were current smokers and 22% were either overweight or obese. In addition, 8% reported having a first degree relative who had suffered a heart attack or a stroke before the age of 60 years. Many people were unaware of their vascular health risks, few were receiving treatments, and knowledge about the causes of cardiovascular disease was very limited. This situation is not at all uncommon in developing regions of Asia [13-15]. Rationale for developing a cardiovascular prevention program The results of the mortality surveillance and the high prevalence of cardiovascular disease identified by the survey highlight the importance of cardiovascular disease in this rural community. The survey also served to highlight huge opportunities for prevention by identifying gaps in knowledge, behaviours and treatments for cardiovascular disease prevention. Prior work done mainly in developed countries has identified a broad range of interventions that would prevent cardiovascular disease amongst this community. For example, dietary changes, smoking cessation and drug therapies could all produce significant reductions in stroke and heart attack in this rural population. However, while there is a good understanding of how to deliver cardiovascular disease control programs in wealthy developed countries, there are few data to define effective strategies for cardiovascular disease prevention in very resource poor settings. Accordingly, the goal of this initiative is to develop, implement and evaluate a sustainable cardiovascular disease prevention program suited to this resource poor rural area. It is important to note that the scientific underpinning of each component of the intervention is based on definitive evidence from many prior studies. The real discovery that will come from this work relates to the simplified low-cost mechanism of delivery that is to be evaluated. Components of the proposed cardiovascular prevention program Broadly, there are three main strategies for cardiovascular disease control. First, risk factor-based approaches, as exemplified by hypertension and hypercholesterolaemia treatment programs. Second, population-wide strategies that seek to modify population determinants of disease through modification of behavioural and dietary risk factors such as saturated fat and tobacco. And third, absolute risk-based approaches that target multifactorial treatment at individuals identified as high risk on the basis of an evaluation encompassing the main cardiovascular risk factors. Page 274 of 291

275 Risk factor-based approaches for the control of cardiovascular disease are now widely recognised to be rather inefficient and almost always much less cost-effective than the other two strategies [16, 17]. Accordingly, the prevention program proposed here incorporates a population-wide approach provided through community-based health promotion and an absolute risk-based strategy for delivery by clinical services. It is of note that the WHO has been instrumental in identifying the merits of these two types of strategies as cost-effective for resource poor settings with a high burden of disease [18, 19]. The program selected has also been significantly influenced by specific WHO recommendations for a CVD-Risk management package for low- and medium- resource regions [20]. The absolute risk-based component This approach is now the preferred means of delivering clinical interventions for cardiovascular prevention in developed countries. The shift to absolute risk-based approaches is also gaining momentum in developing countries and is an underlying theme of the recommendations in the more recently updated national and international guidelines.[21-25] Research evidence from the West has substantively informed the absolute risk based approach to be implemented here, with the interventions selected on the basis of evidence from definitive large-scale research projects. It is of note that while data about optimal mechanisms of implementation of cardiovascular prevention programs is unlikely to transfer well across different settings, evidence about the effects of risk factors and their modification does appear to generalise very well. So, for example, while different levels of health care infrastructure will massively impact upon the mechanism and feasibility of screening for risk factors between developed and developing countries, the prescription of a drug such as aspirin would be expected to produce one fifth reductions in the risk of vascular events in those it is prescribed to in both settings. The population-wide component Population-wide modifications of risk factor levels will be attempted through the delivery of a low-cost health promotion campaign. The goal will be to change population knowledge, attitudes and practices in regard to cardiovascular disease. The prior survey work shows that there is substantial room for improvement in these indicators and that even basic concepts about vascular diseases are not well understood. The campaign will be lead by the village non-physician health worker and the village government (Panchayat) and will include posters, information sessions, street theatre and community rallies. Wherever possible the materials used will be based on those employed by other programs around India. Implementation of the prevention program Routine health care services in the villages comprises a government non-physician health care worker, a BSRF non-physician health care worker and 2 hours of physician clinic time each day provide by BSRF physicians at a nominal charge. There are basic physical facilities in each village to support this work and the BSRF provides selected pharmaceuticals free of charge to the villagers. From a cardiovascular perspective, these include a beta-blocker and a diuretic. Page 275 of 291

