|Year : 2015 | Volume
| Issue : 2 | Page : 43-48
Ten Years Risk Prediction of a Major Cardiovascular Event in a Rural Block in Tamil Nadu
Logaraj Muthunarayanan1, John Kamala Russel1, Shailendra Kumar Hegde2, Balaji Ramraj2
1 Department of Community Medicine, SRM Medical College Hospital and Research Centre, Kancheepuram, Tamil Nadu, India
2 Department of Community Medicine, SRM Medical College, SRM University, Kancheepuram, Tamil Nadu, India
|Date of Web Publication||16-Jun-2015|
Dr. Logaraj Muthunarayanan
Department of Community Medicine, SRM Medical College Hospital and Research Centre, Kattankulathur, Kancheepuram - 603 203, Tamil Nadu
Source of Support: None, Conflict of Interest: None
Background: India has a high burden of cardiovascular diseases (CVDs). High-risk interventions can be initiated only when individuals at high-risk have been identified. Objectives: The objective was to estimate the prevalence and the sociodemographic pattern of cardiovascular risk factors and to predict the 10 years risk of fatal and nonfatal major cardiovascular events in a rural population in Tamil Nadu. Materials and Methods: A cross-sectional study was conducted among 30 villages of a rural block in Tamil Nadu from March 2012 to February 2013 in the age group of 40-79 years attending our fixed mobile clinics using structured interview schedule and subsequently, the World Health Organization/International Society of Hypertension (WHO/ISH) risk charts were used to predict the 10 years absolute risk of fatal or nonfatal cardiovascular event. Results: A total of 482 individuals were studied of which 68.3% were women and 31.7% were men. Prevalence of overweight, diabetes, and systolic hypertension was found to be 60%, 22.8%, and 34.6%, respectively. A majority (79.9%) of the study population had 10 years cardiovascular risk of <10% while only 2.5% had a risk of more than 40%. As the age advances, the proportion of participants with high-risk also increased and this trend was statistically significant (P = 0.001). Conclusion: Less than 10% of the population had a high-risk of CVD based on WHO/ISH risk score. These charts help identify the high-risk groups in the population in resource-scarce setting and thus an appropriate action can be taken.
Keywords: Cardiovascular disease, risk score, World Health Organization/International Society of Hypertension risk prediction charts
|How to cite this article:|
Muthunarayanan L, Russel JK, Hegde SK, Ramraj B. Ten Years Risk Prediction of a Major Cardiovascular Event in a Rural Block in Tamil Nadu. Heart India 2015;3:43-8
|How to cite this URL:|
Muthunarayanan L, Russel JK, Hegde SK, Ramraj B. Ten Years Risk Prediction of a Major Cardiovascular Event in a Rural Block in Tamil Nadu. Heart India [serial online] 2015 [cited 2020 Jul 11];3:43-8. Available from: http://www.heartindia.net/text.asp?2015/3/2/43/158878
| Introduction|| |
Cardiovascular diseases (CVDs) are the number one cause of death globally: More people die annually from CVDs than from any other cause. Low and middle-income countries (LMIC) are disproportionally affected; over 80% of CVD deaths take place in LMIC and occur almost equally in men and women.  India alone is burdened with approximately 25% of CVD-related deaths and would serve as a home to more than 50% of the patients with heart ailments worldwide within next 15 years.  Projections based on the Global Burden of Disease study estimate that by the year 2020, the burden of CVD in India will surpass that in any other region of the world. 
Cardio-vascular deaths account for 34% of all deaths in women and 28% of men. India has not only a high burden of CVDs, but also the effects of these diseases on the productive workforce aged 35-65 years can have devastating consequences for an individual, the family, and society  high-risk strategy of primary prevention of CVDs can be initiated only by identifying once individuals at high-risk have been identified. A number of methods has been used to identify high-risk individuals. ,,,,,,,,,
This study was carried out to estimate the prevalence and the sociodemographic pattern of cardiovascular risk factors and to predict the 10 years risk of fatal and nonfatal major cardiovascular events in a rural population in Tamil Nadu.
| Materials and Methods|| |
A cross-sectional study was carried out through our fixed mobile clinic (visit on fixed day of every week to a village) among 30 villages of Kattankulathur block in Kancheepuram district in Tamil Nadu from March 2012 to February 2013. Individuals in the age group of 40-79 years who attended our fixed mobile clinic were interviewed in person with a structured interview schedule to elicit information on select sociodemographic variables, tobacco and alcohol use, dietary intake, physical activity, and treatment history for diabetes and hypertension. The physical examination of all the participants included measurements of height, weight, blood pressure, resting pulse rate, and collection of blood samples for plasma glucose. All individuals included in the study provided written informed consent. Those individuals who had a nonfatal cardiovascular event and those who did not consent to participate in the study were excluded from the study. The study was approved by the Institutional Ethics Committee.
