Breast Cancer Research and Treatment

, Volume 145, Issue 3, pp 753–763

Association of race/ethnicity, socioeconomic status, and breast cancer subtypes in the National Cancer Data Base (2010–2011)

Authors

    • American Cancer Society, Inc.,
  • Mia Gaudet
    • American Cancer Society, Inc.,
  • Elizabeth M. Ward
    • American Cancer Society, Inc.,
  • W. Dana Flanders
    • American Cancer Society, Inc.,
    • Rollins School of Public HealthEmory University
  • Carol Desantis
    • American Cancer Society, Inc.,
  • Chun Chieh Lin
    • American Cancer Society, Inc.,
  • Ahmedin Jemal
    • American Cancer Society, Inc.,
Epidemiology

DOI: 10.1007/s10549-014-2976-9

Cite this article as:
Sineshaw, H.M., Gaudet, M., Ward, E.M. et al. Breast Cancer Res Treat (2014) 145: 753. doi:10.1007/s10549-014-2976-9

Abstract

To estimate the odds of breast cancer subtypes in minority populations versus non-Hispanic (NH) whites stratified by socioeconomic status (SES) [a composite of individual-level SES (insurance status) and area-level SES (median household income quartile from 2000 U.S. Census data)] using a large nationwide cancer database. We used the National Cancer Data Base to identify breast cancer cases diagnosed in 2010 and 2011, the only 2 years since U.S. cancer registries uniformly began collecting HER2 results. Breast cancer cases were classified into five subtypes based on hormone receptor (HR) and HER2 status: HR+/HER2−, HR+/HER2+, HR−/HER2+ (HER2-overexpressing), HR−/HER2− (TN), and unknown. A polytomous logistic regression was used to estimate odds ratios (ORs) comparing the odds of non-HR+/HER2-subtypes to HR+/HER2− for racial/ethnic groups controlling for and stratifying by SES, using a composite of insurance status and area-level income. Compared with NH whites, NH blacks and Hispanics were 84 % (OR = 1.84; 95 % CI 1.77–1.92) and 17 % (OR = 1.17; 95 % CI 1.11–1.24) more likely to have TN subtype versus HR+/HER2−, respectively. Asian/Pacific Islanders (API) had 1.45 times greater odds of being diagnosed with HER2-overexpressing subtype versus HR+/HER2− compared with NH whites (OR = 1.45; 95 % CI 1.31–1.61). We found similar ORs for race in high and low strata of SES. In a large nationwide hospital-based dataset, we found higher odds of having TN breast cancer in black women and of HER2-overexpressing in API compared with white women in every level of SES.

Keywords

ERPRHER2Breast cancerTriple-negative

Introduction

Breast cancer is a heterogeneous cancer consisting of a number of subtypes that increasingly appear to have distinct risk factors, molecular characteristics, and response to treatment [16]. Five clinically relevant subtypes have been identified based on the combined tumor expression of receptors for estrogen (ER) and progesterone (PR), which are jointly referred as hormone receptor (HR) [79], and human epidermal growth factor receptor 2 (HER2) [2, 5, 10, 11]: HR+/HER2−, HR−/HER2-[triple-negative (TN)], HR+/HER2+, HR−/HER2+ (HER2-overexpressing), and unknown [36, 10, 11]. HR+/HER2− subtype generally has the most favorable prognosis, whereas TN subtype has the least favorable prognosis [3, 5, 6, 9, 1115]. Because of the importance of ER, PR, and HER2 expression in determining treatment options [1], the National Cancer Data Base (NCDB) began collecting information on ER and PR status since 2004 data and on HER2 status since 2010 [16].

A number of reports have indicated that non-Hispanic (NH) blacks and Hispanics were more likely to be diagnosed with TN and HER2-overexpressing breast cancer subtypes and Asian/Pacific Islanders (API) were more likely to be diagnosed with HER2-overexpressing subtype, than NH whites [4, 7, 1214, 1725]. Previous studies also have reported higher odds of TN and HER2-overexpressing breast cancer subtypes in women with lowest socioeconomic status (SES) than in highest SES [12, 14]. In addition, some studies have suggested that the higher odds of these subtypes with unfavorable prognoses in minority racial/ethnic groups could be explained by differences in SES, with lower SES showing higher odds for hormone receptor-negative, TN, or HER2-overexpressing status [13, 17, 2628]. However, these studies were limited because they were based on institutional- or state-level cancer registries with only area-level SES data or without HER2 status and did not simultaneously stratify the analyses by race/ethnicity and SES. In this study, we estimate the odds of breast cancer subtypes in minority populations versus NH whites stratified by SES (a composite of individual-level SES [insurance status] and area-level SES [median household income quartile from 2000 U.S. Census data]) using a large nationwide cancer database. We also estimated odds ratios using individual-level (insurance status) SES.

