Real-word study of racial/ethnic disparities and socioeconomic determinants of overall survival in male breast cancer

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Freeman, Kent Schechter, Long C. Nguyen, Olasubomi J. Omoleye, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5808248/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study assessed racial/ethnic disparities and socioeconomic determinants of overall survival in male breast cancer. Using the 2010–2021 US National Cancer Database, we identified 20,470 patients: 78.2% White, 13.8% Black, 4.0% Hispanic, and 2.5% Asian or Pacific Islander. After adjusting for clinicopathologic characteristics, Black patients had higher mortality than White patients (adjusted hazard ratio [AHR] 1.22, 95% CI: 1.12–1.32); however, when further adjusting for socioeconomic factors, this difference was no longer significant (AHR 1.09, 95% CI: 0.99–1.21). Hispanic patients had better survival. In the TNBC cohort, Asian or Pacific Islander patients had higher mortality than White patients (AHR 2.35, 95% CI: 1.21–4.55), warranting further investigation. In this US male breast cancer cohort, Black patients and White patients had similar mortality risk after further adjusting for socioeconomic indicators. Higher median household income and private insurance were linked to better survival. Strategies addressing socioeconomic inequities may help improve male breast cancer outcomes. Biological sciences/Cancer/Breast cancer Biological sciences/Cancer/Cancer epidemiology Health sciences/Health care/Prognosis Health sciences/Health care/Public health Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Male breast cancer (mBC) is rare, constituting less than 1% of all breast cancer cases in the United States (US) with an estimated 2,800 new cases in 2023. 1 Men who identify as Black or African American have higher rates of breast cancer than those who identify as White, Hispanic, and Asian or Other Pacific Islander​. 2 Due to its rarity, data on mBC outcomes by race/ethnicity and socioeconomic determinants is limited. Additionally, due to comparatively limited evidence, mBC often gets treated following the guidelines created for female breast cancer. Hormone receptor (HR)-positive mBC – similar to estrogen receptor-positive, postmenopausal breast cancer in women – is treated with tamoxifen and/or chemotherapy. Some patients with triple-negative breast cancer (TNBC) may be eligible for pembrolizumab. mBC tumors with specific genetic mutations (e.g., human epidermal growth factor receptor 2 [HER2], PD-1, and PIK3CA ) inform the use of targeted therapies or immune checkpoint inhibitors. 3 Evidence suggests that, although similar in some regards, mBC differs in its biological and clinical behavior compared to female breast cancer, 4 highlighting the need for more focused research to better understand its distinct characteristics and differences to improve male patients’ outcomes​. Although less common than female breast cancer, the incidence of mBC has risen in recent years, 5,6 driven in part by known risk factors such as family history, aging, obesity, and high-penetrance genes (i.e., BRCA1 and BRCA2 ) that can elevate the risk for mBC by up to 80-fold. 7 Previous studies also have documented that mBC patients have lower 3-year and 5-year survival rates than female breast cancer patients for all stages of disease. 8–10 Limited studies of racial/ethnic disparities in mBC mortality have indicated that Black men experience worse survival outcomes than White men, 11–13 mirroring trends seen among women with breast cancer in the US. 14 Social determinants have been shown to contribute to disparities in female breast cancer mortality, including socioeconomic status, access to health care and services, and facility type. 6 While race and ethnicity are closely tied to socioeconomic factors in the US, addressing these factors reduces racial/ethnic disparities in female breast cancer risk and health outcomes. 15 As previous studies in mBC have largely focused on differences between White patients and Black patients and the HR-positive/HER2-negative molecular subtype, little to less is known about mBC mortality disparities in other racial/ethnic groups and other molecular subtypes. 11,16,17 To address these gaps, we examined disparities in overall survival (OS) of mBC by race/ethnicity and social determinants across tumor stages and molecular subtypes, using a large US clinical oncology registry. Results Patient characteristics We identified a total of 20,470 mBC patients. The mean age at diagnosis was 66.2 years (SD 12.6). Most (78.2%) patients self-identified as White, followed by 13.8% as Black, 4.0% as Hispanic, 2.5% as API, and 1.6% as Other (Table 1 ). Overall, 40.2% were at a median household income quartile of ≥ $ 63,333; 37.3% had private insurance, 52.3% were on Medicare while 4.9% were on Medicaid; 91.4% were diagnosed with stage I-III; 83.6% were HR-positive/HER2-negative; and 54.1% had grade 2 tumors (Table 1 ). Compared with White patients, API, Black, or Hispanic patients were diagnosed at younger age, at higher percent no high school degree quartiles, at lower median household income quartiles (except for API), were more likely to be uninsured or on Medicaid (Table 2 ). Black patients and Hispanic patients were more likely to be diagnosed with TNBC and grade 3 tumors compared to other racial/ethnic groups (Table 2 ). Table 1 Overall sociodemographic and clinicopathologic characteristics of male patients with breast cancer Overall (N = 20470) Characteristic n (%) Age at diagnosis (years) , mean (SD) 66.2 (12.6) Race/ethnicity White 16015 (78.2) Black 2817 (13.8) API 503 (2.5) Hispanic 818 (4.0) Other 317 (1.6) Percent no high school degree quartiles a ≥17.6% 3093 (17.5) 10.9–17.5% 4390 (24.8) 6.3–10.8% 5108 (28.9) <6.3% 5099 (28.8) Median household income quartiles b < $ 40,227 2853 (16.2) $ 40,227– $ 50,353 3601 (20.4) $ 50,354– $ 63,332 4110 (23.3) ≥ $ 63,333 7089 (40.2) Type of health insurance Uninsured 407 (2.0) Private/managed care 7637 (37.3) Medicaid 1011 (4.9) Medicare 10702 (52.3) Other government/unknown 713 (3.5) Rural-urban area Metro 17335 (86.8) Urban 2360 (11.8) Rural 271 (1.4) Facility type/cancer program Community 1775 (8.9) Comprehensive community 8171 (40.9) Academic/research 5770 (28.9) Integrated network 4256 (21.3) Charlson-Deyo comorbidity score 0 15346 (75.0) 1 3364 (16.4) ≥ 2 1760 (8.6) Histologic type Ductal 17717 (86.6) Lobular 552 (2.7) Ductal and lobular 387 (1.9) Other 1814 (8.9) AJCC stage group I 9411 (46.0) II 6564 (32.1) III 2725 (13.3) IV 1770 (8.6) Molecular subtype HR+/HER2- 15972 (83.6) HR+/HER2+ 2154 (11.3) HR-/HER2+ 231 (1.2) TNBC 758 (4.0) Tumor grade 1 2653 (14.4) 2 9965 (54.1) 3 5796 (31.5) Median follow-up time in months (IQR) 52.8 (28.7, 84.6) Abbreviations: SD, standard deviation; IQR, interquartile range; API, Asian or Pacific Islander; AJCC, American Joint Committee on Cancer; HR, hormone receptor; HER2, human epidermal growth factor receptor 2; TNBC, triple-negative breast cancer; BCS, breast-conserving surgery. a Defined as education attainment for patient residence areas and measured by matching the zip code of the patient recorded at the time of diagnosis against files derived from the 2016 American Community Survey data. b Based on the 2016 American Community Survey data, spanning years 2012–2016 and adjusted for 2016 inflation. Table 2 Distributions of sociodemographic and clinicopathologic characteristics of male patients with breast cancer by race/ethnicity White Black API Hispanic Other P value a Characteristic n (%) n (%) n (%) n (%) n (%) Age at diagnosis (years) , mean (SD) 67.2 (12.3) 63.2 (12.6) 63.1 (13.4) 61.0 (13.9) 63.4 (13.3) < 0.001 Percent no high school degree quartiles b ≥17.6% 1744 (12.6) 841 (34.9) 97 (22.0) 357 (49.6) 54 (18.8) < 0.001 10.9–17.5% 3272 (23.7) 824 (34.2) 75 (17.0) 150 (20.8) 69 (24.0) 6.3–10.8% 4294 (31.0) 504 (20.9) 109 (24.8) 140 (19.4) 61 (21.2) <6.3% 4522 (32.7) 241 (10.0) 159 (36.1) 73 (10.1) 104 (36.1) Median household income quartiles c < $ 40,227 1642 (11.9) 944 (39.2) 40 (9.1) 191 (26.6) 36 (12.5) < 0.001 $ 40,227– $ 50,353 2836 (20.6) 490 (20.4) 53 (12.0) 163 (22.7) 59 (20.5) $ 50,354– $ 63,332 3354 (24.3) 450 (18.7) 70 (15.9) 181 (25.2) 55 (19.1) ≥ $ 63,333 5967 (43.2) 523 (21.7) 277 (63.0) 184 (25.6) 138 (47.9) Type of health insurance Uninsured 228 (1.4) 95 (3.4) 13 (2.6) 62 (7.6) 9 (2.8) < 0.001 Private/managed care 5925 (37.0) 1008 (35.8) 238 (47.3) 328 (40.1) 138 (43.5) Medicaid 542 (3.4) 298 (10.6) 55 (10.9) 97 (11.9) 19 (6.0) Medicare 8797 (54.9) 1293 (45.9) 189 (37.6) 291 (35.6) 132 (41.6) Other government/unknown 523 (3.3) 123 (4.4) < 10 (< 2.0) 40 (4.9) 19 (6.0) Rural-urban area Metro 13267 (85.1) 2557 (92.3) 471 (96.3) 773 (95.8) 267 (88.4) < 0.001 Urban 2092 (13.4) 189 (6.8) 17 (3.5) 30 (3.7) 32 (10.6) Rural 239 (1.5) 24 (0.9) < 10 (≤ 1.0) < 10 (≤ 1.0) < 10 (≤ 1.0) Facility type/cancer program Community 1483 (9.4) 168 (6.2) 40 (8.4) 59 (7.8) 25 (8.2) < 0.001 Comprehensive community 6660 (42.4) 962 (35.5) 180 (37.7) 269 (35.4) 100 (32.7) Academic/research 4176 (26.6) 1032 (38.1) 174 (36.4) 280 (36.8) 108 (35.3) Integrated network 3397 (21.6) 550 (20.3) 84 (17.6) 152 (20.0) 73 (23.9) Charlson-Deyo comorbidity score 0 12103 (75.6) 1966 (69.8) 396 (78.7) 628 (76.8) 253 (79.8) < 0.001 1 2601 (16.2) 517 (18.4) 69 (13.7) 132 (16.1) 45 (14.2) ≥ 2 1311 (8.2) 334 (11.9) 38 (7.6) 58 (7.1) 19 (6.0) Histologic type Ductal 13950 (87.1) 2365 (84.0) 429 (85.3) 706 (86.3) 267 (84.2) < 0.001 Lobular 464 (2.9) 60 (2.1) < 10(< 1.5) 12 (1.5) < 10 (< 3.0) Ductal and lobular 307 (1.9) 38 (1.3) 11 (2.2) 19 (2.3) 12 (3.8) Other 1294 (8.1) 354 (12.6) 56 (11.1) 81 (9.9) 29 (9.1) AJCC stage group I 7588 (47.4) 1087 (38.6) 244 (48.5) 357 (43.6) 135 (42.6) < 0.001 II 5120 (32.0) 930 (33.0) 150 (29.8) 256 (31.3) 108 (34.1) III 2052 (12.8) 447 (15.9) 60 (11.9) 124 (15.2) 42 (13.2) IV 1255 (7.8) 353 (12.5) 49 (9.7) 81 (9.9) 32 (10.1) Molecular subtype HR+/HER2- 12660 (84.4) 2055 (79.1) 391 (83.2) 618 (82.5) 248 (84.6) < 0.001 HR+/HER2+ 1660 (11.1) 346 (13.3) 48 (10.2) 77 (10.3) 23 (7.8) HR-/HER2+ 165 (1.1) 38 (1.5) 13 (2.8) < 10 (< 1.5) < 10 (< 2.5) TNBC 521 (3.5) 158 (6.1) 18 (3.8) 45 (6.0) 16 (5.5) Tumor grade 1 2161 (14.9) 304 (12.4) 70 (16.0) 93 (12.8) 25 (8.8) < 0.001 2 7865 (54.2) 1299 (52.8) 237 (54.1) 392 (54.0) 172 (60.6) 3 4481 (30.9) 856 (34.8) 131 (29.9) 241 (33.2) 87 (30.6) Median follow-up time in months (IQR) 53.7 (29.3, 85.8) 47.4 (26.2, 78.1) 54.8 (30.0, 83.7) 52.5 (26.7, 80.1) 52.3 (27.3, 85.2) < 0.001 Abbreviations: SD, standard deviation; IQR, interquartile range; API, Asian or Pacific Islander; AJCC, American Joint Committee on Cancer; HR, hormone receptor; HER2, human epidermal growth factor receptor 2; TNBC, triple-negative breast cancer; BCS, breast-conserving surgery. a P values were calculated using ANOVA or Kruskal-Wallis tests for continuous data and Pearson’s X 2 tests for categorical data. b Defined as education attainment for patient residence areas and measured by matching the zip code of the patient recorded at the time of diagnosis against files derived from the 2016 American Community Survey data. c Based on the 2016 American Community Survey data, spanning years 2012–2016 and adjusted for 2016 inflation. Racial/ethnic and socioeconomic