Christine Buttorff, PhD

Biologics account for an increasing share of US prescription drug spending. Biosimilars could lower biologic prices through competition, but barriers to increasing both supply and uptake remain. We projected US biosimilar savings from 2021 to 2025 under different scenarios.

 We projected US spending on biologics over a 5-year period under 3 scenarios: (1) a baseline scenario holding quarter 4 (Q4) of 2020 market conditions constant; (2) under main assumptions allowing for biosimilar market growth and entry; and (3) an upper-bound scenario assuming greater biosimilar uptake, more robust price competition, and quicker biosimilar entry.

 We first analyzed 2014-2020 US volume and price data from IQVIA’s MIDAS database for biologics already facing biosimilar competition to inform model parameter values. We used these inputs to project biosimilar entry, biosimilar volume shares, biosimilar prices, and reference biologic prices. We calculated 2021-2025 new savings from biosimilar competition vs the Q4 2020 baseline.

 Estimated biosimilar savings from 2021 to 2025 under our main approach were $38.4 billion, or 5.9% of projected spending on biologics over the same period. Biologics first facing biosimilar competition from 2021 to 2025 accounted for $26.1 billion of savings, with $12.2 billion from evolving market conditions for already-marketed biosimilars. Furthermore, $24.6 billion of savings under our main approach were from downward pressure on reference biologic prices rather than lower biosimilar prices. Savings were substantially higher ($124.5 billion) under the upper-bound scenario.

 Biosimilar savings from 2021 to 2025 were $38.4 billion under our main assumptions. Greater savings may be feasible if managed care and other settings increase biosimilar utilization and promote competition.

Although biosimilars have the potential to lower prices through competition, barriers to biosimilar uptake may limit these savings. We projected biosimilar savings from 2021 to 2025 under several scenarios.

Projected savings were $38.4 billion under main assumptions vs conditions as of quarter 4 (Q4) of 2020 (5.9% of spending on biologics).

$26.1 billion (68.1%) of estimated savings were from new biosimilars.

$24.6 billion (64.2%) of estimated savings were from downward pressure on reference biologic prices.

Estimated savings were substantially higher ($124.5 billion) under an upper-bound scenario.

Prices and biosimilar market shares already achieved by Q4 2020 resulted in an additional savings of $52.9 billion from 2021 to 2025.

Prescription drugs are often separated into 2 categories: “conventional” small molecule drugs and biologics. Whereas conventional drugs are synthesized chemically and are typically easier to describe scientifically, biologics are manufactured in living systems and are more complex and harder to characterize.

 Average prices for biologics are much higher than those for small molecule drugs for several reasons, including differences in therapeutic classes and clinical use, payment policies, and limited competition.

 In 2017, biologics accounted for 2% of US prescriptions by volume but 37% of net spending on prescription drugs.

 Due in part to favorable returns on investments, many recent and upcoming drugs are biologics, including those to treat cancer and other life-threatening diseases.

Biosimilars, which are highly similar versions of brand-name reference biologics, are a key component of the national strategy to tackle high and growing spending on prescription drugs.

 Compared with other countries, the United States was relatively late in establishing a pathway for biosimilar market entry. The European Medicines Agency implemented a biosimilars approval pathway in 2005, approving its first biosimilar in 2006. In contrast, the US FDA lacked the authority to implement a regulatory approval pathway for biosimilars until the Biologics Price Competition and Innovation Act (BPCIA) was enacted as part of the Affordable Care Act in 2010. The FDA’s regulatory approval pathway, like those in other countries, relies on biosimilars’ similarity to reference biologics as evidence of safety and efficacy.

 The first US biosimilar was approved in 2015. As of December 31, 2020, 7 biologics faced biosimilar competition in the United States, with additional biosimilars on the horizon.

Conceptually, competition between biosimilars and reference biologics has the potential to drive down prices and reduce spending. In practice, the magnitude of these reductions varies depending on the competitive environment for each biologic; the extent to which insurers and pharmacy benefit managers use formularies, utilization management, and other tools to encourage the use of lower-cost alternatives; and patient and prescriber perceptions of biosimilars.

 Managed care organizations, payers, and policy makers have an interest in better understanding the potential for existing and future biosimilars to help control price and spending growth for biologics. Several stakeholders support policy changes to address uptake concerns, the extent of price competition, and incentives for long-term sustainability in the US biosimilar market.

In this paper, we estimate new 2021-2025 US savings from biosimilars under different scenarios vs a baseline of biosimilar market shares and prices already achieved by quarter 4 (Q4) of 2020. For additional context, we also estimate total historical and projected biosimilar savings from 2014 to 2025.

We projected 2021-2025 spending on 60 biologics under 3 scenarios. In the first (“baseline”) scenario, we projected spending for the 7 biologics facing biosimilar competition as of Q4 2020 (bevacizumab, epoetin, filgrastim, infliximab, pegfilgrastim, rituximab, and trastuzumab) while holding biosimilar and reference biologic market shares and prices constant at Q4 2020 levels and with no new biosimilars. In the second (“main approach”) scenario, we estimated spending that allowed existing biosimilar markets to mature and new biosimilar entry based on assumptions grounded in past US biosimilar experience. A third “upper-bound” scenario estimated spending that assumed greater biosimilar entry, volume shares, and price competition compared with assumptions used in the main approach. We then calculated 2021-2025 “new” savings from biosimilar competition (under the main approach and upper-bound scenarios) vs the Q4 2020 baseline scenario.

