|Year : 2015 | Volume
| Issue : 4 | Page : 131-136
A community-based cross-sectional study of cardiovascular risk in a rural community of Puducherry
Saurabh R Shrivastava1, Arun G Ghorpade2, Prateek S Shrivastava1
1 Department of Community Medicine, Shri Sathya Sai Medical College and Research Institute, Kancheepuram, Tamil Nadu, India
2 Department of Community Medicine, Sri Manakula Vinayagar Medical College and Hospital, Pondicherry, India
|Date of Web Publication||18-Dec-2015|
Saurabh R Shrivastava
3rd Floor, Department of Community Medicine, Shri Sathya Sai Medical College and Research Institute, Ammapettai Village, Thiruporur - Guduvancherry Main Road, Sembakkam Post, Kancheepuram - 603 108, Tamil Nadu
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: The World Health Organization (WHO) / International Society of Hypertension (ISH) risk prediction chart can predict the risk of cardiovascular events in any population.
Aim: To assess the prevalence of cardiovascular risk factors and to estimate the cardiovascular risk using the WHO/ISH risk charts.
Materials and Methods: A cross-sectional study was done from November 2011 to January 2012 in a rural area of Puducherry. Method of sampling was a single stage cluster random sampling, and subjects were enrolled depending on their suitability with the inclusion and exclusion criteria. The data collection tool was a piloted and semi-structured questionnaire, while WHO/ISH cardiovascular risk prediction charts for the South-East Asian region was used to predict the cardiovascular risk. Institutional Ethics committee permission was obtained before the start of the study. Statistical analysis was done using SPSS version 16 and appropriate statistical tests were applied.
Results: The mean age in years was 54.2 (±11.1) years with 46.7% of the participants being male. On application of the WHO/ISH risk prediction charts, almost 17% of the study subjects had moderate or high risk for a cardiovascular event. Additionally, high salt diet, alcohol use and low HDL levels, were identified as the major CVD risk factors.
Conclusion: To conclude, stratification of people on the basis of risk prediction chart is a major step to have a clear idea about the magnitude of the problem. The findings of the current study revealed that there is a high burden of CVD risk in the rural Puducherry.
Keywords: Cardiovascular disease, non-communicable disease, Puducherry
|How to cite this article:|
Shrivastava SR, Ghorpade AG, Shrivastava PS. A community-based cross-sectional study of cardiovascular risk in a rural community of Puducherry. Heart Views 2015;16:131-6
|How to cite this URL:|
Shrivastava SR, Ghorpade AG, Shrivastava PS. A community-based cross-sectional study of cardiovascular risk in a rural community of Puducherry. Heart Views [serial online] 2015 [cited 2018 May 24];16:131-6. Available from: http://www.heartviews.org/text.asp?2015/16/4/131/172195
| Introduction|| |
Across many decades, communicable diseases have accounted for a significant number of deaths, compromising public health sector, and even loss of money and other resources.  In contrast to the previous trends, in the last few decades, gradually non-communicable diseases have reached epidemic proportions. ,,
Cardiovascular disease includes a wide spectrum of disorders, namely coronary heart disease, apoplexy, hypertension, peripheral vascular disease, rheumatic heart disease, and congestive cardiac failure.  Current global trends have revealed that irrespective of the decrease in the incidence of coronary heart disease (CHD) in many developed nations, the scenario in developing nations poses a serious challenge as they account for more than 60% of the global burden. 
