Racial and Survival Disparities in Inflammatory Breast Cancer (IBC) and non-IBC: A Population-Based Study Focused on Native Hawaiians and Other Pacific Islanders

Racial and Survival Disparities in Inflammatory Breast Cancer (IBC) and non-IBC: A Population-Based Study Focused on Native Hawaiians and Other Pacific Islanders | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Racial and Survival Disparities in Inflammatory Breast Cancer (IBC) and non-IBC: A Population-Based Study Focused on Native Hawaiians and Other Pacific Islanders Gene Yoshikawa, Kyle Miyazaki, Jared Acoba, Takeo Fujii This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3143571/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 Background Breast cancer has the highest incidence among all malignancies and is the second leading cause of cancer death among women in the United States. It is well known that race is an independent predictor of breast cancer mortality and advanced stage at diagnosis. Inflammatory breast cancer (IBC) is the most aggressive type of breast cancer and has distinct clinical and biological features. Previous studies have shown that Blacks have a higher incidence of IBC than Whites. However, the proportion of IBC and the role of race on prognosis in Native Hawaiian and other Pacific Islander (NH/PI) populations with breast cancer are poorly understood. In this study, we aimed to examine the proportion of IBC to non-IBC in NH/PIs and to identify the clinicopathological, biological, and socioeconomic factors associated with the overall survival of NH/PIs compared to other races. Methods Utilizing a comprehensive cancer registry from the largest hospital in Hawaii, newly diagnosed primary invasive breast cancer patients diagnosed between 2000 and 2018 were identified. Univariate and multivariate Cox proportional hazards models were used to test the association between race and clinical outcomes. Variables with P-values <0.05 in the univariate analysis and race (variable of interest) were included in a multivariate analysis. Results The cohort included 3691 patients, 60 of whom had IBC. NH/PI race had the highest proportion of IBC compared to other races (3.44%) but was not found to be an independent poor prognostic factor in IBC (HR 1.17 [95%CI 0.26-5.22]). Conversely, NH/PI race was associated with worse survival outcomes in patients with non-IBC (HR 1.65 [95%CI, 1.14-2.39]) along with other factors such as lack of insurance, underinsured status, triple-negative breast cancer (TNBC) subtype, age, and advanced clinical stage. Conclusions The findings of this study highlight that NH/PIs had higher rates of IBC and inferior survival in non-IBC compared to other races but not in IBC. It is essential to disaggregate NH/PI race from Asians in future population-based research studies. Further research is needed to understand the factors contributing to higher rates of IBC and poor survival outcomes in NH/PIs with non-IBC as well as targeted interventions to improve breast cancer outcomes in this population to ultimately help improve survival rates and reduce health inequities in NH/PIs with breast cancer. cancer disparities inflammatory breast cancer non-inflammatory breast cancer Native Hawaiian Pacific Islander survival outcomes Introduction Breast cancer has the highest incidence among all malignancies and is the second leading cause of cancer death among women in the United States.[ 1 ] According to the American Cancer Society and National Cancer Institute, in 2023, approximately 300,000 new cases and 44,000 deaths occurred in the United States due to breast cancer.[ 1 ] Health disparities have been a monumental healthcare issue in the United States. For example, given the improvement of screening strategies, the overall incidence of breast cancer has, on average, increased by 0.3% annually from 2004 to 2018, with the increase varying among different races.[ 2 ] Among Asians and Pacific Islanders, the annual increase was 1.4%, which is significantly higher compared to Whites, whose rate of increase was 0.4%[ 2 ]. This suggests that fewer Asian or Pacific Islanders had recommended screening in the past compared to Whites[ 2 ]. Additionally, it is well established that race is an independent predictor of mortality and advanced stage at diagnosis.[ 3 , 4 ] These facts suggest the importance of investigating race in cancer research. Unfortunately, not many studies focus on small racial populations such as Native Hawaiians and Pacific Islanders (NH/PI), given that one of the major challenges in studying these populations is the limited number of patients in health databases. Even in the State of Hawaii, only approximately 10% of the residents are of Native Hawaiian and Pacific Islander race.[ 5 ] In addition to race, it has been found that lower socioeconomic status (SES) is also associated with increased breast cancer mortality and higher clinical stage at diagnosis after adjusting for other potential confounders.[ 6 – 9 ] The interplay between clinicopathologic factors, socioeconomic factors, and race is becoming increasingly recognized.[ 10 ] However, establishing which element has a more significant influence has yet to be determined.[ 11 ] Moreover, although there are previous studies investigating this topic, unfortunately, many populational-based studies comprise primarily Black and White racial groups. As a result, little is known about the influence of racial disparities and SES, particularly on the outcomes of NH/PI patients with breast cancer. However, given the trend in the increase in breast cancer rates among Pacific Islanders,[ 12 , 13 ] disaggregating NH/PI patients in epidemiological cancer-based research is paramount.[ 14 ] It is also essential to differentiate Inflammatory Breast Cancer (IBC) from non-IBC. IBC is a rare but aggressive form of breast cancer, accounting for only 2–6% of all breast cancers but a disproportionate 7% of breast cancer deaths.[ 15 ] Additionally, the clinical features of IBC are distinct from those of non-IBC. Approximately 20–30% of patients with IBC present with metastatic disease at diagnosis compared to 6–10% of non-IBC patients.[ 15 ] Several previous studies demonstrated that the incidence of IBC is highest in Blacks,[ 15 – 17 ] which suggests that the incidence of IBC can vary depending on race. However, to the best of our knowledge, it has never been investigated in NH/PI patients, given the rarity of the disease and the limitations of databases, including the small number of NH/PI patients in previous studies. Understanding the epidemiologic characteristics of IBC among NH/PI patients can better provide new insights into the diagnostic and treatment strategies of breast cancer, potentially leading to higher cost-effectiveness and improved health outcomes. We hypothesize that the proportion of IBC is higher amongst NH/PI populations compared to other races, and by association, these populations are correlated with poor survival outcomes. In this study, our primary objective was to identify and compare the proportion of IBC and non-IBC patients among distinct races (including PI and NH). The secondary objective was to identify the clinicopathological, biological, and socioeconomic factors associated with overall survival. Methods Study population This retrospective chart review study was approved and informed consent was waived due to the study's retrospective nature by the Queen’s Medical Center’s Institutional Review Board (protocol number: RA-2019-027). We used Queen’s Medical Center Tumor Registry to identify patients with newly diagnosed primary invasive breast cancer who were diagnosed between January 1, 2000, and December 31, 2018. The patients diagnosed with other types of co-existing cancers were excluded. From the tumor registry and electronic medical record, we extracted age at diagnosis, race (White, Asian, Native Hawaiian or Pacific Islander, or Others based on the U.S. Office of Management and Budget (OMB) standard), primary insurance (private insurance, MEDICARE, MEDICAID, others, or no insurance), histology (ductal, lobular, mixed, or others), the proportion of IBC vs. non-IBC, clinical stage, estrogen receptor (ER) and/or progesterone receptor (PR) by immunohistochemical staining, and human epidermal growth factor receptor 2 (HER2) status. ER and PR positivity was defined based on the American Society of Clinical Oncology (ASCO)/College of American Pathologists (CAP) guideline.[ 18 ] HER2 positivity was defined as a HER2/CEP17 fluorescence in situ hybridization (FISH) ratio of ≥ 2.0 and/or an immunohistochemical (IHC) staining score of 3+. Black or African American race is rare in Hawaii and thus was categorized as Others. Statistical analysis All the variables were summarized using standard descriptive statistics, and frequency tabulation was used to summarize the data. The chi-square and Fisher’s exact tests were used to assess the association between two categorical variables depending on expected values. The Kruskal-Wallis test was used to compare the distributions of continuous variables between different groups. Univariate and multivariate Cox proportional hazards models were used to investigate the association between each variable and overall survival (OS). OS was defined as the time from diagnosis to death. Patients who were alive at the date of the last follow-up were censored. Variables with P-values < 0.05 in the univariate analysis and race (variable of interest) were included in a multivariate analysis. All tests were two-sided. P-values < 0.05 were considered statistically significant. STATA, version 14 (STATA Corp, College Station, TX), was used for all analyses. Results Patient characteristics A total of 3691 patients were identified. Of those 3691 patients, 60 (1.5%) had IBC (Table 1 ). Twenty-six out of 60 patients with IBC (43.3%) were NH/PI. The overall proportion of NH/PI race was less in patients with non-IBC (730 of 3631 patients with non-IBC; 20.1%). Thirty-six of 60 patients with IBC (60%) had stage III disease. Of note, there is no stage I or II disease in IBC. The subtype was unknown for approximately 35% of patients with non-IBC because this information