A Local Area Analysis of Racial, Ethnic, and Neighborhood Disparities in Breast Cancer Staging

Abstract

Few studies have examined the role of neighborhood socioeconomic condition in shaping breast cancer disparities in defined local areas. We tested associations between three measures of neighborhood socioeconomic condition (poverty, median income, and a composite neighborhood score) on breast cancer staging in two urban counties of the state of New Jersey. Data for these counties were obtained from the New Jersey Surveillance, Epidemiology, and End Results tumor registry and were selected because of their large racial/ethnic and socioeconomic diversity and pilot prevention efforts taking place in these areas. Our study population included Black, Latina, and White women (N = 4,589) diagnosed with breast cancer from 1999 to 2004. Each cancer case was geocoded and linked to socioeconomic data obtained from the 2000 U.S. census. Census tracts served as proxies for neighborhoods. Logistic regression models accounting for clustering of individuals within neighborhoods were fitted with Generalized Estimating Equations. Women living in neighborhoods with lower versus higher neighborhood scores were significantly more likely to have advanced-stage disease (odds ratio, 1.6; confidence intervals, 1.1-2.3), after adjusting for age at diagnosis and race/ethnicity. In analyses stratified by race/ethnicity, results remained significant for all neighborhood measures for White and Black women, but not for Latinas. Moreover, neighborhood poverty showed a weaker socioeconomic gradient in breast cancer staging among White women. Our study findings support the use of a multidimensional neighborhood index to better capture differences in cancer staging risk across racial/ethnic groups and provides evidence that population-based cancer data could be used to identify local needs specific to local populations. (Cancer Epidemiol Biomarkers Prev 2009;18(11):3024-9)

Geographic remoteness, area-level socio-economic disadvantage and advanced breast cancer: a cross-sectional, multilevel study

“…This area-level socio-economic effect has also been reported in international studies,11 13 28 39 although specific reasons for this effect have not been proposed. It is unlikely to be due simply to lower rates of breast screening, since Australian women living in areas of high SES are least likely to participate in population-based screening programmes 40.…”

Neighborhood-level Influences on Delays in Diagnostic Follow-up from Mammography Screening in African-American Women: A Systematic Review

“…Odds ratios (OR) and weighted means were calculated at the 95% Confidence Interval (CI) with multivariate analysis calculating relative risk. Twelve of the 17 studies (67%) examined relationships between residence in disadvantaged neighborhoods and Socio-Economic Status (SES) (age ≥ 45 years, lower education and median household income, and lack of insurance) and delays in diagnostic follow-up from abnormal MS or late-diagnosis of breast cancer [17,[20][21][22][23][24][25][26][27][28]. Three studies examined racial segregation on delays in diagnostic follow-up [18,21,29]; while two studies examined relationships between raciallysegregated neighborhoods and lack of primary care providers and radiologists on later-stage diagnosis of breast cancer.…”

“…Several studies found that female-headed households or single status with less education and/or living in neighborhoods described as crowded, disadvantaged, economically-distressed or medicallyunderserved area with higher levels of unemployment and workingclass Black residents influenced late-diagnosis of breast cancer across race/ethnicities [20][21][22][23][24]29].…”

Variations in Use of PET among Medicare Beneficiaries with Non–Small Cell Lung Cancer, 1998–2007

“…The direct relationship between PET use and disease stage is complicated, because both PET use and disease stage can causally affect each other. Disparities in race, sex, and age have been observed with regard to access to care, treatment, and survival (29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47), particularly with regard to receiving new or higher-technology services (30). Black and white patients are often treated at different hospitals (35), and hospitals that treat large proportions of black patients are less likely to use emerging medical technologies (29).…”