Abstract
Objectives. We examined the combined influence of race/ethnicity and neighborhood socioeconomic status (SES) on short-term survival among women with uniform access to health care and treatment.
Methods. Using electronic medical records data from Kaiser Permanente Northern California linked to data from the California Cancer Registry, we included 6262 women newly diagnosed with invasive breast cancer. We analyzed survival using multivariable Cox proportional hazards regression with follow-up through 2010.
Results. After consideration of tumor stage, subtype, comorbidity, and type of treatment received, non-Hispanic White women living in low-SES neighborhoods (hazard ratio [HR] = 1.28; 95% confidence interval [CI] = 1.07, 1.52) and African Americans regardless of neighborhood SES (high SES: HR = 1.44; 95% CI = 1.01, 2.07; low SES: HR = 1.88; 95% CI = 1.42, 2.50) had worse overall survival than did non-Hispanic White women living in high-SES neighborhoods. Results were similar for breast cancer–specific survival, except that African Americans and non-Hispanic Whites living in high-SES neighborhoods had similar survival.
Conclusions. Strategies to address the underlying factors that may influence treatment intensity and adherence, such as comorbidities and logistical barriers, should be targeted at low-SES non-Hispanic White and all African American patients.
Breast cancer is the most common cancer among women in the United States, and it is the second leading cause of cancer death.1 Despite significant improvements in breast cancer survival from 1992 to 2009,1,2 racial/ethnic and socioeconomic survival disparities have persisted.3,4 African American women have consistently been found to have worse survival after breast cancer,3,5–11 Hispanic women have worse or similar survival,3,9,11,12 and Asian women as an aggregated group have better or similar survival3,9,11,12 than do non-Hispanic White women. Underlying factors thought to contribute to these racial/ethnic disparities include differences in stage at diagnosis,8,12,13 distributions of breast cancer subtypes,14–16 comorbidities,12,13,17 access to and utilization of quality care,13,18 and treatment.12,13
Numerous studies also have found poorer survival after breast cancer diagnosis among women residing in neighborhoods of lower socioeconomic status (SES).6,9,19,20 Research has shown that inadequate use of cancer screening services, and consequent late stage diagnosis and decreased survival, contribute to the SES disparities.21,22 Similar to racial/ethnic disparities, SES disparities have been attributed to inadequate treatment and follow-up care and comorbidities.18 Previous population-based studies have continued to observe racial/ethnic survival disparities after adjusting for neighborhood SES, but these studies have not considered the combined influence of neighborhood SES and race/ethnicity.3,9,11,12,23 These disparities may remain because information on individual-level SES, health insurance coverage, comorbidities, quality of care, and detailed treatment regimens have typically not been available.3,8,9,11,13 Even among studies using national Surveillance Epidemiology and End Results–Medicare linked data, in which more detailed information on treatment and comorbidities are available among some patients aged 65 years and older, survival disparities have remained.12,23,24 However, not all data on medical conditions and health care services are captured in Medicare claims, including data on Medicare beneficiaries enrolled in HMOs (health maintenance organizations).25,26
Using electronic medical records data from Kaiser Permanente Northern California (KPNC) linked to data from the population-based California Cancer Registry (CCR), we recently reported that chemotherapy use followed practice guidelines but varied by race/ethnicity and neighborhood SES in this integrated health system.27 Therefore, to overcome the limitations of previous studies and address simultaneously the multiple social28 and clinical factors affecting survival after breast cancer diagnosis, we used the linked KPNC–CCR database to determine whether racial/ethnic and socioeconomic differences in short-term overall and breast cancer–specific survival persist in women in a membership-based health system. Our study is the first, to our knowledge, to consider the combined influence of neighborhood SES and race/ethnicity and numerous prognostic factors, including breast cancer subtypes and comorbidities, thought to underlie these long-standing survival disparities among women with uniform access to health care and treatment.
METHODS
Women eligible for the study were all 6581 female residents of 23 California counties in the San Francisco Bay Area and the central valley of California who were members of KPNC when newly diagnosed with invasive breast cancer (morphology codes C50.0–C50.9 of International Classification of Diseases for Oncology. 3rd ed.29) during 2004 to 2007. From this group, we excluded patients with inflammatory carcinoma (n = 49), Paget’s disease (n = 2), no mass or tumor found (n = 13), a non–first primary cancer (n = 195), rare histological subtypes (n = 52), and survival time less than 1 day (n = 8). The final study population included 6262 patients.
Data Sources and Linkage
KPNC members constitute nearly one third of the local population and are generally comparable to the underlying population in terms of race/ethnicity and SES.30–33 We extracted KPNC records on newly diagnosed breast cancer between 1999 and 2007 for linkage to CCR tumor-level data, as described previously.27 KPNC chemotherapy infusion databases became fully implemented in 2004; therefore, our analyses are restricted to 2004 to 2007.
We used KPNC pharmacy records to identify filled prescriptions for endocrine therapy of breast cancer, including tamoxifen and aromatase inhibitors.27,34 In addition, from KPNC chemotherapy infusion databases, we extracted data on chemotherapy drug names, infusion dates, and number of infusions, focusing on the 2 most active drug classes for breast cancer, anthracycliness and taxanes.27 We did not consider the use of the monoclonal antibody trastuzumab (Herceptin) because it was not Food and Drug Administration approved for adjuvant treatment of stages I–III human epidermal growth factor receptor 2 (HER2)–positive breast cancer until 2006.27 We used KPNC data on diagnoses associated with inpatient and outpatient encounters to identify comorbidities present from 12 months before to 1 month after diagnosis and to create the Charlson Comorbidity Index.35–37
The CCR is a Surveillance Epidemiology and End Results population-based registry that has collected data about all primary cancers diagnosed among California residents since 1988. Demographic and tumor information is abstracted from medical records according to standard protocols38 in which CCR data have been described.39 Information includes age and marital status at diagnosis, race/ethnicity, tumor size, presence of lymph node involvement, cancer stage according to the American Joint Committee on Cancer classification system,40 tumor grade, subtype, and first course cancer treatments including surgery and radiation, vital status (routinely determined by the CCR through hospital follow-up and database linkages) as of December 31, 2010, and, for the deceased, the underlying cause of death. For this cohort, most clinical information is derived from the KPNC cancer registry; however, CCR data may incorporate additional reports from facilities outside KPNC.27
We categorized breast cancer subtypes according to tumor expression of estrogen receptor (ER), progesterone receptor (PR), HER2, and tumor grade.41,42 We defined (1) low-risk, endocrine-positive tumors as PR-positive, HER2-negative, and well or moderately differentiated tumor grade; (2) higher-risk, endocrine-positive tumors as ER-positive or PR-positive and any of PR-negative, HER2-positive, or poorly or undifferentiated tumor grade; (3) HER2-positive, endocrine-negative tumors as ER-negative, PR-negative, and HER2-positive; and (4) triple-negative tumors as ER-negative, PR-negative, and HER2-negative. Our race/ethnicity and neighborhood SES findings were similar when we considered another definition of breast cancer subtypes.43–45
Neighborhood Socioeconomic Status
Neighborhood SES is a previously developed composite index determined by US Census 2000 block groups in California.46 In the SES measure, we employed principal components analysis to develop a single index from 7 US Census 2000 block group indicator variables (education index, median household income, percentage living 200% below poverty level, percentage blue-collar workers, percentage older than aged 16 years in workforce without a job, median rent, and median house value).46 We classified neighborhood SES into quintiles on the basis of the distribution of the index across the state of California and then into 2 categories: low SES (quintiles 1, 2, and 3) and high SES (quintiles 4 and 5). Because of our previous findings showing interactions between race/ethnicity and neighborhood SES,27 we combined the 2 variables to explore their interactive effects on survival.
