Do ethnic disparities exist in disease burden and healthcare utilization of male breast cancer: a 9-year cohort study of 18.19 million adults in China

Do ethnic disparities exist in disease burden and healthcare utilization of male breast cancer: a 9-year cohort study of 18.19 million adults in China | 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 Do ethnic disparities exist in disease burden and healthcare utilization of male breast cancer: a 9-year cohort study of 18.19 million adults in China Jieying Chen, Liying Qiao, Meng Qi, Yunjing Zhang, Ying Yan, Weiwei Kang, and 21 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3824148/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background As a rare disease, male breast cancer (MBC) is of increasing concern in China. Whether health inequalities of disease burden and healthcare utilization exist by ethnicity in male breast cancer remains unclear. We aim to measure disease burden and healthcare utilization by ethnicity among male breast cancer patients in China. Methods A retrospective cohort study was established during 2012–2021 based on Inner Mongolia Regional Health Information Platform. Disease burden including incidence, 5-year prevalence, mortality, survival rate, and medical cost were analyzed. Results Among 630 participants (mean [SD] age, 59.4 [13.1] years), age-standardized rates of incidence were 1.2 (95% CI: 0.4–2.1) per 100000. All-cause mortality was 50.8 per 1000 person-years (95% CI: 42.4–60.4) but breast cancer-specific mortality was 5.5 per 1000 person-years (95% CI: 3.0-9.3). Regarding ethnicity, Mongolian had a higher age-standardized 5-year prevalence rate than Han (3.2[95% CI: 2.5-4.0] vs 2.3[95% CI: 1.7-3.0], P = 0.016), but no significant differences existed in incidence rates, survival rates, and risk of all-cause and breast cancer-specific mortality. Patients residing in areas of lower GDP level were associated with increased breast-cancer specific mortality (HR, 22.5, [95% CI: 1.6–325.0]; P = 0.022). Conclusions This study revealed a moderate disease burden and relatively lower healthcare utilization for male breast cancer in Inner Mongolia, China. No significant ethnic disparities existed in disease burden and healthcare utilization. However, we still demanded for increasing attention to male breast cancer due to the crucial influence of economic factors on potential ethnic disparities. male breast cancer ethnic disparities rare disease disease burden healthcare utilization Figures Figure 1 BACKGROUND Male breast cancer (MBC) is a rare disease accounting for approximately 1% of all breast cancer [ 1 , 2 ]. Breast cancer has been traditionally regarded as a female-specific disease, leading to the lack of awareness of MBC. However, the disease burden of MBC keeps growing over the past decades [ 3 ]. Due to the deficiency of randomized controlled trials of MBC, treatments regimes were usually extrapolated from guidelines for female breast cancer (FBC) nowadays [ 4 , 5 ], but substantial disparities have been observed between MBC and FBC [ 6 ]. Therefore, treatment regimes for females might not be fully applicable in males, leading to poor adherence and worse prognosis [ 7 ]. However, there are conflicts about prognosis of MBC in Asian. In Korea, the 5-year overall survival rate was lower for MBC than for FBC (76.2% vs 88.9%) [ 8 ], but a study in Hong Kong suggested that MBC did not show lower survival until 10 years later [ 9 ]. Previous studies in China mainland were not convincing enough because of the sample size (ranging from 24 to 106 male patients) [ 10 – 12 ]. Furthermore, healthcare utilization of MBC is of little concern so far with only one study globally [ 13 ]. Therefore, it is crucial to explore the current situation of MBC and provide more evidence for formulating policies and guidelines for this rare disease. It is notable that race/ethnicity plays a part in the disparities in disease burden of breast cancer [ 14 , 15 ]. African Americans have been focused on by most studies about race/ethnicity for both MBC and FBC, whereas Asians were included in only a few studies and often classified into “others” [ 16 – 18 ]. In terms of MBC, a few studies reported the racial/ethnic disparities in incidence in general population, of which Asian was combined with Pacific Islanders, Hispanic or others [ 18 – 22 ]. Significant disparities in disease burden of FBC have been observed within Asian countries at the national level [ 23 , 24 ], but by which the situation of MBC cannot be extrapolated due to the prominent differences between FBC and MBC. The situation within Asian ethnic groups remains unclear as well. In a word, no studies of ethnic disparities have been conducted in China which contributed to the most breast cancer patients (18.4% in 2020) in the world [ 25 ]. As the third largest province in China, Inner Mongolia has a population of 4.25 million Mongolian, accounting for over 70% of the Mongolian population in China and 50% in the world. Inner Mongolia Regional Health Information Platform (IMRHIP) aims to address cancer health disparities across ethnicities by collecting and administrating healthcare information of residents in Inner Mongolia [ 26 ]. In this study, we aimed to outline ethnic disparities of disease burden of male breast cancer based on IMRHIP to provide further understanding of the rare disease. METHODS Data Sources This retrospective cohort study used data from IMRHIP. Established by Inner Mongolia Center for Disease Control and Prevention, IMRHIP integrated and collected data from National Basic Medical Insurance (NBMI), National Cancer Registry (NCR), and Cause-of-death Reporting System since 2012 with provincial coverage of 95% by 2020. The details have been described elsewhere [ 26 ]. The information about the Surveillance Cohort of All Cancers Species has been shared on the China Cohort Consortium (No. CCC2023050801). Patient information including demographics, disease diagnoses, tumor characteristics, cause of death, treatments, and expenses of inpatient and outpatient services were recorded and linked by encrypted ID numbers among databases. All records were deidentified to protect patients’ privacy. Study Population Patients diagnosed with breast cancer between January 1, 2012, and March 9, 2021 in NBMI were identified by diagnostic text and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) code of breast cancer (C50). Natural language processing was applied to standardize the diagnostic text and code, with two researchers conducting this process independently and a third one double checking. Patients without ID numbers (10, 0.03%) were excluded. The patients were followed up until death or the end of the study, whichever came first. Measurements The tumor stage was defined into early (stage 0-Ⅲ), advanced (stage Ⅳ), and unknown according to whether a breast cancer surgery can be operated on the patient. Age at diagnosis was categorized into 3 groups: 18–40, 41–60, and ≥ 61, because the association between age and outcomes is non-linear. Charlson comorbidity index (CCI) score was calculated and categorized as 0, 1, 2, and ≥ 3 [ 27 , 28 ], in which the comorbidities diagnosed both before and after the diagnosis of breast cancer were included. Data on alcohol consumption and smoking rates were obtained from the 2018 Adult Chronic Disease and Nutrition Monitoring Survey in Inner Mongolia [ 26 ]. Gross domestic product (GDP) per capita in 2020 [ 29 ], alcohol consumption rate, and smoking rate were stratified into two levels (high/low) by cities according to the median. Treatments of breast cancer included surgery, chemotherapy, endocrine therapy, targeted therapy, and radiotherapy [ 30 , 31 ]. Statistical Analysis Our analyses included three parts. First, incidence and 5-year prevalence were calculated. A window of two years (2012 and 2013) was set to exclude prevalent cases when calculating the incidence rate for each of the 7 years from 2014 to 2020 [ 32 ]. The numerator of incidence was the number of new cases of breast cancer each year. The numerator of 5-year prevalence was the number of patients alive with breast cancer diagnoses during the past five years for each of the 5 years from 2017 to 2021 [ 26 ]. The denominator for incidence and 5-year prevalence was the total number of residents each year in NBMI. The average estimates in the subgroups were calculated by combining the estimates each year using a random effects meta-analysis based on the Clopper-Pearson method [ 33 ]. The 95% confidence intervals (CIs) were estimated by the Poisson distribution [ 33 ]. Subgroup analyses were conducted according to GDP level of the residence. Age-standardized rates (ASR) were determined using the World Health Organization world standardized rates, 2000 to 2025 [ 34 ]. Second, health utilization, including overall services and specifically for breast cancer of inpatient and outpatient, was measured by costs annually per capita and per episode (US $ 1.00 = 6.47 RMB on July 15, 2020). The number of inpatient and outpatient visits overall and specifically for breast cancer were also calculated. Third, 5-year, 3-year, 2-year, and 1-year survival rates were calculated. Taking the 5-year survival rate as an example, it was defined as the proportion of patients alive for more than 5 years after diagnosis among patients diagnosed that year. In survival analyses, since the proportional hazard assumption was not satisfied by stage, a stratified Cox model was performed for all-cause mortality. The Fine and Gray competing risk model was performed for breast cancer-specific mortality to estimate hazard ratios (HR) and 95% CI. Age at diagnosis, GDP level of residence, alcohol consumption rate, smoking rate, CCI, all treatments and stage were adjusted. Mean ± standard error (SD), median (interquartile range, IQR), and frequency (proportion) were presented for normal continuous variables, non-normal continuous variables, and categorical variables, respectively. χ 2 test, t-test, and Kruskal-Wallis test were performed to compare the difference between variables of interest. All statistical analyses were performed with R version 4.1.3, and a two-sided p < .05 was considered statistically significant. Data were analyzed and reported following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. RESULTS Demographic Characteristic A total of 18.19 million participants were included ( eTable 1 ). 