Methods

We used data from the NCDB, which is a national hospital-based cancer registry jointly sponsored by the American College of Surgeons and the American Cancer Society, and collects data on about 73 % of newly diagnosed breast cancer cases in the U.S. [29, 30]. We identified a total of 260,577 single primary or first primary invasive female breast cancer cases (International Classification of Diseases for Oncology, 3rd edition [ICD-O-3] histology codes 8000–8576, 8980–8981, and 9020/3), diagnosed in 2010–2011, aged 18 years or older, and ICD-O-3 site codes of C500–C506 or C508–C509, after excluding missing/unknown values for diagnosis date. Variables were coded according to the Facility Oncology Registry Data Standards (FORDS) Manual revised for 2010 [31].

We categorized age at diagnosis into five groups (18–39, 40–49, 50–64, 65–74, and ≥75 years), race/ethnicity into six groups (non-Hispanic white, Hispanic, non-Hispanic black, Asian/Pacific Islander, non-Hispanic other, and missing/unknown), and insurance status into six groups: uninsured, Medicaid, Medicare, private, government, and missing/unknown. We categorized comorbidity into three groups (no comorbidity; score = 1; and score ≥2) based on the sum of weighted Charlson/Deyo score [32]. We created a composite SES variable using individual-level insurance status and area-level median household income quartile from 2000 U.S. Census data in the NCDB [31, 33]. We categorized individuals with private or Medicare with supplement insurance and in fourth median income quartile as high SES; those with Medicaid, Medicare with Medicaid supplement, or uninsured status, and/or in first median income quartile as low SES; those with unknown or missing insurance status and median income quartile as “unknown”; and, those who had other types of insurance and/or in second or third median income quartile were categorized as moderate SES. When median income quartile or insurance status was missing/unknown, the one with non-missing information was used to determine the value of the composite SES. Those with “unknown” SES (n = 403) were excluded.

Stage was recoded into stage I, II, III, IV, and unknown/missing using the American Joint Committee on Cancer’s (AJCC) AJCC Cancer Staging Manual (7th edition) [29]. We categorized tumor grade into grade I, II, III, and missing/unknown using AJCC’s collaborative stage site-specific factor 7 and the Bloom–Richardson scoring method [34]. Tumor size was classified into three categories: <2, ≥2 to <5, and ≥5 cm.

We used AJCC’s collaborative stage site-specific factors 1, 2, and 15 to recode ER, PR, and summary HER2 status, respectively, into positive, negative, and unknown. ER and PR positive groups include those with borderline results [35, 36], whereas those with borderline HER2 results were grouped as unknown [37]. We created a joint hormone receptor status using ER and PR status. Those with either ER or PR positive status were grouped as hormone receptor positive, and those with ER and PR negative status were grouped as hormone receptor negative. We then classified all breast cancers into five major subtypes: HR+/HER2−, HR+/HER2+, HR−/HER2+ (HER2-overexpressing), HR−/HER2− (TN), and unclassified (unknown) [4, 9].

Statistical analysis was performed using SAS version 9.3 software (SAS Institute, Cary, NC, USA). We used descriptive analysis to examine the demographic and clinical characteristic distributions of breast cancer subtypes. A polytomous logistic regression model was utilized to calculate odds ratios (OR) and 95 % confidence intervals (CI) for various demographic and clinical characteristics to estimate the odds of having each of the subtypes compared with HR+/HER2−, after accounting for differences in diagnosis age, race/ethnicity, tumor grade, stage, comorbidity, US census region, and SES in the analysis model. We also conducted another polytomous logistic regression analysis stratified by SES, controlling for diagnosis age, race/ethnicity, tumor grade, stage, comorbidity, and US census region. Results were considered statistically significant when the two-sided P value was less than 0.05.

Results

Of the 260,174 breast cancer cases in the NCDB, 64.4 % were HR+/HER2−, 9.2 % HR+/HER2+, 4.1 % HR−/HER2+ (HER2-overexpressing), 11.7 % TN, and 10.8 % were unknown (Table 1). Younger patients (<40) had higher proportions of TN (19.7 %), HR+/HER2+ (15.4 %), and HER2-overexpressing (5.9 %) breast cancer subtypes compared with 8.8, 6.8, and 2.6 %, respectively, in older patients (75+). Triple-negative breast cancer was more common in NH blacks (21.1 %) than other racial/ethnic groups. TN (29.6 %), HR+/HER2+ (13.8 %), and HER2-over expressing (9.0 %) were more common in grade III tumors than other grades. TN was more common in larger tumor size than smaller tumor size. The proportions of TN subtype were higher in uninsured (15.8 %) and Medicaid-insured (16.4 %) patients compared with patients with other insurance types. Patients with low SES had higher proportions of TN subtype than did patients with high or moderate SES (Table 1).
Table 1

