|Year : 2019 | Volume
| Issue : 3 | Page : 186-193
Clustering of cardiovascular disease risk factors – Syndemic approach: Is sit a time to shift toward integrated noncommunicable disease clinic?
Kalaiselvi Selvaraj1, Sitanshu Sekahr Kar2, Gomathi Ramaswamy3, KC Premarajan2, Ganesh Kumar Saya2, Vinodhkumar Kalidoss4
1 Assistant Professor, Department of Community Medicine, All India Institute of Medical Sciences, Nagpur, India
2 Additional Professor, Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Institute of Medical Research (JIPMER), New Delhi, India
3 Research Assistant, Centre for Community Medicine, All India Institute of Medical Sciences, New Delhi, India
4 Tutor, Department of Community Medicine, All India Institute of Medical Sciences, Mangalagiri, Andhra Pradesh, India
|Date of Web Publication||20-Sep-2019|
Sitanshu Sekahr Kar
Department of Preventive and Social Medicine, 4th Floor, Administrative Block, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry - 605 006
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: The concurrent occurrence of many noncommunicable disease (NCD) risk factors is common, and it can play a synergistic role in occurrence of NCDs. Objectives: This study aimed to identify the magnitude of clustering of NCD risk factors, patterns of risk factors emerged in clustering, and variations in clustering of risk factors based on socioeconomic factors. Methods: A cross-sectional survey was undertaken in an urban area of Puducherry among 2399 adults during 2014–2015. Sociodemographic and behavioral risk factors were assessed through a validated STEPS survey tool. Individuals with three or more risk factors were classified to have clustering of NCD risk factors. Socioeconomic positions in relation to clustering were identified through Chi-square analysis followed by multiple logistic regression where clustering at family and area was adjusted through multilevel modeling techniques. Results: Of the 2399 adults, 1741 (73%) had clustering of NCD risk factors. Inadequate consumption of fruits and vegetables, high salt intake, and high waist circumference are the three predominant risk factors across all subgroups. Adults belonging to Christian religion (adjusted odds ratio [adjOR]: 2.8, 95% confidence interval [CI]: 1.5–5.2), aged 35 years and over (adjOR: 2.0, 95% CI: 1.4–6.0), and illiterates (adjOR: 1.8, 95% CI: 1.1–5.5) are more likely to have clustered NCD risk factors compared to others. Conclusions: Clustering of NCD risk factors is highly prevalent in this region and mainly driven by dietary practices and obesity measures. There is an urgent need to reorient the health system toward integrated approach with mandated inclusion of nutritionist in NCD health service delivery.
Keywords: Cardiovascular diseases, chronic disease, cluster analysis, epidemiology, risk factors, STEPS, syndemic
|How to cite this article:|
Selvaraj K, Kar SS, Ramaswamy G, Premarajan K C, Saya GK, Kalidoss V. Clustering of cardiovascular disease risk factors – Syndemic approach: Is sit a time to shift toward integrated noncommunicable disease clinic?. Indian J Public Health 2019;63:186-93
|How to cite this URL:|
Selvaraj K, Kar SS, Ramaswamy G, Premarajan K C, Saya GK, Kalidoss V. Clustering of cardiovascular disease risk factors – Syndemic approach: Is sit a time to shift toward integrated noncommunicable disease clinic?. Indian J Public Health [serial online] 2019 [cited 2020 Dec 4];63:186-93. Available from: https://www.ijph.in/text.asp?2019/63/3/186/267209
| Introduction|| |
Noncommunicable diseases (NCDs) are estimated to cause 68% of global mortality, and major share of the mortality is contributed by low- and middle-income countries. NCDs contribute to 53% of total mortality in India, and there is a 26% probability of dying from any of four major NCDs between the ages of 30 and 70 years. It has been estimated that around 47% of the total out-of-pocket expenditure spent for managing NCDs. The recent high-level expert committee has set the goal of 25 × 25 reducing the premature death due to major NCDs by 25% by the year 2025. The future predicted models had also shown that the reduction of six NCD risk factors, namely tobacco use, alcohol use, high salt intake, obesity, high blood pressure, and glucose, can lead to achieve this target.
