ArticleHealth science reports2021
Predictive model and determinants of odds of neonates dying within 28 days of life in Ghana.
Article in Health science reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 6 citations in OpenAlex.
- A scoping review of the health and health-related sustainable development goals (HHSDGs) in Ghana: progress and challenges.BMC public health · 2026Article
- Predictors of Preterm Neonatal Mortality in India and Pakistan: A Secondary Analysis of Data from PURPOSe Study.Global pediatric health · 2024Article
- Predictors of neonatal mortality in the Eastern Regional Hospital in Ghana: A retrospective cohort study.PLOS global public health · 2024Article
- Multilevel modelling of neonatal mortality in Ghana: Does household and community levels matter?Heliyon · 2023Article
- Influence of the Corporate Image of Nursing Homes on the Loyalty of Residents' Family Members.International journal of environmental research and public health · 2022Article
- Predictive model and determinants of odds of neonates dying within 28 days of life in Ghana.Health science reports · 2021Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOne 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.
methodsThis 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.
resultsThe 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.
conclusionThe 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.
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