Seasonal and Periodic Variations in Child Delivery: A Time Series Study at Federal Medical Centre, Jalingo - Joel, Eviano Israel; Ornguga, Gabriel Ianngi; Augustine, Chibundu Daniel (Faculty of Natural and Applied Sciences Journal of Mathematical and Statistical Computing, 2025)
This study examines seasonal and periodic fluctuations in the modes of child birth especially spontaneous vaginal delivery (SVD), planned cesarean section (CS), and emergency CS—at the Federal Medical Centre (FMC), Jalingo, Nigeria, using a ten-year monthly dataset (2015–2024). Employing a time series analytical framework in STATA 15, the study utilized Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests to confirm stationarity and applied Vector Autoregression (VAR) to assess interdependencies among delivery modes. Descriptive analysis revealed marked seasonal variations: SVD peaked in February (mean = 9.89) and November (mean = 10.00), planned CS recorded its highest average in July (mean = 11.68), and emergency CS exhibited a major spike in March (mean = 11.26; maximum = 50 cases). ADF and PP results indicated all series were stationary (p < 0.001). The optimal model (Lag 1) was selected based on AIC and FPE criteria. VAR estimates demonstrated strong autoregressive and cross-sectional effects: prior SVD significantly predicted current SVD (β = 0.3739, p < 0.001), while prior emergency CS strongly predicted planned CS (β = 0.2349, p < 0.001). Additionally, emergency CS was influenced by prior SVD (β = 0.1988, p = 0.004) and planned labor stimulation (β = 0.2151, p = 0.006). Model diagnostics confirmed robustness, with no residual autocorrelation (p = 0.379). These findings highlight the influence of cultural, climatic, and institutional factors on delivery trends, offering actionable insights for maternal healthcare planning, resource allocation, and emergency preparedness. Strategic scheduling and preventive interventions during high-demand months are recommended to optimize obstetric care.