disparities in mortality With a median follow-up of 52.8 months (IQR: 27.7–84.6), there were differences in OS between racial/ethnic groups overall ( Supplementary Fig. 2 ), with Black patients having the shortest median survival (113.0 months [95% CI: 106.7–130.0]) ( Supplementary Table 1 ). When stratified by tumor stage, Black patients experienced worse OS than other racial/ethnic patients in stage I, II, and III cohorts (Fig. 1 ); the OS rate was similar by race/ethnicity for stage IV disease ( Supplementary Table 1 ). When stratified by molecular subtype, we observed OS differences across racial/ethnic groups in the HR-positive/HER2-negative and TNBC cohorts (Fig. 2 ). Black patients in the HR-positive/HER2-negative cohort and API patients in the TNBC cohort had the shortest median survival ( Supplementary Table 1 ). Among all racial/ethnic groups, Black patients had the lowest 3-year, 5-year, and 10-year rates of OS overall; however, these rates vary across tumor stages and molecular subtypes ( Supplementary Table 2 ). Overall ( Supplementary Table 3 ), mortality risk after adjusting for clinicopathologic characteristics (model 2) was higher in Black patients (AHR 1.22, 95% CI: 1.12–1.32, P < 0.001) and lower in API patients (AHR 0.69, 95% CI: 0.54–0.88, P = 0.003) compared to White patients. When further adjusting for socioeconomic factors (model 3), the OS difference was no longer significant between Black patients and White patients (AHR 1.09, 95% CI: 0.99–1.21, P = 0.075); API patients (AHR 0.70, 95% CI: 0.54–0.90, P = 0.006) and Hispanic patients (AHR 0.76, 95% CI: 0.62–0.94, P = 0.010) had a lower mortality risk ( Supplementary Table 3 ). Compared to patients with a median household income of < $ 40,227, those of $ 40,227- $ 50,353 (AHR 0.89, 95% CI: 0.80–0.99, P = 0.038), $ 50,354- $ 63,332 (AHR 0.85, 95% CI: 0.76–0.96, P = 0.006), or ≥ $ 63,333 (AHR 0.76, 95% CI: 0.67–0.86, P < 0.001) had a lower risk of mortality. Patients with no insurance (AHR 1.79, 95% CI: 1.42–2.26, P < 0.001), Medicaid (AHR 1.60, 95% CI: 1.35–1.89, P < 0.001), or Medicare (AHR 1.19, 95% CI: 1.09–1.30, P < 0.001) had a higher mortality risk than those privately insured. Greater comorbidity scores were associated with worse OS ( Supplementary Table 3 ). When adjusting for clinicopathologic factors in Model 2, Black patients had a greater risk of mortality with stage I (AHR 1.26, 95% CI: 1.05–1.51, P = 0.011), stage II (AHR 1.15, 95% CI: 1.004–1.33, P = 0.044), and stage III tumors (AHR 1.33, 95% CI: 1.12–1.58, P = 0.001) compared to White patients (Fig. 3 ). However, after adjustment for socioeconomic factors in Model 3, this difference was no longer significant across stages I (AHR 1.12, 95% CI: 0.91–1.38, P = 0.281), II (AHR 1.03, 95% CI: 0.87–1.21, P = 0.758), and III (AHR 1.18, 95% CI: 0.95–1.46, P = 0.132) ( Supplementary Tables 4–6 ). No significant difference in mortality was observed by race/ethnicity for patients with stage IV tumors ( Supplementary Table 7 ). Compared to patients with a median household income of < $ 40227, patients with a median household income of ≥ $ 63,333 and either stage I (AHR 0.73, 95% CI: 0.58–0.93, P = 0.012) or stage II tumors (AHR 0.72, 95% CI, 0.59–0.88, P = 0.001) had lower mortality risks ( Supplementary Tables 4 and 5 ). For stages III and IV, no significant differences in mortality between median income quartiles were observed ( Supplementary Tables 6 and 7 ). In patients with stage I tumors, Medicaid was associated with a greater mortality risk compared to private insurance (AHR 1.72, 95% CI: 1.16–2.57, P = 0.007) ( Supplementary Table 4 ). With stage II tumors, patients uninsured (AHR 1.97, 95% CI: 1.34–2.91, P = 0.001) or on Medicare (AHR 1.18, 95% CI: 1.02–1.36, P = 0.028) had a higher risk of mortality compared to private insurance ( Supplementary Table 5 ). For stage III disease, Medicaid was associated with a greater mortality risk compared to private insurance (AHR 1.52, 95% CI: 1.08–2.15, P = 0.016) ( Supplementary Table 6 ). In stage IV disease, lack of insurance, Medicare, or Medicaid was associated with increased mortality risk ( Supplementary Table 7 ). Patients with a Charlson-Deyo comorbidity score of ≥ 2 consistently had a greater mortality risk across all tumor stages ( Supplementary Tables 4–7). When stratifying by molecular subtype, Black patients with HR-positive/HER2-negative mBC had a higher risk of mortality than White patients (AHR 1.23, 95% CI: 1.12–1.35, P < 0.001) (model 2) (Fig. 4 ); however, the difference between the two racial groups was no longer significant when further adjusting for socioeconomic characteristics (AHR 1.09, 95% CI: 0.98–1.22, P = 0.111) (model 3). Patients with a median household income of $ 50,354- $ 63,332 (AHR 0.86, 95% CI: 0.76–0.98, P = 0.019) or ≥ $ 63,333 (AHR 0.73, 95% CI: 0.64–0.84, P < 0.001) had a lower mortality risk than those of < $ 40,227 ( Supplementary Table 8 ). Compared to private insurance, no insurance (AHR 1.68, 95% CI: 1.29–2.19, P < 0.001), Medicare (AHR 1.21, 95% CI: 1.10–1.33, P < 0.001), or Medicaid (AHR 1.73, 95% CI: 1.43–2.09, P < 0.001) was associated with an increased mortality risk ( Supplementary Table 8 ). For HER2-positive tumors, the OS rate was similar by race/ethnicity (Fig. 4 ). Patients with a comorbidity score of ≥ 2 or without insurance experienced worse OS ( Supplementary Table 9 ). Socioeconomic factors were not associated with OS in male TNBC ( Supplementary Table 10 ). In the TNBC cohort, API patients were at an increased mortality risk than White patients (AHR 2.35, 95% CI: 1.21–4.55, P = 0.011), after adjusting for clinicopathologic characteristics (Fig. 4 ). Discussion We compared the mortality of mBC in a large US cohort by racial/ethnic groups, socioeconomic factors across tumor stages and molecular subtypes. Black patients had a higher mortality risk than White patients when adjusting for clinicopathologic factors. However, when further adjusting for socioeconomic indicators, the mortality risk between Black patients and White patients did not vary to a level of statistical significance. Overall, API and Hispanic patients had better OS than White patients. In the TNBC cohort, API patients fared worse OS than White patients, meriting further investigation. Lower median household income quartiles, Medicaid, Medicare, or no insurance, and greater comorbidity scores were associated with a higher mortality risk in mBC. In this study, Black mBC patients consistently had higher mortality than White patients across tumor stages and molecular subtypes, after adjusting for clinicopathologic characteristics. This racial disparity mirrors trends found in female breast cancer studies. 14 Notably, however, after further adjusting for socioeconomic indicators, the survival difference between Black patients and White patients was no longer statistically significant. A recent comparative analysis of mBC has also documented a similar OS rate between the two racial groups after controlling for both clinical and sociodemographic factors. 18 Our findings suggest that alleviating socioeconomic inequities may reduce disparities in mBC mortality between Black patients and White patients. We also found that after adjusting for clinicopathologic characteristics or further for socioeconomic factors, API or Hispanic patients with mBC displayed non-statistically significant trends toward slightly lower mortality risk compared with White patients, and this was consistent across tumor stages and molecular subtypes. However, it is worth noting that in the TNBC cohort, API patients experienced the lowest 5-year and 10-year OS rates compared with other racial/ethnic categories. This is a surprising result, as previous research in TNBC have described Black men having significantly lower OS rates than White men, without much focus on other racial/ethnic groups displaying the same trend. 19 This result may be due to the small sample size for the API patient group. It is also possible that these survival disparities may be masked by aggregation of Asian and other Pacific Islander as a racial category. 20 Between 2010 and 2014, Asian women with TNBC had better survival of any racial demographic with stage I-III tumors but poorer survival in the metastatic setting. 21 Furthermore, API women experienced the steepest increase of all racial/ethnic groups in breast cancer rates from 2012 to 2021, 14 combined with a significant increase in TNBC incidence between 2010 and 2019 among those ages ≥ 55 years. 22 This shift in TNBC incidence and lower survival rates in API women may contain parallels in male TNBC, which warrants future research. Socioeconomic indicators correlated with mortality disparities in mBC, both overall and when stratified by tumor stage or molecular subtype. Across all mBC patients and within stage I-II and HR-positive/HER2-negative disease, higher median household income quartiles were associated with greater survival rates. An analysis of the 2004–2016 NCDB reported that higher income levels were associated with improved survival in mBC. However, this and other studies have not fully stratified patients based on molecular subtype and/or tumor stage. 11,12,16,18 Having private health insurance was associated with a lower mortality risk compared to having no health insurance coverage, Medicaid, or Medicare. Patients with a higher median household income had better survival outcomes, aligning with findings from prior studies. 6,18,23 However, educational attainment, facility type, and rural-urban residence were not significantly or consistently associated with OS across tumor stages and molecular subtypes, though congruent with reports in mBC literature. 11,23 Collectively, these findings suggest that differences in health insurance and household income may contribute to disparities in mBC mortality. Several limitations of this study should be acknowledged. First, although the NCDB includes extensive clinicopathologic and certain relevant sociodemographic data, its mortality statistics only include all-cause mortality. It is worth examining disease-specific, progression-related, or other survival outcomes of mBC in future studies. Moreover, there are many unmeasured potential confounders not being collected by the NCDB, such as environmental factors, lifestyle data, personal and family history, and genetic predispositions, that likely influence the survival differences across racial/ethnic groups observed in the current study. Therefore, investigators should consider these key variables in their future analyses. Lastly, given the nature of the NCDB registry and this retrospective study design, the generalizability of our findings may be limited. However, the racial demographics of mBC patients in the NCDB generally reflect the US population, and our findings are consistent with prior studies using the Surveillance, Epidemiology, and End Results data. 12,16 In conclusion, Black patients with mBC had a greater mortality risk than White patients when controlling for clinicopathologic features; however, the risk was similar between the two racial groups after further controlling for socioeconomic indicators. Compared with White patients, API or Hispanic patients had better survival outcomes, except for API patients who had higher mortality from TNBC. Lower median household income, lack of health insurance coverage or public insurance, and greater comorbidity scores were associated with a higher risk of mortality. Our findings highlight racial/ethnic disparities and socioeconomic determinants of survival outcomes in mBC across tumor stages and molecular subtypes. Strategies to address socioeconomic inequities that impact access to comprehensive cancer care programs and services may help reduce racial/ethnic disparities and improve survival outcomes of the US mBC population. Methods Study design and data source This was a retrospective study of real-world data from mBC patients registered in the 2010–2021 National Cancer Database (NCDB), collected by the Commission on Cancer (CoC) of the American College of Surgeons and the American Cancer Society. 