Childhood underweight in Ethiopia

Modelling non-linear risk factors and geographic hotspots using Bayesian geoadditive methods

Bibliographic Data

ID22077112
AuthorsEndeshaw Assefa Derso (University of Messina), Maria Gabriella Campolo (0000-0002-1075-4573, University of Messina), Angela Alibrandi (0000-0001-6695-151X, University of Messina)
Year2026
Volume14
Pages1760032-1760032
Publication date2026-04-10
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2026.1760032
PMID42040089
OpenAlexW7153203692
LanguageEN
References cited45

Objectives Underweight in children under 5 years of age is defined as a weight-for-age z -score (WAZ) of less than −2 standard deviations (−2SD) from the median of the World Health Organization (WHO) Child Growth Standards (CGS). This study examines the effect of socio-demographic covariates and geographical covariates on underweight, as well as the flexible trends of metrical covariates, to identify communities at a high risk of underweight. 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 2 . Lower and higher maternal BMI are associated with more severe cases of underweight (as indicated by lower WAZ z -scores). There is also significant spatial heterogeneity, and based on inverse distance weighting (IDW) interpolation of predictive values, the western, central, and eastern parts of the country are hotspot areas for underweight children. Conclusion Socio-demographic and community-based programmes should be comprehensively integrated into Ethiopian policy to combat childhood malnutrition

Bayes' theorem · Bayesian inference · Bayesian probability · Body mass index · Covariate · Inverse distance weighting · Markov chain Monte Carlo · Underweight · Weighting · Body Composition Measurement Techniques · Child Nutrition and Water Access · Obesity, Physical Activity, Diet

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