Correcting for Verbal Autopsy Misclassification Bias in Cause-Specific Mortality Estimates
Year: 2023
Type: article
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.
Institutions: University of Pennsylvania, Johns Hopkins University, Centers for Disease Control and Prevention, Center for Global Health, Institució Catalana de Recerca i Estudis Avançats, Hospital Sant Joan de Déu Barcelona, Manhiça Health Research Centre, Barcelona Institute for Global Health, Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública, Universitat de Barcelona, Instituto Nacional de Saúde
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Award: G617214897
OpenAlex ID: W4363677508