PP106 Twenty Years Of Orphan Medicines Regulation: Have Treatments Reached Patients In Need Across Europe And Canada?
Bibliographic record
Abstract
Introduction The European Union regulation for orphan medicinal products (OMPs) was introduced to improve the quality of treatments for patients with rare conditions. To mark 20 years of European Union OMP regulation, this study compared access to OMPs and the length of their reimbursement process in a set of European countries and Canadian provinces. Access refers to their full or partial reimbursement by the public health service. Methods Data were collated on European Medicines Agency orphan designation and marketing authorizations, health technology assessment (HTA) decisions and reimbursement decisions, and the respective dates of these events for all the OMPs centrally authorized in 14 European countries (Belgium, England, France, Germany, Hungary, Italy, the Netherlands, Norway, Poland, Scotland, Slovakia, Spain, Sweden, and Switzerland) and four Canadian provinces (Alberta, British Columbia, Ontario, and Quebec). Results Since the implementation of the OMPs Regulation in 2000, 215 OMPs obtained marketing authorization. We found that Germany had the highest level of coverage, with 91 percent of OMPs being reimbursed. The three countries with the lowest reimbursement rates were Poland, Hungary, and Norway (below 30%). We observed that Germany had the quickest time to reimbursement following marketing authorization, followed by Switzerland and Scotland. We observed that Poland, Hungary, and Slovakia consistently had the longest time to reimbursement. Conclusions We observed substantial variation in the levels and speed of national reimbursement of OMPs, particularly when comparing countries in Eastern and Western Europe, which suggests that an equity gap between the regions may be present. The data also indicated a trend toward faster times to reimbursement over the past 10 years.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".