For comparison, we also calculated total historical and projected biosimilar savings from 2014 to 2025 vs a counterfactual that assumed the absence of biosimilars, as if the BPCIA had never been enacted. This total savings estimate combines 3 separate components: (1) historical savings from 2014 to 2020; (2) projected 2021-2025 savings at already-achieved Q4 2020 market shares and prices; and (3) projected 2021-2025 new savings from expanded biosimilar competition (our “main approach” analysis).

We used Q1 2014 to Q4 2020 IQVIA MIDAS data, which contain US sales and volume estimates for individual biologic products, as the primary data source to project savings.

 IQVIA MIDAS data are designed to support country-level trend and pattern analyses. MIDAS estimates are projected from IQVIA’s audits of standardized manufacturer, wholesaler, and other invoices. This methodology results in reliable estimates of real-world activity for these intended uses, but they remain estimates. The MIDAS data used in this analysis were obtained under license from IQVIA.Our MIDAS extract was prepared on March 2, 2021.

We calculated MIDAS-based prices by dividing manufacturer sales by volume, measured in terms of IQVIA “standard units,” which for biologics are typically a count of vials, syringes, pens, or other counting units. The MIDAS sales data reflect amounts paid to manufacturers prior to the application of rebates and other discounts that can ultimately result in significantly lower net sales (and therefore lower net prices). However, MIDAS data may incorporate certain discounts (for example, prices from the 340B Drug Pricing Program), as long as they appear as lower prices recorded on invoices.

We used MIDAS data with CMS quarterly average sales price (ASP) data as an alternative source for net prices. ASP is a volume-weighted average of net prices, including rebates and most other discounts, reported by manufacturers to CMS with a 2-quarter lag. CMS uses ASP to set payment rates for Medicare Part B drugs, which are often administered by providers. As a result, ASP data were available for only some biologics.

Our modeling includes biologics that are already subject to biosimilar competition and those that may experience biosimilar competition in the future. We identified 256 active ingredients in the MIDAS data that were categorized in MIDAS as biologics and sold in the United States at any time between 2014 and 2020. These active ingredients accounted for $1.14 trillion in 2014-2020 sales at manufacturer prices. We excluded 132 active ingredients that each had less than $100 million in 2020 US sales (at manufacturer prices), as they are unlikely to face biosimilar competition because of their limited potential for profitable biosimilar development. We excluded 64 additional active ingredients for other reasons: (1) 34 with 12-year data exclusivity expiration after December 31, 2025, the end of our study period

; (2) 23 categorized as biologics in MIDAS but not eligible for the biosimilar regulatory approval pathway because their FDA regulatory approval is via the Federal Food, Drug, and Cosmetic Act rather than the Public Health Service Act; and (3) 7 blood products, for which the applicability of the biosimilar regulatory approval pathway is unclear. The resulting list included 60 active ingredients (“biologics”) used in our analysis (see the 

] for a full list). These 60 biologics accounted for $724.4 billion in sales from 2014 to 2020 at manufacturer prices, or 63.4% of sales across all biologics over the same period.

We compiled information on the status of US biosimilar development programs from news releases, news articles, and the Generics and Biosimilars Initiative (GaBI).

 We assigned each of the 60 biologics to 1 of 6 status categories indicating the level of biosimilar competition: biosimilar launched, biosimilar approved but not launched, biosimilar under FDA review, biosimilar in late clinical development, biosimilar in early development, or no available information. Seven had biosimilar versions sold in the United States by Q4 2020: (1) bevacizumab, (2) epoetin, (3) filgrastim, (4) infliximab, (5) pegfilgrastim, (6) rituximab, and (7) trastuzumab.

Neither MIDAS nor ASP data perfectly measure net prices for biologics. ASP data may be lower than MIDAS manufacturer prices if rebates play a significant role in determining net price. For example, rebates to payers may be relatively large when manufacturers of pharmacy-dispensed biologics compete for preferred formulary placement. In other cases, MIDAS prices may be lower than in ASP because MIDAS captures 340B discounts excluded from ASP. We expect that both data sets overestimate actual net prices. Most biologics were present in both data sets, and we used the lower price for each biologic to approximate net prices as closely as possible; prices in MIDAS were lower than in ASP for nearly all biologics. We adjusted MIDAS prices downward for adalimumab and certain insulins to reflect likely omitted rebates in MIDAS manufacturer prices.

A detailed summary of our modeling approach is available in the eAppendix. We projected changes in total volume linearly for each biologic. In practice, volume may deviate from linear trends because of various factors, including changes in clinical practice, availability of new treatments, growing population or disease prevalence, and a demand response from lower-priced biosimilars.

We projected reference biologic prices using linear extrapolation from 2014-2020 quarterly data for biologics without biosimilar competition as of Q4 2020, and from all quarters prior to biosimilar entry for existing biosimilar markets. We developed and applied time-dependent assumptions on biosimilars’ share of total volume, reference biologic prices with and without biosimilar competition, and biosimilar vs reference biologic prices from an analysis of 2014-2020 data for biologics facing biosimilar competition by Q4 2020. For our baseline scenario, we held biosimilar and reference biologic volume shares and prices constant at Q4 2020 levels for these 7 biologics.

