  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.