24 The NCDB captures approximately 72% of new cancer diagnoses each year from more than 1,500 CoC-accredited cancer centers in the US. 25,26 Because NCDB de-identified data does not identify clinical facilities, providers, or patients, the University of Chicago Institutional Review Board exempted the study from review. In compliance with the NCDB’s Data Use Agreement, we suppressed reporting of cell counts < 10 to protect patients’ confidentiality. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. 27 Eligibility and sample selection Sample selection of male patients diagnosed with invasive breast carcinoma was illustrated in Supplementary Fig. 1 . Briefly, patients were eligible if they 1) were at least 18 years of age at diagnosis; 2) were assigned male sex assigned at birth; 3) had tumors classified as stage I, II, III, or IV by the American Joint Committee on Cancer (AJCC) staging system; 4) were diagnosed between 2010 and 2021; and 5) included data on tumor molecular subtype. Measures Tumor stage was defined by the AJCC staging system and classified as stage I, II, III, or IV. 28 Molecular subtypes of breast tumors were categorized as HR-positive/HER2-negative, HR-positive/HER2-positive, HR-negative/HER2-positive, and TNBC. Due to small sample sizes, the HR-positive/HER2-positive and HR-negative/HER2-positive groups were combined into a HER-positive (or HER2-enriched) category for regression analyses. The main outcome of interest was OS, which was defined as the time from the initial breast cancer diagnosis to death from any cause or last patient contact. According to the NCDB, vital status was not available for patients diagnosed in 2021 because of limited time for follow-up. Thus, these patients were not included in our survival analysis. Median follow-up (in months) for the patient cohort was reported. Racial/ethnic groups comprised non-Hispanic Asian or Pacific Islander (API), non-Hispanic Black, Hispanic, non-Hispanic White, and Other. Other is a racial/ethnic group listed in the NCDB and represents patients who were classified as Other by local cancer registries. The NCDB does not specifically define race/ethnicity classified into Other. Demographic and clinicopathologic characteristics included age at diagnosis, year of diagnosis, percent no high school degree quartile based on residential geographic area (≥ 17.6%, 10.9–17.5%, 6.3–10.8%, and < 6.3%), median household income quartile (< $ 40,227, $ 40,227- $ 50,353, $ 50,354- $ 63,332, and ≥ $ 63,333), type of health insurance (Medicaid, Medicare, other government, private, uninsured), rural-urban area, type of facility/cancer program, Charlson-Deyo comorbidity score (0, 1, and ≥ 2), tumor histologic type (ductal, ductal and lobular, lobular, or other), and tumor grade (1 - low, 2 - intermediate, and 3 - high). Statistical analysis First, we described the patient cohort using standard summary statistics. We used analyses of variance or Kruskal-Wallis tests to compare the distributions of continuous variables. Categorical variables were compared using Pearson’s chi-squared tests. For survival analysis, the Kaplan-Meier (K-M) method was used to calculate the median survival time (in months) and estimate 3-year, 5-year, and 10-year OS rates across racial/ethnic groups, overall and stratified by tumor stage and by molecular subtype. We conducted stratified log-rank tests to determine statistical significance when comparing survival functions across racial/ethnic groups, by tumor stage and by molecular subtype. Cox proportional hazards regression models were fit to further assess racial/ethnic and socioeconomic disparities in OS. A stepwise regression approach was employed. Specifically, model 1 included age at diagnosis and race/ethnicity; in addition, model 2 included histologic type, AJCC stage, tumor grade, and Charlson-Deyo comorbidity score; and, in addition, model 3 included percent no high school degree quartile, median household income quartile, type of insurance, rural-urban area, and facility type. A similar approach was implemented in the stratified Cox regression when stratifying by tumor stage and by molecular subtype. Adjusted hazard ratios (AHR) and 95% confidence intervals (95% CI) were calculated. All statistical tests were two-sided at the 0.05 level of significance. All statistical analyses were performed using Stata version 18 (StataCorp, College Station, TX). Forest plots were created using the forestploter package in R (R Foundation for Statistical Computing). Abbreviations API Asian or Pacific Islander HR hormone receptor HER2 human epidermal growth factor receptor 2 TNBC triple-negative breast cancer. Declarations Competing interests The authors declare no competing interests. Author Contribution J.Q.F.: Conceptualization, methodology, formal analysis, Writing—original draft. K.S.: Conceptualization, data visualization, Writing—original draft. J.H.H.: Overall supervision. All authors: Project administration, data interpretation, Writing—review & editing. All authors read and approved the final version of the manuscript. Acknowledgement This study was presented at the 46th Annual San Antonio Breast Cancer Symposium (SABCS 2023), San Antonio, TX, December 5-9, 2023. This work was supported in part by the National Institute on Aging (T32AG000243), the Susan G. Komen® Breast Cancer Foundation (CTA241185923 and TREND21675016), the University of Chicago Comprehensive Cancer Center Sigal Fellowship in Immuno-Oncology, and the University of Chicago Quad Undergraduate Research Grant. The National Cancer Database is a joint project of the Commission on Cancer of the American College of Surgeons and the American Cancer Society. The data used in this study are derived from a de-identified file. The American College of Surgeons and the Commission on Cancer have not verified and are not responsible for the analytic or statistical methodology employed, or the conclusions drawn from these data by the investigators. Additionally, the contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institute on Aging. Data Availability Data for this study were obtained from the US National Cancer Database. Investigators affiliated with Commission on Cancer-accredited cancer programs can request the National Cancer Database Participant User File by submitting an application to the American College of Surgeons via https://www.facs.org/quality-programs/cancer-programs/national-cancer- database. References Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin . Jan 2023;73(1):17–48. doi:10.3322/caac.21763 Giaquinto AN, Sung H, Miller KD, et al. Breast Cancer Statistics, 2022. CA Cancer J Clin . Nov 2022;72(6):524–541. doi:10.3322/caac.21754 Hassett MJ, Somerfield MR, Giordano SH. Management of Male Breast Cancer: ASCO Guideline Summary. JCO Oncol Pract . Aug 2020;16(8):e839-e843. doi:10.1200/JOP.19.00792 Gucalp A, Traina TA, Eisner JR, et al. Male breast cancer: a disease distinct from female breast cancer. Breast Cancer Res Treat . Jan 2019;173(1):37–48. doi:10.1007/s10549-018-4921-9 Liu N, Johnson KJ, Ma CX. Male Breast Cancer: An Updated Surveillance, Epidemiology, and End Results Data Analysis. Clin Breast Cancer . Oct 2018;18(5):e997-e1002. doi:10.1016/j.clbc.2018.06.013 Konduri S, Singh M, Bobustuc G, Rovin R, Kassam A. Epidemiology of male breast cancer. Breast . Dec 2020;54:8–14. doi:10.1016/j.breast.2020.08.010 Fox S, Speirs V, Shaaban AM. Male breast cancer: an update. Virchows Arch . Jan 2022;480(1):85–93. doi:10.1007/s00428-021-03190-7 Greif JM, Pezzi CM, Klimberg VS, Bailey L, Zuraek M. Gender differences in breast cancer: analysis of 13,000 breast cancers in men from the National Cancer Data Base. Ann Surg Oncol . Oct 2012;19(10):3199 − 204. doi:10.1245/s10434-012-2479-z Li X, Yang J, Krishnamurti U, et al. Hormone Receptor-Positive Breast Cancer Has a Worse Prognosis in Male Than in Female Patients. Clin Breast Cancer . Aug 2017;17(5):356–366. doi:10.1016/j.clbc.2017.03.005 Wang F, Shu X, Meszoely I, et al. Overall Mortality After Diagnosis of Breast Cancer in Men vs Women. JAMA Oncol . Nov 1 2019;5(11):1589–1596. doi:10.1001/jamaoncol.2019.2803 Leone J, Hassett MJ, Freedman RA, et al. Mortality Risks Over 20 Years in Men With Stage I to III Hormone Receptor-Positive Breast Cancer. JAMA Oncol . Apr 1 2024;10(4):508–515. doi:10.1001/jamaoncol.2023.7194 Crew KD, Neugut AI, Wang X, et al. Racial disparities in treatment and survival of male breast cancer. J Clin Oncol . Mar 20 2007;25(9):1089-98. doi:10.1200/JCO.2006.09.1710 Ellington TD, Henley SJ, Wilson RJ, Miller JW. Breast Cancer Survival Among Males by Race, Ethnicity, Age, Geographic Region, and Stage - United States, 2007–2016. MMWR Morb Mortal Wkly Rep . Oct 16 2020;69(41):1481–1484. doi:10.15585/mmwr.mm6941a2 Giaquinto AN, Sung H, Newman LA, et al. Breast cancer statistics 2024. CA Cancer J Clin . Nov-Dec 2024;74(6):477–495. doi:10.3322/caac.21863 Hirko KA, Rocque G, Reasor E, et al. The impact of race and ethnicity in breast cancer-disparities and implications for precision oncology. BMC Med . Feb 11 2022;20(1):72. doi:10.1186/s12916-022-02260-0 Leone J, Freedman RA, Lin NU, et al. Tumor subtypes and survival in male breast cancer. Breast Cancer Res Treat . Aug 2021;188(3):695–702. doi:10.1007/s10549-021-06182-y Yadav S, Karam D, Bin Riaz I, et al. Male breast cancer in the United States: Treatment patterns and prognostic factors in the 21st century. Cancer . Jan 1 2020;126(1):26–36. doi:10.1002/cncr.32472 Elimimian EB, Elson L, Li H, et al. Male Breast Cancer: A Comparative Analysis from the National Cancer Database. World J Mens Health . Jul 2021;39(3):506–515. doi:10.5534/wjmh.200164 Yadav SK, Silwal S, Yadav S, Krishnamoorthy G, Chisti MM. A Systematic Comparison of Overall Survival Between Men and Women With Triple Negative Breast Cancer. Clin Breast Cancer . Feb 2022;22(2):161–169. doi:10.1016/j.clbc.2021.07.001 Taparra K, Dee EC, Dao D, Patel R, Santos P, Chino F. Disaggregation of Asian American and Pacific Islander Women With Stage 0-II Breast Cancer Unmasks Disparities in Survival and Surgery-to-Radiation Intervals: A National Cancer Database Analysis From 2004 to 2017. JCO Oncol Pract . Aug 2022;18(8):e1255-e1264. doi:10.1200/OP.22.00001 Wang F, Zheng W, Bailey CE, Mayer IA, Pietenpol JA, Shu XO. Racial/Ethnic Disparities in All-Cause Mortality among Patients Diagnosed with Triple-Negative Breast Cancer. Cancer Res . Feb 15 2021;81(4):1163–1170. doi:10.1158/0008-5472.CAN-20-3094 Du XL, Li Z. Incidence trends in triple-negative breast cancer among women in the United States from 2010 to 2019 by race/ethnicity, age and tumor stage. Am J Cancer Res . 