Health Equity Considerations in Cost-Effectiveness Analysis: Insights from an Umbrella Review
Bibliographic record
Abstract
Jeffrey M Muir,1 Amruta Radhakrishnan,1 Ipek Ozer Stillman,2 Grammati Sarri3 1RWA Health Economics, Cytel Inc., Toronto, ON, Canada; 2Value, Evidence & Health Economics, Takeda Pharmaceuticals, Lexington, MA, USA; 3RWA Health Economics, Cytel Inc., London, UKCorrespondence: Grammati Sarri, Evidence, Value and Access (EVA) Cytel, Inc., Hamilton House, Mabledon Place, London, WC1H 9BB, UK, Email grammati.sarri@cytel.comAbstract: Cost-effectiveness analyses (CEA) are important in healthcare decision-making and resource allocation; however, expanding the scope of CEAs beyond the traditional clinicoeconomic concepts to also include value elements such as health equity has attracted much interest in recent years. This umbrella review aimed to synthesize evidence on how equity concepts have been considered in modified types of CEAs. Publicly available articles in MEDLINE were searched on January 25, 2024, to identify systematic reviews (SLRs) published in English since 2013 that incorporate health equity considerations in CEAs. Title/abstract, full-text article screening and data extraction were conducted by a single reviewer and validated by a second reviewer. Results were qualitatively synthesized to identify common themes. Eight SLRs were included. Distributional CEAs (DCEA), equity-based weighting, extended CEA (ECEA), mathematical programming and multi-criteria decision analysis (MCDA) were the most discussed approaches. A lack of consensus on the best approach for incorporating health equity into CEAs was highlighted, as these approaches are not currently consistently used in decision-making. Important limitations included scarcity of robust data to inform health equity indices, bias associated with commonly used health outcome metrics and the challenge of accounting for additional contextual factors such as fairness and opportunity costs. Proposals to expand CEAs to address equity issues come with challenges due to data unavailability, methods complexity, and decision-makers unfamiliarity with these approaches. Our review indicates that extended and distributional CEAs can support decision-making by capturing the impact of inequity on the clinical and cost-effectiveness assessment of treatments, although future modeling should account for additional contextual factors such as fairness and opportunity costs. Recommendations for actions moving forward include standardization of data collection for outcomes related to equity and familiarity with methodologies to account for the complexities of integrating health equity considerations in CEAs.Keywords: health technology assessment, health equity, cost-effectiveness analysis, value elements, umbrella review
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.233 | 0.473 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.044 | 0.032 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".