International Day Of Women in Statistics and Data Science

Abstract
Birth asphyxia remains a major contributor to neonatal morbidity and mortality in low- and middle-income countries, particularly in sub-Saharan Africa. This study investigated the determinants of birth asphyxia among newborns using a Quasi-Poisson regression model to account for overdispersion in the count data. Secondary data comprising neonatal and maternal records were analysed using descriptive statistics, correlation analysis, and inferential modelling. The Quasi-Poisson model selected after diagnostic assessment confirmed overdispersion in the response variable, making it more appropriate than the standard Poisson model. The Quasi-Poisson regression model for predicting birth asphyxia is given as : Birth Asphyxia = 1.742 +0.385(Birth Weight) + 0.012(Mothers Age)− 0.088(Gestational Age) + 0.0401(Sex)+ 0.301(Mode of Delivery) + 0.158(Parity)+ 0.067(SURVIVE)
The results showed that birth weight, gestational age, mode of delivery, maternal age, parity, and fetal presentation were significant predictors of birth asphyxia. Specifically, lower birth weight and shorter gestational age were associated with a higher incidence of birth asphyxia, while caesarean delivery and abnormal fetal presentation increased the likelihood of adverse birth outcomes.
Keywords: Birth asphyxia, Quasi-Poisson regression, Gestational age, Neonatal outcomes, Maternal health, Ghana.
Authors:
1. Selina Dadzie (Mphil)
2. Irene Kafui Vorsah Amponsah (PhD)