was recorded in a different non-EMR Pathology Department system, which could not be collected. Thirty of 60 patients with IBC (50%) and 2131 of 3631 patients with non-IBC (58.7%) had private insurance. Table 1 Baseline Patient Characteristics IBC (N = 60) n (%) Non-IBC (N = 3631) n (%) P-value Age (Median, range) 59 (29–72) 59 (22–96) 0.11 Race < 0.001 White 12 (20) 712 (19.6) Asian 20 (33.3) 2071 (57) Native Hawaiian or Pacific Islander 26 (43.3) 730 (20.1) Others 2 (3.3) 112 (3.1) Unknown 0 (0) 6 (0.2) Primary Insurance < 0.001 Private insurance 30 (50) 2131 (58.7) Medicare 12 (20) 1053 (29) Medicaid 12 (20) 245 (6.7) Others 3 (5) 168 (4.6) No insurance 3 (5) 21 (0.6) Unknown 0 (0) 13 (0.4) Histology 0.3 Ductal 50 (83.3) 3183 (87.7) Lobular 1 (1.7) 233 (6.4) Mixed 0 (0) 45 (1.2) Others 0 (0) 146 (4) Unknown 9 (15) 24 (0.7) Clinical Stage* 1.0 Stage I N/A 2103 (57.9) Stage II N/A 1057 (29.1) Stage IIII 36 (60) 224 (6.2) Stage IV 20 (33.3) 131 (3.6) Unknown 4 (6.7) 116 (3.2) *Only Stages III and IV were used in the IBC analysis since there is no Stage I or II in IBC Subtype < 0.001 HR-positive/HER2-negative 23 (38.3) 1790 (49.3) HER2-positive 22 (36.7) 316 (8.7) TNBC 6 (10) 225 (6.2) Unknown 9 (15) 1300 (35.8) HR; hormone receptor, HER2; human epidermal growth factor receptor 2, TNBC; triple negative breast cancer Racial difference in the proportion of patients with IBC to those with non-IBC The proportion of those with IBC was highest in the NH/PI race, with 26 of 756 NH/PI having IBC (3.44%). The proportion of Whites with IBC was 1.66% (12 of 712 patients). Compared to the proportion of Whites, the NH/PI race had a significantly high proportion of IBC. (P = 0.003) (Table 2 ). Table 2 Proportion of IBC by Race Race IBC (N = 60) Non-IBC (N = 3631) % of IBC among all BC cases White 12 712 1.66 Asian 20 2071 0.96 Native Hawaiian or Pacific Islander 26 730 3.44 Others 2 112 1.75 Overall survival (OS) in patients with IBC In a univariate cox proportional hazard model, no insurance (HR, 5.38 [95%CI, 1.47–19.7]; P = 0.01), MEDICAID (HR, 3.83 [95%CI, 1.58–9.28]; P < 0.01), TNBC subtype (HR, 5.43 [95%CI, 1.66–17.73]; P < 0.001), and clinical stage IV disease (HR, 10.58 [95%CI, 4.06–27.59]; P < 0.001) were associated with shorter OS, but NH/PI race (HR, 2.46 [95%CI, 0.81–7.45]; P = 0.11) was not. In a multivariate cox proportional hazard model adjusting for the variables with P-values < 0.05 in the univariate analysis and race, MEDICAID insurance (HR, 4.45 [95%CI, 1.1-18.03]; P = 0.04), TNBC subtype (HR, 4.46 [95%CI, 1.08–19.5]; P = 0.04), and clinical stage IV disease (HR, 10.1 [95%CI, 2.35–43.4]; P < 0.001) remained significant. NH/PI race remained a non-significant factor associated with shorter OS (HR, 1.17 [95%CI, 0.26–5.223]; P = 0.84) (Table 3 ). Table 3 Univariate and Multivariate Analysis for OS Among Patients with IBC. (N = 60) Univariate Multivariate HR (95% CI) P-value HR (95% CI) P-value Age 1.0 (0.97–1.03) 0.94 Race White Ref Ref Asian 2.1 (0.66–6.62) 0.21 0.6 (0.11–3.09) 0.54 Native Hawaiian or Pacific Islander 2.46 (0.81–7.45) 0.11 1.17 (0.26–5.22) 0.84 Others 1.34 (0.15–12.01) 0.8 27.85 (1.81-427.97) 0.02 Primary Insurance Private insurance Ref Ref MEDICARE 1.25 (0.47–3.33) 0.66 0.59 (0.12–2.89) 0.51 MEDICAID 3.83 (1.58–9.28) < 0.01 4.45 (1.1-18.03) 0.04 Others 0.64 (0.08–4.98) 0.67 0.64 (0.07–5.56) 0.68 No insurance 5.38 (1.47–19.7) 0.01 4.08 (0.64–26.2) 0.14 Histology Ductal Ref Lobular 1.29 (0.17–9.64) 0.81 Subtype HR-positive/HER2-negative Ref Ref HER2-positive 1.16 (0.48–2.78) 0.75 0.65 (0.22–1.9) 0.43 TNBC 5.43 (1.66–17.73) < 0.001 4.6 (1.08–19.5) 0.04 Clinical Stage Stage III Ref Ref Stage IV 10.58 (4.06–27.59) < 0.001 10.1 (2.35–43.4) < .001 Overall survival (OS) in patients with non-IBC Similarly to the findings in patients with IBC, in the univariate cox proportional hazard model, no insurance (HR, 5.13 [95%CI, 2.63–9.99]; P < 0.001), MEDICAID (HR, 3.1 [95%CI, 2.29–4.17]; P < 0.01), TNBC subtype (HR, 2.38 [95%CI, 1.78–3.19]; P < 0.01), and advanced clinical stage were associated with shorter OS. Additionally, NH/PI race (HR, 1.43 [95%CI, 1.13–1.81]; P < 0.001) and age (HR, 1.05 [95%CI, 1.04–1.06]; P < 0.01) were also associated with shorter OS (Table 4 ). In a multivariate cox proportional hazard model adjusting for the variables with P-values < 0.05, no insurance (HR, 3.34 [95%CI, 0.94–2.81]; P = 0.04), MEDICAID insurance (HR, 1.79 [95%CI, 1.15–2.77]; P = 0.01), TNBC subtype (HR, 1.76 [95%CI, 1.3–2.41]; P < 0.01), age (HR, 1.05 [95%CI, 1.04–1.07]; P < 0.01), advanced clinical stage (HR, 10.1 [95%CI, 2.35–43.4]; P < 0.001), and NH/PI race (HR, 1.65 [95%CI, 1.14–2.39]; P = 0.007) remained significant (Table 4 ). Table 4 Univariate and multivariate analysis for OS among patients with non-IBC. (N = 3631) Univariate Multivariate HR (95% CI) P-value HR (95% CI) P-value Age 1.05 (1.04–1.06) < 0.01 1.05 (1.04–1.07) < 0.01 Race White Ref Ref Asian 0.87 (0.7–1.07) 0.19 0.83 (0.6–1.14) 0.244 Native Hawaiian or Pacific islander 1.43 (1.13–1.81) < 0.001 1.65 (1.14–2.39) 0.007 Others 1.34 (0.86–20.9) 0.86 1.34 (0.63–2.84) 0.45 Primary insurance HMSA Ref Ref MEDICARE 3.4 (2.84–4.08) < 0.01 1.35 (0.94–1.95) 0.1 MEDICAID 3.1 (2.29–4.17) < 0.01 1.79 (1.15–2.77) 0.01 Others 1.82 (1.3–2.55) < 0.01 1.63 (0.94–2.81) 0.08 No insurance 5.13 (2.63–9.99) < 0.01 3.34 (1.03–10.8) 0.04 Histology Ductal Ref Lobular 1.0 (0.7–1.42) 0.98 Mixed 1.21 (0.66–2.2) 0.54 Others 1.07 (0.74–1.54) 0.72 Subtype HR-positive/HER2-negative Ref Ref HER2-positive 1.48 (1.05–2.09) 0.02 1.01 (0.7–1.46) 0.94 TNBC 2.38 (1.78–3.19) < 0.01 1.76 (1.3–2.41) < 0.01 Clinical Stage Stage I Ref Ref Stage II 1.95 (1.59–2.38) < 0.01 2.08 (1.53–2.82) < 0.01 Stage IIII 4.38 (3.36–5.72) < 0.01 4.63 (3.14–6.83) < 0.01 Stage IV 22.77 (17.68–29.3) < 0.01 26.59 (18.3-38.64) < 0.01 Discussion To our knowledge, this is the first study to investigate the association between patient, race, disease characteristics, socioeconomic status, and OS in a unique population focused on Native Hawaiian or other Pacific Islanders (NH/PI) with IBC and non-IBC separately. In accordance with our hypothesis, in our study, we found that NH/PI race had the highest proportion of IBC (3.44%) amongst other races and was statistically significant when compared to the ratio of Whites (P = 0.003). Among patients with non-IBC, weak or no insurance was associated with poor OS, which is consistent with previous studies on the influence of SES on survival outcomes.[ 6 – 8 ] NH/PI race also remained a significant factor associated with OS. However, among patients with IBC, only TNBC subtype and Stage IV disease were significantly associated with OS, and NH/PI was not associated with OS. Despite NH/PI race having a significantly higher proportion of IBC than other races, NH/PI race was not found to be an independent poor prognostic factor amongst those with IBC. Although the reason for this lack of difference in survival in IBC is unclear at this time, we suspect the aggressive nature of IBC is likely one of the highly contributory factors. Small sample size might be another reason. Previous studies have consistently shown that for those with IBC, Black race is associated with poor prognosis.[ 7 , 16 , 19 ] However, other races have not reliably been shown to be independent poor prognostic factors in those with IBC.[ 19 ] One possible reason for this finding is that this could indicate unique disparities (e.g., biological variations) amongst Blacks with IBC that do not necessarily affect other races (including NH/PI race) regardless of the prevalence of the disease. However, given the rarity of IBC, small sample size again should also be considered when interpreting these results. In our study, MEDICAID insurance was also associated with worse survival amongst those with IBC, although no insurance was not a significant factor. This is likely due to small sample size (only three patients with IBC had no insurance) because patients without insurance coverage face similar challenges to patients with MEDICAID insurance, such as access to care and lower income levels. Contrary to that stated above, NH/PI race was found to be associated with worse OS in those with non-IBC in our study. MEDICAID or uninsured, TNBC sub-type, and advanced clinical stage were also found to be poor prognostic factors. Although the exact reasoning why NH/PI race is an adverse prognostic factor is unclear, it is likely multifactorial, influenced by biological and socioeconomic causes. Uninsured and underinsured status are likely substantial contributory factors, although NH/PIs have also been found to have poor health outcomes despite being insured.[ 20 ] It has been well established that low socioeconomic status (SES) is associated with worse outcomes in patients with cancer.[ 6 – 8 , 21 ] Among both men and women, five-year survival for all cancers combined is ten percentage points lower than those of higher SES.[ 21 ] Unfortunately, NH/PI populations have been associated with lower SES, with approximately 15% of NH/PIs living in poverty compared to 11% of Asians or 13% of Americans overall.[ 22 ] Low SES can result in lower quality of life (resulting in higher health risks), fragmentation of care, complications with health insurance, lower education, health literacy, and less access to care.[ 23 ] For example, Sentell et al. found that low health literacy was a significant predictor of poorer health outcomes in adults of NH/PI race.[ 24 ] Additionally, Taparra et al. found that within a total cohort of almost 600,000 women with stage 0-II breast cancer, NH/PI women had worse survival when compared with non-Hispanic White women.[ 25 ] NH/PI women had consistently longer times between surgery and radiation therapy. Thus, delays in care were suggested to be a significant contributory factor to the finding of increased mortality in this population.[ 25 ] These delays in care are suspected to primarily result from poor access to care in NH/PI populations due to financial or geographical hardships. Not only does this apply to local NH/PI patients in the United States but also to the majority of Pacific Islanders originating from Pacific Island Countries where medical care is limited. This requires these patients to travel long distances and at a significant cost to seek appropriate medical treatment, especially when specialty or hospital-based care is needed.