92006082299Cancer Epidemiol Biomarkers PrevCancer Epidemiol. Biomarkers Prev.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology1055-99651538-775524618999401823910.1158/1055-9965.EPI-13-0924NIHMS571918ArticleImpact of neighborhood and individual socioeconomic status on survival after breast cancer varies by race/ethnicity: The Neighborhood and Breast Cancer StudyShariff-MarcoSalma12YangJuan1JohnEsther M.12SangaramoorthyMeera1HertzAndrew1KooJocelyn1NelsonDavid O.12SchuppClayton W.1ShemaSarah J.1CockburnMyles3SatarianoWilliam A.4YenIrene H.5PonceNinez A.6WinklebyMarilyn2KeeganTheresa H. M.12GomezScarlett L.12Cancer Prevention Institute of California, Fremont CAStanford University, School of Medicine, Stanford CAUniversity of Southern California, School of Medicine, Los Angeles CAUniversity of California, Berkeley School of Public Health, Berkeley CAUniversity of California, San Francisco School of Medicine, San Francisco CAUniversity of California, Los Angeles School of Public Health and Center for Health Policy Research, Los Angeles CACorresponding author: Salma Shariff-Marco, PhD, MPH, Research Scientist, Cancer Prevention Institute of California, 2201 Walnut Avenue, Suite 300, Fremont, CA 94538, Tel (510) 608-5276, Fax 510/608-5085, salma.shariff-marco@cpic.org19420141132014520140152015235793811Background

Research is limited on the independent and joint effects of individual- and neighborhood-level socioeconomic status (SES) on breast cancer survival across different racial/ethnic groups.

Methods

We studied individual-level SES, measured by self-reported education, and a composite neighborhood SES (nSES) measure in females (1,068 non-Hispanic whites, 1,670 Hispanics, 993 African-Americans, and 674 Asian-Americans), aged 18–79 years and diagnosed 1995–2008, in the San Francisco Bay Area. We evaluated all-cause and breast cancer-specific survival using stage-stratified Cox proportional hazards models with cluster adjustment for census block groups.

Results

In models adjusting for education and nSES, lower nSES was associated with worse all-cause survival among African-Americans (p-trend=0.03), Hispanics (p-trend=0.01) and Asian-Americans (p-trend=0.01). Education was not associated with all-cause survival. For breast cancer-specific survival, lower nSES was associated with poorer survival only among Asian-Americans (p-trend=0.01). When nSES and education were jointly considered, women with low education and low nSES had 1.4 to 2.7-times worse all-cause survival than women with high education and high nSES across all races/ethnicities. Among African-Americans and Asian-Americans, women with high education and low nSES had 1.6 to 1.9-times worse survival, respectively. For breast cancer-specific survival, joint associations were found only among Asian-Americans with worse survival for those with low nSES regardless of education.

Conclusions

Both neighborhood and individual SES are associated with survival after breast cancer diagnosis, but these relationships vary by race/ethnicity.

Impact

A better understanding of the relative contributions and interactions of SES with other factors will inform targeted interventions towards reducing long-standing disparities in breast cancer survival.

breast cancer survivalneighborhood socioeconomic statuseducationrace/ethnicity
Introduction

Breast cancer is the second leading cancer cause of death in the United States (1). Despite significant improvements in breast cancer survival over the past few decades, racial/ethnic and socioeconomic disparities persist, with African-American, American Indian/Alaska Native, and low income women having worse survival after diagnosis (15). An individual’s socioeconomic status (SES) may influence survival through material and social resources, including access to and quality of health care, and lifestyle risk factors (3, 5). Neighborhood SES (nSES) may influence survival through features of the physical (e.g., goods, services, pollutants) and social (e.g., cohesion, collective efficacy, support, stress, coping) environment (68). Understanding individual-level and neighborhood-level SES associations with survival can identify potential explanatory pathways for informing strategies to reduce these disparities.

Lower nSES (913) and lower individual-level SES (e.g., education, income, wealth) (14,15) have each been associated with worse survival after breast cancer diagnosis. Studies that have examined both individual-level and neighborhood-level SES have found either only nSES (16, 17), only individual-level SES (8), both measures (18), or the interactions between the two measures (19) to be associated with survival. These mixed findings may partly be due to the variation across studies in racial/ethnic composition of the samples, geographic regions, geographic levels used to assess nSES, and measures of SES, or residual confounding and selection bias. Further, prior studies have had limited racial/ethnic diversity, often including non-Hispanic whites and/or African-Americans only (8, 17, 18), and used larger and more heterogeneous geographic units as proxies for residential neighborhoods. The emerging literature on the differential effects of SES on health across population subgroups also may contribute to the mixed results (2022).

We examined race/ethnicity-specific independent and joint associations of individual education and nSES with survival after breast cancer diagnosis using data from the Neighborhood and Breast Cancer (NABC) study which pooled neighborhood and cancer registry data with interview data from two multiethnic population-based studies in the San Francisco Bay Area.

Materials and MethodsStudy Population

NABC includes female breast cancer patients from two epidemiologic studies, the San Francisco Bay Area Breast Cancer Study (SFBCS) and the Northern California Breast Cancer Family Registry (NC-BCFR). Both studies included patients identified through the Greater Bay Area Cancer Registry (GBACR), which participates in the NCI Surveillance, Epidemiology, and End Results (SEER) Program and the California Cancer Registry (CCR). Interview data from the two studies were harmonized and merged with CCR data and neighborhood data from the California Neighborhoods Data System (23). The protocols for the two parent studies and the NABC study were approved by the Cancer Prevention Institute of California Institutional Review Board.

The SFBCS is a population-based case-control study among women aged 35–79 years and residing in Alameda, Contra Costa, San Mateo, San Francisco, or Santa Clara counties at the time of diagnosis (24, 25). Eligible cases newly diagnosed with a first primary invasive breast cancer included all Hispanics diagnosed between 4/1/1995 and 4/30/2002, and all African-Americans and a random 10% sample of non-Hispanic whites diagnosed between 4/1/1995 and 4/30/1999.

The NC-BCFR is a family study that is part of the NCI-funded Breast Cancer Family Registry (26, 27), and included newly-diagnosed invasive breast cancer cases aged 18–64 years who lived in Alameda, Contra Costa, Marin, San Mateo, San Francisco, Santa Clara, Santa Cruz, and Monterey counties at the time of diagnosis. The study included cases of any race/ethnicity diagnosed from 1/1/1995 to 9/30/1998; Hispanic, African-American, Chinese, Filipina and Japanese diagnosed from 10/1/1998 to 4/30/2002; and Hispanic and African-American diagnosed from 5/1/2002 to 12/31/2009. Cases were eligible for the NC-BCFR if they had indicators of increased genetic susceptibility (i.e., diagnosis before age 35 years, personal history of ovarian or childhood cancer, bilateral breast cancer with a first diagnosis before age 50 years, or a first-degree family history of breast, ovarian or childhood cancer). Cases not meeting these criteria (sporadics) were randomly sampled (2.5% of non-Hispanic whites and 33% of other racial/ethnic groups because NC-BCFR focused on minority breast cancer families).

Both studies screened cases by telephone to assess study eligibility, with 84% participation among those contacted. In the SFBCS, 2,571 cases were eligible, and 2,258 (88%) completed the in-person interview, with similar response rates in Hispanics (88%), African-Americans (87%) and non-Hispanic whites (86%). In the NC-BCFR, 4,708 eligible cases were selected for the family study that involved an in-person interview, assistance with recruiting family members, and annual follow-up. Of these, 3,631 (77%) enrolled in the study and completed the interview, with similar response rates in African-Americans (82%), non-Hispanic whites (80%), Hispanics (78%), and Asian-Americans (73%).

For 339 participants that were in both studies, we used data from the SFBCS interview. Our analytic sample included participants with a first primary invasive breast cancer, who completed the questionnaire themselves, had a geocodeable address and follow-up information from the CCR. We excluded participants of American Indian/Alaska Native or mixed race/ethnicity (n=11), or unknown education (n=36). The 4,369 breast cancer cases were interviewed on average 21.0 months (SD=11.1) after diagnosis.

Data Collection and Follow-up

In-person interviews were conducted in English, Spanish, Mandarin or Cantonese using similar structured questionnaires on breast cancer risk factors (2426). CCR data included age and year at diagnosis, American Joint Committee on Cancer (AJCC) stage, histology, grade, tumor size, nodal status, estrogen receptor (ER) and progesterone receptor (PR) status, first course of treatment (surgery, radiation, chemotherapy), subsequent tumors, and marital status. The CCR obtains vital status and underlying cause of death through hospital follow-up and linkages to vital statistics, death records, and other databases. CCR data were used to create hospital-level indicators of percent of cancer patients in highest nSES quintile and percent of cancer patients by race/ethnicity.

Participants’ addresses at time of diagnosis were geocoded to a latitude/longitude coordinate and then assigned a census block group (average of 1,500 residents with range of 600 to 3,000) for 98% of our sample, and address at interview were used for the remaining. Addresses were standardized to conform to U.S. Postal Service specifications using ZP4 software (ZP4. Monterey, CA: Semaphore Corp., 2011). Batch geocoding was performed using ArcGIS 10.0 (ArcGIS. Version10. Redlands, CA: Environmental Systems Research Institute, Inc., 2011). Extensive efforts were made to review addresses that did not batch geocode, resulting in assigning 97% of residences to a latitude and longitude.