2023;13(2):678–691. Restrepo DJ, Boczar D, Huayllani MT, et al. Survival Disparities in Male Patients With Breast Cancer. Anticancer Res . Oct 2019;39(10):5669–5674. doi:10.21873/anticanres.13764 Bilimoria KY, Stewart AK, Winchester DP, Ko CY. The National Cancer Data Base: a powerful initiative to improve cancer care in the United States. Ann Surg Oncol . Mar 2008;15(3):683 − 90. doi:10.1245/s10434-007-9747-3 Boffa DJ, Rosen JE, Mallin K, et al. Using the National Cancer Database for Outcomes Research: A Review. JAMA Oncol . Dec 1 2017;3(12):1722–1728. doi:10.1001/jamaoncol.2016.6905 Mallin K, Browner A, Palis B, et al. Incident Cases Captured in the National Cancer Database Compared with Those in U.S. Population Based Central Cancer Registries in 2012–2014. Ann Surg Oncol . Jun 2019;26(6):1604–1612. doi:10.1245/s10434-019-07213-1 von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med . Oct 16 2007;147(8):573-7. doi:10.7326/0003-4819-147-8-200710160-00010 Amin MB, Greene FL, Edge SB, et al. The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more "personalized" approach to cancer staging. CA Cancer J Clin . Mar 2017;67(2):93–99. doi:10.3322/caac.21388 Additional Declarations No competing interests reported. Supplementary Files Suppl.Info.menBC01.11.25.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5808248","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":415513187,"identity":"908476de-30cb-404b-bc27-5b2d52a81ec2","order_by":0,"name":"Jincong Q. Freeman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACxoaDD0C0HAMDD8MBqKABAS2HwQqMidfCwMAMVpDYANQCA/i1MDceZvvwoWJb+objZw8e+FFTK8fA3rxNgoDDmGfOOHM7d8OZvISDPceOGzPwHCsjoOX8YWbeNqCWGzwGhxnYjiU2SOSYEbSF+e+/2+kGYC3/gFrk3xChhbHhdgJYC2NbDdAWHsJaGHuO3TaceSbH4GBv3wFjNp60Ygt8WgxnHGZm+FFzW57v+BnjDz++1cnxsx/eeAO/lgMofGAI4FMOAvL8DSj8OkIaRsEoGAWjYAQCAJCcUgv+ya85AAAAAElFTkSuQmCC","orcid":"","institution":"The University of Chicago","correspondingAuthor":true,"prefix":"","firstName":"Jincong","middleName":"Q.","lastName":"Freeman","suffix":""},{"id":415513193,"identity":"2cb1a556-1c5d-4b86-ac68-6800754fb61f","order_by":1,"name":"Kent Schechter","email":"","orcid":"","institution":"The University of Chicago","correspondingAuthor":false,"prefix":"","firstName":"Kent","middleName":"","lastName":"Schechter","suffix":""},{"id":415513196,"identity":"d3bd6d65-5ee1-410f-804a-f81abf06a1c3","order_by":2,"name":"Long C. Nguyen","email":"","orcid":"","institution":"The University of Chicago","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"C.","lastName":"Nguyen","suffix":""},{"id":415513202,"identity":"994b9977-1e9f-4b74-9746-f22694f9d20e","order_by":3,"name":"Olasubomi J. Omoleye","email":"","orcid":"","institution":"The University of Chicago Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Olasubomi","middleName":"J.","lastName":"Omoleye","suffix":""},{"id":415513206,"identity":"69f5e01e-6fba-456a-a34a-be3001e8c7fd","order_by":4,"name":"Jared H. Hara","email":"","orcid":"","institution":"The Queen’s Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Jared","middleName":"H.","lastName":"Hara","suffix":""}],"badges":[],"createdAt":"2025-01-11 08:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5808248/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5808248/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76839880,"identity":"0dbe9c2c-bca7-44e0-a88b-cc4da4d799b0","added_by":"auto","created_at":"2025-02-21 10:01:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":478318,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figs14menBC01.11.251.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5808248/v1/4301df803d79bf0846fa1fc2.jpg"},{"id":76839882,"identity":"f6426c16-ff86-47a8-9602-61106e1e7531","added_by":"auto","created_at":"2025-02-21 10:01:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":486528,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figs14menBC01.11.252.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5808248/v1/8521a95b2fd5eadaebf44665.jpg"},{"id":76839881,"identity":"1b1175d1-ca3f-4760-982a-5f2491b187cb","added_by":"auto","created_at":"2025-02-21 10:01:09","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":588420,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figs14menBC01.11.253.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5808248/v1/2abee6cab390d67853061c52.jpg"},{"id":76839885,"identity":"276addb6-c8dd-4fa3-b29c-f6a0e2b8df96","added_by":"auto","created_at":"2025-02-21 10:01:09","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":559502,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figs14menBC01.11.254.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5808248/v1/58ed4dd0b1069df49c631797.jpg"},{"id":80257765,"identity":"48b90de8-0524-4bdd-9302-075264e21045","added_by":"auto","created_at":"2025-04-09 20:01:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3488851,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5808248/v1/bfae7f53-1d93-46af-a2d2-d5cd0bc24eae.pdf"},{"id":76841335,"identity":"de43e70c-5edf-49c8-ac71-df03f9098cb3","added_by":"auto","created_at":"2025-02-21 10:09:10","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":704052,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl.Info.menBC01.11.25.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5808248/v1/7c81d9cd20e7a34f24e58170.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Real-word study of racial/ethnic disparities and socioeconomic determinants of overall survival in male breast cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMale breast cancer (mBC) is rare, constituting less than 1% of all breast cancer cases in the United States (US) with an estimated 2,800 new cases in 2023.\u003csup\u003e1\u003c/sup\u003e Men who identify as Black or African American have higher rates of breast cancer than those who identify as White, Hispanic, and Asian or Other Pacific Islander​.\u003csup\u003e2\u003c/sup\u003e Due to its rarity, data on mBC outcomes by race/ethnicity and socioeconomic determinants is limited. Additionally, due to comparatively limited evidence, mBC often gets treated following the guidelines created for female breast cancer. Hormone receptor (HR)-positive mBC \u0026ndash; similar to estrogen receptor-positive, postmenopausal breast cancer in women \u0026ndash; is treated with tamoxifen and/or chemotherapy. Some patients with triple-negative breast cancer (TNBC) may be eligible for pembrolizumab. mBC tumors with specific genetic mutations (e.g., human epidermal growth factor receptor 2 [HER2], PD-1, and \u003cem\u003ePIK3CA\u003c/em\u003e) inform the use of targeted therapies or immune checkpoint inhibitors.\u003csup\u003e3\u003c/sup\u003e Evidence suggests that, although similar in some regards, mBC differs in its biological and clinical behavior compared to female breast cancer,\u003csup\u003e4\u003c/sup\u003e highlighting the need for more focused research to better understand its distinct characteristics and differences to improve male patients\u0026rsquo; outcomes​.\u003c/p\u003e \u003cp\u003eAlthough less common than female breast cancer, the incidence of mBC has risen in recent years,\u003csup\u003e5,6\u003c/sup\u003e driven in part by known risk factors such as family history, aging, obesity, and high-penetrance genes (i.e., \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e) that can elevate the risk for mBC by up to 80-fold.\u003csup\u003e7\u003c/sup\u003e Previous studies also have documented that mBC patients have lower 3-year and 5-year survival rates than female breast cancer patients for all stages of disease.\u003csup\u003e8\u0026ndash;10\u003c/sup\u003e Limited studies of racial/ethnic disparities in mBC mortality have indicated that Black men experience worse survival outcomes than White men,\u003csup\u003e11\u0026ndash;13\u003c/sup\u003e mirroring trends seen among women with breast cancer in the US.\u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSocial determinants have been shown to contribute to disparities in female breast cancer mortality, including socioeconomic status, access to health care and services, and facility type.\u003csup\u003e6\u003c/sup\u003e While race and ethnicity are closely tied to socioeconomic factors in the US, addressing these factors reduces racial/ethnic disparities in female breast cancer risk and health outcomes.\u003csup\u003e15\u003c/sup\u003e As previous studies in mBC have largely focused on differences between White patients and Black patients and the HR-positive/HER2-negative molecular subtype, little to less is known about mBC mortality disparities in other racial/ethnic groups and other molecular subtypes.\u003csup\u003e11,16,17\u003c/sup\u003e To address these gaps, we examined disparities in overall survival (OS) of mBC by race/ethnicity and social determinants across tumor stages and molecular subtypes, using a large US clinical oncology registry.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eWe identified a total of 20,470 mBC patients. The mean age at diagnosis was 66.2 years (SD 12.6). Most (78.2%) patients self-identified as White, followed by 13.8% as Black, 4.0% as Hispanic, 2.5% as API, and 1.6% as Other (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Overall, 40.2% were at a median household income quartile of \u0026ge;\u003cspan\u003e$\u003c/span\u003e63,333; 37.3% had private insurance, 52.3% were on Medicare while 4.9% were on Medicaid; 91.4% were diagnosed with stage I-III; 83.6% were HR-positive/HER2-negative; and 54.1% had grade 2 tumors (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Compared with White patients, API, Black, or Hispanic patients were diagnosed at younger age, at higher percent no high school degree quartiles, at lower median household income quartiles (except for API), were more likely to be uninsured or on Medicaid (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Black patients and Hispanic patients were more likely to be diagnosed with TNBC and grade 3 tumors compared to other racial/ethnic groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverall sociodemographic and clinicopathologic characteristics of male patients with breast cancer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall (N\u0026thinsp;=\u0026thinsp;20470)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharacteristic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at diagnosis (years)\u003c/b\u003e, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.2 (12.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/ethnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16015 (78.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2817 (13.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e503 (2.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e818 (4.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e317 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePercent no high school degree quartiles\u003c/b\u003e \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;17.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3093 (17.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.9\u0026ndash;17.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4390 (24.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.3\u0026ndash;10.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5108 (28.