Evidence map›Paper›PMID 42814265›Full record

ArticleApplied health economics and health policy2026

A Practical Framework for Understanding Cost-Effectiveness and Decision Risk in Health Benefits Package Design.

Cassandra Nemzoff, Sabine Grimm, Manuela Joore, Andres Madriz-Montero, Stella Matutina Umuhoza, James Humuza, Sedona Sweeney, Rob Baltussen, Anna Vassall

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Article in Applied health economics and health policy, 2026. 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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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Cassandra NemzoffDepartment of Global Health, London School of Hygiene and Tropical Medicine, London, UK. cassandra.nemzoff@radboudumc.nl.ORCID http://orcid.org/0000-0003-2735-0644
Sabine GrimmDepartment of Clinical Epidemiology and Medical Technology Assessment (KEMTA), Maastricht University Medical Centre+, Maastricht, Netherlands.ORCID http://orcid.org/0000-0002-2175-7999
Manuela JooreDepartment of Clinical Epidemiology and Medical Technology Assessment (KEMTA), Maastricht University Medical Centre+, Maastricht, Netherlands.ORCID http://orcid.org/0000-0002-5649-6768
Andres Madriz-MonteroDepartment of Global Health, London School of Hygiene and Tropical Medicine, London, UK.
Stella Matutina UmuhozaSchool of Public Health, University of Rwanda, Kigali, Rwanda.ORCID http://orcid.org/0000-0003-2759-0947
James HumuzaSchool of Public Health, University of Rwanda, Kigali, Rwanda.ORCID http://orcid.org/0000-0002-7660-3663
Sedona SweeneyDepartment of Global Health, London School of Hygiene and Tropical Medicine, London, UK.ORCID http://orcid.org/0000-0003-4233-9080
Rob Baltussen *Radboud University Medical Center, Nijmegen, Netherlands.
Anna Vassall *Department of Global Health, London School of Hygiene and Tropical Medicine, London, UK.ORCID http://orcid.org/0000-0002-2911-1375

Funding

Bill and Melinda Gates Foundation OPP1202541
6 · The paper itself

Abstract

backgroundGlobal cost-effectiveness evidence is often used in health benefits package (HBP) assessments but is uncertain and subject to the risk of making the wrong decision. HBP assessments use 'adaptive' health technology assessment (aHTA) methods, including reviews of existing cost effectiveness studies and eliciting expert opinion to expedite cost-effectiveness evaluations due to substantial time and data constraints. Using aHTA methods poses a risk of making the wrong decision. However, existing methods for assessing risk, such as value of information analysis (VOI), are time- and data-intensive.

objectivesOur objectives were to develop a practical method to evaluate the risk of making the wrong decision in HBP design and to test this approach in Rwanda.

methodsWe developed a heuristic framework to help decision makers determine whether aHTA methods are sufficient for decision making or de novo cost-effectiveness modelling might be needed. The framework was created by adapting the 'Appraisal of Risk Chart' (ARCH), which was originally designed to evaluate the risk of decision making from de novo cost-effectiveness analyses. We tested the adapted ARCH using an assessment of 136 cancer interventions in Rwanda, where each intervention was charted on the ARCH according to its risk profile.

resultsIn Rwanda, 41 cancer interventions were considered low risk on the ARCH and the remainder were uncertain.

conclusionsWe demonstrate that it is feasible to use the ARCH to determine whether aHTA methods are sufficient or more research is needed in HBP assessments.

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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.