Funding innovation for treatment for rare diseases: adopting a cost-based yardstick approach
Bibliographic record
Abstract
BACKGROUND: Manufacturers justify the high prices for orphan drugs on the basis that the associated R&D costs must be spread over few patients. The proliferation of these drugs in the last three decades, combined with high prices commonly in excess of $100,000 per patient per year are placing a substantial strain on the budgets of drug plans in many countries. Do insurers spend a growing portion of their budgets on small patient populations, or leave vulnerable patients without coverage for valuable treatments? We suggest that a third option is present in the form of a cost-based regulatory mechanism. METHODS: This article explores the use of a cost-based price control mechanism for orphan drugs, adapted from the standard models applied in utilities regulation. RESULTS AND CONCLUSIONS: A rate-of-return style model, employing yardsticked cost allocations and a modified two-stage rate of return calculation could be effective in setting a new standard for orphan drugs pricing. This type of cost-based pricing would limit the costs faced by insurers while continuing to provide an efficient incentive for new drug development.
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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 | 0.042 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".