Assessing Trends in Hospitalizations for Breast Cancer Among Women in Korea: A Utilization of the Korea National Hospital Discharge In-depth Injury Survey (2006-2020)

Assessing Trends in Hospitalizations for Breast Cancer Among Women in Korea: A Utilization of the Korea National Hospital Discharge In-depth Injury Survey (2006-2020) | 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 Assessing Trends in Hospitalizations for Breast Cancer Among Women in Korea: A Utilization of the Korea National Hospital Discharge In-depth Injury Survey (2006-2020) Jieun Hwang, Jeong-Hoon Jang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3955810/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Apr, 2024 Read the published version in Journal of Epidemiology and Global Health → Version 1 posted 7 You are reading this latest preprint version Abstract Objective In this study, we analyzed the characteristics of breast cancer patients discharged in Korea over the past 15 years and explored the association between comorbidities and treatment outcomes to propose effective strategies for managing cancer patients. Methods This study utilized cross-sectional data from the Korea National Hospital Discharge In-depth Injury Survey from 2006 to 2020. Breast cancer patients were identified based on the primary diagnosis coded as C50 (Malignant neoplasm of the breast) according to ICD-10. Comorbidities were limited to those specified by the Charlson Comorbidity Index (CCI) and categorized into groups of 0, 1, 2, and 3 or more scores based on the relative risk associated with each condition. Results Between 2006 and 2020, an estimated 499,281 breast cancer patients were discharged, with an average annual percent change of 5.2% (95% CI 4.2–6.2, p < .05). The highest proportion of CCI scores of 3 or more was observed in the 60 and older age group at 12.9%, followed by 10.8% in the 40–59 age group and 8.5% in the under 40 age group. Across all age groups, there was a consistent increasing trend in the risk of mortality as the CCI score increased (p < .05). Conclusions The global trend of aging populations and increasing life expectancy indicate a continued rise in the number of breast cancer patients. Consequently, considering comorbidities when developing treatment plans for breast cancer is expected to result in positive treatment outcomes. Breast Cancer Patients Discharge Comorbidity Figures Figure 1 Figure 2 Background Breast cancer, a prevalent neoplastic disease presenting a substantial health concern for women globally, manifests as a malignant neoplasm in the breast cells( 1 , 2 ). The global incidence of breast cancer continues to surge, driven by factors such as population aging, shifts in demographic structure, and evolving lifestyles( 1 ). Moreover, recent advancements in medical technology have enabled more precise and early diagnosis, consequently unveiling a greater number of breast cancer cases( 3 ). In Korea, breast cancer is recognized as a significant public health concern, posing a threat to the health of women as well( 4 ). In 2021, leading causes of death among Korean women included malignant neoplasms (cancer), heart disease, cerebrovascular disease, pneumonia, and Alzheimer’s disease, in that order. Analyzing cancer mortality rates, lung cancer ranked highest followed by colorectal cancer, pancreatic cancer, liver cancer, breast cancer, and stomach cancer( 5 ). Notably, the mortality rate of breast cancer has increased by 36% compared to a decade ago( 6 ). Breast cancer is a complex disease resulting in the formation of a malignant tumor in the breast cells, influenced by various factors including hormones and genetic background( 1 , 2 ). Individuals with a strong genetic predisposition to breast cancer often harbor genetic variations, notably BRCA1 and BRCA2, which elevate the risk of developing breast cancer( 7 ). Furthermore, alterations in the levels of, including estrogen and progesterone, play a role in the onset of breast cancer hormones( 8 ). Beyond genetic and hormonal factors, a woman's personal history of reproductive events such as menarche, pregnancy, and breastfeeding, alongside lifestyle habits including smoking, alcohol consumption, exercise patterns, and dietary choices, are significantly correlated with the occurrence of breast cancer( 1 , 2 ). Understanding this intricate interplay of factors is crucial for comprehensive approaches to prevention and early detection in the management of breast cancer. Recently, advancements in medical technology have significantly enhanced the diagnosis and treatment of breast cancer( 3 ). Nevertheless, there remains a gap in understanding the discharge trends and outcomes for breast cancer patients, particularly among Korean women. The differences in demographic structures and healthcare services across different countries can contribute to notable variations in treatment outcomes. Therefore, conducting a comprehensive analysis of patient trends becomes crucial to identify changes and differences. Moreover, this analysis can offer valuable insights for the formulation of effective breast cancer-related policies and preventive strategies, addressing the unique challenges and characteristics of the Korean population and healthcare landscape. Numerous studies underscore the critical role of early diagnosis, emphasizing essential healthcare services and access to cancer centers as pivotal factors influencing the mortality rates of breast cancer patients( 9 ). Despite this, there is a notable gap in understanding treatment outcomes linked to comorbidities among individuals with breast cancer( 10 , 11 ). Comorbidities not only shape a patient's treatment approach and methods but also significantly impact discharge results, hospital stays, and overall hospital management aspects( 12 , 13 ). As breast cancer development involves intricate interactions of various factors, an examination of a patient's comorbidities becomes essential, influencing both treatment strategies and prognoses. In our current study, we aimed to analyze the scale and characteristics of discharged breast cancer patients in Korea over the past 15 years, utilizing patient discharge information. We also extended to evaluating the impact of comorbidities on the treatment outcomes of Korean breast cancer patients. Exploring this intricate aspect, we sought to unravel the intricate relationship between comorbidities and breast cancer treatment, contributing valuable insights that can inform the development of effective management methods. This approach may take into account not only the immediate well-being of the patient but also considers the long-term quality of life and the sustainability of healthcare systems. Materials and Methods Study population This study utilized secondary data from the Korea National Hospital Discharge In-depth Injury Survey conducted between 2006 and 2020. The survey, conducted annually by the Korea Disease Control and Prevention Agency(KDCA) since 2005, aims to inform the development of cost-effective health policies by capturing the scale and characteristics of discharged patients ( 14 ). The survey targets patients discharged from general hospitals with 100 beds or more nationwide. Based on the hospital size, 170 hospitals are selected as sample hospitals, collecting demographic information, admission details, disease, and treatment information, as well as injury data of discharged patients from medical records( 14 ). The data incorporates one primary diagnosis and 20 secondary diagnoses. In this study, individuals discharged with the primary diagnosis coded as C50 (Malignant neoplasm of the breast) according to ICD-10 were categorized as breast cancer patients. Research Model and Statistical analyses We examined the trends of breast cancer patients by age group each year. The data follows a two-stage stratified cluster sampling, and thus, applying complex sampling, we calculated the weighted total discharged patients for each year. Subsequently, based on the 2015 population, we calculated the age-standardized discharge rate and presented the average annual percent change(AAPC). To identify characteristics of discharged breast cancer patients, we conducted a frequency analysis of admission route, insurance type, treatment outcomes, number of hospital beds, and operation history information based on age groups (< 40, 40–59, 60+) and tested chi-square analysis for statistical significance of group differences. In this study, secondary diagnoses with the primary diagnosis C50 at discharge were defined as comorbidities. The comorbidities impacting treatment outcomes (mortality) of breast cancer patients were limited to those specified in the Charlson Comorbidity Index (CCI)( 15 ). CCI is a index which calculates comorbidities’ score by the relative risk of 19 major diseases including ischemic heart disease, diabetes mellitus, hypertension and others. Typically, CCI categorizes comorbidities into scores of 0, 1, 2 or 3+. After applying the algorithm that converts ICD-10 codes, CCI used frequency analysis based on age groups and conducted chi-square to test the statistical significance of differences among these groups. Subsequently, to compare the association between treatment outcomes (mortality) among breast cancer patients with CCI comorbidities in each group, multiple logistic regression analysis was analyzed resulting in the calculation of adjusted odds ratio(aOR) and 95% confidence intervals (CI). In analysis, demographic characteristics (admission route, insurance type, hospital beds, operation) were used as covariates for adjustment. All analysis was conducted using SAS software (version 9.4, SAS Institute, Cary, NC, US). We considered the results statistically significant when the p-value was less than the significance level of 0.05 (p < 0.05). Results The number of discharged breast cancer patients over the past 15 years We estimated that 499,281 patients were discharged after hospitalization treatment for breast cancer from 2006 to 2020. The age-standardized incidence rate showed a trend of increasing and decreasing, while AAPC was 5.2% (95% CI 4.2–6.2, p < .05) which showed overall increasing trend (Fig. 1 ). [Insert Fig. 1 here] By age groups, AAPC was highest in the 80 years and older age group at 13.7% (95% CI 11.4–16.0, p < .05), followed by the 70–79 years group (AAPC = 10.1, 95% CI 8.8–11.4, p < .05), 60–69 years group (AAPC = 9.2, 95% CI 8.3–10.2, p < .05), 50–59 years group (AAPC = 6.1, 95% CI 5.3–6.9, p < .05), and 40–49 years group (AAPC = 3.1, 95% CI 2.3–3.9, p < .05) (Fig. 2 ). [Insert Fig. 2 here] Demographic characteristics of discharged breast cancer patients By age group, outpatient admissions were more common than emergency or other types of admissions in all age groups, but the proportion of emergency admissions was highest in those aged 60 and older (p = .0006) (Table 1 ). The type of insurance also showed that health insurance was more common than medical aid or other types, with the highest proportion of medical aid coverage in the group aged 60 and older (p < .0001). In terms of hospital size, the 500–999 bed category had the highest frequency across all age groups, but as age increased, the proportion of 500–999 beds increased, while the proportion of over 1,000 beds decreased (p < .0001). The percentage of patients with an operation history was higher than those without across age groups, but the proportion of surgical history in the 40–59 age group was lower compared to other age groups (p < .0001). The proportion of deceased patients tended to increase with age (p < .0001). Table 1 Comparative analysis of sociodemographic characteristics among discharged breast cancer patient by age groups Total (n = 499,281) < 40 years (n = 221,636) 40–59 years (n = 211,017) 60 + years (n = 66,628) p-value † weighted n weighted % weighted n weighted % weighted n weighted % weighted n weighted % Admission route .0006 Emergency department 51,717 10.4 20,752 9.4 23,502 11.1 7,463 11.2 Outpatient department 445,493 89.2 199,808 90.2 186,661 88.5 59,024 88.6 Others 2,071 0.4 1,076 0.5 854 0.4 141 0.2 Insurance type < .0001 NHI 463,357 92.8 208,589 94.1 196,051 92.9 58,717 88.1 Medicaid 32,239 6.5 11,096 5.0 13,498 6.4 7,645 11.5 Others 3,685 0.7 1,951 0.9 1,468 0.7 266 0.4 Number of hospital beds - - < .0001 100–299 56,364 11.3 25,183 11.4 23,742 11.3 7,439 11.2 300–499 46,383 9.3 19,600 8.8 19,178 9.1 7,605 11.4 500–999 244,960 49.1 104,193 47.0 106,404 50.4 34,363 51.6 ≥1000 151,574 30.4 72,660 32.8 61,693 29.2 17,221 25.8 Operation history 0.0 - - < .0001 No 226,133 45.3 95,895 43.3 100,884 47.8 29,354 44.1 Yes 273,148 54.7 125,741 56.7 110,133 52.2 37,274 55.9 Treatment outcome 0.0 - - < .0001 Alive 481,931 96.5 215,492 97.2 203,014 96.2 63,425 95.2 Death 17,350 3.5 6,144 2.8 8,003 3.8 3,203 4.8 † Rao-Scott Chi-Square test NHI, National Health Insurance [Insert Table 1 here] Comorbidity (CCI) among discharged breast cancer patients In all age groups, metastatic carcinoma was the most common comorbidity (under 40 years 32.9%, 40–59 years 33.0%, 60 years and above 31.8%) (Table 2 ). Additionally, in the group aged under 40, cases with cancers other than breast cancer accounted for 3.8%, and those with diabetes without complications were 1.6%. For the 40–59 age group, cases with diabetes without complications (5.4%) were more prevalent than cases with other cancers (4.1%). Similarly, in the 60 and above age group, cases with diabetes without complications (12.0%) outnumbered cases with other cancers (5.4%). Although the majority of individuals in each age group had a CCI score of 0, those with a CCI score of 3 or higher were most prevalent in the 60 and above age group (12.8%), followed by the 40–59 age group (10.9%), and those under 40 years old (8.9%). Table 2 Comparative analysis of comorbidity distribution among discharged breast cancer patients by age groups CCI < 40 years (n = 221,636) 40–59 years (n = 211,017) 60 + years (n = 66,628) p-value † weighted n weighted % weighted n weighted % weighted n weighted % Myocardial infarction - - 93 0.0 113 0.2 - Congestive heart failure 168 0.1 622 0.3 398 0.6 < .0001 Peripheral vascular disease 90 0.0 54 0.0 - - - Cerebrovascular accident/transient ischemic attacks 214 0.1 920 0.4 477 0.7 < .0001 - Dementia - - 50 0.0 471 0.7 COPD 598 0.3 891 0.4 767 1.2 < .0001 Connective tissue disease 278 0.1 314 0.1 220 0.3 0.0888 Peptic ulcer disease - - - - - - - Liver disease 1,716 0.8 2,194 1.0 818 1.2 0.0898 Diabetes without complications 3,470 1.6 11,402 5.4 7,990 12.0 < .0001 Diabetes with complications 70 0.0 190 0.1 729 1.1 < .0001 Paraplegia and hemiplegia 301 0.1 303 0.1 84 0.1 0.9759 Renal disease 573 0.3 718 0.3 672 1.0 < .0001 Cancer 8,437 3.8 8,709 4.1 3,598 5.4 0.0051 Moderate or severe liver disease 255 0.1 520 0.2 28 0.0 0.0082 Metastatic carcinoma 72,819 32.9 69,689 33.0 21,161 31.8 0.5307 AIDS/HIV 20 0.0 - - - - - CCI score 3 19,754 8.9 23,091 10.9 8,559 12.8 < .0001 CCI score 2 10,446 4.7 12,480 5.9 4,710 7.1 CCI score 1 50,461 22.8 47,976 22.7 16,468 24.7 CCI score 0 140,975 63.6 127,470 60.4 36,891 55.4 † Rao-Scott Chi-Square test CCI, Charlson Comorbidity Index; COPD, Chronic Obstructive Pulmonary Disease; AIDS, Acquired Immunodeficiency Syndrome; HIV, Human Immunodeficiency Virus [Insert Table 2 here] The association between comorbidities in breast cancer patients (primary diagnosis) and treatment outcomes (death) In the age group under 40, moderate or severe liver diseases and metastatic cancer were associated with mortality among discharged breast cancer patients (Table 3 ). Discharged breast cancer patients who were accompanied by moderate or severe liver diseases had a 55.959 times (95% CI 12.678-246.998) higher risk of mortality. Furthermore, patients with metastatic carcinoma had a 8.623 times (95% CI 5.743–12.948) higher risk of mortality. The risk of mortality showed steadily increase as the CCI score rose(CCI score 3 aOR = 19.037, 95% CI 11.637–31.143; CCI score 2 aOR = 14.908, 95% CI 8.299–26.779; CCI score aOR = 3.828, 95% CI 2.228–6.576). In the age group 40–59, congestive heart failure(aOR = 5.906, 95% CI 1.521–22.939), cerebrovascular accident(aOR = 3.823, 95% CI 1.686–8.67), dementia(aOR = 22.821, 95% CI 2.107-247.134), connective tissue disease(aOR = 10.121, 95% CI 2.842–36.041), renal disease(aOR = 5.764, 95% CI 2.303–14.428), moderate or severe liver diseases(aOR = 38.194, 95% CI 12.512-116.594) and metastatic carcinoma(aOR = 8.352, 95% CI 5.726–12.184) were associated with mortality among discharged breast cancer patients. In this group as well, there was a continuous increase in the risk of mortality with the increase in CCI score(CCI score 3 aOR = 17.207, 95% CI 11.048–26.799; CCI score 2 aOR = 11.074, 95% CI 6.826–17.964; CCI score aOR = 3.795, 95% CI 2.312–6.231). In the group aged 60 and above, metastatic carcinoma(aOR = 4.864, 95% CI 2.928–8.079) were associated with mortality of discharged breast cancer patients. In this group, the risk of mortality continued to increase with the rising CCI score(CCI score 3 aOR = 6.816, 95% CI 3.679–12.627; CCI score 2 aOR = 5.541, 95% CI 2.723–11.273). Table 3 Association between comorbidities in discharged breast cancer patient and treatment outcome (death) CCI < 40 years 40–59 years 60 + years aOR (95% CI) aOR (95% CI) aOR (95% CI) Myocardial infarction - - 3.658 (0.239–56.09) Congestive heart failure 1.969 (0.145–26.698) 5.906 (1.521–22.939) * 4.353 (0.956–19.81) Peripheral vascular disease - - - Cerebrovascular accident/ transient ischemic attacks 1.606 (0.138–18.734) 3.823 (1.686–8.67) * 2.288 (0.482–10.854) Dementia 0.085 (0.061–0.117) 22.821 (2.107-247.134) * 4.276 (0.623–29.374) COPD 3.914 (0.477–32.09) 1.223 (0.222–6.741) 0.598 (0.113–3.168) Connective tissue disease 1.195 (0.108–13.188) 10.121 (2.842–36.041) * 7.323 (0.905–59.24) Peptic ulcer disease - - - Liver disease 1.972 (0.449–8.668) 1.564 (0.6-4.081) 1.314 (0.311–5.556) Diabetes without complications 2.344 (0.986–5.573) 0.859 (0.51–1.446) 1.271 (0.75–2.153) Diabetes with complications - 10.38 (0.927-116.182) 1.647 (0.293–9.241) Paraplegia and Hemiplegia 1.805 (0.276–11.805) 1.961 (0.163–23.6) 1.317 (0.068–25.466) Renal Disease 0.997 (0.137–7.245) 5.764 (2.303–14.428) * 2.441 (0.525–11.359) Cancer 1.057 (0.495–2.256) 1.386 (0.746–2.574) 0.938 (0.338–2.603) Moderate or severe liver disease 55.959 (12.678-246.998) * 38.194 (12.512-116.594) * 3.926 (0.212–72.817) Metastatic carcinoma 8.623 (5.743–12.948) * 8.352 (5.726–12.184) * 4.864 (2.928–8.079) * AIDS/HIV - - - CCI score 3 (ref.=0) 19.037 (11.637–31.143) * 17.207 (11.048–26.799) * 6.816 (3.679–12.627) * CCI score 2 (ref.=0) 14.908 (8.299–26.779) * 11.074 (6.826–17.964) * 5.541 (2.723–11.273) * CCI score 1 (ref.=0) 3.828 (2.228–6.576) * 3.795 (2.312–6.231) * 1.674 (0.887–3.161) CCI, Charlson Comorbidity Index; aOR, adjusted Odds Ratio; CI, Confidence Interval; COPD, Chronic Obstructive Pulmonary Disease; AIDS, Acquired Immunodeficiency Syndrome; HIV, Human Immunodeficiency Virus *p < .05 [Insert Table 3 here] Discussion The number of discharged breast cancer patients in Korea has been on a rising trend for the past 15 years. Especially in all age groups over 30, the number of breast cancer patients has been on the rise, with the highest AAPC observed among older age group women. In case of discharged breast cancer patients, there was a demonstrated increase in the association between a higher number of comorbidities and mortality. The extent of this association varied across different age groups. This study's observations indicate that the number of breast cancer patients in Korea is exhibiting an overall increase, aligning with a global trend. The global number of breast cancer patients was estimated to be 2.3 million in 2020, signifying an annual growth rate of approximately 0.33% since 1990( 16 ). In the last 15 years, South Korea has experienced a substantial surge in breast cancer cases, demonstrating an average annual growth of 5.2%. This upward trajectory is particularly noteworthy among the elderly population, a phenomenon likely influenced by the country's extended average life expectancy( 17 ). Indeed, breast cancer arises from various complex factors, with age being recognized as the most significant risk factor ( 18 ). In the United Kingdom, breast cancer primarily affects women aged 70 and older, representing over one-third of total cases, while in developing countries, breast cancer in women under 50 constitutes 50% of the overall occurrences( 17 , 18 ). The findings of this study emphasize the need for more proactive preventive efforts, as breast cancer incidence is projected to increase, ranging from a minimum of 32% in developed countries to as much as 95% in developing nations, due to globalization and economic growth leading to an increase in average life expectancy( 17 ). The CCI demonstrates an increasing risk based on the number of comorbidities and