Statistical Analysis
We used Cox proportional hazards regression to estimate survival hazard ratios (HRs) and associated 95% confidence intervals (CIs) to evaluate the impact of factors on overall and breast cancer–specific survival. For deceased patients, we measured survival time in days from the date of diagnosis to the date of death from any cause for analyses of all-cause survival or to the date of death from breast cancer for analyses of breast cancer–specific survival. We censored patients who died from other causes at the time of death for analyses of breast cancer–specific survival. We censored patients alive at the study end date (December 31, 2010) at this time or at the date of last follow-up (i.e., last known contact). We examined the proportional hazards assumption by statistical testing of the correlation between weighted Schoenfeld residuals and logarithmically transformed survival time. We did not observe any violations of the assumption. Because proportional hazards varied by stage at diagnosis (American Joint Committee on Cancer stages I–IV and unknown), we included stage as a stratifying variable in all Cox regression models, allowing the baseline hazard to vary across stage. We adjusted all models for clustering by block group.
The base model included age and marital status at diagnosis in all models because these variables were of interest a priori. We grouped other significant covariates in unadjusted models, including subtype, tumor size, lymph node involvement, Charlson Comorbidity Index, and treatment modalities, and we added them sequentially into the multivariate models to assess their impact on the HR estimate of the combined race/ethnicity and neighborhood SES variable. The fully adjusted model includes age, marital status, and all the significant covariates in the unadjusted models.
We carried out all statistical tests using SAS version 9.3 (SAS Institute, Cary, NC). All P values reported were 2 sided, and we considered those that were < .05 to be statistically significant.
RESULTS
Our study had up to 7 years of follow-up (mean = 4.8 years; SD = 1.4 years). The majority of women were non-Hispanic White (67.7), were married (58.5), resided in high-SES neighborhoods (63.9), and were diagnosed with breast cancer when aged between 45 and 64 years (51.2; Table 1). Most women were diagnosed with the low-risk (37.1) or higher-risk (28.2) endocrine-positive breast cancer subtypes, stage I (50.4) or II (34.5) disease and had no comorbid diagnosis (77.0). Slightly fewer than half received radiation therapy (49.7) and chemotherapy (45.5). Breast cancer accounted for 50.2% of the deaths (16.4%; n = 1028).
TABLE 1—
Demographic and Clinical Characteristics of Women Diagnosed With Breast Cancer by Race/Ethnicity and Neighborhood SES: Kaiser Permanente Northern California, 2004–2007
| Non-Hispanic White (n = 4237) |
Non-Hispanic African American (n = 479) |
Hispanic (n = 658) |
Asian/Pacific Islander (n = 837) |
||||||
| Demographic and Clinical Characteristics | No. of cases (%) (n = 6262) | High SESa (n = 2837), No. (%) |
Low SES (n = 1400), No. (%) |
High SES (n = 185), No. (%) |
Low SES (n = 294), No. (%) |
High SES (n = 347), No. (%) |
Low SES (n = 311), No. (%) |
High SES (n = 601), No. (%) |
Low SES (n = 236), No. (%) |
| Age at diagnosis, y | |||||||||
| < 45 | 668 (10.7) | 230 (8.1) | 106 (7.6) | 22 (11.9) | 34 (11.6) | 61 (17.6) | 56 (18.0) | 107 (17.8) | 48 (20.3) |
| 45–54 | 1424 (22.7) | 582 (20.5) | 261 (18.6) | 64 (34.6) | 86 (29.3) | 95 (27.4) | 82 (26.4) | 180 (30.0) | 63 (26.7) |
| 55–64 | 1782 (28.5) | 846 (29.8) | 410 (29.3) | 41 (22.2) | 82 (27.9) | 84 (24.2) | 81 (26.0) | 159 (26.5) | 60 (25.4) |
| ≥ 65 | 2388 (38.1) | 1179 (41.6) | 623 (44.5) | 58 (31.4) | 92 (31.3) | 107 (30.8) | 92 (29.6) | 155 (25.8) | 65 (27.5) |
| Marital status at diagnosis | |||||||||
| Married | 3663 (58.5) | 1717 (60.5) | 706 (50.4) | 93 (50.3) | 110 (37.4) | 233 (67.1) | 196 (63.0) | 439 (73.0) | 151 (64.0) |
| Never married | 797 (12.7) | 331 (11.7) | 169 (12.1) | 37 (20.0) | 69 (23.5) | 34 (9.8) | 38 (12.2) | 66 (11.0) | 34 (14.4) |
| Previously married | 1732 (27.7) | 760 (26.8) | 514 (36.7) | 53 (28.6) | 111 (37.8) | 74 (21.3) | 75 (24.1) | 95 (15.8) | 45 (19.1) |
| Unknown | 70 (1.1) | 29 (1.0) | 11 (0.8) | < 5b | < 5 | 6 (1.7) | < 5 | < 5 | 6 (2.5) |
| Subtypec | |||||||||
| Low-risk, endocrine positive | 2321 (37.1) | 1118 (39.4) | 541 (38.6) | 49 (26.5) | 75 (25.5) | 116 (33.4) | 102 (32.8) | 227 (37.8) | 74 (31.4) |
| Higher risk, endocrine positive | 1766 (28.2) | 784 (27.6) | 380 (27.1) | 54 (29.2) | 80 (27.2) | 99 (28.5) | 79 (25.4) | 196 (32.6) | 77 (32.6) |
| HER2-positive, endocrine negative | 273 (4.4) | 104 (3.7) | 61 (4.4) | 9 (4.9) | 12 (4.1) | 22 (6.3) | 16 (5.1) | 27 (4.5) | 19 (8.1) |
| Triple negative | 588 (9.4) | 224 (7.9) | 124 (8.9) | 40 (21.6) | 57 (19.4) | 29 (8.4) | 47 (15.1) | 44 (7.3) | 20 (8.5) |
| Unclassified | 1314 (21.0) | 607 (21.4) | 294 (21.0) | 33 (17.8) | 70 (23.8) | 81 (23.3) | 67 (21.5) | 107 (17.8) | 46 (19.5) |
| AJCC tumor stage | |||||||||
| I | 3158 (50.4) | 1485 (52.3) | 703 (50.2) | 75 (40.5) | 130 (44.2) | 168 (48.4) | 144 (46.3) | 323 (53.7) | 105 (44.5) |
