CAN COST-EFFECTIVENESS ANALYSIS INTEGRATE CONCERNS FOR EQUITY? SYSTEMATIC REVIEW
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to promote approaches to health technology assessment (HTA) that are both evidence-based and values-based. We conducted a systematic review of published studies describing formal methods to consider equity in the context of cost-effectiveness analysis (CEA). METHODS: Candidate studies were identified through an unrestricted search of the Pub Med and EMBASE databases. The search closed on January 20, 2011. We identified additional studies by consulting experts and checking article bibliographies. Two authors independently reviewed each candidate study to determine inclusion and extracted data from studies retained for review. In addition to documenting methods, data extraction identified implicit and explicit notions of fairness. Data were synthesized in narrative form. Study quality was not assessed. RESULTS: Of the 695 candidate articles, 51 were retained for review. We identified three broad methods to facilitate quantitative consideration of equity concerns in economic evaluation: integration of distributional concerns through equity weights and social welfare functions, exploration of the opportunity costs of alternative policy options through mathematical programming, and multi-criteria decision analysis. CONCLUSIONS: Several viable techniques to integrate equity concerns within CEA now exist, ranging from descriptive approaches to the quantitative methods studied in this review. Two obstacles at the normative level have impeded their use in decision making to date: the multiplicity of concepts and values discussed under the rubric of equity, and the lack of a widely accepted normative source on which to ground controversial value choices. Clarification of equity concepts and attention to procedural fairness may strengthen use of these techniques in HTA decision making.
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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 | 0.189 | 0.566 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".