Application of Time Series Analysis in Modelling an Integrated Moving Average for Predicting Caesarean Sections Births

Abstract

Purpose: Periodic variations in caesarean sections incidences at the University of Cape Coast Hospital is currently unclear and the model that can best represent its movement is unknown. This study sought to model and predict the monthly incidence of caesarean sections at the hospital.
 Methods: The study employed a monthly periodicity of 115 time-series data sourced from District health information management systems Two on caesarean section births at the University of Cape Coast Hospital over a 9-year period. The auto regressive integrated moving average models of the classical Box-Jenkins methods of Time Series Analysis were used to analyse the data. Analysis was performed in EViews 12 and R (version 4.0.4)
 Findings: There were six non-seasonal tentative candidate models for the hospital. The best fit model had an autoregressive integrated moving average with a first-order autoregressive and a second-order moving average. The findings revealed an overall rising trend in the incidence of caesarean section rates in the hospital over the study period with an average of 30.54% per month. This is expected to increase to over 40% per month over the next five-year period of August 2021 to July 2026 according to projections.
 Conclusion: The best fit model can be used to forecast the expected number of caesarean section births.
 Recommendations: Clinicians should be trained to improve their skills in the use of instruments in deliveries as well as in the safe conduct of vaginal breech deliveries.