ReviewThe American journal of tropical medicine and hygiene2023
Correcting for Verbal Autopsy Misclassification Bias in Cause-Specific Mortality Estimates.
Review in The American journal of tropical medicine and hygiene, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Validation of malaria-attributed deaths using verbal autopsy studies: a systematic review.Malaria journal · 2024Pooled it
- Rates and causes of neonatal and under-five mortality: insights from a national household survey in Pakistan.Journal of global health · 2026Article
- Country-specific estimates of misclassification rates of computer-coded verbal autopsy algorithms.BMJ global health · 2026Article
- Burden of child mortality from malaria in high endemic areas: Results from the CHAMPS network using minimally invasive tissue sampling.The Journal of infection · 2024Article
- Comparison of causes of stillbirth and child deaths as determined by verbal autopsy and minimally invasive tissue sampling.PLOS global public health · 2024Article
- Addressing the Gaps in Mortality Data: A Case for National Mortality Surveillance.The American journal of tropical medicine and hygiene · 2023Article
Corrections and comments
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Authors and funding
14 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.