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Upcoming Presentations by Staff and Affiliates of CBO’s Health Analysis Division

Upcoming Presentations by Staff and Affiliates of CBO’s Health Analysis Division

This summer, several of my colleagues in the Congressional Budget Office’s Health Analysis Division will share their work with the public, giving a total of five presentations—one at the 2024 IQVIA Institute Research Forum in Boston, Massachusetts, and four at the 13th Annual Conference of the American Society of Health Economists (ASHEcon) in San Diego, California. The presentations are part of the agency’s ongoing efforts to engage with the broader research community in areas related to its work. Those efforts include outreach via blog posts on other health-related topics, such as health care providers’ cost structures and anti-obesity medications. By engaging with researchers outside the agency, CBO improves the quality of its analysis and makes its methods and findings more transparent and available. We look forward to discussion and feedback on the following topics of this summer’s presentations:

How Does Generic Competition Affect Net Prices and Volumes in Part D?

Presenter
Colin Baker

Session
Open Theme (2024 IQVIA Institute Research Forum)

Authors
Colin Baker (CBO), Scott Laughery (CBO), and Rachel Fehr (U.S. Department of Agriculture)

Abstract
In this paper, we quantify changes in prescription drug sales volumes and both retail and net prices and spending in Medicare Part D before and after brand-name drugs are first subject to generic drug competition. Consumers benefit from lower prices when a brand-name prescription drug faces competition from equivalent generic drugs, and hastening generic drug market entry is part of many proposals aimed at making drugs more affordable. Previous estimates of the price change when a brand-name drug faces generic competition have generally relied on retail data that omit rebates or discounts. This analysis uses Medicare Part D data that include confidential drug-level manufacturer rebates and coverage gap discounts reported to CMS, allowing us to estimate average changes in net prices and spending for a sample of approximately 300 drugs whose brand-name version experienced a loss of exclusivity (LOE) between 2010 and 2019. Results show that following LOE, total prescription volume in Part D rose by roughly half, driven by rising generic prescriptions, whereas brand-name prescriptions fell steadily. In that period, average retail prices also declined, whereas net prices of brand-name drugs rose slightly.

Net Price Variation in Medicare Part D: Evidence and Implications

Presenter
Alexander Olssen

Session
Prescription Drug Prices and Rebates in Medicare Part D (ASHEcon)

Authors
Christopher Adams (CBO) and Alexander Olssen (the Wharton School, University of Pennsylvania)

Abstract
We present novel data on the rebates and net prices in Medicare Part D. Using administrative data on rebates at the NDC-plan-year level for all stand-alone and Medicare Advantage Part D plans, we document large heterogeneity in rebates for the same NDC across plans (within year), and we report the R-squared from regression models with increasingly rich controls. As a baseline, we include NDC, plan, and year fixed effects. To account for formulary design, we add NDC-by-tier fixed effects. Furthermore, we control for other observed factors such as competition from alternative drugs, the value of other drugs from the same firm, the number of beneficiaries covered by the plan, the size of the copayments, the ability of the plan to exclude the drug, and the use of utilization management practices such as prior authorization. Substantial unexplained heterogeneity remains in all specifications. We create a flag for branded NDCs that have rebates that vary with their competitors’ formulary placement, and another flag for branded NDCs that have rebates that vary with the formulary placement of other drugs sold by the same manufacturer. We report statistics on the average prevalence of such rebate arrangements. We discuss how net price heterogeneity affects our understanding of pricing in Part D and its implications for models of pricing and benefit design in Part D.

Formulary Restrictions and Drug Rebates: Evidence From Medicare’s Protected Drug Classes

Presenter
Meagan Madden

Session
Prescription Drug Prices and Rebates in Medicare Part D (ASHEcon)

Authors
Christopher Adams (CBO), Meagan Madden (University of North Carolina–Chapel Hill), and Alexander Olssen (the Wharton School, University of Pennsylvania)

Abstract
Health insurers negotiate rebates from drug manufacturers by including drugs on their formulary or offering preferred placement on the formulary. Contracting theory suggests insurers can use the threat of formulary exclusion to negotiate larger rebates. However, when designing formularies for Medicare Part D, insurers cannot exclude drugs in six “protected” classes: anticonvulsants, antidepressants, antineoplastics, antipsychotics, antiretrovirals, and immunosuppressants. We evaluate this policy by comparing Part D rebates with rebates in the commercial insurance market, where insurers have more freedom to exclude drugs from their formulary. Specifically, we compare the difference between Part D and commercial rebates for protected versus nonprotected drug classes.

Part D rebates come from the Medicare Direct and Indirect Remuneration (DIR) data provided by CMS. We calculate commercial rebates as the residual from marketwide rebates, after accounting for Medicare and Medicaid rebates and discounts under the 340B Drug Pricing Program. Marketwide rebates for branded drugs are available from SSR Health. The Medicaid rebates and 340B discounts are provided by CMS and HRSA, respectively. Due to data constraints, we do not observe rebates for physician-administered drugs covered by Medicare Part B, so we exclude drugs with meaningful Part B spending.

