Evidence map›Paper›PMID 26086600›Full record

ArticlePloS one2015

Verbal Autopsy: Evaluation of Methods to Certify Causes of Death in Uganda.

Arthur Mpimbaza, Scott Filler, Agaba Katureebe, Linda Quick, Daniel Chandramohan, Sarah G Staedke

Abstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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  4. A Qualitative Method To Assess a History of Cerebral Malaria in Malian Children.The American journal of tropical medicine and hygiene · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Arthur MpimbazaChild Health & Development Centre, College of Health Sciences, Makerere University, Kampala, Uganda; Infectious Diseases Research Collaboration, Kampala, Uganda.
Scott FillerGlobal Fund to Fight AIDS, Tuberculosis and Malaria, Geneva, Switzerland.
Agaba KatureebeInfectious Diseases Research Collaboration, Kampala, Uganda.
Linda QuickCenters for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Daniel ChandramohanLondon School of Hygiene & Tropical Medicine, London, United Kingdom.
Sarah G StaedkeInfectious Diseases Research Collaboration, Kampala, Uganda; London School of Hygiene & Tropical Medicine, London, United Kingdom.

Funding

University of California Launching Future Leaders in Global Health Research Training ProgramD43TW009343 · FIC · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CRAIG R COHEN, Sung-Jae Lee · 2017 to 2026
$14.7M
FIC NIH HHS D43 TW009343
6 · The paper itself

Abstract

To assess different methods for determining cause of death from verbal autopsy (VA) questionnaire data, the intra-rater reliability of Physician-Certified Verbal Autopsy (PCVA) and the accuracy of PCVA, expert-derived (non-hierarchical) and data-driven (hierarchal) algorithms were assessed for determining common causes of death in Ugandan children. A verbal autopsy validation study was conducted from 2008-2009 in three different sites in Uganda. The dataset included 104 neonatal deaths (0-27 days) and 615 childhood deaths (1-59 months) with the cause(s) of death classified by PCVA and physician review of hospital medical records (the 'reference standard'). Of the original 719 questionnaires, 141 (20%) were selected for a second review by the same physicians; the repeat cause(s) of death were compared to the original,and agreement assessed using the Kappa statistic.Physician reviewers' refined non-hierarchical algorithms for common causes of death from existing expert algorithms, from which, hierarchal algorithms were developed. The accuracy of PCVA, non-hierarchical, and hierarchical algorithms for determining cause(s) of death from all 719 VA questionnaires was determined using the reference standard. Overall, intra-rater repeatability was high (83% agreement, Kappa 0.79 [95% CI 0.76-0.82]). PCVA performed well, with high specificity for determining cause of neonatal (>67%), and childhood (>83%) deaths, resulting in fairly accurate cause-specific mortality fraction (CSMF) estimates. For most causes of death in children, non-hierarchical algorithms had higher sensitivity, but correspondingly lower specificity, than PCVA and hierarchical algorithms, resulting in inaccurate CSMF estimates. Hierarchical algorithms were specific for most causes of death, and CSMF estimates were comparable to the reference standard and PCVA. Inter-rater reliability of PCVA was high, and overall PCVA performed well. Hierarchical algorithms performed better than non-hierarchical algorithms due to higher specificity and more accurate CSMF estimates. Use of PCVA to determine cause of death from VA questionnaire data is reasonable while automated data-driven algorithms are improved.

Indexed as

Cause of DeathAutopsyChild, PreschoolHumansInfantInfant, NewbornObserver VariationPhysiciansSpeechSurveys and QuestionnairesUganda

Identifiers

PMID26086600
PMCPMC4472780

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Registered trials

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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.