For biologics without biosimilar competition by Q4 2020, we calculated savings as if biosimilars entered the market in each quarter during our study period. We then calculated biosimilar savings for each biologic by applying entry probability assumptions. The likelihood of biosimilar entry increased over time and varied based on the current state of biosimilar development.

We separately tallied direct savings from lower biosimilar prices relative to projected reference biologic prices, and indirect savings from downward pressure on reference biologic prices facing biosimilar competition. We report savings estimates overall, separately for direct and indirect savings, and for individual biologics. The sensitivity analyses vary the particular inputs and are described in more detail in the eAppendix.

Spending under the baseline scenario holding Q4 2020 biosimilar conditions constant without new biosimilar entry was $648.0 billion over the 5-year period. Spending under the main approach scenario, including new savings from expanded biosimilar competition, was $609.6 billion over the same period, for savings vs the baseline scenario of $38.4 billion (5.9%). For comparison, US spending on all prescription drugs was roughly $500 billion at manufacturer prices in 2020 based on MIDAS data, and spending on all prescription drugs over a 5-year period would likely be in excess of $2.5 trillion. Our biosimilar savings estimate of $38.6 billion is roughly 1.5% of this rough estimate of total spending on prescription drugs over the 5-year period.

For comparison, total historical and projected biosimilar savings from 2014 to 2025 were $102.5 billion, including $11.2 billion in historical savings from 2014 to 2020, $52.9 billion in projected 2021-2025 savings at already-achieved Q4 2020 market shares and prices, and $38.4 billion in projected 2021-2025 new savings from expanded biosimilar competition (

The bulk of new savings under our main approach, $26.1 billion (68.1% of total new biosimilar savings), is from expected new biosimilars from 2021 to 2025. 

 details new savings for individual biologics under our main approach, grouped by existing vs new biosimilar markets. Savings for other specific biologics are reported in the eAppendix. Despite contributing to savings only from 2023 to 2025, adalimumab accounted for $19.5 billion (50.8%) of savings, whereas savings from new insulin biosimilar entry were $4.1 billion (10.6%). All other biologics without current biosimilar competition accounted for savings of $2.6 billion (6.7%). Additional savings from evolving conditions in existing biosimilar markets from 2021 to 2025 account for $12.2 billion in savings in that time period.

Figure 2 also decomposes the “direct” savings from lower biosimilar vs reference biologic prices and “indirect” savings from downward pressure on reference biologic prices under our main approach. Direct savings were $13.7 billion (35.8%) compared with indirect savings of $24.6 billion (64.2%) across all study biologics.

 illustrates the contributions to savings for select biologics on a quarter-by-quarter basis under our main approach. The area under the curve in Figure 3 represents the total estimated savings of $38.4 billion from 2021 to 2025. The projected entry of adalimumab biosimilars in 2023 results in an inflection point in quarterly savings, whereas contributions of biologics with currently marketed US biosimilars increased slightly over time.

 compares 5-year projected savings from biosimilars under our main approach vs other scenarios. Doubling entry probabilities yielded increased savings of $46.5 billion (a 21.1% increase over our main approach). Increasing biosimilar volume share by 50% raised projected savings to $48.3 billion (an increase of 25.8% over main approach savings), whereas decreasing biosimilar prices by 25% yielded savings of $52.8 billion (an increase of 37.6%). Total savings were $82.4 billion (more than double main approach savings) when we assumed that biosimilar competition drives an additional 25% reduction in reference biologic prices. As noted earlier, downward pressure on reference biologic prices is the primary driver of savings.

The upper-bound scenario, which simultaneously applies all these individual alternative assumptions, increases savings to $124.2 billion, or roughly a fifth of expected spending on biologics ($648.0 billion) over the 5-year period. Insulins contributed $13.9 billion to the upper-bound scenario (vs $4.1 billion under our main approach).

Under our main approach assumptions, we estimate total 2021-2025 savings from biosimilars of $38.4 billion, or 5.9% of projected total spending on biologics from 2021 to 2025. Across all biologics, savings from downward pressure on reference biologic prices accounted for nearly two-thirds of estimated savings ($24.6 billion); the remainder resulted from lower biosimilar prices relative to their reference biologics. Although it can be informative to compare biosimilar and reference product prices,20 this approach omits a significant share of savings resulting from lower reference biologic prices. This is particularly relevant given the consistent price increases observed for reference biologics prior to biosimilar entry.

New biosimilar entrants from 2021 to 2025, including adalimumab and insulins, contributed 68.1% of total new biosimilar savings. Importantly, we assumed that already-approved biosimilars for adalimumab would enter the market in early 2023. Although earlier entry could increase savings, the associated legal risk to manufacturers makes this unlikely. There are few major biologic reference products besides adalimumab and insulins for which biosimilar entry appears likely before 2025. The actual contributions of new biosimilar entry may be less than we estimate because we modeled entry after the end of the data exclusivity period for each biologic, which may occur earlier than the expiration of key patents as in the case of adalimumab.