Analytic Variables

Individual-level SES was measured using self-reported education categorized into four levels: less than high school, high school degree or equivalent, vocational/technical degree or some college, college degree or graduate school. NSES was based on a composite SES measure created by principal component analysis and comprising Census 2000 indicator variables at the block group-level: education index (among individuals age ≥ 25 years: proportion with college, high school, or less than high school weighted by 16, 12 or 9 respectively) (28), proportion with a blue collar job, proportion older than age 16 years without a job, median household income, proportion below 200% of the poverty line, median rent, median house value (29). This nSES index was categorized into statewide quintiles. Due to small numbers, the two lowest SES quintiles were combined for non-Hispanic whites and Asian-Americans. We also created a combination variable using binary indicators for education and nSES. Low education was defined as having a high school degree or less, and high education as having at least a vocational/technical degree or some college; low nSES included quintiles 1–3 and high nSES, quintiles 4–5.

Breast cancer deaths were identified from the underlying cause of death listed on the death certificate (ICD-9 (30) or ICD-10 (31) codes 174–175 and C50, respectively). Survival time was calculated in days from the date of diagnosis to date of death from breast cancer or from any cause, date of last known contact, or December 31, 2009 (the end of the study period), whichever occurred first. Of the 3,463 patients alive at the end of the study period, 97% had a follow-up date in the last year of the study. On average, patients were followed for 7.4 years (SD=3.8) after diagnosis.

Analysis

To assess associations of education and nSES variables with survival, we employed multivariate stage-stratified Cox proportional hazards regression models, with cluster adjustment for block groups, to compute relative rates (hazard ratios, HR) of dying from any cause or from breast cancer. Follow-up time was left-censored. The sandwich estimator of the covariance structure, applied to Cox proportional hazards regression models by Lin & Wei and utilized here in the SAS PHREG procedure, accounts for the intracluster dependence and yields robust standard error estimates even under model misspecification (32). Over 70% of block groups in this study had only one participant across the racial/ethnic strata. The assumption of proportional hazards was checked by including interactions with time and assessing their significance using likelihood ratio tests and confirmed, except for AJCC stage, for which the proportionality did not hold. All Cox models were then stratified on stage allowing the baseline hazards within each model to vary by stage. We checked for and did not detect any effect modification by study type (SFBCS, high-risk NC-BCFR, sporadic NC-BCFR); all models were adjusted for study type. Analyses were conducted using SAS (version 9.3, Cary, NC). We also tested for spatial autocorrelation with Moran’s I and found no evidence of it.

Base models were adjusted for age at diagnosis, year of diagnosis, study, tumor characteristics, treatment, and subsequent tumors. We considered modeling year of diagnosis using categorical intervals and found a consistent gradient of lower mortality over time which suggests that year of diagnosis has a linear effect on both all-cause and breast cancer-specific mortality in our study, therefore we have modeled year of diagnosis linearly. Linear trends for education and nSES in these models were assessed using the p-values associated with the significance of these ordinal variables (33). We are cautious in our interpretation of p-values for linear trends and only report on significant p-trends in the absence of significant main associations when we see a consistent trend with increasing or decreasing HRs across the levels of our ordinal variables. We consider these suggestive of a dose-response relationship between the SES measure and survival.

To assess their relative impact on the associations between SES and survival, additional sets of prognostic factors that may be important mediators were added to the base model if they were independently associated with survival: 1) personal and reproductive risk factors, including history of benign breast disease, years since last full-term pregnancy, use of hormonal contraception, use of menopausal hormone therapy (HT); 2) marital status; 3) behavioral factors, including alcohol consumption in the year before diagnosis, pre-diagnostic body mass index (BMI, calculated as self-reported weight (in kilograms) in the year before diagnosis divided by height (in meters) squared based on measured height for SFBCS participants or self-reported height for NC-BCFR participants), recent recreational physical activity (hours per week during the three years prior to diagnosis) (34); and 4) hospital characteristics.

Results

Non-Hispanic white women were more likely than other groups to be diagnosed with stage I disease, or ER/PR positive tumors, be nulliparous, or have a history of benign breast disease, or HT use (Table 1). They were also more likely to be seen in hospitals with proportionally more white or higher SES patients. African-American women were more likely to have had a lumpectomy, ER & PR negative tumors, be overweight or obese, and less likely to have been married. Hispanic women were more likely to be overweight or obese and report no recent recreational physical activity, and less likely to be nulliparous. Asian-American women were more likely to have had a mastectomy, be younger at diagnosis, be nulliparous, and without a history of hormonal contraceptive use or alcohol consumption.

The distributions of both SES measures varied substantially by race/ethnicity. The percent with less than a high school degree ranged from 4% among non-Hispanic whites to 37% among Hispanics. Proportions of cases living in the highest SES neighborhoods ranged from 63% among non-Hispanic whites to 15% among African-Americans.

Education was correlated with nSES and varied by race/ethnicity (correlation coefficients ranged from 0.25–0.39). Lower proportions of Hispanic and African-American women with higher education lived in higher SES neighborhoods than non-Hispanic white and Asian-American women (Table 2). Conversely, higher proportions of non-Hispanic white and Asian-American women with lower education lived in higher SES neighborhoods than Hispanic and African-American women.

All-Cause Survival

Lower education was associated with worse survival after breast cancer diagnosis among Asian-Americans and African-Americans (marginally significant) in base models, but these associations were attenuated and became non-significant after adjusting for nSES (Table 3). NSES was associated with worse survival among Hispanics and Asian-Americans and a statistically significant trend was found for African-Americans (p=0.01) and a marginally significant trend for non-Hispanic Whites (p=0.05); for all groups, HRs were increasing from high to low quintiles of nSES. The trends for nSES remained significant after adjusting for education among African-Americans, Hispanics, and Asian-Americans.

Education and nSES was jointly associated with survival and this association varied by race/ethnicity. Compared to women of high education and high nSES, survival was worse for those with low education and low nSES, both among non-Hispanic whites [HR=1.62, 95% CI: 1.02–2.56] and Hispanics [HR=1.39 (1.01–1.91)]. Among African-Americans, survival was worse for those living in low SES neighborhoods regardless of education [high education/low nSES HR=1.61(1.16–2.25); low education/low nSES HR=1.67 (1.20–2.32)]. Among Asian-Americans, survival was worse for all other groups [high education/low nSES HR=1.89 (1.02–3.50); low education/high nSES HR=1.84 (1.02–3.37); low education/low nSES HR=2.67 (1.37–5.23)].

Among African-Americans, nSES associations were attenuated after including hospital factors in the model (Table 4). Among Hispanics, further adjusting for behavioral risk factors and hospital characteristics attenuated the association between nSES and survival. Among Asian-Americans, the nSES and survival association was not attenuated after adjusting for personal and hospital factors.

Among non-Hispanic whites, the joint association of education and nSES with all-cause survival was attenuated after adjustment for behavioral factors (low education/low nSES HR=1.44 (0.90–2.28)) and hospital characteristics (low education/low nSES HR=1.60 (0.99–2.60)). Among African-Americans, the joint association remained mostly unchanged after adjusting for additional prognostic factors, demonstrating slight attenuation with adjustment for personal and reproductive factors. Among Hispanic women, adjusting for reproductive, behavioral, and hospital factors attenuated the worse survival for low education/low nSES. Among Asian-Americans, further adjusting for prognostic factors attenuated the association for women with high education and low nSES and slightly attenuated the association for women with low education and high nSES.

Breast Cancer-Specific Survival

We observed significant associations between education and nSES for breast cancer-specific survival only in Asian-Americans (Table 5). Those with a high school degree or less had worse survival compared to those with at least a college degree. However, this association was no longer statistically significant after adjusting for nSES. Asian-American women living in lower SES neighborhoods had worse breast cancer-specific survival compared to those living in the highest SES neighborhoods. Adjusting for education and other prognostic factors did not attenuate this association (Table 6). Among African-Americans, further adjusting for reproductive factors and for marital status resulted in marginally significant (p=0.05) and significant (p=0.04) trends for nSES, respectively, for breast cancer-specific survival, though no significant HRs were observed.

For the joint association of education and nSES, worse survival was observed for Asian-American women with low nSES regardless of education compared to women with high education/high nSES. Adjusting for reproductive factors, marital status, and hospital factors slightly weakened the association for Asian-American women with low education/low nSES, whereas adjusting for behavioral factors completely attenuated the association. Adjusting for marital status and behavioral factors slightly attenuated the association while adjusting for hospital characteristics completely attenuated the association for Asian-American women with high education/low nSES.

Discussion

Combining data from two multiethnic breast cancer studies, we found an independent association between nSES and overall survival that varied by race/ethnicity, and persisted after adjustment for education, and personal and institutional characteristics. Lower nSES was associated with worse all-cause survival among Hispanics and Asian-Americans, with a 3-fold worse survival comparing the lowest to highest nSES groups among Asian-Americans. The nSES associations were not significant among African-Americans though a significant trend was observed, suggesting that the associations are likely modest and undetectable with our sample size. No significant associations were observed among non-Hispanic whites. We also found that lower nSES was associated with a nearly 4-fold higher breast cancer-specific mortality among Asian-Americans, likely due to the high proportion of breast cancer deaths in this group (82%); HRs for the lower nSES quintiles among African-Americans and Hispanics were in the expected direction showing higher, non-significant relative hazards of death. Low education was a significant prognostic factor only in the context of low nSES. Thus, our findings underscore the importance of studying SES and survival associations by race/ethnicity and the need to consider interactions between individual-level and neighborhood-level SES.

