Is it the Patient or is it the Disease – Considerations when Applying Different Cost Effectiveness Thresholds in Making Reimbursement Decisions
Bibliographic record
Abstract
Abstract Background In many jurisdictions, decision makers are considering whether in making decision about reimbursing health technologies they should prioritise specific patient populations or diseases: e.g., cancer or rare disease. This can be achieved through applying higher willingness to pay thresholds which implicitly involves the application of equity weighting of outcomes such as QALYs. Decision makers, however, must choose whether to apply equity weights to a specific disease or to the patient with this disease. The objective of this study is to examine the potential impact of implementation of equity weights under either scenario. Methods In a health care system with a constrained budget, applying equity weights leads to a reduction in the cost effectiveness threshold for those treatments not meeting criteria for prioritization. For illustration, two hypothetical case studies relating to a rare disease illustrate the repercussions of the two potential approaches to equity weights on funding decisions. Results The first case study demonstrates how 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 patient who have a specific rare disease, then funding of a treatment for a common disease may be restricted only to the subset of patients who have this rare disease as a comorbidity. In this scenario, the treatment of the common disease may be restricted to those patients for whom treatment is more costly and less effective. Conclusions As discussions continue with respect to applying equity weights and adopting differential funding criteria for different patient populations it is important that these repercussions 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 | 0.136 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".