[ 26 ] Subsequently, this leads to even further fragmentation of care, given that many patients have family and friends in their home countries, requiring frequent travel back, which can ultimately interrupt treatment plans as well. In addition to socioeconomic causes, biological etiologies are also a consideration for the finding of non-IBC as an independent poor prognostic factor amongst NH/PIs in our study. Previous research has demonstrated higher incidence rates of both hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) expression in Native Hawaiians.[ 27 – 29 ] HR-positive breast cancers generally tend to have a better prognosis than HR-negative breast cancers.[ 30 ] However, HER2 overexpression in invasive breast cancers is associated with higher rates of disease recurrence, brain metastasis, and mortality.[ 31 ] HER2-positive breast cancers have also been found to have the second poorest prognosis amongst all breast cancer subtypes.[ 32 ] Since the advent of HER2-targeted therapies such as trastuzumab or pertuzumab, there has been a paradigm shift amongst patients with HER2-positive breast cancer resulting in decreased mortality rates, but if NH/PI patients are unable to receive these therapies due to poor access to care or other socioeconomic barriers, as discussed above, then perhaps HER2 overexpression could be a biological explanation for this finding. However, further investigation is necessary to determine the impact of receptor expression on mortality in NH/PIs with invasive breast carcinoma. In our study, several limitations should be taken into consideration when interpreting our results. First, this was a retrospective chart review study. Although we controlled for race, insurance status, histology, and clinical stage, given the nature of the study, there are potential unknown confounding factors as well as other variables that were unable to be collected (e.g., distance to health care facilities, family support, and income), which could have affected our results. Second, although the sample size of the patients with non-IBC was favorable, we could only include sixty patients with IBC in our analysis. Unfortunately, this small sample size reduces the power of the study to identify slight differences and variations. However, as previously discussed, IBC is a rare form of breast cancer and can be challenging to observe, especially in smaller populations such as in Hawaii. Third, not all data was present for all patients included in the analysis. Notably, as mentioned above, the subtype of breast cancer was unknown for approximately 35% of patients with non-IBC due to the inability to collect the data from the non-EMR Pathology Department system. Lastly, the patient population included in our study was diagnosed with breast cancer between 2000 and 2018. The standard of care for those patients could have been different from the current standard of care, which could potentially affect survival outcomes. In conclusion, our study demonstrated that NH/PI race had a significantly high proportion of IBC when compared to other races. NH/PI race was an adverse prognostic factor associated with worse OS in those with non-IBC but not in those with IBC. In patients with non-IBC, lack of insurance or underinsured status were also associated with shorter OS. Additional research needs to be conducted to further understand the unique determinants and disparities contributing to poor survival outcomes in NH/PI populations, particularly with non-IBC. Unfortunately, there are not many large, multi-center studies that focus on NH/PI populations, and the research that does include NH/PIs typically aggregates this population with Asians. However, as this study has demonstrated, disaggregating NH/PI race from Asians in population-based research is essential. By further identifying these factors, targeted interventions can be implemented to ultimately help improve survival rates and reduce health inequities in NH/PIs with breast cancer. Declarations Acknowledgments This research was supported in part by the Intramural Research Program of the NIH, NCI, Center for Cancer Research. Authors’ contributions Gene T. Yoshikawa: Conceptualization, Data Curation, Investigation, Writing—Original Draft. Kyle SY. Miyazaki: Conceptualization, Data Curation, Investigation, Writing – Review & Editing. Jared D. Acoba: Conceptualization, Methodology, Investigation, Writing – Review & Editing. Takeo Fujii: Conceptualization, Methodology, Investigation, Formal Analysis, Writing – Review & Editing, Validation, Supervision. Funding This research was supported in part by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Center for Cancer Research (ZIA BC 012130). Availability of data and materials: Data cannot be shared publicly because of protected health information. The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request. Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki. This retrospective chart review study was approved by the Queen’s Medical Center’s Institutional Review Board (RA-2019-027). An informed consent was waived due to the retrospective nature of the study. Consent for publication Not applicable. Competing interests: The authors declare no competing interests. References Siegel Mph RL, Miller KD, Sandeep N, Mbbs W, Ahmedin |, Dvm J, et al. Cancer statistics, 2023. CA Cancer J Clin. 2023;73:17–48. Ellington TD, Miller JW, Henley SJ, Wilson RJ, Wu M, Richardson LC. Trends in Breast Cancer Incidence, by Race, Ethnicity, and Age Among Women Aged ≥20 Years — United States, 1999–2018. Morbidity and Mortality Weekly Report. 2022;71:43. KS A, JM U, JJ C, CA C, DL H. Racial disparities in cancer survival among randomized clinical trials patients of the Southwest Oncology Group. J Natl Cancer Inst. 2009;101:984–92. KW H, WF A, SS D, HA Y, PH L. Trends in inflammatory breast carcinoma incidence and survival: the surveillance, epidemiology, and end results program at the National Cancer Institute. J Natl Cancer Inst. 2005;97:966–75. U.S. Census Bureau QuickFacts: Hawaii. 2022. https://www.census.gov/quickfacts/fact/dashboard/HI/PST045222. Accessed 17 May 2023. A K, ME T, KK C. Associations among socioeconomic status, patterns of care and outcomes in breast cancer patients in a universal health care system: Ontario’s experience. Cancer. 2016;122:893–8. Ji P, Gong Y, Jiang C-C, Hu X, Di G-H, Shao Z-M, et al. Association between socioeconomic factors at diagnosis and survival in breast cancer: A population-based study. 2020. https://doi.org/10.1002/cam4.2842. Kuijer A, Verloop J, Visser O, Sonke G, Jager A, van Gils CH, et al. The influence of socioeconomic status and ethnicity on adjuvant systemic treatment guideline adherence for early-stage breast cancer in the Netherlands. Ann Oncol. 2017;28:1970–8. Orsini M, Tretarre B, Daurès JP, Bessaoud F. Individual socioeconomic status and breast cancer diagnostic stages: a French case–control study. Eur J Public Health. 2016;26:445–50. Mancilla VJ, Peeri NC, Silzer T, Basha R, Felini M, Jones HP, et al. Understanding the Interplay Between Health Disparities and Epigenomics. Front Genet. 2020;11:515278. Kong X, Liu Z, Cheng R, Sun L, Huang S, Fang Y, et al. Variation in Breast Cancer Subtype Incidence and Distribution by Race/Ethnicity in the United States From 2010 to 2015. JAMA Netw Open. 2020;3:e2020303–e2020303. Centers for Disease Control and Prevention. Cancer Incidence and 5-year Survival Among Asian/Pacific Islander People, United States—2008–2017. USCS Data Brief, no 22. Atlanta, GA: Centers for Disease Control and Prevention, US Department of Health and Human Services; 2021. https://www.cdc.gov/cancer/uscs/about/data-briefs/no22-cancer-incidence-survival-Asian-Pacific-Islanders-2008-2017.htm. Accessed 17 May 2023. Liu L, Noone AM, Gomez SL, Scoppa S, Gibson JT, Lichtensztajn D, et al. Cancer Incidence Trends Among Native Hawaiians and Other Pacific Islanders in the United States, 1990–2008. JNCI Journal of the National Cancer Institute. 2013;105:1086. Yoshikawa GT, Simon N, Nakasone RK, Acoba JD. Disaggregating Data on Pacific Islander Gastric Cancer Patients Reveals Survival Disparity. J Gastrointest Cancer. 2021. https://doi.org/10.1007/S12029-020-00579-6. TM F, T K, DD L, Y S, H M, R E-Z, et al. Overall survival differences between patients with inflammatory and noninflammatory breast cancer presenting with distant metastasis at diagnosis. Breast Cancer Res Treat. 2015;152:407–16. Denu RA, Hampton JM, Currey A, Anderson RT, Cress RD, Fleming ST, et al. Racial and Socioeconomic Disparities Are More Pronounced in Inflammatory Breast Cancer Than Other Breast Cancers. 2017. https://doi.org/10.1155/2017/7574946. Goldner B, Behrendt CE, Schoellhammer HF, Lee B, Chen SL. Incidence of Inflammatory Breast Cancer in Women, 1992–2009, United States. Ann Surg Oncol. 2014;21:1267. Wolff AC, Hammond MEH, Hicks DG, Dowsett M, McShane LM, Allison KH, et al. Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update. J Clin Oncol. 2013;31:3997–4013. Gudina AT, Copeland G, Soliman AS, Hirko KA. Racial/ethnic disparities in inflammatory breast cancer survival in the Michigan Cancer Surveillance Program. Breast Cancer Res Treat. 2019;173:693–9. Morisako AK, Taualiʻi M, Adrian ;, Ambrose JH, Withy K. Beyond the Ability to Pay: The Health Status of Native Hawaiians and Other Pacific Islanders in Relationship to Health Insurance. Hawai’i Journal of Medicine & Public Health. 2017;76 3 Suppl 1:36. Ward E, Jemal A, Cokkinides V, Gopal ;, Singh K, Cardinez C, et al. Cancer Disparities by Race/Ethnicity and Socioeconomic Status. CA Cancer J Clin. 2004;54:78–93. Joint Economic Committee of Democrats. The Economic State of Asian Americans, Native Hawaiians and Pacific Islanders in the United States. 2022. Lazar M, Davenport L. Barriers to Health Care Access for Low Income Families: A Review of Literature. J Community Health Nurs. 2018;35:28–37. Sentell T, Baker KK, Onaka A, Braun K. Low Health Literacy and Poor Health Status in Asian Americans and Pacific Islanders in Hawai’i. https://doi.org/101080/108107302011604390. 