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;6.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5099 (28.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian household income quartiles\u003c/b\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u003cspan\u003e$\u003c/span\u003e40,227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2853 (16.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e40,227\u0026ndash;\u003cspan\u003e$\u003c/span\u003e50,353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3601 (20.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e50,354\u0026ndash;\u003cspan\u003e$\u003c/span\u003e63,332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4110 (23.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u003cspan\u003e$\u003c/span\u003e63,333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7089 (40.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of health insurance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUninsured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e407 (2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate/managed care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7637 (37.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1011 (4.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10702 (52.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther government/unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e713 (3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRural-urban area\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17335 (86.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2360 (11.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e271 (1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFacility type/cancer program\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1775 (8.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComprehensive community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8171 (40.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic/research\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5770 (28.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntegrated network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4256 (21.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharlson-Deyo comorbidity score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15346 (75.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3364 (16.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u003c/b\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1760 (8.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistologic type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuctal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17717 (86.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLobular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e552 (2.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuctal and lobular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e387 (1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1814 (8.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAJCC stage group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9411 (46.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6564 (32.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2725 (13.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1770 (8.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMolecular subtype\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR+/HER2-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15972 (83.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR+/HER2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2154 (11.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR-/HER2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e231 (1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e758 (4.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor grade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2653 (14.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9965 (54.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5796 (31.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian follow-up time in months\u003c/b\u003e (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.8 (28.7, 84.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eAbbreviations: SD, standard deviation; IQR, interquartile range; API, Asian or Pacific Islander; AJCC, American Joint Committee on Cancer; HR, hormone receptor; HER2, human epidermal growth factor receptor 2; TNBC, triple-negative breast cancer; BCS, breast-conserving surgery.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003csup\u003ea\u003c/sup\u003e Defined as education attainment for patient residence areas and measured by matching the zip code of the patient recorded at the time of diagnosis against files derived from the 2016 American Community Survey data.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003csup\u003eb\u003c/sup\u003e Based on the 2016 American Community Survey data, spanning years 2012\u0026ndash;2016 and adjusted for 2016 inflation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistributions of sociodemographic and clinicopathologic characteristics of male patients with breast cancer by race/ethnicity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAPI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharacteristic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at diagnosis (years)\u003c/b\u003e, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.2 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.2 (12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.1 (13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.0 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.4 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePercent no high school degree quartiles\u003c/b\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;17.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1744 (12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e841 (34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e357 (49.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.9\u0026ndash;17.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3272 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e824 (34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e150 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69 (24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.3\u0026ndash;10.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4294 (31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e504 (20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;6.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4522 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e241 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e159 (36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e104 (36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian household income quartiles\u003c/b\u003e \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u003cspan\u003e$\u003c/span\u003e40,227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1642 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e944 (39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e191 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e40,227\u0026ndash;\u003cspan\u003e$\u003c/span\u003e50,353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2836 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e490 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e163 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e50,354\u0026ndash;\u003cspan\u003e$\u003c/span\u003e63,332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3354 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e450 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e181 (25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u003cspan\u003e$\u003c/span\u003e63,333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5967 (43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e523 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e277 (63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e184 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e138 (47.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of health insurance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUninsured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate/managed care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5925 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1008 (35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e238 (47.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e328 (40.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e138 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e542 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e298 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8797 (54.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1293 (45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e189 (37.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e291 (35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e132 (41.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther government/unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e523 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10 (\u0026lt;\u0026thinsp;2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRural-urban area\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13267 (85.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2557 (92.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e471 (96.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e773 (95.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e267 (88.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2092 (13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e239 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10 (\u0026le;\u0026thinsp;1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10 (\u0026le;\u0026thinsp;1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10 (\u0026le;\u0026thinsp;1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFacility type/cancer program\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1483 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComprehensive community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6660 (42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e962 (35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180 (37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e269 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic/research\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4176 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1032 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e174 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e280 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e108 (35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntegrated network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3397 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e550 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e152 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73 (23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharlson-Deyo comorbidity score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12103 (75.