American Enterprise Institute: 'Drug Pricing Decisions and Insurance Coverage: Evidence From Medicare Part D'
The working paper was written by
Here are excerpts:
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Abstract
We examine whether the pricing of brand drugs, relative to estimates of their clinical value, is related to the way insurers cover those products in Medicare Part D. The net prices of drugs in our sample vary significantly relative to value-based prices from the
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1 Introduction
Brand drug makers have discretion over the prices of their products. These decisions have attracted significant attention, with many
In a simplified framework, an insurer that is willing to cover drugs priced well above their value to patients will generate premiums that are unattractively high relative to an insurer that is more judicious in coverage. Of course, an insurer need not passively cover drugs independent of their price. Instead, they have a number of tools that can impede beneficiaries' access to such products. Perhaps most directly, a drug's price can influence an insurer's decision to cover drug a drug or not. Even conditional on coverage, insurers can impose differential levels of utilization management tools which have first-order effects on utilization. For example, applying prior authorization to a covered product reduces utilization of a drug by nearly 27 percent in Part D (Brot-Goldberg et al., 2023). Further, they can increase cost sharing for a given product or use coinsurance to make the out-of-pocket spending for beneficiaries a direct function of the (list) price of the drug.
In this paper, we investigate how insurers within Medicare Part D choose to cover drugs with differential pricing strategies. Specifically, we ask whether drugs with particularly high prices, relative to an estimate of their clinical value, are covered at lower rates or subject to greater use of utilization management tools than drugs with lower relative prices. Doing so would reflect a price-volume trade-off for drug makers (while also likely concentrating the use of high-cost products among those with the highest willingness to pay).
Our analysis focuses on pharmacy-dispensed brand drugs without generic competition in the Medicare Part D market in 2022. We place particular emphasis on a subset of drugs which also have external value-based pricing estimates from the
First, coverage and utilization management decisions are, unsurprisingly, responsive to first-order differences in the costs of pharmaceuticals. Branded drugs without generics are often excluded from formularies and are frequently subject to restrictions on their use if covered. The average branded drug in our sample is covered by roughly 50 percent of plans, but only covered without prior authorization or step therapy on 27 percent of plans. Among drugs in our ICER subsample, the average product appears on only 4 percent of plans without such utilization restrictions. Moreover, ICER products are almost always on the highest coverage tier, meaning enrollees' cost sharing is in the form of coinsurance. The typical generic is covered more often without utilization management and on lower cost sharing tiers.
Second, among drugs evaluated by ICER, net prices at which they are purchased are typically higher than estimated value-based prices (under a willingness to pay of
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1/ This result is consistent with findings in Bloudek et al, 2021.
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Third, we show that plans which cover a larger number of relatively "low-value" branded products are generally more permissive in their coverage decisions of all branded products. In other words, they do not appear to make different decisions about which drugs are worth coving, but instead, are more expansive in their coverage decisions generally. These more permissive plans tend to have higher premiums.
Finally, we examine the early lifecycle revenue patterns for drugs that are priced high or low relative to their estimated clinical value. We do not observe evidence that drugs which are offered at relatively good value prices have differentially fast increases in their revenues. However, this analysis has significant limitations owing to sample size and data availability.
These results are perhaps surprising. On one hand, coverage decisions appear responsive to first-order differences in costs. Coverage of generic products is generally more permissive than of brand drugs, for example. Within branded products, coverage of those with ICER estimates, which are often costly, is less permissive than the typical brand drug. On the other hand, coverage decisions do not appear to systematically vary with price within the sample of drugs with ICER estimates. These results are consistent with a few potential explanations.
Most directly, the value-based price estimates may simply not capture demand well. Even if these estimates accurately reflect clinical value to the typical patient with the condition, drug makers may set pricing decisions based on beliefs about how that value varies across patients. That is, drug makers may hypothesize that a significant mass of the willingness-to-pay distribution is clustered meaningfully above the average. Indeed, the use of utilization management tools by insurers for effectively all drugs in our ICER sample may serve to concentrate utilization among higher-willingness to pay patients for some products. Thus, the VBP estimate for a typical consumer may be a poor proxy for the willingness to pay of consumers who can access the product.