630 (1.8%) were male, contributing to 2 539.4 person-years, among whom 62 (9.8%) were Mongolian and 521(82.7%) were Han. Han in MBC were more likely to be diagnosed 5 years older (60.0 years vs 55.4 years, P = 0.016) and more distributed in the residence with high GDP level (56.1% vs 37.1%, P = 0.006). No significant results were found in follow-up years, stage, CCI score, smoking rate, and alcohol consumption rate ( Table 1 ) . Table 1 Baseline characteristics of male breast cancer patients during 2012–2021 in Inner Mongolia, grouped by ethnicity a Overall Han Mongolian P Value Total, no. (%) 630 (1.8) 521 (82.7) 62 (9.8) - Age at diagnosis: x (s) 59.4 ± 13.1 60.0 ± 13.0 55.4 ± 14.1 0.016 Follow-up: median (IQR) 3.7 (2.7–6.8) 3.59 (1.5-7.0) 3.64 (1.5–5.5) 0.653 Stage Early 276 (43.8) 230 (44.2) 26 (41.9) 0.172 Advanced 192 (30.5) 147 (28.2) 26 (41.9) Unknown 162 (25.7) 144 (27.6) 10 (16.1) Charlson comorbidity index score b 0 253 (40.2) 218 (41.8) 21 (33.9) 0.686 1 82 (13.0) 66 (12.7) 9 (14.5) 2 59 (9.4) 49 (9.4) 7 (11.3) ≥ 3 236 (37.5) 188 (36.1) 25 (40.3) GDP level of residence c Low 295 (47.0) 227 (43.6) 39 (62.9) 0.006 High 333 (53.0) 292 (56.1) 23 (37.1) Smoking rate Low 354 (60.9) 315 (60.7) 39 (62.9) 0.736 High 227 (39.1) 204 (39.3) 23 (37.1) Alcohol consumption rate Low 360 (62.0) 326 (62.8) 34 (54.8) 0.222 High 221 (38.0) 193 (37.2) 28 (45.2) Note : Abbreviation: GDP, Gross Domestic Product. a There were missing data for GDP level of residence (2, 0.32%). For ethnic group, 47 (7.46%) patients were categorized into “others”. b Charlson comorbidity index score was calculated and categorized as 0, 1, 2 and ≥ 3. c GDP level of residence were stratified into two levels (high/low) by cities according to the median (66 377 RMB = $10 259.20) Incidence and prevalence The ASR of incidence for MBC during 2014–2020 was 1.2 (95% CI: 0.4–2.1) per 100 000, among whom Han’s was 0.7 (95% CI:0.0-1.5) and Mongolian’s was 0.9 (95% CI: 0.1–1.7), but the difference is not significant ( P = 0.996) ( eTable 2 ). The ASR of prevalence for MBC during 2017–2021 was 4.2 (95% CI:3.0-5.5) per 100 000, and Mongolian’s was significantly higher than Han’s (3.2[2.5-4.0] vs 2.3[1.7-3.0], P = 0.016). The 5-year prevalence in 2021 was 3.0 (95% CI: 2.1–3.9) per 100 000 in MBC, which of Han is 3.1 (95% CI: 2.7–3.5) and of Mongolian is 3.6 (95% CI: 2.5-5.0) (eTable 3) The age-specific incidence and prevalence showed unimodal distributions. For incidence, the age of peak for Han was 10 years older than that for Mongolian while for 5-year prevalence they are the same (Fig. 1 ). Healthcare utilization Only 36.8% of male patients recorded treatments for breast cancer, and 35.7% of Han and 48.4% of Mongolian in MBC were recorded for that ( P = 0.069). For MBC patients, the overall cost and the cost on breast cancer annually per capita were $ 1 734.5 (95% CI: 733.8-4820.9) and $ 507.4 (95% CI: 131.1-1804.9). Cost on breast cancer per inpatient episode was $ 1127.0 (95% CI: 699.4-2076.4) for Han and $ 1057.8 (95% CI: 582.5-1937.9) for Mongolian, and per outpatient episode was $ 147.4 (95% CI: 22.3-681.1) for Han and $ 16.2 (95% CI: 4.3–35.1) for Mongolian. Mongolian patients had higher per outpatient episode overall cost ( $ 9.9[4.3–28.9] vs $ 8.0[3.4–22.2], P = 0.009) and lower per outpatient episode cost for breast cancer( $ 16.2[4.3–35.1] vs $ 147.4[22.3-681.1], P = 0.033) than Han (Table 2 ). Table 2 Cost of MBC patients in Inner Mongolian during 2012–2021, grouped by ethnicity, median (IQR) Characteristics Male Han Mongolian P Patients with treatment of breast cancer (%) 35.70 48.39 0.069 Annual per-capita cost (dollars) 1 770.36 (748.45, 4 993.23) 2 075.39 (893.78, 5 140.43) 0.454 Annual per-capita cost on breast cancer (dollars) 496.56 (130.03, 2 044.58) 701.26 (183.79, 1 082.33) 0.870 Inpatient Per-episode cost (dollars) 1 147.72 (677.38, 1 960.47) 1 227.92 (616.46, 2 487.74) 0.492 Per-episode cost on breast cancer (dollars) 1 126.95 (699.38, 2 076.36) 1 057.80 (582.47, 1 937.89) 0.136 Visit 3.00 (1.00, 7.00) 4.00 (1.00, 6.00) 0.781 Visit on breast cancer 1.00 (1.00, 2.00) 1.00 (1.00, 1.00) 0.161 Length of stay (days) 7.00 (4.00, 13.00) 7.00 (4.00, 12.00) < 0.001 Length of stay on breast cancer (days) 6.00 (4.00, 12.00) 6.00 (3.00, 13.00) 0.399 Outpatient Per-episode cost (dollars) 8.04 (3.40, 22.16) 9.85 (4.33, 28.89) 0.009 Per-episode cost for breast cancer (dollars) 147.44 (22.26, 681.06) 16.23 (4.34, 35.09) 0.033 Visit 57.50 (16.75, 110.00) 52.00 (18.00, 119.50) 0.810 Visit for breast cancer 1.00 (1.00, 2.00) 1.00 (1.00, 2.00) 0.805 Note: The RMB-to-USD exchange rate is based on the July 15, 2020, exchange rate (1.00 USD = 6.47 RMB). Survival and prognosis The all-cause mortality for MBC was 50.8 per 1 000 person-years (95% CI: 42.4–60.4) and breast cancer-specific mortality was 5.5 per 1 000 person-years (95% CI: 3.0-9.3). Mongolian’s all-cause mortality (55.9[29.8–95.6] vs 50.0[40.9–60.5]) and breast cancer-specific (8.6[1.0-31.1] vs 5.2[2.6–9.4]) were insignificantly higher than Han’s, and no significant differences were observed in 5-year, 3-year, 2-year, and 1-year survival rates either (Table 3 ). Table 3 Prognosis and survival rates among male breast cancer patients, grouped by ethnicity Characteristics Han Mongolian P Value No. of death All-cause 105 (20.2) 13 (21.0) 1 Breast cancer-specific 11 (2.1) 2 (3.2) 0.915 Mortality, per 1 000 person-years All-cause 50.0 (40.9–60.5) 55.9 (29.8–95.6) 0.905 Breast cancer-specific 5.2 (2.6–9.4) 8.6 (1.0-31.1) 0.612 5-year survival rate (2012–2015 combined), % 91.3 (87.5–95.1) 96.6 (83.1–100.0) 0.880 3-year survival rate (2012–2017 combined), % 91.1 (86.7–95.5) 94.5 (84.6–100.0) 0.519 2-year survival rate (2012–2018 combined), % 93.2 (89.6–96.7) 93.1 (83.7–100.0) 0.707 1-year survival rate (2012–2019 combined), % 95.7 (92.9–98.5) 94.3 (85.5–100.0) 0.525 After adjusted covariates, Male breast cancer patients residing in cities of low GDP level were associated with increased breast-cancer specific mortality (HR, 22.5, [95% CI: 1.6–325.0]; P = 0.022) and patients diagnosed between the ages of 41–60 and over 61 were associated with decreased breast-cancer specific mortality (HR, 0.1, [95% CI: 0.0-0.6]; P = 0.010). No significant factor was observed in the model of hazard ratios for risk of all-cause mortality (Table 4 ). Table 4 Hazard ratios for risk of all-cause and breast cancer-specific mortality among male breast cancer patients Variable All-cause mortality a Breast cancer-specific mortality b Death Events Person-year HR (95% CI) a P value Death Events Person-year HR (95% CI) P value Age at diagnosis 18–40 8 21.7 1.0 [Reference] 3 5.4 1.0 [Reference] 41–60 31 61.7 0.7 (0.3–1.5) 0.355 2 6.8 0.1 (0.0-0.5) 0.010 >= 61 90 179.0 1.7 (0.8–3.6) 0.169 9 21.1 0.3 (0.1–1.9) 0.260 Ethnic Han 105 220.0 1.0 [Reference] 11 27.0 1.0 [Reference] Mongolian 13 22.7 0.7 (0.4–1.3) 0.300 2 5.9 0.6 (0.1–3.5) 0.680 Others 11 19.6 0.8 (0.4–1.6) 0.537 1 0.3 0.6 (0.1–7.8) 0.740 GDP level of residence High 54 134.0 1.0 [Reference] 2 6.3 1.0 [Reference] Low 75 129.0 0.9 (0.4–2.2) 0.757 12 26.9 22.5 (1.6–325.0) 0.022 Note : Abbreviation: GDP, Gross Domestic Product. a Stratified Cox model adjusted for age at diagnosis, ethnic, GDP level of residence, alcohol consumption rate level, smoking rate level, Charlson comorbidity index, surgery, chemotherapy, endocrine therapy, targeted therapy, radiotherapy, stage (strata) ( n = 628; excluded population = 2). b Fine and Gray competing risk model, adjusted for age at diagnosis, ethnic, GDP level of residence, alcohol consumption rate level, smoking rate level, Charlson comorbidity index, surgery, chemotherapy, endocrine therapy, targeted therapy, radiotherapy, stage (strata) ( n = 628; excluded population = 2). DISCUSSION Based on IMRHIP data during 2012–2021, we described the disease burden and healthcare utilization of male breast cancer in Inner Mongolia, and concentrated on ethnic disparities between Han patients and Mongolian patients. Overall, no significant ethnic disparities were demonstrated in both aspects. We observed a moderate level of incidenceMBC in Inner Mongolia compared to other countries and regions. The ASR of incidence of MBC (1.23 per 100 000) was first reported to be higher than previous studies in Asia including China [ 11 ] (0.17 per 100 000 in Zhongshan, Guangdong in 2016), Singapore [ 35 ] (0.40 in 2017), Japan [ 23 ] (0.18 during 1988–2002) and Korea [ 8 ] (0.22 in 2016). In contrast, it was close to the regions of high income in North America (1.26 in 2017) [ 36 ] and Israel (1.24 person-year