Color-coded, easy-to-use risk charts developed by the World Health Organization (WHO)/International Society of Hypertension (ISH) for the South East Asia sub-region-D were used to predict the 10 years absolute risk of a major cardiovascular event among adults aged 40-79 years. These charts used five risk factors : g0 ender, age, systolic blood pressure (SBP), smoking status, and diabetes. These charts estimate risk without measuring blood cholesterol and are particularly suitable for low-resource settings.  Standard methods were used to measure weights and heights.  Body mass index (BMI) was calculated, and standard cut-offs for Asian adults were used to define overweight and obesity,  according to which, overweight is defined as BMI of more than 23. Overweight is further classified as at-risk of obesity (BMI = 23-24.9), obesity grade 1 (BMI = 25-29.9), and obesity grade 2 (BMI ≥30).
Postprandial blood sugar was measured (2 h after a morning breakfast) using glucometer (Accutrend Plus). A person was considered to have diabetes if the postprandial plasma glucose concentration was found to be >11.0 mmol/l (200 mg/l) or if he or she was taking insulin or oral hypoglycemic drugs. Blood pressure was recorded in the sitting position in the left arm to the nearest 1 mm hg using an electronic OMRON blood pressure measuring device (Omron Corporation, Tokyo, Japan). Two readings were taken : f0 irst one before starting the interview and the second one at the end of the interview and the mean of the two readings was used for analysis. Hypertension was diagnosed using the criteria proposed by the Seventh Joint National Committee.  All current smokers and those who had quit smoking <1 year before the assessment were considered smokers. Similarly, all current alcoholics and those who had quit alcohol <1 year before the assessment were considered alcoholics.
Data entry and analysis was done using SPSS 16 version. Descriptive data were presented as measures of central tendency and dispersion. Chi-square test was used for analyses of categorical variables.
| Results|| |
A total of 482 individuals were studied of which 68.3% were women and 31.7% were men with a mean age of 52.76 (±9.34) years. Most individuals were in the age group of 40-49 years (41.1%), were illiterates (38.1%), and were homemakers (44.4%). When monthly family income was taken, 41.9% of the participants had a monthly family income below 5000 rupees [Table 1].
[Table 2] depicts the prevalence of risk factors in the study population. Prevalence of diabetes mellitus was found to be 22.8% (95% CI: 19.05-26.55%). Among men, the prevalence was 28.8% (95% CI: 21.62-35.98%) while among women it was 20.1% (95% CI: 15.77-24.43%) and this difference was found to be statistically significant (χ2 = 4.49; degrees of freedom = 1; P = 0.034). Mean postprandial blood sugar level was found to be 129.78 mg/dl (±66.35).
Mean SBP was found to be 133.23 mm hg (±20.22). The overall prevalence of systolic hypertension was 34.6% (95% CI: 30.35-38.85%); among men the prevalence was found to be 36% (95% CI: 28.39-43.61%) while among women the prevalence was 34.1% (95% CI: 28.88-39.12%). As age advanced, the proportion of the population with systolic hypertension increased and this trend was found to be statistically significant (χ2 = 17.3; degrees of freedom = 9; P = 0.044) [Table 3].
Mean BMI was found to be 24.6 kg/m 2 (±5.08). The overall prevalence of overweight was found to be 60.0% (95% CI: 55.63-64.37%). Among men the prevalence was 55.5% (95% CI: 47.63-63.37%) while among women the prevalence was 62.0% (95% CI: 56.76-67.24%). The overall prevalence of obesity was found to be 44.4% (95% CI: 39.96-48.84%). Among men the prevalence was 40.5% (95 CI: 32.72-48.28%) while among women the prevalence was 46.2% (95% CI: 40.81-51.59%). The difference in the prevalence of overweight among men and women was not statistically significant.
In the study population, 6.8% were smokers (95% CI: 4.55-9.05%). Among smokers, all were men and two-thirds were under the age of 50 years. Furthermore, 7.3% of the study population was alcoholics and all of them were men.