| II | 2149 (34.3) | 958 (33.8) | 483 (34.5) | 71 (38.4) | 104 (35.4) | 116 (33.4) | 114 (36.7) | 198 (32.9) | 91 (38.6) |
| III | 618 (9.9) | 277 (9.8) | 133 (9.5) | 23 (12.4) | 26 (8.8) | 42 (12.1) | 38 (12.2) | 51 (8.5) | 23 (9.7) |
| IV | 195 (3.1) | 72 (2.5) | 46 (3.3) | 9 (4.9) | 20 (6.8) | 12 (3.5) | 5 (1.6) | 20 (3.3) | 9 (3.8) |
| Unknown | 142 (2.3) | 45 (1.6) | 35 (2.5) | 7 (3.8) | 14 (4.8) | 9 (2.6) | 10 (3.2) | 9 (1.5) | 8 (3.4) |
| Charlson Comorbidity Index | |||||||||
| 0 (no comorbidity) | 4821 (77.0) | 2226 (78.5) | 1047 (74.8) | 140 (75.7) | 197 (67.0) | 274 (79.0) | 237 (76.2) | 469 (78.0) | 185 (78.4) |
| 1 | 982 (15.7) | 427 (15.1) | 240 (17.1) | 29 (15.7) | 64 (21.8) | 47 (13.5) | 53 (17.0) | 87 (14.5) | 33 (14.0) |
| ≥ 2 | 459 (7.3) | 184 (6.5) | 113 (8.1) | 16 (8.6) | 33 (11.2) | 26 (7.5) | 21 (6.8) | 45 (7.5) | 18 (7.6) |
| Endocrine therapy | |||||||||
| No | 3059 (48.9) | 1282 (45.2) | 660 (47.1) | 118 (63.8) | 174 (59.2) | 189 (54.5) | 180 (57.9) | 302 (50.2) | 128 (54.2) |
| Yes | 2935 (46.9) | 1441 (50.8) | 664 (47.4) | 63 (34.1) | 105 (35.7) | 141 (40.6) | 120 (38.6) | 279 (46.4) | 99 (41.9) |
| Unknown | 268 (4.3) | 114 (4.0) | 76 (5.4) | < 5 | 15 (5.1) | 17 (4.9) | 11 (3.5) | 20 (3.3) | 9 (3.8) |
| Surgery | |||||||||
| No | 203 (3.2) | 74 (2.6) | 45 (3.2) | 13 (7.0) | 20 (6.8) | 16 (4.6) | 10 (3.2) | 14 (2.3) | 5 (2.1) |
| Yes | 6059 (96.8) | 2763 (97.4) | 1355 (96.8) | 172 (93.0) | 274 (93.2) | 331 (95.4) | 301 (96.8) | 587 (97.7) | 231 (97.9) |
| Radiation therapy | |||||||||
| No | 3151 (50.3) | 1280 (45.1) | 772 (55.1) | 97 (52.4) | 171 (58.2) | 179 (51.6) | 173 (55.6) | 301 (50.1) | 145 (61.4) |
| Yes | 3111 (49.7) | 1557 (54.9) | 628 (44.9) | 88 (47.6) | 123 (41.8) | 168 (48.4) | 138 (44.4) | 300 (49.9) | 91 (38.6) |
| Chemotherapy | |||||||||
| No chemotherapy | 3193 (51.0) | 1560 (55.0) | 768 (54.9) | 71 (38.4) | 123 (41.8) | 148 (42.7) | 128 (41.2) | 274 (45.6) | 96 (40.7) |
| Anthracycline with or without any other chemotherapy but no taxane | 912 (14.6) | 368 (13.0) | 175 (12.5) | 37 (20.0) | 52 (17.7) | 64 (18.4) | 57 (18.3) | 107 (17.8) | 43 (18.2) |
| Taxane with or without any other chemotherapy but no anthracycline | 120 (1.9) | 54 (1.9) | 25 (1.8) | 8 (4.3) | 10 (3.4) | 8 (2.3) | 7 (2.3) | < 5 | < 5 |
| Anthracycline and taxane with or without any other chemotherapy | 1637 (26.1) | 710 (25.0) | 303 (21.6) | 61 (33.0) | 81 (27.6) | 108 (31.1) | 100 (32.2) | 188 (31.3) | 77 (32.6) |
| Any other chemotherapy (nonanthracycline, nontaxane) | 179 (2.9) | 58 (2.0) | 72 (5.1) | < 5 | 10 (3.4) | 8 (2.3) | 9 (2.9) | 12 (2.0) | 7 (3.0) |
| Unknown | 221 (3.5) | 87 (3.1) | 57 (4.1) | 6 (3.2) | 18 (6.1) | 11 (3.2) | 10 (3.2) | 17 (2.8) | 10 (4.2) |
| Vital status | |||||||||
| Deceased | 1028 (16.4) | 441 (15.5) | 288 (20.6) | 41 (22.2) | 76 (25.9) | 45 (13.0) | 47 (15.1) | 53 (8.8) | 29 (12.3) |
| Alive | 5234 (83.6) | 2396 (84.5) | 1112 (79.4) | 144 (77.8) | 218 (74.1) | 302 (87.0) | 264 (84.9) | 548 (91.2) | 207 (87.7) |
| Cause of death (n = 1028) | |||||||||
| Breast cancer | 516 (50.2) | 208 (47.2) | 133 (46.2) | 18 (43.9) | 44 (57.9) | 31 (68.9) | 27 (57.4) | 32 (60.4) | 516 (50.2) |
| Other cancer | 104 (10.1) | 51 (11.6) | 32 (11.1) | 5 (12.2) | 5 (6.6) | < 5 | < 5 | 6 (11.3) | 104 (10.1) |
| Circulatory disease | 153 (14.9) | 70 (15.9) | 47 (16.3) | 8 (19.5) | 9 (11.8) | 5 (11.1) | < 5 | < 5 | 153 (14.9) |
| Other causes | 190 (18.5) | 82 (18.6) | 55 (19.1) | 9 (22.0) | 13 (17.1) | 6 (13.3) | 12 (25.5) | 9 (17.0) | 190 (18.5) |
| Unknown cause | 65 (6.3) | 30 (6.8) | 21 (7.3) | < 5 | 5 (6.6) | < 5 | < 5 | < 5 | 65 (6.3) |
Note. AJCC = American Joint Committee on Cancer; ER = estrogen receptor; HER2 = human epidermal growth factor receptor 2; PR = progesterone receptor; SES = socioeconomic status.
Low SES includes neighborhood SES quintiles 1, 2, and 3; high SES includes neighborhood SES quintiles 4 and 5.
Cells with < 5 cases are not shown for privacy purposes.
Subtype is defined as follows: low-risk, endocrine positive as PR-positive, HER2-negative, and well or moderately differentiated tumor grade; higher risk, endocrine positive as ER-positive or PR-positive and any of PR-negative, HER2-positive, or poorly or undifferentiated tumor grade; HER2-positive, endocrine-negative tumors as ER-negative, PR-negative, and HER2-positive; and triple negative as ER-negative, PR-negative, and HER2-negative.
With the exception of African Americans, 61.4% of whom were living in a low-SES neighborhood, most women were predominately living in high-SES neighborhoods (52.7% of Hispanics; 67.0% of non-Hispanic Whites; 71.8% of Asian/Pacific Islander [API]; Table 1). African Americans, regardless of neighborhood SES, were more likely than was any other racial/ethnic group to be diagnosed with a triple-negative subtype. Hispanics residing in low-SES neighborhoods had a 6.7% higher triple-negative subtype than did Hispanics residing in high-SES neighborhoods. The distribution of the triple-negative subtype was similar across high- and low-SES neighborhoods for the other racial/ethnic groups. The highest percentages of deaths occurred in African Americans and non-Hispanic Whites living in low-SES neighborhoods.