Further uptake and price reductions from already-approved biosimilars contributed $12.2 billion in savings from 2021 to 2025. Although not the focus of our analysis, we projected that spending on biologics from 2021 to 2025 would be $52.9 billion higher without gains in biosimilar market share and price reductions already achieved by Q4 2020. Depending on context, either our main estimates of incremental new savings vs a Q4 2020 baseline ($38.4 billion) or aggregate savings vs a “no biosimilars” counterfactual ($91.3 billion) may be of greater interest to managed care organizations and policy makers. Our estimate of new savings from biosimilars is relatively modest compared with 2 recent studies that estimated savings of $69 billion to $140 billion from 2020 to 2024,

 and of $24 billion to $150 billion from 2017 to 2026.

 Both studies calculated savings under a range of volume share and price ratio assumptions. However, our analysis uses more recent data to inform estimates, more comprehensively decomposes savings by source and biologic, and is clearer on the baseline case used as a counterfactual to tally savings. Unobserved off-invoice discounts may make our estimates of net prices too high, despite our use of both MIDAS and ASP data, as well as price adjustments for adalimumab and insulins. If so, our savings estimates will be biased.

Although the FDA can designate biosimilars as “interchangeable” with reference biologics, provided certain criteria are satisfied, only 1 currently marketed US biosimilar, Semglee, a biosimilar to insulin glargine, has this designation.

 Interchangeable biosimilars may be perceived as closer substitutes to reference biologics, and as a result, they may achieve larger market shares and yield steeper price reductions in reference biologics than we considered in our modeling scenarios. Interchangeable biosimilars may also give managed care plans greater ability to steer utilization toward the lower-cost versions and give pharmacists incentives to select the version with the lowest acquisition cost. Given consistent insulin price increases,

 recent regulatory actions, and the growing call to designate these products as “interchangeable,”

 additional insulin biosimilar manufacturers may pursue interchangeability. Although insulins accounted for a small share of savings under our main approach ($4.1 billion, or 10.6%), this value increased to $13.9 billion under more aggressive assumptions on biosimilar entry, uptake, and price competition, all of which may be more feasible with an interchangeable designation. Beyond insulins, there is already 1 approved (but not yet sold) interchangeable biosimilar to Humira (adalimumab),

 with potentially more on the horizon. Interchangeable adalimumab biosimilars may accelerate price reductions and volume share growth.

There are many other uncertainties in how biosimilars in the United States will evolve over time. Our savings estimates are based on entry timing, volume share, and pricing assumptions, all of which were chosen to best represent prior US biosimilar and reference biologic trends observed through Q4 2020. Although future US experience for individual biologics will almost certainly deviate from our assumptions, the results presented here were relatively robust to changes in the underlying assumptions. Compared with our main approach, our upper-bound savings estimate of $124.2 billion was, as expected, much larger when we simultaneously increased biosimilar entry, uptake, and price competition across all biologics. Future research should focus on the likely impacts of specific policy proposals and assessing how the number of competitors, market size, and other factors drive the magnitude of savings.

The assumptions used in our alternate scenarios may be feasible today in some parts of the US health care system. Some payers and health systems, such as Kaiser Permanente and the Veterans Health Administration, have used their direct control over prescriber decision-making to achieve much higher biosimilar volume shares than others.

 However, achieving greater use of biosimilars across the entire US health care system may require coordinated new policies and managed care strategies. Several policy changes could increase biosimilar uptake—for example, paying the same rates for reference biologics and their biosimilars,

 keeping separate rates but paying a higher margin for biosimilars,

 or decreasing patient cost sharing for biosimilars.

 More broadly, Congress, the FDA, and individual states could explore ways to promote biosimilars that can be substituted for reference biologics. Many of these strategies also have the potential to increase price competition between biosimilars and reference biologics.

Separately, Congress could revisit BPCIA’s “patent dance” and other provisions governing when and how biosimilars first enter the market, focusing on avoiding cases in which litigation and settlements result in much later biosimilar entry compared with other countries (for example, adalimumab and etanercept, as discussed earlier). Earlier biosimilar entry in these cases would allow biosimilars to gain market share and accumulate savings more quickly.

The United States is unique among higher-income countries in having high reference biologic prices, which typically increase over time. However, broader US policies aiming to reduce reference biologic prices could leave less headroom for biosimilar savings to accrue. Depending on their design, these policies may affect incentives for industry to invest in biosimilar development.

We project that biosimilars will lower spending on biologics by $38.4 billion from 2021 to 2025, with the bulk of savings accruing from new downward pressure on reference biologic prices and from new biosimilar entry. Savings are substantially higher ($124.2 billion) under an alternate upper-bound scenario assuming quicker biosimilar entry, greater biosimilar volume share, and more robust price competition. Each of these alternative market characteristics could be the aim of managed care strategies and future US policy. 

 RAND Corporation (AM, CB), Arlington, VA; Office of the Assistant Secretary for Planning and Evaluation, HHS (KF, ZE, JFO, SM, AJ), Washington, DC.

