ArticleBMC public health2026
Regional vulnerability assessment using poverty, undernutrition, and burden of infectious diseases in India: a spatial syndemic 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
backgroundSocial determinants of health like poverty, undernutrition, and infectious diseases often co-exist in disadvantaged settings, integrating with each other and enhancing health vulnerability. These conditions have been studied separately in India; the limited evidence on their combined spatial distribution remains. This paper applied a syndemic framework to assess the geographic co-occurrence of poverty, undernutrition, and infectious diseases across Indian states.
methodsState-level data were drawn from the National Family Health Survey-5 (2019-21), the National Multidimensional Poverty Index (2023), and the National Tuberculosis Prevalence Survey (2019-21). A composite Syndemic Index was constructed after standardizing and aggregating the indicators of poverty, undernutrition, and infectious disease. Global Moran's I and Local Indicators of Spatial Association were assessed. Associations between the Syndemic Index and selected social and health system determinants were examined using Ordinary Least Squares (OLS) and spatial regression models under centroid, Queen, and Rook contiguity weights, with the spatial lag model under Rook weights selected as the final specification.
resultsThe Syndemic Index showed positive spatial autocorrelation (Moran's I = 0.135), with high-burden clusters in eastern and central states, including Bihar, Jharkhand, and low-burden clusters in southern states such as Kerala and Tamil Nadu. Improved sanitation was associated with lower syndemic vulnerability (β= -0.026), while higher dependency ratio was associated with increased vulnerability (β = 0.023). Spatial regression models provided a better fit than OLS with the spatial lag model with Rook contiguity weights performing best as indicated by lower AIC and higher log-likelihood values. These patterns highlight the need for region-specific and coordinated public health interventions in high-burden areas.
conclusionsSyndemic vulnerability is geographically clustered throughout Indian states, with clear regional disparities. These findings call for multi-sectoral, region-specific interventions focusing on sanitation, population vulnerability and integrated health and social policies.
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