Overall Survival
Non-Hispanic Whites living in low-SES neighborhoods (HR = 1.27; 95% CI = 1.06, 1.51) and African Americans, regardless of neighborhood SES (high SES: HR = 1.62; 95% CI = 1.14, 2.32; low SES: HR = 2.05; 95% CI = 1.56, 2.70) had significantly worse overall survival than did non-Hispanic Whites living in high-SES neighborhoods after adjusting for age, marital status, subtype, stage, and tumor characteristics (Table 2, model 1). Additionally adjusting for treatment and comorbidities did not appreciably change the associations for non-Hispanic Whites in low-SES neighborhoods and somewhat attenuated the associations for African Americans (Table 2, model 3). Hispanics and APIs, regardless of neighborhood SES, had overall survival similar to that of non-Hispanic Whites living in high-SES neighborhoods (Table 2, models 1, 2, and 3).
TABLE 2—
Overall and Breast Cancer–Specific Survival Among Women Diagnosed With Breast Cancer by Race/Ethnicity and Neighborhood SES, Follow-Up Through December 31, 2010: Kaiser Permanente Northern California, 2004–2007
| Overall Survival, HR (95% CI) |
Breast Cancer–Specific Survival, HR (95% CI) |
|||||
| Race/Ethnicity and Neighborhood SES | Model 1 | Model 2 | Model 3 | Model 1 | Model 2 | Model 3 |
| Non-Hispanic White and high SESa (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
| Non-Hispanic White and low SES | 1.27 (1.06, 1.51) | 1.26 (1.06, 1.50) | 1.28 (1.07, 1.52) | 1.29 (1.01, 1.65) | 1.26 (0.98, 1.62) | 1.27 (0.99, 1.64) |
| Non-Hispanic African American and high SES | 1.62 (1.14, 2.32) | 1.50 (1.05, 2.15) | 1.44 (1.01, 2.07) | 1.14 (0.66, 1.98) | 1.09 (0.63, 1.89) | 1.06 (0.61, 1.84) |
| Non-Hispanic African American and low SES | 2.05 (1.56, 2.70) | 1.91 (1.44, 2.52) | 1.88 (1.42, 2.50) | 2.26 (1.58, 3.24) | 2.07 (1.43, 2.98) | 2.13 (1.47, 3.09) |
| Hispanic and high SES | 0.95 (0.67, 1.34) | 0.96 (0.67, 1.36) | 0.94 (0.66, 1.34) | 1.10 (0.71, 1.69) | 1.12 (0.72, 1.74) | 1.12 (0.72, 1.73) |
| Hispanic and low SES | 1.23 (0.87, 1.75) | 1.24 (0.87, 1.76) | 1.21 (0.85, 1.72) | 1.26 (0.79, 2.02) | 1.29 (0.80, 2.06) | 1.30 (0.81, 2.08) |
| Asian/Pacific Islander and high SES | 0.91 (0.66, 1.26) | 0.92 (0.67, 1.27) | 0.88 (0.64, 1.21) | 0.94 (0.62, 1.43) | 0.94 (0.61, 1.43) | 0.92 (0.60, 1.41) |
| Asian/Pacific Islander and low SES | 0.78 (0.50, 1.20) | 0.84 (0.54, 1.29) | 0.81 (0.53, 1.26) | 0.79 (0.46, 1.37) | 0.86 (0.50, 1.49) | 0.84 (0.49, 1.45) |
| Other or unknown | 0.93 (0.38, 2.28) | 0.85 (0.35, 2.08) | 0.91 (0.37, 2.22) | 0.68 (0.17, 2.78) | 0.64 (0.16, 2.63) | 0.65 (0.16, 2.66) |
Note. AJCC = American Joint Committee on Cancer; CI = confidence interval; ER = estrogen receptor; HER2 = human epidermal growth factor receptor 2; HR = hazard ratio; PR = progesterone receptor; SES = socioeconomic status. Model 1 is stratified by AJCC stage, clustered by census block, and adjusted for age at diagnosis (continuous), marital status at diagnosis, subtype (low-risk, endocrine positive; higher risk, endocrine positive; HER2-positive, endocrine negative; triple negative, unclassified), tumor size (continuous), lymph node involvement (negative, positive, unknown), and tumor grade (low, high, unknown). Model 2 includes variables in model 1 and treatment: surgery (yes, no), radiation therapy (yes, no), endocrine therapy (yes, no), and chemotherapy (none; anthracycline with or without any other chemotherapy but no taxane; taxane with or without any other chemotherapy but no anthracycline; anthracycline and taxane with or without any other chemotherapy; any other chemotherapy (nonanthracycline, nontaxane); unknown. Model 3 includes variables in model 2 and Charlson Comorbidity Index (0, 1, ≥ 2).
Low SES includes neighborhood SES quintiles 1, 2, and 3; high SES includes neighborhood SES quintiles 4 and 5.
When we limited analyses to women with stages I, II, or III disease, results for non-Hispanic Whites and African Americans living in low-SES neighborhoods (data not shown) were similar to those for non-Hispanic Whites living in high-SES neighborhoods, but they were attenuated for African Americans in high-SES neighborhoods (HR = 1.29; 95% CI = 0.87, 1.93; data not shown in tables). In fully adjusted models (Table 3), worse overall survival also was associated with non–low-risk, endocrine-positive breast cancer subtypes and Charlson comorbidity score (vs a score of 0) but not marital status.
