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Evidence of health inequity in child survival: spatial and Bayesian network analyses of stillbirth rates in 194 countries
Estimated at 2.6 million annually, stillbirths worldwide have stayed alarmingly high, in contrast to neonatal and under-five mortality rates. It is a neglected public health challenge globally, with less attention to its social determinants. We examined spatial patterns of country-level stillbirth r...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6930217/ https://www.ncbi.nlm.nih.gov/pubmed/31875022 http://dx.doi.org/10.1038/s41598-019-56326-w |
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author | Adeyinka, Daniel Adedayo Olakunde, Babayemi Oluwaseun Muhajarine, Nazeem |
author_facet | Adeyinka, Daniel Adedayo Olakunde, Babayemi Oluwaseun Muhajarine, Nazeem |
author_sort | Adeyinka, Daniel Adedayo |
collection | PubMed |
description | Estimated at 2.6 million annually, stillbirths worldwide have stayed alarmingly high, in contrast to neonatal and under-five mortality rates. It is a neglected public health challenge globally, with less attention to its social determinants. We examined spatial patterns of country-level stillbirth rates and determined the influence of social determinants of health on spatial patterns of stillbirth rates. We also estimated probabilistic relationships between stillbirth rates and significant determinants from the spatial analysis. Using country-level aggregated data from the United Nations databases, it employed ecological spatial analysis and artificial intelligence modeling based on Bayesian network among 194 World Health Organization member countries. From the spatial analysis, thirty-seven countries formed a cluster of high values (hot-spots) for stillbirth and 13 countries formed a cluster of low values (cold-spots). In the multivariate regression, gender inequality and anaemia in pregnancy were significantly associated with spatial patterns of higher stillbirth rates, while higher antenatal care (ANC) coverage and skilled birth attendants during delivery were associated with clusters of lower stillbirth rates. The Bayesian network model suggests strong dependencies between stillbirth rate and gender inequality index, geographic regions and skilled birth attendants during delivery. The Bayesian network predicted that the probability of low stillbirth rate increased from 56% to 100% when the percentage of countries with high skilled birth attendants during delivery increased from 70% to 88%, high ANC coverage increased from 55% to 70%, high prevalence of anaemia in pregnancy decreased from 27% to 11% and high gender inequality index decreased from 43% to 21%. Recognizing the urgency in reducing stillbirths globally, multi-pronged strategies should be designed to promote gender equality and strengthen the reproductive and maternal health services in Africa, Eastern Mediterranean, South Eastern Asia, and other countries with disproportionately high stillbirth rates. |
format | Online Article Text |
id | pubmed-6930217 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-69302172019-12-27 Evidence of health inequity in child survival: spatial and Bayesian network analyses of stillbirth rates in 194 countries Adeyinka, Daniel Adedayo Olakunde, Babayemi Oluwaseun Muhajarine, Nazeem Sci Rep Article Estimated at 2.6 million annually, stillbirths worldwide have stayed alarmingly high, in contrast to neonatal and under-five mortality rates. It is a neglected public health challenge globally, with less attention to its social determinants. We examined spatial patterns of country-level stillbirth rates and determined the influence of social determinants of health on spatial patterns of stillbirth rates. We also estimated probabilistic relationships between stillbirth rates and significant determinants from the spatial analysis. Using country-level aggregated data from the United Nations databases, it employed ecological spatial analysis and artificial intelligence modeling based on Bayesian network among 194 World Health Organization member countries. From the spatial analysis, thirty-seven countries formed a cluster of high values (hot-spots) for stillbirth and 13 countries formed a cluster of low values (cold-spots). In the multivariate regression, gender inequality and anaemia in pregnancy were significantly associated with spatial patterns of higher stillbirth rates, while higher antenatal care (ANC) coverage and skilled birth attendants during delivery were associated with clusters of lower stillbirth rates. The Bayesian network model suggests strong dependencies between stillbirth rate and gender inequality index, geographic regions and skilled birth attendants during delivery. The Bayesian network predicted that the probability of low stillbirth rate increased from 56% to 100% when the percentage of countries with high skilled birth attendants during delivery increased from 70% to 88%, high ANC coverage increased from 55% to 70%, high prevalence of anaemia in pregnancy decreased from 27% to 11% and high gender inequality index decreased from 43% to 21%. Recognizing the urgency in reducing stillbirths globally, multi-pronged strategies should be designed to promote gender equality and strengthen the reproductive and maternal health services in Africa, Eastern Mediterranean, South Eastern Asia, and other countries with disproportionately high stillbirth rates. Nature Publishing Group UK 2019-12-24 /pmc/articles/PMC6930217/ /pubmed/31875022 http://dx.doi.org/10.1038/s41598-019-56326-w Text en © The Author(s) 2019 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Adeyinka, Daniel Adedayo Olakunde, Babayemi Oluwaseun Muhajarine, Nazeem Evidence of health inequity in child survival: spatial and Bayesian network analyses of stillbirth rates in 194 countries |
title | Evidence of health inequity in child survival: spatial and Bayesian network analyses of stillbirth rates in 194 countries |
title_full | Evidence of health inequity in child survival: spatial and Bayesian network analyses of stillbirth rates in 194 countries |
title_fullStr | Evidence of health inequity in child survival: spatial and Bayesian network analyses of stillbirth rates in 194 countries |
title_full_unstemmed | Evidence of health inequity in child survival: spatial and Bayesian network analyses of stillbirth rates in 194 countries |
title_short | Evidence of health inequity in child survival: spatial and Bayesian network analyses of stillbirth rates in 194 countries |
title_sort | evidence of health inequity in child survival: spatial and bayesian network analyses of stillbirth rates in 194 countries |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6930217/ https://www.ncbi.nlm.nih.gov/pubmed/31875022 http://dx.doi.org/10.1038/s41598-019-56326-w |
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