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Bayesian geostatistical modelling of stunting in Rwanda: risk factors and spatially explicit residual stunting burden
BACKGROUND: Stunting remains a significant public health issue in Rwanda and its prevalence exhibits considerable geographical variation. We apply Bayesian geostatistical modelling to study the spatial pattern of stunting in children less than five years considering anthropometric, socioeconomic and...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8785587/ https://www.ncbi.nlm.nih.gov/pubmed/35073893 http://dx.doi.org/10.1186/s12889-022-12552-y |
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author | Uwiringiyimana, Vestine Osei, Frank Amer, Sherif Veldkamp, Antonie |
author_facet | Uwiringiyimana, Vestine Osei, Frank Amer, Sherif Veldkamp, Antonie |
author_sort | Uwiringiyimana, Vestine |
collection | PubMed |
description | BACKGROUND: Stunting remains a significant public health issue in Rwanda and its prevalence exhibits considerable geographical variation. We apply Bayesian geostatistical modelling to study the spatial pattern of stunting in children less than five years considering anthropometric, socioeconomic and demographic risk factors in Rwanda. In addition, we predict the spatial residuals effects to quantify the burden of stunting not accounted for by our geostatistical model. METHODS: We used the data from the 2015 Rwanda Demographic and Health Survey. We fitted two spatial logistic models with similar structures, only differentiated by the inclusion or exclusion of spatially structured random effects. RESULTS: The risk factors of stunting identified in the geostatistical model were being male (OR = 1.32, 95% CI: 1.16, 1.47), lower birthweight (kg) (OR = 0.96, 95% CI: 0.95, 0.97), non-exclusive breastfeeding (OR = 1.24, 95% CI: 1.04, 1.45), occurrence of diarrhoea in the last two weeks (OR = 1.18, 95% CI: 1.02, 1.37), a lower proportion of mothers with overweight (BMI ≥ 25) (OR = 0.82, 95% CI: 0.71, 0.95), a higher proportion of mothers with no or only primary education (OR = 1.14, 95% CI: 0.99, 1.36). Also, a higher probability of living in a house with poor flooring material (OR = 1.22, 95% CI: 1.06, 1.41), reliance on a non-improved water source (OR = 1.13, 95% CI: 1.00, 1.27), and a low wealth index were identified as risk factors of stunting. Mapping of the spatial residuals effects showed that, in particular, the Northern and Western regions, followed by the Southern region of Rwanda, still exhibit a higher risk of stunting even after accounting for all the covariates in the spatial model. CONCLUSIONS: Further studies are needed to identify the still unknown spatially explicit factors associated with higher risk of stunting. Finally, given the spatial heterogeneity of stunting, interventions to reduce stunting should be geographically targeted. |
format | Online Article Text |
id | pubmed-8785587 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-87855872022-01-24 Bayesian geostatistical modelling of stunting in Rwanda: risk factors and spatially explicit residual stunting burden Uwiringiyimana, Vestine Osei, Frank Amer, Sherif Veldkamp, Antonie BMC Public Health Research Article BACKGROUND: Stunting remains a significant public health issue in Rwanda and its prevalence exhibits considerable geographical variation. We apply Bayesian geostatistical modelling to study the spatial pattern of stunting in children less than five years considering anthropometric, socioeconomic and demographic risk factors in Rwanda. In addition, we predict the spatial residuals effects to quantify the burden of stunting not accounted for by our geostatistical model. METHODS: We used the data from the 2015 Rwanda Demographic and Health Survey. We fitted two spatial logistic models with similar structures, only differentiated by the inclusion or exclusion of spatially structured random effects. RESULTS: The risk factors of stunting identified in the geostatistical model were being male (OR = 1.32, 95% CI: 1.16, 1.47), lower birthweight (kg) (OR = 0.96, 95% CI: 0.95, 0.97), non-exclusive breastfeeding (OR = 1.24, 95% CI: 1.04, 1.45), occurrence of diarrhoea in the last two weeks (OR = 1.18, 95% CI: 1.02, 1.37), a lower proportion of mothers with overweight (BMI ≥ 25) (OR = 0.82, 95% CI: 0.71, 0.95), a higher proportion of mothers with no or only primary education (OR = 1.14, 95% CI: 0.99, 1.36). Also, a higher probability of living in a house with poor flooring material (OR = 1.22, 95% CI: 1.06, 1.41), reliance on a non-improved water source (OR = 1.13, 95% CI: 1.00, 1.27), and a low wealth index were identified as risk factors of stunting. Mapping of the spatial residuals effects showed that, in particular, the Northern and Western regions, followed by the Southern region of Rwanda, still exhibit a higher risk of stunting even after accounting for all the covariates in the spatial model. CONCLUSIONS: Further studies are needed to identify the still unknown spatially explicit factors associated with higher risk of stunting. Finally, given the spatial heterogeneity of stunting, interventions to reduce stunting should be geographically targeted. BioMed Central 2022-01-24 /pmc/articles/PMC8785587/ /pubmed/35073893 http://dx.doi.org/10.1186/s12889-022-12552-y Text en © The Author(s) 2022 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 Article Uwiringiyimana, Vestine Osei, Frank Amer, Sherif Veldkamp, Antonie Bayesian geostatistical modelling of stunting in Rwanda: risk factors and spatially explicit residual stunting burden |
title | Bayesian geostatistical modelling of stunting in Rwanda: risk factors and spatially explicit residual stunting burden |
title_full | Bayesian geostatistical modelling of stunting in Rwanda: risk factors and spatially explicit residual stunting burden |
title_fullStr | Bayesian geostatistical modelling of stunting in Rwanda: risk factors and spatially explicit residual stunting burden |
title_full_unstemmed | Bayesian geostatistical modelling of stunting in Rwanda: risk factors and spatially explicit residual stunting burden |
title_short | Bayesian geostatistical modelling of stunting in Rwanda: risk factors and spatially explicit residual stunting burden |
title_sort | bayesian geostatistical modelling of stunting in rwanda: risk factors and spatially explicit residual stunting burden |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8785587/ https://www.ncbi.nlm.nih.gov/pubmed/35073893 http://dx.doi.org/10.1186/s12889-022-12552-y |
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