These results also may reflect institutional features of the Medicare Part D market. Notably, once spending is high enough, plan liability is relatively low. Plans owe just 5 percent of costs in the "coverage gap" and just 15 percent when spending rises higher into the "catastrophic phase" of the benefit (whereupon the federal government covers 80 percent of drug costs). This is in contrast to the initial coverage phase, where plans owe the majority of costs. Thus, plans may have more muted incentives to refine coverage decisions among a sample of highly priced medications that are likely to trigger the catastrophic phase. In the conclusion, we consider potential empirical tests of these theories.
Our work builds on recent research that shows insurers and Pharmacy Benefit Managers can and do influence the utilization of branded pharmaceuticals via formulary design. Naci et al (2022) show that only 20 percent of drugs launched between 2014 and 2018 that were not in protected classes were covered by more than half of plans a year after launch. Conditional on being covered, utilization management was common. Brot-Goldberg et al. (2023) help quantify the effects of prior authorization--a particularly common form of utilization management that requires the insurer's explicit approval before a drug is covered--on use of drugs within the Medicare Part D market. They show that, even though prior authorization applications are typically approved, imposing that restriction reduces the use of targeted drugs by 27 percent relative to plans with no such restrictions. Our empirical work builds on this in a few ways. First, we update some prior findings about coverage in Part D using more recent data and highlight how these decisions vary among a sample of relatively notable, high-cost drugs for which we can observe estimates of value-based prices aimed at purchasers in the US health care market. Second, we connect decisions regarding coverage and formulary design with the pricing decisions of specific drugs and investigate how these decisions affect premiums, plan enrollment, and drug revenues.
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Results
4.1 Coverage of Brand Drugs in Part D
Table 1 summarizes the average coverage rates for drugs in Part D, by branded status. We calculate the percent of plans which cover each drug and report average coverage rates for each product category. The average generic is covered on 77 percent of plans and is often covered without prior authorization or step therapy. Branded drugs with available generics are covered on 31 percent of plans and are typically on a higher average tier than generics. The typical brand drug without a generic in our full sample is covered on 50 percent of plans, but only on 27 percent of plans without prior authorization or step therapy.
Notably, drugs in the ICER subsample are covered at broadly similar rates to all brands without generics. However, they are almost always subject to restrictions. These products are covered without prior authorization or step therapy on just 4.1 percent of plans. As we show later, this is driven by a couple of products that are commonly-covered without restrictions, while most are never covered without utilization management./8 Conditional on being covered, drugs in the ICER subsample are placed on very high average tier. They are almost always covered on tier 4 or above, meaning enrollees face the highest cost sharing in the form of coinsurance.
Taken together, these results indicate that plans in this market are, unsurprisingly, responsive to first-order cost differences of products. Branded drugs--particularly those within our ICER subsample--are less frequently covered, face higher cost sharing, and are subject to greater use of utilization management tools.
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Table 1: Average Coverage by Product Type in Medicare Part D, 2022Q3
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Figure 1 illustrates the distribution of coverage rates across individual products within the sample of all brand drugs without generics (panels A and B) and the ICER subsample (panels C and D). Nearly a quarter of products in the full sample are covered by greater than 95 percent of plans, while 18 percent are covered by less than 5 percent of plans. As panel B shows, widespread coverage without step therapy or prior...
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8/ These general trends are consistent with a related analysis of older data from Naci et al. (2022).
...authorization is the exception rather than the norm. These patterns are broadly similar among branded drugs without generics included in our ICER subsample, though coverage without utilization management is rarer among this group. Because prior authorization and step therapy are so common among the ICER subsample, we focus much of our analysis on extensive margin coverage decisions of insurers where we observe more variation in strategies.