The development of CVD is an extraordinarily complex process which can result because of multiple parameters like age-gender; ,, poor educational status;  adoption of harmful lifestyles; , socioeconomic status;  poor awareness among the general population about risk factors; , poor treatment compliance;  tobacco use;  deranged lipid profiles with high cholesterol level;  presence of chronic diseases like hypertension/diabetes/metabolic syndrome; ,, and dietary habits. , These risk factors have been targeted in separate high risk groups and in community settings and encouraging results have been obtained. ,
As multiple factors can eventually precipitate cardiovascular disease, it is of no use to target only one risk factor. ,,,,,,,,,, Under ideal circumstances, the foremost approach will be to adhere to a risk chart which combines large number of risk factors, and is applied in various settings. ,,, The WHO/ISH cardiovascular risk prediction charts can be applied in different regions of the globe, is cost-effective, and can predict the occurrence of a fatal/non-fatal event in the next ten years. , These charts can even be used as an educational tool to motivate patients to adopt healthy lifestyle practices.  Thus, WHO/ISH cardiovascular risk prediction charts was used in the present study as it neutralizes the limitations of other similar charts. ,
The present community based study has been conducted with objectives to ascertain the prevalence of cardiovascular risk factors and to establish the cardiovascular risk among the population of a rural area in Puducherry using the WHO/ISH risk prediction chart.
| Materials And Methods|| |
The current study was conducted in Puducherry located on the Coromandal coast. A cross-sectional descriptive study was conducted on from November 2011 to January 2012. It has a population of 1.25 million with an adult sex ratio of 1038 females to 1000 males, and a literacy level of 92% and 81% for men and women respectively. 
The study was carried out in two of the four villages under the Rural Health Centre, namely Ramanathapuram and Pillaiyarkuppam. Open Epi Version 2.3.10 software was used to estimate the sample size.  The sampling frame comprised of individuals aged above 40 years and single stage cluster random sampling was employed.
Inclusion and cardiovascular disease exclusion criteria
All individuals aged >40 years were invited to participate in the study. However, those individuals who cannot be contacted despite three home visits or who were not willing to be a part of the study were excluded from the study. Also, individuals with a positive history of an atherosclerotic cardiovascular disease were excluded. Hence, total 570 subjects were finally included in the study.
Each of the study subjects was interviewed using the piloted semi-structured questionnaire, after written informed consent was obtained from them. In addition, the participants were subjected to recording of blood pressure; anthropometry (viz. height, weight & waist circumference); and laboratory tests (viz. lipid profile, fasting and postprandial blood glucose) using standardized methods and tools.
Education was classified using International Standard Classification of Education as (a) No formal schooling and (b) attending school.  Vocational status was categorized as employed and unemployed.  For stratification of study subjects on the basis of their socioeconomic status, Modified BG Prasad classification was employed.  International Physical Activity Questionnaire-15 (short version) was used to classify subjects based on their level of physical activity. 
Smoking refers to the current use of any tobacco product (cigarettes, bidis, chewing tobacco or snuff) on a regular basis for at least the previous six months.  People who said they had never smoked during the last six months were classified as non-smokers. Alcohol use referred to the intake of any form of alcohol in the past 12 months and were further sub-categorized depending on the amount of alcohol consumed. , Subjects with fasting blood glucose of > 125 mg/dL and/or postprandial blood glucose of > 200 mg/dL were labeled as diabetic. , However, subjects who were on oral hypoglycaemics or insulin were also considered diabetic irrespective of their sugar estimates. Blood pressure was determined with digital sphygmomanometer with subjects sitting comfortably. Body mass index (BMI) was calculated and classified as per the WHO classification (<23 kg/m 2 as normal and ≥23 kg/m 2 as overweight and obese). 
Institutional Ethics committee permission was obtained before the start of the study. Written informed consent was obtained from all study participants before eliciting the desired information.
Data were analyzed using the SPSS version 16.0. Frequency and percentages were calculated for different study parameters. The Chi-square test was employed to assess the association between different variables at significance level (P value) of 0.05.
| Results|| |
The mean age in years of the study subjects was 54.2 (±11.1) with 46.7% of the participants being male. [Table 1] depicts the association between various study variables and the gender. A larger proportion of female subjects had never attended the school and were unemployed, in comparison with the male subjects. Although, abdominal obesity was more common among women than in men, however, no statistically significant association was observed. As already known, obesity is the result of a complex interplay of many factors. Though less proportion of samples were inactive and had high calorie intake, it is opined that the unhealthy dietary practices like white rice as a staple food, high salt diet, low intake of fruits and vegetables, frequent intake of fried food items and alcohol use in men were responsible for higher obesity prevalence in the study population.
