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This article provides a summary of Viewpoint and Research articles responding to the 2020 Journal of Managed Care + Specialty Pharmacy Call to Action to address racial and social inequities in medication use. We find great heterogeneity in terms of topic, clinical condition examined, and health disparity addressed. Common recommendations across Viewpoint articles include the need to increase racial and ethnic diversity in clinical trial participants, the need to address drug affordability and health insurance literacy, and the need to incentivize providers and plans to participate in diversity initiatives, such as the better capture of information on social determinants of health (SDOH) in claims data to be able to address SDOH needs. Across research articles, we also find a large range of approaches and study designs, spanning from randomized controlled trials to surveys to observational studies. These articles identify disparities in which minoritized beneficiaries are shown to be less likely to receive medications and vaccines, as well as less likely to be adherent to medications, across a variety of conditions. Finally, we discuss Healthy People 2030 as a potential framework for future health disparity researchers.

Medicare's Part D Medication Therapy Management (MTM) programs serve approximately 4.5 million eligible beneficiaries. Prior research suggested that the thresholds to enter Part D MTM programs would disproportionately favor White beneficiaries compared with Black or Hispanic beneficiaries. This article summarizes those initial studies and compares the results with more recent analyses of racial and ethnic differences in MTM program enrollment, which, in general, show higher odds of MTM enrollment for minority beneficiaries compared with White beneficiaries. Disparities in the utilization of comprehensive medication review (CMR), a core MTM service, are also discussed. Although trends vary, disparities exist for various minority groups. For example, Black beneficiaries and Hispanic beneficiaries are less likely to be offered a CMR compared with White beneficiaries. Additionally, minority (Asian, Hispanic, and North American Native) beneficiaries are less likely to receive a CMR after being offered the service compared with White beneficiaries. Black, Hispanic, and Asian beneficiaries are more likely than White beneficiaries to have a longer duration between MTM enrollment and CMR offer. There is also a distinct difference in the type of pharmacist (ie, plan pharmacist, MTM vendor pharmacist, or community pharmacist) completing the CMR for certain racial and ethnic groups. For example, compared with White beneficiaries, Black beneficiaries were less likely to receive a CMR from a community pharmacist, whereas Asian beneficiaries were more likely.

Background: The use of potentially inappropriate medications (PIMs) is prevalent, costly, and harmful for older adults. These medications are to be avoided among older adults because they generally have (1) a high risk of adverse events in this population and/or (2) limited evidence of benefits in the presence of safer or more effective alternatives. Medication therapy management (MTM) programs can help address PIM use; however, there has not been a synthesis of studies examining the impact of MTM programs on PIM use., Objective: To review published literature evaluating the impact of MTM on PIM use in older adults., Methods: A systematic literature review was conducted in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines using MEDLINE (PubMed) studies were included if they (1) had a Medicare population, (2) were based in the United States, (3) examined an MTM program (ie, used the term "medication therapy management"), (4) focused on the impact of MTM programs on PIM use as the primary outcome, (5) had a randomized controlled trial or an observational study design, and (6) were available in English., Results: Of 221 articles identified, 31 full-text articles were assessed, and 7 met all inclusion and exclusion criteria. The studies took place in various settings, ranging from single-site tertiary medical centers to multisite outpatient clinics, community pharmacies, and nationwide telehealth MTM providers. Patient populations were majority female sex (ranging from 61% to 71%) and majority White (ranging from 81% to 94%), with a mean age of 73 to 78 years. In 5 of the 7 studies, MTM reduced the use of PIMs; however, 3 did not adjust for confounding or apply a comparator group. Measurement of MTM impacts on PIM use varied across studies. Patient-level and plan-level studies mostly assessed shorter-term PIM usage reduction (4 months or less), whereas studies performed at the provider and institutional level assessed PIM usage reduction trends across consecutive measurement years., Conclusions: Based on the current limited evidence, MTM programs in older adults appear to have a positive impact on reducing PIM use. However, evidence was limited by study design, the lack of consistency in outcome measures, and a short follow-up period. Future work should adjust for confounding, apply comparator groups, include longer-term outcomes, and develop a core set of measures that can be consistently applied across studies.