Several surveys including the state-specific surveys conducted by the Indian Council of Medical Research based on the WHO STEPS tool had reported the high prevalence on these risk factors in India.,,, Of the several identified risk factors related to cardiovascular diseases, tobacco and alcohol use, unhealthy diet, obesity, high blood pressure and blood sugar, and low physical activity are recognized as “best buys” strategy. Many of these modifiable risk factors do not present alone. Often, they present with co-occurrence of other risk factors., Although many literature available on the prevalence of individual risk factors, the concurrent influence of these risk factors on each other is not well known. Few countries such as Mexico, Bhutan, and Nepal had reported the clustering of NCDs if the individual had three or more proven noncommunicable risk factors during the assessment.,, In Indian context, studies which describe clustering of NCDs are rare.
Syndemic is being defined as the concurrent influence of more than one attributes ensuing some specific disease. If several risk factors are concurrent that could indicate the common underlying phenomena which predispose to the adoption of those risky behaviors. The models to predict syndemic are commonly explored in the area of HIV., The application of syndemic in NCDs is rare which could assess the need for reorientation of health-care delivery. Previous experience from Bangladesh has reported that the clustering of NCD risk factors can increase the risk to manifold more than the expected additive risk by acting synergistically. In countries, where more than half of the mortality is caused by NCDs which are again entangled by many risk factors, addressing specific individual risk factors may not help to achieve the target on time. Efficient allocation of resources needs the knowledge on whether the magnitude of clustering is substantial amounting to reorient the health system toward multiple risk factor approach. To facilitate target-oriented approach in patient-centric care needs patterns of different NCD risk factor clustering and socioeconomic subgroups of people vulnerable for clustering of NCD risk factors.
In this circumstance, this study was aimed to identify the prevalence of clustering of NCD risk factors, patterns of risk factors emerged in clustering, and variations in clustering of NCD risk factors based on socioeconomic positions.
| Materials and Methods|| |
Study design and setting
This study was a community-based cross-sectional survey conducted among adults 18 years and over living in urban field practice area of a tertiary care teaching institute in Puducherry, South India. Puducherry is one of the seven union territories in India. This union territory caters to the population of 1.24 million, and 68% of population lives in urban areas. Access to health care in this region is good, and it is being provided mainly through eight tertiary care academic medical institutes, government hospitals, and private providers. The study area is functioning under one of the tertiary regional referral care academic institutes in this region. The study area (urban field practice area) caters to the population of around 9600 spread over three census enumeration blocks and 2100 households. Area I has population predominantly belonging to below poverty line, and area II comprises fisherman communities and socioeconomically better adults. Area III mainly has people involved in semiskilled and clerical type of work and majority of them living in house apartments availed under government housing schemes.
The trained investigators made house-to-house visit during January 2014–January 2015 in three areas. During the survey, all available adults who are 18 years and over were considered to be eligible. Among the eligible adults data on sociodemographic factors (age, sex, education, occupation, and income), behavioral factors such as tobacco and alcohol use, physical activity, consumption of unhealthy diet, and family history of NCDs (hypertension, diabetes, stroke, and angina) were collected using the standard WHO STEPS survey tool. Anthropometric measures such as height, weight, waist, and hip circumference were measured as per the methods suggested in the WHO technical report series. Blood pressure was measured thrice using digital sphygmomanometer (OMRON). Details of the study tool used in the study are published elsewhere. Diabetes was recorded as reported by the participants and/or confirmed with already available medical reports. Of the several risk factors assessed using STEPS survey tool, the following nine factors such as tobacco use, alcohol use, infrequent vegetable and fruit consumption, high salt intake, overweight or obesity, high waist circumference, inadequate physical activity, presence of hypertension, and diabetes with or without treatment were considered for classifying clustering of NCD risk factors.