Prior studies have shown that lower education (8, 14, 15) and lower nSES (913) are associated with worse survival after breast cancer diagnosis. Our findings of null associations for education after adjusting for nSES across all racial/ethnic groups is most likely because education alone may not be a sufficient indicator of individual-level SES among women (15, 19, 20). Education is a single dimension of SES; additional measures such as occupation, household income, wealth and assets, and human capital may more accurately characterize a woman’s individual-level SES (35). For example, education may not accurately capture individual-level SES among married women (see Table 1 where marital status varied by race/ethnicity) or groups for whom attained education may not reflect SES, such as immigrant women. Future studies on SES should ensure that individual-level SES measures are multidimensional to better evaluate the associations between SES and health.

In prior studies, findings have been less consistent when both education and nSES were evaluated independently within the same study. In a population-based cohort of primarily non-Hispanic white breast cancer patients from Wisconsin, only nSES was associated with overall and breast cancer-specific mortality after adjustment for individual-level education and income, and established prognostic factors (17). In a subgroup of the American Cancer Society Cancer Prevention Study II cohort, associations of both individual-level and block-level SES with all-cause mortality were attenuated after adjusting for additional prognostic factors (8). Some of these discrepant results may reflect racial/ethnic and geographical differences in the study populations as well as use of different SES measures. In our study, while the nSES gradient for all-cause mortality was evident for most racial/ethnic groups (suggestive for African-Americans), except non-Hispanic whites, the magnitude of the associations varied by race/ethnicity, with Asian-Americans having the largest relative hazard of death as nSES decreased. Sample size and statistical power are likely explanations for significant nSES associations observed for all-cause, but not breast cancer-specific, mortality, except among Asian-Americans. We had adequate power to detect associations between nSES and all-cause mortality because of the larger number of events within each race/ethnicity and magnitude of the effects. For breast cancer-specific mortality, Asian-Americans had a high number of events and large effect magnitude (HRs above 2) allowing us to detect the significance. The other races/ethnicities did not have strong effects (HRs all close to 1), so even though there were more events, we had inadequate power to detect them as significant. For both all cause and breast cancer-specific mortality the HR estimates have trends in similar directions, but the CIs are wide for breast cancer-specific mortality.

To our knowledge, no prior study has examined the joint effect of individual-level and neighborhood-level SES on survival after breast cancer diagnosis. In the general population, all-cause mortality has been found to be highest among those who had low individual-level SES but resided in high SES neighborhoods (3639). It is hypothesized that discordant individual-level and neighborhood-level SES measures may result in worse health through relative deprivation (i.e., those with low education having fewer resources to navigate their high SES neighborhoods which may include higher living costs) or relative standing (i.e., those with low education may have fewer social resources and higher stress compared to their counterparts in high SES neighborhoods and therefore different levels of stress and coping mechanisms) (36). While we observed worse survival for Asian-Americans with low education in high SES neighborhoods, Asian-American women of low education in low SES neighborhoods had the worst survival, 2.7-fold relative to Asian-Americans of high education in high SES neighborhoods. Similarly, women of other race/ethnicities with low education in low SES neighborhoods had lower survival relative to women with high education in high SES neighborhoods. Additionally, among African-Americans, living in a low SES neighborhood was associated with worse survival, regardless of education. The varying interactions between education and nSES across racial/ethnic groups may be due to variations in how well education alone captures individual-level SES as discussed above and whether this association is moderated by race/ethnicity. Further, the range of influence that a specified geographic area has could vary by individual-level SES, in that people who are higher SES may experience a diluted influence by their immediate neighborhoods, as they have access to more resources or people beyond a census block group. However, lower SES people may be more constrained and experience stronger or more concentrated influence of their more immediate surroundings. Nevertheless, our findings support the hypothesis that SES measures do not afford the same protection from death across racial/ethnic groups and the need to consider joint effects between individual-level and neighborhood-level SES (2022).

The variation in the attenuation of our findings from further adjusting for potential mediating factors across racial/ethnic groups also suggest that nSES may be operating through multiple pathways that vary across racial/ethnic groups. Prior studies have shown that associations between education and breast cancer-specific mortality were attenuated after adjustment for behavioral factors and HT use (17) or after adjustment for marital status (8). Similarly for nSES measures, prior studies found that associations with all-cause and all-cancer mortality were attenuated after adjustment for marital status, behavioral factors and HT use (8, 16). Given our focus on examining the independent and joint associations of education and nSES on survival across racial/ethnic groups, we have been able to identify factors, including marital status, behavioral factors, HT use, and hospital characteristics, that are potentially important mediators and should be further studied using mediation analyses, as they may offer opportunities for intervention in reducing socioeconomic inequalities in breast cancer survival.

There are several limitations to our study. First, we defined neighborhoods using administrative boundaries of census block groups. However, we used the smallest level of geography for which rich SES data are available and that has been shown to be a useful approach for defining neighborhoods for health studies as census block groups are more homogenous and better represents neighborhoods where individuals practice healthy behaviors, access services and receive health care (10). Second, we were not able to measure individual-level SES in multiple dimensions. We were also unable to include two important prognostic factors that were not measured by the study surveys, including smoking status and comorbid health conditions. Including these in our models would have most likely further attenuated the SES effects, though we already observed attenuation from the other behavioral factors in our study for the joint SES variable among Hispanic, Asian-American and non-Hispanic whites. Third, for heterogeneous racial/ethnic groups such as Asian-Americans and Hispanics, subgroup differences may confound or modify associations; unfortunately, our sample did not have sufficient statistical power to examine ethnic subgroups. Fourth, we used 2000 Census data for our nSES measure. We carried out sensitivity analyses using 1990 Census data for the 9.5% of our cases diagnosed in 1995 and saw no differences in the results. We are also missing data on length of residency and whether they moved between date of diagnosis and death or censoring date which may result in some misclassification of nSES. Fifth, CCR data on treatment are limited to first course of treatment and may lack meaningful detail, yet, the data are relatively complete and missing rates do not vary greatly by race/ethnicity (40, 41). Lastly, our racial/ethnic-specific analyses were limited by sample size. Although the patterns of associations with nSES appeared to differ by race/ethnicity, tests for interactions between race/ethnicity and the two SES variables were not statistically significant. Future studies with larger samples sizes are needed to sufficiently test such interactions and ensure that such models with adjustment for a variety of factors are not sensitive to issues of model extrapolation due to sparse data.

We have identified several important next steps to further our understanding of socioeconomic disparities in survival after breast cancer diagnosis. While we had a relatively large and diverse sample of breast cancer patients, the associations with individual-level and neighborhood-level SES should be further studied in other populations and geographic locations to extend the generalizaibilty of our findings. Future studies will need to comprehensively measure individual-level SES (e.g., education, wealth, assets) as well as multilevel measures of SES in additional groups (e.g., American Indian/Alaska Native, multiracial patients). It is also important to better understand how living in low SES neighborhoods are more directly contributing to survival and/or interacting with individual-level SES to influence survival. Most importantly, future studies need to work on identifying features of these neighborhoods and the pathways through which they produce better or worse survival. A better understanding of the relative contributions and interactions of SES with other factors will inform targeted interventions towards reducing long-standing disparities in breast cancer survival.

Financial support: This work was supported by National Cancer Institute funds from R21CA133255 (T.H.M.K) and R01CA140058 (S.L.G). The Breast Cancer Family Registry (BCFR) was supported by grant UM1 CA164920 from the National Cancer Institute. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the BCFR, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government or the BCFR. The San Francisco Bay Area Breast Cancer Study was supported by National Cancer Institute grants R01 CA63446 and R01 CA77305; by the U.S. Department of Defense (DOD) grant DAMD17-96-1-6071; and by the California Breast Cancer Research Program (CBCRP) grants 4JB-1106 and 7PB-0068.

We would like to thank J. Kristine Winters and Rita Leung for their help in preparing the neighborhood and cancer registry datasets for this study. The collection of cancer incidence data used in this study was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885; the National Cancer Institute’s Surveillance, Epidemiology and End Results Program under contract HHSN261201000140C awarded to the Cancer Prevention Institute of California, contract HHSN261201000035C awarded to the University of Southern California, and contract HHSN261201000034C awarded to the Public Health Institute; and the Centers for Disease Control and Prevention’s National Program of Cancer Registries, under agreement U58DP003862-01 awarded to the California Department of Public Health. The ideas and opinions expressed herein are those of the author(s) and endorsement by the State of California, Department of Public Health the National Cancer Institute, and the Centers for Disease Control and Prevention or their Contractors and Subcontractors is not intended nor should be inferred.

Conflicts of interest: None of the authors have any potential conflicts of interest to disclose.

