Strategies to reduce out-of-pocket medication costs for Canadians with peripheral arterial disease
Bibliographic record
Abstract
BACKGROUND: Given that peripheral arterial disease (PAD) disproportionately affects people of lower socioeconomic status, out-of-pocket expenses for preventive medications are a major barrier to their use. We carried out a cost comparison of drug therapies for PAD to identify prescribing strategies that minimize out-of-pocket expenses for these medications. METHODS: Between March and June 2019, we contacted outpatient pharmacies in Hamilton, Ontario, Canada, to assess pricing of pharmacologic therapies at dosages included in the 2016 American College of Cardiology/American Heart Association guideline for management of lower extremity PAD. We also gathered pricing information for supplementary charges, including delivery, pill splitting and blister packaging. We calculated prescription prices with and without dispensing fees for 30-day brand-name and generic prescriptions, and 90-day generic prescriptions. RESULTS: Twenty-four pharmacies, including hospital-based, independent and chain, were included in our sample. In the most extreme scenario, total 90-day medication costs could differ by up to $1377.26. Costs were affected by choice of agent within a drug class, generic versus brand-name drug, quantity dispensed, dispensing fee and delivery cost, if any. CONCLUSION: By opting for prescriptions for 90 days or as long as possible, selecting the lowest-cost generic drugs available in each drug class, and identifying dispensing locations with lower fees, prescribers can minimize out-of-pocket patient medication expenses. This may help improve adherence to guideline-recommended therapies for the secondary prevention of vascular events in patients with PAD.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".