Branded prescription drug reimbursement in the United States is complex and comprises multiple transactions among the parties involved in the drug supply chain, including manufacturers, wholesalers, pharmacies, health care providers, health plans or insurers, pharmacy benefit managers, and patients. This primer provides an overview of the parties involved in the reimbursement of brand-name drugs under both the pharmacy and medical benefits of an insurance policy and the flow of products and payments among them. Prescription drug spending in the United States grew from $30 billion in 1980 to $335 billion in 2018, and 80% of spending is on brand-name drugs. 1,2 Pharmaceutical spending and drug prices have been the focus of intense debate in recent years, which has brought attention to the complexity of the drug supply chain. This primer provides a high-level overview of the distribution and reimbursement of brand-name drugs (italicized words are defined in the Glossary) used in the outpatient setting and covered by health plans or insurers in the United States. The focus is on the parties involved and the flow of products and payments among them. The purchase of drug products outside of insurance and differences in drug reimbursement between private and public insurers are outside of the scope of this primer.

BACKGROUND: In 2014, qualified health plans sold in the Affordable Care Act (ACA) marketplaces were accused of providing drug coverage that was too restrictive and costly. After the change in administration in 2016, efforts to repeal portions of the ACA led to increases in premiums, decreases in enrollment, and overall uncertainty. OBJECTIVE: To examine how the number of formulary tiers and medication cost sharing, as well as transparency around these aspects, in qualified bronze and silver health plans in California, Florida, and Illinois changed from 2014 to 2018. METHODS: A search of all bronze and silver qualified health plans in California, Florida, and Illinois was performed in 2014 and in 2018 through the marketplace and issuer websites. RESULTS: From 2014 to 2018, the total number of bronze and silver qualified health plans offered in California, Florida, and Illinois remained relatively stable (36 to 35, 123 to 122, and 60 to 74, respectively). Over the same time period, the median number of formulary tiers remained constant for California and Florida (four and five) and increased from five to seven for Illinois. Of note, most Illinois plans shifted from a formulary with five or fewer tiers (92% of plans) to seven tiers (73% of plans) between 2014 and 2018. There was also an increase in the use of coinsurance instead of copay for each of the four following formulary tiers: generic (19% to 27% of plans), preferred brand (21% to 38%), nonpreferred brand (33% to 52%), and specialty (76% to 91%). Additionally, there was an increase in the median coinsurance rates for each of the aforementioned tiers: 0% to 25%, 0% to 35%, 30% to 40%, and 30% to 40%, respectively. The proportion of plans that provided their formularies on the marketplace website increased from 82% to 97% from 2014 to 2018, with the increase mostly driven by California plans (0% to 80%). There was a small increase in the proportion of plans that reported medication cost sharing through the medical benefit from 2014 (19%) to 2018 (25%). CONCLUSIONS: Between 2014 and 2018, qualified health plans increased their use of formularies with greater numbers of tiers, the use of coinsurance for each tier, and higher coinsurance rates. Availability of formularies on marketplace websites increased, but cost sharing transparency for medications covered by the medical benefit could greatly improve. DISCLOSURES: No funding supported this study. Hung reports past employment by Blue Cross Blue Shield Association, CVS Health, and a grant from PhRMA outside of the submitted work. She was an intern at the Biotechnology Industry Organization when this work began. Sauvageau has no disclosures. This work was presented as a poster at the AMCP 2018 Managed Care & Specialty Pharmacy Annual Meeting; April 23-26, 2018; Boston, MA.