during 1988–2002) [ 23 ], as well as Asian Americans based on The Surveillance, Epidemiology, and End Results (SEER) [ 18 ] (0.64 during 1997–2000). There may be several explanations. First, previous studies were based on data from cancer registries [ 8 , 10 , 11 , 19 , 23 , 35 ] in which the incidence of MBC might be underreported [ 37 ]. Comparative studies about identifying cancer cases have found that claim data can be used as an important implement in many countries including China [ 37 ], Korea [ 38 ], the US [ 39 ], and Germany [ 40 ]. Over 50% of underreporting rates of seven cancers by cancer registries in Inner Mongolia were reported [ 41 ]. Second, population genetic susceptibility may play a role [ 42 ]. BRCA1/2 (e.g. 5296del4 in BRCA1 and 6174delT in BRCA2 ) mutation was considered as the crucial risk factor in breast cancer, and BRCA2 mutations were more common in MBC [ 43 ]. The BRCA2 mutation was more frequently observed in China than in other Asian countries [ 44 ]. Third, allergy may have an impact. The risk of developing this allergy-related disease, which was most prevalent in China than other countries in the world [ 45 ], was related to the mutations on the same gene (RAD51B) with MBC [ 46 , 47 ]. While meta-analyses revealed no increased risk of FBC among patients with rheumatoid arthritis [ 48 ]. No significant ethnic disparities were found in incidence and survival rates. It can be interpreted from the following perspectives. First, according to previous studies, higher incidence of MBC may be associated with endocrine diseases like diabetes [ 47 , 48 ] and obesity and disadvantaged lifestyle such as more alcohol and tobacco consumption [ 49 ]. However, there were no significant differences in the proportion of diabetes (data not shown), smoking rate, and alcohol consumption rate between Mongolian and Han MBC patients in this study, and the latter two factors were also proved to have no significant ethnic differences in other studies [ 50 , 51 ]. Second, adjusted multivariate analysis indicated ethnicity was not significantly associated with higher risks of all-cause and breast cancer-specific mortality. Third, aforementioned population genetic susceptibility is a potential factor, but no specific gene has been found. In sum, no significant ethnic disparities were revealed in disease burden. However, differences related to economic factors should receive attention. Previous studies suggested sociodemographic factors like income and insurance could neutralize ethnic disparities, and poverty was a significant predictor of breast cancer mortality [ 21 , 52 ], with which the results of this study were consistent, warning of potential disease burden in Mongolians. Stakeholders should attach great attention to economic factors and keep eliminating underlying ethnic disparities. We observed a lower level of healthcare utilization compared with other counties and regions. According to prior studies, per-episode cost of visit on breast cancer in China was ranged from $ 2009.3 to $ 3554.9 (from 1996 to 2015) [ 53 ], with a median of approximately $ 3091.2 to $ 3864.0 (from 1996 to 2014) [ 54 ]. The cost on MBC per inpatient episode in Sichuan was $ 1925.0 (from 2015 to 2018) [ 55 ], of which the median was $ 3274.0 in Jilin (2019) [ 56 ]. The only study which estimated hospital costs of male breast cancer at the US population level reported an average cost of $ 9059 per hospital visit (from 2012 to 2016) [ 13 ]. These figures were higher than those reported in this study. Possible explanations are as follows. First, different data sources may lead to bias. Most studies in China collected data from a single hospital or sample in certain areas, and few reported data at the provincial level [ 53 , 54 ] like this study. Second, economic level affects the allocation of medical resources [ 57 ] and patient’s healthcare-seeking behavior. Disadvantaged patients are more likely to reduce expense or quit treatment [ 58 , 59 ]. Inner Mongolia has an economic gap compared with the aforementioned areas. Third, advances in medical technology contributes to the increase in healthcare costs [ 60 ], and high per capita human capital loss is a factor of economic cost for high-income areas like US, where people have better education attainment [ 61 ]. Yet in Inner Mongolia, medical technology and the quantity and quality of health human resources need to be improved [ 62 ]. We first reported ethnic differences in healthcare utilization among MBC patients [ 13 , 63 ], and no significant disparities were observed. It can be illustrated from four aspects. First, health insurance significantly influences the cost by affecting patient’s healthcare-seeking behaviors, which might be hindered if the patient is not being covered [ 59 , 64 – 66 ]. However, the universal medical insurance system based on the National Basic Medical Insurance (NBMI) has covered over 95% of the population in Inner Mongolia in 2020 [ 29 , 67 ], and 630 patients included in the study were all covered. Second, high degree of ethnic mixing co-habitation [ 68 , 69 ] and demographically equitable medical resources distribution [ 70 , 71 ] assist both ethnic groups to access healthcare health equally [ 72 ]. Third, it is proven that the knowledge gaps of MBC [ 73 ] might lead to stigmatization and embarrassment, causing the refusal or non-adherence to medical assistance [ 74 ]. However, few studies directly revealed the ethnic influence on knowledge access in MBC patients [ 75 ]. Educational level indirectly helps understand the result as it potentially affects people’s awareness of disease and treatments [ 75 ]. Studies have shown that educational equity in China at the ethnic level has made great progress since 1949 [ 76 ]. Due to favorable policies and sufficient funds resulting from economic development, the educational gap between Mongolian and Han has gradually narrowed [ 77 ]. Lastly, favorable marital status might affect MBC patients’ healthcare-seeking behaviors by offering psychosocial and emotional support [ 75 , 78 – 80 ]. 630 patients in this study displayed no significant ethnic difference in marital status, which might contribute to the understanding of our finding. Overall, this study highlighted the equity in utilization for MBC patients in Inner Mongolia and provided directions for further development of health equity. To our knowledge, this is the first study to uncover ethnic disparities in disease burden and healthcare utilization of male breast cancer based on 630 patients from 18.19 million general population in China. Our results should be considered within the context of study limitations. First, case ascertainment was limited due to the unavailability of laboratory data and imaging information by medical insurance. However, previous studies using NBMI to identify cancer cases have verified sensitivities and Positive predictive value (PPVs) above 90% [ 37 , 81 ]. Second, the small sample size of Mongolian males and patients with following-ups for more than 5 years led to low statistical power in survival analyses, calling for larger sample sizes. Third, tumor grade, and subtype were not available, influencing the further interpretation of our results. However, the results of disease burden carry substantial importance [ 82 ]. Finally, the aforementioned findings were only observed in Inner Mongolia, which requires external verification in other populations. CONCLUSION In conclusion, we examined the ethnic differences in disease burden and healthcare utilization of male breast cancer based on large-scale population-based data in Inner Mongolia. We reported the incidence, 5-year prevalence, prognosis and healthcare utilization in MBC, and the disparity of disease burden and medical utilization between Mongolian and Han MBC patients is not statistically significant. However, the study still demands greater focus on potential ethnic disparities due to the crucial influence of economic factors. Further research is needed to explore the potential mechanisms and provide policymakers with more precise recommendations. Abbreviations MBC Male breast cancer FBC Female breast cancer SEER The Surveillance, Epidemiology, and End Results IMRHIP Inner Mongolia Regional Health Information Platform NBMI National Basic Medical Insurance NCR National Cancer Registry WHO World Health Organization ICD-10 International Statistical Classification of Diseases and Related Health Problems, Tenth Revision CCI Charlson comorbidity index GDP Gross domestic product ASR Age-standardized rates STROBE the Strengthening the Reporting of Observational Studies in Epidemiology PPV Positive predictive value Declarations Ethics approval and consent to participate Approved by the Medical Ethics Committee of the Inner Mongolia Center for Disease Control and Prevention (NMCDCIRB2021001), informed consent from participants was approved for exemption. Consent for publication Not applicable. Availability of data and materials Yunfeng Xi and Shengfeng Wang had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. The datasets used and analysed during the study are available from Yunfeng Xi on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Natural Science Foundation of China (No. 82173616 and No. 72342015). Author Contributions Concept and design: Jieying Chen, Qiao, Qi, Yunjing Zhang, Ying Yan, Xi, Shengfeng Wang. Acquisition, analysis, or interpretation of data: All authors. Drafting the manuscript: Jieying Chen, Qiao, Xi, Yunjing Zhang, Zhou, Shengfeng Wang. Critical revision of manuscript for important intellectual content: All authors. Statistical analysis: Jieying Chen, Qiao, Yunjing Zhang, Kang, Zhou, Ke, Jiang, Rao, Xu, He, Yu, Ren, Xue Yan, Deng, Xinyu Yang, Yutong Song, Yingzi Yang, Wen, Han, Wu, Liu, Mingyuan Wang, Xiaoyu Zhang, Shengfeng Wang. Administrative, technical or material support: Xi, Shengfeng Wang. Supervision: Xi, Shengfeng Wang Acknowledgements Not applicable. Conflict of Interest Disclosure: Dr. Shengfeng Wang reports grants from Natural Science Foundation of China (No. 82173616 and No. 72342015) during the conduct of the study. Dr. Shengfeng Wang confirmed that the funders did not play a role in design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. No potential conflicts of interest were disclosed for the remaining authors. References Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, et al. Cancer statistics in China, 2015. 