Globally, 31% of deaths are due to CVDs with developing countries contributing more than three quarters of these deaths [1,2]. In 2012, 16 million deaths under 70 years out of total 17.5 million deaths were due to CVDs [1].

Rapidly rising CVD burden can be reduced in a cost-effective manner through health interventions focusing on behavioral risk factors, screening for risk factors and instituting prompt management measures among apparently healthy individuals [1,3]. These measures can prevent premature deaths, loss of Disability Adjusted Life Years (DALYs) by alleviating CVD events [4].

The rising incidence and ever growing burden of CVDs require an operational and pragmatic prevention and control strategies. Current CVD management guidelines recommend absolute risk assessment approach for promotive, preventive and curative services [2,5,6].

However, operationally, absolute risk approach algorithms require laboratory investigations which may not be readily available in resource poor regions including India [7,8]. Majority of risk factors for CVDs can be identified in periphery by auxiliary health care workers in periphery.

Studies carried out to compare non laboratory based cardiovascular disease risk assessment algorithms have found algorithms developed by World Health Organization and International Society of Hypertension (WHO/ISH) which are simple and user friendly for application [9].

Combined risk approach enables peripheral health care workers as well as clinicians to estimate likelihood of CVD events due to atherosclerosis and thus results in effective prioritization and allocation of resources [5,10,11,12]. In fact, across many developed countries, CVD risk assessment algorithms are being utilized for selection of individuals requiring more intense interventions [10].

WHO/ISH risk prediction charts were developed for use in resource poor settings to predict 10 year fatal/non- fatal CVD events. These charts were developed for different regions of the world and includes India in SEAR-D for implementation.

Further, these charts are also helpful in education of the community and can be used as a tool for

The variables for 10 year CVD risk prediction included gender, co-morbidity due to diabetes mellitus, smoking status, age, and blood pressure values.

Operational definitions: Administration of Questionnaire, clinical examination was carried out by resident doctors. Height was measured to nearest centimeter, using a stadiometer. Weight was measured to nearest 100 grams, using a portable weighing scale placed on cemented floor.

The individual was instructed to stand on the scale with minimal clothing, without shoes and socks, and to look straight before measuring weight. Body Mass Index (BMI) was calculated as kg/m² and classified as underweight, normal and at increased risk for BMI values of < 18.5 kg/m², 18.5 – 22.9 kg/ m², and > 23 kg/m²[13,14].

Further, Brocca index was calculated to determine ideal weight as (Height in cm – 100). Any person with weight more than ideal was considered as being overweight. Waist circumference was measured using measuring tape with accuracy up to 0.1 cm at midpoint between lower border of lowest rib and upper border of iliac crest.

Increased risk of metabolic complications was considered for males and females with waist circumference of > 102 cm and > 88 cm respectively. Hip circumference was assessed across the widest portion of the buttocks. Abdominal obesity was considered in males and females for Wait-Hip Ratio (WHR) >1.0 and >0.85 respectively.

Blood pressure was measured using mercury based sphygmomanometer. A person was considered to be hypertensive when Systolic blood pressure was > 140 mm Hg ‘and/or’ diastolic blood pressure > 90 mm Hg.

Level of Physical activity was assessed on basis of activities being performed at the workplace, at home and involvement in regular physical exercises. Adequate level of physical activity was considered if the individual had vigorous activity at home ‘and/or’ strenuous work ‘and/or’ was involved in regular physical exercises. Participant was considered to be a smoker if he/she had been smoking any form of tobacco product in past one year.

An individual was considered to be diabetic if fasting venous blood sugar levels were more than 125 mg/dl. Further, individuals receiving oral hypoglycemic drugs/ insulin for treatment were considered as diabetic irrespective of their blood

* Fischer exact test applied as more than 20% of expected cell count <5

^a Chi square for trend not applied as it did not meet Cochran’s criteria.

Table-2: Prevalence of selected NCD risk factors among CVD risk groups

Distribution of 10 year risk of CVD event, according to WHO/ISH CVD risk charts for SEAR D region is presented in Fig 1. Majority (46%) of the study population had 10 year CVD event risk of less than 10%. Very high 10 year CVD event risk (>40%) was seen in 23% of the study population.

Only 04 (3%) participants had 10 year CVD risk between 30 to 39%. Thus, any person with 10-year CVD risk more than 30% was considered to be at high risk of CVD event and were considered as potential candidates requiring immediate intervention and treatment.

In a study carried out in Northern India, physical inactivity was found to be higher among females and other CVD risk factors were more prevalent among males [16].

A study in Tamil Nadu found prevalence of overweight and obesity to be higher in females [17].Further, studies have also found independent association of age, gender, obesity, physical inactivity and family history with cardiovascular diseases [15].

Globally, 10-year CVD risk has been found to be variable among populations. In the present study, 23% participants were found to have 10 year CVD risk > 40%. This was relatively higher as compared to another carried out in Group C employees in Puducherry where only  4% individuals with CVD risk >10% [15].

Another community based cross sectional study in Puducherry found 17% participants with CVD risk >30% [18]. Internationally, a study carried out in three Asian countries found high CVD risk in 6%, 2.3% and 1.3% study participants in Mongolia, Malaysia and Cambodia respectively [19]. Another study carried out in a selected community in Nepal found ≥10% CVD risk in 14.6% respondents [20].

At a single Latin American center, study found 14.7% having CVD risk >20% [21]. This variation has been attributed to varied age and gender composition of the study subjects as well as their socioeconomic status and lifestyle. Studies have found higher prevalence of CVD risk factors among individuals in lower socioeconomic status residing in urban slums [16,22,23].

In the present study, Obesity and physical inactivity was found to be similar among CVD risk groups whereas hypertension, diabetes, abdominal obesity and smoking was higher among individuals with high CVD risk. These findings support the fact that determining the need for medical intervention on basis of a single risk factor may not be appropriate and combination of risk factors should be utilized.

Atherosclerosis resulting in CVD events has also been documented to be rarely due to single risk factor but mostly due to combination of risk factors. Thus, a combined risk approach is required to manage rapidly growing burden of CVDs at a cost individuals as well as health care systems can afford [6].

The present study brings forward the extent of cardiovascular risk factors in urban slums. In the present study, higher prevalence of Hypertension and Diabetes, abdominal obesity, overweight, and smoking was found in individuals at high risk of CVD event as compared to those at low risk. Further, 23% participants were found to have 10 year CVD risk > 40%. This was relatively higher as compared to previous studies carried in urban and rural areas.

Thus, the present study brings forward two important aspects in prevention of cardiovascular diseases. Firstly, determining the need for medical intervention on basis of a single risk factor may not be appropriate and combination of risk factors should be utilized. Secondly, there is likely to be higher burden of cardiovascular diseases in urban slums. Large multicentric studies should be undertaken to estimate true burden and to make evidence based prevention and control programmes in urban slums.