Uptake and Spending on Biosimilar Infliximab and Etanercept After New Start and Switching Policies in Canada: An Interrupted Time Series Analysis
Bibliographic record
Abstract
OBJECTIVE: Uptake of biosimilars has been suboptimal in North America. This study was undertaken to quantify the impact of various policy interventions (namely, new start and switching policies) on uptake and spending on biosimilar infliximab and etanercept in British Columbia (BC), Canada. METHODS: We used administrative claims data to identify BC residents ≥18 years of age with rheumatoid arthritis, ankylosing spondylitis, psoriatic arthritis, and/or plaque psoriasis who qualified for public drug coverage from January 2013 to November 2020. Using interrupted time series analysis, we studied the change in proportion spent on and prescriptions dispensed of biosimilar infliximab and etanercept out of the total amount per agent after new start and biosimilar switching policies were implemented. RESULTS: Our study included 208,984 individuals living with rheumatoid arthritis, ankylosing spondylitis, plaque psoriasis, and/or psoriatic arthritis, corresponding to 5,884 patients taking infliximab and etanercept. After the new start policy, we detected a small gradual increase in the proportion of dispensed biosimilar etanercept prescriptions of 0.65% per month (95% confidence interval [95% CI] 0.44, 0.85). The trend related to the proportion of total spending on biosimilar etanercept also increased (0.51% [95% CI 0.28, 0.73]). After the switching policy, there was a sustained increase in the proportion of dispensed biosimilar etanercept and infliximab prescriptions of 76.98% (95% CI 75.56, 78.41) and 58.43% (95% CI 52.11, 64.75), respectively. Similarly, there was a persistent increase in monthly spending on biosimilar etanercept and infliximab of 78.22% (95% CI 76.65, 79.79) and 71.23% (95% CI 66.82, 75.65), respectively. CONCLUSION: We found that mandatory switching policies were much more effective than new starting policies for increasing the use of biosimilar medications.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".