Evidence map›Paper›PMID 37284771›Full record

ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2023

Impact of BMI on Prevalence of Dense Breasts by Race and Ethnicity.

Karla Kerlikowske, Michael C S Bissell, Brian L Sprague, Jeffrey A Tice, Katherine Y Tossas, Erin J A Bowles, Thao-Quyen H Ho, Theresa H M Keegan, Diana L Miglioretti

Open access · greenAbstract 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, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
8.0field-weighted citation impact, top 2% 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

23 citing papers in PubMed, 31 citations in OpenAlex.

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  17. Breast MRI to Screen Women With Extremely Dense Breasts.Journal of magnetic resonance imaging : JMRI · 2025
    Review
  18. Article
  19. Mammographic Density in Relation to Breast Cancer Risk Factors among Chinese Women.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025
    Article
  20. Surface Compression Alters in Vivo VisR Stiffness, Viscosity, and Anisotropy Measurements in Human Breast.IEEE open journal of ultrasonics, ferroelectrics, and frequency control · 2025
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 7 institutions in 2 countries.

Karla Kerlikowske *Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, California.ORCID 0000-0001-8793-8779
Michael C S Bissell *Division of Biostatistics, Department of Public Health Sciences, University of California Davis School of Medicine, Davis, California.ORCID 0000-0002-2414-4573
Brian L SpragueDepartments of Surgery and Radiology, Office of Health Promotion Research, Larner College of Medicine at the University of Vermont and University of Vermont Cancer Center, Burlington, Vermont.ORCID 0000-0001-7948-8317
Jeffrey A TiceDivision of General Internal Medicine, Department of Medicine, University of California, San Francisco, California.ORCID 0000-0002-9857-2028
Katherine Y TossasDepartment of Health Behavior and Policy, School of Medicine, and Massey Cancer Center, Virginia Commonwealth University, Richmond, Virginia.ORCID 0000-0001-6872-2294
Erin J A BowlesKaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington.ORCID 0000-0001-6287-7391
Thao-Quyen H HoDepartment of Training and Scientific Research, University Medical Center, Ho Chi Minh City, Vietnam.ORCID 0000-0001-6314-937X
Theresa H M KeeganCenter for Oncology Hematology Outcomes Research and Training (COHORT) and Division of Hematology and Oncology, University of California Davis School of Medicine, Sacramento, California.ORCID 0000-0002-1961-4008
Diana L MigliorettiDivision of Biostatistics, Department of Public Health Sciences, University of California Davis School of Medicine, Davis, California.ORCID 0000-0002-5547-1833
Kaiser Permanente Washington Health Research Institute · USUniversity of California, Davis · USSan Francisco VA Medical Center · USUniversity Medical Center HCMC · VNUniversity of California, San Francisco · USUniversity of Vermont · USVirginia Commonwealth University · US

Funding

Risk-based Imaging Strategies to Improve Breast Cancer Surveillance OutcomesP01CA154292 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Diana L Miglioretti · 2011 to 2026
$52.9M
Vermont Center on Behavior and HealthP20GM103644 · NIGMS · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI KHADANGA, SHERRIE · 2013 to 2022
$21.3M
Vermont Screening Process Documentation UnitU54CA163303 · NCI · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI SPRAGUE, BRIAN L · 2011 to 2017
$6.4M
Identifying effective risk-based supplemental ultrasound screening strategies for women with dense breastsR01CA248068 · NCI · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI Diana L Miglioretti, Brian L Sprague · 2020 to 2026
$5.1M
Comparative effectiveness of breast imaging modalities: a natural experimentR01HS018366 · AHRQ · UNIVERSITY OF ILLINOIS AT CHICAGO · PI RAUSCHER, GARTH H · 2010 to 2014
$1.8M
Group Health Cancer Research Program Research Specialist AwardR50CA211115 · NCI · KAISER FOUNDATION RESEARCH INSTITUTE · PI BOWLES, ERIN JANELLE AIELLO · 2017 to 2021
$919k
AHRQ HHS R01 HS018366NCI NIH HHS P01 CA154292NCI NIH HHS R01 CA248068NCI NIH HHS R50 CA211115NCI NIH HHS U54 CA163303NIGMS NIH HHS P20 GM103644
6 · The paper itself

Abstract

backgroundDensity notification laws require notifying women of dense breasts with dense breast prevalence varying by race/ethnicity. We evaluated whether differences in body mass index (BMI) account for differences in dense breasts prevalence by race/ethnicity.

methodsPrevalence of dense breasts (heterogeneously or extremely dense) according to Breast Imaging Reporting and Data System and obesity (BMI > 30 kg/m2) were estimated from 2,667,207 mammography examinations among 866,033 women in the Breast Cancer Surveillance Consortium (BCSC) from January 2005 through April 2021. Prevalence ratios (PR) for dense breasts relative to overall prevalence by race/ethnicity were estimated by standardizing race/ethnicity prevalence in the BCSC to the 2020 U.S. population, and adjusting for age, menopausal status, and BMI using logistic regression.

resultsDense breasts were most prevalent among Asian women (66.0%) followed by non-Hispanic/Latina (NH) White (45.5%), Hispanic/Latina (45.3%), and NH Black (37.0%) women. Obesity was most prevalent in Black women (58.4%) followed by Hispanic/Latina (39.3%), NH White (30.6%), and Asian (8.5%) women. The adjusted prevalence of dense breasts was 19% higher [PR = 1.19; 95% confidence interval (CI), 1.19-1.20] in Asian women, 8% higher (PR = 1.08; 95% CI, 1.07-1.08) in Black women, the same in Hispanic/Latina women (PR = 1.00; 95% CI, 0.99-1.01), and 4% lower (PR = 0.96; 95% CI, 0.96-0.97) in NH White women relative to the overall prevalence.

conclusionsClinically important differences in breast density prevalence are present across racial/ethnic groups after accounting for age, menopausal status, and BMI. IMPACT: If breast density is the sole criterion used to notify women of dense breasts and discuss supplemental screening it may result in implementing inequitable screening strategies across racial/ethnic groups. See related In the Spotlight, p. 1479.

Indexed as

Breast NeoplasmsMammographyBody Mass IndexBreast DensityEarly Detection of CancerEthnicityFemaleHumansObesityPrevalence

Identifiers

PMID37284771
PMCPMC10701641
OpenAlexW4379600283

What OpenQuestion holds

Textmetadata
LicenceTDM
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.