Potential Life-Years Lost: The Impact of the Cancer Drug Regulatory and Funding Process in Canada
Bibliographic record
Abstract
BACKGROUND: Canada has an established publicly funded health care system with a complex drug approval and funding process. After proof of efficacy (POE; key publication/presentation) and before becoming publicly accessible, each drug undergoes a Health Canada approval process, a health technology assessment (HTA), a pricing negotiation, and finally individual provincial funding agreements. We quantified potential life-years lost during this process. METHODS: We analyzed drugs for advanced lung, breast, and colorectal cancer that underwent the HTA process between 2011 and 2016. Life-years lost were calculated by multiplying documented improvement in progression-free and overall survival, number of eligible patients, and time from POE to first public funding. For conservative calculation, we assumed all eligible patients in Canada had access at the time of first public funding, whereas in reality provinces fund at different time points. RESULTS: We analyzed 21 drugs. Of these, 15 have been funded publicly. The time from POE to first public funding ranged from 14.0 to 99.2 months (median 26.6 months). Total overall life-years lost from POE to first public funding were 39,067 (lung 32,367; breast 6,691). Progression-free life-years lost from POE to first public funding were 48,037 (lung 9,139, breast 15,827, colorectal 23,071). CONCLUSION: The number of potential life-years lost during the drug regulatory and funding process in Canada is substantial, largely driven by delays to funding of colorectal cancer drugs. Recognizing that interprovincial differences exist and that eligible patients may not all receive a given drug, if even a fraction does so, the impact of delays remains substantive. Collaborative national initiatives are required to address this major barrier to treatment access. IMPLICATIONS FOR PRACTICE: Patients may spend lengthy periods of time awaiting access to new and effective cancer drugs. Patients with private drug insurance or personal funds or who reside in certain Canadian provinces may obtain some drugs sooner than others, potentially creating a two-tiered access system. The cancer drug access and public funding system must be expedited to improve equity.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".