Evaluating the Economic Impact of Novel Oncology Drug Coverage Through Patient Assistance Programs
Bibliographic record
Abstract
PURPOSE: Despite a universal public health care system, Canadian oncology patients often enroll in patient assistance programs (PAPs) to access oncology drugs that are awaiting funding decisions. Our multicenter study evaluated the pharmacoeconomic and clinical impact of PAPs for patients with cancer in British Columbia (BC). METHODS: Eligible patients were diagnosed with cancer and enrolled in a PAP between January 2016 and December 2019 in three BC centers. Charts were reviewed for treatment details and survival data. For each drug indication, the hazard ratio from clinical trial data was multiplied by the median overall survival (mOS) or median progression-free survival (mPFS) of our cohort to estimate the mOS or mPFS if the drug was not received. Person life-years gained (PLYG) was the difference between the actual mOS or mPFS and the estimated mOS or mPFS if the drug was not received. Incremental cost-effectiveness ratios and drug costs were obtained from the Canadian Agency for Drugs and Technologies in Health. A total economic value of quality-adjusted life year (QALY) gained for each cohort was calculated and compared with the cost of drugs associated with the gain. RESULTS: Our cohort consisted of 1,025 patients who accessed 40 oncology drugs via PAP. The median time from Health Canada approval to provincial funding was 2.04 years. In the first PAP exposure group (N = 1,025), median PLYG was 0.38 years for OS and 0.80 years for PFS. The total estimated economic value of QALY gained was $83,068,819.32, with total drug costs of $97,026.661.44. CONCLUSION: PAPs were involved in covering up to $100 million in costs to bridge gaps between regulatory approval and public funding. Economic value and PLYG gained from PAPs are substantial.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".