Modeling Caesarean Delivery Determinants Using Logistic Regression: Evidence from the 2022 Tanzania Demographic and Health Survey

Abstract

The rise in Caesarean section rates, which often exceed World health organization recommendations, has sparked global concern, particularly in Tanzania. Despite a notable increase in Caesarean deliveries, differences exist both within and between countries, largely due to factors influencing these procedures. This study aimed to validate logistic regression model in predicting and analyze the determinants of Caesarean delivery among Tanzanian women aged 15 to 49 using data from the 2022 Tanzania Demographic and Health Survey (TDHS). Employing a quantitative cross-sectional design, the research analyzed responses from 3,876 women, focusing on demographic, socioeconomic, and health system variables. Utilizing Pearson chi-square tests and binary logistic regression, it found that education, urban residence, and household wealth significantly increased the likelihood of Caesarean sections. However, the logistic regression model revealed limitations in predicting Caesarean deliveries, correctly identifying only 2.0% of such cases. The study underscores the necessity of refining predictive models to enhance the accuracy of Caesarean delivery predictions, indicating that targeted healthcare interventions could improve maternal health outcomes in Tanzania. Policymakers should improve predictive modeling for Caesarean birth rates, improve women's education, expand access to rural health care, increase health insurance coverage, promote regular prenatal care, and encourage collaboration to achieve equitable maternal health and better outcomes for women to ensure women