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Modelling chronic malnutrition in Zambia: A Bayesian distributional regression approach

BACKGROUND: The burden of child under-nutrition still remains a global challenge, with greater severity being faced by low- and middle-income countries, despite the strategies in the Sustainable Development Goals (SDGs). Globally, malnutrition is the one of the most important risk factors associated...

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Autores principales: Moonga, Given, Böse-O’Reilly, Stephan, Berger, Ursula, Harttgen, Kenneth, Michelo, Charles, Nowak, Dennis, Siebert, Uwe, Yabe, John, Seiler, Johannes
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336812/
https://www.ncbi.nlm.nih.gov/pubmed/34347795
http://dx.doi.org/10.1371/journal.pone.0255073
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author Moonga, Given
Böse-O’Reilly, Stephan
Berger, Ursula
Harttgen, Kenneth
Michelo, Charles
Nowak, Dennis
Siebert, Uwe
Yabe, John
Seiler, Johannes
author_facet Moonga, Given
Böse-O’Reilly, Stephan
Berger, Ursula
Harttgen, Kenneth
Michelo, Charles
Nowak, Dennis
Siebert, Uwe
Yabe, John
Seiler, Johannes
author_sort Moonga, Given
collection PubMed
description BACKGROUND: The burden of child under-nutrition still remains a global challenge, with greater severity being faced by low- and middle-income countries, despite the strategies in the Sustainable Development Goals (SDGs). Globally, malnutrition is the one of the most important risk factors associated with illness and death, affecting hundreds of millions of pregnant women and young children. Sub-Saharan Africa is one of the regions in the world struggling with the burden of chronic malnutrition. The 2018 Zambia Demographic and Health Survey (ZDHS) report estimated that 35% of the children under five years of age are stunted. The objective of this study was to analyse the distribution, and associated factors of stunting in Zambia. METHODS: We analysed the relationships between socio-economic, and remote sensed characteristics and anthropometric outcomes in under five children, using Bayesian distributional regression. Georeferenced data was available for 25,852 children from two waves of the ZDHS, 31% observation were from the 2007 and 69% were from the 2013/14. We assessed the linear, non-linear and spatial effects of covariates on the height-for-age z-score. RESULTS: Stunting decreased between 2007 and 2013/14 from a mean z-score of 1.59 (credible interval (CI): -1.63; -1.55) to -1.47 (CI: -1.49; -1.44). We found a strong non-linear relationship for the education of the mother and the wealth of the household on the height-for-age z-score. Moreover, increasing levels of maternal education above the eighth grade were associated with a reduced variation of stunting. Our study finds that remote sensed covariates alone explain little of the variation of the height-for-age z-score, which highlights the importance to collect socio-economic characteristics, and to control for socio-economic characteristics of the individual and the household. CONCLUSIONS: While stunting still remains unacceptably high in Zambia with remarkable regional inequalities, the decline is lagging behind goal two of the SDGs. This emphasises the need for policies that help to reduce the share of chronic malnourished children within Zambia.
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spelling pubmed-83368122021-08-05 Modelling chronic malnutrition in Zambia: A Bayesian distributional regression approach Moonga, Given Böse-O’Reilly, Stephan Berger, Ursula Harttgen, Kenneth Michelo, Charles Nowak, Dennis Siebert, Uwe Yabe, John Seiler, Johannes PLoS One Research Article BACKGROUND: The burden of child under-nutrition still remains a global challenge, with greater severity being faced by low- and middle-income countries, despite the strategies in the Sustainable Development Goals (SDGs). Globally, malnutrition is the one of the most important risk factors associated with illness and death, affecting hundreds of millions of pregnant women and young children. Sub-Saharan Africa is one of the regions in the world struggling with the burden of chronic malnutrition. The 2018 Zambia Demographic and Health Survey (ZDHS) report estimated that 35% of the children under five years of age are stunted. The objective of this study was to analyse the distribution, and associated factors of stunting in Zambia. METHODS: We analysed the relationships between socio-economic, and remote sensed characteristics and anthropometric outcomes in under five children, using Bayesian distributional regression. Georeferenced data was available for 25,852 children from two waves of the ZDHS, 31% observation were from the 2007 and 69% were from the 2013/14. We assessed the linear, non-linear and spatial effects of covariates on the height-for-age z-score. RESULTS: Stunting decreased between 2007 and 2013/14 from a mean z-score of 1.59 (credible interval (CI): -1.63; -1.55) to -1.47 (CI: -1.49; -1.44). We found a strong non-linear relationship for the education of the mother and the wealth of the household on the height-for-age z-score. Moreover, increasing levels of maternal education above the eighth grade were associated with a reduced variation of stunting. Our study finds that remote sensed covariates alone explain little of the variation of the height-for-age z-score, which highlights the importance to collect socio-economic characteristics, and to control for socio-economic characteristics of the individual and the household. CONCLUSIONS: While stunting still remains unacceptably high in Zambia with remarkable regional inequalities, the decline is lagging behind goal two of the SDGs. This emphasises the need for policies that help to reduce the share of chronic malnourished children within Zambia. Public Library of Science 2021-08-04 /pmc/articles/PMC8336812/ /pubmed/34347795 http://dx.doi.org/10.1371/journal.pone.0255073 Text en © 2021 Moonga et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Moonga, Given
Böse-O’Reilly, Stephan
Berger, Ursula
Harttgen, Kenneth
Michelo, Charles
Nowak, Dennis
Siebert, Uwe
Yabe, John
Seiler, Johannes
Modelling chronic malnutrition in Zambia: A Bayesian distributional regression approach
title Modelling chronic malnutrition in Zambia: A Bayesian distributional regression approach
title_full Modelling chronic malnutrition in Zambia: A Bayesian distributional regression approach
title_fullStr Modelling chronic malnutrition in Zambia: A Bayesian distributional regression approach
title_full_unstemmed Modelling chronic malnutrition in Zambia: A Bayesian distributional regression approach
title_short Modelling chronic malnutrition in Zambia: A Bayesian distributional regression approach
title_sort modelling chronic malnutrition in zambia: a bayesian distributional regression approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336812/
https://www.ncbi.nlm.nih.gov/pubmed/34347795
http://dx.doi.org/10.1371/journal.pone.0255073
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