276 In addition to the services provided in the villages there are also many other private and public health facilities available to the village populace in the local metropolitan areas. The services available extend to include a very broad range of different types of care. However, in almost every case the care must be paid for out-of-pocket and the cost of services is beyond the reach of most of the villagers. For a cardiovascular prevention program to have an impact in rural India it must be made widely available at low cost so that individuals of low income are able to access it. Since resources for the delivery of health care are very limited in such regions traditional Western models of physician-driven care will be of very limited value - there are insufficient physicians to deliver the services and most people would not be able to afford them. The clinical absolute-risk based component of the cardiovascular prevention program will therefore be designed for delivery by physician or non-physician health workers. In the first instance, the program will focus on the very highest risk (those with existing vascular disease) amongst whom some one third of all vascular events occur [17, 26]. Intervention is particularly cost effective for this group. A simple screening tool will be used by non-physician health care workers to identify high risk individuals for management with behavioural advice and low cost drug therapies. In many parts of the world effective prevention programs rely heavily on non-physician staff with specialist training [27, 28]. In the current setting, prescription of drug therapies is restricted to physicians but the program will be implemented in a way that evaluates whether nonphysician health care workers are correctly identifying high risk individuals and whether they are able to make appropriate general recommendations about patient treatment needs. Population-wide modifications of risk factor levels can be attempted through voluntary or compulsory means. While compulsory mechanisms are almost certainly more effective,[29] passing the required legislation and enacting enforcement is a difficult, costly and very time consuming process. In this project, efforts will be made to achieve voluntary changes in population determinants of disease using health promotion techniques to enhance community knowledge, attitudes and practices in regard to cardiovascular diseases. AIM The overall aims of this study are first, to investigate the effects of algorithm-based care on the proportion of individuals at high-risk of cardiovascular disease that can be identified and managed according to basic guidelines and second, to define the effects of a health promotion campaign on population knowledge of the determinants of cardiovascular disease. The study is unique because it will be done in a very resource poor setting in rural India where the burden of vascular disease is high but access to cardiovascular care is currently very limited. The study will test two main hypotheses: Page 276 of 291

277 Algorithm-based care - a low-cost cardiovascular disease prevention strategy based on simple clinical algorithms implemented by primary health care providers will lead to improved identification of individuals at high risk of cardiovascular disease Health promotion - a low-cost health promotion campaign targeting cardiovascular disease prevention will lead to increased knowledge of the determinants of cardiovascular disease amongst the general population For both hypotheses the control condition will be continued usual practice. METHODS This study will be a factorial, cluster-randomised trial in which villages will be exposed to one, both or neither of the interventions for a period of about 12 months. Door-to-door surveys of every household in every village will be used to assess outcomes in all highrisk individuals and a sample of the general population. In practical terms the trial will be a 2x2 factorial cluster randomised trial evaluating the effects of each of the two interventions amongst high risk individuals and a two arm parallel group cluster randomised trial evaluating the effects of the health promotion campaign in the adult population. Study population At least forty villages participating in the Byrraju Foundation-sponsored rural development initiative will take part in this trial. These villages will be a mix of large, medium and small villages of varied distance from the nearest large towns in East and West Godavari. The mean village population will be about 3000 with about one half in each village being aged 30 years or over. Randomisation Assignment to intervention or control will be at random with stratification by geographic region (East or West Godavari), population size (small, medium or large) and distance from a large town (<20km, >=20km). Each village will be randomised twice - once for allocation to algorithm-based care/control and once for allocation to health promotion/control. The randomisation process will be done centrally by epidemiologists based at the George Institute for International Health in Sydney. Intervention and control 1. Algorithm-based care versus control the algorithm is designed to be used by physician or non-physician primary health care workers in poor rural areas. It has two chief goals: First to increase the identification of high-risk individuals in the community through encouraging opportunistic screening, and second to increase the systematic use of appropriate prevention strategies. The algorithm recommendations are based on widely accepted guidelines. This component of the intervention will operate by physician and non-physician health workers opportunistically screening adults with whom they come into contact. This will be done simply by asking the question Has a doctor ever told you that you have had a heart attack, stroke or angina? For any person that responds in the affirmative then Page 277 of 291

