Impact of Introducing Infliximab Biosimilars on Total Infliximab Consumption and Originator Infliximab Prices in Eight Regions: An Interrupted Time-Series Analysis
Bibliographic record
Abstract
OBJECTIVE: We aimed to assess whether the introduction of the first infliximab biosimilar was associated with changes in overall infliximab consumption (originator and biosimilars) and price changes to the originator infliximab. METHODS: An interrupted time series analysis using infliximab sales data from 2010 to 2020 from the IQVIA Multinational Integrated Data Analysis System for eight selected regions: Australia, Canada, Hong Kong, Korea, India, Japan, the UK, and the USA. Quarterly measures of infliximab consumption and list prices were respectively defined as the number of standard units (SU)/1000 inhabitants and as 2020 USA dollars (USD)/SU. RESULTS: Following the introduction of infliximab biosimilars, overall infliximab consumption increased in Australia [immediate change: 0.145 SU/1000 inhabitants (P = 0.014); long-term change: 0.022 SU/1000 inhabitants per quarter (P < 0.001)], Canada [immediate change 0.415 (P = 0.008)], the UK [long-term change 0.024 (P < 0.001)], and Hong Kong [immediate change: 0.042 (P < 0.001)]. The list price of originator infliximab also decreased following biosimilar introduction in Australia [immediate change: - 187.84 USD/SU (P < 0.001); long-term change - 6.46 USD/SU per quarter (P = 0.043)], Canada [immediate change: - 145.58 (P < 0.001)], the UK [immediate change: - 34.95 (P = 0.010); long-term change: - 4.77 (P < 0.001)], and Hong Kong [long-term change: - 4.065 (P = 0.046)]. Consumption and price changes were inconsistent in India, Japan, Korea, and the USA. CONCLUSIONS: Introduction of the first infliximab biosimilar was not consistently associated with increased consumption across regions. Additional policy and healthcare system interventions to support biosimilar infliximab adoption 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".