ESTIMATING THE STILLBIRTH RATE FOR 195 COUNTRIES USING A BAYESIAN SPARSE REGRESSION MODEL WITH TEMPORAL SMOOTHING

• 成果类型: Article
• 署名作者: Wang, Zhengfan; Fix, Miranda J.; Hug, Lucia; Mishra, Anu; You, Danzhen; Blencowe, Hannah; Wakefield, Jon; Alkema, Leontine
• 署名单位: University of Massachusetts System; University of Massachusetts Amherst; University of Washington; University of Washington Seattle; UNICEF; University of London; London School of Hygiene & Tropical Medicine
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1571
• 发表日期: 2022
• 页码: 2101-2121
• 关键词: splines; TRENDS

摘要

Estimation of stillbirth rates globally is complicated because of the paucity of reliable data from countries where most stillbirths occur. We com-piled 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 deter-mined 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 uncer-tainty. 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 In-teragency Group for Child Mortality Estimation to monitor the stillbirth rate for 195 countries.