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1966 (69.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e396 (78.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e628 (76.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e253 (79.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2601 (16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e517 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45 (14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u003c/b\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1311 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e334 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistologic type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuctal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13950 (87.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2365 (84.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e429 (85.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e706 (86.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e267 (84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLobular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e464 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10(\u0026lt;\u0026thinsp;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10 (\u0026lt;\u0026thinsp;3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuctal and lobular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e307 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1294 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e354 (12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAJCC stage group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7588 (47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1087 (38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e244 (48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e357 (43.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e135 (42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5120 (32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e930 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150 (29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e256 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e108 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2052 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e447 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e124 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1255 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e353 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMolecular subtype\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR+/HER2-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12660 (84.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2055 (79.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e391 (83.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e618 (82.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e248 (84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR+/HER2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1660 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e346 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR-/HER2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10 (\u0026lt;\u0026thinsp;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10 (\u0026lt;\u0026thinsp;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e521 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor grade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2161 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e304 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7865 (54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1299 (52.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e237 (54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e392 (54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e172 (60.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4481 (30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e856 (34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131 (29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e241 (33.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87 (30.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian follow-up time in months\u003c/b\u003e (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.7 (29.3, 85.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.4 (26.2, 78.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.8 (30.0, 83.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.5 (26.7, 80.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.3 (27.3, 85.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: SD, standard deviation; IQR, interquartile range; API, Asian or Pacific Islander; AJCC, American Joint Committee on Cancer; HR, hormone receptor; HER2, human epidermal growth factor receptor 2; TNBC, triple-negative breast cancer; BCS, breast-conserving surgery.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e values were calculated using ANOVA or Kruskal-Wallis tests for continuous data and Pearson\u0026rsquo;s \u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e tests for categorical data.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003eb\u003c/sup\u003e Defined as education attainment for patient residence areas and measured by matching the zip code of the patient recorded at the time of diagnosis against files derived from the 2016 American Community Survey data.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ec\u003c/sup\u003e Based on the 2016 American Community Survey data, spanning years 2012\u0026ndash;2016 and adjusted for 2016 inflation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRacial/ethnic and socioeconomic disparities in mortality\u003c/h3\u003e\n\u003cp\u003eWith a median follow-up of 52.8 months (IQR: 27.7\u0026ndash;84.6), there were differences in OS between racial/ethnic groups overall (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e), with Black patients having the shortest median survival (113.0 months [95% CI: 106.7\u0026ndash;130.0]) (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). When stratified by tumor stage, Black patients experienced worse OS than other racial/ethnic patients in stage I, II, and III cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e); the OS rate was similar by race/ethnicity for stage IV disease (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). When stratified by molecular subtype, we observed OS differences across racial/ethnic groups in the HR-positive/HER2-negative and TNBC cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Black patients in the HR-positive/HER2-negative cohort and API patients in the TNBC cohort had the shortest median survival (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). Among all racial/ethnic groups, Black patients had the lowest 3-year, 5-year, and 10-year rates of OS overall; however, these rates vary across tumor stages and molecular subtypes (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e), mortality risk after adjusting for clinicopathologic characteristics (model 2) was higher in Black patients (AHR 1.22, 95% CI: 1.12\u0026ndash;1.32, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and lower in API patients (AHR 0.69, 95% CI: 0.54\u0026ndash;0.88, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) compared to White patients. When further adjusting for socioeconomic factors (model 3), the OS difference was no longer significant between Black patients and White patients (AHR 1.09, 95% CI: 0.99\u0026ndash;1.21, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.075); API patients (AHR 0.70, 95% CI: 0.54\u0026ndash;0.90, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) and Hispanic patients (AHR 0.76, 95% CI: 0.62\u0026ndash;0.94, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010) had a lower mortality risk (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e). Compared to patients with a median household income of \u0026lt;\u003cspan\u003e$\u003c/span\u003e40,227, those of \u003cspan\u003e$\u003c/span\u003e40,227-\u003cspan\u003e$\u003c/span\u003e50,353 (AHR 0.89, 95% CI: 0.80\u0026ndash;0.99, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038), \u003cspan\u003e$\u003c/span\u003e50,354-\u003cspan\u003e$\u003c/span\u003e63,332 (AHR 0.85, 95% CI: 0.76\u0026ndash;0.96, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), or \u0026ge;\u003cspan\u003e$\u003c/span\u003e63,333 (AHR 0.76, 95% CI: 0.67\u0026ndash;0.86, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had a lower risk of mortality. Patients with no insurance (AHR 1.79, 95% CI: 1.42\u0026ndash;2.26, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Medicaid (AHR 1.60, 95% CI: 1.35\u0026ndash;1.89, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), or Medicare (AHR 1.19, 95% CI: 1.09\u0026ndash;1.30, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had a higher mortality risk than those privately insured. Greater comorbidity scores were associated with worse OS (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eWhen adjusting for clinicopathologic factors in Model 2, Black patients had a greater risk of mortality with stage I (AHR 1.26, 95% CI: 1.05\u0026ndash;1.51, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), stage II (AHR 1.15, 95% CI: 1.004\u0026ndash;1.33, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044), and stage III tumors (AHR 1.33, 95% CI: 1.12\u0026ndash;1.58, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) compared to White patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, after adjustment for socioeconomic factors in Model 3, this difference was no longer significant across stages I (AHR 1.12, 95% CI: 0.91\u0026ndash;1.38, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.281), II (AHR 1.03, 95% CI: 0.87\u0026ndash;1.21, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.758), and III (AHR 1.18, 95% CI: 0.95\u0026ndash;1.46, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.132) (\u003cb\u003eSupplementary Tables\u0026nbsp;4\u0026ndash;6\u003c/b\u003e). No significant difference in mortality was observed by race/ethnicity for patients with stage IV tumors (\u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e). Compared to patients with a median household income of \u0026lt;\u003cspan\u003e$\u003c/span\u003e40227, patients with a median household income of \u0026ge;\u003cspan\u003e$\u003c/span\u003e63,333 and either stage I (AHR 0.73, 95% CI: 0.58\u0026ndash;0.93, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012) or stage II tumors (AHR 0.72, 95% CI, 0.59\u0026ndash;0.88, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) had lower mortality risks (\u003cb\u003eSupplementary Tables\u0026nbsp;4 and 5\u003c/b\u003e). For stages III and IV, no significant differences in mortality between median income quartiles were observed (\u003cb\u003eSupplementary Tables\u0026nbsp;6 and 7\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn patients with stage I tumors, Medicaid was associated with a greater mortality risk compared to private insurance (AHR 1.72, 95% CI: 1.16\u0026ndash;2.57, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) (\u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e). With stage II tumors, patients uninsured (AHR 1.97, 95% CI: 1.34\u0026ndash;2.91, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) or on Medicare (AHR 1.18, 95% CI: 1.02\u0026ndash;1.36, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028) had a higher risk of mortality compared to private insurance (\u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e). For stage III disease, Medicaid was associated with a greater mortality risk compared to private insurance (AHR 1.52, 95% CI: 1.08\u0026ndash;2.15, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016) (\u003cb\u003eSupplementary Table\u0026nbsp;6\u003c/b\u003e). In stage IV disease, lack of insurance, Medicare, or Medicaid was associated with increased mortality risk (\u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e). Patients with a Charlson-Deyo comorbidity score of \u0026ge;\u0026thinsp;2 consistently had a greater mortality risk across all tumor stages (\u003cb\u003eSupplementary Tables\u0026nbsp;4\u0026ndash;7).