Characteristics of breast cancer subtypes in the National Cancer Data Base (2010–2011)

Variable

Category

Total

HR+/HER2−

HR-/HER2−

(triple-negative)

HR+/HER2+

HR−/HER2+ (HER2-overexpressing)

Unknown

p value

  

N = 260,174

N = 167,496 (64.4)

N = 30,342 (11.7)

N = 23,914 (9.2)

N = 10,558 (4.1)

N = 27,864 (10.7)

 
  

N (column %)

N (row %)

N (row  %)

N (row %)

N (row %)

N (row %)

 

Age group

 

<40

13,598 (5.2)

6,636 (48.8)

2,681 (19.7)

2,095 (15.4)

804 (5.9)

1,382 (10.2)

p < 0.0001

 

40–49

45,292 (17.4)

27,332 (60.3)

6,224 (13.7)

5,143 (11.4)

2,010 (4.4)

4,583 (10.1)

 
 

50–64

101,222 (38.9)

64,081 (63.3)

12,167 (12)

9,595 (9.5)

4,844 (4.8)

10,535 (10.4)

 
 

65–74

56,437 (21.7)

39,148 (69.4)

5,441 (9.6)

4,129 (7.3)

1,762 (3.1)

5,957 (10.6)

 
 

75+

43,625 (16.8)

30,299 (69.5)

3,829 (8.8)

2,952 (6.8)

1,138 (2.6)

5,407 (12.4)

 

Diagnosis year

 

2010

128,644 (49.4)

80,116 (62.3)

14,788 (11.5)

11,788 (9.2)

5,176 (4)

16,776 (13)

p < 0.0001

 

2011

131,530 (50.6)

87,380 (66.4)

15,554 (11.8)

12,126 (9.2)

5,382 (4.1)

11,088 (8.4)

 

Race/ethnicity

 

NH white

189,877 (73)

126,856 (66.8)

19,375 (10.2)

16,896 (8.9)

7,130 (3.8)

19,620 (10.3)

p < 0.0001

 

NH black

29,485 (11.3)

15,253 (51.7)

6,231 (21.1)

2,948 (10.0)

1,463 (5.0)

3,590 (12.2)

 
 

API

8,065 (3.1)

5,121 (63.5)

814 (10.1)

855 (10.6)

489 (6.1)

786 (9.7)

 
 

Other

1,850 (0.7)

1,060 (57.3)

203 (11.0)

192 (10.4)

91 (4.9)

304 (16.4)

 
 

Hispanic

14,270 (5.5)

8,299 (58.2)

1,874 (13.1)

1,510 (10.6)

740 (5.2)

1,847 (12.9)

 
 

Missing/unknown

16,627 (6.4)

10,907 (65.6)

1,845 (11.1)

1,513 (9.1)

645 (3.9)

1,717 (10.3)

 

Grade

 

I

61,009 (23.4)

50,851 (83.3)

1,864 (3.1)

2,636 (4.3)

547 (0.9)

5,111 (8.4)

p < 0.0001

 

II

94,272 (36.2)

71,257 (75.6)

4,591 (4.9)

8,597 (9.1)

2,159 (2.3)

7,668 (8.1)

 
 

III

66,209 (25.4)

26,027 (39.3)

19,624 (29.6)

9,139 (13.8)

5,987 (9.0)

5,432 (8.2)

 
 

Unknown

38,684 (14.9)

19,361 (50.0)

4,263 (11.0)

3,542 (9.2)

1,865 (4.8)

9,653 (25.0)

 

Stage

 

I

132,027 (50.7)

92,921 (70.4)

11,552 (8.7)

10,215 (7.7)

3,862 (2.9)

13,477 (10.2)

p < 0.0001

 

II

82,022 (31.5)

49,867 (60.8)

12,409 (15.1)

8,305 (10.1)

3,605 (4.4)

7,836 (9.6)

 
 

III

29,651 (11.4)

16,780 (56.6)

4,391 (14.8)

3,448 (11.6)

2,053 (6.9)

2,979 (10.0)

 
 

IV

12,401 (4.8)

5,950 (48.0)

1,570 (12.7)

1,610 (13.0)

897 (7.2)

2,374 (19.1)

 
 

Missing/unknown

4,073 (1.6)

1,978 (48.6)

420 (10.3)

336 (8.2)

141 (3.5)

1,198 (29.4)

 

Comorbidity

 

0

216,440 (83.2)

138,817 (64.1)

25,044 (11.6)

20,274 (9.4)

8,885 (4.1)

23,420 (10.8)

p < 0.0001

 

