Evidence map›Paper›PMID 41802821›Full record

ArticleBMJ global health2026

Country-specific estimates of misclassification rates of computer-coded verbal autopsy algorithms.

Sandipan Pramanik, Emily Wilson, Henry D Kalter, Victor Akelo, Agbessi Amouzou, Robert Black, Dianna Blau, Ivalda Macicame, Jonathan A Muir, Kyu Han Lee and 4 more

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Article in BMJ global health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Sandipan PramanikDepartment of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA spraman4@jhu.edu.ORCID 0000-0002-7196-155X
Emily WilsonDepartment of International Health, Johns Hopkins University, Baltimore, Maryland, USA.
Henry D KalterDepartment of International Health, Johns Hopkins University, Baltimore, Maryland, USA.ORCID 0000-0002-4165-2393
Victor AkeloCenters for Disease Control and Prevention Global Health Kenya, Kisumu, Kenya.
Agbessi AmouzouDepartment of International Health, Johns Hopkins University, Baltimore, Maryland, USA.ORCID 0000-0002-6262-3866
Robert BlackDepartment of International Health, Johns Hopkins University, Baltimore, Maryland, USA.
Dianna BlauGlobal Health Center, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Ivalda MacicameInstituto Nacional de Saúde, Maputo, Mozambique.
Jonathan A MuirEmory Global Health Institute, Emory University, Atlanta, Georgia, USA.
Kyu Han LeeEmory Global Health Institute, Emory University, Atlanta, Georgia, USA.
Li LiuDepartment of International Health, Johns Hopkins University, Baltimore, Maryland, USA.
Cynthia G WhitneyEmory Global Health Institute, Emory University, Atlanta, Georgia, USA.
Scott ZegerDepartment of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA.
Abhirup DattaDepartment of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA.

Funding

Country-Specific Modeling of Verbal Autopsy Misclassification for Improved Child and Neonatal Cause-Specific Mortality Estimates at Low- and High-Resolution CausesK99HD114884 · NICHD · JOHNS HOPKINS UNIVERSITY · PI Sandipan Pramanik · 2025 to 2026
$228k
Bill and Melinda Gates Foundation INV-070577Bill & Melinda Gates Foundation INV-034842NICHD NIH HHS K99 HD114884NIH HHS 1K99HD114884-01A1the Bill and Melinda Gates Foundation INV-034842
6 · The paper itself

Abstract

introductionComputer-coded verbal autopsy (CCVA) algorithms are routinely used to determine individual cause of death (COD) and derive population-level estimates of cause-specific mortality fractions (CSMFs). But frequent COD misclassification leads to biased CSMF estimates. The VA-calibration framework reduces the bias by estimating misclassification rates; but it overlooks systematic patterns and cross-country variation, reducing the accuracy of CSMF estimates.

methodsUsing CHAMPS (Child Health and Mortality Prevention Surveillance) data and the framework in Pramanik

resultsWe report three key findings. First, the country-specific model better fits CHAMPS misclassification rates than the homogeneous model, reducing average absolute loss by 34%-38% for neonates and 13%-24% for children. Second, CCVA algorithms show consistent misclassification patterns, systematically overestimating or underestimating certain causes. Third, calibrating COMSA data increases neonatal CSMF for sepsis/meningitis/infection and decreases it for intrapartum-related events and prematurity; among children, CSMF increases for malaria and decreases for pneumonia.

conclusionsWe present an inventory of VA misclassification rate estimates across two age groups, three CCVA algorithms and eight countries. These publicly available estimates enable the calibration of VA-only data from any country without needing access to CHAMPS data. More generally, these analyses reveal systematic algorithmic biases and highlight opportunities to refine future CCVA algorithms. As reliance on computer-coded and AI-driven approaches to COD determination grows, our integrated VA-calibration workflow, grounded in robust statistical frameworks and open-source software (misclassification matrix modeling, VA-calibration R package on GitHub and CRAN), offers a critical step towards improving the accuracy of mortality surveillance.

Indexed as

AlgorithmsAutopsyCause of DeathChild, PreschoolFemaleHumansInfantInfant, NewbornPopulation SurveillanceChild healthGlobal HealthMathematical modelling

Identifiers

PMID41802821
PMCPMC12983874

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LicenceCC BY
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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.