TEMPORAL MODELS FOR ESTIMATION AND SHORT-TERM FORECASTING OF NEONATAL MORTALITY RATES IN SUB-SAHARAN AFRICA

Accurate estimation and forecasts for neonatal mortality rates (NMRs) in low- and middle-income countries is an urgent problem. Much of child mortality is preventable, and understanding temporal trends is of great interest when evaluating past performance and planning future policy or programming. In countries without robust vital registration, we rely on modeled estimates based on survey data to understand trends. A toolkit of compelling temporal models exists, but these methods have not been comprehensively evaluated for their application for the estimation of the NMR in low- and middle-income countries using household survey data. Using Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS) data from 41 countries in sub-Saharan Africa, we estimate and forecast the national-level NMR for 1970-2030 separately with random walk, auto-regressive, penalized spline, natural spline, and logit-linear latent temporal models. We examine the statistical behavior of these temporal models with both an out-of-sample analysis using the DHS and MICS data and a simulation study. We find that the second-order random walk and the penalized spline have the least bias, and short-term forecasts from the penalized spline tend to have narrower intervals with better out-of-sample performance. From the analysis of the NMR in sub-Saharan Africa, we estimate that 6 or fewer of the 41 countries included are on track to achieve the Sustainable Development Goals target of 12 neonatal deaths per 1000 live births by 2030.