1

33,788 (13.0)

22,365 (66.2)

4,042 (12.0)

2,821 (8.3)

1,301 (3.9)

3,259 (9.6)

 
 

2

9,946 (3.8)

6,314 (63.5)

1,256 (12.6)

819 (8.2)

372 (3.7)

1,185 (11.9)

 

Insurance

 

Uninsured

6,472 (2.5)

3,530 (54.5)

1,025 (15.8)

746 (11.5)

342 (5.3)

829 (12.8)

p < 0.0001

 

Medicaid

18,485 (7.1)

10,289 (55.7)

3,037 (16.4)

2,143 (11.6)

1,065 (5.8)

1,951 (10.6)

 
 

Medicare

91,386 (35.1)

63,155 (69.1)

8,911 (9.8)

6,674 (7.3)

2,791 (3.1)

9,855 (10.8)

 
 

Government

509 (0.2)

316 (62.1)

72 (14.1)

62 (12.2)

12 (2.4)

47 (9.2)

 
 

Private

138,360 (53.2)

88,150 (63.7)

16,898 (12.2)

13,981 (10.1)

6,218 (4.5)

13,113 (9.5)

 
 

Unknown/missing

4,962 (1.9)

2,056 (41.4)

399 (8.0)

308 (6.2)

130 (2.6)

2,069 (41.7)

 

Region

 

Northeast

54,023 (20.8)

35,932 (66.5)

5,802 (10.7)

5,094 (9.4)

2,113 (3.9)

5,082 (9.4)

p < 0.0001

 

Midwest

65,099 (25)

42,236 (64.9)

7,645 (11.7)

5,860 (9.0)

2,581 (4.0)

6,777 (10.4)

 
 

South

96,557 (37.1)

59,038 (61.1)

12,290 (12.7)

8,882 (9.2)

4,090 (4.2)

12,257 (12.7)

 
 

West

43,940 (16.9)

29,986 (68.2)

4,543 (10.3)

4,015 (9.1)

1,736 (4.0)

3,660 (8.3)

 
 

Missing/unknown

555 (0.2)

304 (54.8)

62 (11.2)

63 (11.4)

38 (6.8)

88 (15.9)

 

Tumor size

 

<2 cm

144,558 (55.6)

101,943 (70.5)

12,300 (8.5)

11,538 (8)

4,499 (3.1)

14,278 (9.9)

p < 0.0001

 

2 to <5 cm

85,686 (32.9)

50,950 (59.5)

13,340 (15.6)

9,211 (10.7)

4,102 (4.8)

8,083 (9.4)

 
 

≥5 cm

24,111 (9.3)

12,388 (51.4)

4,079 (16.9)

2,568 (10.7)

1,566 (6.5)

3,510 (14.6)

 
 

Missing/unknown

5,819 (2.2)

2,215 (38.1)

623 (10.7)

597 (10.3)

391 (6.7)

1,993 (34.2)

 

SES

 

High

89,337 (34.3)

59,623 (66.7)

9,176 (10.3)

8,051 (9.0)

3,401 (3.8)

9,086 (10.2)

p < 0.0001

 

Moderate

116,025 (44.6)

75,884 (65.4)

13,168 (11.3)

10,311 (8.9)

4,515 (3.9)

12,147 (10.5)

 
 

Low

54,812 (21.1)

31,989 (58.4)

7,998 (14.6)

5,552 (10.1)

2,642 (4.8)

6,631 (12.1)

 

HR+ hormone receptor positive, HR hormone receptor negative, HER2+ human epidermal growth factor receptor 2 positive, HER2 human epidermal growth factor receptor 2 negative, NH non-Hispanic, API Asian/Pacific Islander

Table 2 shows adjusted odds ratios (ORs) from polytomous logistic regression analysis using HR+/HER2− as the reference group. The odds of having TN breast cancer subtypes versus HR+/HER2− decreases with older age, with women aged 75+ years showing a 43 % lower odds of being diagnosed with TN subtype than women aged <40 years (OR = 0.57; 95 % CI 0.53–0.61). NH blacks had 1.84 times greater odds of being diagnosed with TN subtype (OR = 1.84; 95 % CI 1.77–1.92) versus HR+/HER2− compared with NH whites. Hispanics had 1.26 times greater odds of being diagnosed with HER2-overexpressing subtype (OR = 1.26; 95 % CI 1.16–1.37) versus HR+/HER2− compared with NH whites. Asian/Pacific Islanders had 1.45 times greater odds of being diagnosed with HER2-overexpressing subtype (OR = 1.45; 95 % CI 1.31–1.61) versus HR+/HER2− compared with NH whites. When we conducted sensitivity analyses excluding insurance status and SES from the polytomous regression model, odds ratios changed from 1.84 (95 % CI 1.77–1.92) to 1.91 (95 % CI 1.84–1.98) for TN in NH blacks compared with NH whites, and from 1.45 (95 % CI 1.31–1.61) to 1.46 (95 % CI 1.32–1.61) for HER2-overexpressing in API compared with NH whites, which did not make significant change in the results. Tumors with HER2-overexpressing and TN subtypes versus HR+/HER2− had substantially higher odds to be grade III than grade I ([OR = 18.05; 95 % CI 16.50–19.76], and [OR = 17.86; 95 % CI 16.97–18.79], respectively). Patients with low SES had 1.14 times greater odds of being diagnosed with TN subtype (OR = 1.14; 95 % CI 1.08–1.19) and 1.11 times greater odds of being diagnosed with HER2-overexpressing subtype (OR = 1.11; 95 % CI 1.03–1.19) versus HR+/HER2− compared with high SES.
Table 2