278 specific treatment recommendations are made by following the algorithm. The treatments recommended by the algorithm are for drug therapies proven for secondary prevention [30-33] and established behavioural recommendations. In the villages assigned to control, use of the algorithm will not be introduced and the physician and non-physician health workers will continue their practices unchanged. 2. Health-promotion versus control the health promotion campaign has been designed to increase knowledge of the causes of cardiovascular disease and enhance use of preventive behaviours in the general population. The main messages will focus on tobacco cessation, heart-healthy eating and physical activity. The campaign will use educational materials and other media suitable for communicating health messages to populations with limited literacy. This will include a program of posters, street theatre, walking groups and community presentations. The campaign will draw on experience from developed countries [30]and use materials that have been adapted or developed in collaboration with local experts from a range of local institutions. To ensure wide exposure and a constant presence of the campaign, introduction of the various components will be staged, with posters in the first month, followed by new activities every second month or so. For instance, the street theatre groups will visit every village at least twice in one month, at different times of the day, days of the week and in different public places. The following month there will be a slogan competition launched by a personality, involving young people outside the school setting. The penetration of these campaigns across intervention villages will be checked in a followup survey of intervention villages. In the villages assigned to control, the health promotion campaign will not be introduced. Evaluation of effects Questionnaire The effects of the two interventions will be evaluated through the conduct of a door-todoor survey that will take place in every house in all participating villages. Each house will be visited by an interviewer and in every house inquiry will be made about the presence in the household of anyone aged 30 or over with a history of heart attack, stroke or angina. For each high-risk individual identified the questionnaire will be administered. In addition, a stratified random sample of all adults aged 30 years or over in each village will be administered the same questionnaire. The goal for each village will be to administer the questionnaire to all high risk adults aged 30 or over and to about 100 other adults aged 30 years or over. The latter group will be sampled such that there are selected from each village individuals in 8 age and sex groups (male and female in the age ranges 30-39, 40-49, and 60+). Health workers in intervention and control villages will also be administered a brief questionnaire to evaluate their knowledge attitudes and practices in relation to cardiovascular disease. Page 278 of 291

279 As far as is possible, the questions asked will be those used previously for the baseline survey of risk factors. Those questions were developed from previously validated surveys identified by a comprehensive review of the literature with modification and supplementation by local experts. Comprehensive interviewer training materials will be prepared and detailed interviewer instruction programs will be completed prior to commencement of the evaluation surveys. Physical measurements, blood and urine samples. Blood pressure, weight, height and waist circumference will be measured in every person included in the survey. Blood and urine sampling will also be done in both high risk individuals and in the random sample of the adult population aged thirty years or over. Specifically, all high risk participants and all the randomly selected adults aged 30 years or over will have a random (non-fasting) glucose and a random urine dipstick (for blood, protein and glucose). In addition, a fasting venous blood sample for assay of venous glucose, cholesterol and creatinine will be sought from all the high risk individuals identified. All data will be collected using paper questionnaires in the first instance and entered into a secure data collection and study management system.. Outcomes The same outcomes will be assessed for each of the two randomised comparisons being made (algorithm-based care vs. control and health promotion vs. control) but different primary outcomes have been defined for each (as indicated below). For the comparison of algorithm-based care vs. control the primary analyses will define the effects in high risk individuals only. For the comparison of health promotion vs. control the primary analyses will define the effects in the adult population. The outcomes to be evaluated fall into six broad categories. 1. Effects on identification of high risk individuals: The proportion of high-risk individuals that have been identified will be calculated by dividing the number of high risk patients that have had screening for cardiovascular risk during the intervention period (i.e. responded yes to the question Have you been assessed for your risk of heart disease/ stroke/ angina by a health care provider in the last 12 months? ) by the number of high risk patients identified by the survey. This will be the primary outcome for the comparison of algorithm-based care vs. control. 2. Effects on use of drug treatments for secondary prevention: The proportion of high risk individuals treated with two or more of the recommended drug therapies. This will be calculated by dividing the number of high risk individuals identified during the survey on two or more recommended treatments by the total number of high risk individuals identified during the survey. The mean number of recommended drugs used by high risk individuals. This will be calculated by recording a number (0-4) for each high risk individual based on the number of drug classes they are using. For patients with coronary heart disease Page 279 of 291

