ArticleFrontiers in public health2026
Addressing the poverty-nutrition gap for SDG 1 and SDG 2: a Bayesian geo-additive analysis of spatial heterogeneity and nonlinear maternal risks in childhood stunting in India (NFHS-4 and NFHS-5).
Article in Frontiers in 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
Introduction: Childhood stunting remains a critical indicator of chronic undernutrition and a major barrier to achieving Sustainable Development Goals 1 and 2. Despite gradual improvements, substantial geographic disparities in stunting risk persist across India. This study characterised the spatial and temporal heterogeneity of childhood stunting using harmonized district-level data from NFHS-4 (2015-16) and NFHS-5 (2019-21). Methods: A Bayesian spatial geo-additive logistic regression model was applied to 482,960 children aged 0-59 months from 575 harmonized districts. The framework incorporated district-level BYM2 spatial random effects, district-specific survey-round interaction effects, and RW2 smoothers for child age and maternal BMI, estimated using Integrated Nested Laplace Approximation (INLA). Results: The spatial interaction model demonstrated substantially improved fit over the non-spatial model (ΔWAIC = 4,105.2), with a high estimated spatial fraction (φ = 0.983; 95% CrI: 0.938-0.999), indicating that most residual district-level variation was spatially structured. Model predictions showed strong agreement with weighted survey prevalence estimates (Pearson Discussion: These findings demonstrate that childhood stunting in India remains strongly patterned by geography, with temporal changes varying substantially across districts. Geographically targeted and maternal-focused interventions will be essential for achieving equitable reductions in stunting and accelerating progress toward national nutrition goals.
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