Did a Non‐Medical Biosimilar Switching Policy Cause an Increase in Non‐Biologic/Biosimilar Health Care Resource Utilization or Cost in Patients With Inflammatory Arthritis?
Bibliographic record
Abstract
OBJECTIVE: This study aimed to evaluate the impact of a series of policies that mandated switching patients with inflammatory arthritis (IA) from an originator biologic to a biosimilar in British Columbia, Canada, on health care resource use and cost. METHODS: The health data of patients with IA were obtained from five linked administrative databases held by Population Data BC from January 2013 to December 2022. Our analysis focused on trends in monthly average use and costs of four care resources: physician services, hospital services, emergency department visits, and concomitant drug use. Using interrupted time series analysis, we evaluated the impact of switching policies targeting (1) infliximab or etanercept and (2) adalimumab on total health care costs, excluding biologic and biosimilar costs. RESULTS: We included 3,150 patients in the study. Hospital and physician services accounted for the majority of the total care cost for patients with IA. We found no evidence of any increases in physician services, hospital services, emergency department visits, or concomitant drug use after either nonmedical switch policy. We also found no significant change in level and trend in total health care costs for both policies: infliximab or etanercept (level -$40, 95% confidence interval [CI] -$99 to $19; trend $5.42, 95% CI -$0.62 to $11.46) and adalimumab (level -$34, 95% CI -$139 to $70; trend -$8.97, -$17.94 to $0.00). CONCLUSION: Nonmedical biosimilar switching policies did not lead to increases in other health care service use and costs.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".