ArticleGlobal epidemiology2026
Spatial disparities and associated factors of composite index of anthropometric failure for under-five children across three African countries.
Article in Global epidemiology, 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
Background: Malnutrition is associated with both undernutrition and over nutrition. Child undernutrition remains the greatest public health problem in developing countries. This study assessed factors associated with the Composite Index of Anthropometric Failure (CIAF) and explored spatial patterns across the provinces of three African countries. Methods: A nationally representative secondary record from the recent Demographic and Health Survey was used. Spatial Autocorrelation was used to identify particular provinces clustering with high and low CIAF values. A Bayesian generalized geo-additive mixed modelling approach was applied using Integrated Nested Laplace Approximation with a binomial family and logit link function. Results: The overall prevalence of CIAF among the three African countries was 41.2%, with a difference between countries ranging from Zambia (34.9%) to Democratic Republic of Congo (46.0%). Both Moran's I and Geary's C tests evidenced the existence of spatial autocorrelation of CIAF among children in the three African countries. Bayesian generalized geo-additive model with Besag-York-Mollie mixed effect was found to be the best model to assess the spatial dependencies and the non-linear effects of factors on CIAF. This study showed the existence of spatial disparities in the CIAF. Place of residence, mother's education level, child sex, wealth index, marital status, access to health facilities, child birth size, type of birth, diarrhea, vitamin A supplementation, place of birth, child anemia status, iron supplementation, and country were associated with CIAF. Conclusion: This study revealed a high burden of CIAF across the three African countries, with marked spatial disparities and clustering of CIAF in children. The Bayesian geo-additive model identified multiple socioeconomic, demographic, geographical, and health-related factors associated with the CIAF. These findings highlight the need for geographically targeted, multifaceted interventions to address the underlying factors associated with malnutrition and reduce regional inequalities.
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