Distribution of study subjects with regard to cardiovascular disease risk on the basis of various parameters has been depicted in [Table 2]. Around 86% of study subjects had a very low risk of any fatal/non-fatal outcome in the next 10 years on using WHO/ISH risk charts alone. However, prevalence of low, moderate and high CVD risk in the men was 82.7%, 12.8% and 4.5%, as compared to 88.8%, 5.9% and 5.3% in women respectively.
|Table 2: Risk of CVD with different inclusion criteria for the risk factors |
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[Table 3] highlights the details about the prevalence of non-communicable disease risk factors among the study population. Most of the study subjects had a low calorie intake, nevertheless, salt intake was definitely on the higher side. Further, around 56.3% of participants had lower HDL level, while higher estimates were obtained pertaining to total cholesterol, LDL, and triglyceride levels. The prevalence of obesity, DM, hypertension in the South India has shown a definite rise in recent years. The apparent higher prevalence is also due to the inclusion of the population above the age of 40 years (because CVD risk charts are available for >40 year age only).
|Table 3: Prevalence of noncommunicable disease risk factors in the CVD risk groups |
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The simultaneous presence of risk factors like smoking, diabetes, hypertension and high total cholesterol, resulted in a higher risk of a fatal cardiovascular event. Those subjects who were categorized as having the highest risk, had also a high prevalence of hypertension, and alcohol usage. However, subjects with moderate extent of risk were either tobacco or alcohol users and even had low serum HDL levels.
| Discussion|| |
In accordance with the present study findings, results obtained from various studies has clearly stated that most of the non-communicable diseases result because of a combination of multiple risk factors. , In-fact, documented evidence is available which suggest that very often the health practitioners have failed to predict the extent of cardiovascular risk and hence even there is an immense need for periodic sensitization of the medical fraternity. ,
In the current study it was found that the level of education, employment status, and waist circumference of the study participants was significantly associated with the gender of the participants. However, studies performed in diverse settings have identified heterogeneous parameters (such as age, body mass index, hypertension, diabetes, addiction to tobacco) to be significantly associated. The probable explanation for the dissimilarity being differing socio-demographic profile, family history of CVD risk factors and dietary habits. ,,,
The WHO/ISH cardiovascular risk prediction charts have been widely employed to estimate the risk in heterogeneous settings, owing to the multiple advantages associated with them. ,, In-fact, it has been advocated to extend counseling based on the risk identified so that any adverse consequence can be avoided. , Whenever, the cardiovascular risk is <10%, it means individuals are at low risk and require mainly lifestyle modifications.  However, those with risk of 10% to <20% or >20% are at moderate or high risk for fatal or non-fatal vascular events and periodic follow-up visits should be paid by the individuals. 
Multiple studies across the world have utilized the WHO/ISH cardiovascular risk prediction charts to estimate the risk in heterogeneous settings. ,, In contrast, findings of a study reflected that the WHO/ISH charts were incorrect to discriminate the risk in Malaysian population.  The WHO has recommended that in low-resource settings, measures like individual counseling should be made available based on the extent of cardiovascular risk. ,
Our study showed that almost 4.9% participants were at high risk when only charts were used. However, a variable risk percentage ranging from 1.1% in China to 21.5% in Malaysia has also been observed. ,, Realizing the scope and their utility in varied settings, these risk prediction charts are acknowledged as one of the predominant tools to achieve the target 8 (viz. at least 50% of eligible people receive drug therapy and counselling (including glycaemic control) to prevent heart attacks and strokes), under the NCD action plan. 
In the present study, the prevalence of different risk factors was estimated in mild-moderate (high salt intake, tobacco chewing, alcohol use) and high risk groups (low HDL, alcohol use, high salt intake, hypertension). Findings from an epidemiological study targeting sedentary workers showed deranged lipid profile and presence of hypertension were the most significant predictor of coronary heart disease.  This is probably because of the generalized accumulation of the fat, which generally results because of the sedentary lifestyle pattern. 
