The Cost Burden of Trastuzumab and Bevacizumab Therapy for Solid Tumours in Canada
Bibliographic record
Abstract
OBJECTIVE: Monoclonal antibodies (MAbs) such as trastuzumab and bevacizumab have become important yet expensive components of systemic cancer therapy across a variety of disease sites. We assessed the potential cost implications of adopting trastuzumab and bevacizumab therapy in the context of their potential utilization in breast, lung, and colorectal cancers. DESIGN: We first estimated MAb costs per patient and treatment indication and then included the MAb acquisition cost and the costs of medical resource utilizations required for therapy delivery. Drug costs were based on 2005 average Canadian wholesale prices, assuming full drug delivery and uncomplicated cycles. A direct-payer perspective was undertaken, and results are reported in Canadian dollars. Potential lifetime costs were then derived according to constructed schema, which account for absolute numbers of target patients and systemic therapy utilization. We subsequently estimated costs of MAb therapy relative to total costs of conventional management without MAb therapy. RESULTS: Trastuzumab costs $49,915 and $28,350 per patient treated in the adjuvant and metastatic breast cancer settings, respectively; bevacizumab costs $48,490 and $39,614 per patient treated in the metastatic lung and colorectal cancer settings, respectively. Potential lifetime absolute costs to Canada's health care system were approximately $127 million and $299 million for trastuzumab and bevacizumab respectively, corresponding to an average increase in health care expenditure of approximately 19% for breast cancer and 21% for lung and colorectal cancer over conventional management without MAbs. CONCLUSIONS: Novel Mab-based therapies such as trastuzumab and bevacizumab will likely add a significant cost burden to Canada's publicly funded health care system.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| 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".