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Understanding urban inequalities in children’s linear growth outcomes: a trend and decomposition analysis of 39,049 children in Bangladesh (2000-2018)
BACKGROUND: Despite significant progress in reducing child undernutrition, Bangladesh remains among the top six countries globally with the largest burden of child stunting and has disproportionately high stunting prevalence among the urban poor. We use population representative data to identify key...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8631262/ https://www.ncbi.nlm.nih.gov/pubmed/34847918 http://dx.doi.org/10.1186/s12889-021-12181-x |
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author | Win, Hayman Wallenborn, Jordyn Probst-Hensch, Nicole Fink, Günther |
author_facet | Win, Hayman Wallenborn, Jordyn Probst-Hensch, Nicole Fink, Günther |
author_sort | Win, Hayman |
collection | PubMed |
description | BACKGROUND: Despite significant progress in reducing child undernutrition, Bangladesh remains among the top six countries globally with the largest burden of child stunting and has disproportionately high stunting prevalence among the urban poor. We use population representative data to identify key predictors of child stunting in Bangladesh and assess their contributions to linear growth differences observed between urban poor and non-poor children. METHODS: We combined six rounds of Demographic and Health Survey data spanning 2000-2018 and used official poverty rates to classify the urban population into poor and non-poor households. We identified key stunting determinants using stepwise selection method. Regression-decomposition was used to quantify contributions of these key determinants to poverty-based intra-urban differences in child linear growth status. RESULTS: Key stunting determinants identified in our study predicted 84% of the linear growth difference between urban poor and non-poor children. Child’s place of birth (27%), household wealth (22%), maternal education (18%), and maternal body mass index (11%) were the largest contributors to the intra-urban child linear growth gap. Difference in average height-for-age z score between urban poor and non-poor children declined by 0.31 standard deviations between 2000 and 2018. About one quarter of this observed decrease was explained by reduced differentials between urban poor and non-poor in levels of maternal education and maternal underweight status. CONCLUSIONS: Although the intra-urban disparity in child linear growth status declined over the 2000-2018 period, socioeconomic gaps remain significant. Increased nutrition-sensitive programs and investments targeting the urban poor to improve girls’ education, household food security, and maternal and child health services could aid in further narrowing the remaining linear growth gap. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-12181-x. |
format | Online Article Text |
id | pubmed-8631262 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-86312622021-11-30 Understanding urban inequalities in children’s linear growth outcomes: a trend and decomposition analysis of 39,049 children in Bangladesh (2000-2018) Win, Hayman Wallenborn, Jordyn Probst-Hensch, Nicole Fink, Günther BMC Public Health Research BACKGROUND: Despite significant progress in reducing child undernutrition, Bangladesh remains among the top six countries globally with the largest burden of child stunting and has disproportionately high stunting prevalence among the urban poor. We use population representative data to identify key predictors of child stunting in Bangladesh and assess their contributions to linear growth differences observed between urban poor and non-poor children. METHODS: We combined six rounds of Demographic and Health Survey data spanning 2000-2018 and used official poverty rates to classify the urban population into poor and non-poor households. We identified key stunting determinants using stepwise selection method. Regression-decomposition was used to quantify contributions of these key determinants to poverty-based intra-urban differences in child linear growth status. RESULTS: Key stunting determinants identified in our study predicted 84% of the linear growth difference between urban poor and non-poor children. Child’s place of birth (27%), household wealth (22%), maternal education (18%), and maternal body mass index (11%) were the largest contributors to the intra-urban child linear growth gap. Difference in average height-for-age z score between urban poor and non-poor children declined by 0.31 standard deviations between 2000 and 2018. About one quarter of this observed decrease was explained by reduced differentials between urban poor and non-poor in levels of maternal education and maternal underweight status. CONCLUSIONS: Although the intra-urban disparity in child linear growth status declined over the 2000-2018 period, socioeconomic gaps remain significant. Increased nutrition-sensitive programs and investments targeting the urban poor to improve girls’ education, household food security, and maternal and child health services could aid in further narrowing the remaining linear growth gap. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-12181-x. BioMed Central 2021-11-30 /pmc/articles/PMC8631262/ /pubmed/34847918 http://dx.doi.org/10.1186/s12889-021-12181-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Win, Hayman Wallenborn, Jordyn Probst-Hensch, Nicole Fink, Günther Understanding urban inequalities in children’s linear growth outcomes: a trend and decomposition analysis of 39,049 children in Bangladesh (2000-2018) |
title | Understanding urban inequalities in children’s linear growth outcomes: a trend and decomposition analysis of 39,049 children in Bangladesh (2000-2018) |
title_full | Understanding urban inequalities in children’s linear growth outcomes: a trend and decomposition analysis of 39,049 children in Bangladesh (2000-2018) |
title_fullStr | Understanding urban inequalities in children’s linear growth outcomes: a trend and decomposition analysis of 39,049 children in Bangladesh (2000-2018) |
title_full_unstemmed | Understanding urban inequalities in children’s linear growth outcomes: a trend and decomposition analysis of 39,049 children in Bangladesh (2000-2018) |
title_short | Understanding urban inequalities in children’s linear growth outcomes: a trend and decomposition analysis of 39,049 children in Bangladesh (2000-2018) |
title_sort | understanding urban inequalities in children’s linear growth outcomes: a trend and decomposition analysis of 39,049 children in bangladesh (2000-2018) |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8631262/ https://www.ncbi.nlm.nih.gov/pubmed/34847918 http://dx.doi.org/10.1186/s12889-021-12181-x |
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