Bayesian analysis of verbal autopsy data using factor models with age- and sex-dependent associations between symptoms
Tsuyoshi Kunihama, Zehang Richard Li, Samuel J. Clark, Tyler H. McCormick
The Annals of Applied Statistics · 2025 · Volume 19 · Issue 3
Abstract
Verbal autopsies (VAs) are extensively used to investigate the population-level distributions of deaths by cause in low-resource settings without well-organized vital statistics systems. Computer-based methods are often adopted to assign causes of death to deceased individuals based on the interview responses of their family members or caregivers. In this article we develop a new Bayesian approach that extracts information about cause-of-death distributions from VA data considering the age- and sex-related variation in the associations between symptoms. Its performance is compared with that of existing approaches using gold-standard data from the Population Health Metrics Research Consortium. In addition, we compute the relevance of predictors to causes of death based on information-theoretic measures.
Keywords
Bayesian factor models, causes of death distribution, multivariate data, survey data, verbal autopsies
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