Common drug review recommendations for orphan drugs in Canada: basis of recommendations and comparison with similar reviews in Quebec, Australia, Scotland and New Zealand
Bibliographic record
Abstract
BACKGROUND: Public payer reimbursement for non-oncology drugs in Canada, including orphan drugs, is based on recommendations by the Common Drug Review (CDR) (with the exception of Quebec). CDR has been criticized for negative recommendations for orphan drugs and contributing to delays in patient access to these drugs. However, it is unclear how CDR makes recommendations for orphan drugs and the role clinical and economic factors play in decision making. The objective of the present study was to analyze the basis for CDR orphan drug recommendations and to compare recommendations to those in other jurisdictions. METHODS: A list of orphan drugs reviewed by CDR (between 2004 and 2017) was compiled and final recommendations (list/do not list) assessed. The basis of each recommendation was categorized as clinical only, price only or combined clinical and price factors, based on the ranking of clinical and price parameters in recommendation summaries. The reimbursement status of the same drugs was determined in Quebec and other jurisdictions and level of agreement with CDR decisions assessed using a kappa analysis. RESULTS: Sixty eight orphan drug submissions were identified in the CDR database. Clinical, clinical and price and price parameters were the basis of 48.5%, 44.1% and 7.4% of the reviews, respectively, and corresponding positive recommendation rates were 45.5%, 86.7% and 40.0% (p = 0.0008); overall positive recommendation rate was 63.2%. Positive recommendation rate increased from 50.0% for drugs reviewed between 2004 and 2009 to 86.7% in 2016; however, 84.6% of the latter were conditional on a price reduction. Of the drugs reviewed by CDR, 80.9%, 88.2%, 80.9% and 58.8% were reviewed for the same indications by health technology assessment agencies in Quebec, Scotland, Australia and New Zealand, respectively, with positive listing rates ranging from 60.0% (Quebec) to 92.7% (Australia) with fair (kappa coefficient 0.3307) to poor (kappa coefficient 0.0611) agreement with CDR in listing decisions, respectively. CONCLUSIONS: The positive CDR recommendation rate for orphan drugs was highest when clinical and price parameters supported the assessment. Over time there has been an increase in CDR positive recommendation rates for orphan drugs, although most are conditional on a price reduction. It is unclear if this change in CDR recommendations will impact equitable and timely access to orphan drugs across Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".