Mortgage Lending Bias and Breast Cancer Survival Among Older Women in the United States

Abstract

PURPOSE The objective was to examine the relationship between contemporary redlining (mortgage lending bias on the basis of property location) and survival among older women with breast cancer in the United States. METHODS A redlining index using Home Mortgage Disclosure Act data (2007-2013) was linked by census tract with a SEER-Medicare cohort of 27,516 women age 66-90 years with an initial diagnosis of stage I-IV breast cancer in 2007-2009 and follow-up through 2015. Cox proportional hazards models were used to examine the relationship between redlining and both all-cause and breast cancer–specific mortality, accounting for covariates. RESULTS Overall, 34% of non-Hispanic White, 57% of Hispanic, and 79% of non-Hispanic Black individuals lived in redlined tracts. As the redlining index increased, women experienced poorer survival. This effect was strongest for women with no comorbid conditions, who comprised 54% of the sample. For redlining index values of 1 (low), 2 (moderate), and 3 (high), as compared with 0.5 (least), hazard ratios (HRs) (and 95% CIs) for all-cause mortality were HR = 1.10 (1.06 to 1.14), HR = 1.27 (1.17 to 1.38), and HR = 1.39 (1.25 to 1.55), respectively, among women with no comorbidities. A similar pattern was found for breast cancer–specific mortality. CONCLUSION Contemporary redlining is associated with poorer breast cancer survival. The impact of this bias is emphasized by the pronounced effect even among women with health insurance (Medicare) and no comorbid conditions. The magnitude of this neighborhood level effect demands an increased focus on upstream determinants of health to support comprehensive patient care. The housing sector actively reveals structural racism and economic disinvestment and is an actionable policy target to mitigate adverse upstream health determinants for the benefit of patients with cancer.

Impact of Limited E-Health Literacy on the Overall Survival of Patients With Cancer

“…We can draw a parallel with a recent publication testifying that living in a disadvantaged neighborhood also has a negative impact on the survival of patients with cancer. 28 Of course, research on this frailty factor needs to be deepened and evaluated in other patient cohorts and in other countries by integrating other confounding variables. To improve our knowledge of this population without an e-mail address, a first step would be to carry out a prospective evaluation of the link between the absence of an e-mail address and a precise measurement of their level of digital health literacy using scales of validated evaluation.…”

From describing disparities to understanding why disparities exist: Anti‐racist methods to support dental public health research

“…Examples : Model racism across different levels to measure impact on population‐level oral health outcomes. Data are needed within surveillance systems to help researchers to understand racism's impact on the causal pathway of disease [18]…”

“…Three phases of literature search process interstates through Black communities, and subjecting Black communities to excess law enforcement surveillance have been demonstrated to underlie health disparities in breast cancer survival[18], maternal health outcomes, acute childhood illnesses COVID-19 infection, and other outcomes[2,16,17,19,20]. Oral health researchers can similarly avail themselves of novel methodologies, in particular the use of geospatial data, to investigate how structural decisions produce racialized oral health inequities.Table1describes additional research questions related to oral health service delivery, patient-reported and population-level outcomes, and workforce studies thatT A B L E 1 Examples of research designs and methods to examine different oral health research topics, by level of racism, with examples from the broader health literature…”

Impacts of Neighborhood Characteristics and Surgical Treatment Disparities on Overall Mortality in Stage I Renal Cell Carcinoma Patients

“…Biological factors may contribute to disparities in kidney cancer mortality [ 14 , 15 , 16 ]; however, socioeconomic characteristics are also considered to be major contributing factors for RCC surgical treatment disparities [ 7 , 17 ]. Structural inequality reflected in neighborhood-level socioeconomic factors and residential segregation has been linked to cancer mortality rates [ 18 , 19 , 20 , 21 , 22 ]. However, the way socioeconomic factors affect RCC surgical treatment and mortality has not been fully investigated.…”