age. This trend not only affects the treatment outcomes presented in this study but also has implications for survival, highlighting the importance of managing patients with multiple health conditions( 19 ). In essence, the presence of comorbidities or complications in patients complicates breast cancer management, influencing treatment decisions and inevitably impacting treatment tolerance, leading to potential implications for treatment outcomes( 19 , 20 , 21 ). The findings of this study, particularly the heightened risk of mortality associated with an increasing CCI score in younger age groups, underscore the necessity for comprehensive comorbidity management in patients of lower age ranges. In this context, CCI is increasingly being utilized as a tool to assess the health status of breast cancer patients, and it seems valuable in formulating treatment strategies( 19 , 20 , 21 ). By identifying coexisting medical conditions and predicting treatment outcomes, a more proactive treatment approach can be devised( 19 , 20 , 21 ). The results of this study suggest the need for a more meticulous treatment approach in breast cancer patients with comorbidities, emphasizing the importance of considering concurrent health conditions in treatment planning. Especially in the treatment of breast cancer, decisions are often made based solely on the patient's age, overlooking other factors such as comorbidities that can significantly impact the effectiveness or potential adverse effects of the treatment( 22 , 23 ). While some guidelines suggest that there is no difference in treatment outcomes regardless of the presence of comorbidities when using the same treatment approach, the actual scientific evidence regarding the treatment effectiveness in breast cancer patients with moderate to severe comorbidities is lacking. As indicated in this study, the treatment outcomes vary based on what comorbidities were present and to what extent, underscoring the need to consider not only the patient's age but also the accompanying conditions when formulating breast cancer treatment plans. It is known that there is an association between breast cancer and liver disease( 24 , 25 ). In particular, it is known that breast cancer patients diagnosed with nonalcoholic fatty liver disease (NAFLD) are more likely to exhibit a poor prognosis in terms of recurrence( 26 ). Metabolic syndrome, such as diabetes and obesity, is known to be more prevalent in breast cancer patients( 27 ). Similarly, liver diseases also encompass risk factors such as metabolic abnormalities and obesity, implying a mechanistic connection between breast cancer and liver diseases( 28 ). In this study, it also revealed that there was a close association between mortality in breast cancer discharged patients under the age of 60 and liver diseases, as well as metastatic cancer. The incidence rate of breast cancer in Korea is reported to be lower compared to Western populations. However, it is reported that the proportion of breast cancer patients in younger age groups is higher in Korea compared to Western populations and these patients exhibited rapid tumor progression and metastasis to distant organs( 29 , 30 ). Therefore, it is essential to understand the mechanistic connections between co-existing conditions to elucidate effective treatment approaches against breast cancer. The present study has significance in examining the long-term perspective of breast cancer discharged patients in Korean women and investigating the association between comorbidities in breast cancer patients and treatment outcomes. However, there are several limitations of the present study that should be noted. First, this study defined patients based solely on the primary diagnosis when estimating discharged patients per year. Therefore, even if there were secondary diagnoses with breast cancer codes, those patients were not considered as subjects of the study. As a result, the actual number of discharged breast cancer patients might have been underestimated. Second, the analysis was conducted solely based on the presence of the disease and treatment outcomes for each patient, without considering other risk factors associated with breast cancer such as family history, smoking, alcohol consumption, physical activity, etc. Therefore, caution is needed when interpreting the results due to the omission of these relevant risk factors. Third, it’s challenging to present the casual relationship of treatment outcomes because Discharge In-depth Injury Survey is an analysis of cross-sectional data by year. Furthermore, since there are limitations in proving the temporal sequence of comorbidities, the presence of comorbidities implies an association rather than a casual relationship with treatment outcomes. Therefore, caution is needed when interpreting this aspect. However, it is necessary to establish the relationship between breast cancer and comorbidities presented in this study through mechanisms of subsequent disease occurrence in the future. Conclusion This study aimed to analyze the scale and characteristics of breast cancer discharged patients and evaluate the association between comorbidities and their treatment outcomes using discharge patient information in Korea over the past 15 years. The research findings indicate a continuing upward trend in breast cancer discharged patients in Korea over the past 15 years, particularly showing an increasing trend across all age groups above 30 years, with the highest AAPC observed in older age group. When it comes to discharged breast cancer patients, there is a higher association between comorbidities and mortality, with varying impacts of comorbidities across different age groups. Considering the expected increase in breast cancer patients, especially with the aging population, it becomes crucial to formulate treatment plans that account for comorbidities to ensure positive treatment outcomes. Abbreviations KDCA Korea Disease Control and Prevention Agency AAPC average annual percent change CCI Charlson Comorbidity Index aOR adjusted odds ratio CI confidence intervals NAFLD nonalcoholic fatty liver disease NHI National Health Insurance COPD Chronic Obstructive Pulmonary Disease AIDS Acquired Immunodeficiency Syndrome HIV Human Immunodeficiency Virus Declarations Ethics approval The study protocol was approved by the Institutional Review Board of Dankook University of South Korea (IRB no. DKU 2024-01-008-001). Consent for publication Not applicable Availability of data and material The datasets analysed during the current study are available from the Korea Disease Control and Prevention Agency on reasonable request. [http://www.kdca.go.kr/contents.es?mid=a20303010502] Competing interests The authors have no conflicts of interest to declare for this study. Funding Not applicable Authors’ contributions JH and JJ designed the study. JH analyzed the data and wrote the manuscript. JJ contributed to the interpretation of the results. All authors reviewed and approved the final manuscript. Acknowledgements The authors thank the Korean Disease Control and Prevention Agency. References Feng Y, Spezia M, Huang S, Yuan C, Zeng Z, Zhang L, et al. Breast cancer development and progression: Risk factors, cancer stem cells, signaling pathways, genomics, and molecular pathogenesis. Genes Dis. 2018;5(2):77–106. Akram M, Iqbal M, Daniyal M, Khan AU. Awareness and current knowledge of breast cancer. 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Screening Mammography Outcomes: Risk of Breast Cancer and Mortality by Comorbidity Score and Age. JNCI: Journal of the National Cancer Institute. 2019;112(6):599–606. Fu MR, Axelrod D, Guth AA, Cleland CM, Ryan CE, Weaver KR, et al. Comorbidities and Quality of Life among Breast Cancer Survivors: A Prospective Study. J Personalized Med. 2015;5(3):229–42. Land LH, Dalton SO, Jørgensen TL, Ewertz M. Comorbidity and survival after early breast cancer. A review. Crit Rev Oncol/Hematol. 2012;81(2):196–205. Group EBCTC. Effect of radiotherapy after breast-conserving surgery on 10-year recurrence and 15-year breast cancer death: meta-analysis of individual patient data for 10 801 women in 17 randomised trials. Lancet. 2011;378(9804):1707–16. Kwak M-S, Yim JY, Yi A, Chung G-E, Yang JI, Kim D, et al. Nonalcoholic fatty liver disease is associated with breast cancer in nonobese women. Dig Liver Disease. 2019;51(7):1030–5. Lee Y-S, Lee HS, Chang SW, Lee CU, Kim JS, Jung YK et al. Underlying nonalcoholic fatty liver disease is a significant factor for breast cancer recurrence after curative surgery. Medicine. 2019;98(39). Lee Y-S, Lee HS, Chang SW, Lee CU, Kim JS, Jung YK, et al. Underlying nonalcoholic fatty liver disease is a significant factor for breast cancer recurrence after curative surgery. Medicine. 2019;98(39):e17277. Wani B, Aziz SA, Ganaie MA, Mir MH. Metabolic Syndrome and Breast Cancer Risk. Indian J Med Paediatr Oncol. 2017;38(04):434–9. Zarghamravanbakhsh P, Frenkel M, Poretsky L. Metabolic causes and consequences of nonalcoholic fatty liver disease (NAFLD). Metabolism open. 2021;12:100149. Zhang W, Wu S, Liu J, Zhang X, Ma X, Yang C et al. Metastasis patterns and prognosis in young breast cancer patients: A SEER database analysis. Front Oncol. 2022;12. Frank S, Carton M, Dubot C, Campone M, Pistilli B, Dalenc F, et al. Impact of age at diagnosis of metastatic breast cancer on overall survival in the real-life ESME metastatic breast cancer cohort. Breast. 2020;52:50–7. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Apr, 2024 Read the published version in Journal of Epidemiology and Global Health → Version 1 posted Editorial decision: Revision requested 19 Mar, 2024 Reviews received at journal 18 Mar, 2024 Reviewers agreed at journal 12 Mar, 2024 Reviewers invited by journal 24 Feb, 2024 Editor assigned by journal 22 Feb, 2024 Submission checks completed at journal 21 Feb, 2024 First submitted to journal 14 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3955810","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274018516,"identity":"cd9c61ad-fec3-4671-b9ca-9a9e5a7d64ce","order_by":0,"name":"Jieun Hwang","email":"","orcid":"","institution":"Dankook University","correspondingAuthor":false,"prefix":"","firstName":"Jieun","middleName":"","lastName":"Hwang","suffix":""},{"id":274018517,"identity":"c4ba1f51-727b-432b-9056-722c9a31c371","order_by":1,"name":"Jeong-Hoon Jang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACxhkMCUBKor4fKmBArBYLxpkNxGphkACTFYwbDhCrhXl2w8PPBb8kmI2PnzFg+FHDYGzeQMhhcw4kS8/sk2AzO5NjwNhzjMFM5gAhLTMSEqR5eyR4zG7wGDDwNjDYSBByGFBL8m+gFgnjGTwGjH+J1JImzfNDwsBAgseAGWiLGVFarHkbJBIkzqQVHJY5JmFMUIvhjJzk2zx/6hL42w9vfPimxsZwBkEtDTwJDIxtEM4BWDThBfIM7ECFfwgrHAWjYBSMghEMAOb8OdeQxHU6AAAAAElFTkSuQmCC","orcid":"","institution":"Daegu Catholic University","correspondingAuthor":true,"prefix":"","firstName":"Jeong-Hoon","middleName":"","lastName":"Jang","suffix":""}],"badges":[],"createdAt":"2024-02-14 10:14:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3955810/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3955810/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s44197-024-00229-1","type":"published","date":"2024-04-29T23:28:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51513774,"identity":"687e1a98-f3aa-427d-b2ad-7d87ac625ccd","added_by":"auto","created_at":"2024-02-22 21:34:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":9510,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in the discharge rete of breast cancer patients between 2006 and 2020 in Korea. *p\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3955810/v1/26b6c8476ccb38144b301486.png"},{"id":51513775,"identity":"b6807038-c6c3-4b1b-ab69-fcf4f03bc3a0","added_by":"auto","created_at":"2024-02-22 21:34:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":221988,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in age-standardized incidence rate and average annual percent change by age group\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3955810/v1/be42bcfe979fe771d02c4623.png"},{"id":55695299,"identity":"1a901152-c2f2-4fce-8770-0fa482ecb751","added_by":"auto","created_at":"2024-05-02 01:08:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1055668,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3955810/v1/a0f07a3c-85e1-401e-91d1-8c7bc06112e3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing Trends in Hospitalizations for Breast Cancer Among Women in Korea: A Utilization of the Korea National Hospital Discharge In-depth Injury Survey (2006-2020)","fulltext":[{"header":"Background","content":"\u003cp\u003eBreast cancer, a prevalent neoplastic disease presenting a substantial health concern for women globally, manifests as a malignant neoplasm in the breast cells(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The global incidence of breast cancer continues to surge, driven by factors such as population aging, shifts in demographic structure, and evolving lifestyles(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Moreover, recent advancements in medical technology have enabled more precise and early diagnosis, consequently unveiling a greater number of breast cancer cases(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Korea, breast cancer is recognized as a significant public health concern, posing a threat to the health of women as well(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In 2021, leading causes of death among Korean women included malignant neoplasms (cancer), heart disease, cerebrovascular disease, pneumonia, and Alzheimer\u0026rsquo;s disease, in that order. Analyzing cancer mortality rates, lung cancer ranked highest followed by colorectal cancer, pancreatic cancer, liver cancer, breast cancer, and stomach cancer(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Notably, the mortality rate of breast cancer has increased by 36% compared to a decade ago(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBreast cancer is a complex disease resulting in the formation of a malignant tumor in the breast cells, influenced by various factors including hormones and genetic background(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Individuals with a strong genetic predisposition to breast cancer often harbor genetic variations, notably BRCA1 and BRCA2, which elevate the risk of developing breast cancer(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Furthermore, alterations in the levels of, including estrogen and progesterone, play a role in the onset of breast cancer hormones(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Beyond genetic and hormonal factors, a woman's personal history of reproductive events such as menarche, pregnancy, and breastfeeding, alongside lifestyle habits including smoking, alcohol consumption, exercise patterns, and dietary choices, are significantly correlated with the occurrence of breast cancer(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Understanding this intricate interplay of factors is crucial for comprehensive approaches to prevention and early detection in the management of breast cancer.\u003c/p\u003e \u003cp\u003eRecently, advancements in medical technology have significantly enhanced the diagnosis and treatment of breast cancer(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Nevertheless, there remains a gap in understanding the discharge trends and outcomes for breast cancer patients, particularly among Korean women. The differences in demographic structures and healthcare services across different countries can contribute to notable variations in treatment outcomes. Therefore, conducting a comprehensive analysis of patient trends becomes crucial to identify changes and differences. Moreover, this analysis can offer valuable insights for the formulation of effective breast cancer-related policies and preventive strategies, addressing the unique challenges and characteristics of the Korean population and healthcare landscape.\u003c/p\u003e \u003cp\u003eNumerous studies underscore the critical role of early diagnosis, emphasizing essential healthcare services and access to cancer centers as pivotal factors influencing the mortality rates of breast cancer patients(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Despite this, there is a notable gap in understanding treatment outcomes linked to comorbidities among individuals with breast cancer(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Comorbidities not only shape a patient's treatment approach and methods but also significantly impact discharge results, hospital stays, and overall hospital management aspects(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). As breast cancer development involves intricate interactions of various factors, an examination of a patient's comorbidities becomes essential, influencing both treatment strategies and prognoses.\u003c/p\u003e \u003cp\u003eIn our current study, we aimed to analyze the scale and characteristics of discharged breast cancer patients in Korea over the past 15 years, utilizing patient discharge information. We also extended to evaluating the impact of comorbidities on the treatment outcomes of Korean breast cancer patients. Exploring this intricate aspect, we sought to unravel the intricate relationship between comorbidities and breast cancer treatment, contributing valuable insights that can inform the development of effective management methods. This approach may take into account not only the immediate well-being of the patient but also considers the long-term quality of life and the sustainability of healthcare systems.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis study utilized secondary data from the Korea National Hospital Discharge In-depth Injury Survey conducted between 2006 and 2020. The survey, conducted annually by the Korea Disease Control and Prevention Agency(KDCA) since 2005, aims to inform the development of cost-effective health policies by capturing the scale and characteristics of discharged patients (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe survey targets patients discharged from general hospitals with 100 beds or more nationwide. Based on the hospital size, 170 hospitals are selected as sample hospitals, collecting demographic information, admission details, disease, and treatment information, as well as injury data of discharged patients from medical records(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe data incorporates one primary diagnosis and 20 secondary diagnoses. In this study, individuals discharged with the primary diagnosis coded as C50 (Malignant neoplasm of the breast) according to ICD-10 were categorized as breast cancer patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eResearch Model and Statistical analyses\u003c/h2\u003e \u003cp\u003eWe examined the trends of breast cancer patients by age group each year. The data follows a two-stage stratified cluster sampling, and thus, applying complex sampling, we calculated the weighted total discharged patients for each year. Subsequently, based on the 2015 population, we calculated the age-standardized discharge rate and presented the average annual percent change(AAPC).\u003c/p\u003e \u003cp\u003eTo identify characteristics of discharged breast cancer patients, we conducted a frequency analysis of admission route, insurance type, treatment outcomes, number of hospital beds, and operation history information based on age groups (\u0026lt;\u0026thinsp;40, 40\u0026ndash;59, 60+) and tested chi-square analysis for statistical significance of group differences.