Bayesian Multivariate Logistic Regression Models for Comparing Preeclampsia Risk and Maternal Outcomes in Singleton and Multiple Pregnancies

Preeclampsia is a major contributor to maternal morbidity and mortality, especially in low-resource settings. Evidence comparing preeclampsia risk and maternal outcomes between singleton and multiple pregnancies in Liberia remains limited. This study examined preeclampsia risk and adverse maternal outcomes in singleton and multiple pregnancies using a retrospective hospital records analytic dataset of 180 maternal records structured around the John F. Kennedy Maternity Center (JFKMC), Monrovia, Liberia, for the period July 2024 to July 2025. Two Bayesian multivariate logistic regression models were fitted. Model 1 assessed preeclampsia status using pregnancy type, age, parity, antenatal care (ANC) attendance, history of preeclampsia, and chronic medical condition. Model 2 assessed adverse maternal outcomes using pregnancy type, preeclampsia status, age, parity, ANC attendance, history of preeclampsia, and chronic medical conditions. The results indicate that the frequency of adverse maternal outcomes in multiple pregnancies was higher than that in singleton pregnancies. In Model 1, a history of preeclampsia was strongly associated with current preeclampsia, whereas antenatal care was negatively associated. Multiple pregnancies were negatively associated with preeclampsia; however, the result was clinically inconsistent. In Model 2, multiple pregnancies and increasing maternal age were associated with increased odds of adverse maternal outcomes. Thus, this study provides stronger evidence for the predictors of adverse maternal outcomes than for the predictors of the occurrence of preeclampsia. Improved clinical data are required to provide more reliable risk stratification of mothers in Liberia.