Factors Associated with Biosimilar Exclusions and Step Therapy Restrictions Among US Commercial Health Plans.

BACKGROUND: Biosimilars have been introduced with the goal of competing with high-priced biologic therapies, yet their adoption has been slower than expected and resulted in limited efficiency gains. We aimed to explore factors associated with biosimilar coverage relative to their reference products by commercial plans in the United States (US). METHODS AND DATA: We identified 1181 coverage decisions for 19 commercially available biosimilars, corresponding to 7 reference products and 28 indications from the Tufts Medical Center Specialty Drug Evidence and Coverage database. We also drew on the Tufts Medical Center Cost-Effectiveness Analysis Registry for cost-effectiveness evidence, and the Merative™ Micromedex® RED BOOK® for list prices. We summarized the coverage restrictiveness as a binary variable based on whether the product is covered by the health plan, and if covered, the difference of payers' line of therapy between the biosimilar and its reference product. We used a multivariate logistic regression to examine the association between coverage restrictiveness and a number of potential drivers of coverage. RESULTS: Compared with reference products, health plans imposed coverage exclusions or step therapy restrictions on biosimilars in 229 (19.4%) decisions. Plans were more likely to restrict biosimilar coverage for the pediatric population (odds ratio [OR] 11.558, 95% confidence interval [CI] 3.906-34.203), in diseases with US prevalence higher than 1,000,000 (OR 2.067, 95% CI 1.060-4.029), and if the plan did not contract with one of the three major pharmacy benefit managers (OR 1.683, 95% CI 1.129-2.507). Compared with the reference product, plans were less likely to impose restrictions on the biosimilar-indication pairs if the biosimilar was indicated for cancer treatments (OR 0.019, 95% CI 0.008-0.041), if the product was the first biosimilar (OR 0.225, 95% CI 0.118-0.429), if the biosimilar had two competitors (reference product included; OR 0.060, 95% CI 0.006-0.586), if the biosimilar could generate annual list price savings of more than $15,000 per patient (OR 0.171, 95% CI 0.057-0.514), if the biosimilar's reference product was restricted by the plan (OR 0.065, 95% CI 0.038-0.109), or if a cost-effectiveness measure was not available (OR 0.066, 95% CI 0.023-0.186). CONCLUSION: Our study offered novel insights on the factors associated with biosimilar coverage by commercial health plans in the US relative to their reference products. Cancer treatment, pediatric population, and coverage restriction of the reference products are some of the most significant factors that are associated with biosimilar coverage decisions.

Outcome measures for oncology alternative payment models: practical considerations and recommendations.

OBJECTIVES: This paper aims to synthesize existing scholarship on quality measures in oncology, with a specific focus on outcome-based quality measures, which are often underutilized. We also present a set of "core outcome measures" that may be considered in future oncology alternative payment models (APMs). STUDY DESIGN: Our research consists of a focused literature review, content analysis, and quality measure synthesis and categorization. METHODS: We conducted a focused literature review to generate key evidence on quality measures in oncology. We studied 7 oncology quality assessment frameworks, encompassing 142 quality metrics, and synthesized recommendations using the Center for Medicare and Medicaid Innovation APM toolkit, focusing on outcome measures. RESULTS: We present 34 outcome-based oncology quality measures for consideration, which are classified into 5 domains: clinical care (eg, hospital and emergency department visits, treatment effectiveness, mortality), safety (eg, infections, hospital adverse events), care coordination (for hospital and hospice care), patient and caregiver experience, and population health and prevention. Both general and indication-specific outcome measures should be considered in oncology APMs, as appropriate. Utilizing outcome-based measures will require addressing multiple challenges, ranging from risk adjustment to data quality assurance. CONCLUSIONS: Oncology care will benefit from a more rigorous approach to quality assessment. The success of oncology APMs will require a robust set of quality measures that are relevant to patients, providers, and payers.