Coexisting predictors for undernutrition indices among under-five children in West Africa : application of a multilevel multivariate ordinal logistic regression model

dc.contributor.authorAsmare, Abebew Aklog
dc.contributor.authorTegegne, Awoke Seyoum
dc.contributor.authorBelay, Denekew Bitew
dc.contributor.authorAgmas, Yitateku Adugna
dc.date.accessioned2025-08-29T08:53:20Z
dc.date.available2025-08-29T08:53:20Z
dc.date.issued2025-06
dc.descriptionDATA AVAILABILITY : The data used in this study came from the Measure DHS program (https://dhsprogram.com/Data/terms-of-use.cfm) and can be obtained using the protocol indicated in the Methods section. Additional documentation on ethical problems related to the surveys is accessible at http://dhsprogram.com.
dc.description.abstractBACKGROUND : Due to its immediate and long-term implications for population health and socioeconomic advancement, children’s nutritional status is especially crucial. It is the main reason for morbidity and mortality in children under five. Therefore, this study aimed to investigate predictors for under-nutrition indices among under-five children in West Africa. METHODS : The study was conducted in 14 countries in West Africa. A weighted total sample of 35,162 under-five children was included. Considering the impact of other predictors such as maternal, child, and socioeconomic variables, a multilevel multivariate partial proportional ordinal logistic regression model was conducted to analyze the relationship between stunting, wasting and underweight. RESULTS : Among the participants in the study, approximately 27.7% were stunted, with 17.9% moderately stunted and 10.8% severely stunted. Additionally, about 18.6% (13.3% moderately and 5.3% severely) were underweight, and 9% (6.7% moderately and 2.3% severely) were wasted. More than half of the children (51.1%) were male, and 36.6% lived in urban areas. Compared to singleton children, a child with multiple birth types was 2.701, 3.740, and 1.777 times more likely to be stunted, underweight, and wasted, respectively. CONCLUSION : The findings in the current study revealed a direct correlation among underweight status, wasting, and stunting. Furthermore, even when ignoring the effects of other independent variables, underweight was found to serve as a composite measure of stunting and wasting when examining the link between these three indicators. The results identified several common predictors significantly associated with all three undernourishment indices. Therefore, governments and non-governmental organizations should prioritize public health interventions aimed at improving parental education and increasing antenatal care visits to enhance the nutritional status of under-five children. More attention should be given to improving healthcare delivery at health facilities and homes, promoting children’s health education, and ensuring access to clean drinking water sources.
dc.description.departmentSchool of Health Systems and Public Health (SHSPH)
dc.description.librarianhj2025
dc.description.sdgSDG-03: Good health and well-being
dc.description.urihttps://bmcnutr.biomedcentral.com
dc.identifier.citationAsmare, A.A., Tegegne, A.S., Belay, D.B. et al. Coexisting predictors for undernutrition indices among under-five children in West Africa: application of a multilevel multivariate ordinal logistic regression model. BMC Nutrition 11, 112 (2025). https://doi.org/10.1186/s40795-025-01099-x.
dc.identifier.issn2055-0928 (online)
dc.identifier.other10.1186/s40795-025-01099-x
dc.identifier.urihttp://hdl.handle.net/2263/104058
dc.language.isoen
dc.publisherBioMed Central
dc.rights© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
dc.subjectUnder-five children
dc.subjectMultilevel multivariate partial proportional ordinal logistic regression
dc.subjectStunting
dc.subjectWasting
dc.subjectWest Africa
dc.subjectUnderweight
dc.titleCoexisting predictors for undernutrition indices among under-five children in West Africa : application of a multilevel multivariate ordinal logistic regression model
dc.typeArticle

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