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Correcting for Verbal Autopsy Misclassification Bias in Cause-Specific Mortality Estimates

Domain:

healthcare

Record type:

software
Creator:
JacBriEmiHen
Publisher:
Ame
Host:
ABSTRACT. Verbal autopsies (VAs) 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 (CSMFs), a critical piece in health-policy making. Recent work has demonstrated that the knowledge of the CCVA misclassification rates can be used to calibrate raw VA-based CSMF estimates to account for the misclassification bias. In this manuscript, 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 CSMFs for child (1–59 months) and neonatal deaths using VA data from the Countrywide Mortality Surveillance for Action project in Mozambique.

Visit

doi.org

Licenses

https://creativecommons.org/licenses/by/4.0/

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