280 one point will be scored for use of each of the following classes - anti-platelet therapy, ACE inhibitor, beta-blocker and cholesterol lowering. For patients with cerebrovascular disease one point will be scored for use of each of the following classes - anti-platelet therapy, ACE inhibitor, diuretic and cholesterol lowering. 3 Effects on provision of advice about non-drug preventive strategies: The proportion that have received correct advice about each of six separate determinants of the risk of cardiovascular disease (green leafy vegetables, fruits, oily foods, salt, smoking, physical activity) The mean number of these six determinants of cardiovascular disease (green leafy vegetables, fruits, oily foods, salt, smoking, physical activity) about which the correct advice has been received 4. Effects on knowledge about non-drug preventive strategies: The proportion with correct knowledge about the effects of each of six determinants of the risk of cardiovascular disease (green leafy vegetables, fruits, oily foods, salt, smoking, physical activity) The mean number of these six determinants of cardiovascular disease (green leafy vegetables, fruits, oily foods, salt, smoking, physical activity) about which the correct effect was known. This will be the primary outcome for the comparison of health promotion vs. control. 5. Effects on uptake of non-drug preventive strategies: The mean number of days each week in the last six months that individuals adhere to favourable behaviours relating to five determinants of cardiovascular disease (green leafy vegetables, fruits, oily foods, salt, physical activity) The proportion that have stopped smoking in the last 12 months The proportion that have had their blood pressure checked in the last 12 months The proportion that have had their cholesterol checked in the last 12 months The proportion that have had their blood or urine sugar checked in the last 12 months 6. Physical measurements: The mean blood pressure The mean total cholesterol The mean creatinine The mean blood glucose The mean body mass index In addition, health care providers working in the study villages will be invited to complete a self-administered questionnaire to evaluate their knowledge attitudes and practices relating to cardiovascular diseases. Statistical power Page 280 of 291

281 Key underlying assumptions for this study are: there will be about 1500 adults aged 30 or over in each village. About 5% of those aged 30 years or over will meet the definition of high risk (75 people in each village, 3000 in the forty villages). For the evaluation of the primary outcome for the algorithm-based care intervention there will be 90% power (alpha=0.05) to detect a difference of 10% or more in the proportion of high risk individuals identified between randomised groups. This estimate assumes that only one half (1500) of the 3000 high risk adults will actually participate in the survey and that one quarter (25%) will be identified in the control group (rising to 35% or above in the intervention group).. For the evaluation of the health promotion campaign there will be more than 90% power (alpha=0.05) to detect a 10% difference between randomised groups in the proportion answering correctly three or more of the six questions about the main determinants of cardiovascular disease. An increase of this magnitude or greater is considered eminently feasible since baseline knowledge was limited. This estimate is based upon 100 individuals being surveyed in each of the 40 villages (4000 in total) and assumes that the proportion answering three or more questions correctly will be 50% at baseline and will rise to 60% in the intervention group. For both the algorithm-based care and the health promotion campaign each of the assumptions is considered either reliable or conservative in light of the data from the baseline survey. Ethics review and consent The study protocol will be submitted for review by the University of Sydney Ethics Committee, Sydney, Australia and the CARE Hospital Ethics Committee, Hyderabad, India prior to initiation. No formal review processes exist in the villages but the study and the intervention program will be discussed with each of the villages elders committees. Informed consent will be obtained form all participants prior to participation in the survey. In obtaining informed consent, the study staff will provide the potential participant with information about the purposes, methods, possible risks and benefits of participating in the survey. The consent obtained will include consent for collection and storage of blood samples for evaluation of biochemistry including blood sugar, creatinine and cholesterol sub-fractions. All potential participants will have an opportunity to discuss the study with study staff. The participant and the person obtaining informed consent will each sign and date two copies of the consent form, one copy of which will be provided to the participant and the other copy will be kept by the study site. Amendments to the protocol, participant information sheet or consent form will be submitted to the ethics committee for approval. Such amendments will only be implemented once ethical approval has been obtained, unless an amendment is being made to eliminate immediate hazards to study participants. All data generated by the study will remain strictly confidential and no report will contain any information that would allow an individual participant in the study to be identified. Page 281 of 291

