Descriptive profile of risk factors for cardiovascular diseases using WHO STEP wise approach in Madhya Pradesh

Study design and settings

This was a community based cross-sectional survey conducted in the state of Madhya Pradesh (MP) located in central part of India. Madhya Pradesh state has 51 districts spread across 10 administrative divisions. As per 2011 census, total population of the state was 72.6 million of which 15.34 million (21.1%) belong to Scheduled Tribes. MP is afforded the label of being one of the high priority states under National Health Mission considering its high infant mortality rate (IMR), maternal mortality rate (MMR) and high incidence of malnutrition in children. The present survey was conducted in 10 districts of MP during February 2018 to March 2019.

Sample size and sampling method

We have used multi-stage cluster random sampling method as recommended in WHO-STEPS wise approach. Sample size calculations were done by using STEPS Sample Size Calculator (World Health Organization (WHO), 2020a) for estimating prevalence of any form of tobacco use with 95% confidence interval around relative error of 10% and adjusted for design effect of 1.5, and also for four age-group strata (18–29, 30–44, 45–59 and 60–69 years) for both gender and non-response rate of 20%. Calculated sample size was 5741. Considering logistics feasibility we have decided our cluster size to be 60 participants per cluster and thus a total of 100 clusters were selected.

In the first stage, all divisions from within the state were selected and in second stage one district from each division was randomly selected. Figure 1 shows map of Madhya Pradesh and selected clusters and districts. We have used random number table and census code to identify the districts included in our study.

Then in third stage, sampling frame was prepared by using existing enumeration areas i.e., Primary Sampling Units (PSU) of the Census 2011 and from this unit, sample was chosen. All the villages / Census enumeration blocks (CEB) in wards of the selected districts of Madhya Pradesh were listed as Primary Sampling Unit (PSU). A total of 100 PSUs were selected by proportionate to population size of district considering urban and rural stratification. Total number of urban and rural clusters selected are shown in Table 1.

Then from each district, corresponding number of clusters/PSU were selected by probability proportionate to size (PPS) method. For this we prepared list of villages and CEBs with their population size. Cumulative population of the enlisted villages and CEBs was calculated and it was divided by the requisite number of clusters to arrive at the sampling interval. First cluster was chosen by selecting a random number between 1 to sampling interval. Subsequent clusters were selected by adding sampling interval.

From each cluster we approached 58 eligible individuals for participation in study. For this, field investigators did village street mapping and house listing and then divided total number of households in that cluster by 58. This was the sampling interval for household selection. For selection of first household, a street was randomly selected by using last digit of currency note, and then first household adjacent to that street was selected. Then all eligible individuals from selected household were enlisted and assigned a serial number in paperless data collection tool. This tool had inbuilt logic based on Kish grid method to select a respondent from eligible household members. Subsequently, households on right hand side of the household being interviewed currently were selected by adding sampling interval. The method was thus repeated till the desired sample size was achieved. Fig. S1 shows process of sampling and data collection.

Participants and consent

All adults between 18–69 years of age who were residents of the selected cluster were eligible to be included in the study. Out of all the eligible members in household, respondent was selected as per methods already described above. Purpose of the study and procedures were explained to the respondent and a Participant Information Sheet (PIS) in local language was also provided. Subsequently those who provided written informed consent were included in the study.

Study instrument / tool

We have used WHO -STEPS instrument for collection of data.[6][24] This instrument has got different risk factor assessment tool like socio-demographic information and behavioural risk factor assessment based on various question sets (STEP-1), physical measurement (STEP-2) and biochemical measurement (STEP-3). This questionnaire was translated in to Hindi to suit local needs before using for data collection. Copy of the study instrument is provided in supplementary files. Standard techniques were used for anthropometric measurements like height, weight, waist & hip circumference. Standard procedure mentioned in JNC 8 criteria were used to measure blood pressure of the individuals. For the purpose of measuring weight, we used Seca 803- Electronic Flat Weighing Scale, and for measuring the height we used Seca 213 Portable Stadiometer, both manufactured by Seca, Germany.

Study variables- Key study variables are described in Table 2. We have used operational definitions from WHO-STEPS manual (World Health Organization (WHO), 2020b).

Study procedures- Data collection was done by team of four field investigators and a laboratory technician who were trained in interview technique, anthropometry, blood pressure measurements and laboratory procedures. Quality checks were done by supervisor & study investigators by resurveying selected samples at each PSU to verify errors in data collection. Accordingly, the field supervisor had to check 5 percent of interviews done by field workers in each cluster.

All data collection was done on a paperless data collection system, e-STEPS for android which was deployed on Ona platform. It had a STEPS app which was used for household member listing and respondent selection and e-STEPS app in which data collection forms for each step were available.

Data collection in each cluster was completed in 3–4 days. Field investigators administered Step-1 questionnaire and invited participant for physical measurement and laboratory testing on early morning next day. Individuals not available during the primary visit were visited once again. If they were found to be not available during the second visit also, they were classified as non-respondent.

