International Variability in the Reimbursement of Cancer Drugs by Publically Funded Drug Programs
Bibliographic record
Abstract
PURPOSE: Evaluate inter-country variability in the reimbursement of publically funded cancer drugs, and identify factors such as cost containment measures that may contribute to variability. METHODS: As of February 28, 2010, licensed indications for 10 cancer drugs (bevacizumab, bortezomib, cetuximab, erlotinib, imatinib, pemetrexed, rituximab, sorafenib, sunitinib, and trastuzumab) were obtained from the drug registries of 6 licensing authorities corresponding to 13 countries or regions: Australia, Canada (Ontario), England, Finland, France, Italy, Germany, Japan, New Zealand, the Netherlands, Scotland, Sweden, and the United States (Medicare Parts B and D). Number of licensed indications reimbursed by public payers and the use of cost containment measures were obtained by survey of health authorities involved in reimbursement and through public documents. RESULTS: The 48 identified licensed indications varied between agencies (range: 36-44 indications). Finland, France, Germany, Sweden, and the United States reimbursed the highest percentage of indications (range: 90%-100%). Canada (54%), Australia (46%), Scotland (40%), England (38%), and New Zealand (25%) reimbursed the least. All 5 countries with the lowest rate of reimbursement incorporated a cost-effectiveness analysis into reimbursement decisions and rejected submissions for reimbursement mainly because of lack of cost effectiveness; in New Zealand, lack of cost effectiveness was the second leading cause of rejection after excessive cost. In 9 countries, risk-sharing agreements were used to contain costs. Indications initially not recommended for reimbursement (9 in Australia, 5 in Canada, and 3 in England, New Zealand, and Scotland) were subsequently approved with risk-sharing agreements or special pricing arrangements. CONCLUSIONS: Reimbursement of publically funded cancer drugs varies globally. The cause is multifactorial.
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How this classification was reachedexpand
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 | 0.031 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".