Projected impact of biosimilar substitution policies on drug use and costs in Ontario, Canada: a cross-sectional time series analysis
Bibliographic record
Abstract
BACKGROUND: Several Canadian provinces have introduced reimbursement policies mandating substitution of innovator biologics with lower-cost biosimilars. We estimated the number of patients affected and cost implications if such policy changes were to be implemented in Ontario, Canada. METHODS: We conducted a cross-sectional time series analysis of Ontarians dispensed publicly funded biologics indicated for inflammatory diseases (rheumatic conditions, inflammatory bowel disease: infliximab, etanercept, adalimumab) between January 2018 and December 2019, and forecasted trends to Dec. 31, 2020. The primary source of data was pharmacy claims data for all biologics reimbursed by the public drug program. We modelled the number of patients affected and government expenditures (in nominal Canadian dollars) of several biosimilar policy options, including mandatory nonmedical biosimilar substitution, substitution in new users, introduction of a biosimilar for adalimumab, and price negotiations. In a secondary analysis, we included insulin glargine. RESULTS: In 2018, 14 089 individuals were prescribed a publicly funded biologic for inflammatory diseases. A mandatory nonmedical biosimilar substitution would potentially have affected 7209 patients and saved $238.6 million from 2018 to 2020. A new-user substitution would have affected 757 patients and saved $34.2 million. If an adalimumab biosimilar were to become available, 12 928 patients would be affected by a mandatory nonmedical substitution and the 3-year savings would increase to $645.9 million (all biosimilars priced at 25% of innovator biologics). Finally, an expanded nonmedical substitution policy including insulin glargine would affect 115 895 patients and save $288.7 million (not including adalimumab). INTERPRETATION: Policies designed to curb rising costs of biologics can have substantially different effects on patients and government expenditures. Such analyses warrant careful consideration of the balance between cost savings and effects on patients.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".