Evidence map›Paper›PMID 36705916›Full record

ArticleJAMA health forum2023

Simulated Medicare Drug Price Negotiation Under the Inflation Reduction Act of 2022.

Benjamin N Rome, Sarosh Nagar, Alexander C Egilman, Junyi Wang, William B Feldman, Aaron S Kesselheim

Open access · goldAbstract read
In one paragraph

Article in JAMA health forum, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
24.5field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 43 citations in OpenAlex.

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  18. Estimated discounts generated by Medicare drug negotiation in 2026.Journal of managed care & specialty pharmacy · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Benjamin N RomeProgram on Regulation, Therapeutics, and Law (PORTAL), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
Sarosh NagarProgram on Regulation, Therapeutics, and Law (PORTAL), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
Alexander C EgilmanProgram on Regulation, Therapeutics, and Law (PORTAL), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
Junyi WangProgram on Regulation, Therapeutics, and Law (PORTAL), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
William B FeldmanProgram on Regulation, Therapeutics, and Law (PORTAL), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
Aaron S KesselheimProgram on Regulation, Therapeutics, and Law (PORTAL), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
Brigham and Women's Hospital · USHarvard University · USIo Therapeutics (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: The Inflation Reduction Act of 2022 gives Medicare the authority to negotiate prices for certain prescription drugs. Which drugs will be selected and how prices will be negotiated remain unclear. Objective: To simulate drug selection and the minimum savings that would have been achieved at statutory ceiling prices if Medicare drug price negotiation had been implemented from 2018 to 2020. Design, Setting, and Participants: In this cross-sectional study, a policy simulation analysis of high-spending prescription drugs in Medicare Part B and Part D that were eligible for negotiation from January 2018 to December 2020 was performed from August 5 to November 20, 2022. Exposures: Eligibility criteria for selection and discounts afforded by the statutory ceiling prices for negotiation. Main Outcomes and Measures: The main outcomes were characteristics of drugs subject to negotiation and estimated Medicare savings from 2018 to 2020 that would have been achieved through spending at ceiling prices compared with existing net prices accounting for price concessions. Results: Among the 40 selected drugs, 35 were primarily reimbursed through Medicare Part D and 5 through Part B and 10 were biologics. The most common therapeutic classes were endocrine (11), neurologic or psychiatric (5), pulmonary (4), rheumatologic or immunologic (4), and cardiovascular (4). Median time from US Food and Drug Administration approval to selection was 12 years (IQR, 10-14 years). Three drugs faced generic competition in the 2 years between selection and price negotiation. For the remaining 37 drugs, estimated net Medicare spending from 2018 to 2020 was $55.3 billion; spending at ceiling prices would have been reduced by an estimated $26.5 billion, which represented 5% of estimated net Medicare drug spending during those 3 years. Conclusions and Relevance: In this cross-sectional study, simulating the drug price negotiation provisions in the Inflation Reduction Act of 2022 revealed important limitations, including strict selection criteria and the potential for drugs to become ineligible for negotiation during the 2 years between selection and prices taking effect. Despite these limitations, the policy still delivered substantial savings because ceiling prices offered steep discounts, in part, by erasing excess spending from price increases faster than inflation.

Indexed as

Medicare Part BPrescription DrugsCross-Sectional StudiesDrug CostsNegotiatingUnited StatesPrescription Drugs

Identifiers

PMID36705916
PMCPMC11901806
OpenAlexW4318240943

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.