Modelling chronic malnutrition in Zambia: A Bayesian distributional regression approach.

<h4>Background</h4>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...

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Autores principales: Given Moonga, Stephan Böse-O'Reilly, Ursula Berger, Kenneth Harttgen, Charles Michelo, Dennis Nowak, Uwe Siebert, John Yabe, Johannes Seiler
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Publicado: Public Library of Science (PLoS) 2021
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spelling oai:doaj.org-article:04fd19838d1646be93acec376bb9d7c12021-12-02T20:18:47ZModelling chronic malnutrition in Zambia: A Bayesian distributional regression approach.1932-620310.1371/journal.pone.0255073https://doaj.org/article/04fd19838d1646be93acec376bb9d7c12021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255073https://doaj.org/toc/1932-6203<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.Given MoongaStephan Böse-O'ReillyUrsula BergerKenneth HarttgenCharles MicheloDennis NowakUwe SiebertJohn YabeJohannes SeilerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255073 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Given Moonga
Stephan Böse-O'Reilly
Ursula Berger
Kenneth Harttgen
Charles Michelo
Dennis Nowak
Uwe Siebert
John Yabe
Johannes Seiler
Modelling chronic malnutrition in Zambia: A Bayesian distributional regression approach.
description <h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.
format article
author Given Moonga
Stephan Böse-O'Reilly
Ursula Berger
Kenneth Harttgen
Charles Michelo
Dennis Nowak
Uwe Siebert
John Yabe
Johannes Seiler
author_facet Given Moonga
Stephan Böse-O'Reilly
Ursula Berger
Kenneth Harttgen
Charles Michelo
Dennis Nowak
Uwe Siebert
John Yabe
Johannes Seiler
author_sort Given Moonga
title Modelling chronic malnutrition in Zambia: A Bayesian distributional regression approach.
title_short 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_sort modelling chronic malnutrition in zambia: a bayesian distributional regression approach.
publisher Public Library of Science (PLoS)
publishDate 2021
url https://doaj.org/article/04fd19838d1646be93acec376bb9d7c1
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