Breast Cancer, Race and Place

Welcome to the Breast Cancer, Race and Place (BCRP) project! The goal of this research is to develop new measures of structural racism in housing, examine the relationship between structural racism in housing and breast cancer outcomes, and explore the ways in which racially and ethnically diverse breast cancer survivors navigate survivorship in a racially segregated metropolitan area (Milwaukee, Wisconsin).

On this page, you can learn more about this research and our team as well as download and explore new measures of contemporary mortgage lending bias developed by our study team for metropolitan areas across the country.

Project Highlights

March 2022 (article): Patterns of historical redlining in the 1930’s closely correspond to current partners of redlining and racial settlement.

Project Wonder

The Art of Science at MCW: "Redlining, Race, Bias, and Breast Cancer": Neighborhood characteristics (such as socioeconomic status, racial segregation, home ownership and walkability) influence cancer rates and levels of survivorship as well as many other health outcomes.

About the Project

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Breast Cancer Disparities

Wide gaps in breast cancer survival rates by race persist [1, 2] and may be growing, [3,4] providing troubling evidence that not all populations are benefitting equally from cancer control advances. The gaps exist despite the availability of early detection and treatment therapies known to lengthen survival among diverse population groups. Black/African Americans have the shortest survival among all racial groups for most cancers [5]. While breast cancer mortality rates are generally lower among Hispanic than non-Hispanic White individuals, current five-year cause-specific survival rates indicate poorer survival among Hispanic (87.0) as well as Black (78.9) and American Indian/Alaska Native (85.4) women, relative to non-Hispanic white women (88.6) [6]. Although the survival gap for Hispanic populations is small, national numbers mask substantial variation among subpopulations [7,8], and much poorer outcomes among Hispanic populations in some localities [9-11]. Importantly, the size of racial and ethnic disparities in breast cancer mortality varies geographically [4, 12-14]. This geographical variation suggests that disparities are not inevitable. Determining causes of geographical variation could lead to new strategies to reduce gaps, as knowledge about successful programs or policies could be translated from places where gaps are small to places where gaps are large.

Neighborhood Factors

Research has shown that neighborhoods, including the built, social, and natural environments they contain, are influential determinants of health and health inequity [15]. Numerous neighborhood factors have been found to be associated with cancer disparities, including neighborhood socioeconomic status (SES) and walkability [16]. Among these, residential racial segregation has been recognized as a fundamental neighborhood determinant [17]. Racial segregation results in fundamentally different exposures and experiences based on race, impacting health disparities. Further, evidence indicates that there are wide gaps in home ownership and home equity derived wealth by race and ethnicity in the United States.

Mortgage Lending Bias

An important driver of residential segregation, and the economic health of a neighborhood or an individual, is mortgage lending. Mortgage lending bias – the systematic denial of mortgage financing to specific neighborhoods or applicants – can thus have important implications for housing access, wealth accumulation, economic development, and segregation. By promoting neighborhood economic investment differentials, mortgage lending bias is a key upstream driver of housing access as well as other neighborhood health determinants including access to resources, SES, and built environment features such as parks and tree canopy [18]. By promoting differentials in applicant access to mortgage funding based on the race or ethnicity of the applicant, mortgage lending bias has implications for individual wealth, housing tenure, and socioeconomic disparities among racial and ethnic groups.

Project Summary

We developed three measures of mortgage lending bias using a combination of the disease mapping method adaptive spatial filtering (ASF) and logistic regression models predicting application denial. We then examined the relationship between structural racism in housing metrics and breast cancer survival using the SEER-Medicare database. Finally, we conducted over 100 interviews with breast cancer survivors in the Milwaukee metropolitan area and analyzed these data using qualitative analysis techniques to understand more about their survivorship experiences, including the impact of discrimination and neighborhood characteristics on their health.

