Cost‐Related Medication Behaviors for Patients With and Without Systemic Autoimmune Rheumatic Diseases
Bibliographic record
Abstract
OBJECTIVE: Medication nonadherence challenges the management of systemic autoimmune rheumatic diseases (SARDs). We investigated cost-related medication behaviors among patients with SARDs, and compared them to those of patients without SARDs, in a large diverse cohort across the United States. METHODS: As part of the All of Us (version 7), a nationwide diverse adult cohort with linked electronic health records begun in 2017, participants completed questionnaires concerning cost-related medication behaviors. Chi-square tests compared responses between patients with SARDs, by disease and medication type, and to those without SARDs. Logistic regression analyses were used to calculate odds ratios (95% confidence intervals [CIs]). RESULTS: We analyzed data from 3,997 patients with SARDs and 73,990 participants without SARDs. After adjustment, patients with versus without SARDs had 1.56 times increased odds of reporting unaffordability of prescription medicines (95% CI 1.43-1.70), 1.43 times increased odds of cost-related medication nonadherence (95% CI 1.31-1.56), and 1.23 times increased odds of using cost-reducing strategies (95% CI 1.14-1.32). Patients with SARDs who reported unaffordability were 16.5% less likely to receive a disease-modifying drug (95% CI 0.70-0.99) but 18.1% more likely to receive glucocorticoids (95% CI 0.99-1.42). In addition, unaffordability of prescription medicines was likely to have 1.27 times increased odds of one to two emergency room visits per year (95% CI 1.03-1.57) and 1.38-fold increased odds of three or more emergency room visits per year (95% CI 0.96-1.99). CONCLUSION: In this large diverse cohort, patients with versus without SARDs had more self-reported cost-related medication behaviors, and those who reported medication unaffordability received fewer disease-modifying drugs and had more emergency room visits.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".