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1","display":"","copyAsset":false,"role":"figure","size":61572,"visible":true,"origin":"","legend":"\u003cp\u003eIncidence and 5-year prevalence rates among male breast cancer patients, grouped by ethnicity and age\u003c/p\u003e\n\u003cp\u003eAll results are the rates of disease burden grouped by ethnic group and age group, and the error lines in each bar chart show 95% confidence intervals. (A) Incidence rates; (B) 5-year prevalence rates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote: \u003c/strong\u003eIncidence rate (per 100 000) was calculated as number of new cases of breast cancer each year among population at risk each year. 5-year prevalence rate (per 100 000) was calculated as the number of people alive who diagnosed during the past 5 years among the population each year.\u003c/p\u003e","description":"","filename":"floatimage151.png","url":"https://assets-eu.researchsquare.com/files/rs-3824148/v1/29787355d77f95f8fc8dbdfa.png"},{"id":50389871,"identity":"b953ccf7-74b1-43c4-9074-be50418f7861","added_by":"auto","created_at":"2024-01-30 18:35:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":609318,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3824148/v1/1a4241e4-ad84-4fde-82a2-f95e7122c36d.pdf"},{"id":50389345,"identity":"15ae9971-819b-472f-99fe-e730047cafd2","added_by":"auto","created_at":"2024-01-30 18:27:03","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":32044,"visible":true,"origin":"","legend":"","description":"","filename":"Supplements12.30.docx","url":"https://assets-eu.researchsquare.com/files/rs-3824148/v1/0f31e8ef21b441a91f0c246d.docx"}],"financialInterests":"","formattedTitle":"Do ethnic disparities exist in disease burden and healthcare utilization of male breast cancer: a 9-year cohort study of 18.19 million adults in China","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eMale breast cancer (MBC) is a rare disease accounting for approximately 1% of all breast cancer [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Breast cancer has been traditionally regarded as a female-specific disease, leading to the lack of awareness of MBC. However, the disease burden of MBC keeps growing over the past decades [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Due to the deficiency of randomized controlled trials of MBC, treatments regimes were usually extrapolated from guidelines for female breast cancer (FBC) nowadays [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], but substantial disparities have been observed between MBC and FBC [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, treatment regimes for females might not be fully applicable in males, leading to poor adherence and worse prognosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, there are conflicts about prognosis of MBC in Asian. In Korea, the 5-year overall survival rate was lower for MBC than for FBC (76.2% vs 88.9%) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], but a study in Hong Kong suggested that MBC did not show lower survival until 10 years later [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Previous studies in China mainland were not convincing enough because of the sample size (ranging from 24 to 106 male patients) [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, healthcare utilization of MBC is of little concern so far with only one study globally [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, it is crucial to explore the current situation of MBC and provide more evidence for formulating policies and guidelines for this rare disease.\u003c/p\u003e \u003cp\u003eIt is notable that race/ethnicity plays a part in the disparities in disease burden of breast cancer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. African Americans have been focused on by most studies about race/ethnicity for both MBC and FBC, whereas Asians were included in only a few studies and often classified into \u0026ldquo;others\u0026rdquo; [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In terms of MBC, a few studies reported the racial/ethnic disparities in incidence in general population, of which Asian was combined with Pacific Islanders, Hispanic or others [\u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Significant disparities in disease burden of FBC have been observed within Asian countries at the national level [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], but by which the situation of MBC cannot be extrapolated due to the prominent differences between FBC and MBC. The situation within Asian ethnic groups remains unclear as well.\u003c/p\u003e \u003cp\u003eIn a word, no studies of ethnic disparities have been conducted in China which contributed to the most breast cancer patients (18.4% in 2020) in the world [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. As the third largest province in China, Inner Mongolia has a population of 4.25\u0026nbsp;million Mongolian, accounting for over 70% of the Mongolian population in China and 50% in the world. Inner Mongolia Regional Health Information Platform (IMRHIP) aims to address cancer health disparities across ethnicities by collecting and administrating healthcare information of residents in Inner Mongolia [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In this study, we aimed to outline ethnic disparities of disease burden of male breast cancer based on IMRHIP to provide further understanding of the rare disease.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Sources\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study used data from IMRHIP. Established by Inner Mongolia Center for Disease Control and Prevention, IMRHIP integrated and collected data from National Basic Medical Insurance (NBMI), National Cancer Registry (NCR), and Cause-of-death Reporting System since 2012 with provincial coverage of 95% by 2020. The details have been described elsewhere [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The information about the Surveillance Cohort of All Cancers Species has been shared on the China Cohort Consortium (No. CCC2023050801). Patient information including demographics, disease diagnoses, tumor characteristics, cause of death, treatments, and expenses of inpatient and outpatient services were recorded and linked by encrypted ID numbers among databases. All records were deidentified to protect patients\u0026rsquo; privacy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003ePatients diagnosed with breast cancer between January 1, 2012, and March 9, 2021 in NBMI were identified by diagnostic text and \u003cem\u003eInternational Statistical Classification of Diseases and Related Health Problems, Tenth Revision\u003c/em\u003e (ICD-10) code of breast cancer (C50). Natural language processing was applied to standardize the diagnostic text and code, with two researchers conducting this process independently and a third one double checking. Patients without ID numbers (10, 0.03%) were excluded. The patients were followed up until death or the end of the study, whichever came first.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMeasurements\u003c/h2\u003e \u003cp\u003eThe tumor stage was defined into early (stage 0-Ⅲ), advanced (stage Ⅳ), and unknown according to whether a breast cancer surgery can be operated on the patient. Age at diagnosis was categorized into 3 groups: 18\u0026ndash;40, 41\u0026ndash;60, and \u0026ge;\u0026thinsp;61, because the association between age and outcomes is non-linear. Charlson comorbidity index (CCI) score was calculated and categorized as 0, 1, 2, and \u0026ge;\u0026thinsp;3 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], in which the comorbidities diagnosed both before and after the diagnosis of breast cancer were included. Data on alcohol consumption and smoking rates were obtained from the 2018 Adult Chronic Disease and Nutrition Monitoring Survey in Inner Mongolia [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Gross domestic product (GDP) per capita in 2020 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], alcohol consumption rate, and smoking rate were stratified into two levels (high/low) by cities according to the median. Treatments of breast cancer included surgery, chemotherapy, endocrine therapy, targeted therapy, and radiotherapy [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eOur analyses included three parts. First, incidence and 5-year prevalence were calculated. A window of two years (2012 and 2013) was set to exclude prevalent cases when calculating the incidence rate for each of the 7 years from 2014 to 2020 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The numerator of incidence was the number of new cases of breast cancer each year. The numerator of 5-year prevalence was the number of patients alive with breast cancer diagnoses during the past five years for each of the 5 years from 2017 to 2021 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The denominator for incidence and 5-year prevalence was the total number of residents each year in NBMI.