Adjusted odds ratios with 95 % confidence intervals from polytomous logistic regression analyses for characteristics of breast cancer subtypes

Variable

HR+/HER2−

N = 167,496

HR−/HER2−

(Triple-Negative)

N = 30,342

HR+/HER2+

N = 23,914

HR−/HER2+

(HER2-overexpressing)

N = 10,558

Unknown

N = 27,864

N

Reference

N

OR (95 % CI)

N

OR (95 % CI)

N

OR (95 % CI)

N

OR (95 % CI)

Age group

 <40 (ref)

6,636

 

2,681

1.00

2,095

1.00

804

1.00

1,382

1.00

 40–49

27,332

 

6,224

0.77 (0.72–0.81)

5,143

0.73 (0.69–0.77)

2,010

0.83 (0.76–0.91)

4,583

0.95 (0.89–1.02)

 50–64

64,081

 

12,167

0.73 (0.69–0.77)

9,595

0.62 (0.59–0.66)

4,844

0.96 (0.89–1.05)

10,535

0.97 (0.91–1.03)

 65–74

39,148

 

5,441

0.62 (0.58–0.66)

4,129

0.49 (0.46–0.53)

1,762

0.69 (0.62–0.76)

5,957

0.98 (0.91–1.05)

 75+

30,299

 

3,829

0.57 (0.53–0.61)

2,952

0.45 (0.42–0.49)

1,138

0.57 (0.51–0.64)

5,407

1.12 (1.04–1.21)

Race

 NH white (ref)

126,856

 

19,375

1.00

16,896

1.00

7,130

1.00

19,620

1.00

 NH black

15,253

 

6,231

1.84 (1.77–1.92)

2,948

1.13 (1.08–1.18)

1,463

1.17 (1.10–1.25)

3,590

1.20 (1.15–1.25)

 API

5,121

 

814

0.88 (0.81–0.96)

855

1.07 (0.99–1.15)

489

1.45 (1.31–1.61)

786

1.07 (0.98–1.15)

 NH other

1,060

 

203

1.05 (0.90–1.24)

192

1.17 (1.00–1.37)

91

1.34 (1.08–1.68)

304

1.43 (1.24–1.64)

 Hispanic

8,299

 

1,874

1.17 (1.11–1.24)

1,510

1.11 (1.04–1.17)

740

1.26 (1.16–1.37)

1,847

1.27 (1.20–1.34)

 Missing/unknown

10,907

 

1,845

1.07 (1.01–1.13)

1,513

1.02 (0.96–1.08)

645

1.02 (0.94–1.12)

1,717

0.95 (0.90–1.01)

Grade

 I (ref)

50,851

 

1,864

1.00

2,636

1.00

547

1.00

5,111

1.00

 II

71,257

 

4,591

1.68 (1.59–1.77)

8,597

2.20 (2.10–2.30)

2,159

2.62 (2.38–2.88)

7,668

1.07 (1.03–1.11)

 III

26,027

 

19,624

17.86 (16.97–18.79)

9,139

5.85 (5.58–6.13)

5,987

18.05 (16.50–19.76)

5,432

2.06 (1.97–2.14)

 Unknown

19,361

 

4,263

5.48 (5.17–5.81)

3,542

3.05 (2.89–3.22)

1,865

7.52 (6.81–8.30)

9,653

4.21 (4.05–4.38)

Stage

 I(ref)

92,921

 

11,552

1.00

10,215

1.00

3,862

1.00

13,477

1.00

 II

49,867

 

12,409

1.21 (1.18–1.25)

8,305

1.12 (1.09–1.16)

3,605

1.12 (1.07–1.18)

7,836

0.94 (0.91–0.97)

 III

16,780

 

4,391

1.10 (1.06–1.15)

3,448

1.27 (1.21–1.32)

2,053

1.68 (1.58–1.78)

