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Correcting for misclassification bias in cause-specific mortality estimates

Abhi Datta


Verbal autopsies (VA) are extensively used to determine cause-of-death (COD) in many low and middle income countries. However, COD determination from VA can be inaccurate. Computer-coded-verbal-autopsy (CCVA) algorithms used for this task are imperfect and misclassify COD for a large proportion of deaths. If not accounted for, this misclassification leads to biased estimates of cause-specific mortality fractions (CSMF), a critical piece in health-policy making. We discuss how to estimate and use CCVA misclassification rates to calibrate raw VA-based CSMF estimates to account for the misclassification bias in Bayesian hierarchical model. We review the current practices and issues with raw COD predictions from CCVA algorithms and provide a complete primer on how to use the VA calibration approach with the calibratedVA software to correct for verbal autopsy misclassification bias in cause-specific mortality estimates. We use calibratedVA to obtain CSMF for child (1-59 months) and neonatal deaths using VA data from the Countrywide Mortality Surveillance for Action Mozambique (COMSA) project in Mozambique.