We calculate average percentage rebates weighted by: (1) drug net revenue within segment or (2) drug net revenue in the commercial market to account for differences in utilization across market segments. For nonprotected drug classes, Part D rebates exceed commercial rebates by 10.4 percentage points, on average; whereas, for protected drug classes, Part D rebates are 4.3 percentage points lower than commercial rebates, on average (weighting by own-segment net revenue). Controlling for utilization, we find similar differences in Part D and commercial rebates for nonprotected classes (+5.5 percentage points) versus protected classes (‑5.6 percentage points). Our results suggest that formulary restrictions for protected-class drugs may contribute to higher prices in Medicare Part D since insurers cannot credibly threaten exclusion.

Favorable Selection Into Medicare Advantage and Integrated Plans Among Dual Eligibles

Presenter
Daria Pelech

Session
Dual Eligibles and Medicare Advantage: Plan Enrollment, Service Use, and Transitions Into Dual Eligibility (ASHEcon)

Authors
Ru Ding (CBO), Jing Guo (CBO), Daria Pelech (CBO), and Joyce Shin (CBO)

Abstract
Individuals who are covered by both Medicare and Medicaid (“dual eligibles” or “duals”) account for almost a third of both Medicare and Medicaid spending, despite representing less than 20 percent of enrollment in either program. Duals’ outsized share of spending—along with many examples of how their dual coverage leads to inefficient care—has led to policymakers’ encouraging them to enroll in managed care plans to coordinate their benefits. Although roughly half of all duals are now enrolled in Medicare managed care plans—either in integrated Medicare-Medicaid plans or in unintegrated Medicare Advantage (MA) plans that do not cover Medicaid benefits—it is unclear how these plans affect duals’ spending. The evidence is limited, in part because it is difficult to determine whether the duals who opt into private plans are healthier or sicker than those who stay in Medicare fee-for-service (FFS).

This paper compares the health, spending, and other characteristics of new dual eligibles as they transition from Medicaid and choose Medicare FFS versus different types of Medicare managed care plans. Using linked Medicaid and Medicare claims and enrollment data from 2017 to 2020, we follow Medicaid enrollees as they age into Medicare or complete the two-year disability waiting period and become dual eligible. This population provides an opportunity to examine selection into Medicare managed care plans because we can observe enrollees’ Medicaid spending before it is affected by Medicare plan design and benefits. This population is also less studied than individuals who become dual after being first enrolled in Medicare.

We find evidence of selection among Medicaid beneficiaries transitioning to Medicare. Prior to enrolling in Medicare, duals who later enroll in integrated Medicare-Medicaid plans spent 26 percent less than those who enroll in Medicare FFS; duals who later enroll in unintegrated MA plans spent 32 percent less than those who enroll in Medicare FFS. After controlling for beneficiary and area characteristics, we find that differences in nonprescription drug spending—particularly spending on long-term care—drive plan selection. A 1 percent increase in nondrug spending reduces the probability that an individual will go into a private plan by 1 percentage point. In contrast, a 1 percent increase in drug spending increases the probability that an individual will go into a private plan by 1 percent. Individuals with higher drug spending specifically opt into plans targeted at duals, such as duals’ special needs plans (D-SNPs), whereas individuals with higher medical spending avoid unintegrated plans. Individuals who enroll in Medicare FFS also appear to be in worse health on other dimensions; new enrollees in Medicare FFS have higher rates of mortality and long-term institutionalization than those who enroll in private plans.

This work highlights a key consideration for research on duals in managed care plans. When examining whether managed care plans are successful in reducing Medicare or Medicaid spending or better coordinating benefits, researchers should account for adverse selection across plan types.

Spending in the 340B Drug Pricing Program, 2010 to 2021

Presenter
Rebecca Sachs

Session
Emerging Issues in 340B Pharmacy (ASHEcon)

Authors
Rebecca Sachs (CBO) and Joshua Varcie (CBO)

Abstract
Congress created the 340B Drug Pricing Program in 1992 to decrease the prices that certain hospitals and other providers (for example, Federally Qualified Health Centers), collectively referred to as “covered entities,” pay manufacturers when purchasing outpatient prescription drugs. Inflation-adjusted 340B net spending has grown substantially, from $6.6 billion in 2010 to $43.9 billion in 2021. Because detailed 340B net spending data are generally not accessible to the public, however, little is known about net spending patterns in 340B or the factors driving growth.

As far as we know, our paper is the first to examine these questions using nonpublic NDC-level data from the Health Resources and Services Administration. We merge those data with Red Book data available from IBM Micromedex to explain how 340B net spending has changed from 2010 to 2021. We examine spending by therapeutic class, at each covered entity type by therapeutic class, and at contract pharmacy locations by therapeutic class. Additionally, we use SSR Health data to compare spending by therapeutic class in 340B to the larger market. Nearly half of net spending at 340B hospitals is on cancer drugs. In contrast, anti-infective agents (for example, those that treat HIV or hepatitis C) constitute the greatest share of net spending at non-hospital 340B entities.

We then examine the relative contributions of different factors that may drive net spending growth in the 340B program over the 2010–2021 period. We examine three potential sources of net spending growth. First, we examine how the increase in the number of covered entities in 340B correlates with changes in spending over time, at covered entities. Second, we examine the share of 340B net spending growth that can be accounted for by the increased use of contract pharmacies. Finally, we examine the extent to which the unique mix of drugs in 340B, and disproportionate growth in spending for those drugs over time, contributes to program growth. Our findings will help the Congress and interested stakeholders better understand the current landscape and sources of growth in 340B and the implications of potential policies that would affect the program.

Chapin White is CBO’s Director of Health Analysis.

Originally published at https://www.cbo.gov/publication/60381

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