TABLE 3—
Overall and Breast Cancer–Specific Survival Among Women Diagnosed With Breast Cancer Follow-Up Through December 31, 2010: Kaiser Permanente Northern California, 2004–2007
| Overall Survivala |
Breast Cancer–Specific Survivala |
|||
| Sociodemographic and Clinical Characteristics | No. of Deaths | HR (95% CI) | No. of Deaths | HR (95% CI) |
| Race/ethnicity and neighborhood SES | ||||
| Non-Hispanic White and high SESb | 354 | 1.00 (Ref) | 180 | 1.00 (Ref) |
| Non-Hispanic White and low SES | 227 | 1.28 (1.07, 1.52) | 116 | 1.27 (0.99, 1.64) |
| Non-Hispanic African American and high SES | 35 | 1.44 (1.01, 2.07) | 15 | 1.06 (0.61, 1.84) |
| Non-Hispanic African American and low SES | 69 | 1.88 (1.42, 2.50) | 43 | 2.13 (1.47, 3.09) |
| Hispanic and high SES | 41 | 0.94 (0.66, 1.34) | 30 | 1.12 (0.72, 1.73) |
| Hispanic and low SES | 38 | 1.21 (0.85, 1.72) | 23 | 1.30 (0.81, 2.08) |
| Asian/Pacific Islander and high SES | 47 | 0.88 (0.64, 1.21) | 29 | 0.92 (0.60, 1.41) |
| Asian/Pacific Islander and low SES | 24 | 0.81 (0.53, 1.26) | 16 | 0.84 (0.49, 1.45) |
| Other or unknown | 6 | 0.91 (0.37, 2.22) | < 5 | d |
| Marital status at diagnosis | ||||
| Married | 356 | 1.00 (Ref) | 214 | 1.00 (Ref) |
| Never married | 91 | 1.09 (0.86, 1.39) | 62 | 1.06 (0.78, 1.45) |
| Previously married | 375 | 1.11 (0.94, 1.31) | 167 | 0.95 (0.74, 1.20) |
| Unknown | 19 | 0.94 (0.53, 1.67) | 12 | 0.79 (0.37, 1.69) |
| Subtypec | ||||
| Low-risk, endocrine positive | 165 | 1.00 (Ref) | 54 | 1.00 (Ref) |
| Higher risk, endocrine positive | 272 | 1.73 (1.42, 2.13) | 148 | 2.11 (1.53, 2.92) |
| HER2-positive, endocrine negative | 58 | 2.06 (1.46, 2.91) | 42 | 2.72 (1.72, 4.32) |
| Triple negative | 142 | 3.19 (2.47, 4.12) | 104 | 5.58 (3.85, 8.09) |
| Unclassified | 204 | 1.32 (1.06, 1.65) | 107 | 1.76 (1.24, 2.50) |
| Modified Charlson Comorbidity Index | ||||
| 0 (no comorbidity) | 511 | 1.00 (Ref) | 323 | 1.00 (Ref) |
| 1 | 174 | 1.26 (1.05, 1.52) | 73 | 0.94 (0.72, 1.25) |
| ≥ 2 | 156 | 2.23 (1.83, 2.71) | 59 | 1.44 (1.05, 1.98) |
Note. AJCC = American Joint Committee on Cancer; CI = confidence interval; ER = estrogen receptor; HER2 = human epidermal growth factor receptor 2; HR = hazard ratio; PR = progesterone receptor; SES = socioeconomic status.
Models were adjusted for all variables in the table, clustering by block group and age at diagnosis (continuous), marital status at diagnosis, tumor size (continuous), lymph node involvement (negative, positive, unknown), tumor grade (low, high, unknown), surgery (yes, no), radiation therapy (yes, no), endocrine therapy (yes, no), chemotherapy (none, anthracycline with or without any other chemotherapy but no taxane, taxane with or without any other chemotherapy but no anthracycline, anthracycline and taxane with or without any other chemotherapy), any other chemotherapy (nonanthracycline, nontaxane), and unknown. AJCC stages I, II, III, IV, and unknown were included as a stratifying variable.
Low SES includes neighborhood SES quintiles 1, 2, and 3; high SES includes neighborhood SES quintiles 4 and 5.
Subtype is defined as follows: low-risk, endocrine positive as PR-positive, HER2-negative, and well or moderately differentiated tumor grade; higher risk, endocrine positive as ER-positive or PR-positive and any of PR-negative, HER2-positive, or poorly or undifferentiated tumor grade; HER2-positive, endocrine-negative tumors as ER-negative, PR-negative, and HER2-positive; and triple negative as ER-negative, PR-negative, and HER2-negative.
Cells with < 5 cases are not shown for privacy purposes.
Breast Cancer–Specific Survival
Non-Hispanic Whites (HR = 1.29; 95% CI = 1.01, 1.65) and African Americans (HR = 2.26; 95% CI = 1.58, 3.24) living in low-SES neighborhoods had worse breast cancer–specific survival than did non-Hispanic Whites living in high-SES neighborhoods after adjustment for age, marital status, subtype, stage, and tumor characteristics (Table 2, model 1). Additionally adjusting for treatment and comorbidities did not appreciably attenuate survival differences by neighborhood SES for non-Hispanic Whites, but the survival differences were no longer statistically significant. Adjusting for treatment and comorbidities somewhat attenuated the survival differences for African Americans in low-SES neighborhoods compared with non-Hispanic Whites in high-SES neighborhoods (Table 2, model 3). Hispanics and APIs, regardless of neighborhood SES, had breast cancer–specific survival rates that were similar to non-Hispanic Whites living in high-SES neighborhoods.
When analyses were limited to women with stages I, II, or III disease, results were somewhat stronger for non-Hispanic Whites (HR = 1.35; 95% CI = 1.02, 1.80) and African Americans (HR = 2.29; 95% CI = 1.47, 3.55) living in low-SES neighborhoods than for non-Hispanic Whites living in high-SES neighborhoods (data not shown in tables). In fully adjusted models (Table 3), worse breast cancer survival was associated with non–low-risk, endocrine-positive breast cancer subtypes and a Charlson comorbidity score of 2 or higher (vs a score of 0).
DISCUSSION
In our study of 6262 women with breast cancer in an integrated health care system, we found that non-Hispanic White women living in low-SES neighborhoods and African Americans, regardless of neighborhood SES, had poorer short-term overall survival than did non-Hispanic White women living in high-SES neighborhoods. Results were similar for breast cancer–specific survival, with the exception that African Americans and non-Hispanic Whites living in high-SES neighborhoods had similar breast cancer–specific survival. Hispanics and APIs, regardless of neighborhood SES, had overall and breast cancer–specific survival rates similar to those of non-Hispanic Whites living in high-SES neighborhoods. Racial/ethnic and socioeconomic disparities are thought to result from differences in breast cancer tumor biology and other characteristics3,5–8,13; comorbidities12,13,17; access to, quality of, and utilization of care13,18; and treatment.12,13 Our findings suggest access to care, tumor stage, subtype, comorbidities, and type of treatment received do not eliminate racial/ethnic and socioeconomic differences in survival after breast cancer.
Although the women in our study were members of the same integrated health care system, thus equalizing access to quality health care services, women of all race/ethnic and neighborhood SES groups likely do not perceive their access equally or use health services similarly.47 For example, medical mistrust47 and perceived discrimination48,49 may be more frequent among underserved or minority groups because of negative health system experiences, leading to lower utilization of needed health care.47 Although equal access refers to equal cost or no cost at the point of access,47 there are still copayments and other costs associated with care that may be more of a barrier for low-SES groups. Most breast cancer treatments, especially those administered in an adjuvant setting, require multiple administrations and trips to the clinic (chemotherapy, radiation) or require long-term administration (endocrine therapy). Women of low SES may be more likely to face logistical barriers, such as the inability to take time off work, transportation issues, childcare obligations, or lack of social support, that make it difficult for them to adhere to these treatments.50–53
The poorer survival among African Americans and similar or better survival among Hispanics and APIs observed in our study generally are consistent with previous analyses3,9–13,23 that have tried to explain the persistence of racial/ethnic survival disparities by incorporating health care access and treatment measures, such as neighborhood SES and health insurance. In Surveillance Epidemiology and End Results–Medicare linked data, elderly African American women continued to have worse survival than did White women diagnosed with stages II/III,12 stages I–IIIA,23 and stages I–III24 disease, after consideration of patient demographics12,23,24; mammography screening12; tumor factors12,23,24; comorbidities12,23; treatment,12,23,24 including timing of chemoherapy initiation and completion24; and zip code–level income12 or census tract–level SES.23,24 In a Florida cancer registry analysis of women older than aged 18 years and enhanced with diagnosis and procedure code data from the Agency for Health Care, African Americans had poorer survival than, Hispanics had better survival than, and APIs had survival similar to that of Whites after controlling for patient demographics, health insurance, tumor factors, comorbidities, treatment, and census tract–level SES.9
Our study expands on the findings of previous studies by including women of all ages from an integrated health care system in which members had equal access to quality health care. Additionally, our study found survival differences across both race/ethnicity and neighborhood SES, highlighting the value of this intersectional approach in considering multiple social statuses simultaneously.28
Numerous studies have found poorer survival among women residing in neighborhoods with lower SES,6,9,11,19,20 consistent with our observations of worse survival among non-Hispanic White and African American women in low-SES neighborhoods. Although we controlled for many of the factors attributed to SES disparities, including chemotherapy treatment, for which we found variations in specific drug use according to race/ethnicity and SES in our previous analysis,27 we still observed SES survival disparities among non-Hispanic Whites and African Americans. However, because we did not measure chemotherapy dosing according to body surface area, functional underdosing of chemotherapy, which has been associated with obesity and SES,54 may have influenced our findings. It is also possible that lifestyle factors not available in our study, such as diet, physical activity, smoking, and body size that tend to correlate with SES; race/ethnicity7,13,47; and treatment discontinuation or nonadherence55 could have contributed to these survival disparities.