 Office of the Assistant Secretary for Planning and Evaluation, HHS. Dr Jessup’s contributions to study design and writing primarily occurred when she worked at the Office of the Assistant Secretary for Planning and Evaluation; she is currently affiliated with the HHS Office of Inspector General. The opinions presented in this paper are of the authors only and do not represent the views of their employers.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (AM, KF, ZE, SM, AJ); acquisition of data (AM, CB, ZE, SM); analysis and interpretation of data (AM, CB, KF, ZE, JFO, SM, AJ); drafting of the manuscript (AM, CB, JFO, SM, AJ); critical revision of the manuscript for important intellectual content (AM, CB, KF, ZE, JFO, AJ); statistical analysis (AM); obtaining funding (AM, KF, ZE); administrative, technical, or logistic support (CB); and supervision (AM, ZE).

 Andrew Mulcahy, PhD, MPP, RAND Corporation, 1200 S Hayes St, Arlington, VA 22202. Email: amulcahy@rand.org.

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Articles

Projected savings from biosimilars from 2021 to 2025 were $38.4 billion vs conditions as of quarter 4 of 2020 and were driven by new biosimilar entry. Savings were $124.5 billion under an upper-bound scenario.

Projected US Savings From Biosimilars, 2021-2025

 The use of generics in Medicare Part D generates cost savings for plan sponsors, beneficiaries, and the federal government. However, there is considerable variation in generic use across plans, even within a therapeutic class. Our objective is to understand the extent of variation in generic use in Part D and to understand factors associated with generic use.

 We used an observational study design using Medicare Part D claims from 2006 to 2016.

 We used descriptive statistics and regression analysis to examine the variation in generic and brand use across plans and the extent to which patient, plan, and area characteristics are associated with the choice of medication within a therapeutic class.

 Although generic use has increased markedly over time in Part D, substantial variation across plans persists in a number of common therapeutic classes. Beneficiary characteristics such as gender and health status are associated with higher/lower generic use, as are plan characteristics such as plan type (stand-alone prescription drug plan or Medicare Advantage), premium, and parent company.

 Because we cannot study the impact of brand-name drug rebates on generic use, we can study the variation in generic use across Part D plans as an indirect way to assess pharmacy benefit manager and plan incentives. We find circumstantial evidence that, in certain classes, rebates may play a role in influencing brand over generic use, although the exact relationship is unknowable given the proprietary nature of rebates.

Generic use has increased markedly over time in Medicare Part D, but substantial variation across plans persists in a number of common therapeutic classes.

Beneficiary and plan characteristics are associated with generic use.

We find circumstantial evidence that, in certain classes, rebates may play a role in influencing brand over generic use, although the exact relationship is unknowable given the proprietary nature of rebates.

Changing Part D’s underlying benefit design to reduce incentives to use high-cost drugs or encouraging the use of more tightly managed formularies could increase generic use.

The growing gap between a drug’s list (or “prerebate”) price and what manufacturers actually receive after discounts or rebates (the net price) is at the center of an increasingly public debate over rising drug costs.

 Pharmacy benefit managers (PBMs) typically negotiate rebates or discounts from drug manufacturers on behalf of plan sponsors (“plans”) in return for preferred formulary placement. For example, off-patent brands such as Lipitor (atorvastatin), Nexium (esomeprazole), and Neurontin/Lyrica (pregabalin) remained top-selling medications in Medicare Part D despite widely available generic versions or over-the-counter substitutes.

 A possible explanation for this is that PBMs or plans may place higher-cost drugs on lower cost-sharing tiers to increase the volume used of the drugs in exchange for larger rebates.

 Lower cost sharing for enrollees (involving lower co-payments or coinsurance, for instance) increases utilization.

 Favoring high-cost brands over lower-cost generics may reflect strategic behavior by plans or their sponsors (PBMs) to steer enrollees to heavily rebated drugs.

Medicare Part D is the federal prescription drug program for the disabled and those 65 years and older. Part D plans must use a pharmacy and therapeutics committee to establish formularies.

 Plans must cover at least 2 drugs in each class and must cover substantially all drugs in 6 protected classes (antidepressants, antipsychotics, anticonvulsants, immunosuppressants for transplants, antiretrovirals, and antineoplastics).

 The standard Part D benefit includes a deductible, an initial coverage limit, a coverage gap, and a catastrophic phase that beneficiaries enter when they have spent $6350 in out-of-pocket costs in 2020. In each benefit phase, the extent of cost liability shifts among the plans, manufacturers, beneficiaries, and the federal government.

Although Part D plans are required to report rebates and other discounts to CMS, recent analyses find greater use of high–list price, high-rebate drugs in Part D than in commercial markets.

 This raises concerns that several structural features of Part D may increase plans’ incentives to encourage costly, high-rebate drugs. A majority of drugs in stand-alone plans (but not Medicare Advantage plans with drug coverage) now require coinsurance rather than a co-payment

; in this situation, high prerebate prices increase the beneficiary’s out-of-pocket cost, moving beneficiaries into the catastrophic phase of the benefit sooner—the fastest growing portion of the program’s cost.

 Once a beneficiary reaches the catastrophic phase, plan liability is low and federal liability is high: The federal government pays 80% of the drug costs on average, the plan pays 15%, and nonsubsidized beneficiaries pay 5% of the prerebate price.

