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Estimation of stillbirth rates globally is complicated because of the paucityof reliable data from countries where most stillbirths occur. We compiled dataand developed a Bayesian hierarchical temporal sparse regression model forestimating stillbirth rates for all countries from 2000 to 2019. The modelcombines covariates with a temporal smoothing process so that estimates aredata-driven in country-periods with high-quality data and deter-mined bycovariates for country-periods with limited or no data. Horseshoepriors areused to encourage sparseness. The model adjusts observations with alternativestillbirth definitions and accounts for bias in observations that are subjectto non-sampling errors. In-sample goodness of fit and out-of-sample validationresults suggest that the model is reasonably well calibrated. The model is usedby the UN Inter-agency Group for Child Mortality Estimation to monitor thestillbirth rate for all countries.

Estimating the Stillbirth Rate for 195 Countries Using A Bayesian Sparse Regression Model with Temporal Smoothing