2,979

0.94 (0.90–0.99)

 IV

5,950

 

1,570

1.16 (1.09–1.24)

1,610

1.74 (1.64–1.85)

897

2.10 (1.93–2.27)

2,374

1.61 (1.52–1.70)

 Unknown

1,978

 

420

1.22 (1.09–1.37)

336

1.29 (1.14–1.45)

141

1.26 (1.06–1.51)

1,198

2.66 (2.46–2.88)

Comorbidity score

 0 (ref)

138,817

 

25,044

1.00

20,274

1.00

8,885

1.00

23,420

1.00

 1

22,365

 

4,042

1.00 (0.96–1.04)

2,821

0.92 (0.88–0.96)

1,301

0.94 (0.88–1.00)

3,259

0.88 (0.85–0.92)

 ≥2

6,314

 

1,256

1.07 (1–10.15)

819

0.97 (0.90–1.05)

372

0.95 (0.85–1.06)

1,185

1.04 (0.97–1.11)

Insurance

 Private (ref)

88,150

 

16,898

1.00

13,981

1.00

6,218

1.00

13,113

1.00

 Uninsured

3,530

 

1,025

1.06 (0.98–1.16)

746

1.02 (0.93–1.12)

342

0.92 (0.80–1.05)

829

0.99 (0.90–1.08)

 Medicaid

10,289

 

3,037

1.08 (1.02–1.15)

2,143

1.04 (0.97–1.11)

1,065

1.04 (0.95–1.14)

1,951

0.95 (0.89–1.02)

 Medicare

63,155

 

8,911

1.00 (0.96–1.05)

6,674

0.95 (0.90–1.00)

2,791

0.94 (0.87–1.01)

9,855

0.98 (0.94–1.02)

 Government

316

 

72

0.94 (0.71–1.24)

62

1.07 (0.81–1.42)

12

0.41 (0.23–0.74)

47

0.79 (0.57–1.08)

 Unknown/missing

2,056

 

399

1.01 (0.90–1.13)

308

0.95 (0.84–1.07)

130

0.84 (0.70–1.01)

2,069

4.44 (4.14–4.75)

Region

 Northeast (ref)

35,932

 

5,802

1.00

5,094

1.00

2,113

1.00

5,082

1.00

 Midwest

42,236

 

7,645

1.13 (1.08–1.17)

5,860

1.01 (0.97–1.05)

2,581

1.09 (1.03–1.16)

6,777

1.36 (1.31–1.42)

 South

59,038

 

12,290

1.18 (1.14–1.23)

8,882

1.05 (1.01–1.10)

4,090

1.18 (1.11–1.24)

12,257

1.51 (1.46–1.57)

 West

29,986

 

4,543

0.97 (0.93–1.01)

4,015

0.94 (0.90–0.99)

1,736

0.96 (0.90–1.03)

3,660

1.02 (0.97–1.07)

 Missing/unknown

304

 

62

0.94 (0.70–1.26)

63

1.16 (0.88–1.54)

38

1.57 (1.10–2.24)

88

1.68 (1.31–2.16)

SES

 High

59,623

 

9,176

1.00

8,051

1.00

3,401

1.00

9,086

1.00

 Moderate

75,884

 

13,168

1.10 (1.07–1.14)

10,311

1.05 (1.02–1.09)

4,515

1.07 (1.02–1.12)

12,147

1.03 (1.00–1.06)

 Low

31,989

 

7,998

1.14 (1.08–1.19)

5,552

1.09 (1.03–1.14)

2,642

1.11 (1.03–1.19)

6,631

1.21 (1.15–1.26)

Adjusted for diagnosis age, race, grade, stage, comorbidity, insurance status, census region, and SES

HR+ hormone receptor positive, HR hormone receptor negative, HER2+ human epidermal growth factor receptor 2 positive, HER2 human epidermal growth factor receptor 2 negative, OR odds ratio, CI confidence interval, NH non-Hispanic, API Asian/Pacific Islander, SES socioeconomic status

Table 3 illustrates adjusted ORs of breast cancer subtypes using HR+/HER2− as the reference outcome stratified by SES from polytomous logistic regression model for racial/ethnic groups with NH whites being the reference group. NH blacks, compared with NH whites, were at greater odds of being diagnosed with TN than HR+/HER2− in all SES strata, with odds ratio varying little across strata from 1.79 (1.68–1.91) in the low stratum to 1.91 (1.80–2.03) in moderate stratum. Asian/Pacific Islanders had higher odds of having HER2-overexpressing subtype versus HR+/HER2− than NH whites at each level of SES.
Table 3

Adjusted odds ratios from polytomous logistic regression analyses showing race/ethnicity stratified by a composite socioeconomic status variable for breast cancer subtypes