Tobacco use was defined to be present if at the time survey the adult has consumed tobacco in any form either daily or occasionally during the past month. For alcohol consumption use, people were shown show cards which kind of drink they had during the corresponding period. Accordingly, the standardized alcohol content was derived. Alcohol consumption is said to be present if the person has consumed at least one standard drink of alcohol (equivalent to 10 g ethanol) in the year preceding the survey. Body mass index (BMI) was further classified into undernutrition, normal, overweight, and obese based on the guidelines of consensus for the Indian population. Based on the same consensus, >100 cm in men and >90 cm in women were considered as raised waist circumference. Physical activity level was estimated using the Global Physical Activity Questionnaire with the help of show cards. Using the show card, participants were asked to respond regarding work, transport, and leisure time-related type of activity and frequency of those activities. These physical activity levels are converted in terms of metabolic equivalents (METs).
In the past week, if the persons' cumulative physical activity (work, transport, and leisure time physical activities) was estimated to be <600 METs, physical inactivity was present. Participants were shown different standardized cup sizes in the range of 40, 80, 160, 200, and 250 g. They were asked to show how much fruits and vegetables they ate corresponding to the cup size. One serving size was considered if it is 80 g. Accordingly, the serving size was derived. If an adult has consumed <5 servings (serving size ~80 g) of fruits and vegetables, it was defined as an inadequate consumption of fruits and vegetables. People were asked to report how many salt packets were consumed in the last month and how much was used. From the total consumption and number of family members, per capita daily salt intake was estimated. As per the Dietary Approach to Stop Hypertension (DASH) diet recommendation, <2.3 g of sodium is recommended which is equivalent to 6 g sodium chloride. High salt intake was considered if per capita daily consumption exceeds >6 g in a day.
This study was approved by the scientific committee and institute ethics committee of a tertiary care medical institute in Puducherry.
Data were entered in Excel after subjecting to its completeness and validity by the field supervisor. Data were analyzed using STATA 11 (, StataCorp, Texas, USA). Characteristics of study participants are summarized in the form of a mean (standard deviation) and percentages. Nine noncommunicable risk factors were coded as present or absent using the above-mentioned operational guidelines. Individual risk factor prevalence based on the sociodemographic characters was presented as percentages. Of the nine NCD risk factors, a person who had three or more was considered to have clustering of NCD risk factors. Since age and gender are the universal confounders which can distort the estimates, gender-disaggregated weighted age prevalence was reported for clustering of NCD risk factors. Factors associated with clustering of NCD risk factors were identified using the Chi-square test. Background characteristics which were significant at P < 0.1 level were subjected to multilevel logistic regression modeling. In this analysis, clustering of NCD risk factors (≥3 NCD risk factors) was the dependent variable. Age, gender, literacy, poverty status, and occupation were the independent variables. Intracluster correlation (ICC) values were estimated to test whether family identity and area are associated with clustering. Random intercept model with two levels of hierarchy was followed where individual participants are nested within family and families are nested within the area of living. The final estimate of measures of association obtained from multilevel modeling after adjusting the family- and area-level clustering was reported as adjusted odds ratio (AOR) with 95% confidence interval (CI).
| Results|| |
Totally 2399 adults had participated in the study. Details of participant characteristics and associated risk factor profile are shown in [Table 1]. Of the 2399 adults participated in the study, 1741 (73%) had clustering of NCD risk factors (three or more) identified and one-fifth of the adults had five or more risk factors. The median number of risk factors observed in this population was 3 with interquartile range (2–4). Inadequate consumption of fruits and vegetables, high salt intake, and high waist circumference are the three predominant risk factors identified across all subgroups.
|Table 1: Distribution of substance use and dietary factors related to noncommunicable disease risk factors by sociodemographic characteristics in urban Puducherry, 2015-2016*|
Click here to view
Except few, all adults (97.5%) were consuming <5 servings of vegetables and fruits. Around two-third of adults had the risk factor of high waist circumference (74.6%), high salt (>6 g/day) intake (63.5%), and BMI of overweight or obese (BMI ≥23 kg/m2) category (64.4%) [Table 1] and [Table 2].
|Table 2: Distribution of cardiovascular disease risk factors (physical activity, body mass index, and coexistent noncommunicable diseases) by sociodemographic characteristics in urban Puducherry, 2015-2016*|
Click here to view
Among the 1741 individuals with clustered NCD risk factors, the predominant coexisting pattern found was high salt intake, inadequate fruit and vegetable consumption with high waist circumference (483/1741 = 48.4%), followed by high salt intake, inadequate fruit and vegetable consumption and overweight or obesity (660/1741 = 37.9%). About 60% of the clustering had coexisting risk factors of high salt intake and inadequate vegetable and fruit consumption.
