Historical redlining and clustering of present-day breast cancer factors

被引:0
作者
Lima, Sarah M. [1 ]
Palermo, Tia M. [1 ,2 ]
Aldstadt, Jared [3 ]
Tian, Lili [4 ]
Meier, Helen C. S. [5 ]
Louis Jr, Henry Taylor [6 ,7 ]
Ochs-Balcom, Heather M. [1 ]
机构
[1] SUNY Buffalo, Sch Publ Hlth & Hlth Profess, Dept Epidemiol & Environm Hlth, 265 Farber Hall, Buffalo, NY 14214 USA
[2] Policy Res Solut LLC PRESTO, Buffalo, NY USA
[3] SUNY Buffalo, Dept Geog, Buffalo, NY USA
[4] SUNY Buffalo, Sch Publ Hlth & Hlth Profess, Dept Biostat, Buffalo, NY USA
[5] Univ Michigan, Inst Social Res, Survey Res Ctr, Ann Arbor, MI USA
[6] SUNY Buffalo, Sch Architecture & Planning, Dept Urban & Reg Planning, Buffalo, NY USA
[7] SUNY Buffalo, Community Hlth Equ Res Inst, Buffalo, NY USA
关键词
Breast cancer; Redlining; Neighborhoods; Disparities; RACIAL DISPARITIES; HEALTH; NEIGHBORHOODS; SEGREGATION;
D O I
10.1007/s10552-024-01950-9
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Purpose Historical redlining, a 1930s-era form of residential segregation and proxy of structural racism, has been associated with breast cancer risk, stage, and survival, but research is lacking on how known present-day breast cancer risk factors are related to historical redlining. We aimed to describe the clustering of present-day neighborhood-level breast cancer risk factors with historical redlining and evaluate geographic patterning across the US. Methods This ecologic study included US neighborhoods (census tracts) with Home Owners' Loan Corporation (HOLC) grades, defined as having a score in the Historic Redlining Score dataset; 2019 Population Level Analysis and Community EStimates (PLACES) data; and 2014-2016 Environmental Justice Index (EJI) data. Neighborhoods were defined as redlined if score >= 2.5. Prevalence quintiles of established adverse and protective breast cancer factors relating to behavior, environment, and socioeconomic status (SES) were used to classify neighborhoods as high-risk or not. Factor analysis grouped factors into domains. Overall and domain-specific scores were calculated for each neighborhood according to historical redlining status. Percent difference in score by historical redlining was used to assess differences in average scores, with Wilcoxon-Mann-Whitney test used to estimate significance. Kappa statistic was used to estimate concordance between historical redlining status and high-risk status. Heatmaps of scores were created to compare spatial clustering of high-risk factors to historical redlining. Results We identified two domains: (1) behavior + SES; (2) healthcare. Across the US, redlined neighborhoods had significantly more breast cancer factors than non-redlined (redlined neighborhoods = 5.41 average high-risk factors vs. non-redlined = 3.55 average high-risk factors; p < 0.0001). Domain-specific results were similar (percent difference for redlined vs. non-redlined: 39.1% higher for behavior + SES scale; 23.1% higher for healthcare scale). High-scoring neighborhoods tended to spatially overlap with D-grades, with heterogeneity by scale and region. Conclusion Breast cancer risk factors clustered together more in historically redlined neighborhoods compared to non-redlined neighborhoods. Our findings suggest there are regional differences for which breast cancer factors cluster by historical redlining, therefore interventions aimed at redlining-based cancer disparities need to be tailored to the community.
引用
收藏
页码:483 / 495
页数:13
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