Comparing access to orphan medicinal products in Europe
Bibliographic record
Abstract
OBJECTIVES: The primary objective of this study was to compare the availability and access of orphan medicinal products (OMPs) in the devolved nations in the United Kingdom (UK), France, Germany, Italy and Spain. Availability is defined as the possibility to prescribe OMPs. Access refers to their full or partial reimbursement by the public health service. METHODS: Data were collated on: marketing authorisations, Health Technology Assessment (HTA) decisions, commissioning, and reimbursement decisions, and respective dates of these events for all the OMPs centrally authorised. Indicators of availability of and access to OMPs were calculated in each country and compared. RESULTS: We found that since the implementation of the OMPs Regulation in 2000 to end of May 2016, 143 OMPs obtained a marketing authorisation in the European Union. These OMPs are most widely accessible in Germany and France. In the other countries between 30 and 60% of OMPs are reimbursed. In particular in England, less than 50% of centrally authorised OMPs are routinely funded by the NHS, with one-third of these recommended by NICE. In Germany reimbursement is automatically granted to all medicines which receive a marketing authorisation, immediately after authorisation - but since 2011, there is an evaluation and potentially a pricing negotiation between companies and sickness funds (third party payers). In the other countries, the shortest time from authorisation to a reimbursement decision is observed in Italy and France where it takes 18.6 and 19.5 months respectively on average. CONCLUSIONS: Marketing authorisation granted to OMPs is only the first step, as medicines reach patients when reimbursement decisions are implemented by national health systems (this applies to non-OMPs too). We found that more than a half of centrally authorised OMPs were available in the five selected countries, but that access to patients was further restricted by different national reimbursement policies, especially in the UK, Italy and Spain.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 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".