Evidence map›Paper›PMID 41416872›Full record

ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2026

Social Drivers of Guideline-Discordant Breast Cancer Screening by Age and Mortality Risk.

Michelle L Lui, Erica J Lee Argov, Rebecca D Kehm, Anita G Karr, Nathalie Moise, Rachel C Shelton, Parisa Tehranifar

Abstract read
In one paragraph

Article in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2026. 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

7 authors.

Michelle L LuiDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York.ORCID 0000-0001-9707-1996
Erica J Lee ArgovDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York.ORCID 0000-0002-7045-2673
Rebecca D KehmDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York.ORCID 0000-0002-6089-6799
Anita G KarrDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York.ORCID 0000-0001-5777-438X
Nathalie MoiseDepartment of Medicine, Columbia University Irving Medical Center, New York, New York.ORCID 0000-0002-5660-5573
Rachel C SheltonDepartment of Sociomedical Sciences, Mailman School of Public Health, Columbia University, New York, New York.ORCID 0000-0001-6496-6339
Parisa TehranifarDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York.ORCID 0000-0002-0605-3934

Funding

Supplemental Training in Making Data FAIR and AI/ML ReadyT32ES007322 · NIEHS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Pam R Factor-Litvak, Allison Kupsco · 2000 to 2026
$12.1M
De-implementation of Mammography Overuse in Primary Care SettingsR01CA255382 · NCI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI MOISE, NATHALIE, SHELTON, RACHEL C · 2021 to 2025
$2.6M
National Cancer Institute (NCI) R01CA255382National Institute of Environmental Health Sciences (DEHS) T32ES007322NCI NIH HHS R01 CA255382NIEHS NIH HHS T32 ES007322
6 · The paper itself

Abstract

backgroundUnderstanding social drivers of mammography screening is critical to implementing breast cancer screening guidelines that maximize benefits and minimize harms across diverse populations. We examined racial/ethnic, socioeconomic, and geographic patterns in guideline-discordant underscreening and overscreening according to guidelines based on age and mortality risk.

methodsWe used 2022 Behavioral Risk Factor Surveillance System data and major screening guidelines to define screening participation. Underscreening captured mammography in the past 2 years among women ages 50 to 74 years. Overscreening included any mammography beyond age 74 years or among women ages 50+ years with high mortality risk. We used modified Poisson regression to examine screening by race/ethnicity, regular healthcare provider access, and metropolitan, educational, and marital status.

resultsAmong 88,326 women ages 50 to 74 years, compared with non-Hispanic (NH) White, NH American Indian/Alaskan Native [adjusted prevalence ratio (aPR) = 0.88, 95% confidence interval (CI), 0.79-0.97] and NH women of unknown race (aPR = 0.86, 95% CI, 0.78-0.94) were less likely to be screened, whereas NH Black (aPR = 1.11, 95% CI, 1.09-1.13) women were more likely. Among 31,477 women ages 75+ years, NH Black women were more likely to be screened than NH White women (aPR = 1.07, 95% CI, 1.01-1.14). Among women with high mortality risk, NH Black (aPR = 1.20, 95% CI, 1.12-1.28) women were more likely to be screened than NH White women. Screening was lower among women with more limited socioeconomic resources regardless of age or mortality risk.

conclusionsFindings reveal social drivers of underscreening and overscreening and the need for equitable breast cancer screening delivery. IMPACT: This work calls for strengthening implementation and de-implementation efforts to optimize breast cancer screening.

Indexed as

Breast NeoplasmsEarly Detection of CancerMammographyAgedAge FactorsBehavioral Risk Factor Surveillance SystemFemaleHumansMass ScreeningMiddle AgedPractice Guidelines as TopicUnited States

Identifiers

PMID41416872
PMCPMC12908925

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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.