Does Etanercept Biosimilar Prescription in a Rheumatology Center Bend the Medication Cost Curve?
Bibliographic record
Abstract
Objective The market entry of biosimilars is expected to bring budgetary relief. Our objective was to determine how the introduction of biosimilars influences medication costs in patients with rheumatoid arthritis (RA) and which patients gain access to biologics due to the availability of biosimilars. Methods Using hospital data of patients with RA between 2014 and 2018, an interrupted time series was performed. The interruption in the time series was placed at June 2016 (i.e., the introduction of the etanercept biosimilar). The changes in trends for rheumatic medication costs before and after the interruption were measured. Secondary analyses focused on explaining these trends. Results In the first quarter after the interruption, there was a decrease in total costs for biologic users of –€63,020 (95% CI –€96,487 to –€29,553,P= 0.001). The postinterruption trend did not differ from the preinterruption trend (95% CI –€6695 to €6715,P= 0.998) and after 3 quarters, the medication costs were back at the interruption level. After the interruption, the average cost per biologic user decreased by –€370 (95% CI –€602 to –€138,P= 0.005), followed by a quarterly decrease (relative to the preinterruption trend; 95% CI –€86 to –€14,P= 0.010), bending the average cost curve. The percentage of patients being treated with biologics increased in postinterruption by 0.50 percentage points quarterly (95% CI 0.38–0.62,P< 0.001). Also, the average age at the start of the first biologic increased after the interruption (P= 0.057). Conclusion The average cost per patient treated with biologics decreased after the introduction of biosimilars with a persistent trend. However, the budgetary relief due to market entry of biosimilars vanished quickly due to an increase in patients treated with biologics.
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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 | 0.007 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".