Clustering of NCD factors was more commonly observed among adults belonging to below poverty line, aged beyond 35 years, and less commonly seen among people living in area III (clustered houses provided under housing schemes) compared to others.
Across all categories of socioeconomic patterns, except in the higher literacy level, age-adjusted prevalence of clustered NCD risk factors was more in males compared to females [Table 3].
|Table 3: Age-weighted prevalence of clustering of noncommunicable disease risk factors among adults from urban Puducherry, 2014-2015|
Click here to view
While building the predictive model, family and area were included to look for the presence of clustering effect. Since there was a high intracluster correlation observed among the family members (ICC 0.56) and adults whose census enumeration blocks were same (ICC 0.42), family identity and area codes were subjected to multilevel modeling by random intercept models. Adults belonging to Christian religion are 2.8 times more likely to have more than three NCD risk factors compared to others (AOR: 2.8, 95% CI: 1.5–5.2); adults aged beyond 35 years are two times more likely to have more than three clustered NCD risk factors compared to adults <35 years of age (AOR: 2.0, 95% CI: 1.4–6.0); similarly, adults who had literacy beyond primary level were around two times more likely to have clustered NCD risk factors (AOR: 1.8, 95% CI: 1.1–5.5) compared to illiterates. However, such association was not found among adults who studied beyond higher secondary level [Table 4].
|Table 4: Factors associated with clustering of noncommunicable disease risk factors among adults from urban Puducherry, 2014-2015|
Click here to view
Like the clustering of NCD risk factors, certain subgroups, namely age beyond 65 years, Muslim religion, male, low level of literacy, and those who involved in the semiskilled job, had more than six NCD risk factors.
| Discussion|| |
This study from an urban area of the union territory had demonstrated a high prevalence of co-occurring several NCD risk factors which were mainly driven by the unhealthy dietary practices, namely high salt intake and low consumption of fruits and vegetables, followed by obesity and high waist circumference.
Although in India, several studies addressed the burden of individual NCD risk factors,,,, literature on patterns of various coexisting NCD risk factors and its relation to socioeconomic positions are rare. Union territory which has more proportions of migrants and where the access to health care is optimal, reorienting the health system for escalating NCD-related health-care delivery needs a better understanding on patterns of various coexisting NCD risk factors.
Several lifestyle-related risk factors for NCDs are interrelated, and many a times, they act synergistically to accelerate the disease process. There is an emerging global felt need for addressing the risk factors in an integrated manner rather than the fragmented approach.,,,
In the current study, three-fourth of participating adults (72.6%) are presenting with more than three co-occurring identified NCD risk factors. These results are in consistent with the report from industrial employees where around 67% of the employees found to have more than three risk factors. Similarly, a multicountry analysis by Wu et al. also had reported, in India, more than 45% of the adults have multiple risk factors and which is more compared to countries such as China and Ghana and lesser compared to South Africa, Russia, and Mexico. This phenomenon is not only prevailing in India and the other South Asian countries such as Bangladesh, Nepal, and Bhutan also has reported a similarly high prevalence of multiple coexisting NCD risk factors.,, A multicentric study from nine surveillance sites which come under the Demographic and Health Surveillance System conducted among 25–64-year-olds reported the prevalence of tobacco use, low consumption of vegetables, and low-level physical activity to be the major contributing risk factor for clustering. In this study, the Indian population has found to have clustering <30%, and the maximum clustering was observed from Bangladesh. Despite the different eligibility of age group between the multicountry study and the current study, the present study also revealed the role of unhealthy dietary practices as a major determinant to cause clustering.
Although the current study reports are similar to other study estimates in terms of individual risk factors and clustering at three or more NCD risk factors, proportions of people who are having more than four NCD risk factors are alarmingly high (19%).