BACKGROUND: The prevalence of financial medication assistance (FMA), including patient assistance programs, coupons/copayment cards, vouchers, discount cards, and programs/pharmacy services that help patients apply for such programs, has increased. The impact of FMA on medication adherence and persistence has not been synthesized. OBJECTIVE: The primary objective of this study was to review published studies evaluating the impact of FMA on the three phases of medication adherence (initiation [or primary adherence], implementation [or secondary adherence], and discontinuation) and persistence. Among these studies, the secondary objective was to report the impact of FMA on patient out-of-pocket costs and clinical outcomes. METHODS: A systematic review was performed using MEDLINE and Web of Science. RESULTS: Of 656 articles identified, eight studies met all inclusion criteria. Seven studies examined FMA for medications treating cardiovascular diseases, while one study assessed FMA for cancer medications. Among included studies, FMA had a positive impact on medication adherence or persistence, and most measured this impact over one year or less. Of the three phases of medication adherence, implementation (5 of 8) was most commonly reported, followed by discontinuation (3 of 8), and then initiation (1 of 8). Regarding implementation, users of FMA had a higher mean medication possession ratio (MPR) than nonusers, ranging from 7 to 18 percentage points higher. The percentage of patients who discontinued medication was 7 percentage points lower in users of FMA versus nonusers for cardiovascular disease states. In one cancer study, FMA had a larger impact on initiation than discontinuation, ie, compared to nonusers, users of FMA were less likely to abandon an initial prescription (risk ratio= 0.12, 95% confidence interval [CI]: 0.08-0.18), and this effect was larger than the decreased likelihood of discontinuing the medication (hazard ratio = 0.76, 95% CI: 0.66-0.88). In 3 of 8 studies reporting on medication persistence, FMA increased the odds of medication persistence for one year ranged from 11% to 47%, depending on the study. In addition to adherence, half of the studies reported on FMA impacts on patient out-of-pocket costs and 3 of 8 studies reported on clinical outcomes. Impacts on patient out-of-pocket costs were mixed; two studies reported that out-of-pocket costs were higher for users of a coupon or a voucher versus nonusers, one study reported the opposite, and one study reported null effects. Impacts on clinical outcomes were either positive or null. CONCLUSIONS: We found that FMA has positive impacts on all phases of medication adherence as well as medication persistence over one year. Future studies should assess whether FMA has differential impacts based on phase of medication adherence and report on its longer-term (ie, beyond one year) impacts on medication adherence. DISCLOSURES: This work was sponsored by a grant from Pharmaceutical Research and Manufacturers of America (PhRMA). PhRMA had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Hung reports past employment by Blue Cross Blue Shield Association and CVS Health and a grant from PhRMA outside of the submitted work. Zullig reports research funding from Proteus Digital Health and the PhRMA Foundation. consulting fees from Novartis. Reed reports receiving research support from Abbott Vascular, AstraZeneca, Janssen Research & Development, Monteris, PhRMA Foundation, and TESARO and consulting fees from Sanofi/Regeneron, NovoNordisk, SVC Systems, and Minomic International, Inc. Bosworth reports research grants from the PhRMA Foundation, Proteus Digital Health, Otsuka, Novo Nordisk, Sanofi, Improved Patient Outcomes, Boehinger Ingelheim, NIH, and VA, as well as consulting fees from Sanofi, Novartis, Otsuka, Abbott, Xcenda, Preventric Diagnostics, and the Medicines Company. The other authors have nothing to report. This work was presented as a poster presentation at the ESPACOMP Annual Meeting in November 2020.