282 Participant records transmitted to the Data Management Centre will be identified only by a unique number, date of birth and initials. REFERENCES 1. Murray, C. and A. Lopez, The Global Burden of Disease: A Comprehensive Assessment of Mortality and Disability from Disease, Injuries and Risk Factors in 1990 and Projected to Boston, Mass: Harvard School of Public Health., Neal B, C.N., Patel A. Managing the global burden of cardiovascular disease. Eur Heart J 2002; 4(Suppl F): F2-F6., Managing the global burden of cardiovascular disease. Eur Heart J, (Suppl F): p. F2-F6. 3. Yusuf S, R.S., Ounpuu S, Anand S, Global burden of cardiovascular diseases: part I: general considerations, the epidemiologic transition, risk factors, and impact of urbanization. Circulation, (22): p Yusuf S, R.S., Ounpuu S, Anand S, Global burden of cardiovascular diseases: Part II: Variations in cardiovascular disease by specific ethnic groups and geographic regions and prevention stategies. Circulation, (23): p Pitt, B., et al., The QUinapril Ischaemic Event Trial (QUIET): Evaluation of chronic ACE inhibitor therapy in patients with ischaemic heart disease and preserved left ventricular function. Am J Cardiol, : p Anand, S.S., et al., Differences in risk factors, atherosclerosis, and cardiovascular disease between ethnic groups in Canada: the Study of Health Assessment and Risk in Ethnic groups (SHARE)[see comment]. Lancet, (9226): p Srinath Reddy, K., et al., Responding to the threat of chronic diseases in India. The Lancet, (9498): p Ghaffar, A., K.S. Reddy, and M. Singhi, Burden of non-communicable diseases in South Asia. BMJ, (7443): p Reynolds, K., et al., Geographic variations in the prevalence, awareness, treatment and control of hypertension in China. Journal of Hypertension., (7): p Mahapatra, P. and C. Rao, Cause of death reporting in India. A performance analysis. Natl Med J Inida, : p Gruen, R.L., et al., Improving access to specialist care for remote Aboriginal communities: evaluation of a specialist outreach service. Medical Journal of Australia., (10): p Perry, H., et al., Effect of treating isolated systolic hypertension on the risk of developing various types and subtypes of stroke: the Systolic Hypertension in the Elderly Program (SHEP). JAMA, : p Gu, D.F., et al., Prevalence, awareness, treatment and control of hypertension in Chinese adults. Chung-Hua Yu Fang i Hsueh Tsa Chih [Chinese Journal of Preventive Medicine], (2): p Page 282 of 291

283 14. Gu, D., et al., Prevalence of diabetes and impaired fasting glucose in the Chinese adult population: International Collaborative Study of Cardiovascular Disease in Asia (InterASIA). Diabetologia, (9): p Aekplakorn, W., et al., The prevalence and management of diabetes in Thai adults - The International Collaborative Study of Cardiovascular Disease in Asia. Diabetes Care, (10): p Gaziano, T.A., L.H. Opie, and M.C. Weinstein, Cardiovascular disease prevention with a multidrug regimen in the developing world: a cost-effectiveness analysis. Lancet, (9536): p Manuel, D., et al., Revisiting Rose: strategies for reducing coronary heart disease. BMJ, : p WHO, World Health Report. Geneva: World Health Organisation, Murray, C. and A. Lopez, Mortality by cause for eight regions of the world: Global Burden of Disease Study. Lancet., : p WHO. WHO CVD-Risk Management Package [cited; Available from: Williams, B., et al., British Hypertension Society Guidelines for management of hypertension: report of the fourth working party of the British Hypertension Society, 2004 BHS IV. J Human Hypertens, : p Guidelines Committee, European Society of Hypertension - European Society of Cardiology Guidelines for the management of arterial hypertension. J Hypertens, : p The Intercollegiate Working Party for Stroke, National Clinical Guidelines for Stroke. Update , Royal College of Physicians: London. 24. The Intercollegiate Working Party for Stroke, National Guidelines for Stroke. 2002, Royal College of Physicians: London. 25. National Heart Foundation of Australia and Cardiac Society of Australia and New Zealand, Lipid management guidelines National Heart Foundation of Australia, The Cardiac Society of Australia and New Zealand. Med J Australia, : p. Suppl:S Rothwell, P.M., et al., Population-based study of event-rate, incidence, case fatality, and mortality for all acute vascular events in all arterial territories (Oxford Vascular Study). Lancet [NLM - MEDLINE], (9499): p Chen, P., Providing primary health care with non-physicians.. Annals of the Academy of Medicine, (2): p Nichter, M.A., The primary health center as a social system: PHC, social status, and the issue of team-work in South Asia. Social Science & Medicine, (4): p Murray, C., J. Lauer, and et al, Reducing the risk of cardiovascular disease: effectiveness and costs of interventions to reduce systolic blood pressure and cholesterol - a global and regional analysis. Lancet, : p Shaper, A.G., et al., Risk factors for ischaemic heart disease: the prospective phase of the British Regional Heart Study. J Epidemiol Community Health, (3): p Page 283 of 291

