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

Jacob Fiksel, Brian Gilbert, Emily Wilson, Henry Kalter, Almamy Kante, Aveika Akum, Dianna Blau, Quique Bassat, Ivalda Macicame, Eduardo Samo Gudo, Robert Black, Scott Zeger, Agbessi Amouzou, Abhirup Datta

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)66-77
Number of pages12
JournalAmerican Journal of Tropical Medicine and Hygiene
Volume108
DOIs
StatePublished - May 2023

ASJC Scopus subject areas

  • Infectious Diseases
  • Virology
  • Parasitology

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