References

Ward E, Jemal A, Cokkinides V, et al. Cancer disparities by race/ethnicity and socioeconomic status. CA Cancer J Clin. 2004;54(2):78-93.
Newman LA, Mason J, Cote D, et al. African‐American ethnicity, socioeconomic status, and breast cancer survival. Cancer. 2002;94(11):2844-2854.
Orsi JM, Margellos-Anast H, Whitman S. Black-white health disparities in the united states and chicago: A 15-year progress analysis. Am J Public Health. 2010;100(2):349-356.
Hunt BR, Whitman S, Hurlbert MS. Increasing black: White disparities in breast cancer mortality in the 50 largest cities in the united states. Cancer epidemiology. 2014;38(2):118-123.
DeSantis C, Naishadham D, Jemal A. Cancer statistics for african americans, 2013. CA: a cancer journal for clinicians. 2013;63(3):151-166.
DeSantis C, Ma J, Bryan L, Jemal A. Breast cancer statistics, 2013. CA: a cancer journal for clinicians. 2014;64(1):52-62.
Keegan TH, Quach T, Shema S, Glaser SL, Gomez SL. The influence of nativity and neighborhoods on breast cancer stage at diagnosis and survival among california hispanic women. BMC Cancer. 2010;10(1):1.
Pinheiro PS, Williams M, Miller EA, Easterday S, Moonie S, Trapido EJ. Cancer survival among latinos and the hispanic paradox. Cancer Causes & Control. 2011;22(4):553-561.
Beyer KM, Zhou Y, Matthews K, et al. Breast and colorectal cancer survival disparities in southeastern wisconsin. WMJ. 2016;115(1):17-21.
Boone S, Baumgartner K, Joste N, Pinkston C, Yang D, Baumgartner R. The joint contribution of tumor phenotype and education to breast cancer survival disparity between hispanic and non-hispanic white women. Cancer Causes & Control. 2014;25(3):273-282.
Li CI, Malone KE, Daling JR. Differences in breast cancer stage, treatment, and survival by race and ethnicity. Arch Intern Med. 2003;163(1):49-56.
DeSantis CE, Fedewa SA, Goding Sauer A, Kramer JL, Smith RA, Jemal A. Breast cancer statistics, 2015: Convergence of incidence rates between black and white women. CA: a cancer journal for clinicians. 2015.
Whitman S, Orsi J, Hurlbert M. The racial disparity in breast cancer mortality in the 25 largest cities in the united states. Cancer epidemiology. 2012;36(2):e147-e151.
Freund KM. The racial disparity in breast cancer mortality in the 25 largest cities in the united states. Cancer epidemiology. 2012;36(5):497.
Gomez, S.L., et al., The impact of neighborhood social and built environment factors across the cancer continuum: Current research, methodological considerations, and future directions. Cancer, 2015. 121(14): p. 2314-30.
Shariff-Marco, S., et al., Neighborhood archetypes and breast cancer survival in California. Annals of Epidemiology, 2021. 57: p. 22-29.
Williams, D.R. and C. Collins, Racial Residential Segregation: A Fundamental Cause of Racial Disparities in Health. Public Health Reports, 2001. 116(5): p. 404-416.
Namin, S., et al., The legacy of the Home Owners’ Loan Corporation and the political ecology of urban trees and air pollution in the United States. Social Science Medicine, 2020. 246: p. 112758.

Our Measures

Our team at the Medical College of Wisconsin created three primary mortgage lending bias metrics to help researchers and the public measure bias in access to mortgage lending across neighborhoods and individuals. These metrics can be used to identify bias, map spatial patterns of bias, and link bias with health outcomes or other socioeconomic metrics. We developed these measures using the Home Mortgage Disclosure Act (HMDA) database and a combination of the disease mapping method adaptive spatial filtering (ASF) and logistic regression models predicting application denial. The three bias metrics are:

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Contemporary Redlining

The odds ratio of denial of a mortgage application for a property in a local area, compared to properties across the metropolitan statistical area (MSA).

Racial Bias

The odds ratio of denial of a mortgage application for a Black/African American applicant, compared to a non-Hispanic white applicant, for a property in the local area.

Ethnic Bias

The odds ratio of denial of a mortgage application for a Hispanic applicant, compared to a non-Hispanic white applicant, for a property in the local area.

Technical Notes

Methods used to calculate measures are described in detail in Measures of Bias in Mortgage Lending Summary & Technical Notes (DOCX).