Cost-related Medication Nonadherence in Older Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Economic access to costly medications including biologic agents can be challenging. Our objective was to examine whether patients with rheumatoid arthritis (RA) are at particular risk for cost-related medication nonadherence (CRN) and spending less on basic needs. METHODS: We identified a nationally representative sample of older adults with RA (n = 1100) in the Medicare Current Beneficiary Survey (2004-2008) and compared them to older adults with other morbidities categorized by chronic disease count: 0 (n = 5898), 1-2 (n = 30,538), and ≥ 3 (n = 34,837). We compared annual rates of self-reported CRN (skipping or reducing medication doses or not obtaining prescriptions because of cost) as well as spending less on basic needs to afford medications and tested for differences using survey-weighted logistic regression analyses adjusted for demographic characteristics, health status, and prescription drug coverage. RESULTS: In the RA sample, the unadjusted weighted prevalence of CRN ranged from 20.7% in 2004 to 18.4% in 2008 as compared to 18.5% and 11.9%, respectively, in patients with 3 or more non-RA conditions. In adjusted analyses, having RA was associated with a 3.5-fold increase in the risk of CRN (OR 3.52, 95% CI 2.63-4.71) and almost a 2.5-fold risk of spending less on basic needs (OR 2.41, 95% CI 1.78-3.25) as compared to those without a chronic condition. CONCLUSION: Patients with RA experience a high prevalence of CRN and forgoing of basic needs, more than do older adults with multiple other chronic conditions. The situation did not improve during a period of policy change aimed at alleviating high drug costs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".