Frequentist and Bayesian Regression Approaches for Determining Risk Factors of Child Mortality in Ghana

Abstract

Background. Child mortality is a global health problem. The United Nations’ 2018 report on levels and trends on child mortality indicated that under-five mortality is one of the major public health problems in Ghana with a rate of 60 deaths per 1000 live births. To further mitigate this problem, it is important to identify the drivers of under-five mortality in order to achieve the United Nations SDG Goal 3 target 2. Methods. In this study, we investigated the effects of some selected risk factors on child mortality using data from the 2014 Ghana Demographic Health Survey. We modelled the relationship between child mortality and the risk factors using a logistic regression model under the frequentist and Bayesian frameworks. We used the Metropolis-Hastings Algorithm to simulate parameter estimates from the posterior distributions, and statistical analyses were carried out using STATA version 14.1. Results. Results from the frequentist framework are in line with those from the Bayesian framework. The results showed an increased risk of death among children who were delivered through caesarean and reduced relative odds of death among children whose sizes are average or large at birth and whose mothers have formal education. Conclusions. There is a need for improved health facilities for better health-care for mothers and children. Education should, among other things, emphasise on the need for mothers to go for regular check-ups during antinatal and postnatal periods for improved mother and child health.