Additionally, the Affordable Care Act and the Balanced Budget Amendment of 2018 restructured the Part D benefit to reduce beneficiary cost sharing in the coverage gap.

 The primary change required manufacturers to provide discounts on brand drugs purchased in the coverage gap, with the discount reaching 70% of the prerebate price in 2019. The manufacturer discount counts toward beneficiaries’ out-of-pocket spending, which also pushed more beneficiaries into the catastrophic phase where plan liability is low.

Because rebates are proprietary, we cannot directly assess their impact on plans’ formulary design and medication use. (CMS requires plans to submit information on Direct and Indirect Remuneration in order to reconcile expenditures at the end of the year. However, this information is proprietary and is not available to researchers.) Rather, we assess this indirectly by examining the extent of variation in generic use across Part D plans. We use Medicare Part D data from 2006 to 2016 to examine differences in brand and generic drug use within a therapeutic class and the extent to which beneficiary, plan, and area characteristics explain the variation.

We used Part D Prescription Drug Event (PDE) data from January 2006 through December 2016 for a 20% random sample of Medicare beneficiaries enrolled in Part D. Part D plans are sold either as stand-alone prescription drug plans (PDPs) or in conjunction with a Medicare Advantage plan that also includes medical benefits (MAPD). The PDE data capture drug name, National Drug Code (NDC), dosage, quantity, date of service, and payments made by the beneficiary, plan, and other third-party coverage. We aggregated individual NDCs into therapeutic classes based on First Databank (FDB) definitions, focusing on the 10 most commonly used classes in Part D: antiasthmatics, antihistamines, antidiabetics, antineoplastics, anticoagulants, antihypertensives, antihyperlipidemics, anticonvulsants, antidepressants, and antiulcerants. Antineoplastics, anticonvulsants, and antidepressants are 3 of the 6 protected classes. We linked the PDE data to the plan characteristics file, which contains detailed information on each plan’s formulary and benefit design at the NDC level. This information was then linked to plan formulary files containing additional information on whether a given plan requires prior authorization or step therapy for a specific drug.

Most Part D plans use tiered formularies to steer beneficiaries to preferred medications. In 2015, the vast majority of Part D enrollees were in plans that used 5 cost-sharing tiers: preferred and nonpreferred tiers for generic drugs, preferred and nonpreferred tiers for brand drugs, and a tier for high-cost specialty drugs, with average co-payments of $1, $4, $38, and $80 for the first 4 tiers, respectively, and coinsurance of 29% in the specialty tier.

 Plans may also combine formularies with other rules—such as step therapy and prior authorization—to manage beneficiaries’ use of prescription drugs. Restrictive formularies that list fewer drugs in each therapeutic class will generate higher rebates because plans can drive volume toward these drugs.

We relied on FDB definitions to classify drugs as either single-source brand, multisource brand, or generic. Multisource brands have generic equivalents with the same active ingredient(s), and almost all prescriptions for multisource brand drugs are dispensed as generics.

 Single-source brands have no generic substitutes, so prescriptions written for single-source drugs limit Part D plans’ opportunities for generic substitution.

Our key outcome is the generic dispensing rate (GDR), defined as the percentage of generic fills of all fills in a therapeutic class per year, weighted by the days supplied. We additionally examined the rate of single-source drug use within a class, which is the inverse of the generic utilization rate minus the multisource use rate. We did not use the generic substitution rate because this is typically 95% or higher. Therapeutic substitution is where a generic is substituted for a brand of a different molecule. The appropriateness of therapeutic substitution is complex and beyond the scope of this paper.

We regressed a set of beneficiary and plan characteristics in a logit model to assess the odds that a given fillwas for a generic drug, within a therapeutic class. The unit of analysis is the PDE (or claim). We adjusted for several beneficiary characteristics that come from the Master Beneficiary Summary and Enrollment files, including age, gender, race/ethnicity, and beneficiary status (dual eligible, low-income subsidy [LIS], non-LIS). We used the number of different drug classes that a beneficiary took in a year as a proxy for health status.

We included several plan characteristics such as each plan’s monthly premium and whether it provides some coverage in the coverage gap (also known as the “donut hole”). Managed plans tend to have more control over physician prescribing behavior and typically make greater use of utilization management tools, so we include a binary indicator for MAPDs. Given that 3 firms—UnitedHealth, Humana, and CVS Health—account for more than half of all Part D enrollees and the 10 largest sponsors account for nearly 90% of enrollment, we include binary indicators for each of the 10 largest plan sponsors to assess variation in generic use across parent companies.

 Finally, we included state, year, and drug class fixed-effects to control for any unmeasured differences in generic use across states, years, or drug classes. We used Stata/MP 14.1 (StataCorp) for all analyses. Significance levels were determined using clustered standard errors at the plan level.

