Increase in Drug Spending in Canada Due to Extension of Data Protection for Biologics: A Descriptive Study
Bibliographic record
Abstract
Introduction: Biologics are currently protected from competition by eight years of data protection. The renegotiated North American Free Trade Agreement (NAFTA) increases data protection from 8 to 10 years. This study investigates the effect of such an extension on drug spending in Canada. Methods: A list of currently available biologics eligible for data protection along with their 2017 sales was compiled. Two years were added to the current expiration date of data protection to see if it exceeded patent protection, and any theoretical change in spending due to delayed competition was calculated. The number of biologics approved after January 1, 1995, that have competition and the time until competition started was analyzed. Theoretical competition due to increased data protection for biologics where data protection has already expired was examined. Results: Depending on how much of the market is captured by biologic competitors and how strong the patents are, lost savings from data protection extension could range from $0 to $305.8 million. One biologic competitor currently on the market could theoretically have been affected by an increase in data protection. Increased data protection would have had minor effects on products that have already lost data protection. Discussion: The potential impact on drug expenditures of a two-year extension in data protection is highly variable. Possible increases in spending on biologics strengthen the rationale for a national pharmacare plan where monopsony buying power would help to control drug prices overall and offset increased spending on biologics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".