Limitations and Strengths
Our study was limited by not having a measure of individual-level SES, and, thus, our neighborhood-level measure of SES likely encompasses individual-level SES effects in addition to neighborhood effects.56 Additional environmental, cultural, and behavioral factors must be taken into account in future studies. Our study is also subject to the potential misclassification of race/ethnicity, although we have detected excellent overall agreement with self-reported race/ethnicity for Whites and African Americans and good agreement for Hispanics and Asians.57,58 Lastly, we lacked information about recurrence and adherence to the full course of endocrine therapy.
Despite these limitations, our study was able to leverage high-quality electronic medical record data available through a large, integrated health care system covering a diverse, representative population as well as Surveillance Epidemiology and End Results cancer registry data. Using the linked KPNC–CCR database of patients with uniform access to health care and treatment, we were able to address the limitations of previous studies by being the first, to our knowledge, to consider the combined influence of neighborhood SES and race/ethnicity while adjusting for numerous prognostic factors, including breast cancer subtypes and comorbidities, factors known to vary by race/ethnicity13,42 and to be associated with survival after breast cancer.16,17,41,59,60
Although KPNC patients are unlikely to seek out-of-network care because of the coverage structure, reducing the chance that we missed treatment administered at other institutions,27 we were also able, through linkage to the CCR, to obtain treatment information administered in contract or satellite facilities. Another strength of our study was the availability of specific chemotherapy agent data,27 data not available in most previous analyses.3,8,9,11,13
Conclusions
Our study expands on the findings of previous studies by being the first study, to our knowledge, to consider the combined influence of race/ethnicity and neighborhood SES on survival after breast cancer among women with uniform access to health care and treatment. Our findings suggest that socioeconomic disparities in short-term survival exist among non-Hispanic White and African American women after considering many of the factors thought to underlie well-known survival disparities, including access to care, tumor stage, subtype, comorbidities, and type of treatment received.
Future studies should identify specific aspects of SES that are responsible for these observed survival disparities and assess whether these disparities persist with longer follow-up and after considering lifestyle factors and comorbidities. Strategies to address the underlying factors that may influence treatment intensity and adherence, such as comorbidities,61 treatment side effects, and logistical barriers, should be targeted to low-SES non-Hispanic White and all African American patients to reduce these disparities.
Acknowledgments
This research was supported by the National Cancer Institute’s Surveillance, Epidemiology and End Results Program (contract HHSN261201000140C) and the Surveillance Epidemiology and End Results Rapid Response Surveillance Study (contract N01-PC-35136) awarded to the Cancer Prevention Institute of California and under a subcontract to Kaiser Permanente Northern California, Division of Research. The collection of cancer incidence data used in this study was supported by the California Department of Health Services 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 (contract HHSN261201000140C) awarded to the Cancer Prevention Institute of California (contract HHSN261201000035C), the University of Southern California (contract HHSN261201000034C), and the Public Health Institute; and the Centers for Disease Control and Prevention’s National Program of Cancer Registries (agreement #1U58 DP000807-01) awarded to the Public Health Institute. The Kaiser Foundation Research Institute received research funding from Takeda, Sanofi Aventis via a subcontract from University of North Carolina and Genentech (to L. A. H.). S. L.G. received research funding from Genentech.
Note. The opinions expressed herein are those of the authors, and endorsement by the state of California, the California Department of Health Services, the National Cancer Institute, or the Centers for Disease Control and Prevention or their contractors and subcontractors is not intended nor should it be inferred.
Human Participant Protection
Analyses were approved by the institutional review boards of the state of California, the Cancer Prevention Institute of California, and the Kaiser Permanente Northern California Division of Research.
References
- 1.Siegel R, Naishadham D, Jemal A. Cancer statistics, 2013. CA Cancer J Clin. 2013;63(1):11–30. doi: 10.3322/caac.21166. [DOI] [PubMed] [Google Scholar]
- 2.Jemal A, Simard EP, Dorell C et al. Annual Report to the Nation on the Status of Cancer, 1975–2009, featuring the burden and trends in human papillomavirus (HPV)-associated cancers and HPV vaccination coverage levels. J Natl Cancer Inst. 2013;105(3):175–201. doi: 10.1093/jnci/djs491. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Ooi SL, Martinez ME, Li CI. Disparities in breast cancer characteristics and outcomes by race/ethnicity. Breast Cancer Res Treat. 2011;127(3):729–738. doi: 10.1007/s10549-010-1191-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Akinyemiju TF, Soliman AS, Copeland G, Banerjee M, Schwartz K, Merajver SD. Trends in breast cancer stage and mortality in Michigan (1992–2009) by race, socioeconomic status, and area healthcare resources. PLoS One. 2013;8(4) doi: 10.1371/journal.pone.0061879. e61879. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.DeSantis C, Naishadham D, Jemal A. Cancer statistics for African Americans, 2013. CA Cancer J Clin. 2013;63(3):151–166. doi: 10.3322/caac.21173. [DOI] [PubMed] [Google Scholar]
- 6.Vona-Davis L, Rose DP. The influence of socioeconomic disparities on breast cancer tumor biology and prognosis: a review. J Womens Health (Larchmt) 2009;18(6):883–893. doi: 10.1089/jwh.2008.1127. [DOI] [PubMed] [Google Scholar]
- 7.Newman LA, Griffith KA, Jatoi I, Simon MS, Crowe JP, Colditz GA. Meta-analysis of survival in African American and White American patients with breast cancer: ethnicity compared with socioeconomic status. J Clin Oncol. 2006;24(9):1342–1349. doi: 10.1200/JCO.2005.03.3472. [DOI] [PubMed] [Google Scholar]