In a cohort study higher socioeconomic status was considered to be associated with a more unfavorable clinical outcome.  Similar results were obtained from a study done in the northern part of India.  In another study, BMI was found to be a significant predictor of occurrence of cardiovascular disease among families.  Furthermore, it was observed that the presence of pre-hypertension and hypertension is significantly associated with the occurrence of other cardiovascular risk factors. 
Our study findings clearly indicate that the existing WHO/ISH risk prediction charts does not estimate the actual risk, as many of the risk factors like abdominal obesity, positive family history of CVDs, tobacco chewing, high salt intake, are not yet incorporated. Thus, it is of extreme priority to develop a risk prediction chart which should assess all the potential risk factors.
The present study had some limitations. Cross sectional assessment of blood pressure may have overestimated the subjects with blood pressure persistently higher than 160/100 mm Hg. Also, because of the small sample size, findings of the study cannot be generalized.
| Conclusion|| |
To conclude, stratification of people on the basis of risk prediction chart is a major step to have a clear idea about the magnitude of the problem. The findings of the current study revealed that there is a high burden of CVD risk in the rural Puducherry.
| References|| |
Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CJ. Global and regional burden of disease and risk factors, 2001: Systematic analysis of population health data. Lancet 2006;367:1747-57.
Abegunde DO, Mathers CD, Adam T, Ortegon M, Strong K. The burden and costs of chronic diseases in low-income and middle-income countries. Lancet 2007;370:1929-38.
Mathers CD, Boerma T, Ma Fat D. Global and regional causes of death. Br Med Bull 2009;92:7-32.
Chandola T, Plewis I, Morris JM, Mishra G, Blane D. Is adult education associated with reduced coronary heart disease risk? Int J Epidemiol 2011;40:1499-509.
Saidi O, Ben Mansour N, O′Flaherty M, Capewell S, Critchley JA, Ben Romdhane H. Analyzing recent coronary heart disease mortality trends in Tunisia between 1997 and 2009. PLoS One 2013;8:e63202.
Kar SS, Thakur JS, Virdi NK, Jain S, Kumar R. Risk factors for cardiovascular diseases: Is the social gradient reversing in northern India? Natl Med J India 2010;23:206-9.
Samuel P, Antonisamy B, Raghupathy P, Richard J, Fall CH. Socio-economic status and cardiovascular risk factors in rural and urban areas of Vellore, Tamilnadu, South India. Int J Epidemiol 2012;41:1315-27.
Kamble PH, Rode MV, Phatak MS, Tayade P. Is smokeless tobacco use a risk factor for coronary artery disease? A comparative study of smokers and smokeless tobacco users. Indian J Basic Appl Med Res 2011;1:22-30.
Cooney MT, Dudina AL, Graham IM. Value and limitations of existing scores for the assessment of cardiovascular risk: A review for clinicians. J Am Coll Cardiol 2009;54:1209-27.
Ndindjock R, Gedeon J, Mendis S, Paccaud F, Bovet P. Potential impact of single-risk-factor versus total risk management for the prevention of cardiovascular events in Seychelles. Bull World Health Organ 2011;89:286-95.
Kuklina EV. Assessing and managing risk for cardiovascular disease: A worldwide perspective. North Am J Med Sci 2010;3:94-103.
Mendis S, Lindholm LH, Mancia G, Whitworth J, Alderman M, Lim S, et al.
World Health Organization (WHO) and International Society of Hypertension (ISH) risk prediction charts: Assessment of cardiovascular risk for prevention and control of cardiovascular disease in low and middle-income countries. J Hypertens 2007;25:1578-82.
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.
Zhang XF, Attia J, D′Este C, Yu XH, Wu XG. A risk score predicted coronary heart disease and stroke in a Chinese cohort. J Clin Epidemiol 2005;58:951-8.
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.
