Evidence map›Paper›PMID 41500658›Full record

ArticleBMJ global health2026

Introducing and testing the maternal vulnerability segmentation tool (MVST) for essential health services use: demonstrating its use in Oromia, Ethiopia.

Yihunie Lakew, Habtamu Tamene, Bee-Ah Kang, Nandita Kapadia-Kundu, Simon Heliso Kuka, Rajiv Rimal

Abstract read
In one paragraph

Article in BMJ global health, 2026. 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

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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

6 authors.

Yihunie LakewCenter for Communication Programs, Johns Hopkins University, Addis Ababa, Ethiopia yihunierh@yahoo.com.ORCID 0000-0002-1520-0844
Habtamu TameneCenter for Communication Programs, Johns Hopkins University, Addis Ababa, Ethiopia.ORCID 0009-0004-3681-8338
Bee-Ah KangDepartment of Health, Behavior and Society, Johns Hopkins University, Baltimore, MD, USA.ORCID 0000-0002-1124-2481
Nandita Kapadia-KunduDepartment of Health, Behavior and Society, Johns Hopkins University, Baltimore, MD, USA.ORCID 0009-0004-5359-9923
Simon Heliso KukaCenter for Communication Programs, Johns Hopkins University, Addis Ababa, Ethiopia.ORCID 0009-0003-7531-7352
Rajiv RimalDepartment of Health, Behavior and Society, Johns Hopkins University, Baltimore, MD, USA.ORCID 0000-0003-1413-9305

Funding

Gates Foundation INV-035431
6 · The paper itself

Abstract

Ethiopia has made substantial progress in improving maternal health outcomes; however, significant disparities remain among populations whose individual, interpersonal, community and cultural factors place them at greater risk, as compared with those with better access to resources and healthcare facilities. Addressing these disparities requires initiating advanced and targeted segmentation tools to effectively identify and reach vulnerable groups.This study developed and validated a maternal vulnerability segmentation tool designed to assist community health workers to identify pregnant women who are least likely to access essential maternal care services. Key predictors of maternal health service utilisation were identified and incorporated into an initial 20-item questionnaire. Through a process of refinement and validation, these were condensed into six items for rural and four for urban-rural settings. Findings indicated that women with higher vulnerability scores were significantly less likely to attend antenatal care and more likely to deliver at home (p<0.01, for each). Based on these results, a maternal vulnerability segmentation tool was tested and implemented by community health workers to identify and support underserved pregnant women in Ethiopia.

Indexed as

Health Services AccessibilityMaternal Health ServicesVulnerable PopulationsAdolescentAdultCommunity Health WorkersEthiopiaFemaleHumansPregnancyPrenatal CareRural PopulationSurveys and QuestionnairesYoung AdultMaternal healthScreening

Identifiers

PMID41500658
PMCPMC12781986

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.