ArticleFrontiers in public health2026
Childhood underweight in Ethiopia: modelling non-linear risk factors and geographic hotspots using Bayesian geoadditive methods.
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
Objectives: Underweight in children under 5 years of age is defined as a weight-for-age Methods: This study utilized cross-sectional data on underweight from the 2016 Ethiopian Demographic and Health Survey (EDHS). A Bayesian geoadditive Gaussian regression model was used to analyse a sample of 10,641 children. Appropriate prior distributions were established for the scale parameters in the models, and the inference was conducted within a fully Bayesian framework using Markov chain Monte Carlo (MCMC) simulation. Results: The results indicate that the effects of metrical covariates, such as child age, the mother's body mass index (BMI), and maternal age, on underweight were non-linear. Specifically, the relationship between the mother's BMI and her child's underweight appears to be an inverted U-shape within the maternal BMI range between 12 and 50 kg/m Conclusion: Socio-demographic and community-based programmes should be comprehensively integrated into Ethiopian policy to combat childhood malnutrition.
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