BACKGROUND: Tenofovir alafenamide (TAF) is a new formulation of tenofovir disoproxil fumarate (TDF) that was approved in 2015. While clinical trial evidence suggests that TAF has more favorable outcomes related to kidney injury and loss of bone mineral density, TAF also leads to higher lipid levels compared with TDF. OBJECTIVES: To (a) determine prescribing rates of TDF and TAF among new recipients from 2014 to 2018 in a large academic health system and (b) compare baseline patient characteristics of those newly prescribed TDF versus TAF before and after the approval of TAF in November 2015. METHODS: Electronic health record data were used to identify new recipients of TDF or TAF from 2014 to 2018 and describe their total monthly TDF and TAF prescriptions by indication. Patient characteristics were compared among new recipients of TDF before November 2015, new recipients of TDF after November 2015, and new recipients of TAF. RESULTS: Monthly TAF prescribing rates increased to match TDF prescribing rates by April 2018 (82 vs. 88 prescriptions per month). TAF recipients and new recipients of TDF before November 2015 had similar racial distributions; both of these groups were more likely to be Black compared with new recipients of TDF after November 2015 (55% and 53% vs. 37%; P < 0.0001). TAF recipients also tended to have more comorbidities, including chronic kidney disease (7% vs. 2% and 2%; P < 0.0001), hepatitis C virus (8% vs. 5% and 3%; P < 0.0001), diabetes (13% vs. 5% and 6%; P < 0.0001), hypertension (27% vs. 13% and 13%; P < 0.0001), coronary artery disease (5% vs. 3% and 2%; P < 0.0001), hyperlipidemia (21% vs. 6% and 7%; P < 0.0001), and congestive heart failure (3% vs. 1% and 1%; P < 0.0001), compared with both new recipients of TDF before and after November 2015. CONCLUSIONS: TAF prescribing rates grew substantially in the 2.5 years after FDA approval. TAF is being prescribed more often than TDF in patients with chronic kidney disease and in patients with cardiovascular disease, suggesting that prescribers may be prioritizing the kidney safety profile over the effect on lipids. DISCLOSURES: This work was supported by the Duke Clinical Research Institute Executive Director's Pathway for Supplemental Funding. The research team received additional support from the National Institute of Diabetes, Digestive, and Kidney Disease R01DK112258 and P01DK056492 (CW) and from the National Institute of Allergy and Infectious Diseases 5T32AI100851 (MHM). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Hung reports past employment by Blue Cross Blue Shield Association and CVS Health and a grant from Pharmaceutical Research and Manufacturers of America (PhRMA), unrelated to this work. The other authors have nothing to disclose. This work was accepted as a poster presentation for the AMCP Nexus 2020 Virtual, October 19-23, 2020.

The number of people in the United States living with Alzheimer disease (AD) is growing, resulting in significant clinical and economic impact. Substantial research investment has led to drug development in stages of AD before symptomatic dementia, such as preclinical AD. Although there are no treatments approved for preclinical AD, there are currently 6 phase 3 clinical trials for preclinical AD treatments. In this article, we review these clinical trials and highlight considerations for future coverage decisions. In line with the definition of preclinical AD, enrollment in these trials focuses on cognitively unimpaired patients that are at high risk of AD because of family history and then genetic testing or brain imaging. Enrollment in most of these trials also allows for younger patients, including those aged under 65 years. Primary clinical trial endpoints focus on cognition often 4 or more years after treatment. Secondary endpoints include other measures of cognition and function, as well as biomarkers. Review of these trials brings to light a few potential considerations when covering these new medications in the future. First, novel and potentially costly approaches involving genetic testing and/or positron emission tomography imaging may be needed to identify appropriate patients and should be developed efficiently. Second, the long duration of these clinical trials suggest that there may be a need for alternative payment approaches in the United States that encourage early payers to pay for a medication for which the long-term benefits may not be realized until after the beneficiary is no longer with the health plan. Third, the value of AD treatments may differ across populations, creating a potential role for indication-based or population-based contracting. Finally, considering the potentially high budgetary impact and little real-world evidence for a new drug class, payers and manufacturers may want to consider outcomes-based payment approaches and coverage with evidence development to mitigate uncertainty about the value of the treatment demonstrated in well-defined populations in clinical trials versus more heterogeneous real-world settings. DISCLOSURES: This work was funded through a generous gift from the Global CEO Initiative on Alzheimer Disease. Hung reports grants from Agency for Healthcare Research and Quality and Pharmaceutical Research and Manufacturers of America outside the submitted work and past employment at CVS Health and BlueCross BlueShield Association. McClellan is an independent board member on the boards of Johnson & Johnson, Cigna, Alignment Healthcare, and Seer; co-chairs the Accountable Care Learning Collaborative and the Guiding Committee for the Health Care Payment Learning and Action Network; and receives fees for serving as an advisor for Cota and MITRE. Hamilton Lopez and Schneider have nothing to disclose. Part of this work was presented at the 2019 AMCP Nexus Meeting, October 29-November 1, 2019, in National Harbor, MD.

