Considerations when applying equity weights within economic evaluation when making drug reimbursement decisions
Bibliographic record
Abstract
When decision-makers use economic evaluation to facilitate making decisions about reimbursing whether to reimburse pharmaceuticals within a publicly funded health care system, they may consider whether to prioritise specific patient populations or diseases: e.g., cancer or rare disease. This can be achieved through applying equity weights to outcomes such as QALYs. Decision makers, however, must choose whether equity weights are applied to solely the treatment of a specific disease or to treatments of the patient with the specific disease. Without such clarification, confusion may arise which can hinder the work of those who must make reimbursement recommendations and decisions. This study examines the repercussions of implementation of equity weights. For illustration, two hypothetical case studies relating to a rare disease are considered. The first case study demonstrates that applying equity weights only to the treatment of the rare disease of interest can lead to a patient with that rare disease accruing less benefits at a higher cost to the payer. The second case study demonstrates that if equity weights are applied to the patients who have a specific rare disease, then funding of a treatment for a common disease may be restricted only to those patients for whom treatment is more costly and less effective. As discussions continue with respect to applying equity weights within economic evaluation, it is important that the repercussions outlined are recognised.
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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 | 0.409 | 0.594 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.034 | 0.052 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".