HowladerNNooneAMKrapchoMNeymanNAminouRWaldronWSEER Cancer Statistics Review, 1975–2009 (Vintage 2009 Populations)National Cancer InstituteBethesda, MDhttp://seer.cancer.gov/csr/1975_2009_pops09/, based on November 2011 SEER data submission, posted to the SEER web site42012HarperSLynchJMeersmanSCBreenNDavisWWReichmanMCTrends in area-socioeconomic and race-ethnic disparities in breast cancer incidence, stage at diagnosis, screening, mortality, and survival among women ages 50 years and over (1987–2005)Cancer Epidemiol Biomarkers Prev1200918112113119124489KlassenACSmithKCThe enduring and evolving relationship between social class and breast cancer burden: a review of the literatureCancer Epidemiol6201135321723421470929McKenzieFJeffreysMDo lifestyle or social factors explain ethnic/racial inequalities in breast cancer survival?Epidemiol Rev200931526619675112Vona-DavisLRoseDPThe influence of socioeconomic disparities on breast cancer tumor biology and prognosis: a reviewJ Womens Health (Larchmt)6200918688389319514831RobertSAStrombomITrentham-DietzAHamptonJMMcElroyJANewcombPASocioeconomic risk factors for breast cancer: distinguishing individual- and community-level effectsEpidemiology7200415444245015232405MeijerMRohlJBloomfieldKGrittnerUDo neighborhoods affect individual mortality? A systematic review and meta-analysis of multilevel studiesSoc Sci Med420127481204121222365939SteenlandKHenleyJCalleEThunMIndividual- and area-level socioeconomic status variables as predictors of mortality in a cohort of 179,383 personsAm J Epidemiol612004159111047105615155289BassettMTKriegerNSocial class and black-white differences in breast cancer survivalAm J Public Health1219867612140014033777285KriegerNChenJTWatermanPDSoobaderMSubramanianSVCarsonRGeocoding and monitoring of US socioeconomic inequalities in mortality and cancer incidence: does the choice of area-based measure and geographic level matter? The Public Health Disparities Geocoding ProjectAmerican Journal of Epidemiology2002156547148212196317ByersTEWolfHJBauerKRBolick-AldrichSChenVWFinchJLThe impact of socioeconomic status on survival after cancer in the United States : findings from the National Program of Cancer Registries Patterns of Care StudyCancer812008113358259118613122YuXQSocioeconomic disparities in breast cancer survival: relation to stage at diagnosis, treatment and raceBMC Cancer2009936419828016ClarkeCAMillerTChangETYinDCockburnMGomezSLRacial and social class gradients in life expectancy in contemporary CaliforniaSoc Sci Med520107091373138020171001FerriCPAcostaDGuerraMHuangYLlibre-RodriguezJJSalasASocioeconomic factors and all cause and cause-specific mortality among older people in Latin America, India, and China: a population-based cohort studyPLoS Med2201292e100117922389633AlbanoJDWardEJemalAAndersonRCokkinidesVEMurrayTCancer mortality in the United States by education level and raceJ Natl Cancer Inst919200799181384139417848670MajorJMDoubeniCAFreedmanNDParkYLianMHollenbeckARNeighborhood socioeconomic deprivation and mortality: NIH-AARP diet and health studyPLoS One2010511e1553821124858SpragueBLTrentham-DietzAGangnonRERamchandaniRHamptonJMRobertSASocioeconomic status and survival after an invasive breast cancer diagnosisCancer41201111771542155121425155DasguptaPBaadePDAitkenJFTurrellGMultilevel determinants of breast cancer survival: association with geographic remoteness and area-level socioeconomic disadvantageBreast Cancer Res Treat42012132270171022160639BentleyRKavanaghAMSubramanianSVTurrellGArea disadvantage, individual socio-economic position, and premature cancer mortality in Australia 1998 to 2000: a multilevel analysisCancer Causes Control3200819218319318027094BravemanPACubbinCEgerterSChideyaSMarchiKSMetzlerMSocioeconomic status in health research: one size does not fit allJAMA12142005294222879288816352796WilliamsDRSternthalMUnderstanding racial-ethnic disparities in health: sociological contributionsJ Health Soc Behav201051 SupplS152720943580WilliamsDRKontosEZViswanathKHaasJSLathanCSMacConaillLEIntegrating multiple social statuses in health disparities research: the case of lung cancerHealth Serv Res62012473 Pt 21255127722568674GomezSLGlaserSLMcClureLAShemaSJKealeyMKeeganTHThe California Neighborhoods Data System: a new resource for examining the impact of neighborhood characteristics on cancer incidence and outcomes in populationsCancer Causes Control4201122463164721318584JohnEMHorn-RossPLKooJLifetime physical activity and breast cancer risk in a multiethnic population: the San Francisco Bay area breast cancer studyCancer Epidemiol Biomarkers Prev1120031211 Pt 11143115214652273JohnEMPhippsAIDavisAKooJMigration history, acculturation, and breast cancer risk in Hispanic womenCancer Epidemiol Biomarkers Prev12200514122905291316365008JohnEMHopperJLBeckJCKnightJANewhausenSLSenieRTThe Breast Cancer Family Registry: an infrastructure for cooperative multinational, interdisciplinary and translational studies of the genetic epidemiology of breast cancerBreast Cancer Res200464R37538915217505JohnEMMironAGongGPhippsAIFelbergALiFPPrevalence of pathogenic BRCA1 mutation carriers in 5 US racial/ethnic groupsJAMA12262007298242869287618159056LiuLCozenWBernsteinLRossRKDeapenDChanging relationship between socioeconomic status and prostate cancer incidenceJ Natl Cancer Inst52200193970570911333293YostKPerkinsCCohenRMorrisCWrightWSocioeconomic status and breast cancer incidence in California for different race/ethnic groupsCancer Causes Control10200112870371111562110International Classification of Diseases, Ninth RevisionGeneva, SwitzerlandWorld Health Organization1980International Classification of Diseases, 10th RevisionGeneva, SwitzerlandWorld Health Organization1992LinDYWeiLJThe robust inference for the Cox Proportional Hazards ModelJournal of the American Statistical Association19898440810741078LiuHCochran-Armitage Trend Test using SASPaper SP05Merck Research Labs, Merck & Co., IncRahway, NJ2007(http://www.lexjansen.com/pharmasug/2007/sp/sp05.pdf)HellmannSSThygesenLCTolstrupJSGronbaekMModifiable risk factors and survival in women diagnosed with primary breast cancer: results from a prospective cohort studyEur J Cancer Prev9201019536637320502344YaoLRobertSAExamining the Racial Crossover in Mortality between African American and White Older Adults: A Multilevel Survival Analysis of Race, Individual Socioeconomic Status, and Neighborhood Socioeconomic ContextJ Aging Res2011201113207321792390WinklebyMCubbinCAhnDEffect of cross-level interaction between individual and neighborhood socioeconomic status on adult mortality ratesAm J Public Health12200696122145215317077398YenIHKaplanGANeighborhood social environment and risk of death: multilevel evidence from the Alameda County StudyAm J Epidemiol51519991491089890710342798VeugelersPJYipAMKephartGProximate and contextual socioeconomic determinants of mortality: multilevel approaches in a setting with universal health care coverageAm J Epidemiol10152001154872573211590085RoosLLMagoonJGuptaSChateauDVeugelersPJSocioeconomic determinants of mortality in two Canadian provinces: multilevel modelling and neighborhood contextSoc Sci Med1020045971435144715246172CooperGSYuanZStangeKCDennisLKAminiSBRimmAAAgreement of Medicare claims and tumor registry data for assessment of cancer-related treatmentMed Care4200038441142110752973CooperGSVirnigBKlabundeCNSchusslerNFreemanJWarrenJLUse of SEER-Medicare data for measuring cancer surgeryMed Care82002408 SupplIV-4348

Characteristics of NABC breast cancer patients (N=4639), San Francisco Bay Area, 1995–2008

Non-Hispanic WhiteAfrican- AmericanHispanicAsian-AmericanChi Square p-valueTotal

n%n%n%n%n%
Total patients106798816426724369
Number of deaths
 All-cause27728025792906
 Breast cancer-specific157571736216263758256763
Study<0.01
  NC-BCFR high risk44842241243372125939128529
  NC-BCFR sporadic999293303652241362117027
  SFBCS52049454469405700191444
Age at diagnosis (years)<0.01
  <30192111292142732
  30–347973236944872285
  35–3952553513995582997
  40–44100912112229141151756513
  45–491531416216297181271973917
  50–541711618018238151191870816
  55–591571514214241141061664615
  60–64149141521519312881358213
  65+1871813514207130052912
AJCC Stage<0.01
  I53550398406854229143190944
  II42940449457424531948193944
  III53580814393253087
  IV172253242132792
  Unknown3333644831731343
Nodal involvement<0.01
  No69465576589685939759263560
  Yes32330363376273825538156836
  Unknown5054954732031664
Histology0.19
  Ductal839797868012997955182347580
  Lobular151141121121213721154712
  Other77790913184973478
Histological grade<0.01
  1192181251324315851364515
  242340314325923627841160737
  3 & 432430437446544025238166738
  Unknown1281211211153957945010
Estrogen and progesterone receptor status<0.01
  ER & PR negative1601526126378231271992621
  ER/PR positive796756276411226847671302169
  Unknown11110100101429691042210
Surgerya<0.01
  None101404292122912
  Lumpectomy58155592608995531747238955
  Mastectomy47545356367144434351188843
Radiation<0.01
  No42140398406564032248179741
  Yes64661590609866035052257259
Chemotherapy<0.01
  No54551453466604024837190644
  Yes50547519539685941762240955
  Unknown17216214171541
Education<0.01
  <High school454143156064051884519
  High school degree or equivalent175161942034921641078218
  Vocational/Technical degree or some college37435430444362717626141632
  College degree/graduate school47344221222511538157132630
Neighborhood (block group) SESb<0.01
  Quintile 1-Low SES9113213986912486
  Quintile 2303282292401531558313
  Quintile 3100920721363221011577118
  Quintile 425724216224342616424107125
  Quintile 5-High SES67163151155073136755169639
% Poverty (block group)<0.01
  0–0.049, High SES62759202205913635052177041
  0.05–0.0927926191194292619930109825
  0.1–0.19135133173243827941498423
  >= 0.2, Low SES262278281841129451712
Marital status<0.01
  Single/never married164152822925816981580218
  Married681643974010186252278261860
  Separated/divorced11411197202071234555213
  Widowed78786911971022937
  Unknown303263402811042
History of benign breast disease<0.01
  No817777948014058657686359282
  Yes250231921923414961477218
  Unknown0020300050
Years since last full-term pregnancy<0.01
  Nulliparous2722618018234141732685920
  <2252141211173772
  2–44042638254771955
  5+726687677813037943565323174
  Unknown4010200070
History of oral contraceptive use<0.01
  No28527271275353336554145633
  Yes730686796910526430646276763
  Unknown525384553101463
History of menopausal hormone therapy usec<0.01
  No540516867011266951477286666
  Past197192012023214901372017
  Recent330311011028417681078318
Alcohol consumption (g/day) in the year before diagnosis<0.01
  0380366146210456461792265661
  <524023125132631621364915
  5–913513971010771223518
  10–1410910485996912656
  ≥151961810110124810243110
  Unknown71304030170
BMI (kg/m2) in year prior to diagnosis<0.01
  <25.060457262275293247170186643
  25.0–29.925724312325433314622125829
  ≥30.0197194004154933487119427
  Unknown9114121171511
Recent recreational physical activity (hours/week)<0.01
  0, None26024288296864221131144533
  Quartiles 1 & 233031460474762924136150735
  Quartile 3 & 447645240244792921832141332
  Unknown1000102040
Percent of race/ethnic-specific cancer patients in reporting hospital (%)<0.01
  <25106466514568956284266561
  25–497073423518611861368416
  50–7447244000010248211
  ≥7552449000014253812
Percent of cancer patients in highest SES quintile in reporting hospital (%)<0.01
  <2516916317324202612419103024
  25–4928627491504923024937151835
  50–7452149163176594025137159437
  ≥759191727144872275

Distributions are based on known status.