Background: Concurrent use of opioids and benzodiazepines is associated with increased risk of opioid overdose and death. Clinical guidelines recommend against this practice and quality measures incentivize plans to minimize concurrent use., Objective: To compare comorbidities, risky opioid-related behaviors such as high daily doses or multiple prescribers or pharmacies, and outcomes of users of opioids with and without benzodiazepine in the 2017-2018 North Carolina Medicaid population., Methods: This was a retrospective claims analysis that used 2017-2018 North Carolina Medicaid enrollment and administrative claims data to describe 3 populations: (1) opioid users who concurrently used benzodiazepine for at least 30 days, (2) opioid users who used some benzodiazepine for 0 to less than 30 overlapping days, and (3) opioid users who did not use benzodiazepines., Results: From 2017 to 2018, 6% of opioid users concurrently used opioids and benzodiazepines for at least 30 days, and 14% used some benzodiazepine for less than 30 overlapping days. Persons filling prescriptions for opioids and benzodiazepines were more likely to have mood disorders and more likely to have depression than opioid users who did not use benzodiazepines. Compared with those not using benzodiazepines, opioid users using benzodiazepine were also more likely to have higher daily opioid doses (at least 90 morphine milligram equivalents), at least 3 prescribers, and at least 3 pharmacies for opioid prescriptions. Although enrollees with at least 30 days of overlapping benzodiazepines and opioids had a higher percentage diagnosed with opioid use disorder compared with those with less than 30 days (30% vs. 13%), a similar percentage received medication-assisted treatment continuously for 90 days (2.6% vs. 2.7%) during 2017-2018. Users of opioids and benzodiazepines, whether for at least 30 overlapping days or less, had higher 1-year cumulative incidences of all-cause outpatient emergency department visits (64% and 65% vs. 52%) and all-cause hospitalizations (25% and 21% vs. 14%) compared with opioid users without benzodiazepine use., Conclusions: Despite guidelines and quality measures, patients continue to use opioids and benzodiazepines concurrently. Addressing underlying mood disorders and depression, curbing risky opioid-related behaviors, and increasing access to medication-assisted treatment may benefit this population., Disclosures: This project was supported by Arnold Ventures (formerly Arnold Foundation). Hung reports personal fees from CVS Health and Blue Cross Blue Shield Association, unrelated to this work. Maciejewski reports Amgen stock ownership due to spouse employment, unrelated to this work. McKethan reports personal fees from North Carolina Department of Health and Human Services. All other authors have nothing to disclose. Part of this content was presented as a poster at AMCP Nexus 2019; October 29-November 1, 2019; National Harbor, MD.