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Figure 1: Distribution of coverage among branded drugs without generics, 2022Q3
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4.2 Pricing and Coverage Decisions
The VBPs of branded drugs in our ICER sample are generally lower than net prices, but by varying degrees. Figure 2 plots the histogram of this VBP-to-net price ratio. Note that a ratio below one implies a drug's net price exceeds its VBP and vice versa. Among our sample, 71.7 percent of products have a ratio less than one. Eighteen drugs have a VPB that is less than 50 percent of the net price. In other words, the VBP estimated by ICER is far below the observed net price of some drugs. On the other hand, some drugs are quite good values by this metric--selling for net prices that are lower than the VBP (indicated by a ratio over one). Appendix A includes the VBP-to-net price ratio of all the drugs in our ICER subsample. Brand drug makers clearly make significantly different pricing decisions relative to this measure of value. This level of variation provides a useful context to consider how plans choose to treat these different products.
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Figure 2: Ratio of VPB to
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Given the substantial variation illustrated in figure 2, we next ask whether insurance coverage varies systematically with pricing decisions within this sample of products. In figure 3, we plot the percent of plans covering each product in this sample against their VBP-to-net price ratio. There is clearly variation in how drugs are treated across plans, but not in a way that varies with price. Drugs with higher VBP-to-net price ratios (indicating a "better value" based on this assessment of clinical value) are not systematically covered with greater frequency than those which have lower VPB-to-net price ratios.
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Figure 3: Coverage Relative VBP-to-
The patterns shown in figure 3 may be influenced by variation in drug characteristics. For example, those that have been on the market for longer may face greater market competition over time, potentially influencing coverage decisions and pricing. In column 1 of table 2 we replicate the result from figure 3 with a simple regression. A higher VBP-to-net price ratio has a very small and insignificant negative association with the percent of plans covering a drug. A one unit increase in the ratio, representing more than doubling of the average ratio, is associated with just a 5 percent decrease in the average rate of coverage. Column 2 shows that this result does not change when we control for the time a drug has been on the market. In column 3, we show that the same is true if we control for the number of branded products within class reported in our SSR data (this result is similar if we instead merge data from Redbook that provides a measure of brand and generic competition within class). In other words, these additional controls do not change the relationship that is evident in figure 3--we observe no systematic relationship between coverage of these products and VBP-to-net price ratios.
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Table 2: VBP-to-
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As figure 3 illustrates, plans clearly make different decisions about whether to cover many branded drugs in our ICER sample, even among "low-value" drugs with prices above their VBP. This could reflect alternative explanations. Plans may simply come to different decisions about the value or market demand for different products. In such a scenario, we might observe different plans covering a comparable number of products, but where they make different choices about which specific drugs to include on their formulary. Alternatively, some plans may simply be more permissive in their coverage decisions of all products. For example, plans which aim to be "benchmark" plans--plans that are made available to enrollees who receive low-income subsides at no premium--may take a low-cost coverage approach relative to plans targeting other parts of the market which may have a higher willingness to pay.
In figure 4, we investigate whether coverage of low-value products, defined as having VBP-to-net ratios less than 1 (N=31) is indicative of broader patterns of coverage by plans. Because plans using the same formulary will mechanically cover the same percentage of drugs, we illustrate these data at the formulary level. Each observation is then weighted by the number of enrollees in plans which use each formulary. Panel A indicates that formularies including a larger percentage of low-value branded drugs in the ICER sample are not systematically doing so at the exclusion of high-value products (N=14). Panel B shows that formularies which do cover larger percentages of these low-value products tend have more permissive coverage of all branded drugs without generics, however.
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Figure 4: Formulary Coverage ICER Subsample and All Branded Drugs, 2022Q3
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4.3 Plan Coverage Decisions and Premium Levels
Figure 5 investigates how different coverage strategies by plans translate to monthly premium levels. Again, because plans using the same formulary cover the same percent of products, we illustrate this at the formulary level. For each formulary, we average the premiums of plans using it. Each observation is weighted by the number of enrollees in plans using it. Unsurprisingly, plans using formularies which cover a larger percent of low-value drugs (panel A) or all brand drugs (panel B) tend to have higher premium levels. It is also implicitly evident from the size of each observation that enrollment tends to be larger in lower-premium plans.