2011;16 SUPPL. 3:279–94. 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. 2022;18:e1255–64. World Health Organization (WHO). Strengthening Pacific Health Systems. https://www.who.int/westernpacific/activities/strengthening-pacific-health-systems. Accessed 17 May 2023. Ihenacho U, McKinley MA, Vu A, Hernandez BY, Loo LWM, Gomez SL, et al. Characterizing breast cancer incidence and trends among Asian American, Native Hawaiian, and non-Hispanic White women in Hawaiʻi, 1990-2014. Cancer Causes and Control. 2022;34:241–9. Loo LWM, Williams M, Hernandez BY. The High and Heterogeneous Burden of Breast Cancer in Hawaii: a Unique Multiethnic U.S. Population. Cancer Epidemiol. 2019;58:71. Kathryn L Braun 1 MFCCGCDKC. Ethnic differences in breast cancer in Hawai’i: age, stage, hormone receptor status, and survival. Pac Health Dialog. 2004;11:146–53. Dunnwald LK, Rossing MA, Li CI. Hormone receptor status, tumor characteristics, and prognosis: a prospective cohort of breast cancer patients. Breast Cancer Research. 2007;9:R6. Patel A, Unni N, Peng Y. The Changing Paradigm for the Treatment of HER2-Positive Breast Cancer. Cancers (Basel). 2020;12:1–17. Vu T, Claret FX. Trastuzumab: Updated Mechanisms of Action and Resistance in Breast Cancer. Front Oncol. 2012;2. Additional Declarations No competing interests reported. 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-3143571","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":222007294,"identity":"e68657e5-6e49-4f57-b33b-e53677e41705","order_by":0,"name":"Gene Yoshikawa","email":"","orcid":"","institution":"University of Hawaii at Manoa","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gene","middleName":"","lastName":"Yoshikawa","suffix":""},{"id":222007295,"identity":"bc767a5c-9b7e-4708-adf0-e5e7d5899a93","order_by":1,"name":"Kyle Miyazaki","email":"","orcid":"","institution":"University of Hawaii at Manoa","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kyle","middleName":"","lastName":"Miyazaki","suffix":""},{"id":222007296,"identity":"d6dc0892-cb85-4445-9dba-9b7aed39978d","order_by":2,"name":"Jared Acoba","email":"","orcid":"","institution":"University of Hawaiʻi Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jared","middleName":"","lastName":"Acoba","suffix":""},{"id":222007297,"identity":"3916962b-c8b2-4dc9-9cf4-757a8fe0516b","order_by":3,"name":"Takeo Fujii","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYFACNhAhx8DYzNj4gMHgAGENPBAtxiAtzQakaQHZJ8HAQIQWe/ZjiQ8+MBgkNrczt1UXFNxhkHfvMcBvC0/aYcMZQC2NzYxtt2cYPGMwPHOGgBaG9DZpHoY/EC08BocZDGekJeDXwv+8/TcP1JZi4rRIpB1jhmlhBmmRl0g+gF/LjWfJkjMMDIyBWpqlgVp4DHgO49fC3p9m+OFDhYHsxv7jDz/z/DksJ9/e2IBXCwQAg8gQqo6HmNiEAHk4gxg7RsEoGAWjYEQBALujQpgZhjkCAAAAAElFTkSuQmCC","orcid":"","institution":"National Cancer Institute","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Takeo","middleName":"","lastName":"Fujii","suffix":""}],"badges":[],"createdAt":"2023-07-05 19:29:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3143571/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3143571/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":46763359,"identity":"5399730e-a84c-444c-9fdc-dbbe7ba8970b","added_by":"auto","created_at":"2023-11-20 10:23:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":533315,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3143571/v1/b4626288-b0eb-4eaa-bc44-b77c8a6f9ce5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Racial and Survival Disparities in Inflammatory Breast Cancer (IBC) and non-IBC: A Population-Based Study Focused on Native Hawaiians and Other Pacific Islanders","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer has the highest incidence among all malignancies and is the second leading cause of cancer death among women in the United States.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] According to the American Cancer Society and National Cancer Institute, in 2023, approximately 300,000 new cases and 44,000 deaths occurred in the United States due to breast cancer.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] Health disparities have been a monumental healthcare issue in the United States. For example, given the improvement of screening strategies, the overall incidence of breast cancer has, on average, increased by 0.3% annually from 2004 to 2018, with the increase varying among different races.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Among Asians and Pacific Islanders, the annual increase was 1.4%, which is significantly higher compared to Whites, whose rate of increase was 0.4%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This suggests that fewer Asian or Pacific Islanders had recommended screening in the past compared to Whites[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Additionally, it is well established that race is an independent predictor of mortality and advanced stage at diagnosis.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] These facts suggest the importance of investigating race in cancer research. Unfortunately, not many studies focus on small racial populations such as Native Hawaiians and Pacific Islanders (NH/PI), given that one of the major challenges in studying these populations is the limited number of patients in health databases. Even in the State of Hawaii, only approximately 10% of the residents are of Native Hawaiian and Pacific Islander race.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn addition to race, it has been found that lower socioeconomic status (SES) is also associated with increased breast cancer mortality and higher clinical stage at diagnosis after adjusting for other potential confounders.[\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] The interplay between clinicopathologic factors, socioeconomic factors, and race is becoming increasingly recognized.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] However, establishing which element has a more significant influence has yet to be determined.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Moreover, although there are previous studies investigating this topic, unfortunately, many populational-based studies comprise primarily Black and White racial groups. As a result, little is known about the influence of racial disparities and SES, particularly on the outcomes of NH/PI patients with breast cancer. However, given the trend in the increase in breast cancer rates among Pacific Islanders,[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] disaggregating NH/PI patients in epidemiological cancer-based research is paramount.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIt is also essential to differentiate Inflammatory Breast Cancer (IBC) from non-IBC. IBC is a rare but aggressive form of breast cancer, accounting for only 2\u0026ndash;6% of all breast cancers but a disproportionate 7% of breast cancer deaths.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] Additionally, the clinical features of IBC are distinct from those of non-IBC. Approximately 20\u0026ndash;30% of patients with IBC present with metastatic disease at diagnosis compared to 6\u0026ndash;10% of non-IBC patients.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] Several previous studies demonstrated that the incidence of IBC is highest in Blacks,[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] which suggests that the incidence of IBC can vary depending on race. However, to the best of our knowledge, it has never been investigated in NH/PI patients, given the rarity of the disease and the limitations of databases, including the small number of NH/PI patients in previous studies. Understanding the epidemiologic characteristics of IBC among NH/PI patients can better provide new insights into the diagnostic and treatment strategies of breast cancer, potentially leading to higher cost-effectiveness and improved health outcomes.\u003c/p\u003e \u003cp\u003eWe hypothesize that the proportion of IBC is higher amongst NH/PI populations compared to other races, and by association, these populations are correlated with poor survival outcomes. In this study, our primary objective was to identify and compare the proportion of IBC and non-IBC patients among distinct races (including PI and NH). The secondary objective was to identify the clinicopathological, biological, and socioeconomic factors associated with overall survival.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003e This retrospective chart review study was approved and informed consent was waived due to the study's retrospective nature by the Queen\u0026rsquo;s Medical Center\u0026rsquo;s Institutional Review Board (protocol number: RA-2019-027). We used Queen\u0026rsquo;s Medical Center Tumor Registry to identify patients with newly diagnosed primary invasive breast cancer who were diagnosed between January 1, 2000, and December 31, 2018. The patients diagnosed with other types of co-existing cancers were excluded. From the tumor registry and electronic medical record, we extracted age at diagnosis, race (White, Asian, Native Hawaiian or Pacific Islander, or Others based on the U.S. Office of Management and Budget (OMB) standard), primary insurance (private insurance, MEDICARE, MEDICAID, others, or no insurance), histology (ductal, lobular, mixed, or others), the proportion of IBC vs. non-IBC, clinical stage, estrogen receptor (ER) and/or progesterone receptor (PR) by immunohistochemical staining, and human epidermal growth factor receptor 2 (HER2) status. ER and PR positivity was defined based on the American Society of Clinical Oncology (ASCO)/College of American Pathologists (CAP) guideline.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] HER2 positivity was defined as a HER2/CEP17 fluorescence in situ hybridization (FISH) ratio of \u0026ge;\u0026thinsp;2.0 and/or an immunohistochemical (IHC) staining score of 3+. Black or African American race is rare in Hawaii and thus was categorized as Others.