Potential cost-savings from the use of the biosimilars filgrastim, infliximab and insulin glargine in Canada: a retrospective analysis
Bibliographic record
Abstract
BACKGROUND: In 2014 and 2015, biosimilars for the drugs filgrastim, infliximab, and insulin glargine were approved for use in Canada. The introduction of biosimilars in Canada could provide significant cost savings for the Canadian healthcare system over originator biologic drugs, however it is known that the use of biosimilars varies widely across the world. The aim of this study was to estimate the use of biosimilars in Canada and potential cost-savings from their use. METHODS: We performed a retrospective analysis of Canadian drug purchases for filgrastim, infliximab, and insulin glargine from July 2016 to June 2018. This was a cross-sectional study and the time horizon was limited to the study period. As a result, no discounting of effects over time was included. Canadian drugstore and hospital purchases data, obtained from IQVIA™, were used to estimate the costs per unit and unit volume for biosimilars and originator biologic drugs within each province. Potential cost-savings were calculated as a product of the units of reference originator product purchased and the cost difference between the originator biologic and its corresponding biosimilar. RESULTS: The purchase of biosimilars varied by each province in Canada, ranging from a low of 0.1% to a high of 81.6% of purchases. In total, $1,048,663,876 Canadian dollars in savings could have been realized with 100% use of biosimilars over the originator products during this 2 year time period. The potential savings are highest in the province of Ontario ($349 million); however, even in smaller markets (PEI and Newfoundland), $28 million could have potentially been saved. Infliximab accounted for the vast majority of the potential cost-savings, whereas the purchases of the biosimilar filgrastim outpaced that of the originator drug in some provinces. In sensitivity analyses assuming only 80% of originator units would be eligible for use as a biosimilar, $838 million dollars in cost savings over this two-year time period would still have been realized. CONCLUSIONS: The overall use of biosimilar drugs in Canada is low. Policy makers, healthcare providers, and patients need to be informed of potential savings by increased use of biosimilars, particularly in an increasingly costly healthcare 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".