The effect of evidence-based drug coverage policies on pharmaceutical R&D: a case study from British Columbia
Bibliographic record
Abstract
Background: To manage public expenditures in the mid-1990s, British Columbia implemented evidence-based drug coverage policies, including “reference pricing.” Industry lobbied against the province's policy, arguing that reference pricing harms patients and that it is inconsistent with federal and provincial legislation. Researchers and the courts have studied and rejected industry's claims. However, industry also threatened to halt R&D investment in British Columbia and continues to so threaten other provinces contemplating evidence-based drug coverage policies. The purpose of this study is to review evidence regarding these threats. Methods: Provincial-level R&D data for 1988–2006 were used to analyze the impact of BC PharmaCare's policies on pharmaceutical R&D in British Columbia. We used statistical analyses to determine whether the province's policies affected BC-based R&D as expressed in two ways: (1) as inflation-adjusted expenditure per capita in British Columbia and (2) as the ratio of expenditure per capita in the province to expenditure per capita in the rest of Canada. Results: Evidence-based drug coverage policies had no statistically significant negative effects on BC-based pharmaceutical R&D. BC R&D was slightly above expected trends in 1997 and slightly below expected trends in 1998 and 1999 (though not statistically significantly in either case). From 2001 to 2003, BC R&D was (statistically significantly) above expected trends. Conclusions: While they are part of the politics of the pharmaceutical sector, claims and threats regarding connections between coverage policy and location of R&D investment are not borne out in British Columbia's experience. This is likely because, as suggested by business and economic literature, firms locate R&D based on the expected cost-to-firm and productivity of the R&D investment itself. Prudent policy would therefore manage pharmaceutical expenditures using evidence-based policies and pursue scientific and economic development goals through direct and strategic government investment in local scientific capacity.
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How this classification was reachedexpand
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 teacher head, 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".