\u003c/p\u003e \u003cp\u003eThe average estimates in the subgroups were calculated by combining the estimates each year using a random effects meta-analysis based on the Clopper-Pearson method [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The 95% confidence intervals (CIs) were estimated by the Poisson distribution [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Subgroup analyses were conducted according to GDP level of the residence. Age-standardized rates (ASR) were determined using the World Health Organization world standardized rates, 2000 to 2025 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSecond, health utilization, including overall services and specifically for breast cancer of inpatient and outpatient, was measured by costs annually per capita and per episode (US \u003cspan\u003e$\u003c/span\u003e1.00\u0026thinsp;=\u0026thinsp;6.47 RMB on July 15, 2020). The number of inpatient and outpatient visits overall and specifically for breast cancer were also calculated.\u003c/p\u003e \u003cp\u003eThird, 5-year, 3-year, 2-year, and 1-year survival rates were calculated. Taking the 5-year survival rate as an example, it was defined as the proportion of patients alive for more than 5 years after diagnosis among patients diagnosed that year. In survival analyses, since the proportional hazard assumption was not satisfied by stage, a stratified Cox model was performed for all-cause mortality. The Fine and Gray competing risk model was performed for breast cancer-specific mortality to estimate hazard ratios (HR) and 95% CI. Age at diagnosis, GDP level of residence, alcohol consumption rate, smoking rate, CCI, all treatments and stage were adjusted.\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error (SD), median (interquartile range, IQR), and frequency (proportion) were presented for normal continuous variables, non-normal continuous variables, and categorical variables, respectively. χ\u003csup\u003e2\u003c/sup\u003e test, t-test, and Kruskal-Wallis test were performed to compare the difference between variables of interest. All statistical analyses were performed with R version 4.1.3, and a two-sided \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05 was considered statistically significant. Data were analyzed and reported following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDemographic Characteristic\u003c/h2\u003e \u003cp\u003eA total of 18.19\u0026nbsp;million participants were included (\u003cb\u003eeTable 1\u003c/b\u003e). 630 (1.8%) were male, contributing to 2 539.4 person-years, among whom 62 (9.8%) were Mongolian and 521(82.7%) were Han. Han in MBC were more likely to be diagnosed 5 years older (60.0 years vs 55.4 years, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016) and more distributed in the residence with high GDP level (56.1% vs 37.1%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). No significant results were found in follow-up years, stage, CCI score, smoking rate, and alcohol consumption rate \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of male breast cancer patients during 2012\u0026ndash;2021 in Inner Mongolia, grouped by ethnicity\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHan\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMongolian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e 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\u003eTotal, no. (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e630 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e521 (82.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at diagnosis: x (s)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFollow-up: median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.7 (2.7\u0026ndash;6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.59 (1.5-7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.64 (1.5\u0026ndash;5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e276 (43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e230 (44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (41.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192 (30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147 (28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (41.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144 (27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (16.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharlson comorbidity index score\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e253 (40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e218 (41.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (14.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (11.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e236 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188 (36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (40.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGDP level of residence\u003c/b\u003e\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e295 (47.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e227 (43.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (62.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333 (53.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e292 (56.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (37.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking rate\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e354 (60.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e315 (60.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (62.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e227 (39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204 (39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (37.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption rate\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e360 (62.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e326 (62.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221 (38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193 (37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (45.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e: Abbreviation: GDP, Gross Domestic Product.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u003c/sup\u003eThere were missing data for GDP level of residence (2, 0.32%). For ethnic group, 47 (7.46%) patients were categorized into \u0026ldquo;others\u0026rdquo;.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003eb\u003c/sup\u003eCharlson comorbidity index score was calculated and categorized as 0, 1, 2 and \u0026ge;\u0026thinsp;3.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ec\u003c/sup\u003eGDP level of residence were stratified into two levels (high/low) by cities according to the median (66 377 RMB = $10 259.20)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIncidence and prevalence\u003c/h2\u003e \u003cp\u003eThe ASR of incidence for MBC during 2014\u0026ndash;2020 was 1.2 (95% CI: 0.4\u0026ndash;2.1) per 100 000, among whom Han\u0026rsquo;s was 0.7 (95% CI:0.0-1.5) and Mongolian\u0026rsquo;s was 0.9 (95% CI: 0.1\u0026ndash;1.7), but the difference is not significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.996) (\u003cb\u003eeTable 2\u003c/b\u003e). The ASR of prevalence for MBC during 2017\u0026ndash;2021 was 4.2 (95% CI:3.0-5.5) per 100 000, and Mongolian\u0026rsquo;s was significantly higher than Han\u0026rsquo;s (3.2[2.5-4.0] vs 2.3[1.7-3.0], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016). The 5-year prevalence in 2021 was 3.0 (95% CI: 2.1\u0026ndash;3.9) per 100 000 in MBC, which of Han is 3.1 (95% CI: 2.7\u0026ndash;3.5) and of Mongolian is 3.6 (95% CI: 2.5-5.0) \u003cb\u003e(eTable 3)\u003c/b\u003e The age-specific incidence and prevalence showed unimodal distributions. For incidence, the age of peak for Han was 10 years older than that for Mongolian while for 5-year prevalence they are the same (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eHealthcare utilization\u003c/h2\u003e \u003cp\u003eOnly 36.8% of male patients recorded treatments for breast cancer, and 35.7% of Han and 48.4% of Mongolian in MBC were recorded for that (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.069). For MBC patients, the overall cost and the cost on breast cancer annually per capita were \u003cspan\u003e$\u003c/span\u003e1 734.5 (95% CI: 733.8-4820.9) and \u003cspan\u003e$\u003c/span\u003e507.4 (95% CI: 131.1-1804.9). Cost on breast cancer per inpatient episode was \u003cspan\u003e$\u003c/span\u003e1127.0 (95% CI: 699.4-2076.4) for Han and \u003cspan\u003e$\u003c/span\u003e1057.8 (95% CI: 582.5-1937.9) for Mongolian, and per outpatient episode was \u003cspan\u003e$\u003c/span\u003e147.4 (95% CI: 22.3-681.1) for Han and \u003cspan\u003e$\u003c/span\u003e16.2 (95% CI: 4.3\u0026ndash;35.1) for Mongolian. Mongolian patients had higher per outpatient episode overall cost (\u003cspan\u003e$\u003c/span\u003e9.9[4.3\u0026ndash;28.9] vs \u003cspan\u003e$\u003c/span\u003e8.0[3.4\u0026ndash;22.2], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) and lower per outpatient episode cost for breast cancer(\u003cspan\u003e$\u003c/span\u003e16.2[4.3\u0026ndash;35.1] vs \u003cspan\u003e$\u003c/span\u003e147.4[22.3-681.1], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033) than Han (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\u003eCost of MBC patients in Inner Mongolian during 2012\u0026ndash;2021, grouped by ethnicity, median (IQR)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHan\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMongolian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePatients with treatment of breast cancer (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e35.