SESa

HR+/HER2−

N = 167,496

HR−/HER2−

(triple-negative)

N = 30,342

HR+/HER2+

N = 23,914

HR−/HER2+

(HER2-overexpressing)

N = 10,558

Unknown

N = 27,864

N

Reference

N

OR (95 % CI)

N

OR (95 % CI)

N

OR (95 % CI)

N

OR (95 % CI)

High status

59,623

 

9,176

 

8,051

 

3,401

 

9,086

 

 NH white ref)

48,674

 

6,777

1.00

6,288

1.00

2,568

1.00

7,095

1.00

 NH black

2,858

 

1,067

1.85 (1.70–2.02)

563

1.19 (1.08–1.31)

269

1.24 (1.08–1.42)

624

1.22 (1.11–1.34)

 API

2,434

 

357

0.92 (0.81–1.04)

393

1.06 (0.95–1.19)

209

1.42 (1.22–1.66)

331

1.01 (0.89–1.14)

 NH other

332

 

56

1.02 (0.76–1.39)

52

1.05 (0.78–1.41)

29

1.42 (0.96–2.10)

142

2.33 (1.89–2.88)

 Hispanic

1,748

 

373

1.36 (1.20–1.55)

298

1.12 (0.99–1.28)

134

1.24 (1.03–1.50)

368

1.37 (1.21–1.55)

 Missing/unknown

3,577

 

546

1.07 (0.97–1.19)

457

0.99 (0.89–1.10)

192

1.01 (0.86-1.18)

526

0.95 (0.87–1.05)

Moderate status

75,884

 

13,168

 

10,311

 

4,515

 

12,147

 

 NH white (ref)

59,905

 

9,116

1.00

7,708

1.00

3,323

1.00

9,119

1.00

 NH black

5,697

 

2,288

1.91 (1.80–2.03)

1,068

1.15 (1.07–1.24)

490

1.09 (0.98–1.20)

1,262

1.21 (1.13–1.29)

 API

1,565

 

264

0.93 (0.80–1.07)

252

1.08 (0.94–1.24)

141

1.40 (1.16–1.68)

249

1.15 (1.00–1.32)

 NH other

376

 

63

0.97 (0.73–1.29)

72

1.32 (1.02–1.71)

28

1.18 (0.80–1.76)

92

1.48 (1.167–1.88)

 Hispanic

2,860

 

561

1.04 (0.94–1.15)

448

1.02 (0.92–1.14)

218

1.12 (0.96–1.29)

618

1.36 (1.24–1.50)

 Missing/unknown

5,481

 

876

1.02 (0.94–1.11)

763

1.06 (0.97–1.15)

315

1.01 (0.89–1.14)

807

0.94 (0.86–1.01)

Low status

31,989

 

7,998

 

5,552

 

2,642

 

6,631

 

 NH white (ref)

18,277

 

3,482

1.00

2,900

1.00

1,239

1.00

3,406

1.00

 NH black

6,698

 

2,876

1.79 (1.68–1.91)

1,317

1.08 (1.00–1.16)

704

1.27 (1.15–1.40)

1,704

1.20 (1.12–1.28)

 API

1,122

 

193

0.80 (0.68–0.95)

210

1.07 (0.91–1.25)

139

1.60 (1.32–1.95)

206

1.00 (0.86–1.18)

 NH other

352

 

84

1.11 (0.86–1.44)

68

1.14 (0.87–1.49)

3,411

1.32 (0.91–1.90)

70

1.07 (0.82–1.40)

 Hispanic

3,691

 

940

1.19 (1.09–1.30)

764

1.16 (1.06–1.27)

388

1.38 (1.22–1.57)

861

1.24 (1.13–1.35)

 Missing/unknown

1,849

 

423

1.20 (1.06–1.35)

293

0.98 (0.86–1.12)

138

1.11 (0.92–1.33)

384

1.07 (0.94–1.20)

Adjusted for diagnosis age, race, grade, stage, comorbidity, census region

HR+ hormone receptor positive, HR hormone receptor negative, HER2+ human epidermal growth factor receptor 2 positive, HER2 human epidermal growth factor receptor 2 negative, OR odds ratio, CI confidence interval, NH non-Hispanic, API Asian/Pacific Islander

aA composite variable created using insurance status and area-level median income quartile

Discussion

Using a large nationwide cancer database, we showed that the odds of being diagnosed with TN breast cancer subtype were greater in NH blacks than NH whites, in younger than older adults, and in low SES than high SES. We also showed that the odds of being diagnosed with HER2-overexpressing breast cancer subtype were greater in Asian/Pacific Islanders and Hispanics than NH whites, in younger than older adults, and in high grade than low grade breast cancers. Notably, the higher odds of having TN breast cancer subtype in blacks and of HER2-overexpressing in Asian/Pacific Islanders compared with NH whites were similar at each level of SES.

