Forecasting cesarean deliveries with robust time series models in a tertiary care hospital

Abstract

Introduction: Caesarian section rate was 47% at Patan Hospital in 2014 despite the recommendation of keeping it below 15%. This has become a public health problem and it is debated as human right violation of childbearing women. This study aims use robust time series model to forecast caesarian deliveries to keep track of it at the hospital.

Method: Univariate time series models were used to forecast 3-year caesarean deliveries at Patan Hospital using 60-month (2010-2014) data. A robust time series model with low mean average percentage error from validation period and without autocorrelation problem was selected and used to forecast caesarean deliveries for 2015-2017 period.  

Result: Winter’s additive model had lowest validation forecasting error and showed decreasing trend of caesarian deliveries but it showed autocorrelation. Quadratic regression gave similar result but is also autocorrelation problem. Artificial Neural Network – Multilayer Perceptron model gave close forecasts but autocorrelation was not assessed. Best Autoregressive Integrated Moving Average (ARIMA) model gave valid forecasts without autocorrelation problem.

Conclusion: ARIMA (0,1,1),(0,0,0) model with one difference and one level of moving error correction gave valid forecasts for Patan Hospital. Advanced univariate and multivariate time series models with large samples can be used to get precise forecasts of caesarean deliveries in Nepal.