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e48.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAnnual per-capita cost (dollars)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1 770.36 (748.45, 4 993.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e2 075.39 (893.78, 5 140.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAnnual per-capita cost on breast cancer (dollars)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e496.56 (130.03, 2 044.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e701.26 (183.79, 1 082.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInpatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePer-episode cost (dollars)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1 147.72 (677.38, 1 960.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1 227.92 (616.46, 2 487.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePer-episode cost on breast cancer (dollars)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1 126.95 (699.38, 2 076.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1 057.80 (582.47, 1 937.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVisit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3.00 (1.00, 7.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e4.00 (1.00, 6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVisit on breast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.00 (1.00, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLength of stay (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e7.00 (4.00, 13.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e7.00 (4.00, 12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLength of stay on breast cancer (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e6.00 (4.00, 12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e6.00 (3.00, 13.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutpatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePer-episode cost (dollars)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e8.04 (3.40, 22.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e9.85 (4.33, 28.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePer-episode cost for breast cancer (dollars)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e147.44 (22.26, 681.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e16.23 (4.34, 35.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVisit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e57.50 (16.75, 110.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e52.00 (18.00, 119.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVisit for breast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.00 (1.00, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1.00 (1.00, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: The RMB-to-USD exchange rate is based on the July 15, 2020, exchange rate (1.00 USD\u0026thinsp;=\u0026thinsp;6.47 RMB).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSurvival and prognosis\u003c/h2\u003e \u003cp\u003eThe all-cause mortality for MBC was 50.8 per 1 000 person-years (95% CI: 42.4\u0026ndash;60.4) and breast cancer-specific mortality was 5.5 per 1 000 person-years (95% CI: 3.0-9.3). Mongolian\u0026rsquo;s all-cause mortality (55.9[29.8\u0026ndash;95.6] vs 50.0[40.9\u0026ndash;60.5]) and breast cancer-specific (8.6[1.0-31.1] vs 5.2[2.6\u0026ndash;9.4]) were insignificantly higher than Han\u0026rsquo;s, and no significant differences were observed in 5-year, 3-year, 2-year, and 1-year survival rates either (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\u003ePrognosis and survival rates among male breast cancer patients, grouped by ethnicity\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\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHan\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMongolian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNo. of death\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast cancer-specific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMortality, per 1 000\u003c/b\u003e \u003cb\u003eperson-years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.0 (40.9\u0026ndash;60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.9 (29.8\u0026ndash;95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast cancer-specific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.2 (2.6\u0026ndash;9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.6 (1.0-31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5-year survival rate (2012\u0026ndash;2015 combined), %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.3 (87.5\u0026ndash;95.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.6 (83.1\u0026ndash;100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3-year survival rate (2012\u0026ndash;2017 combined), %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.1 (86.7\u0026ndash;95.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.5 (84.6\u0026ndash;100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2-year survival rate (2012\u0026ndash;2018 combined), %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.2 (89.6\u0026ndash;96.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.1 (83.7\u0026ndash;100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1-year survival rate (2012\u0026ndash;2019 combined), %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.7 (92.9\u0026ndash;98.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.3 (85.5\u0026ndash;100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter adjusted covariates, Male breast cancer patients residing in cities of low GDP level were associated with increased breast-cancer specific mortality (HR, 22.5, [95% CI: 1.6\u0026ndash;325.0]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022) and patients diagnosed between the ages of 41\u0026ndash;60 and over 61 were associated with decreased breast-cancer specific mortality (HR, 0.1, [95% CI: 0.0-0.6]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010). No significant factor was observed in the model of hazard ratios for risk of all-cause mortality (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\u003eHazard ratios for risk of all-cause and breast cancer-specific mortality among male breast cancer patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eAll-cause mortality\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e \u003cp\u003eBreast cancer-specific mortality\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eEvents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerson-year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CI) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eEvents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePerson-year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at diagnosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7 (0.3\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1 (0.0-0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;= 61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e179.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7 (0.8\u0026ndash;3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3 (0.1\u0026ndash;1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMongolian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7 (0.4\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6 (0.1\u0026ndash;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8 (0.4\u0026ndash;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6 (0.1\u0026ndash;7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGDP level of residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9 (0.4\u0026ndash;2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.5 (1.6\u0026ndash;325.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cb\u003eNote\u003c/b\u003e: Abbreviation: GDP, Gross Domestic Product.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003ea\u003c/sup\u003eStratified Cox model adjusted for age at diagnosis, ethnic, GDP level of residence, alcohol consumption rate level, smoking rate level, Charlson comorbidity index, surgery, chemotherapy, endocrine therapy, targeted therapy, radiotherapy, stage (strata) ( n\u0026thinsp;=\u0026thinsp;628; excluded population\u0026thinsp;=\u0026thinsp;2).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003eb\u003c/sup\u003eFine and Gray competing risk model, adjusted for age at diagnosis, ethnic, GDP level of residence, alcohol consumption rate level, smoking rate level, Charlson comorbidity index, surgery, chemotherapy, endocrine therapy, targeted therapy, radiotherapy, stage (strata) ( n\u0026thinsp;=\u0026thinsp;628; excluded population\u0026thinsp;=\u0026thinsp;2).