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

Journal Article (Journal Article)

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, allow both the mean and covariance to vary with COD category, and can include covariate information on the decedent, region, or events surrounding death. Taking a Bayesian approach to inference, this work develops an MCMC algorithm and validates the FActor Regression for Verbal Autopsy (FARVA) model in simulation experiments. An application of FARVA to real VA data shows improved goodness-of-fit and better predictive performance in inferring COD and CSMF over competing methods. Code and a user manual are made available at https://github.com/kelrenmor/farva.

Full Text

Duke Authors

Cited Authors

  • Moran, KR; Turner, EL; Dunson, D; Herring, AH

Published Date

  • June 2021

Published In

Volume / Issue

  • 70 / 3

Start / End Page

  • 532 - 557

PubMed ID

  • 34334826

Pubmed Central ID

  • PMC8320757

International Standard Serial Number (ISSN)

  • 0035-9254

Digital Object Identifier (DOI)

  • 10.1111/rssc.12468

Language

  • eng

Conference Location

  • England