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Spatio-temporal variations in neonatal mortality rates in Ghana: An application of hierarchical Bayesian methods

Ghana might not meet the SDGs target 3.2 of reducing neonatal mortality to 12 deaths per 1000 live births by 2030. Identifying core determinants of neonatal deaths provide policy guidelines and a framework aimed at mitigating the effect of neonatal deaths. Most studies have identified household and...

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Autores principales: Kwami Takramah, Wisdom, Dwomoh, Duah, Aheto, Justice Moses K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10021147/
https://www.ncbi.nlm.nih.gov/pubmed/36962797
http://dx.doi.org/10.1371/journal.pgph.0000649
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author Kwami Takramah, Wisdom
Dwomoh, Duah
Aheto, Justice Moses K.
author_facet Kwami Takramah, Wisdom
Dwomoh, Duah
Aheto, Justice Moses K.
author_sort Kwami Takramah, Wisdom
collection PubMed
description Ghana might not meet the SDGs target 3.2 of reducing neonatal mortality to 12 deaths per 1000 live births by 2030. Identifying core determinants of neonatal deaths provide policy guidelines and a framework aimed at mitigating the effect of neonatal deaths. Most studies have identified household and individual-level factors that contribute to neonatal mortality. However, there are relatively few studies that have rigorously assessed geospatial covariates and spatiotemporal variations of neonatal deaths in Ghana. This study focuses on modeling and mapping of spatiotemporal variations in the risk of neonatal mortality in Ghana using Bayesian Hierarchical Spatiotemporal models. This study used data from the Ghana Demographic and Health Surveys (GDHS) conducted in 1993, 1998, 2003, 2008, and 2014. We employed Bayesian Hierarchical Spatiotemporal regression models to identify geospatial correlates and spatiotemporal variations in the risk of neonatal mortality. The estimated weighted crude neonatal mortality rate for the period under consideration was 33.2 neonatal deaths per 1000 live births. The results obtained from Moran’s I statistics and CAR model showed the existence of spatial clustering of neonatal mortality. The map of smooth relative risk identified Ashanti region as the most consistent hot-spot region for the entire period under consideration. Small body size babies contributed significantly to an increased risk of neonatal mortality at the regional level [Posterior Mean: 0.003 (95% CrI: 0.00,0.01)]. Hot spot GDHS clusters exhibiting high risk of neonatal mortality were identified by LISA cluster map. Rural residents, small body size babies, parity, and aridity contributed significantly to a higher risk of neonatal mortality at the GDHS cluster level. The findings provide actionable and insightful information to prioritize and distribute the scarce health resources equitably to tackle the menace of neonatal mortality. The regions and GDHS clusters with excess risk of neonatal mortality should receive optimum attention and interventions to reduce the neonatal mortality rate.
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spelling pubmed-100211472023-03-17 Spatio-temporal variations in neonatal mortality rates in Ghana: An application of hierarchical Bayesian methods Kwami Takramah, Wisdom Dwomoh, Duah Aheto, Justice Moses K. PLOS Glob Public Health Research Article Ghana might not meet the SDGs target 3.2 of reducing neonatal mortality to 12 deaths per 1000 live births by 2030. Identifying core determinants of neonatal deaths provide policy guidelines and a framework aimed at mitigating the effect of neonatal deaths. Most studies have identified household and individual-level factors that contribute to neonatal mortality. However, there are relatively few studies that have rigorously assessed geospatial covariates and spatiotemporal variations of neonatal deaths in Ghana. This study focuses on modeling and mapping of spatiotemporal variations in the risk of neonatal mortality in Ghana using Bayesian Hierarchical Spatiotemporal models. This study used data from the Ghana Demographic and Health Surveys (GDHS) conducted in 1993, 1998, 2003, 2008, and 2014. We employed Bayesian Hierarchical Spatiotemporal regression models to identify geospatial correlates and spatiotemporal variations in the risk of neonatal mortality. The estimated weighted crude neonatal mortality rate for the period under consideration was 33.2 neonatal deaths per 1000 live births. The results obtained from Moran’s I statistics and CAR model showed the existence of spatial clustering of neonatal mortality. The map of smooth relative risk identified Ashanti region as the most consistent hot-spot region for the entire period under consideration. Small body size babies contributed significantly to an increased risk of neonatal mortality at the regional level [Posterior Mean: 0.003 (95% CrI: 0.00,0.01)]. Hot spot GDHS clusters exhibiting high risk of neonatal mortality were identified by LISA cluster map. Rural residents, small body size babies, parity, and aridity contributed significantly to a higher risk of neonatal mortality at the GDHS cluster level. The findings provide actionable and insightful information to prioritize and distribute the scarce health resources equitably to tackle the menace of neonatal mortality. The regions and GDHS clusters with excess risk of neonatal mortality should receive optimum attention and interventions to reduce the neonatal mortality rate. Public Library of Science 2022-09-08 /pmc/articles/PMC10021147/ /pubmed/36962797 http://dx.doi.org/10.1371/journal.pgph.0000649 Text en © 2022 Kwami Takramah 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
Kwami Takramah, Wisdom
Dwomoh, Duah
Aheto, Justice Moses K.
Spatio-temporal variations in neonatal mortality rates in Ghana: An application of hierarchical Bayesian methods
title Spatio-temporal variations in neonatal mortality rates in Ghana: An application of hierarchical Bayesian methods
title_full Spatio-temporal variations in neonatal mortality rates in Ghana: An application of hierarchical Bayesian methods
title_fullStr Spatio-temporal variations in neonatal mortality rates in Ghana: An application of hierarchical Bayesian methods
title_full_unstemmed Spatio-temporal variations in neonatal mortality rates in Ghana: An application of hierarchical Bayesian methods
title_short Spatio-temporal variations in neonatal mortality rates in Ghana: An application of hierarchical Bayesian methods
title_sort spatio-temporal variations in neonatal mortality rates in ghana: an application of hierarchical bayesian methods
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10021147/
https://www.ncbi.nlm.nih.gov/pubmed/36962797
http://dx.doi.org/10.1371/journal.pgph.0000649
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