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Mapping stunted children in Ethiopia using two decades of data between 2000 and 2019. A geospatial analysis through the Bayesian approach
BACKGROUND: Childhood stunting is a major public health problem globally, resulting in poor cognition and educational performance, low adult wages, low productivity, and an increased risk of nutrition-related chronic diseases in adulthood life. Accurate and reliable data on the prevalence of stuntin...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10601230/ https://www.ncbi.nlm.nih.gov/pubmed/37885003 http://dx.doi.org/10.1186/s41043-023-00412-3 |
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author | Atalell, Kendalem Asmare Techane, Masresha Asmare Terefe, Bewuketu Tamir, Tadesse Tarik |
author_facet | Atalell, Kendalem Asmare Techane, Masresha Asmare Terefe, Bewuketu Tamir, Tadesse Tarik |
author_sort | Atalell, Kendalem Asmare |
collection | PubMed |
description | BACKGROUND: Childhood stunting is a major public health problem globally, resulting in poor cognition and educational performance, low adult wages, low productivity, and an increased risk of nutrition-related chronic diseases in adulthood life. Accurate and reliable data on the prevalence of stunting over time with a sub-national estimate are scarce in Ethiopia. OBJECTIVE: Our objective was to investigate the spatiotemporal distributions and ecological level drivers of stunting among under-five children over time in Ethiopia. METHODS: A geospatial analysis using the Bayesian framework was employed to map the spatial variations of stunting among children aged less than five years. The data for the primary outcome were obtained from the Ethiopian Demographic and Health Surveys (2000–2019) and covariates data were accessed from different publicly available credible sources. The spatial binomial regression model was fitted to identify drivers of child stunting using the Bayesian approach. RESULT: The national prevalence of stunting was 47.9 in 2000, 43.3 in 2005, 37.3 in 2011, 36.6 in 2016, and 35.9 in 2019, with a total reduction rate of 25%. Substantial spatial clustering of stunting was observed in the Northern (Tigray), Northcentral (Amhara), and Northwestern (Amhara) parts of Ethiopia. Temperature (mean regression coefficient (β): −0.19; 95% credible interval (95% CrI): −0.25, −0.12) and population density (β: −0.012; 95% CrI: −0.016, −0.009) were negatively associated with stunting, whereas travel time to the nearest cities (β: 0.12; 95% CrI: 0.064, 0.17) was positively associated with child stunting in Ethiopia. CONCLUSION: The prevalence of stunting varied substantially at subnational and local levels over time. Clustering of stunted children were observed in the Northern parts of Ethiopia. Temperature, population density and travel time to the nearest cities were identified as the drivers of stunting in children. Improving community awareness of child nutrition through community health extension programs should be strengthened. |
format | Online Article Text |
id | pubmed-10601230 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106012302023-10-27 Mapping stunted children in Ethiopia using two decades of data between 2000 and 2019. A geospatial analysis through the Bayesian approach Atalell, Kendalem Asmare Techane, Masresha Asmare Terefe, Bewuketu Tamir, Tadesse Tarik J Health Popul Nutr Research BACKGROUND: Childhood stunting is a major public health problem globally, resulting in poor cognition and educational performance, low adult wages, low productivity, and an increased risk of nutrition-related chronic diseases in adulthood life. Accurate and reliable data on the prevalence of stunting over time with a sub-national estimate are scarce in Ethiopia. OBJECTIVE: Our objective was to investigate the spatiotemporal distributions and ecological level drivers of stunting among under-five children over time in Ethiopia. METHODS: A geospatial analysis using the Bayesian framework was employed to map the spatial variations of stunting among children aged less than five years. The data for the primary outcome were obtained from the Ethiopian Demographic and Health Surveys (2000–2019) and covariates data were accessed from different publicly available credible sources. The spatial binomial regression model was fitted to identify drivers of child stunting using the Bayesian approach. RESULT: The national prevalence of stunting was 47.9 in 2000, 43.3 in 2005, 37.3 in 2011, 36.6 in 2016, and 35.9 in 2019, with a total reduction rate of 25%. Substantial spatial clustering of stunting was observed in the Northern (Tigray), Northcentral (Amhara), and Northwestern (Amhara) parts of Ethiopia. Temperature (mean regression coefficient (β): −0.19; 95% credible interval (95% CrI): −0.25, −0.12) and population density (β: −0.012; 95% CrI: −0.016, −0.009) were negatively associated with stunting, whereas travel time to the nearest cities (β: 0.12; 95% CrI: 0.064, 0.17) was positively associated with child stunting in Ethiopia. CONCLUSION: The prevalence of stunting varied substantially at subnational and local levels over time. Clustering of stunted children were observed in the Northern parts of Ethiopia. Temperature, population density and travel time to the nearest cities were identified as the drivers of stunting in children. Improving community awareness of child nutrition through community health extension programs should be strengthened. BioMed Central 2023-10-26 /pmc/articles/PMC10601230/ /pubmed/37885003 http://dx.doi.org/10.1186/s41043-023-00412-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Atalell, Kendalem Asmare Techane, Masresha Asmare Terefe, Bewuketu Tamir, Tadesse Tarik Mapping stunted children in Ethiopia using two decades of data between 2000 and 2019. A geospatial analysis through the Bayesian approach |
title | Mapping stunted children in Ethiopia using two decades of data between 2000 and 2019. A geospatial analysis through the Bayesian approach |
title_full | Mapping stunted children in Ethiopia using two decades of data between 2000 and 2019. A geospatial analysis through the Bayesian approach |
title_fullStr | Mapping stunted children in Ethiopia using two decades of data between 2000 and 2019. A geospatial analysis through the Bayesian approach |
title_full_unstemmed | Mapping stunted children in Ethiopia using two decades of data between 2000 and 2019. A geospatial analysis through the Bayesian approach |
title_short | Mapping stunted children in Ethiopia using two decades of data between 2000 and 2019. A geospatial analysis through the Bayesian approach |
title_sort | mapping stunted children in ethiopia using two decades of data between 2000 and 2019. a geospatial analysis through the bayesian approach |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10601230/ https://www.ncbi.nlm.nih.gov/pubmed/37885003 http://dx.doi.org/10.1186/s41043-023-00412-3 |
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