Evidence map›Paper›PMID 37967477›Full record

ArticleGeriatric nursing (New York, N.Y.)

Rural racial disparities and barriers in mammography utilization among Medicare beneficiaries in Texas: A longitudinal study.

Zhaoli Liu, Yong Shan, Yong-Fang Kuo, Sharon H Giordano

Abstract read
In one paragraph

Article in Geriatric nursing (New York, N.Y.). 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.

  1. Article
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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

4 authors.

Zhaoli LiuCollege of Nursing and Health Innovation, The University of Texas at Arlington, Texas, United States. Electronic address: zhaoli.liu@uta.edu.
Yong ShanDepartment of Biostatistics & Data Science, School of Public and Population Health, University of Texas Medical Branch, Galveston, Texas, United States.
Yong-Fang KuoDepartment of Biostatistics & Data Science, School of Public and Population Health, University of Texas Medical Branch, Galveston, Texas, United States.
Sharon H GiordanoDepartments of Health Services Research and Breast Medical Oncology, MD Anderson Cancer Center, The University of Texas, Houston, Texas, United States.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Fabien Delerue · 1985 to 2026
$290.8M
NCI NIH HHS P30 CA016672
6 · The paper itself

Abstract

This study examined rural racial/ethnic disparities in long-term mammography screening practices among Medicare beneficiaries. A retrospective longitudinal study was conducted using 100% Texas Medicare data for women aged 65-74 who enrolled in Medicare between 2010-2013. Of the 114,939 eligible women, 21.2% of Hispanics, 33.3% of non-Hispanic Blacks (NHB), and 38.4% non-Hispanic Whites (NHW) in rural areas were regular users of mammography, compared to 33.5%, 44.9%, and 45.3% of their counterparts in urban areas, respectively. Stratification analyses showed rural Hispanics and NHB were 33% (95% CI, 25% - 40%) and 22% (95% CI, 6% - 36%) less likely to be regular users of mammography compared to their urban counterparts. Major barriers to routine mammography screening included the lack of a primary care provider, frequent hospitalization, and comorbidity. The findings of this study highlight the importance of addressing rural racial disparities in mammography utilization among older women to ensure equitable screening practices for all populations.

Indexed as

MammographyMedicareAgedFemaleHealthcare DisparitiesHumansLongitudinal StudiesRetrospective StudiesTexasUnited StatesBreast cancer screeningMammographyRacial disparityRural disparity

Identifiers

PMID37967477
PMCPMC10976317

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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.