Biologic Disease-modifying Drug Treatment Patterns and Associated Costs for Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To assess the influence of biologic treatment patterns on healthcare costs for patients with rheumatoid arthritis (RA) initiating tumor necrosis factor-α (TNF-α) antagonist therapy. METHODS: Patients with 2 RA diagnoses (International Classification of Diseases, 9th ed, 714.xx), and without psoriasis or Crohn's disease, were identified in a US employer-based insurance claims database. A sample of 2545 was constructed based on an index event of initiating TNF-α antagonist therapy and 30 months of continuous enrollment. Baseline characteristics were assessed in the 6-month pre-index period and treatment patterns were determined during the 12-month post-index period. Medical service and prescription drug costs were analyzed for Months 13-24 using multivariate regression analysis to control for baseline characteristics and time-varying confounding associated with treatment and disease severity. RESULTS: In the first year after TNF-α initiation, 89% used a single TNF-α antagonist; only 9% and 2% had switched TNF-α antagonists or received non-TNF biologic disease-modifying antirheumatic drugs, respectively. Descriptive analyses revealed pairwise differences between groups (p < 0.05) in baseline characteristics (comorbidities, RA-related procedure use, and prescription drug use). Controlling for observed baseline characteristics, costs were greater for those treated with multiple vs single TNF-α antagonists: annual RA-related prescription drug costs ($8,340 vs $7,058; p = 0.012), RA-related healthcare costs ($15,048 vs $13,312; p = 0.008), and total healthcare costs ($26,697 vs $21,381; p < 0.001). CONCLUSION: In this sample, the majority of patients with RA were treated with a single TNF-α antagonist over the first year on therapy. For those who switched therapy, Year 2 RA-related and total direct healthcare costs were higher, adjusting for claims-based measures of RA disease severity.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".