This study has the following strengths. First, this is a community-based household survey where risk factors for NCDs are systematically assessed using the WHO STEPS approach among adults. Second, since all the family members are sharing the diet patterns and environment, clustering is expected to occur at a household level. Similarly, risk factors are proposed to get influenced by neighborhood effects. Hence, clustering at household and area level was adjusted by appropriate multilevel modeling techniques. However, the study has the following limitations. Despite the multiple visits, certain proportions (11%) of individuals were unable to be contacted during the survey. However, when we analyzed from the Health Information and Management System maintained in the health center, the characteristics of these nonresponded individuals were not statistically significant from those who participated in the survey. Hence, the nonresponse rate of 11% unlikely could have influenced our estimates. Although this study comprehensively assessed several risk factors, biological parameters such as blood glucose and lipid profile were not tested. In some of the subgroups such as religion found to have more clustering compared to others. Further, qualitative approaches could identify the reason for clustering in the specific groups.
This study has the following clinical and programmatic implications. First, since three-fourth of the individuals are found to have clustered NCD risk factors, this necessitates the obvious need for reorienting the current NCD clinics toward integrated NCD clinic under the national program. Second, the individual health-care providers, especially who treat specifically for diabetes and hypertension morbidities, should be sensitized toward assessing the person for integrated NCD risk factors. Third, clustering is mainly driven by coexisting patterns of high salt intake, low consumptions of fruits and vegetables associated with obesity, and raised waist circumference. This emphasizes the need for introducing a cadre of nutritionists and health educationists to raise awareness and demonstrate nutritional values not only in the NCD clinic but also in the community as well. The prevailing clustering of NCD risk factors reported among illiterates and elderly age groups also supports this requirement. Furthermore, the clustering pattern which is mainly driven by unhealthy dietary practices and low level of physical activity reemphasizes the need for policy-level changes and increasing the access to healthy foods and enabling the environment to be conducive for the regular physical activity. At primary care level, the possibilities of task shifting to instill better dietary practices as health-promoting initiatives through gross-root level Anganwadi workers are being already explored and proven.
| Conclusions|| |
Clustering of NCD risk factors is highly prevalent (73%) in this region, and it is mainly driven by unhealthy dietary practices, namely consumption of low vegetables and fruits, high salt intake, and obesity measures. Clustering was more commonly observed among illiterates and adults aged 35 years and above compared to others. Hence, there is an urgent need to reorient the health system toward integrated approach with mandated inclusion of nutritionist to work in the NCD clinic as well as in the community.
We thank the staffs working in JIPMER Urban Health Centre for their support in enumeration of eligible participants and sharing the updated database of Health Management Information System.
Financial support and sponsorship
This study was conducted with the funding from resources allocated under Intramural Research Grants, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry.
Conflicts of interest
There are no conflicts of interest.
| References|| |
World Health Organization. Impact of Chronic Disease in India. Global Health Observatory Data. Vol. 51. World Health Organization; 2014. p. 51-6210. Available from: http://www.who.int/gho/malaria/en/
. [Last accessed on 2017 Nov 29].
Kontis V, Mathers CD, Bonita R, Stevens GA, Rehm J, Shield KD, et al.
Regional contributions of six preventable risk factors to achieving the 25 × 25 non-communicable disease mortality reduction target: A modelling study. Lancet Glob Health 2015;3:e746-57.
World Health Organization. From Burden to “Best Buys”: Reducing the Economic Impact of Non-Communicable Diseases in Low-and Middle-Income Countries. Geneva, Switzerland: World Health Organization; 2011.
National Institute of Medical Statistics. IDSP Non-Communicable Disease Risk Factors Survey, Phase-I States of India, 2007-2008. New Delhi: National Institute of Medical Statistics; 2009.
Bhardwaj S, Misra A, Misra R, Goel K, Bhatt SP, Rastogi K, et al.
High prevalence of abdominal, intra-abdominal and subcutaneous adiposity and clustering of risk factors among urban Asian Indians in North India. PLoS One 2011;6:e24362.
