Orphan drug pricing and payer management in the United States: are we approaching the tipping point?
Bibliographic record
Abstract
The Orphan Drug Act of 1983 paved the way for the development of drugs that treat rare diseases, defined in the United States as those affecting fewer than 200,000 patients. Orphan drugs can cost hundreds of thousands of dollars annually, but insurers have traditionally covered these therapies because the small populations involved did not typically lead to significant cost exposure. Payer sensitivity to the cost of orphan drugs is rising, however, with the accelerated rate of new launches of these agents amid intensified economic pressure. Payers are showing increasing levels of concern and scrutiny about coverage of orphan drugs. A new payer survey conducted between February 2008 and March 2009 provides insights on how payers are managing orphan drugs and the way it is likely to evolve in the future. Survey findings show that the patient share of orphan drug costs is rising and is expected to continue to rise, barring sweeping changes in public health policy. This shift in benefit design could affect patient access to orphan agents and, therefore, drug utilization. Manufacturers will have to invest in research to understand payer impact on the uptake of their orphan drugs in development. They will also benefit from being prepared to develop strategies to ensure patient access to and affordability of their orphan agents.
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.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".