Prevalence and nature of manufacturer-sponsored patient support programs for prescription drugs in Canada: a cross-sectional study
Bibliographic record
Abstract
Background: Globally, pharmaceutical companies offer patient support programs in tandem with their products, which aim to enhance medication adherence and patient experience through education, training, support and financial assistance. We sought to identify the proportion and characteristics of such patient support programs in Canada and to describe the nature of supports provided. Methods: We conducted a crosssectional study to identify and characterize all marketed prescription drugs available in Canada as of Aug. 23, 2022, using the Health Canada Drug Product and CompuScript databases. To describe the nature of supports provided, we conducted a content analysis of publicly available patient support program websites and Web-based documents. Using logistic regression, we identified characteristics of drugs associated with having a patient support program including brand-name or branded generic (generic medications with a proprietary name), orphan (medications for rare diseases) or biologic drug status; estimated total cost of prescriptions dispensed at retail pharmacies; and price per unit. Results: Of the 2556 prescription drugs marketed by 89 companies in the study period, 256 (10.0%) had a patient support program in Canada. Many of the 89 drug manufacturers (n = 55, 61.8%) offered at least 1 patient support program, frequently relying on third-party administrators for delivery. Brandname and branded generic medications, biologic agents and drugs with orphan status were more likely to have a patient support program than generic drugs. Compared with drugs priced $1.01–$10.00 per unit, drugs priced $10.01–$100.00 per unit were nearly 8 times more likely to have a patient support program (adjusted odds ratio 7.54, 95% confidence interval 4.07– 14.64). Most sampled patient support programs included reimbursement navigation (n = 231, 90.2%) and clinical case management (n = 223, 87.1%). Interpretation: About 1 in 10 drugs marketed in Canada has a manufacturersponsored patient support program, but these are concentrated around brand-name, branded generic, biologic and high-cost drugs, often for rare diseases. To understand the impact of patient support programs on health outcomes and sustainable access to cost-effective medicines, greater transparency and independent evaluation of patient support programs is necessary.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".