Bayesian factor models for probabilistic cause of death assessment with verbal autopsies. (English) Zbl 1439.62220

Kunihama, Tsuyoshi; Li, Zehang Richard; Clark, Samuel J.; Mccormick, Tyler H.

Ann. Appl. Stat. 14, No. 1, 241-256 (2020).

Summary: 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.

MSC:

62P10	Applications of statistics to biology and medical sciences; meta analysis
92C60	Medical epidemiology
92D30	Epidemiology
62N05	Reliability and life testing
62H25	Factor analysis and principal components; correspondence analysis

Keywords:

Bayesian latent model; cause of death; conditional dependence; multivariate data; verbal autopsies; survey data

Software:

BayesDA; Ox; R

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