Neighborhood SES was measured using a composite measure of 7 Census indicator measures known as the Yost SES Index (29).

Past = stopped prior to diagnosis; recent = stopped or continued to use at diagnosis.

Distributions of education and neighborhood SES by race/ethnicity for NABC breast cancer patients, San Francisco Bay Area, 1995–2008

Neighborhood SESbEducation
<High School graduationHigh School graduationVocational School/Some CollegeCollege+Totala
N%N%N%N%N%
Non-Hispanic White
Neighborhood SES
 Q1-Low SES111.1333.3333.3222.290.8
 Q2310.0826.71446.7516.7302.8
 Q31212.02424.04141.02323.01009.4
 Q4218.15220.28633.39838.025824.2
 Q5-High SES81.28813.123034.334551.467162.8
 Total454.217516.437435.047344.31068100.0

African-American
Neighborhood SES
 Q1-Low SES3828.63425.65138.396.813313.4
 Q25519.45920.813346.83512.328428.6
 Q32311.04622.09344.54521.520921.0
 Q4198.83918.110347.75525.521621.8
 Q5-High SES85.31610.65033.17751.015115.2
 Total14314.419419.543043.322122.3993100.0

Hispanic
Neighborhood SES
 Q1-Low SES6464.61313.11616.255.1995.9
 Q214658.94216.94116.5114.424814.9
 Q318449.27520.17520.1297.837422.4
 Q412328.011526.112628.67015.944026.3
 Q5-High SES8917.510420.417835.013626.750930.5
 Total60636.334920.943626.125115.01670100.0

Asian-American
Neighborhood SES
 Q1-Low SES333.3222.2111.1333.391.3
 Q2722.6825.8825.8825.8314.6
 Q31413.91615.82221.84948.510115.0
 Q4148.51911.65734.87445.116424.3
 Q5-High SES133.5195.18823.824766.936954.7
 Total517.6649.517626.138156.5674100.0

Totals include patients with unknown education and therefore row numbers may not add up to the total. Note that those with unknown education were excluded from the analytic sample.

Neighborhood SES was measured using a composite measure of 7 Census indicator measures the YOST SES Index (29).

Association of individual and neighborhood SES with all-cause mortality by race/ethnicity: Hazard Ratios (HRs) with 95% Confidence Intervals, San Francisco Bay Area, 1995–2008 (with follow-up through 2009)

SES VariablesNon-Hispanic WhiteAfrican-AmericanHispanicAsian-American

Base ModelaBase + Education + SES ModelbBase ModelaBase + Education + SES ModelbBase ModelaBase + Education + SES ModelbBase ModelaBase + Education + SES Modelb
HR (95% CI)HR (95% CI)HR (95% CI)HR (95% CI)HR (95% CI)HR (95% CI)HR (95% CI)HR (95% CI)
Education
 College degree+1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 Vocational/Some College1.03 (0.78–1.36)1.01 (0.76–1.34)0.92 (0.65–1.31)0.88 (0.62–1.26)1.31 (0.83–2.06)1.25 (0.79–1.97)1.29 (0.74–2.23)1.11 (0.63–1.97)
 =High School Degreec1.27 (0.92–1.74)1.19 (0.86–1.65)1.03 (0.69–1.54)0.92 (0.60–1.40)1.20 (0.75–1. 92)1.08 (0.66–1.77)2.13 (1.283.52)1.53 (0.89–2.62)
 <High School Degree1.40 (0.96–2.06)1.20 (0.80–1.80)1.38 (0.90–2.11)1.15 (0.73–1.81)
  p-trend0.170.340.050.310.190.760.010.13

Neighborhood SES (nSES)e
 Q5-High SES1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 Q41.15 (0.86–1.54)1.12 (0.84–1.50)0.65 (0.41–1.02)0.66 (0.42–1.03)1.12 (0.77–1.63)1.12 (0.76–1.64)2.14 (1.313.51)1.99 (1.193.34)
 Q31.28 (0.86–1.91)1.24 (0.83–1.85)1.03 (0.67–1.59)1.03 (0.66–1.61)1.38 (0.96–1.99)1.37 (0.92–2.02)2.29 (1.244.23)2.02 (1.073.82)
 Q2d1.64 (0.91–2.94)1.56 (0.86–2.83)1.17 (0.80–1.71)1.16 (0.78–1.71)1.59 (1.072.36)1.57 (1.032.38)3.79 (1.897.61)3.18 (1.516.70)
 Q1-Low SES1.33 (0.86–2.05)1.29 (0.82–2.03)1.59 (0.94–2.67)1.57 (0.92–2.71)
  p-trend0.050.080.010.030.010.010.010.01

Education & nSESf
 ≥ Some College, High SES1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 ≥ Some College, Low SES1.22 (0.78–1.91)1.61 (1.162.25)1.41 (0.89–2.23)1.89 (1.023.50)
 ≤HS Degree, High SES1.19 (0.86–1.63)1.44 (0.86–2.42)0.99 (0.70–1.41)1.84 (1.00–3.38)
 ≤HS Degree, Low SES1.62 (1.022.56)1.67 (1.202.32)1.39 (1.011.92)2.68 (1.375.23)

Adjusted for age at diagnosis (continuous), year of diagnosis (continuous), study eligibility (NC-BCFR high risk, NC-BCFR sporadic, SFBCS), nodal involvement (no, yes, unknown), histology (ductal, lobular, other), histological grade (1,2,3 & 4, unknown), joint ERPR status (ER−PR−, ER+ or PR+, unknown), type of surgery (none, lumpectomy, mastectomy, not otherwise specified, unknown), radiation (no, yes, unknown), chemotherapy (no, yes, unknown), subsequent primary tumor (yes, no) and clustering by block group, and stratified by AJCC stage (I, II, III, IV, unknown).

Adjusted for covariates of model 1 and clustering by block group (individual education and neighborhood SES in the same model), and stratified by AJCC stage (I, II, III, IV, unknown).

Education levels <high school graduate and high school graduate collapsed as for non-Hispanic whites and Asian-Americans due to small sample sizes.

Neighborhood SES quintiles 1 & 2 were collapsed for non-Hispanic Whites and Asian-Americans due to small sample sizes.

Neighborhood SES was measured using a composite measure of 7 Census indicator measures the YOST SES Index (29).

HS, high school. Education levels (<high school graduate and high school graduate) collapsed as <=high school graduate. Neighborhood SES levels collapsed as Q1–Q3:low SES; Q4–Q5: high SES.

Association of individual and neighborhood SES with all-cause mortality by race/ethnicity: Hazard Ratios (HRs) with 95% Confidence Intervals, San Francisco Bay Area, 1995–2008 (with follow-up through 2009)

SES VariablesCasesAll Cause Mortality
DeathsBase + SES + Reproductive Factor ModelaBase + SES + Marital Status ModelbBase + SES + Behavioral Factors ModelcBase + SES + Hospital Characteristics Modeld
n (%)n (%)HR (95% CI)HR (95% CI)HR (95% CI)HR (95% CI)
Non-Hispanic Whites
Education
 College+473 (44.3%)112 (40.4%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 Vocational/Some College374 (35.1%)95 (34.3%)1.01 (0.76–1.34)1.01 (0.76–1.34)0.96 (0.72–1.28)1.05 (0.78–1.40)
 <=High School Graduatee220 (20.6%)70 (25.3%)1.18 (0.85–1.63)1.23 (0.89–1.69)1.09 (0.78–1.53)1.25 (0.90–1.73)
  p-trend0.370.260.660.21
Neighborhood SES (nSES)f
 Q5-High SES671 (62.9%)161 (58.1%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 Q4257 (24.1%)70 (25.3%)1.11 (0.82–1.50)1.09 (0.81–1.47)1.08 (0.80–1.45)1.09 (0.81–1.46)
 Q3100 (9.4%)32 (11.6%)1.24 (0.83–1.85)1.18 (0.78–1.77)1.17 (0.78–1.79)1.18 (0.78–1.79)
 Q1, Q2-Low SES39 (3.7%)14 (5.1%)1.49 (0.82–2.72)1.51 (0.84–2.72)1.51 (0.81–2.80)1.48 (0.79–2.76)
  p-trend0.120.150.170.18

