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Predictive model and determinants of odds of neonates dying within 28 days of life in Ghana
BACKGROUND: One of the priorities and important current problem in public health research globally is modeling of neonatal mortality and its risk factors in using the appropriate statistical methods. It is believed that multiple risk factors interplay to increase the risk of neonatal mortality. To u...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7883380/ https://www.ncbi.nlm.nih.gov/pubmed/33614984 http://dx.doi.org/10.1002/hsr2.248 |
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author | Takramah, Wisdom Kwami Aheto, Justice Moses K. |
author_facet | Takramah, Wisdom Kwami Aheto, Justice Moses K. |
author_sort | Takramah, Wisdom Kwami |
collection | PubMed |
description | BACKGROUND: One of the priorities and important current problem in public health research globally is modeling of neonatal mortality and its risk factors in using the appropriate statistical methods. It is believed that multiple risk factors interplay to increase the risk of neonatal mortality. To understand the risk factors of neonatal mortality in Ghana, the current study carefully evaluated and compared the predictive accuracy and performance of two classification models. METHODS: This study reviewed the birth history data collected on 5884 children born in the 5 years preceding the 2014 Ghana Demographic and Health Survey (GDHS). The 2014 GDHS is a cross‐sectional nationally representative household sample survey. The relevant variables were selected using leaps‐and‐bounds method, and the area under curves were compared to evaluate the predictive accuracy of unweighted penalized and weighted single‐level multivariable logistic regression models for predicting neonatal mortality using the 2014 GDHS data. RESULTS: The study found neonatal mortality prevalence of 2.8%. A sample of 4514 children born in the 5 years preceding the 2014 GDHS was included in the inferential analysis. The results of the current study show that for the unweighted penalized single‐level multivariable logistic model, there is an increased risk of neonatal death among babies born to mothers who received prenatal care from non‐skilled worker [OR: 3.79 (95% CI: 2.52, 5.72)], multiple births [OR: 3.10 (95% CI: 1.89, 15.27)], babies delivered through caesarian section [OR: 2.24 (95% CI: 1.30, 3.85)], and household with 1 to 4 members [OR: 5.74 (95% CI: 3.16, 10.43)], respectively. The predictive accuracy of the unweighted penalized and weighted single‐level multivariable logistic regression models was 82% and 80%, respectively. CONCLUSION: The study advocates that prudent and holistic interventions should be institutionalized and implemented to address the risk factors identified in order to reduce neonatal death and, by large, improve child and maternal health outcomes to achieve the SDG target 3.2. |
format | Online Article Text |
id | pubmed-7883380 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78833802021-02-19 Predictive model and determinants of odds of neonates dying within 28 days of life in Ghana Takramah, Wisdom Kwami Aheto, Justice Moses K. Health Sci Rep Research Articles BACKGROUND: One of the priorities and important current problem in public health research globally is modeling of neonatal mortality and its risk factors in using the appropriate statistical methods. It is believed that multiple risk factors interplay to increase the risk of neonatal mortality. To understand the risk factors of neonatal mortality in Ghana, the current study carefully evaluated and compared the predictive accuracy and performance of two classification models. METHODS: This study reviewed the birth history data collected on 5884 children born in the 5 years preceding the 2014 Ghana Demographic and Health Survey (GDHS). The 2014 GDHS is a cross‐sectional nationally representative household sample survey. The relevant variables were selected using leaps‐and‐bounds method, and the area under curves were compared to evaluate the predictive accuracy of unweighted penalized and weighted single‐level multivariable logistic regression models for predicting neonatal mortality using the 2014 GDHS data. RESULTS: The study found neonatal mortality prevalence of 2.8%. A sample of 4514 children born in the 5 years preceding the 2014 GDHS was included in the inferential analysis. The results of the current study show that for the unweighted penalized single‐level multivariable logistic model, there is an increased risk of neonatal death among babies born to mothers who received prenatal care from non‐skilled worker [OR: 3.79 (95% CI: 2.52, 5.72)], multiple births [OR: 3.10 (95% CI: 1.89, 15.27)], babies delivered through caesarian section [OR: 2.24 (95% CI: 1.30, 3.85)], and household with 1 to 4 members [OR: 5.74 (95% CI: 3.16, 10.43)], respectively. The predictive accuracy of the unweighted penalized and weighted single‐level multivariable logistic regression models was 82% and 80%, respectively. CONCLUSION: The study advocates that prudent and holistic interventions should be institutionalized and implemented to address the risk factors identified in order to reduce neonatal death and, by large, improve child and maternal health outcomes to achieve the SDG target 3.2. John Wiley and Sons Inc. 2021-02-15 /pmc/articles/PMC7883380/ /pubmed/33614984 http://dx.doi.org/10.1002/hsr2.248 Text en © 2021 The Authors. Health Science Reports published by Wiley Periodicals LLC. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Research Articles Takramah, Wisdom Kwami Aheto, Justice Moses K. Predictive model and determinants of odds of neonates dying within 28 days of life in Ghana |
title | Predictive model and determinants of odds of neonates dying within 28 days of life in Ghana |
title_full | Predictive model and determinants of odds of neonates dying within 28 days of life in Ghana |
title_fullStr | Predictive model and determinants of odds of neonates dying within 28 days of life in Ghana |
title_full_unstemmed | Predictive model and determinants of odds of neonates dying within 28 days of life in Ghana |
title_short | Predictive model and determinants of odds of neonates dying within 28 days of life in Ghana |
title_sort | predictive model and determinants of odds of neonates dying within 28 days of life in ghana |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7883380/ https://www.ncbi.nlm.nih.gov/pubmed/33614984 http://dx.doi.org/10.1002/hsr2.248 |
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