Our primary regression analyses report the years 2010-2016 because in those years the indicator about whether the fill required prior authorization was available. We provide the results for the full set of years (2006-2016) without the utilization management indicator in the 

Following trends in the commercial market, the GDR in Part D reached 90% for MAPDs and 88% for PDPs in 2016 across all drug classes, up from 63% and 53%, respectively, in 2006 (

). Generic use remained higher in MAPDs compared with stand-alone PDPs, but the difference narrowed over time.

 shows the distribution of GDRs in 2016 at the plan-class level for the 10 classes we examined in detail, separately for PDPs and MAPDs. The ends of the boxes show the interquartile range of GDRs (25th and 75th percentiles), and the whiskers represent the 10th and 90th percentiles of plans. The variation in generic use across drug classes is largely due to different product cycles. Classes such as antiasthmatics were brand dominant in 2016, with average generic dispensing rates of 20% to 25%. Plan-level variation in generic use tends to be lower in generic-dominant classes, such as antidepressants, and higher in classes with over-the-counter substitutes, such as antihistamines and antiulcerants.

More importantly, Figure 2 highlights variation in generic use 

 a therapeutic class. For example, in the treatment of type 2 diabetes, 10% of PDPs have GDRs of 50% or less, whereas 1 in 10 plans have GDRs higher than 70%. Given that each plan faces the same set of treatment options, large differences in generic use within a class may be due in part to nonclinical factors, such as coverage generosity, formulary restrictions, or differential strategies to favor high-rebate drugs. Not all plans cover the exact same mix of drugs in each class; however, there are 3 protected classes (antidepressants, anticonvulsants, and antineoplastics) shown in Figure 2 for which plans must cover substantially all of the active ingredients in the class, and there is still variation in generic use for these classes.

To better understand how plans differed in their generic use, the 

 shows class-level market shares within selected classes among Part D plans in the top and bottom quartiles of generic dispensing in 2016. In each of the 4 classes, plans in the bottom quartile of GDR have higher use of a brand drug relative to the top-quartile GDR plans. For example, esomeprazole (Nexium) was the third most widely used antiulcerant in low GDR plans, with an average market share of 12.7%, but it was rarely filled in the highest GDR plans.

Another possible explanation for these differences is that some plans favor highly rebated drugs over lower-cost alternatives by placing them on a lower tier with lower patient cost sharing to encourage use. Although we cannot infer causality, we observe this pattern occurring in some classes. The Table includes a column for the most common tier placement for the highest utilized single-source drugs among plans in the bottom quartile of the distribution of GDR. Esomeprazole was often not covered in the highest GDR plans; most low GDR plans considered it a “preferred brand” (tier 3).

Thus far, our results have been descriptive and have not controlled for a range of beneficiary, plan, provider, and area characteristics that can also affect medication choices, ranging from health status to perceptions that generics are less effective.

 displays the odds ratios of filling a generic prescription from class-level regressions that control for patient, plan, and area characteristics. Dual-eligible beneficiaries and those receiving the LIS were 24% and 28%, respectively, less likely to receive a generic compared with nonsubsidized beneficiaries, conditional on receiving a drug in the class.

Plan-level factors also affect the mix of brand and generic drugs. Beneficiaries enrolled in plans with higher premiums were less likely to fill a generic drug, presumably because coverage of brand drugs was more generous in higher-premium plans. Those enrolled in stand-alone PDPs were 10% less likely to fill a generic prescription than similar beneficiaries in MAPDs.

As we noted earlier, the Part D market is highly concentrated,

 and some of these plans also operate PBMs. We hypothesized that market concentration and other organizational features may play roles in influencing generic usage. Accordingly, we found that beneficiaries in Kaiser Permanente, a tightly managed staff model health maintenance organization, were 49% more likely to fill a generic than beneficiaries in other plans, and all of Kaiser’s plans are MAPD. They were followed closely by WellCare, a large insurer predominantly operating in government markets (Medicaid, Medicare Advantage, and Part D). On the other end, GDRs were lowest in plans operating as both PBMs and Part D plans (Express Scripts, CVS Caremark, and UnitedHealth). Beneficiaries in UnitedHealth and CVS plans were 19% and 20%, respectively, less likely to fill generics than other beneficiaries.

Beyond beneficiary and plan characteristics, prior work has shown that Medicare spending on pharmaceuticals varies widely across areas of the country such as hospital referral regions, even after adjustment for demographic characteristics, insurance coverage, and individual health status.

 Across the country, average GDRs across the 10 classes range from 0.83 to 0.88 (

). The states with the lowest and highest predicted GDR are New Jersey and Massachusetts, respectively.

Despite high overall use of generic drugs in Part D, generic utilization rates within a therapeutic class vary across plans. Some of the variation is due to the mix of beneficiaries enrolled in a plan. Dual-eligible beneficiaries and those receiving the LIS face little or no cost sharing and thus are less price sensitive to high-cost drugs. Plan features also matter. Generic dispensing rates are higher in MAPDs compared with PDPs, although the gap is narrowing over time. Generic rates are lower in plans with higher premiums that typically have lower cost sharing.

Nonetheless, significant variation persists after controlling for beneficiary and plan characteristics, raising concerns that the program’s design and institutional features may incentivize plans to favor high–list price, high-rebate drugs over lower-cost alternatives. A widely cited example of this is esomeprazole (Nexium), which accounted for the most spending in Medicare Part D in 2013 at $2.5 billion, despite widespread availability of close therapeutic substitutes and over-the-counter antiulcerants.

 Even after the drug lost patent protection in 2014, we still found that plans in the lowest quartile of GDR still had fills for the brand-name version.