Education & nSESg
 >=College, High SES759 (71.1%)180 (65.0%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 >=College, Low SES88 (8.3%)27 (9.8%)1.19 (0.76–1.86)1.18 (0.75–1.85)1.18 (0.76–1.85)1.17 (0.73–1.85)
 <=High School Graduate, High SES169 (15.8%)51 (18.4%)1.16 (0.83–1.61)1.21 (0.88–1.66)1.12 (0.81–1.55)1.20 (0.88–1.65)
 <=High School Graduate, Low SES51 (4.8%)19 (6.9%)1.62 (1.032.54)1.59 (1.012.51)1.44 (0.90–2.28)1.60 (0.99–2.60)

African-Americans

Education(N=980)(N=280)
 College+221 (22.4%)49 (17.5%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 Vocational/Some College430 (43.5%)112 (40.0%)0.90 (0.62–1.31)0.89 (0.62–1.29)0.86 (0.60–1.25)0.91 (0.63–1.31)
 High School Graduate194 (19.6%)59 (21.1%)0.91 (0.58–1.41)0.94 (0.62–1.44)0.87 (0.56–1.35)0.97 (0.63–1.50)
 <High School Graduate143 (14.5%)60 (21.4%)1.18 (0.78–1.80)1.24 (0.82–1.88)1.17 (0.77–1.77)1.26 (0.83–1.90)
  p-trend0.380.240.400.20
Neighborhood SES (nSES)f
 Q5-High SES151 (15.3%)42 (15.0%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 Q4216 (21.9%)46 (16.4%)0.67 (0.42–1.05)0.64 (0.41–1.01)0.68 (0.43–1.08)0.65 (0.41–1.03)
 Q3207 (21.0%)54 (19.3%)0.99 (0.62–1.59)1.02 (0.64–1.61)0.98 (0.63–1.55)1.04 (0.65–1.66)
 Q2282 (28.5%)88 (31.4%)1.11 (0.75–1.66)1.14 (0.77–1.69)1.16 (0.78–1.72)1.09 (0.72–1.66)
 Q1-Low SES132 (13.4%)50 (17.9%)1.26 (0.79–2.01)1.29 (0.81–2.03)1.29 (0.81–2.04)1.19 (0.73–1.92)
  p-trend0.040.020.030.06

Education & nSESg
 >=College, High SES285 (28.9%)61 (21.8%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 >=College, Low SES366 (37.0%)100 (35.7%)1.62 (1.152.27)1.62 (1.152.27)1.57 (1.122.20)1.58 (1.122.22)
 <=High School Graduate, High SES82 (8.3%)27 (9.6%)1.53 (0.91–2.57)1.44 (0.86–2.42)1.44 (0.85–2.42)1.46 (0.87–2.47)
 <=High School Graduate, Low SES255 (25.8%)92 (32.9%)1.58 (1.112.23)1.70 (1.222.36)1.58 (1.132.22)1.68 (1.202.35)

Hispanics

Education(N=1,642)(N=257)
 College+251 (15.3%)29 (11.3%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 Vocational/Some College436 (26.6%)58 (22.6%)1.20 (0.75–1.89)1.24 (0.78–1.97)1.24 (0.78–1.95)1.23 (0.78–1.94)
 High School Graduate349 (21.3%)50 (19.5%)1.06 (0.64–1.74)1.11 (0.67–1.83)1.06 (0.64–1.74)1.07 (0.65–1.75)
 <High School Graduate606 (36.9%)120 (46.7%)1.04 (0.65–1.64)1.17 (0.74–1.86)1.06 (0.66–1.71)1.06 (0.67–1.68)
  p-trend0.790.720.820.85
Neighborhood SES (nSES)f
 Q5-High SES507 (30.9%)67 (26.1%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 Q4434 (26.4%)65 (25.3%)1.14 (0.77–1.67)1.13 (0.77–1.66)1.08 (0.74–1.59)1.07 (0.73–1.57)
 Q3363 (22.1%)62 (24.1%)1.33 (0.89–1.97)1.35 (0.91–2.00)1.29 (0.86–1.92)1.27 (0.86–1.88)
 Q2240 (14.6%)48 (18.7%)1.65 (1.082.50)1.61 (1.072.43)1.50 (0.99–2.27)1.41 (0.91–2.17)
 Q1-Low SES98 (6.0%)15 (5.8%)1.51 (0.87–2.61)1.65 (0.97–2.82)1.55 (0.91–2.64)1.36 (0.78–2.39)
  p-trend0.01<0.010.020.08

Education & nSESg
 >=College, High SES510 (31.1%)64 (24.9%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 >=College, Low SES177 (10.8%)23 (9.0%)1.34 (0.86–2.10)1.40 (0.89–2.23)1.34 (0.84–2.13)1.26 (0.79–2.03)
 <=High School Graduate, High SES431 (26.3%)68 (26.5%)0.94 (0.65–1.35)1.02 (0.71–1.45)0.93 (0.64–1.34)0.93 (0.65–1.33)
 <=High School Graduate, Low SES524 (31.9%)102 (39.7%)1.29 (0.93–1.80)1.43 (1.041.98)1.25 (0.87–1.78)1.20 (0.85–1.71)

Asian-Americans

Education(N=672)(N=92)
 College+381 (56.7%)41 (44.6%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 Voct/Some College176 (26.2%)24 (26.1%)1.19 (0.66–2.15)1.18 (0.65–2.15)1.03 (0.58–1.85)1.08 (0.61–1.90)
 <=High School Graduatee115 (17.1%)27 (29.4%)1.45 (0.83–2.53)1.57 (0.90–2.73)1.31 (0.69–2.47)1.46 (0.84–2.53)
  p-trend0.200.120.450.21

Neighborhood SES (nSES)f
 Q5-High SES367 (54.6%)38 (41.3%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 Q4164 (24.4%)27 (29.4%)1.85 (1.093.11)1.91 (1.133.23)2.01 (1.173.47)1.93 (1.153.25)
 Q3101 (15.0%)18 (19.6%)1.79 (0.91–3.54)1.93 (1.003.69)1.89 (1.003.54)1.86 (0.95–3.62)
 Q1, Q2-Low SES40 (6.0%)9 (9.8%)3.20 (1.52–6.73)2.68 (1.17–6.13)3.11 (1.42–6.78)3.05 (1.39–6.70)
  p-trend<0.01<0.01<0.01<0.01

Education & nSESg
 >=College, High SES466 (69.4%)50 (54.4%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 >=College, Low SES91 (13.5%)15 (16.3%)1.85 (0.99–3.47)1.72 (0.91–3.27)1.78 (0.96–3.29)1.62 (0.80–3.27)
 <=High School Graduate, High SES65 (9.7%)15 (16.3%)1.79 (0.94–3.42)1.82 (0.97–3.40)1.67 (0.82–3.43)1.64 (0.88–3.05)
 <=High School Graduate, Low SES50 (7.4%)12 (13.0%)2.32 (1.134.78)2.54 (1.265.10)2.21 (1.064.60)2.43 (1.204.92)

Adjusted for age at diagnosis (continuous), year of diagnosis (continuous), study eligibility (Northern California site of the Breast Cancer Family Registry (NC-BCFR) high risk, NC-BCFR sporadic, SFBCS), histology (ductal, lobular, other), histological grade (1,2,3 & 4, unknown), joint ERPR status (ER−PR−, ER+ or PR+, unknown), nodal involvement (none, positive, unknown), type of surgery (none, lumpectomy, mastectomy, NOS, unknown), radiation (no, yes, unknown), chemotherapy (no, yes, unknown), 1st subsequent primary tumor (yes, no), 2nd subsequent primary tumor (yes, no), days between the dates of diagnosis of study qualifying tumor and the 1st subsequent tumor(continuous), days between the dates of diagnosis of the 1st and 2nd subsequent tumor (continuous), benign breast disease (no, yes, unknown), years since last full-term pregnancy (<2, 2–4, 5+, unknown), pre-diagnosis hormonal contraception use (never, ever, unknown), pre-diagnosis hormone therapy use (never, past, recent, unknown) and clustering by block group, and stratified by AJCC stage (I, II, III, IV, unknown).

Adjusted for age at diagnosis (continuous), study eligibility (Northern California site of the Breast Cancer Family Registry (NC-BCFR) high risk, NC-BCFR sporadic, SFBCS), histology (ductal, lobular, other), histological grade (1,2,3 & 4, unknown), joint ERPR status (ER−PR−, ER+ or PR+, unknown), nodal involvement (none, positive, unknown), type of surgery (none, lumpectomy, mastectomy, NOS, unknown), radiation (no, yes, unknown), chemotherapy (no, yes, unknown), 1st subsequent primary tumor (yes, no), 2nd subsequent primary tumor (yes, no), days between the dates of diagnosis of study qualifying tumor and the 1st subsequent tumor(continuous), days between the dates of diagnosis of the 1st and 2nd subsequent tumor (continuous), marital status (single/never married, married, separated/divorced, widowed, unknown), and clustering by block group, and stratified by AJCC stage (I, II, III, IV, unknown).

Adjusted for age at diagnosis (continuous), study eligibility (Northern California site of the Breast Cancer Family Registry (NC-BCFR) high risk, NC-BCFR sporadic, SFBCS), histology (ductal, lobular, other), histological grade (1,2,3 & 4, unknown), joint ERPR status (ER−PR−, ER+ or PR+, unknown), nodal involvement (none, positive, unknown), type of surgery (none, lumpectomy, mastectomy, NOS, unknown), radiation (no, yes, unknown), chemotherapy (no, yes, unknown), 1st subsequent primary tumor (yes, no), 2nd subsequent primary tumor (yes, no), days between the dates of diagnosis of study qualifying tumor and the 1st subsequent tumor(continuous), days between the dates of diagnosis of the 1st and 2nd subsequent tumor (continuous), grams per day of alcohol in reference year (0,<5, 5–9, 10–14, 15+, unknown), pre-diagnosis BMI (<25, 25–29, 30+, unknown), recent recreational physical activity (0, Q1/ Q2, Q3/Q4, unknown) and clustering by block group, and stratified by AJCC stage (I, II, III, IV, unknown).