284 32. Zargar, A.H., et al., Prevalence of type 2 diabetes mellitus and impaired glucose tolerance in the Kashmir Valley of the Indian subcontinent. Diabetes Res Clin Pract, (2): p Convery, F.R., et al., The relative safety of polymethylmethacrylate. A controlled clinical study of randomly selected patients treated with Charnley and ring total hip replacements. J Bone Joint Surg [Am], (1): p Page 284 of 291

285 APRHI - CARDIOVASCULAR DISEASE ALGORITHM ASK THESE TWO QUESTIONS EVERY ADULT YOU SEE: 1. Has a doctor ever told you that you have had a heart attack or that you have angina? 2. Has a doctor ever told you that you have had a stroke? 1. MULTIPURPOSE HEALTHCARE WORKER OR PHYSICIAN TO COMPLETE THIS SECTION IF THE ANSWER IS YES TO EITHER QUESTION COMPLETE THIS FORM PATIENT DETAILS 1 Full name 3 Sex (circle one) Female Male 2 Age 4 Identification number Years MULTIPURPOSE HEALTHCARE WORKER TO COMPLETE THIS SECTION (leave this section blank if physician sees this patient first) 5. Date of Assessment (Health worker) / / (dd/mm/yyyy) DISEASE HISTORY CURRENT TREATMENT RECOMMENDED TREATMENT* 6 Yes No 7 Yes No 8 Yes No If NO to Recommended record reason why Heart attack or angina Y N Aspirin Beta-blocker ACE inhibitor Statin Check for risk factors Make lifestyle recommendations Y Y Y Y Y Y N N N N N N Aspirin Beta-blocker ACE inhibitor Statin Check for risk factors Make lifestyle recommendations Y Y Y Y Y Y N N N N N N Stroke Y N Aspirin Diuretic ACE inhibitor Statin Check for risk factors Make lifestyle recommendations Y Y Y Y Y Y N N N N N N Aspirin Diuretic ACE inhibitor Statin Check for risk factors Make lifestyle recommendations Y Y Y Y Y Y N N N N N N APPOINTMENT FOR REVIEW BY PHYSICIAN Page 285 of 291

286 12 Date (dd/mm/yyyy) / / 13 Multipurpose health worker name *All of these treatments should usually be recommended for every patient. It is of note that patients with a history of heart disease or stroke will get huge benefit from the recommended blood pressure lowering and cholesterol lowering treatments even if their blood pressure and cholesterol levels are normal. 3. PHYSICIAN TO COMPLETE THIS SECTION 14. Date of Assessment (Physician) / / (dd/mm/yyyy) DISEASE HISTORY CURRENT TREATMENT RECOMMENDED TREATMENT* 15 Yes No 16 Yes No 17 Yes No Heart Aspirin Y N Aspirin Y N attack Y N Beta-blocker Y N Beta-blocker Y N or ACE inhibitor Y N ACE inhibitor Y N angina Statin Y N Statin Y N Check for risk factors Y N Check for risk factors Y N Make lifestyle recommendations Y N Make lifestyle recommendations Y N Stroke Y N Aspirin Diuretic ACE inhibitor Statin Check for risk factors Make lifestyle recommendations Y Y Y Y Y Y N N N N N N Aspirin Diuretic ACE inhibitor Statin Check for risk factors Make lifestyle recommendations Y Y Y Y Y Y N N N N N N If NO to Recommended record reason why Notes: APPOINTMENT FOR FOLLOW-UP BY PHYSICIAN 21 Date (dd/mm/yyyy) / / 22 Physician Name Page 286 of 291