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Figure 5: Coverage Rates and Premium Levels, 2022Q3
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There is still non-trivial variation in premiums across plans that cover similar numbers of products in either case, which may reflect other differences in plan design. Column 1 of table 3 replicates the results from figure 5a in a regression. Covering one additional percent of low-value drugs covered is associated with an increase of
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Table 3: Regression Results: Premium Levels and Coverage Rates, 2022Q3
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4.4 Drug Revenues
Throughout our analysis, we have effectively considered coverage decisions as a proxy for utilization of products. Lower coverage rates, or greater rates of utilization management when covered, should lower utilization of products. Thus, the inconsistent relationship between pricing decisions and coverage of products in our ICER subsample suggest that there would be a similarly inconsistent relationship between pricing and revenues.
However, this is not necessarily mechanically true. First, we only observe decisions made by insurers in Part D, which may or may not be indicative of coverage decisions in other markets. Moreover, our analysis implicitly assumes that impediments to utilization, like prior authorization, are similar across drugs. This need not be true. Utilization management for well-priced drugs may represent a lower bar (e.g., requiring only that the drug is prescribed for an on-label indication) than for more expensive medications (e.g., requiring failure of other therapy types and a minimum level of severity of a condition).
Considering how pricing decisions affect revenue is relatively challenging, though, because it is unclear how to define the counterfactual--namely, how much a given product would have sold under different pricing strategies. Nonetheless, we can provide some information about how revenues of products with different pricing strategies evolve early in their lifecycle.
Because we cannot observe revenue data prior to 2008, we exclude 5 products in our ICER sample that launched prior to that. We divide the remaining products into low-value (VBP-to-net price ratio between 0 and 0.5), medium value (ratios between 0.5 and 1), and high value (ratios over 1). We then ask how revenues evolved over the first three full years of sales for products in these groups. For context, most drugs see rising revenues over this period as it takes some time to build market share, but it is easy to imagine pricing strategies affecting this diffusion. If drug makers choose to set relatively low prices with the hopes of targeting a large swath of patients with a condition, one might imagine this growth to be relatively rapid. Instead, we observe (1) low-value products tend to have relatively high revenues, and (2) grow at roughly the same rate as those products priced much lower relative to VBP estimates. There are clear limits to this analysis, however, the results are consistent with the lack of a clear relationship between pricing decisions and coverage of products by the insurers we study.
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Figure 6: Revenue Growth Early in Product Lifecycle
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5 Conclusion
In this paper we investigated whether the pricing strategies of brand drugs was related to the way that insurers chose to cover those drugs. Namely, are drugs priced closer to value-based pricing estimates covered in ways that are likely to result in greater access and utilization? In short, we find limited evidence in favor of such a hypothesis.
Our data suggest that insurers in the Medicare Part D market react to first-order differences in the costs of pharmaceuticals. This is evident in the relatively permissive coverage of generic products relative to brands. It is also illustrated in the very aggressive use of utilization management tools for branded products--particularly those in our ICER subsample, which are covered without prior authorization or step therapy on only 4 percent of plans in 2022.
We show that branded drugs within our ICER subsample make meaningfully different decisions about how to price their products relative to value-based price estimates that are based on cost-effectiveness analysis. However, we observe little systematic relationship between these pricing decisions and coverage by insurers. High value products are not covered at greater frequency than low value products in this market.
Moreover, insurers that tend to cover more low-value products appear to be relatively permissive in their coverage decisions more generally, as opposed to making different decisions about which drugs to cover. We provide some evidence that this tends to result in higher premiums. This is consistent with insurers targeting different parts of the market (i.e., some insurers taking a lower-cost approach to qualify as "benchmark" plans).
While our analysis of drug revenues is limited by data availability, we find that the early revenue trends of high, medium, and low value products are generally similar. Notably, low value products, appear to have higher revenues initially, but with similar revenue trajectories as utilization increases.
