Biosimilar Optimization in Community Oncology Practices
Author(s)
Owanate Briggs, MPH1, Carolyn Brown, PhD, FAPhA1, Chanhyun Park, MEd, RPh, PhD1, Puneeth Indurlal, MBBS, MD, MS2, Jody Garey, PharmD3, Michael Johnsrud, MS, RPh, PhD1;
1University of Texas at Austin, College of Pharmacy, Austin, TX, USA, 2American Oncology Network, Fort Myers, FL, USA, 3The US Oncology Network, The Woodlands, TX, USA
1University of Texas at Austin, College of Pharmacy, Austin, TX, USA, 2American Oncology Network, Fort Myers, FL, USA, 3The US Oncology Network, The Woodlands, TX, USA
OBJECTIVES: Biosimilars reduce the burden of cost on patients and payers, thereby increasing access to life-saving care. Nevertheless, biosimilar uptake in the US has been inconsistent. Previous studies revealed favorable biosimilar perceptions among oncologists, however, research on predictors of biosimilar use is limited. We examined how biosimilar utilization differs across oncology care provider and provider group characteristics, as well as patient clinical factors.
METHODS: This retrospective cohort study used data from a community oncology network spanning 25 provider groups. The outcome was biosimilar utilization. Operational predictors, or predictors associated with practice infrastructure and clinical decision-making, included patient age, gender, diagnosis, line of therapy, clinical trial participation, provider time in practice, practice payer mix, and drug class (e.g., rituximab and its biosimilars). Economic predictors, or financial factors, included patient payer type, and Oncology Care Model (OCM) status. A generalized linear mixed model estimated the odds ratios (ORs) for biosimilar utilization.
RESULTS: A total of 478,709 drug administrations were analyzed. Biosimilar utilization was predicted by diagnosis, line of therapy, clinical trial participation, provider time in practice, OCM status, practice payer mix, patient payer type, and drug class. Patients diagnosed with lymphoma had the highest odds of receiving a biosimilar (OR=2.20, p<0.01). Patients in their first line (1L) of therapy were less likely to receive a biosimilar when compared to 2L (OR=1.55, p<0.01), 4L and above (OR=1.38, p=0.02), adjuvant setting (OR=1.84, p<0.01), and neoadjuvant setting (OR=2.53, p<0.01). The odds of receiving a biosimilar decreased for clinical trial participants (OR=0.01, p<0.01) and increased for OCM practices (OR=1.40,p<0.01). Patients continuing a class of therapy were more likely (OR=8.27, p<0.01) to receive a biosimilar than those starting a new class.
CONCLUSIONS: In a setting where provider perceptions toward biosimilars are generally favorable and barriers are infrequent, operational and economic factors may drive biosimilar optimization.
METHODS: This retrospective cohort study used data from a community oncology network spanning 25 provider groups. The outcome was biosimilar utilization. Operational predictors, or predictors associated with practice infrastructure and clinical decision-making, included patient age, gender, diagnosis, line of therapy, clinical trial participation, provider time in practice, practice payer mix, and drug class (e.g., rituximab and its biosimilars). Economic predictors, or financial factors, included patient payer type, and Oncology Care Model (OCM) status. A generalized linear mixed model estimated the odds ratios (ORs) for biosimilar utilization.
RESULTS: A total of 478,709 drug administrations were analyzed. Biosimilar utilization was predicted by diagnosis, line of therapy, clinical trial participation, provider time in practice, OCM status, practice payer mix, patient payer type, and drug class. Patients diagnosed with lymphoma had the highest odds of receiving a biosimilar (OR=2.20, p<0.01). Patients in their first line (1L) of therapy were less likely to receive a biosimilar when compared to 2L (OR=1.55, p<0.01), 4L and above (OR=1.38, p=0.02), adjuvant setting (OR=1.84, p<0.01), and neoadjuvant setting (OR=2.53, p<0.01). The odds of receiving a biosimilar decreased for clinical trial participants (OR=0.01, p<0.01) and increased for OCM practices (OR=1.40,p<0.01). Patients continuing a class of therapy were more likely (OR=8.27, p<0.01) to receive a biosimilar than those starting a new class.
CONCLUSIONS: In a setting where provider perceptions toward biosimilars are generally favorable and barriers are infrequent, operational and economic factors may drive biosimilar optimization.
Conference/Value in Health Info
2025-05, ISPOR 2025, Montréal, Quebec, CA
Value in Health, Volume 28, Issue S1
Code
P56
Topic
Health Service Delivery & Process of Care
Disease
SDC: Oncology, STA: Biologics & Biosimilars