Country-Specific Estimates of Misclassification Rates of Computer-Coded Verbal Autopsy Algorithms

Background

Computer-coded verbal autopsy (CCVA) algorithms are commonly used to determine individual causes of death (COD) and population-level cause-specific mortality fractions (CSMF), but frequent COD misclassification leads to biased CSMF estimates. The VA-calibration framework [1,2] reduces bias by estimating misclassification rates from limited CHAMPS data, but it overlooks country-level variation in these rates, reducing the accuracy of CSMF estimates.

Methods

Utilizing CHAMPS data and the framework from [3], we estimate VA misclassification rates for three widely used CCVA algorithms (EAVA, InSilicoVA, InterVA), two age groups (neonates 0-27 days and children 1-59 months), and eight countries (Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, South Africa, other). We then use the Mozambique-specific rates to calibrate VA-only data from the COMSA project in Mozambique.

Results

We report three key findings. First, the country-specific model better fits CHAMPS misclassification rates than the homogeneous model, reducing average absolute loss by 34-38% for neonates and 13-24% for children. Second, CCVA algorithms show consistent misclassification patterns, systematically over- or underestimating certain causes. Third, calibrating COMSA data increases neonatal CSMF for sepsis/meningitis/infection and decreases it for intrapartum-related events (IPRE) and prematurity; among children, CSMF increases for malaria and decreases for pneumonia.

Conclusion

We generate VA misclassification rate estimates across two age groups, three CCVA algorithms, and eight countries. These publicly available estimates enable calibration of VA-only data from any country without needing access to CHAMPS data. The analysis also highlights systematic algorithm biases, providing direction for future improvements.

Key Messages

• We improve VA-calibration by using CHAMPS data and a country-specific Bayesian model to account for systematic and cross-country variation in CCVA misclassification rates.

• We provide uncertainty-quantified, country-specific misclassification estimates across two age groups, three CCVA algorithms, and eight country categories (including an ‘other’ group for countries outside CHAMPS), enabling VA-calibration for any country without requiring access to CHAMPS data.

• We showcase their utility by using Mozambique-specific misclassification estimate to calibrate VA-only data from Mozambique’s COMSA project, refining CSMF estimates among neonates and children.