Prevalence and risk factors of diabetes in a large community-based study in North India: results from a STEPS survey in Punjab, India

This study reports results of a cross-sectional survey conducted in the state of Punjab, India using a multistage stratified sample.

Punjab is a prosperous state in northern part of India bordering Pakistan with a population of 27 million according to 2011 national census. It ranks second in terms of human development index among all states [12]. A state wide non communicable disease (NCD) risk factor survey based on WHO-STEPS approach was undertaken in Punjab in 2014–2015. The survey adopted a multistage stratified, geographically clustered sampling approach using the 2011 census sampling frame. A three-stage design was employed in urban areas whereas in rural areas a two-stage sampling design was followed. Out of a total of 100 Primary Sampling Units (PSUs), there were 60 villages from rural areas and 40 Census Enumeration Blocks from the urban locality. From each selected PSU, 54 households were selected using systematic random sampling. The ultimate sampling units were the households and one individual in the age group of 18–69 years residing in the selected household was selected using the KISH method. The details of the methodology can be found in another paper [13].

A culturally adapted, Punjabi (local language) translated and pre-tested version of the WHO STEP Surveillance (STEPS) questionnaire (version 3.1) was used with minor adaptations [14]. As part of the household survey, socio demographic and behavioural information on tobacco and alcohol use, diet, physical activity, history of chronic diseases, family history of chronic conditions, health screening, and health care costs were collected in Step 1. Physical measurements such as height, weight, blood pressure and waist circumference were collected in Step 2. Biochemical tests were conducted to measure fasting blood glucose, total cholesterol, triglycerides, HDL and LDL cholesterol in Step 3.

Data were collected by trained investigators. SECA adult portable stadiometer was used to measure height after removing shoes, socks, slippers and any head gear. It was measured in centimetres up to 0.1 cm. SECA digital weighing scale was used to measure weight of the individuals. The scale was regularly calibrated against a standard weight. The participants were asked to remove footwear and socks and weight was recorded in kilograms up to 0.1 kg. Waist circumference was measured using a SECA constant tension tape to the nearest 0.1 cm at the level of the midpoint between the inferior margin of the last rib and the iliac crest in the mid-axillary plane. The measurement was taken at the end of a normal expiration with the arms relaxed at the sides.

One serving of vegetable was considered to be one cup of raw green leafy vegetables or 1/2 cup of other vegetables (cooked or chopped raw). One serving of fruit was considered to be one medium size piece of apple, banana or orange, 1/2 cup of chopped, canned fruit or 1/2 cup of fruit juice.

Physical activity was assessed using the Global Physical Activity Questionnaire (GPAQ), which has been developed by the World Health Organization and used in the STEPS questionnaire. This questionnaire assesses physical activity behaviour in three different domains: work, transport and during leisure time. Activities are classified into three intensity levels: vigorous, moderate and light. ‘Vigorous-intensity activities’ are activities that require hard physical effort and cause large increases in breathing or heart rate, ‘moderate-intensity activities’ are activities that require moderate physical effort and cause small increases in breathing or heart rate. Participants were classified as sufficiently active who exceed the minimum duration of physical activity per week recommended by WHO i.e. 150 min of moderate intensity physical activity or 75 min of vigorous intensity physical activity or an equivalent combination of moderate- and vigorous-intensity physical activity achieving at least 600 MET-minutes per week with each activity performed in bouts of at least 10-min duration [15]. Obesity was defined as BMI ≥ 27.5. Show cards (pictorial, adapted to the local context) were used to explain to the participants the type of physical activity, servings of fruits and vegetables and salty food intake.

Biochemical measurements (step 3): Every alternate individual (50%) of the initial sample was subjected to steps 3 (around 2700). A mix of dry and wet chemistry methods was used to assess biochemical profile of participants using blood and urine samples. For blood glucose, dry chemistry method was used by blood glucose measurement device (Optium H, Freestyle). For lipid profile i.e. cholesterol and triglycerides measurements, blood samples were drawn on individuals after 10–12 h fasting. 5 ml of venous blood was taken in sitting position, and centrifuged immediately to separate serum, and transferred under cold chain condition to the Central Reference Laboratory of Department of Biochemistry, Post Graduate Institute of Medical Education and Research, Chandigarh, India.

Laboratory measurement of triglyceride was made on Modular P 800 autoanalyzer (Roche Diagnostics, Germany) using commercially available kits (Roche Diagnostics, Germany).

Sample size of 4609 was calculated using the estimated prevalence of physical activity as 50%, 0.05 margin of error and 95% confidence interval. Assuming a response rate of 85%, sample size was raised to 5400. Every 2nd individual was subjected to Step 3 i.e. biochemical assessment. Out of 2700 respondents, 2499 (93%) gave consent to blood sampling for biochemical assessment.

Categorical variables are summarized using proportions and continuous variables using mean or median, whichever is applicable, with 95% confidence intervals. Chi square test was used for comparison of proportions across groups and ANOVA test for comparison of means across groups. Univariate and multiple logistic regression analysis (backward conditional method) was done to determine the predictors of diabetes. Variables entered into the multiple regression models were selected on the basis of significance (p < 0.2) in the univariate analysis. Statistical analysis was done using SPSS version 16.0.

Table 1 shows the socio-demographic, behavioural and clinical characteristics of the respondents in the study. Majority of the respondents are females (64%), adults in the age group 25–44 years (51%), rural residents (61%) and belong to the general caste (48%). The prevalence of hypertension among the respondents was 36%. Nearly 15% were current alcohol users whereas around 4% were found to be current smokers (Table 1).

Overall prevalence of DM among the study participants was found out to be 8.3% (95% CI 7.3–9.4%) which was higher in urban areas (9.4%, 95% CI 7.7–11.4%) compared to rural (7.6, 6.4–9.1%), though not significant. The prevalence of prediabetes was 6.3% (5.4–7.3%) based on ADA criteria and 6.0% (5.0–7.1%) based on NPCDCS guidelines, again with a higher proportion among the urban dwellers (Table 2). On univariate analysis, the prevalence of DM was found to be significantly higher among those aged 45–69 years (18.0%), hypertensives (14.3%), obese (14.4%), with family history of DM (11.9%) and those with hypertriglyceridemia. No difference was found in prevalence by sex, residence, social group, educational status, smoking and alcohol use (Table 3).

Age group (45–69 years), marital status, hypertension, obesity and family history of DM were found to be the risk factors significantly associated with DM in a multivariate regression model (Table 3).

Among all persons with DM, only 37 (18%) were known case of DM or on treatment whereas the rest were newly diagnosed. Among those already on treatment or known cases of DM, 13 (35%) had controlled blood glucose status (Table 4).

The study had few limitations. Firstly, this being a cross-sectional study, prevents us from drawing causal inferences. Secondly, measurement of blood glucose was done by a glucometer device instead of venous blood glucose estimation due to logistic constraints. However, regular quality control check on blood glucose measurement was done in a reference laboratory as per the manufacturer’s instructions. Thirdly, only fasting blood glucose was used to diagnose diabetes and pre-diabetes.