ArticleBMC public health2026
Subnational inequalities and spatially varying associations of elevated blood glucose among women in India: a national district-level biomarkers analysis.
Article in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundElevated blood sugar is a major modifiable risk factor for cardiovascular disease and premature mortality. In India, national estimates obscure substantial small-scale disparities, particularly among women. This study presents a national district-level geospatial analysis of elevated blood sugar among Indian women using data from the National Family Health Survey 2019-21 (NFHS-5),
methodsWe analyzed blood sugar data from 724,115 women aged ≥ 15 years across 707 districts of India (NFHS-5, 2019-21) using random blood glucose ≥ 140 mg/dL or current diabetes medication as definition of elevated blood glucose. Socio-demographic, nutritional, maternal health and behavioural variables were considered. Ordinary least squares (OLS) regression assessed global associations, while geographically weighted regression (GWR) and multi-scale GWR (MGWR) captured spatial heterogeneity.
findingsDistrict-level prevalence ranged from approximately 6% in parts of north-eastern India to over 30% in Punjab, Kerala and Himachal Pradesh. Overweight/obesity (β = 0.56), female literacy (β = 0.12), early marriage (β = 0.03) was consistently associated with higher prevalence, with notable geographic variation. MGWR substantially improved model performance (adjusted R² = 0.83 compared with 0.65 for OLS), revealing that obesity had the strongest effect in southern districts, while early marriage was more influential in central and northern regions. Unexpected positive associations with iron-folic acid supplementation and female literacy likely reflect complex interactions related to the nutrition transition and differential detection of metabolic risk.
conclusionsHyperglycemia among Indian women is highly clustered and driven by spatially heterogeneous factors. National averages conceal more than threefold differences across districts. Targeted, district-specific interventions addressing obesity, early marriage and structural inequities are essential to achieve the World Health Organization (WHO) targets for reducing the burden of non-communicable diseases (NCDs). Integrating geospatial analytics into surveillance can enhance precision public health-that is, the use of data-driven, location-specific strategies to improve population health outcomes in low- and middle-income countries.
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