Evidence map›Paper›PMID 36637815›Full record

ArticleJAMA health forum2023

Estimates of Medicaid and Non-Medicaid Net Prices of Top-Selling Brand-name Drugs Incorporating Best Price Rebates, 2015 to 2019.

Lisa Clemans-Cope, Marni Epstein, Jessica Banthin, Aaron S Kesselheim, Thomas J Hwang

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 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
5.7field-weighted citation impact, top 4% 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

5 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Prescription drug spending by payer: Implications for managed care.Exploratory research in clinical and social pharmacy · 2024
    Article
  5. Reforming Drug Price Regulation: Using Tools That Work.Inquiry : a journal of medical care organization, provision and financing
    Article
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

5 authors at 4 institutions in 1 country.

Lisa Clemans-CopeHealth Policy Center, The Urban Institute, Washington, DC.
Marni EpsteinSalt Lake County Human Services Department, Salt Lake City, Utah.
Jessica BanthinHealth Policy Center, The Urban Institute, Washington, DC.
Aaron S KesselheimProgram on Regulation, Therapeutics, and Law (PORTAL), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
Thomas J HwangProgram on Regulation, Therapeutics, and Law (PORTAL), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
Urban Institute · USBrigham and Women's Hospital · USHarvard University · USLake County · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: The US spends far more on brand-name prescription drugs than other comparable countries. However, studies of prescription drug spending in the US are often limited because there can be substantial differences in the confidential rebates that drug manufacturers pay to Medicaid vs other payers. Objectives: To demonstrate an approach for improved estimation of Medicaid rebates through case studies of 18 top-selling drugs to better understand trends in net Medicaid and non-Medicaid spending and prices for brand-name drugs. Design, Settings, and Participants: This was a cross-sectional study of US pricing data from 2015 to 2019 derived from Medicaid State Drug Utilization data SSR Health, Medi-Span, the Federal Supply Schedule, and IQVIA. Pricing data for 18 top-selling brand-name drugs measured consistently in both SSR Health, which captures US sales reported by publicly traded companies, and IQVIA's top US prescription drugs by nondiscounted spending in 2015 to 2019. Data were accessed and analyzed from January 2019 to June 2021. Main Outcomes and Measures: Gross and net Medicaid and non-Medicaid drug spending for the sample of 18 drugs and prices corresponding to a 30-day supply of medication. Results: Medicaid aggregate gross spending for the 18 drugs in the sample increased 173%, from $3.6 billion in 2015 to $9.9 billion in 2019, and estimated net spending after discounts increased by 119%, from $1.4 billion to $3.0 billion. Medicaid inflation-linked rebates reduced average gross price per 30-day supply by an estimated 43% in 2019, and up to 67% for individual drugs. In addition to the basic rebate, the best price provision reduced the average gross price per 30-day supply by an estimated 3% in 2019 and up to 54% for individual drugs. Between 2015 and 2019 across all study drugs, estimated average non-Medicaid net 30-day prices were between 1.9 and 2.6 times higher than Medicaid net prices. Excluding adalimumab-a spending anomaly because of the entry of a new high-cost formulation-net prices weighted by average gross spending decreased annually by 1% from 2015 through 2019 for Medicaid, while increasing by 2% for non-Medicaid payers. Conclusions and Relevance: In this cross-sectional study of 18 top-selling brand-name drugs, excluding 1 anomaly, Medicaid average net prices declined from 2015 to 2019. Simultaneously, for non-Medicaid payers, net price increased more than previously published marketwide growth rates, raising the importance of restraining drug price growth in non-Medicaid markets. Rigorous and transparent methods to estimate Medicaid discounts are imperative to understand patterns in Medicaid and non-Medicaid prices and develop policies that better align drug prices with clinical benefits.

Indexed as

Drug CostsPrescription DrugsCosts and Cost AnalysisCross-Sectional StudiesMedicaidPrescription Drugs

Identifiers

PMID36637815
PMCPMC9857139
OpenAlexW4315928508

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.