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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".