Self-Monitoring of Blood Glucose: Impact of Quantity Limits in Public Drug Formularies on Provincial Costs Across Canada
Bibliographic record
Abstract
OBJECTIVES: For most patients with diabetes, routine use of blood glucose test strips (BGTS) has not been shown to be beneficial, yet the economic implications of broad publicly funded reimbursement for BGTS are substantial. We assessed the potential impact of BGTS quantity limits on utilization and costs for 6 publicly funded drug plans across Canada. METHODS: A cross-sectional analysis was conducted in 6 provinces (Alberta, Saskatchewan, Manitoba, Nova Scotia, Newfoundland and Labrador and Prince Edward Island) for patients who received at least 1 prescription for BGTS in 2014 through the public drug program. We determined the number of BGTS that would have exceeded the quantity limits and the associated costs to the provincial drug program. RESULTS: A total of $38,051,026 was spent on BGTS reimbursed through public drug programs among the 6 provinces. In provinces where BGTS use is largely restricted to patients using insulin, the potential annual savings were minimal, ranging from 0.4% to 2.3%, whereas in provinces with more liberal listings, potential savings ranged from 12.4% to 19.8%. Combining these results with data from a previous analysis in Ontario and British Columbia, the cost savings associated with BGTS quantity limits for 8 provinces across Canada (capturing approximately three-quarters of the Canadian population) is estimated to be $30.3 million annually. CONCLUSIONS: The national implementation of a quantity limit policy for BGTS that aligns with evidence of efficacy, optimal prescribing and patient safety can lead to considerable savings for most public drug plans across Canada.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".