Intellectual Property Protection And Drug Plan Coverage: Evidence From Ontario
Bibliographic record
Abstract
Canada has strengthened intellectual property (IP) protections for pharmaceutical drugs several times over the last three decades. These changes were intended to lengthen the period of market exclusivity for new brand drugs and thereby allow them to earn additional sales revenues that could be used to recoup R&D investments. Whether these policies achieved their objective of increasing sales revenues is unclear, however. Whether they did depends on the coverage decisions of the major drug plans. Longer periods of market exclusivity amount to a price increase for brand drugs. In response to higher prices, drug plans could have become more selective in the drugs they cover, and they could have waited longer to list these drugs on their formularies, reducing formulary exclusivity periods. To investigate, we assembled data on the coverage of brand drugs approved for use in Canada over the last 35 years by the Ontario Drug Benefit (ODB) program, the largest and most influential drug plan in Canada. We find that, except for a brief period of time, the marked strengthening of Canadian pharmaceutical IP laws over the last 25 years have not lead to an increase in the exclusivity period that brand-name drugs enjoy on the ODB formulary. In fact, exclusivity periods have been dropping more or less consistently since the mid 1970s. The causes of these changes remain to be explored.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| 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) | 0.008 | 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".