Clustering of hypertension and clustering of diabetes within households across districts of India: A cross-sectional analysis using a nationally representative household survey

TL;DR

This study finds that hypertension and diabetes often cluster within households in India, with certain factors increasing the likelihood of such clustering.

Contribution

The study provides new quantitative evidence on the clustering of hypertension and diabetes within Indian households and identifies key influencing factors.

Findings

14.9% of Indian households had hypertension clustering, contributing to half of all hypertension cases.

7.7% of households had diabetes clustering, accounting for 39.3% of diabetes cases in India.

Abstract

Despite the rising prevalence of hypertension and diabetes, limited evidence exists on clustering of hypertension and clustering of diabetes within households in India, prompting this study to examine the issue among individuals aged 15 years and above across all 707 districts in India. Clustering here is defined as two or more household members having a disease. We examined clustering for hypertension and clustering for diabetes separately, using cross-sectional data from 5th round of India DHS (National Family Health Survey-5, 2019–21). The factors influencing clustering at the community, district, and state levels were evaluated by multi-level analysis. In India, 14.9% of households had hypertension clustering, contributing to half of the total cases of hypertension in India, while 7.7% had diabetes clustering, accounting for 39.3% of total cases of diabetes in India. Distinct concentrated regions with high prevalence of clustering were noted across districts in India. The regressions at national level revealed that clustering for both diseases was more likely in large households with higher number of older members, wealthier households, households with an overweight woman, regular consumption of fish and fried food, and urban residence. The ICC for clustering was highest at the community level, highlighting the highest impact of factors in the immediate neighbourhood. Clustering within households is evident for both hypertension and diabetes. By providing quantitative estimates of disproportionate case burden among clustered households, our findings underscore the importance of targeting households for effective hypertension and diabetes management interventions. These results provide valuable insights about district-wise distribution of hypertension and diabetes within the unique context of household level clustering, equipping health systems with information on concentrated disease burden and key driving factors influencing clustering in India. This may inform intensified interventions, accelerating progress towards SDG 3·4.

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