Neighborhood disadvantage and individual-level life stressors in relation to breast cancer incidence in US Black women

Abstract

Background Research on psychosocial stress and risk of breast cancer has produced conflicting results. Few studies have assessed this relation by breast cancer subtype or specifically among Black women, who experience unique chronic stressors. Methods We used prospective data from the Black Women’s Health Study, an ongoing cohort study of 59,000 US Black women, to assess neighborhood- and individual-level psychosocial factors in relation to risk of breast cancer. We used factor analysis to derive two neighborhood score variables after linking participant addresses to US Census data (2000 and 2010) on education, employment, income and poverty, female-headed households, and Black race for all households in each residential block group. We used Cox proportional hazards regression to estimate hazard ratios (HR) and 95% confidence intervals (CI) adjusted for established breast cancer risk factors. Results During follow-up from 1995 to 2017, there were 2167 incident invasive breast cancer cases (1259 estrogen receptor positive (ER +); 687 ER negative (ER−)). For ER− breast cancer, HRs were 1.26 (95% CI 1.00–1.58) for women living in the highest quartile of neighborhood disadvantage relative to women in the lowest quartile, and 1.24 (95% CI 0.98–1.57) for lowest versus highest quartile of neighborhood socioeconomic status (SES). For ER+ breast cancer, living in the lowest quartile of neighborhood SES was associated with a reduced risk of ER+ breast cancer (HR = 0.83, 95% CI 0.70–0.98). With respect to individual-level factors, childhood sexual abuse (sexual assault ≥ 4 times vs. no abuse: HR = 1.35, 95% CI 1.01–1.79) and marital status (married/living together vs. single: HR = 1.29, 95% CI 1.08–1.53) were associated with higher risk of ER+, but not ER− breast cancer. Conclusion Neighborhood disadvantage and lower neighborhood SES were associated with an approximately 25% increased risk of ER− breast cancer in this large cohort of Black women, even after control for multiple behaviors and lifestyle factors. Further research is need to understand the underlying reasons for these associations. Possible contributing factors are biologic responses to the chronic stress/distress experienced by individuals who reside in neighborhoods characterized by high levels of noise, crime and unemployment or the direct effects of environmental toxins.

Racial disparities in triple negative breast cancer: toward a causal architecture approach

“…As reviewed elsewhere, several potential patient-level risk factors for TNBC have been identified, with varying levels of supporting evidence, including reproductive (age at menarche and menopause, parity, breastfeeding), metabolic (obesity, type 2 diabetes, alcohol use), and genetic ( BRCA1 , BRCA2 ) factors [ 11 , 13 , 14 ]. More recent studies have found that area-level measures of socioeconomic status (SES) are inversely associated with TNBC risk, even after adjusting for patient characteristics, providing support for neighborhood effects [ 15 – 17 ]. Further, neighborhood effects have been found to aid in improving the targeting of prevention and early detection interventions by identifying areas with a high cancer burden that could be attributable to potentially modifiable risk factors.…”

Does place connectivity moderate the association between concentrated disadvantage and COVID-19 fatality in the United States?

“…Several studies have shown that areas with concentrations of socioeconomically disadvantaged people are strongly associated with more health problems, including disproportionately high transmission and deaths of COVID-19 (Levy et al, 2022; Pierce, & Jacob, 2021; Samuels-Kalow et al, 2021). Disadvantaged populations live in house conditions that do not allow for effective voluntary isolation and quarantine (Duque, 2020; Khanijahani, & Tomassoni, 2022), and suffer from poorer health status (Barber et al, 2021). Another significant factor could be the higher population mobility of disadvantaged populations.…”

“…Concentrated disadvantaged areas aggregate groups such as low-income earners, welfare recipients, and single households (Sampson, Raudenbush, & Earls, 1997; Sampson, Sharkey, & Raudenbush, 2008), and some may also include minority groups (Lee, Maume, & Ousey, 2003). These groups face higher levels of health risks (Barber et al, 2021) and crime risks (Lee, Maume, & Ousey, 2003; Wang, & Arnold, 2008). Many studies have revealed the significant relationships between concentrated disadvantages and differential forms of health inequalities, such as the increased risk of breast cancer (Barber et al, 2021; DeGuzman, 2017), increased incidence of lung cancer (Adie et al, 2020), diabetes and cholesterol control (Durfey et al, 2019), mental health (Kim, 2010).…”

“…These groups face higher levels of health risks (Barber et al, 2021) and crime risks (Lee, Maume, & Ousey, 2003; Wang, & Arnold, 2008). Many studies have revealed the significant relationships between concentrated disadvantages and differential forms of health inequalities, such as the increased risk of breast cancer (Barber et al, 2021; DeGuzman, 2017), increased incidence of lung cancer (Adie et al, 2020), diabetes and cholesterol control (Durfey et al, 2019), mental health (Kim, 2010).…”

Racial Disparities in Triple Negative Breast Cancer: Toward a Causal Architecture Approach

“…11,13,14 More recent studies have found that area-level measures of socioeconomic status (SES) are inversely associated with TNBC risk, even after adjusting for patient characteristics, providing support for neighborhood effects. [15][16][17] Further, neighborhood effects have been found to aid in improving the targeting of prevention and early detection interventions by identifying areas with a high cancer burden that could be attributable to potentially modi able risk factors. 18 Viewed through a causal architecture framework, neighborhood effects can be conceptualized as a system of exposure that contributes to variations in TNBC risk across residentially segregated populations.…”