[Table 4] and [Table 5] depict the relationship between risk factors (age, gender, BMI, SBP, diabetes, and smoking) and 10 years risk of a cardiovascular event of the study population. A majority of the study population had a risk of <10% (79.9%). A small proportion of the study population (2.5%) had a risk of over 40%. As age advanced the proportion of participants with the high-risk increased, and this trend was statistically significant (χ 2 = 167; degrees of freedom = 12; P = 0.001). A higher proportion of women (83.3%) had a risk of <10% as compared to men (72.5%) but this was not statistically significant (χ 2 = 7.84; degrees of freedom = 4; P = 0.098). Among diabetics, 20% had 10 years cardiovascular risk of more than 20% while among nondiabetics it was 6% and this difference was found to be statistically significant (χ 2 = 26.9; degrees of freedom = 4; P = 0.001)
Among those with a SBP of more than 140 mm hg, the 10 years cardiovascular risk of more than 20% was 25.1% while among those with a SBP of <140 mm hg it was 0.6%. The proportion of population with more than 20% risk increases as the SBP of the population increases and it was found to be statistically significant (χ 2 = 441; degrees of freedom = 12; P = 0.001). Among smokers, 21.2% had 10 years cardiovascular risk of more than 20% while among nonsmokers it was 4.6% and this difference was found to be statistically significant (χ 2 = 9.66; degrees of freedom = 4; P = 0.046).
Almost one-fifth (19.5%) of those with overweight and obesity had more than 20% risk of cardiovascular event over the next 10 years.
| Discussion|| |
The present study was conducted among 482 adults in the age groups of 40-79 years. The prevalence of diabetes mellitus in the present study was found to be 22.8%. Patel and Singh  reported that the prevalence of diabetes mellitus among males was 15.47%. Chow et al.  reported that the prevalence of diabetes among rural adults over the age of 30 years was 13.2%. Muninarayana et al.  reported that the prevalence of diabetes mellitus among the rural population was found to be 10%.
The prevalence of systolic hypertension was found to be 34.6% in the present study. As age advanced the proportion of the population with SBP also increased and this was statistically significant. Subburam et al.  reported that the prevalence of hypertension among adults in the age group of 45-60 years rural Tamil Nadu was 33%. However, Gupta  reported that the prevalence of hypertension among rural adult Indians was 10-15%. Kinra et al.  and Panesar et al.  in their respective studies reported that the prevalence of hypertension ranged between 17% and 22%. The higher prevalence in our study can be attributed to the older age of our study population as compared to the other studies where the study population aged between 20 and 69 years.
In the present study, 60% of the study population was overweight (BMI >23). Mohan et al.  reported that the prevalence of overweight among the industrial population over the age of 20 years in Chennai was 60.2%. Anuradha et al.  reported a lower prevalence of overweight at 27.7% among urban slum dwelling women over the age of 20 years. Kinra et al.  reported that the prevalence of overweight among rural Indian men were 33.7% and women were 41.9%.
The prevalence of obesity (BMI >25) in the study population was found to be 44.4%; among women the prevalence of obesity were 42.6% while among men it were 40.5%. Kinra et al.  reported an obesity prevalence of 19% (18.8% among men and 27.7% among women) among the Indian population between 20 and 69 years. Higher prevalence in the present study was probably due to the inclusion of higher age groups of 40-79 years.
In the present study, 6.8% of the population smoked tobacco and all of them were men. Among smokers, two-third were under the age of 50 years. Jayakrishnan et al.  reported that the prevalence of smoking was 28% among selected rural community in Thiruvananthapuram among men aged between 18 and 60 years. Kaur et al.  reported a prevalence of smoking to be 37.6% among rural adults between 25 and 64 years in Tamil Nadu. Chockalingam et al.  reported that among individuals above the age of 15 years in Tamil Nadu, the prevalence of smoking was 23.7%.
The present study, showed that 9.2% of the population had 10 years cardiovascular risk of ≥20%. Otgontuya et al.  reported a slightly lesser proportion among Mongolians, Malaysians, and Cambodians at 6%, 2.3%, and 1.3%, respectively using the same charts. Otgontuya et al.  also reported that among 40-64-year-old adults in Seychelles, 5.1% had high total CVD risk. Mendis et al.  reported that 1.1% of the Chinese, 1.7% of the Iranians, 2.2% of the Srilankans, 2.8% of the Cubans, 5.0% of the Nigerians, 9.6% of the Georgians, and 10% of Pakistanis had 10 years cardiovascular risk of ≥20%. Kanjilal  using the Framingham model predicted that 5.3% of the Indian cohort was at high-risk.