Historically, budget impact models have been used to demonstrate how a new drug or technology entering the market could influence payer budgets, with the hope of influencing coverage of the drug or technology. This article proposes the adaptation of a budget impact model framework to answer the payer question "What is the budget impact of our approaches to managing the utilization of drugs and technologies?" Throughout this article, we also describe lessons learned while collaborating with a U.S. payer to evaluate the budgetary impact of specific antidiabetic drug utilization management approaches. DISCLOSURES: The study discussed in this commentary was supported by grant number F32HS024857 from the Agency for Healthcare Research and Quality (AHRQ). The content is solely the responsibility of the authors and does not necessarily represent the official views of AHRQ, which had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or design to submit the manuscript for publication. The findings discussed in this manuscript represent the views of the authors and do not necessarily reflect the views of the Department of Defense, the Defense Health Agency, nor the Departments of the Army, Navy, and Air Force. Hung reports personal fees from CVS Health and BlueCross BlueShield Association, outside the submitted work. Lugo has nothing to disclose. Mullins reports grants and other fees from AHRQ, during the conduct of the study; grants and personal fees from Bayer and Pfizer; and personal fees from Boehringer-Ingelheim, Janssen/J&J, Regeneron, and Sanofi, outside the submitted work. Parts of the study were presented as a poster presentation at the 2017 AMCP Managed Care & Specialty Pharmacy Annual Meeting; March 27-30, 2017; Denver, CO, and as poster and oral presentations at the 2017 AMCP Nexus Meeting; October 16-19, 2017; Dallas, TX. The study was also published as Hung's PhD dissertation.

Background: There is a paucity of studies validating budget impact models. The lack of such studies may contribute to the underuse of budget impact models by payers in formulary decision making., Objective: To assess the face validity, internal verification, and predictive validity of a previously published model that assessed the budgetary impact of antidiabetic formulary changes., Methods: 4 experts with diverse backgrounds were selected and asked questions regarding the face validity of the structure/conceptual model, input data, and results from the budget impact model. To assess internal verification, structured "walk-throughs," unit tests, extreme condition tests, traces, replication tests, and double programming techniques were used. The predictive validity of the model was evaluated by comparing the predicted and realized budget using mean absolute scaled error. "Realized" budgetary impact of the formulary changes was calculated by taking the difference between realized budget in the year after the formulary changes and the budget had there been no formulary changes (i.e., the counterfactual). The counterfactual budget was modeled using the best fit autoregressive integrated moving average model., Results: When assessing the face validity of the model, the 4 experts brought up issues such as how to incorporate other health insurance, recent policy changes, cost inflation, and potential impacts on insulin use. The 6 internal verification techniques caught mistakes in equations, missing data, and misclassified data. The realized budget was found to be lower than the predicted budget, with 13% error and an absolute scaled error of 2.60. After removing the model assumption that past utilization trends would continue, the model's predictive accuracy improved (the absolute scaled error dropped below 1 to 0.48). The "realized" budgetary impact was found to be greater than the predicted budgetary impact, largely because of lower-than-expected utilization., Conclusions: The budget impact model overpredicted utilization in the year after the formulary changes. Discoveries through the validation process improved the accuracy and transparency of the model., Disclosures: This project was supported by grant number F32HS024857 from the Agency for Healthcare Research and Quality (AHRQ). The content is solely the responsibility of the authors and does not necessarily represent the official views of AHRQ. AHRQ had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or design to submit the manuscript for publication. The findings discussed in this manuscript represent the views of the authors and do not necessarily reflect the views of the Department of Defense, the Defense Health Agency, nor the Departments of the Army, Navy, and Air Force. Hung reports a grant from the AHRQ, during the conduct of the study, and personal fees from CVS Health and BlueCross BlueShield Association, outside the submitted work. Mullins reports grants and personal fees from Bayer and Pfizer and personal fees from Boehringer Ingelheim, Janssen/J&J, Regeneron, and Sanofi-Aventis, outside the submitted work. Mullins, Slejko, and Shaya are employed by the University of Maryland School of Pharmacy. Haines and Lugo have nothing to disclose. Part of this content was previously presented as a poster at the 2017 AMCP Managed Care & Specialty Pharmacy Annual Meeting; March 27-30, 2017; Denver, CO, and as poster and oral presentations at the 2017 AMCP Nexus Meeting; October 16-19, 2017; Dallas, TX. Part of this content was published as Hung's PhD dissertation.

