Biologic DMARD Access and Medication Cost-related Nonadherence in Rheumatology Patients in Canada: A Cross-sectional Survey
Bibliographic record
Abstract
Abstract Background Cost-related nonadherence to prescription medications affects many Canadians and is associated with negative self-perceptions of health. Biologic disease modifying anti-arthritic drugs (bDMARDs) are costly drugs recommended for certain patients with rheumatoid or psoriatic arthritis and ankylosing spondylitis. We investigated access and cost-related nonadherence (CRN) to bDMARDs compared to other therapies for such patients in Ontario. Methods We conducted a cross-sectional telephone survey of adult patients recruited from two academic rheumatology practices in Hamilton, Ontario, asking demographic and socioeconomic characteristics, drug plan coverage, medication cost-related cutbacks, opinions on the value of bDMARDs, and assistance with costs from health professionals. CRN was defined by patient self-report of not using or using less than prescribed amount of medication, due to cost. Results 104 patients (mean age (SD) 61(12) years) participated, including 77 (74%) women, 57 (54.8%) taking bDMARDs, and 27 (25.9%) with household income <$40,000 annually. CRN was reported by 19 (18.3%) participants with no significant difference between those taking versus not taking bDMARDs (risk difference (95% CI): -0.10 (−0.25, 0.04); p=0.19). 37 (64.9%) of those taking bDMARDs reported that they would not take them if they had to pay the full cost. Overall, few patients reported that they would ask their doctor (17.3%) or pharmacist (15.4%) for help with reducing prescription costs. Conclusion CRN prevalence was relatively high amongst these rheumatology patients despite access to public and private funding mechanisms. Patients expressed a reluctance to ask their doctor or pharmacist for help in reducing their medication costs.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".