In the present study, the proportion of population with 10 years CVD risk of 30% or more in men were 0, 7.5%, 10.9%, and 18.8% and in women it were 0, 5.8%, 4.8%, and 17.6% in the age groups of 40-49 years, 50-59 years, 60-69 years, and 70-79 years, respectively. The WHO in their study conducted in South East Asia Region including India reported that the proportion of population with 10 years CVD risk of 30% or more in men were 0.47%, 5.12%, 22.3%, and 31.39% and in women it were 0.22%, 3.31%, 22.23%, and 29.9% in the age groups of <50 years, 50-59 years, 60-69 years, and 70-79 years, respectively. 
In the present study, among the lower age groups of 40-49 years, 97.5% of the participants had 10 years cardiovascular risk of <10%, 1.5% of the participants had risk of 10-20% risk, and none had >20% risk. In the highest age group of 70-79 years, only 39.4% had 10 years risk <10%, 33.3% had risk of 10-20%, and 27.3% had risk of more than 20%. Similar findings have been reported by Bansal et al.  in their study among asymptomatic office executive in Delhi based on Framingham risk score that in lower age quartile 95.5% patients had 10 years cardiovascular risk of <10%, 4.1% patients had 10-20% risk, and none had >20% risk; but in the highest age quartile <½ of the patients had 10 years cardiovascular risk <10% and nearly half had 10-20% risk of cardiovascular risk.
Khanna et al.  based on the distribution of Framingham risk score among different age groups reported that in patients <45 years, none of the patients were categorized as high-risk. Only 13% and 15% patients could be categorized as having "high-risk" in age groups 45-55 years and 55-65 years, respectively, while in patients older than 65 years, 30% were classified as having high-risk.
| Conclusion|| |
About half the study population was obese, one-third had systolic hypertension and one-fifth were diabetics, but <10% of the population was at high-risk of CVD based on WHO/ISH risk score.
Estimating, monitoring, and prevention of multiple risk factors for CVDs will be cost effective over monitoring and prevention of individual risk factors. Even though WHO/ISH risk score may not help those already suffering from CVD and on treatment, it will help as a tool to assess and categorize the population. This in turn will help in giving more care and counseling for those at high-risk by means of regular follow-up (every 3-6 months), thus helping in the prevention of fatal and nonfatal CVDs at primary care level in resource-scarce settings.
| References|| |
Gupta R, Joshi P, Mohan V, Reddy KS, Yusuf S. Epidemiology and causation of coronary heart disease and stroke in India. Heart 2008;94:16-26.
Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CJ, editors. Global Burden of Disease and Risk Factors: Disease Control Priorities Project. Washington (DC): World Bank & Oxford University Press; 2006. Available from: http://www.files.dcp2.org/pdf/GBD/GBD.pdf
. [Last cited on 2014 Feb 09].
Yusuf S, Reddy S, Ounpuu S, Anand S. Global burden of cardiovascular diseases: part I: General considerations, the epidemiologic transition, risk factors, and impact of urbanization. Circulation 2001;104:2746-53.
Anderson KM, Odell PM, Wilson PW, Kannel WB. Cardiovascular disease risk profiles. Am Heart J 1991;121:293-8.
Asia Pacific Cohort Studies Collaboration, Barzi F, Patel A, Gu D, Sritara P, Lam TH, et al.
Cardiovascular risk prediction tools for populations in Asia. J Epidemiol Community Health 2007;61:115-21.
Balkau B, Hu G, Qiao Q, Tuomilehto J, Borch-Johnsen K, Pyörälä K, et al.
Prediction of the risk of cardiovascular mortality using a score that includes glucose as a risk factor. The DECODE Study. Diabetologia 2004;47:2118-28.
Bhopal R, Fischbacher C, Vartiainen E, Unwin N, White M, Alberti G. Predicted and observed cardiovascular disease in South Asians: Application of FINRISK, Framingham and SCORE models to Newcastle Heart Project data. J Public Health (Oxf) 2005;27:93-100.
Conroy RM, Pyörälä K, Fitzgerald AP, Sans S, Menotti A, De Backer G, et al
. Estimation of ten-year risk of fatal cardiovascular disease in Europe: The SCORE project. Eur Heart J 2003;24:987-1003.
D′Agostino RB Sr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al.
General cardiovascular risk profile for use in primary care: The Framingham Heart Study. Circulation 2008;117:743-53.
Greenland P, LaBree L, Azen SP, Doherty TM, Detrano RC. Coronary artery calcium score combined with Framingham score for risk prediction in asymptomatic individuals. JAMA 2004;291:210-5.