Managed care organizations are growing more sophisticated in their ability to analyze data. There are increasing numbers of data analysts at managed care organizations, as well as more types of real-time, or "live," data available. These data range from pharmacy claims and enrollment files to medical claims, medical records, and linkages to external data. Moreover, the data are often curated in a way that allows for easier data analysis. Using these data, managed care residents are often required to perform a project to evaluate a utilization management policy or clinical program. Yet, there is a lack of guidance specific to managed care organizations on how to conduct such a research study using "live" claims data. This Viewpoint article provides a primer for managed care residents and other managed care professionals who are seeking to use data to help inform decisions on how to manage their beneficiaries' health and costs. DISCLOSURES: There was no funding source for this manuscript. Hung reports a grant from the Agency for Healthcare Research and Quality and personal fees from Blue Cross Blue Shield Association, outside the submitted work. Gedey, Groeneweg, and Jay have nothing to disclose.

Background: Traditional budget impact models predict the financial consequences of a new drug entering the market. This study provides an example of applying the budget impact framework to a new research question of interest to managed care organizations-what is the budget impact of our formulary and utilization management (UM) policy changes?, Objective: To predict the 3-year annual budgetary impact of TRICARE's antidiabetic formulary and UM policy changes using TRICARE claims data., Methods: A budget impact model was built in Microsoft Excel using health plan claims data for a 3-year time horizon. Model outcomes included spending on antidiabetic medications and medications used for side effect treatment. In sensitivity analyses, medical costs from inpatient, outpatient, and emergency room visits were also estimated. Model inputs included health plan antidiabetic medication utilization, as well as publicly available drug cost, rebate, dispensing fee, and patient cost-sharing estimates. Type of enrollee and pharmacy were also incorporated into the model. Sensitivity analyses varied estimates for utilization switch rates between preferred and nonpreferred agents, drug costs, rebates, and dispensing fees, as well as predicted impact from implementation delays., Results: For the 623,827 affected by the formulary and UM policy changes, the model predicted annual savings that increased from $24 million in the first year to $43 million in the third year after the changes. The majority of savings came from drug acquisition costs, as opposed to rebates, copays, and dispensing fees. Sensitivity analyses found savings across all varied parameters and scenarios except an unlikely scenario when 0% of utilization switched from nonpreferred to preferred agents. The model also predicted that the formulary and UM policy changes would lead to $529,439 in savings from medical visit costs in Year 3., Conclusions: This budget impact model predicted cost savings from the payer's formulary and UM policy changes., Disclosures: This project was supported by grant number F32HS024857 from the Agency for Healthcare Research and Quality (AHRQ), which contracted with the University of Maryland School of Pharmacy to conduct this study. The content is solely the responsibility of the authors and does not necessarily represent the official views of the AHRQ, which had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or design to submit the manuscript for publication. The findings discussed in this manuscript represent the views of the authors and do not necessarily reflect the views of the Department of Defense, the Defense Health Agency, nor the Departments of the Army, Navy, and Air Force. Hung reports a grant from the AHRQ, during the conduct of the study, and personal fees from CVS Health and BlueCross BlueShield Association, outside the submitted work. Mullins reports grants and personal fees from Bayer and Pfizer and personal fees from Boehringer-Ingelheim, Janssen/J&J, Regeneron, and Sanofi, outside the submitted work. Mullins, Slejko, and Shaya are employed by the University of Maryland School of Pharmacy. Haines and Lugo have nothing to disclose. Part of this content was previously presented as a poster at the 2017 AMCP Managed Care & Specialty Pharmacy Annual Meeting; March 27-30, 2017; Denver, CO, and as poster and oral presentations at the 2017 AMCP Nexus Meeting; October 16-19, 2017; Dallas, TX. Part of this content was published as Hung's PhD dissertation.

