Household Level Clustering of Hypertension and Diabetes across Districts of India: Evidence from a Nationally Representative Household Survey

Sarang Pedgaonkar, International Institute for Population Sciences (IIPS)
Kaushlendra Kumar, Guide
Wahengbam Bigyananda Meitei, International Institute for Population Sciences (IIPS)
Shubham Kumar, International Institute for Population Sciences (IIPS)
Ashish Kumar Upadhyay, International Institute for Population Sciences (IIPS)
Jürgen Maurer, Université de Lausanne
Abhishek Singh, International Institute for Population Sciences (IIPS)

Abstract: Despite the rising prevalence of hypertension and diabetes, limited evidence exists on their household-level clustering in India, prompting this study to examine the issue among individuals aged 15 and above using NFHS-5 data. Clustering, defined as two or more household members having hypertension or diabetes, was evaluated using multi-level analysis to assess influencing factors at the community, district, and state levels. In India, 14.9% of households had hypertension clustering, contributing to half of the cases, while 8.2% had diabetes clustering, accounting for 39.3% of cases. Regional concentration of clustering across districts was noted, with clustering more likely in households with higher alcohol/tobacco use, overweight women, greater fried food consumption, more members aged 15+, wealth, and rural residence. The ICC for clustering was highest at the community level, highlighting the highest impact of factors in the immediate neighbourhood. By demonstrating clustering at household level for both hypertension and diabetes, our findings underscore the importance of targeting households for interventions of hypertension and diabetes management and equip health systems with information on patterns of concentrated pockets of disease burden within households. This may inform intensified interventions for rapid progress towards SDG 3·4.

Keywords: Families, Unions and Households, Health and Morbidity, Mortality and Longevity, Multi-level modeling

See extended abstract.