Evidence map›Paper›PMID 24495312›Full record

SynthesisBMC medicine2014

Comparison of physician-certified verbal autopsy with computer-coded verbal autopsy for cause of death assignment in hospitalized patients in low- and middle-income countries: systematic review.

Jordana Leitao, Nikita Desai, Lukasz Aleksandrowicz, Peter Byass, Pierre Miasnikof, Stephen Tollman, Dewan Alam, Ying Lu, Suresh Kumar Rathi, Abhishek Singh and 3 more

Registry-linked trialAbstract readComparative StudySystematic Review
In one paragraph

Synthesis in BMC medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02810366 (Physician Versus Computer Coding of Verbal Autopsies, a Randomised Control Trial), which is not on this map. Cited by 68 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
68citing papers in PubMed, 1 pooled it
–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.

NCT02810366 naunknown statusnot on this mapstarted 2015, after this paper: background citation

Physician Versus Computer Coding of Verbal Autopsies, a Randomised Control Trial

TypeinterventionalSponsorCentre for Global Health Research, TorontoRan2015 to 2016Enrolled12,500ConditionsFatal OutcomeArmsPhysician versus Computer Coded Verbal Autopsy
3 · Its place in the literature

Who cites it

68 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  3. Verbal autopsy-assigned causes of death among adults being investigated for TB in South Africa.Transactions of the Royal Society of Tropical Medicine and Hygiene · 2016
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8 more citing papers are in PubMed but not listed here.

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

13 authors.

Jordana Leitao
Nikita Desai
Lukasz Aleksandrowicz
Peter Byass
Pierre Miasnikof
Stephen Tollman
Dewan Alam
Ying Lu
Suresh Kumar Rathi
Abhishek Singh
Wilson Suraweera
Faujdar Ram
Prabhat JhaCentre for Global Heath Research, St Michael's Hospital, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada. Prabhat.jha@utoronto.ca.

Funding

Canadian Institutes of Health Research
6 · The paper itself

Abstract

backgroundComputer-coded verbal autopsy (CCVA) methods to assign causes of death (CODs) for medically unattended deaths have been proposed as an alternative to physician-certified verbal autopsy (PCVA). We conducted a systematic review of 19 published comparison studies (from 684 evaluated), most of which used hospital-based deaths as the reference standard. We assessed the performance of PCVA and five CCVA methods: Random Forest, Tariff, InterVA, King-Lu, and Simplified Symptom Pattern.

methodsThe reviewed studies assessed methods' performance through various metrics: sensitivity, specificity, and chance-corrected concordance for coding individual deaths, and cause-specific mortality fraction (CSMF) error and CSMF accuracy at the population level. These results were summarized into means, medians, and ranges.

resultsThe 19 studies ranged from 200 to 50,000 deaths per study (total over 116,000 deaths). Sensitivity of PCVA versus hospital-assigned COD varied widely by cause, but showed consistently high specificity. PCVA and CCVA methods had an overall chance-corrected concordance of about 50% or lower, across all ages and CODs. At the population level, the relative CSMF error between PCVA and hospital-based deaths indicated good performance for most CODs. Random Forest had the best CSMF accuracy performance, followed closely by PCVA and the other CCVA methods, but with lower values for InterVA-3.

conclusionsThere is no single best-performing coding method for verbal autopsies across various studies and metrics. There is little current justification for CCVA to replace PCVA, particularly as physician diagnosis remains the worldwide standard for clinical diagnosis on live patients. Further assessments and large accessible datasets on which to train and test combinations of methods are required, particularly for rural deaths without medical attention.

Indexed as

Cause of DeathHospitalizationPhysician's RolePovertyAutopsyElectronic Data ProcessingHumansRandomized Controlled Trials as Topic

Identifiers

PMID24495312
PMCPMC3912516

What OpenQuestion holds

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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.