The impact of the introduction of a formulary into a large Canadian private drug plan: an interrupted time-series analysis
Bibliographic record
Abstract
BACKGROUND: Most private drug plans in Canada do not use a formulary, which leads to suboptimal drug use. We studied the impact of the adoption of the public formulary by a large private health benefits plan in British Columbia. METHODS: We studied the impact of a change by members of the BC Hospital Employees' Union to have their private drug plan mirror the public formulary as of June 2013. With data from Pacific Blue Cross, we conducted a before-and-after descriptive study using interrupted time-series analysis to study changes in covered drug costs and use for 18 months preceding and following the change. RESULTS: Our cohort averaged 66 000 plan members and dependents over our study period. Following the implementation of the formulary, the number of prescriptions covered by the plan declined by 0.46 prescriptions per member per month (95% confidence interval -0.50 to -0.42), a decline of 23.8% at 1 year. This decreased plan spending by $1.32 million over the 18 months after the coverage change, a 49.7% decline. INTERPRETATION: The adoption of the public formulary by a large private drug plan in BC substantially reduced drug plan expenditures and the volume of prescriptions paid for by the plan. Overall, these results suggest that carefully designed formulary changes could substantially reduce spending by private-sector drug plans on drugs that have more cost-effective therapeutic alternatives.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".