  Estimation of stillbirth rates globally is complicated because of the paucity
of reliable data from countries where most stillbirths occur. We compiled data
and developed a Bayesian hierarchical temporal sparse regression model for
estimating stillbirth rates for all countries from 2000 to 2019. The model
combines covariates with a temporal smoothing process so that estimates are
data-driven in country-periods with high-quality data and deter-mined by
covariates for country-periods with limited or no data. Horseshoepriors are
used to encourage sparseness. The model adjusts observations with alternative
stillbirth definitions and accounts for bias in observations that are subject
to non-sampling errors. In-sample goodness of fit and out-of-sample validation
results suggest that the model is reasonably well calibrated. The model is used
by the UN Inter-agency Group for Child Mortality Estimation to monitor the
stillbirth rate for all countries.