Biochemical investigations- Fasting capillary glucose, total cholesterol and triglycerides tests were done by a trained laboratory technician by using point-of-care technology. For this we used Aina Mini Blood Glucose Monitoring System and Aina Lipids System of Jana Care Inc, Boston (MA).

Ethics issues- The study protocol was approved by Institutional Human Ethics Committee (IHEC) of AIIMS Bhopal. (Approval No IHEC-LOP/2014/EF0018 Dated 30th Jan 2015). Permission for data collection at field sites was obtained from Directorate Health Services, Government of Madhya Pradesh. All participants were explained about the study in Hindi and provided with participant information sheet. Interviews were conducted only after written informed consent was obtained. Individuals who were found to have high risk factors including raised blood pressure, blood glucose and total cholesterol were informed about it and referred to nearest public health facility for healthcare services.

Data analysis

Data collected through mobile phone application was downloaded from server in the form of spreadsheet and was cleaned for any inconsistencies of implausible values. We have used IBM SPSS Statistics for Macintosh, Version 26.0. (Armonk, NY: IBM Corp.) for data analysis. Mean and standard deviation, or median and IQR (whichsoever was applicable) were used to summarize numerical variables as per their distribution. Nominal variables were summarized as frequency and percentage. Sampling weights for each STEP were derived by considering selection probabilities at different sampling stages. Subsequently weighted prevalence of behavioural and biological risk factors were calculated with their 95% confidence interval. Global NCD Monitoring Framework developed by WHO has recommended 25 indicators categorized under four broad domains viz mortality and morbidity, behavioural risk factors, biological risk factors and national health system response (World Health Organization (WHO), 2020c). Out of these 9 are estimated in results of this study.

Prevalence of behavioural risk factors

Gender wise weighted prevalence of behavioural risk factors is shown in Table 4. One in four male and less than one percent females were found to be smoking tobacco daily. The mean age for starting smoking among both sexes was 22.18 years [95% CI [21.58–22.78]] and the mean number of manufactured cigarettes smoked per day was 0.28 [95% CI [0.11–0.44]]. About 5.7% of the daily smokers were reported of smoking manufactured cigarettes. Current alcohol consumption, i.e., alcohol consumption in anytime in the past 30 days was reported by 11.8% of males but only 0.3% of females. At the same time, 1.2% [95% CI [0.8–1.6]] professed to have engaged in heavy drinking in the last 30 days. The mean number of days fruit and vegetables were consumed by the respondents in a typical week were 1.36 days [95% CI [1.2–1.51]] and 5.09 days [95% CI [4.9–5.27]] respectively. The mean number of servings of fruit and vegetables that were consumed per day on an average was 0.82 [95% CI [0.76–0.88]] and 1.56 [95% CI [1.52–1.61]] respectively. Majority (98.5% [95% CI [97.9–98.9]]) of the respondents ate less than 5 servings of fruit and/or vegetables on an average per day, and about one fourth (25.5% [95% CI [22.8–28.4]]) of the respondents always or often added salt or salty sauce to their food before eating or as they were eating. 11.9% [95% CI [10.1–13.9]] always or frequently ate processed foods high in salt.

Prevalence of biologic risk factors

The mean BMI of respondents was 20.88 kg/m². About 15.3% [95% CI [12.8–18.2]] of the respondents were overweight and 3.4% [95% CI [2.6–4.3]] were obese. More females were found to be overweight and obese as compared to males. The mean systolic and diastolic blood pressure of the respondents was 125.94 [95% CI [124.98–126.89]] and 77.68 [95% CI [77.09–78.27]] mm Hg respectively. Almost a fourth (22.3% [95% CI [20.5–24.1]]) of the respondents had raised BP or were currently on medication for raised BP. The mean fasting blood glucose of the respondents including those currently on medication for raised blood glucose was 92.69 [95% CI [90.2–95.18]] mg/dl. Prevalence of raised fasting glucose was 6.8% (95% CI [5.6–8.3]) which was higher in males (8.4% [95% CI [6.6–10.7]]).

Summary of risk factors

We estimated the percentage of participants who had 3 or more risk factors and stratified it by age (below 45 years and above 45 years) and gender (male and female). Men were found to be having more multiple risk factors in both the age categories (36.6 and 54.6 percent) as compared to females. Within females, burden of multiple risk factors increases sharply from 17.0 percent below 45 years to 38.1 percent after 45 years.

The alluvial diagram (Fig. 2) attempts to connect the demographic characteristics (age and gender) to behavioural, anthropological and biological NCD risk magnitude in cohesion. There seems to be some distinctive patterns here- first, age and behavioural risk factors show an incremental relationship which is more lucid in male. Second, females are more prone to ’being overweight’ in spite of lesser propensity to affect with behavioural risk . This rivulet of alluvial diagram can be seen translated into biological risk for NCD. Another interesting observation is related to biological risk which shows a pattern seemingly disfavorable to male.