287 *All of these treatments should usually be recommended for every patient. It is of note that patients with a history of heart disease or stroke will get huge benefit from the recommended blood pressure lowering and cholesterol lowering treatments even if their blood pressure and cholesterol levels are normal. If the treatments are not prescribed because they are too expensive, not available or for some other reason please write this down in the space provided. Timetable of health promotion events for intervention villages June July August September October November December January February March April May Rajupalem RPoster 1 ST 2 Mim 3 Rposter1 Rally Rposter2 CP Rposter3 Rposter4 Eethakota RPoster ST Mim Rposter1 Rally Rposter2 CP Rposter3 Rposter4 Mosalapalli RPoster ST Mim Rposter1 Rally Rposter2 CP Rposter3 Rposter4 N Kothapalli RPoster ST Mim Rposter1 Rally Rposter2 CP Rposter3 Rposter4 Lolla RPoster ST Mim Rposter1 Rally Rposter2 CP Rposter3 Rposter4 Allavaram RPoster ST Mim Rposter1 Rally Rposter2 CP Rposter3 Rposter4 Pulidindi RPoster ST Mim Rposter1 Rally Rposter2 CP Rposter3 Rposter4 Vedireswaram RPoster ST Mim Rposter1 Rally Rposter2 CP Rposter3 Rposter4 Chinchinada Rposter ST Mim Rally Rposter1 CP Rposter2 Rposter4 Rposter3 Kopalle Rposter ST Mim Rally Rposter1 CP Rposter2 Rposter4 Rposter3 Poduru Rposter ST Mim Rally Rposter1 CP Rposter2 Rposter4 Rposter3 Jallikakinada Rposter ST Mim Rally Rposter1 CP Rposter2 Rposter4 Rposter3 Nelapogula Rposter ST Mim Rally Rposter1 CP Rposter2 Rposter4 Rposter3 Kolamuru Rposter ST Mim Rally Rposter1 CP Rposter2 Rposter4 Rposter3 Gummuluru RPoster ST Mim Rally Rposter1 CP Rposter2 Rposter4 Rposter3 Goteru Rposter Rally 4 ST Mim CP 5 Rposter1 Rposter2 Rposter4 Rposter3 Kasipadu Rposter Rally ST Mim CP Rposter1 Rposter2 Rposter4 Rposter3 Cherukuwada Rposter Rally ST Mim CP Rposter1 Rposter2 Rposter4 Rposter3 Chinthaparru Rposter Rally ST Mim CP Rposter1 Rposter2 Rposter4 Rposter3 Juvvalapalem Rposter Rally ST Mim CP Rposter1 Rposter2 Rposter4 Rposter3 Cherukumilli Rposter Rally ST Mim CP Rposter1 Rposter2 Rposter4 Rposter3 Mogallu Rposter Rally ST Mim CP Rposter1 Rposter2 Rposter4 Rposter3 1 RAPCAPS Posters - Posters are developed for the use in rural India to educate the general population about different key health messages. 2 Street Theatre Artists will use this media to raise awareness and communicate health related messages across all strata of the population including those who are illiterate. 3 Mimicry Play Is a form of media used in villages for entertainment and will be used for raising awareness and important messages of cardiovascular disease prevention. 4 Rally School children will participate in the rally event promoting RAPCAPS health promotion key messages. 5 Community Presentation Aims to raise general awareness of healthy life style modification and the importance of the prevention of cardiovascular diseases. Page 287 of 291

288 Andhra Pradesh Rural Health Initiative Participant Consent Form Study identification number: I I I I I I I Sex (F/M): I I Rural Andhra Pradesh Cardiovascular Prevention Study (RAPCAPS) Please give one copy to the participant and keep one copy for the Investigator Participant: Mr/Mrs/Miss/Ms (first and last name). Address (House number, Street, Village) Unique ID By signing this form, I give my free and informed consent to being interviewed for this study and to having some basic physical measurements made. I also consent to giving a blood sample for laboratory testing for the purposes specified in the Patient Information Sheet I have been given full information about the study and I understand its nature, purpose, and duration as well as the procedures involved including any known or expected inconvenience. I have had a chance to ask questions about the survey and the tests and all of my questions have been answered to my satisfaction. My medical data are strictly confidential and I authorize only persons involved in the research and my health care providers to look at them. I understand that I am free to withdraw from the study at any given time. I have been given a copy of this consent form to keep. By signing this form I have not given up my legal rights. I understand that I am entirely free to decide whether or not to take part in this study, and if I choose not to take part, this will not affect the health care I am currently receiving. I am also free to discontinue my involvement with the study at any time. Printed name of participant: Signature of participant: Date: Printed name of person obtaining consent: Signature of interviewer: Date: Page 288 of 291

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