- 8.Deshpande AD, Jeffe DB, Gnerlich J, Iqbal AZ, Thummalakunta A, Margenthaler JA. Racial disparities in breast cancer survival: an analysis by age and stage. J Surg Res. 2009;153(1):105–113. doi: 10.1016/j.jss.2008.05.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Tannenbaum SL, Koru-Sengul T, Miao F, Byrne MM. Disparities in survival after female breast cancer diagnosis: a population-based study. Cancer Causes Control. 2013;24(9):1705–1715. doi: 10.1007/s10552-013-0246-5. [DOI] [PubMed] [Google Scholar]
- 10.Silber JH, Rosenbaum PR, Clark AS et al. Characteristics associated with differences in survival among Black and White women with breast cancer. JAMA. 2013;310(4):389–397. doi: 10.1001/jama.2013.8272. [DOI] [PubMed] [Google Scholar]
- 11.Niu X, Pawlish KS, Roche LM. Cancer survival disparities by race/ethnicity and socioeconomic status in New Jersey. J Health Care Poor Underserved. 2010;21(1):144–160. doi: 10.1353/hpu.0.0263. [DOI] [PubMed] [Google Scholar]
- 12.Curtis E, Quale C, Haggstrom D, Smith-Bindman R. Racial and ethnic differences in breast cancer survival: how much is explained by screening, tumor severity, biology, treatment, comorbidities, and demographics? Cancer. 2008;112(1):171–180. doi: 10.1002/cncr.23131. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Maskarinec G, Sen C, Koga K, Conroy SM. Ethnic differences in breast cancer survival: status and determinants. Womens Health (Lond Engl) 2011;7(6):677–687. doi: 10.2217/whe.11.67. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Bauer KR, Brown M, Cress RD, Parise CA, Caggiano V. Descriptive analysis of estrogen receptor (ER)-negative, progesterone receptor (PR)-negative, and HER2-negative invasive breast cancer, the so-called triple-negative phenotype: a population-based study from the California cancer Registry. Cancer. 2007;109(9):1721–1728. doi: 10.1002/cncr.22618. [DOI] [PubMed] [Google Scholar]
- 15.Clarke CA, Keegan TH, Yang J et al. Age-specific incidence of breast cancer subtypes: understanding the Black–White crossover. J Natl Cancer Inst. 2012;104(14):1094–1101. doi: 10.1093/jnci/djs264. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Keegan TH, Press DJ, Tao L et al. Impact of breast cancer subtypes on 3-year survival among adolescent and young adult women. Breast Cancer Res. 2013;15(5):R95. doi: 10.1186/bcr3556. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Tammemagi CM, Nerenz D, Neslund-Dudas C, Feldkamp C, Nathanson D. Comorbidity and survival disparities among Black and White patients with breast cancer. JAMA. 2005;294(14):1765–1772. doi: 10.1001/jama.294.14.1765. [DOI] [PubMed] [Google Scholar]
- 18.Woods LM, Rachet B, Coleman MP. Origins of socio-economic inequalities in cancer survival: a review. Ann Oncol. 2006;17(1):5–19. doi: 10.1093/annonc/mdj007. [DOI] [PubMed] [Google Scholar]
- 19.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;10:603. doi: 10.1186/1471-2407-10-603. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Gomez SL, Clarke CA, Shema SJ, Chang ET, Keegan TH, Glaser SL. Disparities in breast cancer survival among Asian women by ethnicity and immigrant status: a population-based study. Am J Public Health. 2010;100(5):861–869. doi: 10.2105/AJPH.2009.176651. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Barry J, Breen N. The importance of place of residence in predicting late-stage diagnosis of breast or cervical cancer. Health Place. 2005;11(1):15–29. doi: 10.1016/j.healthplace.2003.12.002. [DOI] [PubMed] [Google Scholar]
- 22.Dailey AB, Kasl SV, Holford TR, Calvocoressi L, Jones BA. Neighborhood-level socioeconomic predictors of nonadherence to mammography screening guidelines. Cancer Epidemiol Biomarkers Prev. 2007;16(11):2293–2303. doi: 10.1158/1055-9965.EPI-06-1076. [DOI] [PubMed] [Google Scholar]
- 23.Du XL, Fang S, Meyer TE. Impact of treatment and socioeconomic status on racial disparities in survival among older women with breast cancer. Am J Clin Oncol. 2008;31(2):125–132. doi: 10.1097/COC.0b013e3181587890. [DOI] [PubMed] [Google Scholar]
- 24.Nurgalieva ZZ, Franzini L, Morgan RO, Vernon SW, Liu CC, Du XL. Impact of timing of adjuvant chemotherapy initiation and completion after surgery on racial disparities in survival among women with breast cancer. Med Oncol. 2013;30(1):419. doi: 10.1007/s12032-012-0419-1. [DOI] [PubMed] [Google Scholar]
- 25.Engels EA, Pfeiffer RM, Ricker W, Wheeler W, Parsons R, Warren JL. Use of surveillance, epidemiology, and end results–Medicare data to conduct case–control studies of cancer among the US elderly. Am J Epidemiol. 2011;174(7):860–870. doi: 10.1093/aje/kwr146. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Warren JL, Klabunde CN, Schrag D, Bach PB, Riley GF. Overview of the SEER–Medicare data: content, research applications, and generalizability to the United States elderly population. Med Care. 2002;40(8 suppl) doi: 10.1097/01.MLR.0000020942.47004.03. IV-3–IV-18. [DOI] [PubMed] [Google Scholar]
- 27.Kurian AW, Lichtensztajn DY, Keegan TH et al. Patterns and predictors of breast cancer chemotherapy use in Kaiser Permanente Northern California, 2004–2007. Breast Cancer Res Treat. 2013;137(1):247–260. doi: 10.1007/s10549-012-2329-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Williams DR, Kontos EZ, Viswanath K et al. Integrating multiple social statuses in health disparities research: the case of lung cancer. Health Serv Res. 2012;47(3 pt 2):1255–1277. doi: 10.1111/j.1475-6773.2012.01404.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.International Classification of Diseases for Oncology. 3rd ed. Geneva, Switzerland: World Health Organization; 2000. [Google Scholar]
- 30.Wagner EH, Greene SM, Hart G et al. Building a research consortium of large health systems: the Cancer Research Network. J Natl Cancer Inst Monogr. 2005;35:3–11. doi: 10.1093/jncimonographs/lgi032. [DOI] [PubMed] [Google Scholar]
- 31.Gordon N. Similarity of the Adult Kaiser Permanente Membership in Northern California to the Insured and General Population in Northern California: Statistics From the 2007 California Health Interview Survey. Oakland, CA: Kaiser Permanente Division of Research; 2012. [Google Scholar]
- 32.Gordon N. A Comparison of Sociodemographic and Health Characteristics of the Kaiser Permanente Northern California Membership Derived From Two Data Sources: The 2008 Member Health Survey and the 2007 California Health Interview Survey. Oakland, CA: Kaiser Permanente Division of Research; 2012. [Google Scholar]
- 33.Gordon N. Similarity of the Adult Kaiser Permanente Membership in Northern California to the Insured and General Population in Northern California: Statistics From the 2009 California Health Interview Survey. Oakland, CA: Kaiser Permanente Division of Research; 2012. [Google Scholar]
- 34.Hershman DL, Kushi LH, Shao T et al. Early discontinuation and nonadherence to adjuvant hormonal therapy in a cohort of 8,769 early-stage breast cancer patients. J Clin Oncol. 2010;28(27):4120–4128. doi: 10.1200/JCO.2009.25.9655. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–383. doi: 10.1016/0021-9681(87)90171-8. [DOI] [PubMed] [Google Scholar]
