Modeling Structure and Country-specific Heterogeneity in Misclassification Matrices of Verbal Autopsy-based Cause of Death Classifiers

Verbal autopsy (VA) algorithms are routinely used to determine individual-level causes of death (COD) in many low-and-middle-income countries, which are then aggregated to derive population-level cause-specific mortality fractions (CSMF), 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 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 misclassification matrices in data-scarce settings. We first introduce a novel base model based on two latent mechanisms: intrinsic accuracy and systematic preference to parsimoniously characterize misclassifications. We prove that they 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 tradeoff by adaptively favoring simpler models. We publish uncertainty-quantified estimates of VA misclassification rates for 6 countries. This effort broadens VA-calibration's future applicability and strengthens ongoing efforts of using VA for mortality surveillance.