Al-Lawati JA, Barakat MN, Al-Lawati NA, Al-Maskari MY, Elsayed MK, Mikhailidis DP, et al
. Cardiovascular risk assessment in diabetes mellitus: Comparison of the general Framingham risk profile versus the World Health Organization/International Society of Hypertension risk prediction charts in Arabs - Clinical implications. Angiology 2013;64:336-42.
Sawhney JP, Sawhney A. The total risk approach to prevention of coronary heart disease. J Prev Cardiol 2011;1:16-21.
Majgi SM, Soudarssanane BM, Roy G, Das AK. Risk factors of diabetes mellitus in rural Puducherry. Online J Health Allied Sci 2012;11:4.
Agarwal A. Social classification: The need to update in the present scenario. Indian J Community Med 2008;33:50-1.
International Physical Activity Questionnaire. IPAQ Scoring Protocol; 2005. Available from: https://www.sites.google.com/site/theipaq/scoring-protocol. [Last accessed on 2014 Dec 19].
World Health Organization. STEPwise approach to surveillance (STEPS) field manual appendices. Geneva: WHO Press; 2003. Available from: http://www.who.int/chp/steps/en/
. [Last accessed on 2014 Dec 19].
Rhem J, Room R, Monteiro M, Gmel G, Grahm K, Rhen N, et al
. Alochol use. In: Ezzati M, Lopez A, Rodgers A, Murray C, editors. Comapritive Quantification of Health Risk: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors. Geneva: WHO Press; 2004. p. 959-1108.
Indian Council of Medical Research. Guidelines for Management of Type 2 Diabetes. New Delhi: Indian Council of Medical Research; 2005.
Misra A, Chowbey P, Makkar BM, Vikram NK, Wasir JS, Chadha D, et al.
Consensus statement for diagnosis of obesity, abdominal obesity and the metabolic syndrome for Asian Indians and recommendations for physical activity, medical and surgical management. J Assoc Physicians India 2009;57:163-70.
Cooney MT, Dudina A, D′Agostino R, Graham IM. Cardiovascular risk-estimation systems in primary prevention: Do they differ? Do they make a difference? Can we see the future? Circulation 2010;122:300-10.
Wassenberg MW, Willemsen JM, Gaillard CA, Braam B. Hypertension management in primary care: Standard care and attitude towards a disease management model. Neth J Med 2004;62:375-82.
Erhardt L, Moller R, Puig JG. Comprehensive cardiovascular risk management - What does it mean in practice? Vasc Health Risk Manag 2007;3:587-603.
Ferket BS, Colkesen EB, Visser JJ, Spronk S, Kraaijenhagen RA, Steyerberg EW, et al.
Systematic review of guidelines on cardiovascular risk assessment: Which recommendations should clinicians follow for a cardiovascular health check? Arch Intern Med 2010;170:27-40.
World Health Organization. Prevention of Cardiovascular Diseases - Pocket Guidelines for Assessment and Management of Cardiovascular Risk. Geneva: WHO Press; 2007.
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.
Koju R, Gurung R, Pant P, Humagain S, Yogol CM, Koju A, et al.
Prediction of cardiovascular disease in suburban population of 3 municipalities in Nepal. Nepal Heart J 2011;8:3-7.
Shivaramakrishna HR, Wantamutte AS, Sangolli HN, Mallapur MD. Risk factors of coronary heart disease among bank employees of Belgaum city - Cross-sectional study. Al Ameen J Med Sci 2010;3:152-9.
Singh RB, Singh V, Kulshrestha SK, Singh S, Gupta P, Kumar R, et al.
Social class and all-cause mortality in an urban population of North India. Acta Cardiol 2005;60:611-7.
Mora S, Yanek LR, Moy TF, Fallin MD, Becker LC, Becker DM. Interaction of body mass index and framingham risk score in predicting incident coronary disease in families. Circulation 2005;111:1871-6.
Yadav S, Boddula R, Genitta G, Bhatia V, Bansal B, Kongara S, et al.
Prevalence and risk factors of pre-hypertension and hypertension in an affluent north Indian population. Indian J Med Res 2008;128:712-20.
[Table 1], [Table 2], [Table 3]