Entry to market of new medicines and medicines treating rare diseases: issues arising from value assessment processes in the European Union, the United Kingdom and Canada
Bibliographic record
Abstract
To achieve access to new medicines within markets, manufacturers need to first receive marketing authorisation and subsequently seek funding from healthcare insurance. In countries where access to healthcare is free, the allocation of finite resources poses substantial challenges. Increasingly in these settings, health technology assessment (HTA) is used to inform funding decisions whilst seeking to promote healthcare financial sustainability and macro- and micro-economic efficiency. Variations in access to medicines can occur as countries implement HTA differently. These variations are further highlighted in medicines used to treat rare diseases, known as orphan medicines. Due to their high prices and high uncertainty about their clinical benefit, some HTA bodies have specialised assessment frameworks for orphan medicines to safeguard equity by considering additional dimensions of value beyond clinical- and/or cost-effectiveness. In this thesis, I explored how differences in HTA systems and processes may contribute to access variations of new medicines and medicines for rare diseases across settings. First, I outlined a conceptual framework that showed how HTA is operationalised; I also mapped HTA systems across 32 countries. Second, through a Delphi panel of European stakeholders, I identified features of HTA that facilitate access to new medicines. Third, I observed whether the presence of specialised assessment frameworks might translate to more favourable funding recommendations for and timely access to orphan medicines by comparing two settings where these medicines are treated differently. Finally, I evaluated whether HTA recommendations are aligned with funding decisions for orphan medicines in a decentralised healthcare setting where the HTA body has an advisory role. The main contributions of this thesis are fivefold: (i) it develops a conceptual framework that allows comparisons of HTA systems regardless of how well-developed they are; (ii) it generates evidence on the performance of HTA features, looking at HTA holistically, against different access metrics; (iii) it examines whether efforts to optimise access to orphan medicines across the market access pathway may translate into more favourable reimbursement decisions; (iv) it studies whether HTA recommendations are followed in funding decisions; and (v) it provides recommendations on what features of HTA need improvement to optimise patient access.
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 | 0.082 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.017 | 0.021 |
| Scholarly communication | 0.045 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".