Better treatment at what cost? A study of myeloma spending.
Bibliographic record
Abstract
60 Background: Multiple myeloma represents less than 1.5% of new cancer cases in Canada. Currently, the estimated median overall survival is at least 5-6 years, primarily driven by therapeutic advances over the past decade. As treatment protocols routinely use doublet and triplet combinations, there are increasing concerns about the ability of health systems to afford growing costs of treatment. To inform system planning in Ontario, we examined trends in costs and utilization of myeloma drugs funded by Ontario’s New Drug Funding Program (NDFP) and the Ontario Drug Benefit program (ODB). Methods: NDFP primarily funds IV cancer drugs while ODB funds take-home cancer drugs (THCD). Treatment volumes and government costs, including drug costs and pharmacy fees where applicable, were obtained from ODB and NDFP claims data. Based on the available data, trends were examined from the 2010/11 to the second quarter of the 2019/20 fiscal year. Results: A total of 7 myeloma drugs (3-IV cancer drugs, 4-THCD) were examined. Over 9 years (2010/11 - 2018/19), spending on publicly-funded myeloma drugs increased by 303% while treatment volumes increased by 116%. Between 2014/15 and 2018/19, bortezomib spending decreased by 72%, largely due to generic pricing policies, while lenalidomide spending increased by 158%, likely due to new indications. By 2018/19, these 7 drugs accounted for 17% of the total cancer drug costs under Ontario's publicly funded programs. NDFP spending on IV cancer drugs by the second quarter of 2019/20 has surpassed the annual expenditures in 2018/19 due to the addition of daratumumab. Conclusions: Since 2010/11, growth in Ontario's public expenditures on myeloma drugs has outpaced savings from pricing policies and this growth is mainly driven by the high cost of the novel agents.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".