Background: Biosimilars undergo an abbreviated licensure pathway called 351(k), which was created by the Biologics Price Competition and Innovation Act of 2009. This approval process is different from the 351(a) pathway for original biologic approval and, as of August 2017, has been used to approve 5 biosimilars in the United States., Objective: To identify the types and quantities of evidence required by the FDA for biosimilar approval and the corresponding evidence manufacturers have provided in their 351(k) biosimilar approval applications., Methods: To collect data for this review, we searched through drug-specific FDA approval documents and approval-related FDA webcasts for approval indications and dates; reference product names; formulations; postmarketing requirements and commitments; evidence used for extrapolation claims; advisory committee votes; and evidence on similarity in analytical and functional characteristics, pharmacokinetics, pharmacodynamics, efficacy, safety, and immunogenicity., Results: All biosimilars approved in the United States provided a large evidence base to demonstrate similarity in analytical and functional characteristics-3 to 5 clinical studies showed similarity in pharmacokinetics and pharmacodynamics, and 1 to 2 clinical studies demonstrated efficacy. Safety and immunogenicity were evaluated across all clinical studies. All biosimilars were compared with either the U.S.-licensed reference product or the reference product licensed by the European Union. Extrapolation allowed biosimilars to be approved for indications in which clinical studies were not conducted. The few indications for which biosimilars did not share approval with the reference product were due to market exclusivity protection. None of the 5 biosimilars have been approved as interchangeable in the United States., Conclusions: The approval process for the first 5 biosimilars on the market in the United States provides a baseline understanding of what type and degree of evidence is required for biosimilar approval., Disclosures: There was no external funding for this study. Hung reports employment as a pharmacist for CVS Health, an AHRQ F32 grant, and meeting/accommodation/travel support from AACP, DIA, and ISPOR, all outside the submitted work. Vu and Mostovoy have nothing to disclose. Study concept and design were contributed by Hung and Mostovoy, along with Vu. Hung and Vu collected the data, and data interpretation was performed primarily by Hung, along with Mostovoy. The manuscript was written by Hung and Vu and revised by all the authors. Some of the study results were previously presented as a poster at the ISPOR 22nd Annual International Meeting; May 20-24, 2017; in Boston, Massachusetts.

Background: Medicare Prescription Drug Plans (PDPs) do not have incentives to consider long-term outcomes and costs associated with both medical and pharmacy benefits (LTOCMP) when making formulary decisions., Objective: To identify existing quality measures, payment models, and public reporting tools for PDPs that are related to formulary decision making and that could be used as potential incentives for PDPs to consider LTOCMP., Methods: PubMed, Google, and quality measure databases, as well as Center for Medicare and Medicaid Innovation and Centers for Medicare & Medicaid Services (CMS) websites, were searched for appropriate quality measures, payment models, and public reporting tools., Results: Few quality measures and other mechanisms exist that are related to formulary decision making and have the potential to be incentives for PDPs to consider LTOCMP. Only 3 such tools were identified: (1) Medicare Part D star ratings quality measures in use by CMS, (2) the Medicare Plan Finder website, and (3) URAC PBM Accreditation standards and measures. Furthermore, the majority of the quality initiatives identified were only indirectly related to motivating PDPs to consider LTOCMP., Conclusions: Efforts are needed to develop mechanisms to align PDP incentives with LTOCMP., Disclosures: No outside funding supported this research/study. Hung reports part-time employment by CVS Health and Blue Cross Blue Shield Association, as well as past paid internships with Biotechnology Industry Organization and AMCP Foundation/Allergan. Perfetto reports advisory board, consultancy, and lecture fees from Pfizer, Otsuka, Sanofi, National Pharmaceutical Council, and AMCP Foundation, as well as employment by the National Health Council and Journal of Managed Care & Specialty Pharmacy. Both authors contributed to study concept and design, data interpretation, and manuscript revision. Hung collected the data and prepared the manuscript.