Evaluating the impact of novel oncology drug coverage through patient assistance programs in British Columbia (BC), Canada.
Bibliographic record
Abstract
13 Background: Despite a universal public healthcare system, Canadian oncology patients often enroll in patient assistance programs (PAPs) through drug manufacturers to access oncology drugs that are awaiting funding decisions by national and provincial regulatory bodies. Our multicenter study evaluated the pharmacoeconomic and clinical impact of PAPs for cancer patients in BC. Methods: Eligible patients were diagnosed with cancer and enrolled in a PAP for an oncology drug between January 1, 2016 - December 31, 2019 in 3 BC centres. Charts were reviewed for treatment details and survival data. We referenced median overall survival (mOS) or median progression-free survival (mPFS) data from phase III trials. mPFS data was used if mOS was unavailable or not statistically significant. For each drug indication, the hazard ratio from trial data was multiplied by the mOS or mPFS of our cohort to estimate the mOS or mPFS if the drug was not received. Life-years gained (LYG) was defined as the difference between the actual mOS or mPFS in our cohort and the estimated mOS or mPFS if the drug was not received. Mean OS was used if median OS was not reached by the cut-off date January 1, 2021. Quality-adjusted life years (QALY) and cost per drug were obtained from the Canadian Agency for Drugs and Technologies in Health (CADTH). QALY gained per drug was calculated as QALY multiplied by LYG. Assuming $100,000 Canadian dollars (CAD) for 1 QALY gained (based on prior studies), the economic value of QALY gained was calculated as $100,000 CAD multiplied by QALY gained per drug. Results: Our cohort consisted of 1025 patients that accessed 40 oncology drugs via PAP. By the cut-off date, 290 patients continued on treatment while 735 had stopped, most often due to disease progression (69%) or toxicity (14%). 74 patients then accessed another drug in their second exposure to PAP, and 5 patients accessed drugs in their third exposure to PAP. Median time from Health Canada approval to public funding was 2.04 years (IQR 1.53 – 2.34). QALY gained per drug ranged from 0.007-2.997 years for OS and 0.001-1.339 years for PFS. This translated to a total of 268.3 QALY gained in OS and 117.6 QALY gained in PFS. In the first exposure group, total economic value gained from PAP was $26,788,512 for those with OS benefits, $11,763,571 for those with PFS benefits, and total drug costs were $94,150,774. In the second exposure group, total economic value gained was $566,253 for those with OS benefits, $947,134 for those with PFS benefits, and total costs were $4,917,017. In the third exposure group, $44,754 total economic value was gained and total costs were $931,207. Conclusions: Nearly $100 million CAD of non-public funding was required to bridge gaps between regulatory approval and public funding. The economic value and QALY gained from PAPs are substantial. Future studies should focus on strategies to optimize the funding process for novel cancer therapies.
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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 | 0.025 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".