Patterns of Infliximab Biosimilar Uptake for Medicare, Medicaid, and Private Insurance from 2016 to 2022
Bibliographic record
Abstract
OBJECTIVE: Biosimilars have the potential to reduce spending on biologic drugs, yet uptake has been slower than anticipated. We investigated how successive introductions of infliximab biosimilars influenced their adoption by major US insurance providers. METHODS: Data came from the Rheumatology Informatics System for Effectiveness, a national registry with electronic health records from more than 1,100 US rheumatologists. All infliximab administrations (bio-originator or biosimilar) to patients aged ≥18 years from April 2016 to September 2022 were included. We used an interrupted time series to model the effect of each infliximab biosimilar release (infliximab-dyyb, November 2016; infliximab-adba, July 2017; and infliximab-axxq, July 2020) on uptake across Medicare, Medicaid, and private insurers. RESULTS: With the first and second biosimilar releases, biosimilar uptake rose slowly, with average annual increases of ≤5% from 2016 to June 2020 (Medicare 3.2%, Medicaid 5.2%, and private insurance 1.8%). With the third biosimilar release in July 2020, the average annual increase reached 13% for Medicaid and 16.4% for private insurance but remained low for Medicare (5.6%). By September 2022, uptake was higher for Medicaid (43.8%) and private insurance (38.5%) than for Medicare (24%). CONCLUSION: Our results have two key findings for policy makers. First, our results suggest that one or two biosimilars may not generate enough competition to speed adoption rates for biosimilars. Second, Medicare, which covers most patients receiving biologics nationally, had slow adoption rates even after the third biosimilar was introduced. Policy levers to speed adoption among Medicare beneficiaries are needed.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".