\u003c/p\u003e \u003cp\u003eIn this study, secondary diagnoses with the primary diagnosis C50 at discharge were defined as comorbidities. The comorbidities impacting treatment outcomes (mortality) of breast cancer patients were limited to those specified in the Charlson Comorbidity Index (CCI)(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). CCI is a index which calculates comorbidities\u0026rsquo; score by the relative risk of 19 major diseases including ischemic heart disease, diabetes mellitus, hypertension and others. Typically, CCI categorizes comorbidities into scores of 0, 1, 2 or 3+. After applying the algorithm that converts ICD-10 codes, CCI used frequency analysis based on age groups and conducted chi-square to test the statistical significance of differences among these groups. Subsequently, to compare the association between treatment outcomes (mortality) among breast cancer patients with CCI comorbidities in each group, multiple logistic regression analysis was analyzed resulting in the calculation of adjusted odds ratio(aOR) and 95% confidence intervals (CI). In analysis, demographic characteristics (admission route, insurance type, hospital beds, operation) were used as covariates for adjustment. All analysis was conducted using SAS software (version 9.4, SAS Institute, Cary, NC, US). We considered the results statistically significant when the p-value was less than the significance level of 0.05 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eThe number of discharged breast cancer patients over the past 15 years\u003c/h2\u003e \u003cp\u003eWe estimated that 499,281 patients were discharged after hospitalization treatment for breast cancer from 2006 to 2020. The age-standardized incidence rate showed a trend of increasing and decreasing, while AAPC was 5.2% (95% CI 4.2\u0026ndash;6.2, p\u0026thinsp;\u0026lt;\u0026thinsp;.05) which showed overall increasing trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e[Insert\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cem\u003ehere]\u003c/em\u003e\u003c/p\u003e \u003cp\u003eBy age groups, AAPC was highest in the 80 years and older age group at 13.7% (95% CI 11.4\u0026ndash;16.0, p\u0026thinsp;\u0026lt;\u0026thinsp;.05), followed by the 70\u0026ndash;79 years group (AAPC\u0026thinsp;=\u0026thinsp;10.1, 95% CI 8.8\u0026ndash;11.4, p\u0026thinsp;\u0026lt;\u0026thinsp;.05), 60\u0026ndash;69 years group (AAPC\u0026thinsp;=\u0026thinsp;9.2, 95% CI 8.3\u0026ndash;10.2, p\u0026thinsp;\u0026lt;\u0026thinsp;.05), 50\u0026ndash;59 years group (AAPC\u0026thinsp;=\u0026thinsp;6.1, 95% CI 5.3\u0026ndash;6.9, p\u0026thinsp;\u0026lt;\u0026thinsp;.05), and 40\u0026ndash;49 years group (AAPC\u0026thinsp;=\u0026thinsp;3.1, 95% CI 2.3\u0026ndash;3.9, p\u0026thinsp;\u0026lt;\u0026thinsp;.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e[Insert\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003ehere]\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDemographic characteristics of discharged breast cancer patients\u003c/h2\u003e \u003cp\u003eBy age group, outpatient admissions were more common than emergency or other types of admissions in all age groups, but the proportion of emergency admissions was highest in those aged 60 and older (p\u0026thinsp;=\u0026thinsp;.0006) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The type of insurance also showed that health insurance was more common than medical aid or other types, with the highest proportion of medical aid coverage in the group aged 60 and older (p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). In terms of hospital size, the 500\u0026ndash;999 bed category had the highest frequency across all age groups, but as age increased, the proportion of 500\u0026ndash;999 beds increased, while the proportion of over 1,000 beds decreased (p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). The percentage of patients with an operation history was higher than those without across age groups, but the proportion of surgical history in the 40\u0026ndash;59 age group was lower compared to other age groups (p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). The proportion of deceased patients tended to increase with age (p\u0026thinsp;\u0026lt;\u0026thinsp;.0001).\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\u003eComparative analysis of sociodemographic characteristics among discharged breast cancer patient by age groups\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" 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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;499,281)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40 years\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;221,636)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e40\u0026ndash;59 years\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;211,017)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e60\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;66,628)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eweighted n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eweighted %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eweighted n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eweighted %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eweighted n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eweighted %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eweighted n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eweighted %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdmission route\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.0006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmergency department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51,717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20,752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23,502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutpatient department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e445,493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e199,808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e186,661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e59,024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e88.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsurance type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e463,357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e208,589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e196,051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e58,717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e88.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32,239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11,096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13,498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of hospital beds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u0026ndash;299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56,364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25,183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23,742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e300\u0026ndash;499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46,383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19,600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19,178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500\u0026ndash;999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e244,960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e104,193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e106,404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e34,363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e51.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e151,574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72,660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e61,693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17,221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e226,133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100,884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e29,354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e273,148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e125,741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e110,133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e37,274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e55.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment outcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e481,931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e215,492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e203,014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e96.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e63,425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17,350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8,003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3,203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eRao-Scott Chi-Square test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNHI, National Health Insurance\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e[Insert\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cem\u003ehere]\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eComorbidity (CCI) among discharged breast cancer patients\u003c/h2\u003e \u003cp\u003eIn all age groups, metastatic carcinoma was the most common comorbidity (under 40 years 32.9%, 40\u0026ndash;59 years 33.0%, 60 years and above 31.8%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, in the group aged under 40, cases with cancers other than breast cancer accounted for 3.8%, and those with diabetes without complications were 1.6%. For the 40\u0026ndash;59 age group, cases with diabetes without complications (5.4%) were more prevalent than cases with other cancers (4.1%). Similarly, in the 60 and above age group, cases with diabetes without complications (12.0%) outnumbered cases with other cancers (5.4%). Although the majority of individuals in each age group had a CCI score of 0, those with a CCI score of 3 or higher were most prevalent in the 60 and above age group (12.8%), followed by the 40\u0026ndash;59 age group (10.9%), and those under 40 years old (8.9%).\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\u003eComparative analysis of comorbidity distribution among discharged breast cancer patients by age groups\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\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40 years\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;221,636)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e40\u0026ndash;59 years\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;211,017)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e60\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;66,628)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eweighted n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eweighted %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eweighted n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eweighted %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eweighted n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eweighted %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral vascular disease\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\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular accident/transient ischemic attacks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConnective tissue disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e220\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\u003e0.0888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeptic ulcer disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0898\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes without complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7,990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes with complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParaplegia and hemiplegia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.9759\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,598\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\u003e0.0051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate or severe liver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastatic carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72,819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69,689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21,161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIDS/HIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI score 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19,754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23,091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8,559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI score 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI score 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50,461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47,976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16,468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI score 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140,975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127,470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36,891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eRao-Scott Chi-Square test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eCCI, Charlson Comorbidity Index; COPD, Chronic Obstructive Pulmonary Disease; AIDS, Acquired Immunodeficiency Syndrome; HIV, Human Immunodeficiency Virus\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e[Insert\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003ehere]\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eThe association between comorbidities in breast cancer patients (primary diagnosis) and treatment outcomes (death)\u003c/h2\u003e \u003cp\u003eIn the age group under 40, moderate or severe liver diseases and metastatic cancer were associated with mortality among discharged breast cancer patients (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Discharged breast cancer patients who were accompanied by moderate or severe liver diseases had a 55.959 times (95% CI 12.678-246.998) higher risk of mortality. Furthermore, patients with metastatic carcinoma had a 8.623 times (95% CI 5.743\u0026ndash;12.948) higher risk of mortality. The risk of mortality showed steadily increase as the CCI score rose(CCI score 3 aOR\u0026thinsp;=\u0026thinsp;19.037, 95% CI 11.637\u0026ndash;31.143; CCI score 2 aOR\u0026thinsp;=\u0026thinsp;14.908, 95% CI 8.299\u0026ndash;26.779; CCI score aOR\u0026thinsp;=\u0026thinsp;3.828, 95% CI 2.228\u0026ndash;6.576).\u003c/p\u003e\u003cp\u003eIn the age group 40\u0026ndash;59, congestive heart failure(aOR\u0026thinsp;=\u0026thinsp;5.906, 95% CI 1.521\u0026ndash;22.939), cerebrovascular accident(aOR\u0026thinsp;=\u0026thinsp;3.823, 95% CI 1.686\u0026ndash;8.67), dementia(aOR\u0026thinsp;=\u0026thinsp;22.821, 95% CI 2.107-247.134), connective tissue disease(aOR\u0026thinsp;=\u0026thinsp;10.121, 95% CI 2.842\u0026ndash;36.041), renal disease(aOR\u0026thinsp;=\u0026thinsp;5.764, 95% CI 2.303\u0026ndash;14.428), moderate or severe liver diseases(aOR\u0026thinsp;=\u0026thinsp;38.194, 95% CI 12.512-116.594) and metastatic carcinoma(aOR\u0026thinsp;=\u0026thinsp;8.352, 95% CI 5.726\u0026ndash;12.184) were associated with mortality among discharged breast cancer patients. In this group as well, there was a continuous increase in the risk of mortality with the increase in CCI score(CCI score 3 aOR\u0026thinsp;=\u0026thinsp;17.207, 95% CI 11.048\u0026ndash;26.799; CCI score 2 aOR\u0026thinsp;=\u0026thinsp;11.074, 95% CI 6.826\u0026ndash;17.964; CCI score aOR\u0026thinsp;=\u0026thinsp;3.795, 95% CI 2.312\u0026ndash;6.231).\u003c/p\u003e \u003cp\u003eIn the group aged 60 and above, metastatic carcinoma(aOR\u0026thinsp;=\u0026thinsp;4.864, 95% CI 2.928\u0026ndash;8.079) were associated with mortality of discharged breast cancer patients. In this group, the risk of mortality continued to increase with the rising CCI score(CCI score 3 aOR\u0026thinsp;=\u0026thinsp;6.816, 95% CI 3.679\u0026ndash;12.627; CCI score 2 aOR\u0026thinsp;=\u0026thinsp;5.541, 95% CI 2.723\u0026ndash;11.273).\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\u003eAssociation between comorbidities in discharged breast cancer patient and treatment outcome (death)\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\u003eCCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40 years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e40\u0026ndash;59 years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e60\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e(0.239\u0026ndash;56.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(0.145\u0026ndash;26.698)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(1.521\u0026ndash;22.939) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e(0.956\u0026ndash;19.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral vascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular accident/\u003c/p\u003e \u003cp\u003etransient ischemic attacks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(0.138\u0026ndash;18.734)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(1.686\u0026ndash;8.67) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e(0.482\u0026ndash;10.854)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(0.061\u0026ndash;0.117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(2.107-247.134) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e(0.623\u0026ndash;29.374)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(0.477\u0026ndash;32.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(0.222\u0026ndash;6.741)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e(0.113\u0026ndash;3.168)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConnective tissue disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(0.108\u0026ndash;13.188)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(2.842\u0026ndash;36.041) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e(0.905\u0026ndash;59.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeptic ulcer disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(0.449\u0026ndash;8.668)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(0.6-4.081)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e(0.311\u0026ndash;5.556)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes without complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(0.986\u0026ndash;5.573)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(0.51\u0026ndash;1.446)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e(0.75\u0026ndash;2.153)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes with complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \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 \u003cp\u003e10.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(0.927-116.182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e(0.293\u0026ndash;9.241)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParaplegia and Hemiplegia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(0.276\u0026ndash;11.805)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(0.163\u0026ndash;23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e(0.068\u0026ndash;25.466)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(0.137\u0026ndash;7.245)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(2.303\u0026ndash;14.428) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e(0.525\u0026ndash;11.359)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(0.495\u0026ndash;2.256)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(0.746\u0026ndash;2.574)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e(0.338\u0026ndash;2.603)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate or severe liver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(12.678-246.998) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(12.512-116.594) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e(0.212\u0026ndash;72.817)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastatic carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(5.743\u0026ndash;12.948) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(5.726\u0026ndash;12.184) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e(2.928\u0026ndash;8.079) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIDS/HIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI score 3 (ref.=0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(11.637\u0026ndash;31.143) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(11.048\u0026ndash;26.799) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e(3.679\u0026ndash;12.627) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI score 2 (ref.=0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(8.299\u0026ndash;26.779) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(6.826\u0026ndash;17.964) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e(2.723\u0026ndash;11.273) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI score 1 (ref.=0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(2.228\u0026ndash;6.576) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(2.312\u0026ndash;6.231) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e(0.887\u0026ndash;3.161)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eCCI, Charlson Comorbidity Index; aOR, adjusted Odds Ratio; CI, Confidence Interval; COPD, Chronic Obstructive Pulmonary Disease; AIDS, Acquired Immunodeficiency Syndrome; HIV, Human Immunodeficiency Virus\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e[Insert\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cem\u003ehere]\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe number of discharged breast cancer patients in Korea has been on a rising trend for the past 15 years. Especially in all age groups over 30, the number of breast cancer patients has been on the rise, with the highest AAPC observed among older age group women. In case of discharged breast cancer patients, there was a demonstrated increase in the association between a higher number of comorbidities and mortality. The extent of this association varied across different age groups.