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll the variables were summarized using standard descriptive statistics, and frequency tabulation was used to summarize the data. The chi-square and Fisher\u0026rsquo;s exact tests were used to assess the association between two categorical variables depending on expected values. The Kruskal-Wallis test was used to compare the distributions of continuous variables between different groups. Univariate and multivariate Cox proportional hazards models were used to investigate the association between each variable and overall survival (OS). OS was defined as the time from diagnosis to death. Patients who were alive at the date of the last follow-up were censored. Variables with P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariate analysis and race (variable of interest) were included in a multivariate analysis. All tests were two-sided. P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. STATA, version 14 (STATA Corp, College Station, TX), was used for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eA total of 3691 patients were identified. Of those 3691 patients, 60 (1.5%) had IBC (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Twenty-six out of 60 patients with IBC (43.3%) were NH/PI. The overall proportion of NH/PI race was less in patients with non-IBC (730 of 3631 patients with non-IBC; 20.1%). Thirty-six of 60 patients with IBC (60%) had stage III disease. Of note, there is no stage I or II disease in IBC. The subtype was unknown for approximately 35% of patients with non-IBC because this information was recorded in a different non-EMR Pathology Department system, which could not be collected. Thirty of 60 patients with IBC (50%) and 2131 of 3631 patients with non-IBC (58.7%) had private insurance.\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\u003eBaseline Patient Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIBC (N\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-IBC (N\u0026thinsp;=\u0026thinsp;3631)\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\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\u003eAge (Median, range)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (29\u0026ndash;72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (22\u0026ndash;96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\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 \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\u003eWhite\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e712 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAsian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2071 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNative Hawaiian or Pacific Islander\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e730 (20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOthers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnknown\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary 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 \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\u003ePrivate insurance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2131 (58.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedicare\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1053 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedicaid\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOthers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo insurance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnknown\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistology\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 \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDuctal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3183 (87.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLobular\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e233 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMixed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOthers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnknown\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical Stage*\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 \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage I\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2103 (57.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage II\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1057 (29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage IIII\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e224 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage IV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnknown\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e*Only Stages III and IV were used in the IBC analysis since there is no Stage I or II in IBC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubtype\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 \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\u003eHR-positive/HER2-negative\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1790 (49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHER2-positive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e316 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTNBC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnknown\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1300 (35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eHR; hormone receptor, HER2; human epidermal growth factor receptor 2, TNBC; triple negative breast cancer\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eRacial difference in the proportion of patients with IBC to those with non-IBC\u003c/h2\u003e \u003cp\u003eThe proportion of those with IBC was highest in the NH/PI race, with 26 of 756 NH/PI having IBC (3.44%). The proportion of Whites with IBC was 1.66% (12 of 712 patients). Compared to the proportion of Whites, the NH/PI race had a significantly high proportion of IBC. (P\u0026thinsp;=\u0026thinsp;0.003) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eProportion of IBC by Race\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIBC (N\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-IBC (N\u0026thinsp;=\u0026thinsp;3631)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% of IBC among all BC cases\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\u003eWhite\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAsian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNative Hawaiian or Pacific Islander\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOthers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eOverall survival (OS) in patients with IBC\u003c/h2\u003e \u003cp\u003eIn a univariate cox proportional hazard model, no insurance (HR, 5.38 [95%CI, 1.47\u0026ndash;19.7]; P\u0026thinsp;=\u0026thinsp;0.01), MEDICAID (HR, 3.83 [95%CI, 1.58\u0026ndash;9.28]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), TNBC subtype (HR, 5.43 [95%CI, 1.66\u0026ndash;17.73]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and clinical stage IV disease (HR, 10.58 [95%CI, 4.06\u0026ndash;27.59]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with shorter OS, but NH/PI race (HR, 2.46 [95%CI, 0.81\u0026ndash;7.45]; P\u0026thinsp;=\u0026thinsp;0.11) was not. In a multivariate cox proportional hazard model adjusting for the variables with P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariate analysis and race, MEDICAID insurance (HR, 4.45 [95%CI, 1.1-18.03]; P\u0026thinsp;=\u0026thinsp;0.04), TNBC subtype (HR, 4.46 [95%CI, 1.08\u0026ndash;19.5]; P\u0026thinsp;=\u0026thinsp;0.04), and clinical stage IV disease (HR, 10.1 [95%CI, 2.35\u0026ndash;43.4]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) remained significant. NH/PI race remained a non-significant factor associated with shorter OS (HR, 1.17 [95%CI, 0.26\u0026ndash;5.223]; P\u0026thinsp;=\u0026thinsp;0.84) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and Multivariate Analysis for OS Among Patients with IBC. (N\u0026thinsp;=\u0026thinsp;60)\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=\"left\" 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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\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 \u003cp\u003e1.0 (0.97\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \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\u003cb\u003eRace\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\u003e\u003cb\u003eWhite\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 \u003cp\u003eRef\u003c/p\u003e \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 \u003cp\u003eRef\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\u003eAsian\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 \u003cp\u003e2.1 (0.66\u0026ndash;6.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6 (0.11\u0026ndash;3.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNative Hawaiian or Pacific Islander\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 \u003cp\u003e2.46 (0.81\u0026ndash;7.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.17 (0.26\u0026ndash;5.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOthers\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 \u003cp\u003e1.34 (0.15\u0026ndash;12.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.85 (1.81-427.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary 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\u003e\u003cb\u003ePrivate 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 \u003cp\u003eRef\u003c/p\u003e \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 \u003cp\u003eRef\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\u003eMEDICARE\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 \u003cp\u003e1.25 (0.47\u0026ndash;3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.59 (0.12\u0026ndash;2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMEDICAID\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 \u003cp\u003e3.83 (1.58\u0026ndash;9.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.45 (1.1-18.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOthers\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 \u003cp\u003e0.64 (0.08\u0026ndash;4.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64 (0.07\u0026ndash;5.