Evidence map›Paper›PMID 31242925›Full record

Trial reportBMC medicine2019

Automated versus physician assignment of cause of death for verbal autopsies: randomized trial of 9374 deaths in 117 villages in India.

Prabhat Jha, Dinesh Kumar, Rajesh Dikshit, Atul Budukh, Rehana Begum, Prabha Sati, Patrycja Kolpak, Richard Wen, Shyamsundar J Raithatha, Utkarsh Shah and 7 more

Registry-linked trialOpen access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in BMC medicine, 2019. 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 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
2.6field-weighted citation impact, top 11% of its field
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 map

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

21 citing papers in PubMed, 30 citations in OpenAlex.

  1. Trial
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  9. Correcting for Verbal Autopsy Misclassification Bias in Cause-Specific Mortality Estimates.The American journal of tropical medicine and hygiene · 2023
    Review
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  11. Mortality Associated with AmbientEnvironmental health perspectives · 2022
    Article
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  17. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors at 10 institutions in 4 countries.

Prabhat JhaCentre for Global Health Research, St Michael's Hospital and Dalla Lana School of Public Health, University of Toronto, Toronto, Canada. Prabhat.jha@utoronto.ca.
Dinesh KumarDepartment of Community Medicine, Pramukhswami Medical College, Anand, Gujarat, India.
Rajesh DikshitCentre for Cancer Epidemiology, Tata Memorial Centre, Mumbai, India.
Atul BudukhCentre for Cancer Epidemiology, Tata Memorial Centre, Mumbai, India.
Rehana BegumCentre for Global Health Research, St Michael's Hospital and Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
Prabha SatiCentre for Global Health Research, St Michael's Hospital and Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
Patrycja KolpakCentre for Global Health Research, St Michael's Hospital and Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
Richard WenCentre for Global Health Research, St Michael's Hospital and Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
Shyamsundar J RaithathaDepartment of Community Medicine, Pramukhswami Medical College, Anand, Gujarat, India.
Utkarsh ShahDepartment of Community Medicine, Pramukhswami Medical College, Anand, Gujarat, India.
Zehang Richard LiDepartment of Biostatistics, Yale University, New Haven, USA.
Lukasz AleksandrowiczLondon School of Hygiene & Tropical Medicine, London, UK.
Prakash ShahCentre for Global Health Research, St Michael's Hospital and Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
Kapila PiyasenaCentre for Global Health Research, St Michael's Hospital and Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
Tyler H McCormickDepartment of Statistics, University of Washington, Seattle, USA.
Hellen GelbandCentre for Global Health Research, St Michael's Hospital and Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
Samuel J ClarkLondon School of Hygiene & Tropical Medicine, London, UK.
Pramukhswami Medical College · INSt. Michael's Hospital · CAUniversity of Toronto · CACentre for Global Health Research · CAAdvanced Centre for Treatment, Research and Education in Cancer · INLondon School of Hygiene & Tropical Medicine · GBSeattle University · USTata Memorial Hospital · INThe Ohio State University · USYale University · US

Funding

Verbal Autopsy: Reimagining Data & Automated Cause Assignment (using ALPHA Network data)R01HD086227 · NICHD · OHIO STATE UNIVERSITY · PI CLARK, SAMUEL J · 2016 to 2021
$2.4M
Estimating vital rates in the developing world: A Bayesian process modeling approachK01HD078452 · NICHD · UNIVERSITY OF WASHINGTON · PI MCCORMICK, TYLER · 2015 to 2019
$611k
Measuring Tobacco Mortality within the Million Death Study in IndiaR01TW007939 · FIC · ST. MICHAEL'S HOSPITAL · PI JHA, PRABHAT · 2007 to 2010
$540k
CIHR FDN 154277FIC NIH HHS R01 TW007939NICHD NIH HHS K01 HD078452NICHD NIH HHS R01 HD086227
6 · The paper itself

Abstract

backgroundVerbal autopsies with physician assignment of cause of death (COD) are commonly used in settings where medical certification of deaths is uncommon. It remains unanswered if automated algorithms can replace physician assignment.

methodsWe randomized verbal autopsy interviews for deaths in 117 villages in rural India to either physician or automated COD assignment. Twenty-four trained lay (non-medical) surveyors applied the allocated method using a laptop-based electronic system. Two of 25 physicians were allocated randomly to independently code the deaths in the physician assignment arm. Six algorithms (Naïve Bayes Classifier (NBC), King-Lu, InSilicoVA, InSilicoVA-NT, InterVA-4, and SmartVA) coded each death in the automated arm. The primary outcome was concordance with the COD distribution in the standard physician-assigned arm. Four thousand six hundred fifty-one (4651) deaths were allocated to physician (standard), and 4723 to automated arms.

resultsThe two arms were nearly identical in demographics and key symptom patterns. The average concordances of automated algorithms with the standard were 62%, 56%, and 59% for adult, child, and neonatal deaths, respectively. Automated algorithms showed inconsistent results, even for causes that are relatively easy to identify such as road traffic injuries. Automated algorithms underestimated the number of cancer and suicide deaths in adults and overestimated other injuries in adults and children. Across all ages, average weighted concordance with the standard was 62% (range 79-45%) with the best to worst ranking automated algorithms being InterVA-4, InSilicoVA-NT, InSilicoVA, SmartVA, NBC, and King-Lu. Individual-level sensitivity for causes of adult deaths in the automated arm was low between the algorithms but high between two independent physicians in the physician arm.

conclusionsWhile desirable, automated algorithms require further development and rigorous evaluation. Lay reporting of deaths paired with physician COD assignment of verbal autopsies, despite some limitations, remains a practicable method to document the patterns of mortality reliably for unattended deaths.

trial registrationClinicalTrials.gov , NCT02810366. Submitted on 11 April 2016.

Indexed as

AdultAutopsyChildData CollectionDeathFemaleHumansIndiaMalePhysiciansAlgorithmsCOD classificationPhysician codingVerbal autopsies

Identifiers

PMID31242925
PMCPMC6595581
OpenAlexW2955512129

What OpenQuestion holds

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LicenceCC BY
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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.