Evidence mapPaperPMID 38613660Full record

ArticlePharmacoEconomics2024

A Blueprint for Multi-use Disease Modeling in Health Economics: Results from Two Expert-Panel Consultations.

Junfeng Wang, Xavier Pouwels, Bram Ramaekers, Geert Frederix, Chris van Lieshout, Rudolf Hoogenveen, Xinyu Li, G Ardine de Wit, Manuela Joore, Hendrik Koffijberg and 4 more

Abstract read
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Article in PharmacoEconomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Model-based cost-effectiveness studies in nuclear medicine: an unavoidable fact of life.European journal of nuclear medicine and molecular imaging · 2025
    Article
  6. Article
  7. 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

14 authors.

Junfeng WangDepartment of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Xavier PouwelsDepartment of Health Technology and Services Research, Faculty of Behavioural, Management, and Social Sciences, TechMed Centre, University of Twente, Enschede, The Netherlands.
Bram RamaekersDepartment of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center+, CAPHRI Care and Public Health Research Institute, Maastricht, The Netherlands.
Geert FrederixDepartment of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Chris van LieshoutDepartment of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Rudolf HoogenveenDepartment of Statistics, Modelling and Data Science, Center of Research and Data services, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
Xinyu LiUniversity of Groningen, Faculty of Science and Engineering, Groningen Research Institute of Pharmacy, Groningen, The Netherlands.
G Ardine de WitDepartment of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Manuela JooreDepartment of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center+, CAPHRI Care and Public Health Research Institute, Maastricht, The Netherlands.
Hendrik KoffijbergDepartment of Health Technology and Services Research, Faculty of Behavioural, Management, and Social Sciences, TechMed Centre, University of Twente, Enschede, The Netherlands.
Anoukh van GiessenDepartment of Statistics, Modelling and Data Science, Center of Research and Data services, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
Saskia KniesNational Health Care Institute, Diemen, The Netherlands.
Talitha FeenstraUniversity of Groningen, Faculty of Science and Engineering, Groningen Research Institute of Pharmacy, Groningen, The Netherlands. t.l.feenstra@rug.nl.ORCID http://orcid.org/0000-0002-5788-0454
Dutch multi-use disease modeling consortium

Funding

Zorginstituut Nederland 2019007362
6 · The paper itself

Abstract

backgroundThe current use of health economic decision models in HTA is mostly confined to single use cases, which may be inefficient and result in little consistency over different treatment comparisons, and consequently inconsistent health policy decisions, for the same disorder. Multi-use disease models (MUDMs) (other terms: generic models, whole disease models, disease models) may offer a solution. However, much is uncertain about their definition and application. The current research aimed to develop a blueprint for the application of MUDMs.

methodsWe elicited expert opinion using a two-round modified Delphi process. The panel consisted of experts and stakeholders in health economic modelling from various professional backgrounds. The first questionnaire concerned definition, terminology, potential applications, issues and recommendations for MUDMs and was based on an exploratory scoping review. In the second round, the panel members were asked to reconsider their input, based on feedback regarding first-round results, and to score issues and recommendations for priority. Finally, adding input from external advisors and policy makers in a structured way, an overview of issues and challenges was developed during two team consensus meetings.

resultsIn total, 54 respondents contributed to the panel results. The term 'multi-use disease models' was proposed and agreed upon, and a definition was provided. The panel prioritized 10 potential applications (with comparing alternative policies and supporting resource allocation decisions as the top 2), while 20 issues (with model transparency and stakeholders' roles as the top 2) were identified as challenges. Opinions on potential features concerning operationalization of multi-use models were given, with 11 of these subsequently receiving high priority scores (regular updates and revalidation after updates were the top 2).

conclusionsMUDMs would improve on current decision support regarding cost-effectiveness information. Given feasibility challenges, this would be most relevant for diseases with multiple treatments, large burden of disease and requiring more complex models. The current overview offers policy makers a starting point to organize the development, use, and maintenance of MUDMs and to support choices concerning which diseases and policy decisions they will be helpful for.

Indexed as

Delphi TechniqueHealth PolicyModels, EconomicTechnology Assessment, BiomedicalConsensusDecision MakingEconomics, MedicalHumansSurveys and Questionnaires

Identifiers

PMID38613660
PMCPMC11180025

What Socratic holds

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LicenceCC BY-NC
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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 Socratic graph.