In a competitive market, why would Part D plans favor a higher-cost drug? One possible explanation is the growth in rebates.

 Rebates are most common for high-cost brand drugs in competitive therapeutic classes, where there are multiple choices of similar products. Within Medicare Part D, rebates have more than doubled as a share of total spending since the program’s inception, from 9.6% in 2007 to 21.8% in 2017.

 Previous work found that 70% of Part D plans place at least 1 brand drug on a lower tier when a generic version is available, which is often cited as evidence of increasing rebates in return for more favorable tier placement to increase volume.

 Other work has found that a slight majority of generic drugs (53%) are placed on nongeneric tiers.

 More recent work found that by 2019, plans generally covered the generic version of a drug instead of the brand, when both were available.

 Besides more recent data, these findings may differ from those of prior work because the authors analyzed a small number of active ingredients and excluded brand drugs that did not have an exact generic equivalent in terms of form and dose.

While rebates lower plans’ overall costs, the tier placement also affects beneficiary liability, which in turn affects plan spending in Part D. A majority of drugs in PDPs (but not in MAPDs) now require coinsurance rather than a co-payment.

 Coinsurance on drugs with a higher prerebate price means that beneficiaries hit the catastrophic phase sooner, increasing costs to both beneficiaries and the federal government, but decreasing plan liability.

Consolidation at the parent level may allow the largest insurers to negotiate better discounts with manufacturers, but the potential for rebates to drive utilization becomes greater.

 We found lower generic utilization among firms operating as both a PBM and drug plan, specifically UnitedHealth, CVS, and Express Scripts. The Part D marketplace is dynamic and continues to undergo consolidation. In recent years, several large mergers have been completed or proposed between CVS Health and Aetna,

 Although Anthem and WellPoint merged in 2004, WellPoint did not begin using the Anthem brand name until 2014.

 Torchmark sold its Part D plans to SilverScript, a subsidiary of CVS, in 2016.

 Increased consolidation and vertical integration in Part D may restrict competition and raise total program costs.

Stand-alone PDPs manage prescription drug coverage only, whereas MAPD plans are at risk for both medical and drug expenditures. Recent work suggests that MAPDs provide more generous coverage of drugs that reduce downstream medical costs (including generic maintenance drugs), which is consistent with our findings of modestly higher generic dispensing rates in MAPDs.

2016 is the most recent year of claims data available to us, meaning more recent changes in generic use are not reflected here. Some drugs may be more effective or have fewer adverse effects than other medications in the class, at least for some patients, and physicians may have different experiences that affect their prescribing behavior. These unmeasured factors could bias our results if they differ systematically differ across plans, but there is no evidence to suggest that it is a concern.

Despite some widely publicized examples of excess profiteering in generic markets, new medicines will continue to drive drug spending. However, there are still opportunities for significant cost savings from generics, given the variation in GDR we found. One option is for CMS to prohibit Part D plans from giving branded products more favorable formulary placement than generic products. Another option would be the Trump administration’s proposal to apply rebates at the point of sale that was abandoned last year.

 One potential impact of that policy would be to reduce the incentive to place branded drugs on lower cost-sharing tiers, thus increasing generic use rates.

Variation in generic use has real consequences for plans, beneficiaries, and taxpayers. We find that overall generic utilization is increasing in Part D, but variation across plans within a class persists. Beneficiary and plan characteristics, state, and parent organization are all factors contributing to differences in GDR within a therapeutic class. We find evidence that rebates may be playing a role in discouraging generic use in particular therapeutic classes, but there is no public information on the extent of these rebates. Focusing on changing the underlying benefit design to reduce incentives to use high-cost drugs or encouraging the use of more tightly managed formularies could increase generic use.

 RAND Corporation (CB), Arlington, VA; IQVIA (YX), San Francisco, CA; University of Southern California, School of Pharmacy (YX, GJ) and Schaeffer Center for Health Policy & Economics (GJ), Los Angeles, CA.

 Dr Buttorff acknowledges support from the Schaeffer-RAND initiative; Dr Joyce was supported by a grant from the National Institute on Aging, R01AG059234.

 Concept and design (CB, YX, GJ); acquisition of data (YX, GJ); analysis and interpretation of data (CB, YX, GJ); drafting of the manuscript (CB, GJ); critical revision of the manuscript for important intellectual content (CB, GJ); statistical analysis (YX, GJ); and administrative, technical, or logistic support (CB).

 Christine Buttorff, PhD, RAND Corporation, 1200 S Hayes St, Arlington, VA 22202. Email: buttorff@rand.org.

1. Thomas K, Goodnough A. Trump’s efforts to rein in drug prices face setbacks. 

. July 11, 2019. Accessed October 8, 2020. https://www.nytimes.com/2019/07/11/health/drug-prices-rebate-donald-trump.html

2. Aitken M, Kleinrock M. Medicine use and spending in the U.S.: a review of 2017 and outlook to 2022. IQVIA. April 19, 2018. Accessed October 8, 2020. https://www.iqvia.com/insights/the-iqvia-institute/reports/medicine-use-and-spending-in-the-us-review-of-2017-outlook-to-2022

Generic use has increased over time in Medicare Part D, but substantial variation across plans persists in a number of common classes.

Christine Buttorff, PhD

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