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo 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 \u003cp\u003e5.38 (1.47\u0026ndash;19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.08 (0.64\u0026ndash;26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistology\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\u003e\u003cb\u003eDuctal\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 \u003cp\u003eRef\u003c/p\u003e \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\u003cb\u003eLobular\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 \u003cp\u003e1.29 (0.17\u0026ndash;9.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \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\u003cb\u003eSubtype\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\u003e\u003cb\u003eHR-positive/HER2-negative\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 \u003cp\u003eRef\u003c/p\u003e \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 \u003cp\u003eRef\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\u003eHER2-positive\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 \u003cp\u003e1.16 (0.48\u0026ndash;2.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65 (0.22\u0026ndash;1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTNBC\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 \u003cp\u003e5.43 (1.66\u0026ndash;17.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.6 (1.08\u0026ndash;19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical Stage\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\u003e\u003cb\u003eStage III\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 \u003cp\u003eRef\u003c/p\u003e \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 \u003cp\u003eRef\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\u003eStage IV\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 \u003cp\u003e10.58 (4.06\u0026ndash;27.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.1 (2.35\u0026ndash;43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eOverall survival (OS) in patients with non-IBC\u003c/h2\u003e \u003cp\u003eSimilarly to the findings in patients with IBC, in the univariate cox proportional hazard model, no insurance (HR, 5.13 [95%CI, 2.63\u0026ndash;9.99]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), MEDICAID (HR, 3.1 [95%CI, 2.29\u0026ndash;4.17]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), TNBC subtype (HR, 2.38 [95%CI, 1.78\u0026ndash;3.19]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and advanced clinical stage were associated with shorter OS. Additionally, NH/PI race (HR, 1.43 [95%CI, 1.13\u0026ndash;1.81]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and age (HR, 1.05 [95%CI, 1.04\u0026ndash;1.06]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were also associated with shorter OS (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In a multivariate cox proportional hazard model adjusting for the variables with P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05, no insurance (HR, 3.34 [95%CI, 0.94\u0026ndash;2.81]; P\u0026thinsp;=\u0026thinsp;0.04), MEDICAID insurance (HR, 1.79 [95%CI, 1.15\u0026ndash;2.77]; P\u0026thinsp;=\u0026thinsp;0.01), TNBC subtype (HR, 1.76 [95%CI, 1.3\u0026ndash;2.41]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), age (HR, 1.05 [95%CI, 1.04\u0026ndash;1.07]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), advanced clinical stage (HR, 10.1 [95%CI, 2.35\u0026ndash;43.4]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and NH/PI race (HR, 1.65 [95%CI, 1.14\u0026ndash;2.39]; P\u0026thinsp;=\u0026thinsp;0.007) remained significant (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analysis for OS among patients with non-IBC. (N\u0026thinsp;=\u0026thinsp;3631)\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=\"left\" 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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\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 \u003cp\u003e1.05 (1.04\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05 (1.04\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\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\u003e\u003cb\u003eWhite\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 \u003cp\u003eRef\u003c/p\u003e \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 \u003cp\u003eRef\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\u003eAsian\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 \u003cp\u003e0.87 (0.7\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.83 (0.6\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNative Hawaiian or Pacific islander\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 \u003cp\u003e1.43 (1.13\u0026ndash;1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.65 (1.14\u0026ndash;2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOthers\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 \u003cp\u003e1.34 (0.86\u0026ndash;20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.34 (0.63\u0026ndash;2.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary 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\u003e\u003cb\u003eHMSA\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 \u003cp\u003eRef\u003c/p\u003e \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 \u003cp\u003eRef\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\u003eMEDICARE\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 \u003cp\u003e3.4 (2.84\u0026ndash;4.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.35 (0.94\u0026ndash;1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMEDICAID\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 \u003cp\u003e3.1 (2.29\u0026ndash;4.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.79 (1.15\u0026ndash;2.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOthers\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 \u003cp\u003e1.82 (1.3\u0026ndash;2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.63 (0.94\u0026ndash;2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo 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 \u003cp\u003e5.13 (2.63\u0026ndash;9.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.34 (1.03\u0026ndash;10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistology\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\u003e\u003cb\u003eDuctal\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 \u003cp\u003eRef\u003c/p\u003e \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\u003cb\u003eLobular\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 \u003cp\u003e1.0 (0.7\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \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\u003cb\u003eMixed\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 \u003cp\u003e1.21 (0.66\u0026ndash;2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \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\u003cb\u003eOthers\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 \u003cp\u003e1.07 (0.74\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \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\u003cb\u003eSubtype\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\u003e\u003cb\u003eHR-positive/HER2-negative\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 \u003cp\u003eRef\u003c/p\u003e \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 \u003cp\u003eRef\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\u003eHER2-positive\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 \u003cp\u003e1.48 (1.05\u0026ndash;2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01 (0.7\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTNBC\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 \u003cp\u003e2.38 (1.78\u0026ndash;3.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.76 (1.3\u0026ndash;2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical Stage\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\u003e\u003cb\u003eStage I\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 \u003cp\u003eRef\u003c/p\u003e \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 \u003cp\u003eRef\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\u003eStage II\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 \u003cp\u003e1.95 (1.59\u0026ndash;2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.08 (1.53\u0026ndash;2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage IIII\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 \u003cp\u003e4.38 (3.36\u0026ndash;5.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.63 (3.14\u0026ndash;6.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage IV\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 \u003cp\u003e22.77 (17.68\u0026ndash;29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.59 (18.3-38.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the first study to investigate the association between patient, race, disease characteristics, socioeconomic status, and OS in a unique population focused on Native Hawaiian or other Pacific Islanders (NH/PI) with IBC and non-IBC separately. In accordance with our hypothesis, in our study, we found that NH/PI race had the highest proportion of IBC (3.44%) amongst other races and was statistically significant when compared to the ratio of Whites (P\u0026thinsp;=\u0026thinsp;0.003). Among patients with non-IBC, weak or no insurance was associated with poor OS, which is consistent with previous studies on the influence of SES on survival outcomes.[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] NH/PI race also remained a significant factor associated with OS. However, among patients with IBC, only TNBC subtype and Stage IV disease were significantly associated with OS, and NH/PI was not associated with OS.\u003c/p\u003e \u003cp\u003eDespite NH/PI race having a significantly higher proportion of IBC than other races, NH/PI race was not found to be an independent poor prognostic factor amongst those with IBC. Although the reason for this lack of difference in survival in IBC is unclear at this time, we suspect the aggressive nature of IBC is likely one of the highly contributory factors. Small sample size might be another reason. Previous studies have consistently shown that for those with IBC, Black race is associated with poor prognosis.