Health technology assessment of new drugs for rare disorders in Canada: impact of disease prevalence and cost
Bibliographic record
Abstract
BACKGROUND: Authors from the Canadian Agency for Drugs and Technologies in Health (CADTH) presented an analysis of submissions to the Common Drug Review (CDR) between 2004 and February 3, 2016 for drugs for rare disorders (disorders with a prevalence of <50 per 100,000). OBJECTIVE: The aim of this analysis was to examine the same CDR submissions to evaluate whether the negative reimbursement recommendation rate, clinical evidence of efficacy and statements concerning the drug's cost in the CDR reports varied with the prevalence of the disorder treated by the drug grouped into three decreasing categories: <50 to >10, ≤10 to >1, and ≤1 per 100,000. RESULTS: As the prevalence of the treated disorder decreased, the median daily cost of the drug, the negative recommendation rate and the proportion of submissions with statements in the CDR reports highlighting the cost of the drug increased, while the proportion of submissions with acceptable evidence of clinical efficacy decreased. Moreover, although the CADTH authors reported that only two submissions received a negative recommendation due to a "lack of cost-effectiveness/high cost," high cost was mentioned in the CDR reports of 15 drugs with negative recommendations, all for disorders with a prevalence of ≤10 per 100,000. CONCLUSIONS: The aggregated analysis of CDR submissions for drugs for disorders with wide ranging prevalence rates concealed information of concern to patients. The negative reimbursement recommendation rate and the significance of cost in the CDR assessments increased as the prevalence of the treated disorder decreased. Since 2012, the manner in which high cost drugs for rare disorders have been dealt with by the CDR has changed. Cost has ceased to be a factor in negative recommendations but is included in criteria accompanying positive recommendations. This trend is associated with the integration of the CDR process with the system for price negotiation between public drug plans and pharmaceutical companies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".