Mechanically, the results suggest that the value-based prices from ICER do not capture willingness to pay for these products in the Part D market. It is important to consider what might underlie this fact. Perhaps most directly, the VBPs may simply not accurately reflect the full value of the product--be it clinical or non-clinical value. Alternatively, our results may implicitly reflect beliefs of drug makers about the distribution of willingness-to-pay levels across patients with a given condition. A drug maker that sets a relatively high price may believe that the willingness-to-pay distribution is not tightly grouped around the central estimate provided by ICER at
Our results may also reflect institutional features of this market which can mute the competitive benefits for insurers that do make coverage decisions in a more value-based manner. Beneficiary choice across Part D plans is heavily focused on premium levels and whether a plan covers the medications an enrollee takes, independent of how they are priced compared to a measure of value. (Indeed, the Medicare Plan Finder directs enrollees to list their specific medications when guiding them to a plan selection). Enrollees can also change plans over time. This begs the question: how do coverage decisions for drugs not taken by the enrollee affect competition among insurers? An insurer employing a value-based approach may be able to charge lower premiums than one using a generally permissive approach. However, this may still be dominated by a simple, low-coverage approach that pays little attention to VBPs (within coverage constraints imposed by Medicare Part D). Such an approach would tradeoff lower enrollment of beneficiaries taking those expensive medications against the ability to offer more competitive premiums more generally. These incentives may differ in settings where purchasers have to consider the demand for products among a large group of individuals (e.g., the employer market) rather than the drug consumption of a single person. Future research can help inform this hypothesis by comparing behavior of insurers in the employer-sponsored markets to those studied here.
The competitive benefits from employing a value-based coverage rule for high-cost drugs may be further muddied by the design of the Part D benefit during this period. Because insurers have relatively low liability once spending progresses beyond the initial coverage phase, they may have limited incentive to refine coverage decisions based on price within a set of costly medications. This theory is testable in the coming years because of provisions included in the Inflation Reduction Act of 2022 (Inflation Reduction Act, 2022). Specifically, the IRA will alter the Part D benefit design so that insurers have much greater liability for high-cost enrollees. Starting in 2025, federal spending in the catastrophic phase will fall from 80 percent of brand drug spending, under current law, to just 20 percent. Plans will be responsible for 60 percent of brand spending, while drug makers will be responsible for 20 percent. Enrollees will owe nothing in this portion of the benefit and will have total annual out-of-pocket spending capped at
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References
Bloudek, L. M., Nguyen, V., Grueger, J., & Sullivan, S. D. (2021). Are Drugs Priced in Accordance with Value? A Comparison of
Blume-Kohout, M. E., & Sood, N. (2013). Market size and innovation: Effects of Medicare Part D on pharmaceutical research and development.
Brot-Goldberg, Z. C., Burn, S., Layton, T., & Vabson, B. (2023). Rationing Medicine Through Bureaucracy: Authorization Restrictions in Medicare. NBER Working Paper No. 30878.
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Cubianski, J. & Damico, A. (2022). Key Facts About Medicare Part D Enrollment and Costs in 2022,
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Hwang TJ, Dusetzina SB, Feng J, Maini L, Kesselheim AS. Price increases of protected-class drugs in Medicare Part D, relative to inflation, 2012-2017. JAMA. 2019 Jul 16;322(3):267-9.
Inflation Reduction Act, Pub. L. No. 117-169, 136 Stat. 1818 (2022). https://www.govinfo.gov/content/pkg/PLAW-117publ169/html/PLAW-117publ169.htm
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Ippolito, B., & Levy, J. (2022). Best Practices Using SSR Health Net Drug Pricing Data, Health Affairs Forefront,
Lavetti, K., & Simon, K. (2018). Strategic Formulary Design in Medicare Part D Plans.
Naci, H., Kyriopoulos, I., Feldman, W. B., Hwang, T. J., Kesselheim, A. S., & Chandra, A. (2022). Coverage of New Drugs in Medicare Part D. The Milbank Quarterly, 100(2), 562-588.
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The working paper is posted at: https://www.aei.org/wp-content/uploads/2023/06/Ippolito-Levy-Drug-Pricing-Decisions-and-Insurance-Coverage-WP.pdf?x91208
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