Forecasting of neonatal mortality trend at a special new-born care unit in Odisha, India: A time-series analysis

Abstract

New born mortality is a public health problem in the state of Odisha. Newborn mortality is a dynamic process and variations in mortality are observed temporally and seasonally, and also across health facilities. Prior knowledge of mortality burden can enable health system’s readiness in terms of resources allocation and timely intervention, thereby improving the chances of survival of sick newborns admitted in the hospitals. Hence, this study aimed to examine temporal trends of newborn mortality in a Special Newborn Care Unit of Saheed Laxman Nayak Medical College and Hospital (SLNMCH) in Odisha and forecast a short-term monthly projection.The Box-Jenkins approach was used to fit a seasonal autoregressive integrated moving average (SARIMA) model to the monthly recorded mortality among the hospitalized new borns in the SNCU during 2016-2020. The best-fit model for forecasting was found based on the Akaike Information Criterion.The time-series analysis revealed a modest upward trend in newborn mortality rate among SNCU admitted newborns, with peaks in the late winter and late summer months. The seasonal ARIMA (0,1,1)(1,1,1)12 model offered the best fit for time-series data. This model predicted the monthly percentage of mortality in SNCU admitted newborns in the range of 9% to 35% with respective 95% confidence interval for two years period (2021-2022).SARIMA models are useful for monitoring newborn mortality and provide an estimate of temporal trends and seasonality. The models are helpful for predicting occurrence of mortality in the SNCU of SLNMCH and could be useful for developing early warning systems. It may help in early detection, timely treatment, and prevention of serious complications in admitted sick newborns.