Adjusted for age at diagnosis (continuous), study eligibility (Northern California site of the Breast Cancer Family Registry (NC-BCFR) high risk, NC-BCFR sporadic, SFBCS), histology (ductal, lobular, other), histological grade (1,2,3 & 4, unknown), joint ERPR status (ER−PR−, ER+ or PR+, unknown), nodal involvement (none, positive, unknown), type of surgery (none, lumpectomy, mastectomy, NOS, unknown), radiation (no, yes, unknown), chemotherapy (no, yes, unknown), 1st subsequent primary tumor (yes, no), 2nd subsequent primary tumor (yes, no), days between the dates of diagnosis of study qualifying tumor and the 1st subsequent tumor(continuous), days between the dates of diagnosis of the 1st and 2nd subsequent tumor (continuous), percent of White cancer patients in reporting hospital (<25%, 25–49%, 50–74%, ≥ 75%, unknown), percent of cancer patients in highest SES quintile in reporting hospital (<25%, 25–49%, 50–74%, ≥ 75%, unknown), and clustering by block group, and stratified by AJCC stage (I, II, III, IV, unknown).

Education levels (<high school graduate and high school graduate) collapsed as <=high school graduate.

Neighborhood SES was measured using a composite measure of 7 Census indicator measures known as the Yost SES Index (29).

SES levels collapsed as Q1–Q3: low SES; Q4–Q5: high SES.

Association of individual and neighborhood SES with breast cancer-specific mortality by race/ethnicity: Hazard Ratios (HRs) with 95% Confidence Intervals, San Francisco Bay Area, 1995–2002 (with follow-up through 2009)

SES VariablesNon-Hispanic WhiteAfrican-AmericanHispanicAsian-American

Base ModelaBase + Education + SES ModelbBase ModelaBase + Education + SES ModelbBase ModelaBase + Education + SES ModelbBase ModelaBase + Education + SES Modelb
HR (95% CI)HR (95% CI)HR (95% CI)HR (95% CI)HR (95% CI)HR (95% CI)HR (95% CI)HR (95% CI)
Education
 College Degree+1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 Vocational/Some College0.87 (0.59–1.29)0.87 (0.59–1.29)0.74 (0.49–1.14)0.70 (0.45–1.09)1.41 (0.83–2.37)1.36 (0.80–2.32)1.11 (0.59–2.09)0.93 (0.49–1.76)
 =High School Degreec1.15 (0.75–1.77)1.14 (0.73–1.77)0.65 (0.39–1.10)0.56 (0.32–1.00)1.17 (0.66–2.08)1.09 (0.60–2.00)1.93 (1.093.43)1.23 (0.66–2.31)
 <High School Degree0.97 (0.59–1.60)0.83 (0.49–1.40)1.37 (0.82–2.27)1.22 (0.70–2.13)
  p-trend0.700.740.610.310.430.860.040.51

Neighborhood SESe
 Q5-High SES1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 Q41.12 (0.76–1.65)1.09 (0.73–1.62)0.63 (0.37–1.09)0.67 (0.39–1.15)0.85 (0.54–1.35)0.84 (0.53–1.36)2.63 (1.514.60)2.55 (1.424.58)
 Q31.02 (0.61–1.70)1.00 (0.59–1.68)0.78 (0.45–1.37)0.88 (0.50–1.57)1.05 (0.67–1.66)1.03 (0.63–1.69)2.97 (1.475.98)2.78 (1.365.71)
 Q2d1.08 (0.41–2.87)1.05 (0.40–2.75)0.95 (0.60–1.52)1.08 (0.67–1.74)1.41 (0.87–2.30)1.38 (0.83–2.30)4.25 (1.999.08)3.91 (1.728.91)
 Q1-Low SES1.20 (0.71–2.02)1.42 (0.81–2.47)1.48 (0.78–2.84)1.47 (0.74–2.91)
  p-trend0.750.800.170.100.100.140.010.01

Education & nSESf
 ≥ Some College, High SES1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 ≥ Some College, Low SES0.78 (0.41–1.50)1.45 (0.99–2.14)1.08 (0.59–1.97)2.26 (1.144.49)
 ≤HS Degree, High SES1.09 (0.69–1.73)1.25 (0.65–2.43)0.84 (0.52–1.34)1.74 (0.88–3.44)
 ≤HS Degree, Low SES1.52 (0.82–2.82)1.11 (0.72–1.70)1.24 (0.83–1.86)2.82 (1.296.14)

Adjusted for age at diagnosis (continuous), year of diagnosis (continuous), study eligibility (FRBC high risk, case-control, FRBC sporadic), nodal involvement (no, yes, unknown), histology (ductal, lobular, other), histological grade (1,2,3 & 4, unknown), joint ERPR status (ER−PR−, ER+ or PR+, unknown), type of surgery (none, lumpectomy, mastectomy, NOS, unknown), radiation (no, yes, unknown), chemotherapy (no, yes, unknown), subsequent primary tumor (yes, no) and clustering by block group, and stratified by AJCC stage (I, II, III, IV, unknown).

Adjusted for covariates of model 1 and clustering by block group (individual education and neighborhood SES in the same model), and stratified by AJCC stage (I, II, III, IV, unknown).

Education levels (<high school graduate and high school graduate) collapsed as <=high school graduate for non-Hispanic whites and Asian-Americans due to small sample sizes.

Neighborhood SES quintiles 1 & 2 were collapsed for non-Hispanic Whites and Asian-Americans due to small sample sizes.

Neighborhood SES was measured using a composite measure of 7 Census indicator measures the YOST SES Index (29).

HS, high school. Education levels (<high school graduate and high school graduate) collapsed as <=high school graduate. Neighborhood SES levels collapsed as Q1–Q3:low SES; Q4–Q5: high SES.

Association of individual and neighborhood SES with breast cancer-specific mortality by race/ethnicity: Hazard Ratios (HRs) with 95% Confidence Intervals, San Francisco Bay Area, 1995–2002 (with follow-up through 2009)

SES VariablesCasesBreast Cancer Mortality
DeathsBase + SES+ Reproductive Factor ModelaBase + SES + Marital Status ModelbBase + SES + Behavioral Factors ModelcBase + SES + Hospital Characteristics Modeld
n (%)n (%)HR (95% CI)HR (95% CI)HR (95% CI)HR (95% CI)

Non-Hispanic Whites
Education(N=1,067)(N=157)
 College+473 (44.3%)74 (47.1%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 Vocational/Some College374 (35.1%)49 (31.2%)0.89 (0.60–1.31)0.90 (0.61–1.33)0.89 (0.59–1.33)0.91 (0.60–1.38)
 <=High School Graduatee220 (20.6%)34 (21.7%)1.13 (0.72–1.76)1.17 (0.76–1.82)1.12 (0.71–1.76)1.16 (0.74–1.82)
  p-trend0.750.630.760.64
Neighborhood SES (nSES)f
 Q5-High SES671 (62.9%)93 (59.2%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 Q4257 (24.1%)42 (26.8%)1.07 (0.71–1.60)1.06 (0.71–1.60)1.07 (0.71–1.61)0.97 (0.64–1.46)
 Q3100 (9.4%)16 (10.2%)0.95 (0.55–1.64)0.91 (0.53–1.58)0.94 (0.56–1.57)0.81 (0.46–1.43)
 Q1, Q2-Low SES39 (3.7%)6 (3.8%)1.01 (0.38–2.70)1.02 (0.40–2.61)1.12 (0.44–2.86)0.84 (0.30–2.34)
  p-trend1.000.940.920.52

Education & nSESg
 >=College, High SES759 (71.1%)111 (70.7%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 >=College, Low SES88 (8.3%)12 (7.6%)0.74 (0.38–1.43)0.74 (0.39–1.42)0.78 (0.41–1.46)0.64 (0.33–1.27)
 <=High School Graduate, High SES169 (15.8%)24 (15.3%)1.06 (0.66–1.69)1.11 (0.71–1.76)1.07 (0.67–1.72)1.06 (0.67–1.68)
 <=High School Graduate, Low SES51 (4.8%)10 (6.4%)1.48 (0.80–2.75)1.43 (0.75–2.70)1.44 (0.78–2.68)1.33 (0.69–2.53)

African-Americans

Education(N=980)(N=173)
 College+221 (22.4%)38 (22.0%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 Vocational/Some College430 (43.5%)78 (45.1%)0.67 (0.42–1.07)0.70 (0.45–1.10)0.68 (0.42–1.08)0.72 (0.46–1.13)
 High School Graduate194 (19.6%)31 (17.9%)0.52 (0.290.95)0.58 (0.33–1.03)0.55 (0.30–1.00)0.58 (0.33–1.04)
 <High School Graduate143 (14.5%)26 (15.0%)0.79 (0.46–1.36)0.92 (0.53–1.58)0.84 (0.49–1.45)0.87 (0.51–1.46)
  p-trend0.250.480.360.39
Neighborhood SES (nSES)f
 Q5-High SES151 (15.3%)29 (16.8%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 Q4216 (21.9%)33 (19.1%)0.69 (0.40–1.20)0.68 (0.39–1.18)0.67 (0.39–1.16)0.66 (0.38–1.14)
 Q3207 (21.0%)29 (16.8%)0.85 (0.47–1.56)0.91 (0.50–1.67)0.81 (0.45–1.45)0.89 (0.50–1.58)
 Q2282 (28.5%)53 (30.6%)1.11 (0.68–1.81)1.12 (0.68–1.83)1.02 (0.62–1.67)1.04 (0.63–1.72)
 Q1-Low SES132 (13.4%)29 (16.8%)1.45 (0.82–2.54)1.50 (0.85–2.62)1.36 (0.78–2.37)1.34 (0.75–2.40)
  p-trend0.050.040.100.09

