  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 collection.