Previous studies suggested that the black–white differences in prevalence of hormone receptor negative or TN status could be explained by SES [26, 3840]. However, the effect of race/ethnicity on the odds of having these breast cancer subtypes is still evident even after controlling for differences in SES [12, 14, 27]. For example, DeSantis et al. [27]. reported that adjusting for SES explained 18 % black–white difference in the odds of being diagnosed with hormone receptor-negative breast cancer, which was not statistically significant. A number of studies also reported that NH blacks had about twofold higher odds of being diagnosed with TN breast cancer than NH whites after controlling for SES, which is similar to the magnitude of the association in our study [12, 14, 41]. However, these studies used state-level cancer registry data with area-level SES did not show black–white odds ratio difference across SES strata, or showed results for breast cancer subtypes without including HER2 status. In our study, after controlling for SES and other factors, NH blacks had about 1.8 times higher odds of TN subtype than NH whites. Even in women with high SES, NH black women still had higher odds of being diagnosed with TN breast cancer compared with NH whites, implying a role for other uncontrolled factors such as genetic or non-genetic factors [42, 43]. In addition, race could be a proxy for other unmeasured factors [44]. We also tried to check the inter-play between race and SES in a sensitivity analysis creating a composite variable of race and SES and using NH white with high SES as a reference category, which did not show significant change in our results (Supplementary Table 1). The factors that drive higher odds of TN breast cancer subtypes in black women in the U.S. still need more elaboration.

In our study, we also found higher odds of HER2-overexpressing subtype in API than NH whites, which was consistent with previous reports [12, 13]. For instance, Parise et al. [12] showed that API had 1.41 times higher odds being diagnosed with HER2-overexpressing subtype than did NH whites after adjusting for SES. Our findings on the higher odds of HER2-overexpressing breast cancer subtypes in API women were similar in every level of SES. Lund et al. [41] reported that HER2-positivity did not vary by SES, although they did not examine racial differences across SES levels. The higher odds of HER2-overexpressing subtype in API could be due to genetic, sociocultural, or other risk factors, which require further studies [45, 46].

The higher odds of TN and HER2-overexpressing breast cancer subtypes in younger than older women are also in agreement with previous reports [3, 7, 12, 14, 47]. Anders et al. [47] found unique gene sets that distinguished breast cancers arising in younger (≤45) but not in older (≥65) women, suggesting that breast cancer in younger women is biologically distinct. The difference in subtype distributions of younger and older women could be due to differences in lifestyle, environmental, and other unidentified risk factors between these two groups.

A particular strength of our study is the use of the NCDB, as its large number of breast cancer cases permitted simultaneous stratified analyses of molecular subtypes (including HER2 status) by race/ethnicity and SES for the first time. Furthermore, our results were robust to choice of SES measures or analytic methods. Specifically, we found similar results when we used individual-level SES (insurance) instead of a composite of individual- and area-level SES measure and multiple logistic regression instead of polytomous logistic regression analyses (Supplementary Tables 2, 3). We also conducted sensitivity analysis by excluding stage and grade from the model and then including them subsequently, which did not change our results significantly.

One of the limitations of our study is that the NCDB is a national hospital-based cancer registries database, rather than a population-based cancer registry generalizable to the overall US population. However, the demographic and tumor characteristics of women diagnosed with breast cancer are found to be similar between the NCDB and the Surveillance, Epidemiology and End Results (SEER) program database, which is a population-based cancer registries database [48]. Second, information for HER2 status was missing for 10.5 % of breast cancer cases although there were no significant demographic or tumor characteristics differences between these cases and those with HER2 status information. In addition, we grouped borderline laboratory test results of ER and PR as ER+ and PR+, and borderline HER2 as unknown, which might have led to misclassification. Hospital differences in laboratory techniques for breast cancer markers, especially HER2 status, might also be an issue [49, 50].

Conclusions

In a large nationwide hospital-based dataset in the U.S., we found that NH black women had nearly twofold higher odds of being diagnosed with triple-negative breast cancer subtype than did their white counterparts, regardless of their socioeconomic group. Similarly, the higher odds of presenting with HER2-overexpressing breast cancer in Asian/Pacific Islander women compared with white women were observed also at every level of SES. Further studies are needed to identify factors that contribute to the higher odds of TN or HER2-overexpressing breast cancer subtypes in minority women.

Disclosure

The authors declare that they have no conflict of interest.

Supplementary material

10549_2014_2976_MOESM1_ESM.docx (28 kb)
Supplementary material 1 (DOCX 28 kb)

Copyright information

© Springer Science+Business Media New York 2014