Evidence map›Paper›PMID 29320495›Full record

ArticlePLoS medicine2018

The WHO 2016 verbal autopsy instrument: An international standard suitable for automated analysis by InterVA, InSilicoVA, and Tariff 2.0.

Erin K Nichols, Peter Byass, Daniel Chandramohan, Samuel J Clark, Abraham D Flaxman, Robert Jakob, Jordana Leitao, Nicolas Maire, Chalapati Rao, Ian Riley and 2 more

Abstract read
In one paragraph

Article in PLoS medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 108 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
108citing 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.

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

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

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48 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

12 authors.

Erin K NicholsNational Center for Health Statistics, Centers for Disease Control and Prevention, United States Public Health Service, Hyattsville, Maryland, United States of America.ORCID 0000-0002-4917-1412
Peter ByassWHO Collaborating Centre for Verbal Autopsy, Umeå Centre for Global Health Research, Division of Epidemiology and Global Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden.ORCID 0000-0001-5474-4361
Daniel ChandramohanLondon School of Hygiene & Tropical Medicine, London, United Kingdom.
Samuel J ClarkMRC-Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Abraham D FlaxmanInstitute for Health Metrics and Evaluation, Department of Global Health, University of Washington, Seattle, Seattle, Washington, United States of America.ORCID 0000-0001-6033-4713
Robert JakobWorld Health Organization (WHO), Geneva, Switzerland.
Jordana LeitaoWorld Health Organization (WHO), Geneva, Switzerland.
Nicolas MaireDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland.ORCID 0000-0002-1961-9343
Chalapati RaoDepartment of Global Health, Research School of Population Health, Australian National University, Canberra, Australia.
Ian RileyMelbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.
Philip W SetelVital Strategies, New York, New York, United States of America.
WHO Verbal Autopsy Working Group

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
NICHD NIH HHS R01 HD086227
6 · The paper itself

Abstract

backgroundVerbal autopsy (VA) is a practical method for determining probable causes of death at the population level in places where systems for medical certification of cause of death are weak. VA methods suitable for use in routine settings, such as civil registration and vital statistics (CRVS) systems, have developed rapidly in the last decade. These developments have been part of a growing global momentum to strengthen CRVS systems in low-income countries. With this momentum have come pressure for continued research and development of VA methods and the need for a single standard VA instrument on which multiple automated diagnostic methods can be developed. METHODS AND

findingsIn 2016, partners harmonized a WHO VA standard instrument that fully incorporates the indicators necessary to run currently available automated diagnostic algorithms. The WHO 2016 VA instrument, together with validated approaches to analyzing VA data, offers countries solutions to improving information about patterns of cause-specific mortality. This VA instrument offers the opportunity to harmonize the automated diagnostic algorithms in the future.

conclusionsDespite all improvements in design and technology, VA is only recommended where medical certification of cause of death is not possible. The method can nevertheless provide sufficient information to guide public health priorities in communities in which physician certification of deaths is largely unavailable. The WHO 2016 VA instrument, together with validated approaches to analyzing VA data, offers countries solutions to improving information about patterns of cause-specific mortality.

Indexed as

Vital StatisticsWorld Health OrganizationAutopsyCause of DeathHumans

Identifiers

PMID29320495
PMCPMC5761828

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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.