Market access for medicines treating rare diseases: Association between specialised processes for orphan medicines and funding recommendations
Bibliographic record
Abstract
Access to medicines treating rare diseases ('orphan medicines') has proven challenging due to high prices and clinical uncertainty. To optimise market access to these medicines, some healthcare systems are implementing specialised pathways and/or processes during marketing authorisation (MA) and/or health technology assessment (HTA). Comparing one setting where these medicines are classed as "orphan" (Scotland) to another where they considered "non-orphan" (Canada), this study aims to explore whether the presence of specialised pathways and processes at MA and HTA levels is associated with more favourable funding recommendations and faster time to market access. A matched sample of 116 medicine-indication pairs with MA approval from 2001 to 2019 in Europe and Canada was identified, and publicly available sources were used for data extraction. Descriptive statistics were used for data analysis. All medicines were commercially marketed in both countries, except one instance in Scotland. In Scotland, more orphan medicines (68.1%) had a favourable HTA recommendation than in Canada (60.4%), while Canada issued more negative HTA recommendations (20.7%) than Scotland (15.5%). Low levels of agreement on HTA recommendations and the main reasons driving recommendations were found between settings. In both countries, medicines with specialised MA approval were less likely to receive negative HTA recommendations than medicines with standard MA. Time to market access was faster in Canada than Scotland, though medicines with specialised MA approval had slower timelines than medicines with standard MA approval in both countries. However, it is unclear whether the presence of orphan designation and HTA specialised processes alone could result in favourable funding recommendations without accounting for other healthcare system-related factors and differences in the decision-making processes across settings. Holistic approaches and better alignment of evidentiary requirements across regulators are needed to optimise access to orphan medicines.
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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 | 0.012 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".