Considerations when applying equity weights within economic evaluation when making drug reimbursement decisions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.068 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.023 |
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 it