Evidence map›Paper›PMID 41462338›Full record

ArticleBMC research notes2025

Adapting the 2022 WHO verbal autopsy tool for use in Lagos State, Nigeria: insights from the LVASA-SRS project.

Adeyemi Okunowo, Uchenna Gwacham-Anisiobi, Ndubuisi Ezumezu, Hameed Adelabu, Adedoyin Ogunyemi, Teniola Lawanson, Brenda Isikekpei, Bosede B Afolabi, Aduragbemi Banke-Thomas

Abstract read
In one paragraph

Article in BMC research notes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Adeyemi OkunowoCentre for Clinical Trials, Research, and Implementation Science, College of Medicine, University of Lagos, Idi Araba, Lagos State, Nigeria.
Uchenna Gwacham-AnisiobiCentre for Clinical Trials, Research, and Implementation Science, College of Medicine, University of Lagos, Idi Araba, Lagos State, Nigeria.
Ndubuisi EzumezuCentre for Clinical Trials, Research, and Implementation Science, College of Medicine, University of Lagos, Idi Araba, Lagos State, Nigeria.
Hameed AdelabuCentre for Clinical Trials, Research, and Implementation Science, College of Medicine, University of Lagos, Idi Araba, Lagos State, Nigeria.
Adedoyin OgunyemiCentre for Clinical Trials, Research, and Implementation Science, College of Medicine, University of Lagos, Idi Araba, Lagos State, Nigeria.
Teniola LawansonCentre for Clinical Trials, Research, and Implementation Science, College of Medicine, University of Lagos, Idi Araba, Lagos State, Nigeria.
Brenda IsikekpeiCentre for Clinical Trials, Research, and Implementation Science, College of Medicine, University of Lagos, Idi Araba, Lagos State, Nigeria.
Bosede B AfolabiCentre for Clinical Trials, Research, and Implementation Science, College of Medicine, University of Lagos, Idi Araba, Lagos State, Nigeria.
Aduragbemi Banke-ThomasCentre for Clinical Trials, Research, and Implementation Science, College of Medicine, University of Lagos, Idi Araba, Lagos State, Nigeria. Aduragbemi.Banke-Thomas@lshtm.ac.uk.

Funding

Bill and Melinda Gates Foundation INV-056956
6 · The paper itself

Abstract

objectiveAccurate and routine cause-of-death data are essential for health planning, yet many low- and middle-income countries lack comprehensive systems for such data, resulting in undocumented maternal and perinatal deaths. This article details the adaptation of the 2022 WHO Verbal Autopsy (VA) tool for maternal mortality surveillance in a linguistically diverse urban setting with high rates of out-of-facility deaths as part of the Lagos Verbal and Social Autopsy Sample Registration System project in Nigeria.

resultsThe adaptation process enhanced the tool’s cultural sensitivity, language clarity, and system compatibility. Impersonal or potentially disrespectful terms (e.g., “the deceased”) were replaced, complex medical terms simplified (e.g., “abdominal pain” to “tummy pain”), and locally relevant options added for care providers and death locations. Context-specific skip patterns improved efficiency by focusing on maternal deaths and a rigorous translation process (forward/back translation in Yoruba and Pidgin English) ensured linguistic fidelity and local resonance. Validation included face and content review, role-play simulations, and pilot-testing with bereaved families. The adapted tool improved respondent comfort, data accuracy, and digital integration, incorporating global positioning system coordinates and interviewer identification numbers. The adapted tool retained compatibility with automated VA coding platforms and was incorporated into training regimen for data collectors.

Indexed as

Maternal MortalityAutopsyCause of DeathFemaleHumansNigeriaPregnancyWorld Health OrganizationAdaptationHealth systemsMaternal mortalityNigeriaPublic health surveillanceStillbirthVerbal autopsy

Identifiers

PMID41462338
PMCPMC12751495

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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.