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] However, other races have not reliably been shown to be independent poor prognostic factors in those with IBC.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] One possible reason for this finding is that this could indicate unique disparities (e.g., biological variations) amongst Blacks with IBC that do not necessarily affect other races (including NH/PI race) regardless of the prevalence of the disease. However, given the rarity of IBC, small sample size again should also be considered when interpreting these results. In our study, MEDICAID insurance was also associated with worse survival amongst those with IBC, although no insurance was not a significant factor. This is likely due to small sample size (only three patients with IBC had no insurance) because patients without insurance coverage face similar challenges to patients with MEDICAID insurance, such as access to care and lower income levels.\u003c/p\u003e \u003cp\u003eContrary to that stated above, NH/PI race was found to be associated with worse OS in those with non-IBC in our study. MEDICAID or uninsured, TNBC sub-type, and advanced clinical stage were also found to be poor prognostic factors. Although the exact reasoning why NH/PI race is an adverse prognostic factor is unclear, it is likely multifactorial, influenced by biological and socioeconomic causes. Uninsured and underinsured status are likely substantial contributory factors, although NH/PIs have also been found to have poor health outcomes despite being insured.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] It has been well established that low socioeconomic status (SES) is associated with worse outcomes in patients with cancer.[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] Among both men and women, five-year survival for all cancers combined is ten percentage points lower than those of higher SES.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] Unfortunately, NH/PI populations have been associated with lower SES, with approximately 15% of NH/PIs living in poverty compared to 11% of Asians or 13% of Americans overall.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Low SES can result in lower quality of life (resulting in higher health risks), fragmentation of care, complications with health insurance, lower education, health literacy, and less access to care.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] For example, Sentell et al. found that low health literacy was a significant predictor of poorer health outcomes in adults of NH/PI race.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Additionally, Taparra et al. found that within a total cohort of almost 600,000 women with stage 0-II breast cancer, NH/PI women had worse survival when compared with non-Hispanic White women.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] NH/PI women had consistently longer times between surgery and radiation therapy. Thus, delays in care were suggested to be a significant contributory factor to the finding of increased mortality in this population.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] These delays in care are suspected to primarily result from poor access to care in NH/PI populations due to financial or geographical hardships. Not only does this apply to local NH/PI patients in the United States but also to the majority of Pacific Islanders originating from Pacific Island Countries where medical care is limited. This requires these patients to travel long distances and at a significant cost to seek appropriate medical treatment, especially when specialty or hospital-based care is needed.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] Subsequently, this leads to even further fragmentation of care, given that many patients have family and friends in their home countries, requiring frequent travel back, which can ultimately interrupt treatment plans as well.\u003c/p\u003e \u003cp\u003eIn addition to socioeconomic causes, biological etiologies are also a consideration for the finding of non-IBC as an independent poor prognostic factor amongst NH/PIs in our study. Previous research has demonstrated higher incidence rates of both hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) expression in Native Hawaiians.[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] HR-positive breast cancers generally tend to have a better prognosis than HR-negative breast cancers.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] However, HER2 overexpression in invasive breast cancers is associated with higher rates of disease recurrence, brain metastasis, and mortality.[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] HER2-positive breast cancers have also been found to have the second poorest prognosis amongst all breast cancer subtypes.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] Since the advent of HER2-targeted therapies such as trastuzumab or pertuzumab, there has been a paradigm shift amongst patients with HER2-positive breast cancer resulting in decreased mortality rates, but if NH/PI patients are unable to receive these therapies due to poor access to care or other socioeconomic barriers, as discussed above, then perhaps HER2 overexpression could be a biological explanation for this finding. However, further investigation is necessary to determine the impact of receptor expression on mortality in NH/PIs with invasive breast carcinoma.\u003c/p\u003e \u003cp\u003eIn our study, several limitations should be taken into consideration when interpreting our results. First, this was a retrospective chart review study. Although we controlled for race, insurance status, histology, and clinical stage, given the nature of the study, there are potential unknown confounding factors as well as other variables that were unable to be collected (e.g., distance to health care facilities, family support, and income), which could have affected our results. Second, although the sample size of the patients with non-IBC was favorable, we could only include sixty patients with IBC in our analysis. Unfortunately, this small sample size reduces the power of the study to identify slight differences and variations. However, as previously discussed, IBC is a rare form of breast cancer and can be challenging to observe, especially in smaller populations such as in Hawaii. Third, not all data was present for all patients included in the analysis. Notably, as mentioned above, the subtype of breast cancer was unknown for approximately 35% of patients with non-IBC due to the inability to collect the data from the non-EMR Pathology Department system. Lastly, the patient population included in our study was diagnosed with breast cancer between 2000 and 2018. The standard of care for those patients could have been different from the current standard of care, which could potentially affect survival outcomes.\u003c/p\u003e \u003cp\u003eIn conclusion, our study demonstrated that NH/PI race had a significantly high proportion of IBC when compared to other races. NH/PI race was an adverse prognostic factor associated with worse OS in those with non-IBC but not in those with IBC. In patients with non-IBC, lack of insurance or underinsured status were also associated with shorter OS. Additional research needs to be conducted to further understand the unique determinants and disparities contributing to poor survival outcomes in NH/PI populations, particularly with non-IBC. Unfortunately, there are not many large, multi-center studies that focus on NH/PI populations, and the research that does include NH/PIs typically aggregates this population with Asians. However, as this study has demonstrated, disaggregating NH/PI race from Asians in population-based research is essential. By further identifying these factors, targeted interventions can be implemented to ultimately help improve survival rates and reduce health inequities in NH/PIs with breast cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported in part by the Intramural Research Program of the NIH, NCI, Center for Cancer Research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene T. Yoshikawa: Conceptualization, Data Curation, Investigation, Writing\u0026mdash;Original Draft. Kyle SY. Miyazaki: Conceptualization, Data Curation, Investigation, Writing \u0026ndash; Review \u0026amp; Editing. Jared D. Acoba: Conceptualization, Methodology, Investigation, Writing \u0026ndash; Review \u0026amp; Editing. Takeo Fujii: Conceptualization, Methodology, Investigation, Formal Analysis, Writing \u0026ndash; Review \u0026amp; Editing, Validation, Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported in part by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Center for Cancer Research (ZIA BC 012130).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData cannot be shared publicly because of protected health information. The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki. This retrospective chart review study was approved by the Queen\u0026rsquo;s Medical Center\u0026rsquo;s Institutional Review Board (RA-2019-027). An informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel Mph RL, Miller KD, Sandeep N, Mbbs W, Ahmedin |, Dvm J, et al. Cancer statistics, 2023. CA Cancer J Clin. 2023;73:17\u0026ndash;48.\u003c/li\u003e\n\u003cli\u003eEllington TD, Miller JW, Henley SJ, Wilson RJ, Wu M, Richardson LC. Trends in Breast Cancer Incidence, by Race, Ethnicity, and Age Among Women Aged \u0026ge;20 Years \u0026mdash; United States, 1999\u0026ndash;2018. Morbidity and Mortality Weekly Report. 2022;71:43.\u003c/li\u003e\n\u003cli\u003eKS A, JM U, JJ C, CA C, DL H. Racial disparities in cancer survival among randomized clinical trials patients of the Southwest Oncology Group. J Natl Cancer Inst. 2009;101:984\u0026ndash;92.\u003c/li\u003e\n\u003cli\u003eKW H, WF A, SS D, HA Y, PH L. Trends in inflammatory breast carcinoma incidence and survival: the surveillance, epidemiology, and end results program at the National Cancer Institute. J Natl Cancer Inst. 2005;97:966\u0026ndash;75.