Potential Cost Implications of Mandatory Non‐Medical Switching Policies for Biologics for Rheumatic Conditions and Inflammatory Bowel Disease in Canada
Bibliographic record
Abstract
In 2018, TNFα inhibitors were the highest cost drug class for Canadian public drug programs. In 2019, two Canadian provinces announced mandatory nonmedical switching policies in an attempt to reduce their costs by increasing biosimilar uptake. The national impact of similar policies across Canada is unknown. We conducted a cross-sectional analysis of monthly publicly funded prescription claims for infliximab, etanercept, and adalimumab between June 2015 and December 2019. We reported the market share of biosimilars for infliximab and etanercept in 2019 for each province and estimated the cost savings that public payers could have realized in 2019 if mandatory switching policies had been implemented across Canada, including a sensitivity analysis, which assumed that governments receive a 25% rebate on all biologics. Provincial drug programs spent CAD $991.84 million on infliximab, etanercept, and adalimumab in 2019, and, when biosimilars were available, they constituted only 15.5% of national utilization of these drugs. In British Columbia, the implementation of a mandatory switching policy for patients with rheumatic conditions increased the biosimilar market share of infliximab and etanercept by 299% (from 19.7% to 78.5%). If applied nationwide to all three biologics for all indications, we estimate such policies could lead to annual savings of between CAD $179.71 million and CAD $425.64 million nationally. The overall market share of biosimilars remains low in all provinces where mandatory switching policies have not been introduced. The cost implications of successfully increasing biosimilar uptake would be substantial, particularly as more biosimilars reach the Canadian market.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".