State-level nutritional disparities and policy priorities in India

Nutritional inequality persists in India despite sustained economic growth, expanding healthcare infrastructure, and large-scale social welfare programs. National averages mask substantial subnational disparities, concealing deep inequalities that continue to shape nutritional outcomes across States and Union Territories. This study quantifies nutritional inequality across Indian States and Union Territories using the latest NFHS-6 factsheet (2023-24) data, integrating multidimensional and spatial methods to provide a comprehensive evidence base for geographically targeted nutrition policy.

Data from 36 States/UTs (excluding Manipur) were analyzed using an ecological, cross-sectional, and comparative research design. Outcome indicators included child stunting, wasting, and underweight prevalence, which were standardized and aggregated into a Child Undernutrition Burden Index (CUBI). A Dietary Vulnerability Index (DVI) was also constructed using standardized indicators of dietary diversity, nutrient-rich food consumption, and household food insecurity. Inequality was assessed via coefficient of variation, Theil decomposition, and Moran's I spatial autocorrelation. Advanced methods included quantile regression to examine heterogeneous associations across burden percentiles and spatial Durbin models to assess direct, indirect, and total spillover effects across neighboring states.

National prevalence was 29.3% for stunting, 19.0% for wasting, and 31.8% for underweight. Jharkhand (CUBI = 1.35), Gujarat (0.94), and Bihar (0.81) had the highest burden, while Goa (-1.45), Kerala (-1.32), and Tamil Nadu (-1.21) had the lowest. Significant spatial autocorrelation (Moran's I = 0.47, p < 0.01) indicated clustering in eastern and central India. Female education showed stronger protective associations in high-burden states (β=-0.487) than in low-burden states (β=-0.346). The spatial Durbin model identified significant spillovers (ρ = 0.413, p < 0.01), including direct (β=-0.362) and indirect (β=-0.184) effects of female education. Within-region inequality accounted for 61.4% of total inequality. Female education, sanitation, and women's empowerment showed the strongest protective associations, explaining 64% of state-level CUBI variation. DVI was strongly associated with CUBI (ρ = 0.842, p < 0.001). All findings represent ecological associations and should not be interpreted as individual-level causal effects.

India's nutritional transition remains characterized by persistent spatial clustering and substantial within-region inequality. State-level priorities should focus on female education, sanitation, dietary diversity, and women's empowerment, while CUBI, quantile regression, and spatial spillover evidence can guide targeted interventions and coordinated regional nutrition policies.