\u003c/li\u003e\n\u003cli\u003eU.S. Census Bureau QuickFacts: Hawaii. 2022. https://www.census.gov/quickfacts/fact/dashboard/HI/PST045222. Accessed 17 May 2023.\u003c/li\u003e\n\u003cli\u003eA K, ME T, KK C. Associations among socioeconomic status, patterns of care and outcomes in breast cancer patients in a universal health care system: Ontario\u0026rsquo;s experience. Cancer. 2016;122:893\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eJi P, Gong Y, Jiang C-C, Hu X, Di G-H, Shao Z-M, et al. Association between socioeconomic factors at diagnosis and survival in breast cancer: A population-based study. 2020. https://doi.org/10.1002/cam4.2842.\u003c/li\u003e\n\u003cli\u003eKuijer A, Verloop J, Visser O, Sonke G, Jager A, van Gils CH, et al. The influence of socioeconomic status and ethnicity on adjuvant systemic treatment guideline adherence for early-stage breast cancer in the Netherlands. Ann Oncol. 2017;28:1970\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eOrsini M, Tretarre B, Daur\u0026egrave;s JP, Bessaoud F. Individual socioeconomic status and breast cancer diagnostic stages: a French case\u0026ndash;control study. Eur J Public Health. 2016;26:445\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eMancilla VJ, Peeri NC, Silzer T, Basha R, Felini M, Jones HP, et al. Understanding the Interplay Between Health Disparities and Epigenomics. Front Genet. 2020;11:515278.\u003c/li\u003e\n\u003cli\u003eKong X, Liu Z, Cheng R, Sun L, Huang S, Fang Y, et al. Variation in Breast Cancer Subtype Incidence and Distribution by Race/Ethnicity in the United States From 2010 to 2015. JAMA Netw Open. 2020;3:e2020303\u0026ndash;e2020303.\u003c/li\u003e\n\u003cli\u003eCenters for Disease Control and Prevention. Cancer Incidence and 5-year Survival Among Asian/Pacific Islander People, United States\u0026mdash;2008\u0026ndash;2017. USCS Data Brief, no 22. Atlanta, GA: Centers for Disease Control and Prevention, US Department of Health and Human Services; 2021. https://www.cdc.gov/cancer/uscs/about/data-briefs/no22-cancer-incidence-survival-Asian-Pacific-Islanders-2008-2017.htm. Accessed 17 May 2023.\u003c/li\u003e\n\u003cli\u003eLiu L, Noone AM, Gomez SL, Scoppa S, Gibson JT, Lichtensztajn D, et al. Cancer Incidence Trends Among Native Hawaiians and Other Pacific Islanders in the United States, 1990\u0026ndash;2008. JNCI Journal of the National Cancer Institute. 2013;105:1086.\u003c/li\u003e\n\u003cli\u003eYoshikawa GT, Simon N, Nakasone RK, Acoba JD. Disaggregating Data on Pacific Islander Gastric Cancer Patients Reveals Survival Disparity. J Gastrointest Cancer. 2021. https://doi.org/10.1007/S12029-020-00579-6.\u003c/li\u003e\n\u003cli\u003eTM F, T K, DD L, Y S, H M, R E-Z, et al. Overall survival differences between patients with inflammatory and noninflammatory breast cancer presenting with distant metastasis at diagnosis. Breast Cancer Res Treat. 2015;152:407\u0026ndash;16.\u003c/li\u003e\n\u003cli\u003eDenu RA, Hampton JM, Currey A, Anderson RT, Cress RD, Fleming ST, et al. Racial and Socioeconomic Disparities Are More Pronounced in Inflammatory Breast Cancer Than Other Breast Cancers. 2017. https://doi.org/10.1155/2017/7574946.\u003c/li\u003e\n\u003cli\u003eGoldner B, Behrendt CE, Schoellhammer HF, Lee B, Chen SL. Incidence of Inflammatory Breast Cancer in Women, 1992\u0026ndash;2009, United States. Ann Surg Oncol. 2014;21:1267.\u003c/li\u003e\n\u003cli\u003eWolff AC, Hammond MEH, Hicks DG, Dowsett M, McShane LM, Allison KH, et al. Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update. J Clin Oncol. 2013;31:3997\u0026ndash;4013.\u003c/li\u003e\n\u003cli\u003eGudina AT, Copeland G, Soliman AS, Hirko KA. Racial/ethnic disparities in inflammatory breast cancer survival in the Michigan Cancer Surveillance Program. Breast Cancer Res Treat. 2019;173:693\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eMorisako AK, Taualiʻi M, Adrian ;, Ambrose JH, Withy K. Beyond the Ability to Pay: The Health Status of Native Hawaiians and Other Pacific Islanders in Relationship to Health Insurance. Hawai\u0026rsquo;i Journal of Medicine \u0026amp; Public Health. 2017;76 3 Suppl 1:36.\u003c/li\u003e\n\u003cli\u003eWard E, Jemal A, Cokkinides V, Gopal ;, Singh K, Cardinez C, et al. Cancer Disparities by Race/Ethnicity and Socioeconomic Status. CA Cancer J Clin. 2004;54:78\u0026ndash;93.\u003c/li\u003e\n\u003cli\u003eJoint Economic Committee of Democrats. The Economic State of Asian Americans, Native Hawaiians and Pacific Islanders in the United States. 2022.\u003c/li\u003e\n\u003cli\u003eLazar M, Davenport L. Barriers to Health Care Access for Low Income Families: A Review of Literature. J Community Health Nurs. 2018;35:28\u0026ndash;37.\u003c/li\u003e\n\u003cli\u003eSentell T, Baker KK, Onaka A, Braun K. Low Health Literacy and Poor Health Status in Asian Americans and Pacific Islanders in Hawai\u0026rsquo;i. https://doi.org/101080/108107302011604390. 2011;16 SUPPL. 3:279\u0026ndash;94.\u003c/li\u003e\n\u003cli\u003eTaparra 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. 2022;18:e1255\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization (WHO). Strengthening Pacific Health Systems. https://www.who.int/westernpacific/activities/strengthening-pacific-health-systems. Accessed 17 May 2023.\u003c/li\u003e\n\u003cli\u003eIhenacho U, McKinley MA, Vu A, Hernandez BY, Loo LWM, Gomez SL, et al. Characterizing breast cancer incidence and trends among Asian American, Native Hawaiian, and non-Hispanic White women in Hawaiʻi, 1990-2014. Cancer Causes and Control. 2022;34:241\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eLoo LWM, Williams M, Hernandez BY. The High and Heterogeneous Burden of Breast Cancer in Hawaii: a Unique Multiethnic U.S. Population. Cancer Epidemiol. 2019;58:71.\u003c/li\u003e\n\u003cli\u003eKathryn L Braun 1 MFCCGCDKC. Ethnic differences in breast cancer in Hawai\u0026rsquo;i: age, stage, hormone receptor status, and survival. Pac Health Dialog. 2004;11:146\u0026ndash;53.\u003c/li\u003e\n\u003cli\u003eDunnwald LK, Rossing MA, Li CI. Hormone receptor status, tumor characteristics, and prognosis: a prospective cohort of breast cancer patients. Breast Cancer Research. 2007;9:R6.\u003c/li\u003e\n\u003cli\u003ePatel A, Unni N, Peng Y. The Changing Paradigm for the Treatment of HER2-Positive Breast Cancer. Cancers (Basel). 2020;12:1\u0026ndash;17.\u003c/li\u003e\n\u003cli\u003eVu T, Claret FX. Trastuzumab: Updated Mechanisms of Action and Resistance in Breast Cancer. Front Oncol. 2012;2.\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":"cancer disparities, inflammatory breast cancer, non-inflammatory breast cancer, Native Hawaiian, Pacific Islander, survival outcomes","lastPublishedDoi":"10.21203/rs.3.rs-3143571/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3143571/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBreast cancer has the highest incidence among all malignancies and is the second leading cause of cancer death among women in the United States. It is well known that race is an independent predictor of breast cancer mortality and advanced stage at diagnosis. Inflammatory breast cancer (IBC) is the most aggressive type of breast cancer and has distinct clinical and biological features. Previous studies have shown that Blacks have a higher incidence of IBC than Whites. However, the proportion of IBC and the role of race on prognosis in Native Hawaiian and other Pacific Islander (NH/PI) populations with breast cancer are poorly understood. In this study, we aimed to examine the proportion of IBC to non-IBC in NH/PIs and to identify the clinicopathological, biological, and socioeconomic factors associated with the overall survival of NH/PIs compared to other races.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUtilizing a comprehensive cancer registry from the largest hospital in Hawaii, newly diagnosed primary invasive breast cancer patients diagnosed between 2000 and 2018 were identified. Univariate and multivariate Cox proportional hazards models were used to test the association between race and clinical outcomes. Variables with P-values \u0026lt;0.05 in the univariate analysis and race (variable of interest) were included in a multivariate analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cohort included 3691 patients, 60 of whom had IBC. NH/PI race had the highest proportion of IBC compared to other races (3.44%) but was not found to be an independent poor prognostic factor in IBC (HR 1.17 [95%CI 0.26-5.22]). Conversely, NH/PI race was associated with worse survival outcomes in patients with non-IBC (HR 1.65 [95%CI, 1.14-2.39]) along with other factors such as lack of insurance, underinsured status, triple-negative breast cancer (TNBC) subtype, age, and advanced clinical stage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings of this study highlight that NH/PIs had higher rates of IBC and inferior survival in non-IBC compared to other races but not in IBC. It is essential to disaggregate NH/PI race from Asians in future population-based research studies. Further research is needed to understand the factors contributing to higher rates of IBC and poor survival outcomes in NH/PIs with non-IBC as well as targeted interventions to improve breast cancer outcomes in this population to ultimately help improve survival rates and reduce health inequities in NH/PIs with breast cancer.\u003c/p\u003e","manuscriptTitle":"Racial and Survival Disparities in Inflammatory Breast Cancer (IBC) and non-IBC: A Population-Based Study Focused on Native Hawaiians and Other Pacific Islanders","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-02 02:35:23","doi":"10.21203/rs.3.rs-3143571/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":"ac9d8f9f-f47f-4a9a-b395-9bcfee12f5be","owner":[],"postedDate":"August 2nd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-20T10:15:01+00:00","versionOfRecord":[],"versionCreatedAt":"2023-08-02 02:35:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3143571","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3143571","identity":"rs-3143571","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}