- 36.Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD–9–CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619. doi: 10.1016/0895-4356(92)90133-8. [DOI] [PubMed] [Google Scholar]
- 37.Quan H, Li B, Couris CM et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. 2011;173(6):676–682. doi: 10.1093/aje/kwq433. [DOI] [PubMed] [Google Scholar]
- 38.Yu H, Rohan T. Role of the insulin-like growth factor family in cancer development and progression. J Natl Cancer Inst. 2000;92(18):1472–1489. doi: 10.1093/jnci/92.18.1472. [DOI] [PubMed] [Google Scholar]
- 39.Rocha RL, Hilsenbeck SG, Jackson JG et al. Insulin-like growth factor binding protein-3 and insulin receptor substrate-1 in breast cancer: correlation with clinical parameters and disease-free survival. Clin Cancer Res. 1997;3(1):103–109. [PubMed] [Google Scholar]
- 40.Irwin ML, McTiernan A, Bernstein L et al. Relationship of obesity and physical activity with C-peptide, leptin, and insulin-like growth factors in breast cancer survivors. Cancer Epidemiol Biomarkers Prev. 2005;14(12):2881–2888. doi: 10.1158/1055-9965.EPI-05-0185. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Caan BJ, Sweeney C, Habel LA et al. Intrinsic subtypes from the PAM50 gene expression assay in a population-based breast cancer survivor cohort: prognostication of short- and long-term outcomes. Cancer Epidemiol Biomarkers Prev. 2014;23(5):725–734. doi: 10.1158/1055-9965.EPI-13-1017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Sweeney C, Bernard PS, Factor RE et al. Intrinsic subtypes from PAM50 gene expression assay in a population-based breast cancer cohort: differences by age, race, and tumor characteristics. Cancer Epidemiol Biomarkers Prev. 2014;23(5):714–724. doi: 10.1158/1055-9965.EPI-13-1023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Bernstein L, Lacey JV., Jr Receptors, associations, and risk factor differences by breast cancer subtypes: positive or negative? J Natl Cancer Inst. 2011;103(6):451–453. doi: 10.1093/jnci/djr046. [DOI] [PubMed] [Google Scholar]
- 44.Perou CM, Sørlie T, Eisen MB et al. Molecular portraits of human breast tumours. Nature. 2000;406(6797):747–752. doi: 10.1038/35021093. [DOI] [PubMed] [Google Scholar]
- 45.Sørlie T, Perou CM, Tibshirani R et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA. 2001;98(19):10869–10874. doi: 10.1073/pnas.191367098. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Yost K, Perkins C, Cohen R, Morris C, Wright W. Socioeconomic status and breast cancer incidence in California for different race/ethnic groups. Cancer Causes Control. 2001;12(8):703–711. doi: 10.1023/a:1011240019516. [DOI] [PubMed] [Google Scholar]
- 47.McKenzie F, Jeffreys M. Do lifestyle or social factors explain ethnic/racial inequalities in breast cancer survival? Epidemiol Rev. 2009;31:52–66. doi: 10.1093/epirev/mxp007. [DOI] [PubMed] [Google Scholar]
- 48.Burgess DJ, Ding Y, Hargreaves M, van Ryn M, Phelan S. The association between perceived discrimination and underutilization of needed medical and mental health care in a multi-ethnic community sample. J Health Care Poor Underserved. 2008;19(3):894–911. doi: 10.1353/hpu.0.0063. [DOI] [PubMed] [Google Scholar]
- 49.Masi CM, Gehlert S. Perceptions of breast cancer treatment among African-American women and men: implications for interventions. J Gen Intern Med. 2009;24(3):408–414. doi: 10.1007/s11606-008-0868-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Hendren S, Chin N, Fisher S et al. Patients’ barriers to receipt of cancer care, and factors associated with needing more assistance from a patient navigator. J Natl Med Assoc. 2011;103(8):701–710. doi: 10.1016/s0027-9684(15)30409-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Mujahid MS, Janz NK, Hawley ST, Griggs JJ, Hamilton AS, Katz SJ. The impact of sociodemographic, treatment, and work support on missed work after breast cancer diagnosis. Breast Cancer Res Treat. 2010;119(1):213–220. doi: 10.1007/s10549-009-0389-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Gerend MA, Pai M. Social determinants of Black–White disparities in breast cancer mortality: a review. Cancer Epidemiol Biomarkers Prev. 2008;17(11):2913–2923. doi: 10.1158/1055-9965.EPI-07-0633. [DOI] [PubMed] [Google Scholar]
- 53.Celaya MO, Rees JR, Gibson JJ, Riddle BL, Greenberg ER. Travel distance and season of diagnosis affect treatment choices for women with early-stage breast cancer in a predominantly rural population (United States) Cancer Causes Control. 2006;17(6):851–856. doi: 10.1007/s10552-006-0025-7. [DOI] [PubMed] [Google Scholar]
- 54.Griggs JJ, Culakova E, Sorbero ME et al. Effect of patient socioeconomic status and body mass index on the quality of breast cancer adjuvant chemotherapy. J Clin Oncol. 2007;25(3):277–284. doi: 10.1200/JCO.2006.08.3063. [DOI] [PubMed] [Google Scholar]
- 55.Hershman DL, Shao T, Kushi LH et al. Early discontinuation and non-adherence to adjuvant hormonal therapy are associated with increased mortality in women with breast cancer. Breast Cancer Res Treat. 2011;126(2):529–537. doi: 10.1007/s10549-010-1132-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Krieger N. Overcoming the absence of socioeconomic data in medical records: validation and application of a census-based methodology. Am J Public Health. 1992;82(5):703–710. doi: 10.2105/ajph.82.5.703. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Clegg LX, Reichman ME, Hankey BF et al. Quality of race, Hispanic ethnicity, and immigrant status in population-based cancer registry data: implications for health disparity studies. Cancer Causes Control. 2007;18(2):177–187. doi: 10.1007/s10552-006-0089-4. [DOI] [PubMed] [Google Scholar]
- 58.Gomez SL, Glaser SL. Misclassification of race/ethnicity in a population-based cancer registry (United States) Cancer Causes Control. 2006;17(6):771–781. doi: 10.1007/s10552-006-0013-y. [DOI] [PubMed] [Google Scholar]
- 59.Bauer K, Parise C, Caggiano V. Use of ER/PR/HER2 subtypes in conjunction with the 2007 St Gallen consensus statement for early breast cancer. BMC Cancer. 2010;10:228. doi: 10.1186/1471-2407-10-228. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60.Patnaik JL, Byers T, Diguiseppi C, Denberg TD, Dabelea D. The influence of comorbidities on overall survival among older women diagnosed with breast cancer. J Natl Cancer Inst. 2011;103(14):1101–1111. doi: 10.1093/jnci/djr188. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Søgaard M, Thomsen RW, Bossen KS, Sørensen HT, Nørgaard M. The impact of comorbidity on cancer survival: a review. Clin Epidemiol. 2013;5(suppl 1):3–29. doi: 10.2147/CLEP.S47150. [DOI] [PMC free article] [PubMed] [Google Scholar]