Work

Modeling structure and country-specific heterogeneity in misclassification matrices of verbal autopsy-based cause of death classifiers

Year: 2025

Type: article

Abstract: Verbal autopsy (VA) algorithms are routinely used to determine individual-level causes of death (COD) in many low- and-middle-income countries. The individual CODs are then aggregated to derive population-level cause-specific mortality fractions (CSMF), which are essential to informing public health policies. However, VA algorithms frequently misclassify COD and introduce bias in CSMF estimates. A recent method, VA-calibration, can correct for this bias using a VA misclassification rate matrix estimated from paired data on COD from both VA and minimally invasive tissue sampling (MITS) from the Child Health and Mortality Prevention Surveillance (CHAMPS) Network. Due to the limited sample size, CHAMPS data are pooled across all countries, implicitly assuming that the misclassification rates are homogeneous. In this research we show that the VA misclassification matrices are substantially heterogeneous across countries, thereby biasing the VA-calibration. We develop a coherent framework for modeling country-specific VA misclassification matrices in data-scarce settings. We first introduce a novel base model to parsimoniously characterize misclassifications via two latent mechanisms—intrinsic accuracy and systematic preference. We prove that these mechanisms are identifiable from the data and manifest as a form of invariance in certain misclassification odds, a pattern evident in the CHAMPS data. Then we expand from this base model, adding higher complexity and country-specific heterogeneity via interpretable effect sizes. Shrinkage priors balance the bias-variance trade-off by adaptively favoring simpler models. We publish uncertainty-quantified estimates of VA misclassification rates for six countries. This effort broadens VA-calibration’s future applicability and strengthens ongoing efforts of using VA for mortality surveillance.

Source: The Annals of Applied Statistics

DOI: 10.1214/24-aoas2006

Authors: Sandipan Pramanik, Scott Zeger, Dianna Blau, Abhirup Datta

Institutions: Johns Hopkins University, Centers for Disease Control and Prevention, Centers for Disease Control and Prevention

Language: English

Topic: Statistical Methods in Epidemiology

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: Bill and Melinda Gates Foundation, Centers for Disease Control and Prevention