A study on spatial association of poor child health outcomes and multidimensional poverty in India

India faces persistent challenges of poor child health and multidimensional poverty, which together create a cycle of intergenerational poverty and poor health. So, identifying the spatial association between these issues is crucial for targeted interventions and achieving health and poverty-related SDGs.

This study used secondary data from NITI Aayog's MPI Progress Review 2023 and NFHS-5 state fact sheets. To achieve the study objectives, various methods were used. Getis-Ord statistics were used to identify spatial clusters of hot spots and cold spots. Univariate Moran's I identified spatial clusters of poor child health outcomes. Bivariate LISA Moran's I examined their spatial relationship with the Multidimensional Poverty Index (MPI). Finally, a spatial lag model was applied to assess the effect of MPI on child health outcomes at the district level.

The results demonstrated a strong link between poor child health outcomes and multidimensional poverty in various Indian districts. Districts such as Satna, Rewa, and Chhatarpur in Madhya Pradesh; Banda, Sonbhadra, and Bahraich in Uttar Pradesh; Simdega and West Singhbhum in Jharkhand; Bastar and Dantewada in Chhattisgarh; Baran and Dholpur in Rajasthan; Jamui and Purnia in Bihar; Malkangiri and Koraput in Odisha; and Purulia and Jhargram in West Bengal displayed on-going weaknesses in both areas.

The findings highlight the need for specific policies, as one-size-fits-all approaches across districts have not worked. High cluster districts should be named High-Priority Development Zones. These zones should receive mobile health services for mothers and children, increased funding, inter-sector committees, and improved existing programs.