Open Access
March 2020 Bayesian factor models for probabilistic cause of death assessment with verbal autopsies
Tsuyoshi Kunihama, Zehang Richard Li, Samuel J. Clark, Tyler H. McCormick
Ann. Appl. Stat. 14(1): 241-256 (March 2020). DOI: 10.1214/19-AOAS1253


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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Tsuyoshi Kunihama. Zehang Richard Li. Samuel J. Clark. Tyler H. McCormick. "Bayesian factor models for probabilistic cause of death assessment with verbal autopsies." Ann. Appl. Stat. 14 (1) 241 - 256, March 2020.


Received: 1 March 2018; Revised: 1 March 2019; Published: March 2020
First available in Project Euclid: 16 April 2020

zbMATH: 07200170
MathSciNet: MR4085092
Digital Object Identifier: 10.1214/19-AOAS1253

Keywords: Bayesian latent model , cause of death , Conditional dependence , multivariate data , survey data , verbal autopsies

Rights: Copyright © 2020 Institute of Mathematical Statistics

Vol.14 • No. 1 • March 2020
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