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhen stratifying by molecular subtype, Black patients with HR-positive/HER2-negative mBC had a higher risk of mortality than White patients (AHR 1.23, 95% CI: 1.12\u0026ndash;1.35, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (model 2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e); however, the difference between the two racial groups was no longer significant when further adjusting for socioeconomic characteristics (AHR 1.09, 95% CI: 0.98\u0026ndash;1.22, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.111) (model 3). Patients with a median household income of \u003cspan\u003e$\u003c/span\u003e50,354-\u003cspan\u003e$\u003c/span\u003e63,332 (AHR 0.86, 95% CI: 0.76\u0026ndash;0.98, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019) or \u0026ge;\u003cspan\u003e$\u003c/span\u003e63,333 (AHR 0.73, 95% CI: 0.64\u0026ndash;0.84, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had a lower mortality risk than those of \u0026lt;\u003cspan\u003e$\u003c/span\u003e40,227 (\u003cb\u003eSupplementary Table\u0026nbsp;8\u003c/b\u003e). Compared to private insurance, no insurance (AHR 1.68, 95% CI: 1.29\u0026ndash;2.19, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Medicare (AHR 1.21, 95% CI: 1.10\u0026ndash;1.33, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), or Medicaid (AHR 1.73, 95% CI: 1.43\u0026ndash;2.09, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was associated with an increased mortality risk (\u003cb\u003eSupplementary Table\u0026nbsp;8\u003c/b\u003e). For HER2-positive tumors, the OS rate was similar by race/ethnicity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Patients with a comorbidity score of \u0026ge;\u0026thinsp;2 or without insurance experienced worse OS (\u003cb\u003eSupplementary Table\u0026nbsp;9\u003c/b\u003e). Socioeconomic factors were not associated with OS in male TNBC (\u003cb\u003eSupplementary Table\u0026nbsp;10\u003c/b\u003e). In the TNBC cohort, API patients were at an increased mortality risk than White patients (AHR 2.35, 95% CI: 1.21\u0026ndash;4.55, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), after adjusting for clinicopathologic characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe compared the mortality of mBC in a large US cohort by racial/ethnic groups, socioeconomic factors across tumor stages and molecular subtypes. Black patients had a higher mortality risk than White patients when adjusting for clinicopathologic factors. However, when further adjusting for socioeconomic indicators, the mortality risk between Black patients and White patients did not vary to a level of statistical significance. Overall, API and Hispanic patients had better OS than White patients. In the TNBC cohort, API patients fared worse OS than White patients, meriting further investigation. Lower median household income quartiles, Medicaid, Medicare, or no insurance, and greater comorbidity scores were associated with a higher mortality risk in mBC.\u003c/p\u003e \u003cp\u003eIn this study, Black mBC patients consistently had higher mortality than White patients across tumor stages and molecular subtypes, after adjusting for clinicopathologic characteristics. This racial disparity mirrors trends found in female breast cancer studies.\u003csup\u003e14\u003c/sup\u003e Notably, however, after further adjusting for socioeconomic indicators, the survival difference between Black patients and White patients was no longer statistically significant. A recent comparative analysis of mBC has also documented a similar OS rate between the two racial groups after controlling for both clinical and sociodemographic factors.\u003csup\u003e18\u003c/sup\u003e Our findings suggest that alleviating socioeconomic inequities may reduce disparities in mBC mortality between Black patients and White patients.\u003c/p\u003e \u003cp\u003eWe also found that after adjusting for clinicopathologic characteristics or further for socioeconomic factors, API or Hispanic patients with mBC displayed non-statistically significant trends toward slightly lower mortality risk compared with White patients, and this was consistent across tumor stages and molecular subtypes. However, it is worth noting that in the TNBC cohort, API patients experienced the lowest 5-year and 10-year OS rates compared with other racial/ethnic categories. This is a surprising result, as previous research in TNBC have described Black men having significantly lower OS rates than White men, without much focus on other racial/ethnic groups displaying the same trend.\u003csup\u003e19\u003c/sup\u003e This result may be due to the small sample size for the API patient group. It is also possible that these survival disparities may be masked by aggregation of Asian and other Pacific Islander as a racial category.\u003csup\u003e20\u003c/sup\u003e Between 2010 and 2014, Asian women with TNBC had better survival of any racial demographic with stage I-III tumors but poorer survival in the metastatic setting.\u003csup\u003e21\u003c/sup\u003e Furthermore, API women experienced the steepest increase of all racial/ethnic groups in breast cancer rates from 2012 to 2021,\u003csup\u003e14\u003c/sup\u003e combined with a significant increase in TNBC incidence between 2010 and 2019 among those ages\u0026thinsp;\u0026ge;\u0026thinsp;55 years.\u003csup\u003e22\u003c/sup\u003e This shift in TNBC incidence and lower survival rates in API women may contain parallels in male TNBC, which warrants future research.\u003c/p\u003e \u003cp\u003eSocioeconomic indicators correlated with mortality disparities in mBC, both overall and when stratified by tumor stage or molecular subtype. Across all mBC patients and within stage I-II and HR-positive/HER2-negative disease, higher median household income quartiles were associated with greater survival rates. An analysis of the 2004\u0026ndash;2016 NCDB reported that higher income levels were associated with improved survival in mBC. However, this and other studies have not fully stratified patients based on molecular subtype and/or tumor stage.\u003csup\u003e11,12,16,18\u003c/sup\u003e Having private health insurance was associated with a lower mortality risk compared to having no health insurance coverage, Medicaid, or Medicare. Patients with a higher median household income had better survival outcomes, aligning with findings from prior studies.\u003csup\u003e6,18,23\u003c/sup\u003e However, educational attainment, facility type, and rural-urban residence were not significantly or consistently associated with OS across tumor stages and molecular subtypes, though congruent with reports in mBC literature.\u003csup\u003e11,23\u003c/sup\u003e Collectively, these findings suggest that differences in health insurance and household income may contribute to disparities in mBC mortality.\u003c/p\u003e \u003cp\u003eSeveral limitations of this study should be acknowledged. First, although the NCDB includes extensive clinicopathologic and certain relevant sociodemographic data, its mortality statistics only include all-cause mortality. It is worth examining disease-specific, progression-related, or other survival outcomes of mBC in future studies. Moreover, there are many unmeasured potential confounders not being collected by the NCDB, such as environmental factors, lifestyle data, personal and family history, and genetic predispositions, that likely influence the survival differences across racial/ethnic groups observed in the current study. Therefore, investigators should consider these key variables in their future analyses. Lastly, given the nature of the NCDB registry and this retrospective study design, the generalizability of our findings may be limited. However, the racial demographics of mBC patients in the NCDB generally reflect the US population, and our findings are consistent with prior studies using the Surveillance, Epidemiology, and End Results data.\u003csup\u003e12,16\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn conclusion, Black patients with mBC had a greater mortality risk than White patients when controlling for clinicopathologic features; however, the risk was similar between the two racial groups after further controlling for socioeconomic indicators. Compared with White patients, API or Hispanic patients had better survival outcomes, except for API patients who had higher mortality from TNBC. Lower median household income, lack of health insurance coverage or public insurance, and greater comorbidity scores were associated with a higher risk of mortality. Our findings highlight racial/ethnic disparities and socioeconomic determinants of survival outcomes in mBC across tumor stages and molecular subtypes. Strategies to address socioeconomic inequities that impact access to comprehensive cancer care programs and services may help reduce racial/ethnic disparities and improve survival outcomes of the US mBC population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and data source\u003c/h2\u003e \u003cp\u003eThis was a retrospective study of real-world data from mBC patients registered in the 2010\u0026ndash;2021 National Cancer Database (NCDB), collected by the Commission on Cancer (CoC) of the American College of Surgeons and the American Cancer Society.\u003csup\u003e24\u003c/sup\u003e The NCDB captures approximately 72% of new cancer diagnoses each year from more than 1,500 CoC-accredited cancer centers in the US.\u003csup\u003e25,26\u003c/sup\u003e Because NCDB de-identified data does not identify clinical facilities, providers, or patients, the University of Chicago Institutional Review Board exempted the study from review. In compliance with the NCDB\u0026rsquo;s Data Use Agreement, we suppressed reporting of cell counts\u0026thinsp;\u0026lt;\u0026thinsp;10 to protect patients\u0026rsquo; confidentiality. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.\u003csup\u003e27\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEligibility and sample selection\u003c/h2\u003e \u003cp\u003eSample selection of male patients diagnosed with invasive breast carcinoma was illustrated in \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e. Briefly, patients were eligible if they 1) were at least 18 years of age at diagnosis; 2) were assigned male sex assigned at birth; 3) had tumors classified as stage I, II, III, or IV by the American Joint Committee on Cancer (AJCC) staging system; 4) were diagnosed between 2010 and 2021; and 5) included data on tumor molecular subtype.