Gupta V, Millett C, Walia GK, Kinra S, Aggarwal A, Prabhakaran P, et al.
Socio-economic patterning of cardiometabolic risk factors in rural and peri-urban India: Andhra Pradesh Children And Parents Study (APCAPS). Z Gesundh Wiss 2015;23:129-36.
Walia R, Bhansali A, Ravikiran M, Ravikumar P, Bhadada SK, Shanmugasundar G, et al.
High prevalence of cardiovascular risk factors in Asian Indians: A community survey – Chandigarh Urban Diabetes Study (CUDS). Indian J Med Res 2014;139:252-9.
] [Full text]
Ahmed SM, Hadi A, Razzaque A, Ashraf A, Juvekar S, Ng N, et al.
Clustering of chronic non-communicable disease risk factors among selected Asian populations: Levels and determinants. Glob Health Action 2009;2:68-75.
Wu F, Guo Y, Chatterji S, Zheng Y, Naidoo N, Jiang Y, et al.
Common risk factors for chronic non-communicable diseases among older adults in China, Ghana, Mexico, India, Russia and South Africa: The study on global AGEing and adult health (SAGE) wave 1. BMC Public Health 2015;15:88.
Aryal KK, Mehata S, Neupane S, Vaidya A, Dhimal M, Dhakal P, et al.
The burden and determinants of non communicable diseases risk factors in Nepal: Findings from a nationwide STEPS survey. PLoS One 2015;10:e0134834.
Pelzom D, Isaakidis P, Oo MM, Gurung MS, Yangchen P. Alarming prevalence and clustering of modifiable noncommunicable disease risk factors among adults in Bhutan: A nationwide cross-sectional community survey. BMC Public Health 2017;17:975.
Tsai AC, Venkataramani AS. Syndemics and health disparities: A methodological note. AIDS Behav 2016;20:423-30.
Tsai AC, Burns BF. Syndemics of psychosocial problems and HIV risk: A systematic review of empirical tests of the disease interaction concept. Soc Sci Med 2015;139:26-35.
Zaman MM, Bhuiyan MR, Karim MN, Zaman M, Rahman MM, Akanda AW, et al.
Clustering of non-communicable diseases risk factors in Bangladeshi adults: An analysis of STEPS survey 2013. BMC Public Health 2015;15:659.
Kar SS, Kalaiselvi S, Archana R, Saya GK, Premarajan KC. Is rule of halves still an occurrence in South India: Findings from community-based survey in a selected urban area of Puducherry. J Postgrad Med 2017;63:232-6.
] [Full text]
World Health Organization. STEPwise Approach to Surveillance (STEPS). World Health Organization; 2015. Available from: http://www.who.int/chp/steps/en/
. [Last accessed on 2017 Nov 29].
World Health Organization. Physical Status: The Use of and Interpretation of Anthropometry, Report of A WHO Expert Committee. Geneva : World Health Organization; 1995. Available from: http://apps.who.int/iris/handle/10665/37003
. [Last accessed on 2017 Oct 13].
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.
Derkach A, Sampson J, Joseph J, Playdon MC, Stolzenberg-Solomon RZ. Effects of dietary sodium on metabolites: The dietary approaches to stop hypertension (DASH)-sodium feeding study. Am J Clin Nutr 2017;106:1131-41.
Patel SA, Dhillon PK, Kondal D, Jeemon P, Kahol K, Manimunda SP, et al.
Chronic disease concordance within Indian households: A cross-sectional study. PLoS Med 2017;14:e1002395.
Girin N, Brostrom R, Ram S, McKenzie J, Kumar AM, Roseveare C. Describing the burden of non-communicable disease risk factors among adults with diabetes in Wallis and Futuna. Public Health Action 2014;4:S39-43.
Jeemon P, Narayanan G, Kondal D, Kahol K, Bharadwaj A, Purty A, et al.
Task shifting of frontline community health workers for cardiovascular risk reduction: Design and rationale of a cluster randomised controlled trial (DISHA study) in India. BMC Public Health 2016;16:264.
[Table 1], [Table 2], [Table 3], [Table 4]