Incorporating equity in economic evaluations: a multi-attribute equity state approach
Bibliographic record
Abstract
In publicly funded health care systems, decision makers must continually balance often conflicting priorities of efficiency and equity. Health economists have developed a set of highly sophisticated analytical methods for assessing efficiency, but less attention has been paid to formally incorporating equity considerations into analyses. As a result, where equity is considered is often informal, ad hoc and/or simplistic. This paper is a proposal for a mechanism for formally incorporating equity within the decision process. It begins with an overview of the current literature on equity weighting. It then considers the case of a single equity domain and illustrates how this is currently applied in practice by the UK's National Institute for Health and Care Excellence. It then proposes a more comprehensive method for considering the multi-attribute equity state, where a population exhibits more than one trait considered worthy of differential weighting. Finally, the paper proposes a mechanism by which this could be applied in practice, and concludes with a discussion of the challenges for applying multi-attribute equity weighting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.173 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; a candidate call from one teacher head, not a consensus.
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".