Publication:

Bayesian logistic regression of stillbirth cases in the Bolgatanga Municipality of Ghana, West Africa

Background Stillbirth, as an adverse outcome of pregnancy, represents a growing worldwide public health challenge. The risks of stillbirth have been reported to exhibit considerable variation across different factors due to variability in socio-economic and geographical settings. Thus, this study was aimed at modelling the risk of stillbirth in the Bolgatanga Municipality of the Upper East region of Ghana and identifying some possible risk factors.

Methods A retrospective cohort study design was utilized in this work. Thus, all the data were obtained from the medical recorded histories of all single birth outcomes at Bolgatanga Regional hospital in Ghana from September 2023 to December 2024. Bayesian logistic regression was applied in fitting the data on stillbirth in this study. R studio was the statistical software that was utilized in analysing the data.

Results Based on the results of the posterior estimation of the Bayesian logistic regression, maternal age, educational level and hypertension status were established as significant risk factors of stillbirth in the Bolgatanga Municipality. Overall, women with low maternal age (<20 years) and those with advanced maternal age (≥35 years), women with no formal education, and women with hypertension during pregnancy were established to have a higher risk of stillbirth in the Bolgatanga Municipality.

Conclusions The study concluded by indicating the need for various agencies of healthcare in the Bolgatanga Municipality to institute targeted interventions that will help control the effects of the risk factors and enhance improved overall pregnancy outcomes.