Predictive model and determinants of odds of neonates dying within 28 days of life in Ghana
Abstract 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 mortali...
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doaj-1d071523fa7a454da131e09b28cd3eb22021-05-03T04:05:45ZengWileyHealth Science Reports2398-88352021-03-0141n/an/a10.1002/hsr2.248Predictive model and determinants of odds of neonates dying within 28 days of life in GhanaWisdom Kwami Takramah0Justice Moses K. Aheto1Department of Epidemiology and Biostatistics, School of Public Health University of Health and Allied Sciences Ho GhanaDepartment of Biostatistics, School of Public Health University of Ghana Accra GhanaAbstract 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.https://doi.org/10.1002/hsr2.248Ghananeonatal mortalityrisk factorsunder‐five mortalityunweighted penalized multivariable logistic regressionweighted multivariable logistic regression |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wisdom Kwami Takramah Justice Moses K. Aheto |
spellingShingle |
Wisdom Kwami Takramah Justice Moses K. Aheto Predictive model and determinants of odds of neonates dying within 28 days of life in Ghana Health Science Reports Ghana neonatal mortality risk factors under‐five mortality unweighted penalized multivariable logistic regression weighted multivariable logistic regression |
author_facet |
Wisdom Kwami Takramah Justice Moses K. Aheto |
author_sort |
Wisdom Kwami Takramah |
title |
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_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_sort |
predictive model and determinants of odds of neonates dying within 28 days of life in ghana |
publisher |
Wiley |
series |
Health Science Reports |
issn |
2398-8835 |
publishDate |
2021-03-01 |
description |
Abstract 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. |
topic |
Ghana neonatal mortality risk factors under‐five mortality unweighted penalized multivariable logistic regression weighted multivariable logistic regression |
url |
https://doi.org/10.1002/hsr2.248 |
work_keys_str_mv |
AT wisdomkwamitakramah predictivemodelanddeterminantsofoddsofneonatesdyingwithin28daysoflifeinghana AT justicemoseskaheto predictivemodelanddeterminantsofoddsofneonatesdyingwithin28daysoflifeinghana |
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