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eTumor stage was defined by the AJCC staging system and classified as stage I, II, III, or IV.\u003csup\u003e28\u003c/sup\u003e Molecular subtypes of breast tumors were categorized as HR-positive/HER2-negative, HR-positive/HER2-positive, HR-negative/HER2-positive, and TNBC. Due to small sample sizes, the HR-positive/HER2-positive and HR-negative/HER2-positive groups were combined into a HER-positive (or HER2-enriched) category for regression analyses.\u003c/p\u003e \u003cp\u003eThe main outcome of interest was OS, which was defined as the time from the initial breast cancer diagnosis to death from any cause or last patient contact. According to the NCDB, vital status was not available for patients diagnosed in 2021 because of limited time for follow-up. Thus, these patients were not included in our survival analysis. Median follow-up (in months) for the patient cohort was reported.\u003c/p\u003e \u003cp\u003eRacial/ethnic groups comprised non-Hispanic Asian or Pacific Islander (API), non-Hispanic Black, Hispanic, non-Hispanic White, and Other. Other is a racial/ethnic group listed in the NCDB and represents patients who were classified as Other by local cancer registries. The NCDB does not specifically define race/ethnicity classified into Other. Demographic and clinicopathologic characteristics included age at diagnosis, year of diagnosis, percent no high school degree quartile based on residential geographic area (\u0026ge;\u0026thinsp;17.6%, 10.9\u0026ndash;17.5%, 6.3\u0026ndash;10.8%, and \u0026lt;\u0026thinsp;6.3%), median household income quartile (\u0026lt;\u003cspan\u003e$\u003c/span\u003e40,227, \u003cspan\u003e$\u003c/span\u003e40,227-\u003cspan\u003e$\u003c/span\u003e50,353, \u003cspan\u003e$\u003c/span\u003e50,354-\u003cspan\u003e$\u003c/span\u003e63,332, and \u0026ge;\u003cspan\u003e$\u003c/span\u003e63,333), type of health insurance (Medicaid, Medicare, other government, private, uninsured), rural-urban area, type of facility/cancer program, Charlson-Deyo comorbidity score (0, 1, and \u0026ge;\u0026thinsp;2), tumor histologic type (ductal, ductal and lobular, lobular, or other), and tumor grade (1 - low, 2 - intermediate, and 3 - high).\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFirst, we described the patient cohort using standard summary statistics. We used analyses of variance or Kruskal-Wallis tests to compare the distributions of continuous variables. Categorical variables were compared using Pearson\u0026rsquo;s chi-squared tests. For survival analysis, the Kaplan-Meier (K-M) method was used to calculate the median survival time (in months) and estimate 3-year, 5-year, and 10-year OS rates across racial/ethnic groups, overall and stratified by tumor stage and by molecular subtype. We conducted stratified log-rank tests to determine statistical significance when comparing survival functions across racial/ethnic groups, by tumor stage and by molecular subtype. Cox proportional hazards regression models were fit to further assess racial/ethnic and socioeconomic disparities in OS. A stepwise regression approach was employed. Specifically, model 1 included age at diagnosis and race/ethnicity; in addition, model 2 included histologic type, AJCC stage, tumor grade, and Charlson-Deyo comorbidity score; and, in addition, model 3 included percent no high school degree quartile, median household income quartile, type of insurance, rural-urban area, and facility type. A similar approach was implemented in the stratified Cox regression when stratifying by tumor stage and by molecular subtype. Adjusted hazard ratios (AHR) and 95% confidence intervals (95% CI) were calculated. All statistical tests were two-sided at the 0.05 level of significance. All statistical analyses were performed using Stata version 18 (StataCorp, College Station, TX). Forest plots were created using the \u003cem\u003eforestploter\u003c/em\u003e package in R (R Foundation for Statistical Computing).\u003c/p\u003e \u003c/div\u003e "},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAsian or Pacific Islander\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehormone receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHER2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehuman epidermal growth factor receptor 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etriple-negative breast cancer.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.Q.F.: Conceptualization, methodology, formal analysis, Writing\u0026mdash;original draft. K.S.: Conceptualization, data visualization, Writing\u0026mdash;original draft. J.H.H.: Overall supervision. All authors: Project administration, data interpretation, Writing\u0026mdash;review \u0026amp; editing. All authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis study was presented at the 46th Annual San Antonio Breast Cancer Symposium (SABCS 2023), San Antonio, TX, December 5-9, 2023. This work was supported in part by the National Institute on Aging (T32AG000243), the Susan G. Komen\u0026reg; Breast Cancer Foundation (CTA241185923 and TREND21675016), the University of Chicago Comprehensive Cancer Center Sigal Fellowship in Immuno-Oncology, and the University of Chicago Quad Undergraduate Research Grant. The National Cancer Database is a joint project of the Commission on Cancer of the American College of Surgeons and the American Cancer Society. The data used in this study are derived from a de-identified file. The American College of Surgeons and the Commission on Cancer have not verified and are not responsible for the analytic or statistical methodology employed, or the conclusions drawn from these data by the investigators. Additionally, the contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institute on Aging.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData for this study were obtained from the US National Cancer Database. Investigators affiliated with Commission on Cancer-accredited cancer programs can request the National Cancer Database Participant User File by submitting an application to the American College of Surgeons via https://www.facs.org/quality-programs/cancer-programs/national-cancer- database.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e. Jan 2023;73(1):17\u0026ndash;48. doi:10.3322/caac.21763\u003c/li\u003e\n\u003cli\u003eGiaquinto AN, Sung H, Miller KD, et al. Breast Cancer Statistics, 2022. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e. Nov 2022;72(6):524\u0026ndash;541. doi:10.3322/caac.21754\u003c/li\u003e\n\u003cli\u003eHassett MJ, Somerfield MR, Giordano SH. Management of Male Breast Cancer: ASCO Guideline Summary. \u003cem\u003eJCO Oncol Pract\u003c/em\u003e. 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Mortality Risks Over 20 Years in Men With Stage I to III Hormone Receptor-Positive Breast Cancer. \u003cem\u003eJAMA Oncol\u003c/em\u003e. Apr 1 2024;10(4):508\u0026ndash;515. doi:10.1001/jamaoncol.2023.7194\u003c/li\u003e\n\u003cli\u003eCrew KD, Neugut AI, Wang X, et al. Racial disparities in treatment and survival of male breast cancer. \u003cem\u003eJ Clin Oncol\u003c/em\u003e. Mar 20 2007;25(9):1089-98. doi:10.1200/JCO.2006.09.1710\u003c/li\u003e\n\u003cli\u003eEllington TD, Henley SJ, Wilson RJ, Miller JW. Breast Cancer Survival Among Males by Race, Ethnicity, Age, Geographic Region, and Stage - United States, 2007\u0026ndash;2016. \u003cem\u003eMMWR Morb Mortal Wkly Rep\u003c/em\u003e. Oct 16 2020;69(41):1481\u0026ndash;1484. doi:10.15585/mmwr.mm6941a2\u003c/li\u003e\n\u003cli\u003eGiaquinto AN, Sung H, Newman LA, et al. Breast cancer statistics 2024. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e. Nov-Dec 2024;74(6):477\u0026ndash;495. doi:10.3322/caac.21863\u003c/li\u003e\n\u003cli\u003eHirko KA, Rocque G, Reasor E, et al. The impact of race and ethnicity in breast cancer-disparities and implications for precision oncology. \u003cem\u003eBMC Med\u003c/em\u003e. Feb 11 2022;20(1):72. doi:10.1186/s12916-022-02260-0\u003c/li\u003e\n\u003cli\u003eLeone J, Freedman RA, Lin NU, et al. Tumor subtypes and survival in male breast cancer. \u003cem\u003eBreast Cancer Res Treat\u003c/em\u003e. Aug 2021;188(3):695\u0026ndash;702. doi:10.1007/s10549-021-06182-y\u003c/li\u003e\n\u003cli\u003eYadav S, Karam D, Bin Riaz I, et al. Male breast cancer in the United States: Treatment patterns and prognostic factors in the 21st century. \u003cem\u003eCancer\u003c/em\u003e. Jan 1 2020;126(1):26\u0026ndash;36. doi:10.1002/cncr.32472\u003c/li\u003e\n\u003cli\u003eElimimian EB, Elson L, Li H, et al. 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Oct 16 2007;147(8):573-7. doi:10.7326/0003-4819-147-8-200710160-00010\u003c/li\u003e\n\u003cli\u003eAmin MB, Greene FL, Edge SB, et al. The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more \"personalized\" approach to cancer staging. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e. Mar 2017;67(2):93\u0026ndash;99. doi:10.3322/caac.21388\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5808248/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5808248/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study assessed racial/ethnic disparities and socioeconomic determinants of overall survival in male breast cancer. Using the 2010\u0026ndash;2021 US National Cancer Database, we identified 20,470 patients: 78.2% White, 13.8% Black, 4.0% Hispanic, and 2.5% Asian or Pacific Islander. After adjusting for clinicopathologic characteristics, Black patients had higher mortality than White patients (adjusted hazard ratio [AHR] 1.22, 95% CI: 1.12\u0026ndash;1.32); however, when further adjusting for socioeconomic factors, this difference was no longer significant (AHR 1.09, 95% CI: 0.99\u0026ndash;1.21). Hispanic patients had better survival. In the TNBC cohort, Asian or Pacific Islander patients had higher mortality than White patients (AHR 2.35, 95% CI: 1.21\u0026ndash;4.55), warranting further investigation. In this US male breast cancer cohort, Black patients and White patients had similar mortality risk after further adjusting for socioeconomic indicators. Higher median household income and private insurance were linked to better survival. Strategies addressing socioeconomic inequities may help improve male breast cancer outcomes.\u003c/p\u003e","manuscriptTitle":"Real-word study of racial/ethnic disparities and socioeconomic determinants of overall survival in male breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-21 10:01:05","doi":"10.21203/rs.3.rs-5808248/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8eb62174-4b26-42e1-adea-a1aaf2841a45","owner":[],"postedDate":"February 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44310047,"name":"Biological sciences/Cancer/Breast cancer"},{"id":44310048,"name":"Biological sciences/Cancer/Cancer epidemiology"},{"id":44310049,"name":"Health sciences/Health care/Prognosis"},{"id":44310050,"name":"Health sciences/Health care/Public health"}],"tags":[],"updatedAt":"2025-04-09T19:53:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-21 10:01:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5808248","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5808248","identity":"rs-5808248","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}