The Economic Impact of Originator-to-Biosimilar Non-medical Switching in the Real-World Setting: A Systematic Literature Review
Bibliographic record
Abstract
INTRODUCTION: To save costs to the healthcare system, forced non-medical switch (NMS) policies that cut drug coverage for originator biologics and fund only less expensive biosimilars are being implemented. However, costs related to the impact of NMS on healthcare resource utilization (HCRU) must also be considered. This study aims to summarize the evidence on the economic impact of an originator-to-biosimilar NMS. METHODS: A systematic literature review (SLR) was conducted. Publications reporting on HCRU or costs associated with originator-to-biosimilar NMS in the real-world setting were searched in MEDLINE and EMBASE from January 2008 to February 2020. In addition to hand searching the reference lists of relevant publications and SLRs, key conference websites, PubMed, and various government sites were also searched for the 2 years preceding the search (2018-2020). RESULTS: A total of 1845 citations were identified, of which 49 were retained for data extraction. Most studies reporting on the HCRU associated with NMS reported on post-NMS HCRU alone without a comparison pre-NMS. However, four studies described a difference in HCRU (i.e., investigations pre- vs post-switch or between non-switchers vs switchers), all of which reported a relative increase in HCRU, including laboratory testing, imaging, medical visits, and hospitalizations, amongst patients who underwent an originator-to-biosimilar NMS. Most studies reporting on the costs associated with NMS reported significant savings following NMS on the basis of drug costs alone. However, four studies specifically reporting on the difference of costs following originator-to-biosimilar NMS all demonstrated an increase in HCRU-related costs associated with NMS (increase in HCRU-related costs of 4-37% or 148-2234 2020 Canadian dollars). CONCLUSION: Amongst the studies that reported on the difference in HCRU pre- vs post-switch or between non-switchers and switchers, all showed an increase in HCRU and related costs associated with NMS, suggesting that the expected overall savings due to less costly drug prices may be reduced as a result of an increase in HCRU and its associated costs post-switch. Nevertheless, more real-world studies that include NMS-related healthcare costs in addition to drug costs 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 | 0.016 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".