Review of 11 national policies for rare diseases in the context of key patient needs
Bibliographic record
Abstract
Rare diseases collectively exert a global public health burden in the severity of their manifestations and the total number of people they afflict. For many patients, considerable barriers exist in terms of access to appropriate care, delayed diagnosis and limited or non-existing treatment options. Motivated by these challenges, the rare disease patient community has played a critical role, elevating the patient voice and mobilizing legislation to support the development of programs that address the needs of patients with rare diseases.The US Orphan Drug Act of 1983 served as a key milestone in this journey, providing a roadmap for other countries to introduce and implement similar orphan drug legislation; more recently, the European Union (EU) has gone further to encourage the widespread adoption and implementation of rare disease plans or strategies designed to more adequately address the comprehensive needs of patients with rare diseases. Despite these legislative efforts and the growing contributions of patient advocacy groups in moving forward implementation and adoption of rare disease programs, gaps still exist across the policy landscape for several countries. To gain deeper insights into the challenges and opportunities to address key needs of rare disease patients, it is critical to define the current status of rare disease legislation and policy across a geographically and economically diverse selection of countries. We analyzed the rare disease policy landscape across 11 countries: Germany, France, the United Kingdom, Canada, Bulgaria, Turkey, Argentina, Mexico, Brazil, China, and Taiwan. The status and implementation of policy was evaluated for each country in the context of key patient needs across 5 dimensions: improving coordination of care, diagnostic resources, access to treatments, patient awareness and support, and promoting innovative research. Our findings highlight the continuing role of the patient community in driving the establishment and adoption of legislation and programs to improve rare disease care. Further, we found that while national rare disease plans provide important guidance for improving care, implementation of plans is uneven across countries. More research is needed to demonstrate the effect of specific elements of rare disease plans on patient outcomes.
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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 | 0.022 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".