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eBased on IMRHIP data during 2012\u0026ndash;2021, we described the disease burden and healthcare utilization of male breast cancer in Inner Mongolia, and concentrated on ethnic disparities between Han patients and Mongolian patients. Overall, no significant ethnic disparities were demonstrated in both aspects.\u003c/p\u003e \u003cp\u003eWe observed a moderate level of incidenceMBC in Inner Mongolia compared to other countries and regions. The ASR of incidence of MBC (1.23 per 100 000) was first reported to be higher than previous studies in Asia including China [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] (0.17 per 100 000 in Zhongshan, Guangdong in 2016), Singapore [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] (0.40 in 2017), Japan [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] (0.18 during 1988\u0026ndash;2002) and Korea [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] (0.22 in 2016). In contrast, it was close to the regions of high income in North America (1.26 in 2017) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and Israel (1.24 person-year during 1988\u0026ndash;2002) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], as well as Asian Americans based on The Surveillance, Epidemiology, and End Results (SEER) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] (0.64 during 1997\u0026ndash;2000). There may be several explanations. First, previous studies were based on data from cancer registries [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] in which the incidence of MBC might be underreported [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Comparative studies about identifying cancer cases have found that claim data can be used as an important implement in many countries including China [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], Korea [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], the US [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and Germany [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Over 50% of underreporting rates of seven cancers by cancer registries in Inner Mongolia were reported [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Second, population genetic susceptibility may play a role [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. \u003cem\u003eBRCA1/2\u003c/em\u003e (e.g. 5296del4 in \u003cem\u003eBRCA1\u003c/em\u003e and 6174delT in \u003cem\u003eBRCA2\u003c/em\u003e) mutation was considered as the crucial risk factor in breast cancer, and \u003cem\u003eBRCA2\u003c/em\u003e mutations were more common in MBC [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The \u003cem\u003eBRCA2\u003c/em\u003e mutation was more frequently observed in China than in other Asian countries [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Third, allergy may have an impact. The risk of developing this allergy-related disease, which was most prevalent in China than other countries in the world [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], was related to the mutations on the same gene (RAD51B) with MBC [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. While meta-analyses revealed no increased risk of FBC among patients with rheumatoid arthritis [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNo significant ethnic disparities were found in incidence and survival rates. It can be interpreted from the following perspectives. First, according to previous studies, higher incidence of MBC may be associated with endocrine diseases like diabetes [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and obesity and disadvantaged lifestyle such as more alcohol and tobacco consumption [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. However, there were no significant differences in the proportion of diabetes (data not shown), smoking rate, and alcohol consumption rate between Mongolian and Han MBC patients in this study, and the latter two factors were also proved to have no significant ethnic differences in other studies [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Second, adjusted multivariate analysis indicated ethnicity was not significantly associated with higher risks of all-cause and breast cancer-specific mortality. Third, aforementioned population genetic susceptibility is a potential factor, but no specific gene has been found. In sum, no significant ethnic disparities were revealed in disease burden. However, differences related to economic factors should receive attention. Previous studies suggested sociodemographic factors like income and insurance could neutralize ethnic disparities, and poverty was a significant predictor of breast cancer mortality [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], with which the results of this study were consistent, warning of potential disease burden in Mongolians. Stakeholders should attach great attention to economic factors and keep eliminating underlying ethnic disparities.\u003c/p\u003e \u003cp\u003eWe observed a lower level of healthcare utilization compared with other counties and regions. According to prior studies, per-episode cost of visit on breast cancer in China was ranged from \u003cspan\u003e$\u003c/span\u003e2009.3 to \u003cspan\u003e$\u003c/span\u003e3554.9 (from 1996 to 2015) [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], with a median of approximately \u003cspan\u003e$\u003c/span\u003e3091.2 to \u003cspan\u003e$\u003c/span\u003e3864.0 (from 1996 to 2014) [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The cost on MBC per inpatient episode in Sichuan was \u003cspan\u003e$\u003c/span\u003e1925.0 (from 2015 to 2018) [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], of which the median was \u003cspan\u003e$\u003c/span\u003e3274.0 in Jilin (2019) [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The only study which estimated hospital costs of male breast cancer at the US population level reported an average cost of \u003cspan\u003e$\u003c/span\u003e9059 per hospital visit (from 2012 to 2016) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These figures were higher than those reported in this study. Possible explanations are as follows. First, different data sources may lead to bias. Most studies in China collected data from a single hospital or sample in certain areas, and few reported data at the provincial level [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] like this study. Second, economic level affects the allocation of medical resources [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] and patient\u0026rsquo;s healthcare-seeking behavior. Disadvantaged patients are more likely to reduce expense or quit treatment [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Inner Mongolia has an economic gap compared with the aforementioned areas. Third, advances in medical technology contributes to the increase in healthcare costs [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], and high per capita human capital loss is a factor of economic cost for high-income areas like US, where people have better education attainment [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Yet in Inner Mongolia, medical technology and the quantity and quality of health human resources need to be improved [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe first reported ethnic differences in healthcare utilization among MBC patients [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], and no significant disparities were observed. It can be illustrated from four aspects. First, health insurance significantly influences the cost by affecting patient\u0026rsquo;s healthcare-seeking behaviors, which might be hindered if the patient is not being covered [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. However, the universal medical insurance system based on the National Basic Medical Insurance (NBMI) has covered over 95% of the population in Inner Mongolia in 2020 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], and 630 patients included in the study were all covered. Second, high degree of ethnic mixing co-habitation [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e] and demographically equitable medical resources distribution [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e] assist both ethnic groups to access healthcare health equally [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Third, it is proven that the knowledge gaps of MBC [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e] might lead to stigmatization and embarrassment, causing the refusal or non-adherence to medical assistance [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. However, few studies directly revealed the ethnic influence on knowledge access in MBC patients [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Educational level indirectly helps understand the result as it potentially affects people\u0026rsquo;s awareness of disease and treatments [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Studies have shown that educational equity in China at the ethnic level has made great progress since 1949 [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Due to favorable policies and sufficient funds resulting from economic development, the educational gap between Mongolian and Han has gradually narrowed [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Lastly, favorable marital status might affect MBC patients\u0026rsquo; healthcare-seeking behaviors by offering psychosocial and emotional support [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan additionalcitationids=\"CR79\" citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. 