Time Series Modeling of Livebirths and Stillbirths at a Nigeria Teaching Hospital 2001 -2020 - I. A Okegbade; Abiola Opeyemi Egbewumi (International Journal of Interdisciplinary Research, 2026)
Livebirths and stillbirths are key public health indicators, with significant social and economic consequences. This study employs time series modeling to analyze quarterly records of livebirths and stillbirths obtained fom Obafemi Awolowo University Teaching Hospitals Complex (OAUTHC), Ile-Ife, Nigeria, covering the period from 2001 to 2020. The Augmented Dickey Fuller test confirmed the stationarity of the series, after which the Autocorrelation (ACF) and Partial Autocorrelation (PACF) functions were examined, identify suitable ARMA models. Model selection was guided by the Akaike Information criteria (AIC), Bayesian Information Criterion (BIC), and Hannan Quinn Criterion (HQIC). The models with the lowest information criteria ARMA (2,3) for livebirths and ARMA(1,3) for stillbirths were selected as optimal. Diagnostic checks revealed no significant autocorrelation in the residuals with ACF and PACF plots showing values close to zero across lags, confirming model adequacy and reliability. Forecast results indicate that livebirths will experience short term fluctuations before reaching a stable trend while stillbirth are projected to remain relatively constant at approximately 30 cases per quarter, though with broader confidence intervals at the start of the forest. The relatively low R2 values, particularly for stillbirths suggest that other unobserved variables such as healthcare accessibility, maternal risk factors and socio-economic conditions may also influence outcomes. Overall, the study emphasizes the importance of continuous improvements in maternal and child healthcare as well as the value of data driven decision making for public health planning and intervention.