Education & nSESg
 >=College, High SES285 (28.9%)46 (26.6%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 >=College, Low SES366 (37.0%)70 (40.5%)1.48 (0.99–2.21)1.48 (1.00–2.20)1.40 (0.94–2.09)1.45 (0.97–2.15)
 <=High School Graduate, High SES82 (8.3%)16 (9.3%)1.30 (0.67–2.54)1.23 (0.63–2.41)1.36 (0.70–2.65)1.27 (0.65–2.46)
 <=High School Graduate, Low SES255 (25.8%)41 (23.7%)1.05 (0.66–1.65)1.18 (0.76–1.83)1.07 (0.70–1.64)1.12 (0.73–1.73)

Hispanics

Education(N=1,642)(N=162)
 College+251 (15.3%)20 (12.4%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 Vocational/Some College436 (26.6%)42 (25.9%)1.29 (0.76–2.21)1.34 (0.79–2.29)1.35 (0.78–2.31)1.31 (0.77–2.23)
 High School Graduate349 (21.3%)32 (19.8%)1.07 (0.58–1.98)1.09 (0.59–2.01)1.12 (0.61–2.06)1.05 (0.57–1.92)
 <High School Graduate606 (36.9%)68 (42.0%)1.10 (0.62–1.97)1.23 (0.70–2.15)1.23 (0.68–2.24)1.08 (0.61–1.91)
  p-trend0.930.760.780.82
Neighborhood SES (nSES)f
 Q5-High SES507 (30.9%)47 (29.0%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 Q4434 (26.4%)39 (24.1%)0.86 (0.53–1.41)0.84 (0.52–1.35)0.86 (0.53–1.41)0.82 (0.51–1.33)
 Q3363 (22.1%)36 (22.2%)1.07 (0.64–1.78)1.02 (0.62–1.67)1.02 (0.60–1.71)0.93 (0.57–1.52)
 Q2240 (14.6%)30 (18.5%)1.48 (0.88–2.48)1.43 (0.86–2.37)1.38 (0.82–2.33)1.20 (0.70–2.08)
 Q1-Low SES98 (6.0%)10 (6.2%)1.53 (0.77–3.02)1.50 (0.76–2.97)1.56 (0.79–3.09)1.14 (0.57–2.28)
  p-trend0.090.110.130.47

Education & nSESg
 >=College, High SES510 (31.1%)48 (29.6%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 >=College, Low SES177 (10.8%)14 (8.6%)1.05 (0.58–1.89)1.09 (0.60–1.98)1.08 (0.58–2.02)0.93 (0.50–1.73)
 <=High School Graduate, High SES431 (26.3%)38 (23.5%)0.78 (0.48–1.28)0.85 (0.53–1.37)0.86 (0.53–1.41)0.77 (0.48–1.25)
 <=High School Graduate, Low SES524 (31.9%)62 (38.3%)1.23 (0.81–1.87)1.27 (0.85–1.89)1.26 (0.80–1.97)1.01 (0.65–1.55)

Asian-Americans

Education(N=672)(N=75)
 College+381 (56.7%)35 (46.7%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 Vocational/Some College176 (26.2%)18 (24.0%)1.00 (0.51–1.94)1.01 (0.52–1.98)0.86 (0.45–1.64)0.90 (0.48–1.68)
 <=High School Graduatee115 (17.1%)22 (29.3%)1.14 (0.59–2.20)1.31 (0.69–2.48)0.98 (0.47–2.03)1.13 (0.59–2.15)
  p-trend0.710.460.870.80
Neighborhood SES (nSES)f
 Q5-High SES367 (54.6%)29 (38.7%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 Q4164 (24.4%)22 (29.3%)2.44 (1.364.39)2.41 (1.334.37)2.51 (1.374.61)2.51 (1.404.49)
 Q3101 (15.0%)16 (21.3%)2.58 (1.195.56)2.64 (1.275.49)2.59 (1.285.22)2.51 (1.135.58)
 Q1, Q2-Low SES40 (6.0%)8 (10.7%)3.98 (1.78–8.92)3.21 (1.20–8.63)4.07 (1.76–9.41)3.76 (1.52–9.30)
  p-trend<0.01<0.01<0.01<0.01

Education & nSESg
 >=College, High SES466 (69.4%)39 (52.0%)1.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
 >=College, Low SES91 (13.5%)14 (18.7%)2.27 (1.15–4.49)2.05 (1.01–4.16)2.09 (1.05–4.14)1.92 (0.82–4.52)
 <=High School Graduate, High SES65 (9.7%)12 (16.0%)1.67 (0.80–3.46)1.72 (0.85–3.46)1.38 (0.58–3.28)1.50 (0.75–3.01)
 <=High School Graduate, Low SES50 (7.4%)10 (13.3%)2.46 (1.055.75)2. 74 (1.216.23)2.29 (0.99–5.32)2.42 (1.035.69)

Adjusted for age at diagnosis (continuous), year of diagnosis (continuous), study eligibility (Northern California site of the Breast Cancer Family Registry (NC-BCFR) high risk, NC-BCFR sporadic, SFBCS), histology (ductal, lobular, other), histological grade (1,2,3 & 4, unknown), joint ERPR status (ER−PR−, ER+ or PR+, unknown), nodal involvement (none, positive, unknown), type of surgery (none, lumpectomy, mastectomy, NOS, unknown), radiation (no, yes, unknown), chemotherapy (no, yes, unknown), 1st subsequent primary tumor (yes, no), 2nd subsequent primary tumor (yes, no), days between the dates of diagnosis of study qualifying tumor and the 1st subsequent tumor(continuous), days between the dates of diagnosis of the 1st and 2nd subsequent tumor (continuous), benign breast disease (no, yes, unknown), years since last full-term pregnancy (<2, 2–4, 5+, unknown), pre-diagnosis hormonal contraception use (never, ever, unknown), pre-diagnosis hormone therapy use (never, past, recent, unknown) and clustering by block group, and stratified by AJCC stage (I, II, III, IV, unknown).

Adjusted for age at diagnosis (continuous), study eligibility (Northern California site of the Breast Cancer Family Registry (NC-BCFR) high risk, NC-BCFR sporadic, SFBCS), histology (ductal, lobular, other), histological grade (1,2,3 & 4, unknown), joint ERPR status (ER−PR−, ER+ or PR+, unknown), nodal involvement (none, positive, unknown), type of surgery (none, lumpectomy, mastectomy, NOS, unknown), radiation (no, yes, unknown), chemotherapy (no, yes, unknown), 1st subsequent primary tumor (yes, no), 2nd subsequent primary tumor (yes, no), days between the dates of diagnosis of study qualifying tumor and the 1st subsequent tumor(continuous), days between the dates of diagnosis of the 1st and 2nd subsequent tumor (continuous), marital status (single/never married, married, separated/divorced, widowed, unknown), and clustering by block group, and stratified by AJCC stage (I, II, III, IV, unknown).

Adjusted for age at diagnosis (continuous), study eligibility (Northern California site of the Breast Cancer Family Registry (NC-BCFR) high risk, NC-BCFR sporadic, SFBCS), histology (ductal, lobular, other), histological grade (1,2,3 & 4, unknown), joint ERPR status (ER−PR−, ER+ or PR+, unknown), nodal involvement (none, positive, unknown), type of surgery (none, lumpectomy, mastectomy, NOS, unknown), radiation (no, yes, unknown), chemotherapy (no, yes, unknown), 1st subsequent primary tumor (yes, no), 2nd subsequent primary tumor (yes, no), days between the dates of diagnosis of study qualifying tumor and the 1st subsequent tumor(continuous), days between the dates of diagnosis of the 1st and 2nd subsequent tumor (continuous), grams per day of alcohol in reference year (0,<5, 59, 1014, 15+, unknown), pre-diagnosis BMI (<25, 2529, 30+, unknown), recent recreational physical activity (0, Q1/ Q2, Q3/Q4, unknown) and clustering by block group, and stratified by AJCC stage (I, II, III, IV, unknown).

Adjusted for age at diagnosis (continuous), study eligibility (Northern California site of the Breast Cancer Family Registry (NC-BCFR) high risk, NC-BCFR sporadic, SFBCS), histology (ductal, lobular, other), histological grade (1,2,3 & 4, unknown), joint ERPR status (ER−PR−, ER+ or PR+, unknown), nodal involvement (none, positive, unknown), type of surgery (none, lumpectomy, mastectomy, NOS, unknown), radiation (no, yes, unknown), chemotherapy (no, yes, unknown), 1st subsequent primary tumor (yes, no), 2nd subsequent primary tumor (yes, no), days between the dates of diagnosis of study qualifying tumor and the 1st subsequent tumor(continuous), days between the dates of diagnosis of the 1st and 2nd subsequent tumor (continuous), percent of White cancer patients in reporting hospital (<25%, 2549%, 5074%, ≥ 75%, unknown), percent of cancer patients in highest SES quintile in reporting hospital (<25%, 2549%, 50–74%, ≥ 75%, unknown), and clustering by block group, and stratified by AJCC stage (I, II, III, IV, unknown).

Education levels (<high school graduate and high school graduate) collapsed as <=high school graduate.

Neighborhood SES was measured using a composite measure of 7 Census indicator measures known as the Yost SES Index (29).

SES levels collapsed as Q1–Q3: low SES; Q4–Q5: high SES.