International comparison on the factors influencing reimbursement of expensive cancer drugs.
Bibliographic record
Abstract
6614 Background: Reimbursement policy for expensive cancer drugs is heterogeneous and inconsistent among countries. We compared the pattern of publically funded drug programs of ten countries. Methods: We investigated reimbursement policies of 19 indications with expensive cancer drugs (nilotinib, dasatinib, imatinib, pemetrexed, bevacizumab, sunitinib, temsirolimus, lapatinib, crizotinib, erlotinib, cetuximab, sorafenib and lenalidomide) in ten countries (Australia, Canada, England, France, Germany, Japan, Korea, Sweden, Taiwan, and the United States). Microarray analysis methodology is adopted to cluster the countries according to the pattern of reimbursement, based on incremental cost effectiveness ratio (ICER) and other variables of 19 indications. The earlier a country starts reimbursement for the indication, the higher reimbursement score (RS) is assigned to the country. Fairness index (FI) was determined for each country, depending on whether reimbursement decision follows ICER sequence. Results: Except for US, Japan and France were more likely to reimburse the indications (16/19), whereas Sweden and UK were less likely to reimburse them (5/19 and 6/19, respectively). From array analysis, we observed three separate groups: 1) France and Germany, 2) Korea and Japan, 3) UK, Sweden, Canada, Taiwan and Austrailia. The details of analysis will be presented. Regarding FI, Sweden (0.75), France (0.73) and UK (0.71) were the countries with high FI, in which health system was financed by general taxation and health technology assessment (HTA) was performed by government agency. Korea (0.34), Taiwan (0.40) and Germany (0.46) were the countries with low FI, in which social health insurance was financed by payroll tax or premium rather than general taxation. HTA was performed by non-government agency (Germany) or recently established government agency (2007 in Taiwan and 2009 in Korea) in these countries. Conclusions: Reimbursement policies for expensive cancer drugs are variable among the nations. Countries where health system is funded by general taxation and HTA is performed by government agency showed consistency of reimbursement policy based on ICER.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".