Evidence map›Paper›PMID 39872412›Full record

ArticleInnovation in aging2025

Medicare Resource Use Differs by English Reading Proficiency.

Lilly Estenson, Eric T Roberts, Mireille Jacobson

Abstract read
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Lilly EstensonLeonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA.ORCID https://orcid.org/0000-0002-0437-9287
Eric T RobertsDivision of General Internal Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Mireille JacobsonLeonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA.ORCID https://orcid.org/0000-0002-4977-8783

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objectives: Medicare coverage decisions are complex. The Centers for Medicare & Medicaid Services (CMS) provides a hotline, handbook, and website to help Medicare beneficiaries understand their benefits and coverage options, yet it is unclear what factors affect beneficiary use of these resources. We examined limited English speaking and English reading proficiency status as predictors of CMS Medicare informational resource use in a nationally representative sample of Medicare beneficiaries. Research Design and Methods: We used 2016-2018 Medicare Current Beneficiary Survey data and linear probability models to assess whether the likelihood of using CMS Medicare informational resources differed among beneficiaries with limited English proficiency. We adjusted models for demographic, socioeconomic, health, and local market factors and, in sensitivity analyses, health insurance characteristics. Our primary outcome was an indicator of having previously used at least 1 of the 3 CMS resources. Results: Among noninstitutionalized beneficiaries ( Discussion and Implications: These findings suggest that English reading proficiency may be an underappreciated factor in Medicare beneficiaries' use of the Medicare handbook, hotline, and website. Alternative approaches to providing and publicizing informational resources may reduce barriers to Medicare resource use among beneficiaries with limited English reading proficiency.

Indexed as

Consumer health informationHealth equityHealth insuranceLimited English proficiency

Identifiers

PMID39872412
PMCPMC11771191

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.