Evidence map›Paper›PMID 40717766›Full record

ArticleHealth affairs scholar2025

What drug characteristics explain the wide range of manufacturer rebates?

Molly T Beinfeld, Fariel LaMountain, Glenn Phillips, Tom Hughes, Peter J Neumann, James D Chambers

Abstract read
In one paragraph

Article in Health affairs scholar, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Molly T BeinfeldCenter for the Evaluation of Value and Risk in Health, Tufts Medical Center, Boston, MA 02111, USA.
Fariel LaMountainCenter for the Evaluation of Value and Risk in Health, Tufts Medical Center, Boston, MA 02111, USA.ORCID https://orcid.org/0009-0002-1934-7058
Glenn PhillipsHEOR, Argenx, Inc, Gent 9052, Belgium.ORCID https://orcid.org/0000-0002-9676-1909
Tom HughesHEOR, Argenx, Inc, Gent 9052, Belgium.ORCID https://orcid.org/0009-0002-9261-7819
Peter J NeumannCenter for the Evaluation of Value and Risk in Health, Tufts Medical Center, Boston, MA 02111, USA.ORCID https://orcid.org/0000-0003-1226-4073
James D ChambersCenter for the Evaluation of Value and Risk in Health, Tufts Medical Center, Boston, MA 02111, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Prescription drug list prices, often cited in policy discussions, do not account for rebates negotiated between manufacturers and payers. Methods: This study examines factors influencing rebates in the United States, focusing on specialty drugs-ie, high priced therapies (often biologics) for which payers issue specific coverage policies. Rebate data came from SSR Health and drug attributes from the Tufts Medical Center Specialty Drug Evidence and Coverage Database. We stratified rebate data by drug type and characteristics. Results: We identified 161 drugs found in SSR Health and SPEC as of December 2023. We found that rebates vary substantially across drugs (median of 27%, IQR 16%-53%). Biosimilar and originator drugs have the highest, most variable rebates (median of 71%, IQR 53%-79%), while rebates for cancer treatments and orphan drugs are lower (medians of 19% and 23%, respectively) and vary less (IQR of 12%-28% and 14%-29%, respectively). Drugs that face more competition from alternative options within the same therapeutic class, are self-administered, or received Food and Drug Administration approval further in the past have higher rebates. Conclusion: Our findings indicate that rebates are sizeable and vary along several dimensions, many relating to market competition, complicating policy discussions around drug pricing.

Indexed as

health economicsprescription drug pricesrebatesspecialty drugs

Identifiers

PMID40717766
PMCPMC12290394

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