Bayesian hierarchical factor regression models to infer cause of death from verbal autopsy data.

ABSTRACT: In low-resource settings where vital registration of death is not routine it is often of critical interest to determine and study the cause of death (COD) for individuals and the cause-specific mortality fraction (CSMF) for populations. Post-mortem autopsies, considered the gold standard for COD assignment, are often difficult or impossible to implement due to deaths occurring outside the hospital, expense, and/or cultural norms. For this reason, Verbal Autopsies (VAs) are commonly conducted, consisting of a questionnaire administered to next of kin recording demographic information, known medical conditions, symptoms, and other factors for the decedent. This article proposes a novel class of hierarchical factor regression models that avoid restrictive assumptions of standard methods, allo

SUBMITTER: Moran KR 

PROVIDER: S-EPMC8320757 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature