Estimating the Impact of Delayed Access to Oncology Drugs on Patient Outcomes in Canada
Bibliographic record
Abstract
INTRODUCTION: New requirements in Canada's pricing processes for patented drugs may exacerbate delays in regulatory and reimbursement reviews. This study seeks to better understand the impact of any additional delays on non-small cell lung cancer (NSCLC) patients by measuring the following: (a) durations and outcomes of regulatory and reimbursement reviews of NSCLC drugs in Canada and reference countries; (b) delays in Canada's reviews of three NSCLC drugs (nivolumab, afatinib, and pemetrexed [NAP]); and (c) estimating clinical, patient, and economic impacts of delays in Canada's reviews on access to NAP. METHODS: Information from the Context Matters database and the literature (2005-2020) was used to evaluate the durations and outcomes of reimbursement reviews of NSCLC drugs in Canada and comparator countries. Public information was used to assess delays in Canada's reviews of NAP. Empirical modeling with data from the literature and the Southern Alberta Lung Cancer database was used to estimate the impact of delays in Canada's NAP reviews on patients (i.e., as losses in person-years of life and quality-adjusted life-years [QALYs]). RESULTS: Regulatory and reimbursement reviews in countries of interest take 12-18 months. In Canada, reviews of NSCLC drugs took 216 days (median), with a 24% rejection rate (mean = 19%). Delays in NAP reviews ranged from 5 to 94 days at Health Canada, 0-80 days at CADTH/pCODR, and 12-797 days in Canadian provinces. These delays may have affected 6400 patients, who lost up to 1740 person-years of life and 1122 QALYs (valued at CA$112 million). CONCLUSION: Changes to Canada's prescription drug pricing processes may prolong reviews.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".