\u003c/p\u003e \u003cp\u003eThis study's observations indicate that the number of breast cancer patients in Korea is exhibiting an overall increase, aligning with a global trend. The global number of breast cancer patients was estimated to be 2.3\u0026nbsp;million in 2020, signifying an annual growth rate of approximately 0.33% since 1990(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In the last 15 years, South Korea has experienced a substantial surge in breast cancer cases, demonstrating an average annual growth of 5.2%. This upward trajectory is particularly noteworthy among the elderly population, a phenomenon likely influenced by the country's extended average life expectancy(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Indeed, breast cancer arises from various complex factors, with age being recognized as the most significant risk factor (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In the United Kingdom, breast cancer primarily affects women aged 70 and older, representing over one-third of total cases, while in developing countries, breast cancer in women under 50 constitutes 50% of the overall occurrences(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The findings of this study emphasize the need for more proactive preventive efforts, as breast cancer incidence is projected to increase, ranging from a minimum of 32% in developed countries to as much as 95% in developing nations, due to globalization and economic growth leading to an increase in average life expectancy(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe CCI demonstrates an increasing risk based on the number of comorbidities and age. This trend not only affects the treatment outcomes presented in this study but also has implications for survival, highlighting the importance of managing patients with multiple health conditions(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). In essence, the presence of comorbidities or complications in patients complicates breast cancer management, influencing treatment decisions and inevitably impacting treatment tolerance, leading to potential implications for treatment outcomes(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The findings of this study, particularly the heightened risk of mortality associated with an increasing CCI score in younger age groups, underscore the necessity for comprehensive comorbidity management in patients of lower age ranges.\u003c/p\u003e \u003cp\u003eIn this context, CCI is increasingly being utilized as a tool to assess the health status of breast cancer patients, and it seems valuable in formulating treatment strategies(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). By identifying coexisting medical conditions and predicting treatment outcomes, a more proactive treatment approach can be devised(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The results of this study suggest the need for a more meticulous treatment approach in breast cancer patients with comorbidities, emphasizing the importance of considering concurrent health conditions in treatment planning.\u003c/p\u003e \u003cp\u003eEspecially in the treatment of breast cancer, decisions are often made based solely on the patient's age, overlooking other factors such as comorbidities that can significantly impact the effectiveness or potential adverse effects of the treatment(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). While some guidelines suggest that there is no difference in treatment outcomes regardless of the presence of comorbidities when using the same treatment approach, the actual scientific evidence regarding the treatment effectiveness in breast cancer patients with moderate to severe comorbidities is lacking. As indicated in this study, the treatment outcomes vary based on what comorbidities were present and to what extent, underscoring the need to consider not only the patient's age but also the accompanying conditions when formulating breast cancer treatment plans.\u003c/p\u003e \u003cp\u003eIt is known that there is an association between breast cancer and liver disease(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). In particular, it is known that breast cancer patients diagnosed with nonalcoholic fatty liver disease (NAFLD) are more likely to exhibit a poor prognosis in terms of recurrence(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Metabolic syndrome, such as diabetes and obesity, is known to be more prevalent in breast cancer patients(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Similarly, liver diseases also encompass risk factors such as metabolic abnormalities and obesity, implying a mechanistic connection between breast cancer and liver diseases(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). In this study, it also revealed that there was a close association between mortality in breast cancer discharged patients under the age of 60 and liver diseases, as well as metastatic cancer. The incidence rate of breast cancer in Korea is reported to be lower compared to Western populations. However, it is reported that the proportion of breast cancer patients in younger age groups is higher in Korea compared to Western populations and these patients exhibited rapid tumor progression and metastasis to distant organs(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Therefore, it is essential to understand the mechanistic connections between co-existing conditions to elucidate effective treatment approaches against breast cancer.\u003c/p\u003e \u003cp\u003eThe present study has significance in examining the long-term perspective of breast cancer discharged patients in Korean women and investigating the association between comorbidities in breast cancer patients and treatment outcomes. However, there are several limitations of the present study that should be noted. First, this study defined patients based solely on the primary diagnosis when estimating discharged patients per year. Therefore, even if there were secondary diagnoses with breast cancer codes, those patients were not considered as subjects of the study. As a result, the actual number of discharged breast cancer patients might have been underestimated. Second, the analysis was conducted solely based on the presence of the disease and treatment outcomes for each patient, without considering other risk factors associated with breast cancer such as family history, smoking, alcohol consumption, physical activity, etc. Therefore, caution is needed when interpreting the results due to the omission of these relevant risk factors. Third, it\u0026rsquo;s challenging to present the casual relationship of treatment outcomes because Discharge In-depth Injury Survey is an analysis of cross-sectional data by year. Furthermore, since there are limitations in proving the temporal sequence of comorbidities, the presence of comorbidities implies an association rather than a casual relationship with treatment outcomes. Therefore, caution is needed when interpreting this aspect. However, it is necessary to establish the relationship between breast cancer and comorbidities presented in this study through mechanisms of subsequent disease occurrence in the future.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study aimed to analyze the scale and characteristics of breast cancer discharged patients and evaluate the association between comorbidities and their treatment outcomes using discharge patient information in Korea over the past 15 years. The research findings indicate a continuing upward trend in breast cancer discharged patients in Korea over the past 15 years, particularly showing an increasing trend across all age groups above 30 years, with the highest AAPC observed in older age group. When it comes to discharged breast cancer patients, there is a higher association between comorbidities and mortality, with varying impacts of comorbidities across different age groups. Considering the expected increase in breast cancer patients, especially with the aging population, it becomes crucial to formulate treatment plans that account for comorbidities to ensure positive treatment outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKorea Disease Control and Prevention Agency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAAPC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eaverage annual percent change\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\"\u003eaOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eadjusted odds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence intervals\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNAFLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enonalcoholic fatty liver disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Health Insurance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic Obstructive Pulmonary Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcquired Immunodeficiency Syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHIV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman Immunodeficiency Virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Institutional Review Board of Dankook University of South Korea (IRB no. DKU 2024-01-008-001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are available from the Korea Disease Control and Prevention Agency on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[http://www.kdca.go.kr/contents.es?mid=a20303010502]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJH and JJ designed the study. JH analyzed the data and wrote the manuscript. JJ contributed to the interpretation of the results. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Korean Disease Control and Prevention Agency.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFeng Y, Spezia M, Huang S, Yuan C, Zeng Z, Zhang L, et al. Breast cancer development and progression: Risk factors, cancer stem cells, signaling pathways, genomics, and molecular pathogenesis. Genes Dis. 2018;5(2):77\u0026ndash;106.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkram M, Iqbal M, Daniyal M, Khan AU. Awareness and current knowledge of breast cancer. Biol Res. 2017;50(1):33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTilli TM. Precision Medicine: Technological Impact into Breast Cancer Diagnosis, Treatment and Decision Making. J Pers Med. 2021;11(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang SY, Lee SB, Kim YS, Kim Z, Kim HY, Kim HJ, et al. Breast Cancer Statistics in Korea, 2018. J Breast Cancer. 