Evidence map›Paper›PMID 39916970›Full record

ArticleHealth affairs scholar2025

Understanding health care price variation: evidence from Transparency-in-Coverage data.

Christopher Whaley, Nandita Radhakrishnan, Michael Richards, Kosali Simon, Benjamin Chartock

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. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

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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

5 authors.

Christopher WhaleySchool of Public Health, Brown University, Providence, RI 02903, United States.ORCID https://orcid.org/0000-0002-9405-1989
Nandita RadhakrishnanSchool of Public Health, Brown University, Providence, RI 02903, United States.ORCID https://orcid.org/0009-0001-1183-5017
Michael RichardsJeb E. Brooks School of Public Policy, Cornell University, Ithaca, NY 14853, United States.
Kosali SimonO'Neill School of Public Affairs, Indiana University, Bloomington IN 47405, United States.ORCID https://orcid.org/0000-0002-3231-1466
Benjamin ChartockDepartment of Economics, Bentley University, Waltham, MA 02452, United States.ORCID https://orcid.org/0000-0001-7037-7426

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Competition in health care markets should lead to lower prices and less dispersion, with consumer choice as the driving mechanism. Several studies document price variation, suggesting room for improvement; however, they relied on selected data from insurers who provide access to data, limiting generalizability. We document the nature of price variation in the private US market across geography, payer, and provider by leveraging a new dataset, implementing a descriptive analysis using the most comprehensive data available: Transparency-in-Coverage. We measured health care prices in 3 ways: percentile distribution prices for common services, state-level and insurer-level facility fee price indices, and regression-adjusted mean inpatient and outpatient prices. Variation is large: the mean facility fee for a foot X-ray, for example, is $86 at Anthem and $190 at UnitedHealth. Pricing does not appear to be uniform; there is just 22% correlation between an insurer's inpatient price and outpatient facility price. And there is little difference in ordering of high-price states depending on alternative measures, such as relative to Medicare. Results suggest greater consideration of policies to address high and variable prices for US health care.

Indexed as

commercial insurancecosts and spendinghealth care marketsprice transparency

Identifiers

PMID39916970
PMCPMC11798183

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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.