Drivers of infliximab biosimilar uptake: A comparative analysis of new biosimilar initiations versus switching in a national rheumatology registry
Bibliographic record
Abstract
OBJECTIVE: To analyze the variability in new infliximab biosimilar starts as well as switching from bio-originator to biosimilar infliximab, across insurance payers and rheumatology practices nationally. STUDY SETTING AND DESIGN: Data came from Rheumatology Informatics System for Effectiveness, a national registry with electronic health records from over 1100 US rheumatologists. Key outcomes include ever use of a biosimilar, date of initiation, and date of switching. Key variables of interest include insurance payer and practice. DATA SOURCES AND ANALYTIC SAMPLE: Secondary analysis of 37,560 patients aged ≥18 years administered infliximab (bio-originator or biosimilar) between April 2016 and September 2022 in Rheumatology Informatics System for Effectiveness. We tested for differences in use of biosimilar infliximab by demographic characteristics, socioeconomic status, and diagnosis using standard mean differences and multivariable modified Poisson regression. We used generalized estimating equations to assess the adjusted effect of insurance and year of initiation on new biosimilar starts. We analyzed variation in biosimilar switching by insurance, date of switch, and practice. PRINCIPAL FINDINGS: A total of 8196 (21.8%) infliximab users ever used a biosimilar and use did not differ significantly by demographic or clinical characteristics. In 2022, uptake among new users was higher among those with Medicaid (55%; 95%CI 43%-68%) and private insurance (51%; 95%CI 46%-57%) compared to Medicare (36%; 95%CI 29%-43%). Few prevalent bio-originator infliximab users switched to a biosimilar, and switching was lowest among Medicare beneficiaries (7% vs. 14.2% in Medicaid and 16.9% among privately insured). In adjusted analyses, practice level differences explained 37% of variation among new biosimilar starts and 34% of variation among those switching to a biosimilar. CONCLUSIONS: Our findings underscore two critical areas for enhancing biosimilar infliximab usage: increasing switching among prevalent users and increasing uptake among Medicare beneficiaries initiating treatment. Significant variation in uptake across practices also suggests that local switching policies are likely key drivers of uptake.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".