The Impact of reference pricing on prescribing patterns, costs, and health services utilization of proton pump inhibitors: A quasi-experimental study in British Columbia, Canada
Bibliographic record
Abstract
INTRODUCTION: The Reference Drug Program (RDP) was established to steer patients toward equally safe and cost-effective medication under British Columbia's public drug coverage. Each RDP class covers at least one reference drug, and non-reference drugs are reimbursed up to the cost of the reference drug. In 2016, the RDP updated to include proton pump inhibitors (PPIs). This study evaluated the impact on drug expenditures, prescription patterns, and health services utilization. METHODS: We identified a cohort of individuals covered by Fair Pharmacare who used PPIs, and a control group of H2 Blockers users. We used interrupted time series analysis on administrative data from June 2014 to December 2019 on the following outcomes: new users, day supply, expenditures, drug costs, reference drug use, and physician visits and costs. RESULTS: The RDP had little impact on overall PPI use patterns. We did not observe any changes in reference drug uptake, new users, physician visits, cost-savings, or significant changes to days supplied post-policy. Cost expenditure results were likely biased due to co-occurring changes to drug prices. CONCLUSION: Inclusion of PPIs to the RDP saw no cost-savings for the provincial drug program and had little impact on prescribing patterns. Overall, our findings are consistent with existing evidence that the RDP is safe for similar therapeutic alternatives, but the impact on PPI costs remains unclear.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".