Evidence map›Paper›PMID 35377290›Full record

ReviewGlobal health action2021

Estimating causes of death where there is no medical certification: evolution and state of the art of verbal autopsy.

Daniel Chandramohan, Edward Fottrell, Jordana Leitao, Erin Nichols, Samuel J Clark, Carine Alsokhn, Daniel Cobos Munoz, Carla AbouZahr, Aurelio Di Pasquale, Robert Mswia and 14 more

Abstract readReview
In one paragraph

Review in Global health action, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

  1. Article
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  3. Hierarchical latent class models for mortality surveillance using partially verified verbal autopsies.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2026
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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

24 authors.

Daniel ChandramohanDepartment of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.ORCID 0000-0002-6974-5620
Edward FottrellInstitute for Global Health, University College London, London, UK.
Jordana LeitaoWorld Health Organization Verbal Autopsy Reference Group Secretariat, Luanda, Angola.
Erin NicholsCenters for Disease Control, National Center for Health Statistics, US Public Health Service, Hyattsville, MD, USA.ORCID 0000-0002-4917-1412
Samuel J ClarkInstitute for Population Research and the Department of Sociology, Ohio State University, Columbus, Ohio, USA.ORCID 0000-0002-4929-6231
Carine AlsokhnDepartment of Data Analytics and Delivery for Impact, World Health Organization, Geneva, Switzerland.
Daniel Cobos MunozDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland.ORCID 0000-0002-5920-1304
Carla AbouZahrConsultant, Saint-Legier, Switzerland.ORCID 0000-0001-7421-7884
Aurelio Di PasqualeDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland.
Robert MswiaVital Strategies, New York, USA.
Eungang ChoiInstitute for Population Research and the Department of Sociology, Ohio State University, Columbus, Ohio, USA.
Frank BaidenDepartment of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.
Jason ThomasInstitute for Population Research and the Department of Sociology, Ohio State University, Columbus, Ohio, USA.
Isaac LyatuuDepartment of Environmental Health and Ecological Services, Ifakara Health Institute, Dar Es Salaam, Tanzania.ORCID 0000-0001-5694-7648
Zehang LiDepartment of Statistics, University of California, Santa Cruz, USA.
Patrick Larbi-DebrahGhana Ministry of Health, Ghana Health Service, Accra, Ghana.
Yue ChuInstitute for Population Research and the Department of Sociology, Ohio State University, Columbus, Ohio, USA.
Samuel CheburetMinistry of Health, Nairobi, Kenya.
Osman SankohStatistics Sierra Leone, Freetown, Sierra Leone.ORCID 0000-0003-4405-9808
Azza Mohamed BadrDepartment of Data Analytics and Delivery for Impact, World Health Organization, Geneva, Switzerland.
Doris Ma FatDepartment of Data Analytics and Delivery for Impact, World Health Organization, Geneva, Switzerland.
Philip SetelVital Strategies, New York, USA.ORCID 0000-0002-9042-5699
Robert JakobDepartment of Data Analytics and Delivery for Impact, World Health Organization, Geneva, Switzerland.
Don de SavignyDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland.ORCID 0000-0002-6903-4722

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 HD086227World Health Organization 001
6 · The paper itself

Abstract

Over the past 70 years, significant advances have been made in determining the causes of death in populations not served by official medical certification of cause at the time of death using a technique known as Verbal Autopsy (VA). VA involves an interview of the family or caregivers of the deceased after a suitable bereavement interval about the circumstances, signs and symptoms of the deceased in the period leading to death. The VA interview data are then interpreted by physicians or, more recently, computer algorithms, to assign a probable cause of death. VA was originally developed and applied in field research settings. This paper traces the evolution of VA methods with special emphasis on the World Health Organization's (WHO)'s efforts to standardize VA instruments and methods for expanded use in routine health information and vital statistics systems in low- and middle-income countries (LMICs). These advances in VA methods are culminating this year with the release of the 2022 WHO Standard Verbal Autopsy (VA) Toolkit. This paper highlights the many contributions the late Professor Peter Byass made to the current VA standards and methods, most notably, the development of InterVA, the most commonly used automated computer algorithm for interpreting data collected in the WHO standard instruments, and the capacity building in low- and middle-income countries (LMICs) that he promoted. This paper also provides an overview of the methods used to improve the current WHO VA standards, a catalogue of the changes and improvements in the instruments, and a mapping of current applications of the WHO VA standard approach in LMICs. It also provides access to tools and guidance needed for VA implementation in Civil Registration and Vital Statistics Systems at scale.

Indexed as

Vital StatisticsAutopsyCause of DeathCertificationHumansMalePovertyCivil Registration and Vital Statistics SystemsInSilicoVAInterVAMortality surveillanceSmartVA

Identifiers

PMID35377290
PMCPMC8986278

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.