2021;24(2):123\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJung K-W, Won Y-J, Hong S, Kong H-J, Im J-S, Seo HG. Prediction of Cancer Incidence and Mortality in Korea, 2021. crt. 2021;53(2):316\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStatistics Korea. Causes of Death Statistics in 2021 2022 [Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://kostat.go.kr/board.es?mid=a20108100000\u003c/span\u003e\u003cspan address=\"https://kostat.go.kr/board.es?mid=a20108100000\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u0026amp;bid=11773\u0026amp;act=view\u0026amp;list_no=421206.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVeronesi U, Boyle P, Goldhirsch A, Orecchia R, Viale G. Breast cancer. Lancet. 2005;365(9472):1727\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Y-S, Zhao Z, Yang Z-N, Xu F, Lu H-J, Zhu Z-Y, et al. Risk factors and preventions of breast cancer. Int J Biol Sci. 2017;13(11):1387.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbu Abeelh E, AbuAbeileh Z. Impact of Mammography Screening Frequency on Breast Cancer Mortality Rates. Cureus. 2023;15(11):e49066.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNechuta S, Lu W, Zheng Y, Cai H, Bao PP, Gu K, et al. Comorbidities and breast cancer survival: a report from the Shanghai Breast Cancer Survival Study. Breast Cancer Res Treat. 2013;139(1):227\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHouterman S, Janssen-Heijnen MLG, Verheij CDGW, Louwman WJ, Vreugdenhil G, van der Sangen MJC, et al. Comorbidity has negligible impact on treatment and complications but influences survival in breast cancer patients. Br J Cancer. 2004;90(12):2332\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHidalgo NJ, Pando E, Mata R, Fernandes N, Villasante S, Barros M, et al. Impact of comorbidities on hospital mortality in patients with acute pancreatitis: a population-based study of 110,021 patients. BMC Gastroenterol. 2023;23(1):81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOwens PL, Liang L, Barrett ML, Fingar KR. Statistical Brief# 303 Comorbidities Associated With Adult Inpatient Stays, 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee Y-K, Hong SO, Park S-J, Park M, Wang K, Jo M et al. Data resource profile: the Korea national hospital discharge in-depth injury survey. Epidemiol health. 2021;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewschaffer CJ, Bush TL, Penberthy LT. Comorbidity measurement in elderly female breast cancer patients with administrative and medical records data. J Clin Epidemiol. 1997;50(6):725\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Y, Gong M, Wang Y, Yang Y, Liu S, Zeng Q. Global trends and forecasts of breast cancer incidence and deaths. Sci Data. 2023;10(1):334.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKontis V, Bennett JE, Mathers CD, Li G, Foreman K, Ezzati M. Future life expectancy in 35 industrialised countries: projections with a Bayesian model ensemble. Lancet. 2017;389(10076):1323\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrapani D, Ginsburg O, Fadelu T, Lin NU, Hassett M, Ilbawi AM, et al. Global challenges and policy solutions in breast cancer control. Cancer Treat Rev. 2022;104:102339.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma N, Narayan S, Sharma R, Kapoor A, Kumar N, Nirban R. Association of comorbidities with breast cancer: an observational study. Trop J Med Res. 2016;19(2):168.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemb J, Abraham L, Miglioretti DL, Sprague BL, O\u0026rsquo;Meara ES, Advani S et al. Screening Mammography Outcomes: Risk of Breast Cancer and Mortality by Comorbidity Score and Age. JNCI: Journal of the National Cancer Institute. 2019;112(6):599\u0026ndash;606.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu MR, Axelrod D, Guth AA, Cleland CM, Ryan CE, Weaver KR, et al. Comorbidities and Quality of Life among Breast Cancer Survivors: A Prospective Study. J Personalized Med. 2015;5(3):229\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLand LH, Dalton SO, J\u0026oslash;rgensen TL, Ewertz M. Comorbidity and survival after early breast cancer. A review. Crit Rev Oncol/Hematol. 2012;81(2):196\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGroup EBCTC. Effect of radiotherapy after breast-conserving surgery on 10-year recurrence and 15-year breast cancer death: meta-analysis of individual patient data for 10 801 women in 17 randomised trials. Lancet. 2011;378(9804):1707\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKwak M-S, Yim JY, Yi A, Chung G-E, Yang JI, Kim D, et al. Nonalcoholic fatty liver disease is associated with breast cancer in nonobese women. Dig Liver Disease. 2019;51(7):1030\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee Y-S, Lee HS, Chang SW, Lee CU, Kim JS, Jung YK et al. Underlying nonalcoholic fatty liver disease is a significant factor for breast cancer recurrence after curative surgery. Medicine. 2019;98(39).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee Y-S, Lee HS, Chang SW, Lee CU, Kim JS, Jung YK, et al. Underlying nonalcoholic fatty liver disease is a significant factor for breast cancer recurrence after curative surgery. Medicine. 2019;98(39):e17277.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWani B, Aziz SA, Ganaie MA, Mir MH. Metabolic Syndrome and Breast Cancer Risk. Indian J Med Paediatr Oncol. 2017;38(04):434\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZarghamravanbakhsh P, Frenkel M, Poretsky L. Metabolic causes and consequences of nonalcoholic fatty liver disease (NAFLD). Metabolism open. 2021;12:100149.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang W, Wu S, Liu J, Zhang X, Ma X, Yang C et al. Metastasis patterns and prognosis in young breast cancer patients: A SEER database analysis. Front Oncol. 2022;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrank S, Carton M, Dubot C, Campone M, Pistilli B, Dalenc F, et al. Impact of age at diagnosis of metastatic breast cancer on overall survival in the real-life ESME metastatic breast cancer cohort. Breast. 2020;52:50\u0026ndash;7.\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-epidemiology-and-global-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Epidemiology and Global Health](https://www.springer.com/journal/44197)","snPcode":"44197","submissionUrl":"https://submission.nature.com/new-submission/44197/3","title":"Journal of Epidemiology and Global Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast Cancer, Patients, Discharge, Comorbidity","lastPublishedDoi":"10.21203/rs.3.rs-3955810/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3955810/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eIn this study, we analyzed the characteristics of breast cancer patients discharged in Korea over the past 15 years and explored the association between comorbidities and treatment outcomes to propose effective strategies for managing cancer patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study utilized cross-sectional data from the Korea National Hospital Discharge In-depth Injury Survey from 2006 to 2020. Breast cancer patients were identified based on the primary diagnosis coded as C50 (Malignant neoplasm of the breast) according to ICD-10. Comorbidities were limited to those specified by the Charlson Comorbidity Index (CCI) and categorized into groups of 0, 1, 2, and 3 or more scores based on the relative risk associated with each condition.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBetween 2006 and 2020, an estimated 499,281 breast cancer patients were discharged, with an average annual percent change of 5.2% (95% CI 4.2\u0026ndash;6.2, p\u0026thinsp;\u0026lt;\u0026thinsp;.05). The highest proportion of CCI scores of 3 or more was observed in the 60 and older age group at 12.9%, followed by 10.8% in the 40\u0026ndash;59 age group and 8.5% in the under 40 age group. Across all age groups, there was a consistent increasing trend in the risk of mortality as the CCI score increased (p\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe global trend of aging populations and increasing life expectancy indicate a continued rise in the number of breast cancer patients. Consequently, considering comorbidities when developing treatment plans for breast cancer is expected to result in positive treatment outcomes.\u003c/p\u003e","manuscriptTitle":"Assessing Trends in Hospitalizations for Breast Cancer Among Women in Korea: A Utilization of the Korea National Hospital Discharge In-depth Injury Survey (2006-2020)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-22 21:33:56","doi":"10.21203/rs.3.rs-3955810/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-19T10:47:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-18T06:35:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4cb30035-d8a4-466e-9033-e26596aea0c4","date":"2024-03-12T07:33:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-24T16:17:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-22T09:46:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-21T05:47:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Epidemiology and Global Health","date":"2024-02-14T09:59:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-epidemiology-and-global-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Epidemiology and Global Health](https://www.springer.com/journal/44197)","snPcode":"44197","submissionUrl":"https://submission.nature.com/new-submission/44197/3","title":"Journal of Epidemiology and Global Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"95e57052-c9e6-41e1-9e96-3914d3232a26","owner":[],"postedDate":"February 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-01T23:28:41+00:00","versionOfRecord":{"articleIdentity":"rs-3955810","link":"https://doi.org/10.1007/s44197-024-00229-1","journal":{"identity":"journal-of-epidemiology-and-global-health","isVorOnly":false,"title":"Journal of Epidemiology and Global Health"},"publishedOn":"2024-04-29 23:28:41","publishedOnDateReadable":"April 29th, 2024"},"versionCreatedAt":"2024-02-22 21:33:56","video":"","vorDoi":"10.1007/s44197-024-00229-1","vorDoiUrl":"https://doi.org/10.1007/s44197-024-00229-1","workflowStages":[]},"version":"v1","identity":"rs-3955810","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3955810","identity":"rs-3955810","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}