630 patients in this study displayed no significant ethnic difference in marital status, which might contribute to the understanding of our finding. Overall, this study highlighted the equity in utilization for MBC patients in Inner Mongolia and provided directions for further development of health equity.\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first study to uncover ethnic disparities in disease burden and healthcare utilization of male breast cancer based on 630 patients from 18.19\u0026nbsp;million general population in China. Our results should be considered within the context of study limitations. First, case ascertainment was limited due to the unavailability of laboratory data and imaging information by medical insurance. However, previous studies using NBMI to identify cancer cases have verified sensitivities and Positive predictive value (PPVs) above 90% [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Second, the small sample size of Mongolian males and patients with following-ups for more than 5 years led to low statistical power in survival analyses, calling for larger sample sizes. Third, tumor grade, and subtype were not available, influencing the further interpretation of our results. However, the results of disease burden carry substantial importance [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. Finally, the aforementioned findings were only observed in Inner Mongolia, which requires external verification in other populations.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn conclusion, we examined the ethnic differences in disease burden and healthcare utilization of male breast cancer based on large-scale population-based data in Inner Mongolia. We reported the incidence, 5-year prevalence, prognosis and healthcare utilization in MBC, and the disparity of disease burden and medical utilization between Mongolian and Han MBC patients is not statistically significant. However, the study still demands greater focus on potential ethnic disparities due to the crucial influence of economic factors. Further research is needed to explore the potential mechanisms and provide policymakers with more precise recommendations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMale breast cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFemale breast cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSEER\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Surveillance, Epidemiology, and End Results\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIMRHIP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInner Mongolia Regional Health Information Platform\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Basic Medical Insurance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Cancer Registry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICD-10\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Statistical Classification of Diseases and Related Health Problems, Tenth Revision\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCharlson comorbidity index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGDP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGross domestic product\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAge-standardized rates\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTROBE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe Strengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePositive predictive value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproved by the Medical Ethics Committee of the Inner Mongolia Center for Disease Control and Prevention (NMCDCIRB2021001), informed consent from participants was approved for exemption.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYunfeng Xi and Shengfeng Wang had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. The datasets used and analysed during the study are available from Yunfeng Xi on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of China (No. 82173616 and No. 72342015).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcept and design: Jieying Chen, Qiao, Qi, Yunjing Zhang, Ying Yan, Xi, Shengfeng Wang.\u003c/p\u003e\n\u003cp\u003eAcquisition, analysis, or interpretation of data: All authors.\u003c/p\u003e\n\u003cp\u003eDrafting the manuscript: Jieying Chen, Qiao, Xi, Yunjing Zhang, Zhou, Shengfeng Wang.\u003c/p\u003e\n\u003cp\u003eCritical revision of manuscript for important intellectual content: All authors.\u003c/p\u003e\n\u003cp\u003eStatistical analysis: Jieying Chen, Qiao, Yunjing Zhang, Kang, Zhou, Ke, Jiang, Rao, Xu, He, Yu, Ren, Xue Yan, Deng, Xinyu Yang, Yutong Song, Yingzi Yang, Wen, Han, Wu, Liu, Mingyuan Wang, Xiaoyu Zhang, Shengfeng Wang.\u003c/p\u003e\n\u003cp\u003eAdministrative, technical or material support: Xi, Shengfeng Wang.\u003c/p\u003e\n\u003cp\u003eSupervision: Xi, Shengfeng Wang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Disclosure:\u0026nbsp;\u003c/strong\u003eDr. Shengfeng Wang reports grants from Natural Science Foundation of China (No. 82173616 and No. 72342015) during the conduct of the study. Dr. Shengfeng Wang confirmed that the funders did not play a role in design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. No potential conflicts of interest were disclosed for the remaining authors.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21338\u003c/span\u003e\u003cspan address=\"10.3322/caac.21338\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. 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Eur Heart J. 2019;40(48):3889\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"orphanet-journal-of-rare-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ojrd","sideBox":"Learn more about [Orphanet Journal of Rare Diseases](http://ojrd.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ojrd/default.aspx","title":"Orphanet Journal of Rare Diseases","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"male breast cancer, ethnic disparities, rare disease, disease burden, healthcare utilization","lastPublishedDoi":"10.21203/rs.3.rs-3824148/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3824148/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAs a rare disease, male breast cancer (MBC) is of increasing concern in China. Whether health inequalities of disease burden and healthcare utilization exist by ethnicity in male breast cancer remains unclear. We aim to measure disease burden and healthcare utilization by ethnicity among male breast cancer patients in China.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective cohort study was established during 2012\u0026ndash;2021 based on Inner Mongolia Regional Health Information Platform. Disease burden including incidence, 5-year prevalence, mortality, survival rate, and medical cost were analyzed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 630 participants (mean [SD] age, 59.4 [13.1] years), age-standardized rates of incidence were 1.2 (95% CI: 0.4\u0026ndash;2.1) per 100000. All-cause mortality was 50.8 per 1000 person-years (95% CI: 42.4\u0026ndash;60.4) but breast cancer-specific mortality was 5.5 per 1000 person-years (95% CI: 3.0-9.3). Regarding ethnicity, Mongolian had a higher age-standardized 5-year prevalence rate than Han (3.2[95% CI: 2.5-4.0] vs 2.3[95% CI: 1.7-3.0], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016), but no significant differences existed in incidence rates, survival rates, and risk of all-cause and breast cancer-specific mortality. Patients residing in areas of lower GDP level were associated with increased breast-cancer specific mortality (HR, 22.5, [95% CI: 1.6\u0026ndash;325.0]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study revealed a moderate disease burden and relatively lower healthcare utilization for male breast cancer in Inner Mongolia, China. No significant ethnic disparities existed in disease burden and healthcare utilization. However, we still demanded for increasing attention to male breast cancer due to the crucial influence of economic factors on potential ethnic disparities.\u003c/p\u003e","manuscriptTitle":"Do ethnic disparities exist in disease burden and healthcare utilization of male breast cancer: a 9-year cohort study of 18.19 million adults in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-30 18:26:58","doi":"10.21203/rs.3.rs-3824148/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-07-03T13:00:18+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-24T18:17:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-04T11:18:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Orphanet Journal of Rare Diseases","date":"2023-12-30T09:03:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"orphanet-journal-of-rare-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ojrd","sideBox":"Learn more about [Orphanet Journal of Rare Diseases](http://ojrd.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ojrd/default.aspx","title":"Orphanet Journal of Rare Diseases","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"54a9a827-77e3-4a89-9629-084dcef984e2","owner":[],"postedDate":"January 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-01-30T18:26:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-30 18:26:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3824148","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3824148","identity":"rs-3824148","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}