BAYESIAN FACTOR MODELS FOR PROBABILISTIC CAUSE OF DEATH ASSESSMENT WITH VERBAL AUTOPSIES
成果类型：
Article
署名作者：
Kunihama, Tsuyoshi; Li, Zehang Richard; Clark, Samuel J.; McCormick, Tyler H.
署名单位：
Kwansei Gakuin University; Yale University; University System of Ohio; Ohio State University; University of Washington; University of Washington Seattle
刊物名称：
ANNALS OF APPLIED STATISTICS
ISSN/ISSBN：
1932-6157
DOI：
10.1214/19-AOAS1253
发表日期：
2020
页码：
241-256
关键词：
摘要：
The distribution of deaths by cause provides crucial information for public health planning, response and evaluation. About 60% of deaths globally are not registered or given a cause, limiting our ability to understand disease epidemiology. Verbal autopsy (VA) surveys are increasingly used in such settings to collect information on the signs, symptoms and medical history of people who have recently died. This article develops a novel Bayesian method for estimation of population distributions of deaths by cause using verbal autopsy data. The proposed approach is based on a multivariate probit model where associations among items in questionnaires are flexibly induced by latent factors. Using the Population Health Metrics Research Consortium labeled data that include both VA and medically certified causes of death, we assess performance of the proposed method. Further, we estimate important questionnaire items that are highly associated with causes of death. This framework provides insights that will simplify future data
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