Modeling and mapping under-nutrition among under-five children in Ethiopia: a Bayesian spatial analysis.

Malnutrition remains a critical global challenge, characterized by an imbalance between nutrient requirements and consumption. Under-nutrition, a specific form of malnutrition, results from inadequate intake of essential nutrients and has severe implications for young children, especially in developing countries. This study aims to model under-nutrition cases among children under five in Ethiopia, utilizing Bayesian spatial models to identify effective interventions. Four models were considered: Generalized Linear Model (GLM), Generalized Linear Mixed Models (GLMM), Intrinsic Conditional Autoregressive (ICAR), and Conditional Autoregressive Besag-York-Mollié (CAR BYM) with negative binomial distribution. The rationale for employing multiple models stems from the need to compare performance and accuracy in capturing spatial heterogeneity. The data were obtained from the Ethiopian Demographic and Health Survey 2019. The parameter estimation was carried out using Bayesian Markov Chain Monte Carlo (MCMC) through the brms package in R, which interfaces with Stan for efficient sampling. The models were evaluated based on the Watanabe Akaike Information Criterion (WAIC) and Leave-One-Out (LOO) cross-validation, with CAR BYM emerging as the best-fitting model. Spatial modeling revealed that maternal age, breastfeeding practices, access to clean water and sanitation facilities, cooking practices, maternal education, and wealth status significantly influence the number of under-nutrition cases among children under five in Ethiopia. Specifically, lower maternal education, poorer wealth status, and inadequate access to clean water and sanitation were associated with an increased number of under-nutrition cases, while improved breastfeeding practices, rich wealth status and higher maternal education were associated with decreased number of cases. Regional disparities also played a significant role, with the CAR BYM model effectively identifying high-risk regions such as Somali, Afar, and parts of Oromia, identified as areas requiring targeted intervention.