Work

Multidimensional Correlates of Childhood Stunting in India: A Spatial Machine Learning and Explainable AI Approach

Year: 2026

Type: article

Abstract: Childhood stunting remains a major public health challenge in India and is influenced by multiple socioeconomic and environmental factors. This ecological study examined district-level correlates of childhood stunting, including Crimes Against Women (CAW), the Multidimensional Poverty Index (MPI), and drought severity, using data from NFHS-5, the National Crime Records Bureau, NITI Aayog’s MPI reports, and the Drought Atlas of India. Spatial autocorrelation and Spatial regression models were applied alongside machine learning approaches and SHAP-based Explainable AI (XAI) interpretation. Childhood stunting exhibited significant spatial clustering (Moran’s I = 0.520, p < 0.001), with hotspots in northern, central, and eastern India. Higher stunting was associated with higher birth order, low maternal BMI, child anaemia, and MPI, and negative associations with iodised salt usage, electricity access, and timely postnatal care. A significant spatial lag parameter (ρ = 0.348) indicated substantial spillover effects. Machine learning models consistently identified MPI, drought severity, and CAW as key predictors. The integrated spatial and machine learning framework identifies key correlates and spatial dependencies of childhood stunting, highlighting the need for region-specific, multisectoral interventions.

Source: Stats

DOI: 10.3390/stats9020034

Authors: Bhagyajyothi Rao, Md Gulzarull Hasan, Bandhavya Putturaya, Asha Kamath, Mohammad Aatif, Yousif M. Elmosaad

Institutions: Manipal Academy of Higher Education, Kyoto University, King Faisal University

Language: English

Topic: Child Nutrition and Water Access

Subfield: Nutrition and Dietetics

Field: Nursing

Domain: Health Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: gold

Estimated APC paid: $1,930