Evidence map›Paper›PMID 40333649›Full record

ArticlePLOS global public health2025

Co-developing a comprehensive disease policy model with stakeholders: The case of malaria during pregnancy.

Silke Fernandes, Andrew Briggs, Kara Hanson

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Article in PLOS global public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Silke FernandesDepartment of Health Services Research and Policy, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom.ORCID https://orcid.org/0000-0001-7694-4233
Andrew BriggsDepartment of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Kara HansonDepartment of Health Services Research and Policy, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the holistic impact of malaria during pregnancy is essential for improving maternal and child outcomes in malaria endemic settings. To be able to design appropriate research and conduct robust policy analyses, a comprehensive model of the underlying disease, representing the current understanding of mechanisms and consequences, is needed. This study aimed to illustrate a methodology to co-develop a disease policy model with expert stakeholders using malaria during pregnancy as a case study. An initial steering group was convened to develop a first model of malaria during pregnancy and its consequences for mother and child based on their understanding of the literature. Subsequently, this model was refined using a Delphi process to gain consensus amongst twelve experts working in the field of malaria during pregnancy, representing the disciplines of health economics, mathematical modelling, epidemiology and clinical medicine. The experts reviewed drafts of the conceptual model and provided feedback in two rounds of semi-structured questionnaires with the aim of identifying the most important health outcomes and relationships in both mother and child as well as the most relevant stratifiers for the model. Final consensus on any areas of disagreement was reached after two online meetings. The final model is a comprehensive disease policy model of malaria during pregnancy, including ten maternal and ten child health outcomes with four stratifiers. The model developed in this study should be of value to malaria researchers, funders, evaluators and decision makers, though some adaptation will be required for each specific context and purpose. In addition, the methodology and process followed in this study is replicable and can guide researchers aiming to develop a conceptual model for other conditions. The model resulting from this study highlights the complexity required to depict fully the consequences of malaria during pregnancy for both the mother and the child. It also demonstrates how to conduct a rigorous process to develop a disease policy model. In addition, the study has helped to identify a number of areas with scarce data and need for further research.

Identifiers

PMID40333649
PMCPMC12057981

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