Lima Sarah M, Palermo Tia M, Aldstadt Jared, Tian Lili, Meier Helen C S, Louis Henry Taylor, Ochs-Balcom Heather M
Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, State University of New York at Buffalo, 265 Farber Hall, Buffalo, NY, 14214, USA.
Policy Research Solutions LLC (PRESTO), Buffalo, NY, USA.
Cancer Causes Control. 2025 May;36(5):483-495. doi: 10.1007/s10552-024-01950-9. Epub 2025 Jan 4.
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.
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.
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.
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.
历史上的红线划定是20世纪30年代的一种居住隔离形式，也是结构性种族主义的代表，它与乳腺癌风险、分期及生存率相关，但目前尚缺乏关于已知的当今乳腺癌风险因素与历史红线划定之间关系的研究。我们旨在描述当今邻里层面的乳腺癌风险因素与历史红线划定的聚集情况，并评估美国各地的地理模式。
这项生态学研究纳入了具有房主贷款公司（HOLC）等级的美国邻里（普查区），这些等级在历史红线划定分数数据集中有分数定义；2019年人口水平分析和社区估计（PLACES）数据；以及2014 - 2016年环境正义指数（EJI）数据。如果分数≥2.5，则邻里被定义为红线划定区域。与行为、环境和社会经济地位（SES）相关的既定不良和保护性乳腺癌因素的患病率五分位数被用于将邻里分类为高风险或非高风险。因子分析将因素分组为不同领域。根据历史红线划定状态为每个邻里计算总体和特定领域的分数。历史红线划定导致的分数差异百分比用于评估平均分数差异，采用Wilcoxon - Mann - Whitney检验来估计显著性。Kappa统计量用于估计历史红线划定状态与高风险状态之间的一致性。创建分数热图以比较高风险因素与历史红线划定的空间聚集情况。
我们确定了两个领域：（1）行为 + SES；（2）医疗保健。在美国，红线划定的邻里比非红线划定的邻里有显著更多的乳腺癌因素（红线划定的邻里平均有5.41个高风险因素，而非红线划定的邻里平均有3.55个高风险因素；p < 0.0001）。特定领域的结果相似（红线划定与非红线划定的差异百分比：行为 + SES量表高39.1%；医疗保健量表高23.1%）。高分邻里在空间上往往与D级区域重叠，且存在规模和区域上的异质性。
与非红线划定的邻里相比，乳腺癌风险因素在历史上被红线划定的邻里中聚集得更多。我们的研究结果表明，乳腺癌因素按历史红线划定聚集存在区域差异，因此针对基于红线划定的癌症差异的干预措施需要根据社区情况进行调整。
