Abstract
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 195 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 determined by covariates for country-periods with limited or no data. Horseshoe priors are used to encourage sparseness. The model adjusts observations with alternative stillbirth definitions and accounts for various sources of uncertainty. 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 Interagency Group for Child Mortality Estimation to monitor the stillbirth rate for 195 countries.
Funding Statement
The stillbirth estimation work was supported by the Bill & Melinda Gates Foundation (OPP1180460 OP190601 and INV-001395), and the National Institute of Environmental Health Sciences of the National Institutes of Health under award number T32ES015459.
The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders.
Acknowledgments
We thank all members of the Core Stillbirth Estimation Group (Leontine Alkema, Dianna M. Blau, Hannah Blencowe, Simon Cousens, Andreea Creanga, Trevor Croft, Kenneth Hill, KS Joseph, Salome Maswime, Elizabeth M. McClure, Robert Pattinson, Jon Pedersen, Lucy Smith, Jennifer Zeitlin) and the members of the Technical Advisory Group of the UN Inter-agency Group for Child Mortality Estimation (Leontine Alkema, Robert Black, Simon Cousens, Trevor Croft, Michel Guillot, Kenneth Hill, Bruno Masquelier, Colin Mathers, Jon Pedersen, Jon Wakefield, Neff Walker) for providing input and feedback on estimating stillbirths. We thank the members of the UN Interagency Group for Child Mortality Estimation (Allisyn Moran, Emi Suzuki, Victor Gaigbe-Togbe, Danzhen You) for additional guidance. We also thank Remy Wang, Serena Wang, and Zitong Wang for their contributions to the modeling in an earlier stage of the project.
Citation
Zhengfan Wang. Miranda J. Fix. Lucia Hug. Anu Mishra. Danzhen You. Hannah Blencowe. Jon Wakefield. Leontine Alkema. "Estimating the stillbirth rate for 195 countries using a Bayesian sparse regression model with temporal smoothing." Ann. Appl. Stat. 16 (4) 2101 - 2121, December 2022. https://doi.org/10.1214/21-AOAS1571
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