Bayesian Hierarchical Factor Regression Models to Infer Cause of Death From Verbal Autopsy Data

TL;DR

This paper introduces a Bayesian hierarchical factor regression model called FARVA for inferring causes of death from verbal autopsy data, improving accuracy and flexibility over existing methods.

Contribution

It develops a novel hierarchical factor regression approach that captures complex dependencies and incorporates covariates, enhancing cause of death inference from verbal autopsies.

Findings

FARVA outperforms competing methods in predictive accuracy.

The model provides better goodness-of-fit in real data applications.

Incorporates covariates to improve cause of death classification.

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, 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.