Potential Cost Implications of Mandatory Non‐Medical Switching Policies for Biologics for Rheumatic Conditions and Inflammatory Bowel Disease in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it