Hillier TA, Rousseau A, Lange C, Lépinay P, Cailleau M, Novak M, et al
. Practical way to assess metabolic syndrome using a continuous score obtained from principal components analysis. Diabetologia 2006;49:1528-35.
Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, May M, Brindle P. Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: Prospective open cohort study. BMJ 2007;335:136.
Kanjilal S, Rao VS, Mukherjee M, Natesha BK, Renuka KS, Sibi K, et al.
Application of cardiovascular disease risk prediction models and the relevance of novel biomarkers to risk stratification in Asian Indians. Vasc Health Risk Manag 2008;4:199-211.
World Health Organization. The Asia Pacific Perspective: Redefining Obesity and its Treatment. Regional Office for the Western Pacific, International Association for the study of Obesity & International Obesity Task Force; February, 2000. [Table 2].2; 2000. p. 18. Available from: http://www.wpro.who.int/nutrition/documents/docs/Redefiningobesity.pdf
. [Last cited on 2014 Feb 09].
United States Department of Health and Human Services. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. National Institutes of Health, National Heart, Lung and Blood Institute & National High Blood Pressure Education Program; August, 2004. Classification of Blood Pressure. [Table 3]; 2004. p. 12. Available from: http://www.nhlbi.nih.gov/guidelines/hypertension/jnc7full.pdf
. [Last cited on 2014 Feb 09].
Patel DN, Singh MP. Comparison of Anthropometric Indicator of General Obesity (BMI) to Anthropometric Indicators of Central Obesity (WC, WHR) in Relation to Diabetes Mellitus in Male Population. Natl J Community Med 2013;4:377-80.
Chow CK, Raju PK, Raju R, Reddy KS, Cardona M, Celermajer DS, et al.
The prevalence and management of diabetes in rural India. Diabetes Care 2006;29:1717-8.
Muninarayana C, Balachandra G, Hiremath SG, Iyengar K, Anil NS. Prevalence and awareness regarding diabetes mellitus in rural Tamaka, Kolar. Int J Diabetes Dev Ctries 2010;30:18-21.
Subburam R, Sankarapandian M, Gopinath DR, Selvarajan SK, Kabilan L. Prevalence of hypertension and correlates among adults of 45-60 years in a rural area of Tamil Nadu. Indian J Public Health 2009;53:37-40.
Gupta R. Trends in hypertension epidemiology in India. J Hum Hypertens 2004;18:73-8.
Kinra S, Bowen LJ, Lyngdoh T, Prabhakaran D, Reddy KS, Ramakrishnan L, et al.
Sociodemographic patterning of non-communicable disease risk factors in rural India: A cross sectional study. BMJ 2010;341:c4974.
Mohan V, Deepa M, Farooq S, Prabhakaran D, Reddy KS. Surveillance for risk factors of cardiovascular disease among an industrial population in southern India. Natl Med J India 2008;21:8-13.
Jayakrishnan R, Mathew A, Uutela A, Finne P. A community based smoking cessation intervention trial for rural Kerala, India. Asian Pac J Cancer Prev 2011;12:3191-5.
Kaur P, Rao SR, Radhakrishnan E, Ramachandran R, Venkatachalam R, Gupte MD. High prevalence of tobacco use, alcohol use and overweight in a rural population in Tamil Nadu, India. J Postgrad Med 2011;57:9-15.
Chockalingam K, Vedhachalam C, Rangasamy S, Sekar G, Adinarayanan S, Swaminathan S, et al.
Prevalence of tobacco use in urban, semi urban and rural areas in and around Chennai City, India. PLoS One 2013;8:e76005.
Otgontuya D, Oum S, Buckley BS, Bonita R. Assessment of total cardiovascular risk using WHO/ISH risk prediction charts in three low and middle income countries in Asia. BMC Public Health 2013;13:539.
Mendis S, Lindholm LH, Anderson SG, Alwan A, Koju R, Onwubere BJ, et al.
Total cardiovascular risk approach to improve efficiency of cardiovascular prevention in resource constrain settings. J Clin Epidemiol 2011;64:1451-62.
Bansal M, Shrivastava S, Mehrotra R, Agarwal V, Kasliwal RR. Low Framingham risk score despite high prevalence of metabolic syndrome in asymptomatic North-Indian population. J Assoc Physicians India 2009;57:17-22.
Khanna R, Kapoor A, Kumar S, Tewari S, Garg N, Goel PK. Metabolic syndrome & Framingham Risk Score: Observations from a coronary angiographic study in Indian patients. Indian J Med Res 2013;137:295-301.
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]