The impact of biosimilar use on healthcare utilization among new users of etanercept for inflammatory arthritis: a population-based regression discontinuity analysis
Bibliographic record
Abstract
Background: Epidemiological evidence on biosimilars' real-world performance is limited. On July 18th, 2017, a biosimilar health policy was implemented in British Columbia (BC), Canada, mandating all patients initiating a new biologic medication to be prescribed a biosimilar (if/when available). Exploiting a policy change as a natural experiment, we assessed the real-world impact of biosimilar use for inflammatory arthritis (IA) on health resource utilization as a surrogate marker of real-world effectiveness and safety. Methods: Using administrative health data, we identified all incident etanercept users for IA in BC with initiation dates between 2014 and 2020 (n = 3004) [63·6% female; mean (S.D.) age at IA disease diagnosis 52·5 (16·6) years]. Healthcare utilization over three years after initiation was assessed using outcomes including - physician visits (PV), all-cause hospitalizations (ACH), infection-related hospitalizations (IRH), length of hospital stays (LOS), and emergency room visits (ERV). Using regression discontinuity design, we compared healthcare utilization risk in patients initiating etanercept immediately before/after policy-change date, representing the intention-to-treat effect. Additionally, we estimated the complier average causal effect of biosimilar use with instrumental variable (IV) control function method. Findings: Intention-to-treat analyses showed no significant impact of biosimilar policy implementation on PV, HOSP, IRH, LOS, or ERV, with respective adjusted RRs of 0·96 (95% CI: 0·82-1·12), 0·84 (95% CI: 0·49-1·44), 0·91 (95% CI: 0·21-3·86), 0·94 (95% CI: 0·41-2·15), and 0·91 (95% CI: 0·44-1·88). IV analyses indicated biosimilar use in routine settings did not significantly change healthcare utilization, compared to originator etanercept. Interpretation: No significant impact of biosimilar policy or actual biosimilar use on healthcare utilization was observed, suggesting equivalent real-world effectiveness and safety of biosimilars to originators and no unintended consequences of the policy change. Funding: CHIR and NSERC.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".