Evidence map›Paper›PMID 42684705›Full record

ArticleJAMA network open2026

Historical Redlining and Spatiotemporal Patterns in Breast Cancer Screening.

Md Atikur Rahman, Isuru Ratnayake, Sam Pepper, Derrick Asante, Brahian Cano Urrego, Susan Beckman, Leah Lambart, Dinesh Pal Mudaranthakam

Abstract read
In one paragraph

Article in JAMA network open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

8 authors.

Md Atikur RahmanDepartment of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City.
Isuru RatnayakeDepartment of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City.
Sam PepperDepartment of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City.
Derrick AsanteDepartment of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City.
Brahian Cano UrregoDepartment of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City.
Susan BeckmanThe University of Kansas Cancer Center, University of Kansas Medical Center, Kansas City.
Leah LambartThe University of Kansas Cancer Center, University of Kansas Medical Center, Kansas City.
Dinesh Pal MudaranthakamDepartment of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Geographic variation in breast cancer screening may reflect persistent structural inequities in access to preventive care. Understanding whether historical redlining remains associated with screening, independent of contemporary social vulnerability, neighborhood conditions, and geographic access, is critical for targeting interventions within cancer center catchment areas. Objectives: To examine the association between historical redlining and breast cancer screening prevalence, accounting for social vulnerability, neighborhood characteristics, and geographic access, and to characterize spatiotemporal screening patterns. Design, Setting, and Participants: This cohort study used Census tract-level data from 2016 to 2024 across the University of Kansas Cancer Center catchment area, including communities in Kansas and adjacent Missouri counties. The analytic sample included Census tracts with available Homeowners' Loan Corporation (HOLC) grades A (indicating the least redlining) to D (indicating the most redlining). Statistical analysis was performed from July to December 2025. Exposures: HOLC grades; Social Vulnerability Index quintiles (with the first quintile indicating the lowest vulnerability and the fifth quintile indicating the highest); Census tract-level socioeconomic, housing, and transportation indicators; and distance to the nearest mammography facility. Main Outcomes and Measures: Census tract-level breast cancer screening prevalence from the Centers for Disease Control and Prevention's PLACES database, reported as odds ratios (ORs) with 95% credible intervals (CrIs). Results: A total of 1152 historically redlined Census tracts were analyzed. Tracts were categorized by HOLC grades A (n = 27), B (n = 99), C (n = 423), and D (n = 603). The median (IQR) screening prevalence was highest in grade A tracts (79.6% [79.0%-80.0%]) and lowest in grade D tracts (75.2% [71.7%-79.2%]). Models demonstrated substantial spatial and temporal dependence with geographic clustering and localized variability. Compared with grade A tracts, grade C (OR, 0.94; 95% CrI, 0.90-0.99) and grade D tracts (OR, 0.94; 95% CrI, 0.89-0.99) had lower screening prevalence after adjustment. The highest Social Vulnerability Index quintile was associated with increased screening odds (OR, 1.08; 95% CrI, 1.01-1.14). Lower educational attainment (OR, 0.94; 95% CrI, 0.92-0.96) and higher mobile home prevalence (OR, 0.98; 95% CrI, 0.97-1.00) were associated with lower screening odds. Residual spatial heterogeneity persisted (711 of 1152 tracts [61.7%] excluding the null). Screening peaked in 2018 to 2019, stabilized through 2023, and declined in 2024, with most areas remaining below the Healthy People 2030 target of 80.3%. Conclusions and Relevance: This study found that historical redlining was associated with lower breast cancer screening prevalence independent of contemporary social vulnerability, neighborhood conditions, and geographic access. These findings support sustained, place-based strategies addressing structural and socioeconomic barriers to improve screening uptake and progress toward national screening targets.

Indexed as

Breast NeoplasmsEarly Detection of CancerHealth Services AccessibilityMass ScreeningNeighborhood CharacteristicsAgedCohort StudiesFemaleHumansKansasMammographyMiddle AgedMissouriResidence CharacteristicsResidential SegregationSocial Vulnerability

Identifiers

PMID42684705
PMCPMC13539404

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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.