Evidence map›Paper›PMID 42524049›Full record

ArticleJournal of tropical medicine2026

Predictive Modeling of Healthcare Workers' Priorities of the WHO 2030 Key Activities for Snakebite Prevention and Control in Ghana.

Eric Nyarko, Iddrisu Abugbil Atubiga, Fafa Shalom Tchorly, Nicholas Amani Hamman, Aashna Uppal, Nuhu Mohammed, Leonard Kyei, Eduardo Alberto Fernandez, Isaac Baidoo

Abstract read
In one paragraph

Article in Journal of tropical medicine, 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

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.

Eric NyarkoDepartment of Statistics and Actuarial Science, College of Basic and Applied Sciences, University of Ghana, Legon, Accra, Ghana, ug.edu.gh.ORCID https://orcid.org/0000-0002-1666-7489
Iddrisu Abugbil AtubigaDepartment of Statistics and Actuarial Science, College of Basic and Applied Sciences, University of Ghana, Legon, Accra, Ghana, ug.edu.gh.
Fafa Shalom TchorlyDepartment of Statistics and Actuarial Science, College of Basic and Applied Sciences, University of Ghana, Legon, Accra, Ghana, ug.edu.gh.ORCID https://orcid.org/0009-0007-0062-068X
Nicholas Amani HammanSnakebite Treatment and Research Hospital, Kaltungo 770110, Gombe, Nigeria.ORCID https://orcid.org/0009-0000-2105-3062
Aashna UppalNuffield Department of Medicine, The Global Health Network, Centre for Global Health and Tropical Medicine, University of Oxford, Oxford OX2 7SG, UK, ox.ac.uk.ORCID https://orcid.org/0000-0003-3712-2442
Nuhu MohammedSnakebite Treatment and Research Hospital, Kaltungo 770110, Gombe, Nigeria.ORCID https://orcid.org/0009-0009-4964-6623
Leonard KyeiMathematics Applications Department, Wisconsin International University College, Legon, Accra, Ghana.
Eduardo Alberto FernandezDepartment of Health Sciences, Brock University, St Catharines, Ontario, Canada, brocku.ca.ORCID https://orcid.org/0000-0002-4645-947X
Isaac BaidooDepartment of Statistics and Actuarial Science, College of Basic and Applied Sciences, University of Ghana, Legon, Accra, Ghana, ug.edu.gh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Snakebite is a serious public health issue that causes a significant amount of morbidity and mortality. This has led the World Health Organization (WHO) to develop a roadmap to reduce snakebite envenoming (SBE) by half by 2030. This study predicted the activities healthcare workers consider most important among the 24 WHO SBE roadmap activities for preventing and controlling snakebites. The research involved healthcare workers and took place in two poor and mostly rural districts in the Eastern Region of Ghana. The study used traditional MaxDiff and five machine learning (ML) models to analyze the data. Healthcare workers prioritized "Making safe, effective antivenoms available, accessible, and affordable to all" as the most important activity, with a utility estimate (UE) of 0.8256 (95% confidence interval (CI): 0.7300 to 0.9213). This was followed by "Effective first aid care and ambulance transport" (UE = 0.4348, 95% CI: 0.3266 to 0.5430), "Coordinated data management and analysis" (UE = 0.3744, 95% CI: 0.2353 to 0.5134), and "Promoting advocacy, effective communication, and productive engagement" (UE = 0.3630, 95% CI: 0.2528 to 0.4732). Using choice experiments and ML models has helped reveal healthcare workers' priorities regarding the WHO's SBE roadmap activities. This approach gives a clear view of local opinions on key WHO activities, which is crucial for tackling the snakebite problem. By using this information in the broader health system, we can improve health outcomes and enhance the quality of life for snakebite patients in Ghana and other low- and middle-income countries.

Indexed as

Ghanamachine learning modelmaximum difference choice experimentssnakebitesWHO 2030 snakebite strategic key activities

Identifiers

PMID42524049
PMCPMC13409028

What OpenQuestion holds

Textmetadata
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.