Evaluation of the breast cancer disease burden in China from 1990 to 2021: based on the Global Burden of Disease Study 2021

Evaluation of the breast cancer disease burden in China from 1990 to 2021: based on the Global Burden of Disease Study 2021 | 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 Evaluation of the breast cancer disease burden in China from 1990 to 2021: based on the Global Burden of Disease Study 2021 Yuanfeng Tan, Zhiyu Wang, Xiaoqing Yu, Qin Deng, Lu Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6893644/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective This study aims to assess the disease burden of breast cancer (BC) in China from 1990 to 2021, identifying key population groups at increased risk and provide reference data for improving breast cancer prevention and treatment strategies. Methods Data from the Global Burden of Disease Study 2021 (GBD 2021) were analyzed, focusing on six key indicators: mortality, prevalence, incidence, disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost (YLLs). Temporal trends were characterized via the Joinpoint regression model and Age-Period-Cohort (APC) model, with age-standardized rates calculated using the global age structure as a reference. Results From 1990 to 2021, the incidence of BC in China increased by 364.5%, increaseing from 86,709 cases (95% UI: 70,225 − 105,273) to 402,794 cases (95% UI: 312,117–505,644). Among females, incidence increased by 355% (from 84,793 to 385,838 cases), while male incidence saw a striking 785.2% rise (from 1,916 to 16,956 cases), highlighting a significant gender disparity. The age-standardized incidence rate (ASIR) grew by 113.3%, from 9.1 per 100,000 (95% UI: 7.4–11.0) in 1990 to 19.4 per 100,000 (95% UI: 15.0–24.3) in 2021. Mortality rose by 122%, from 41,218 deaths (95% UI: 33,621 − 50,194) to 91,484 deaths (95% UI: 71,739 − 113,710), with female mortality increasing by 118.3% and male mortality surging by 292.3%. However, the age-standardized mortality rate (ASMR) declined slightly by 6.3%, driven by an 8.2% decrease in females, while male ASMR increased by 62.3%, underscoring divergent survival trends. Disability-adjusted life years (DALYs) rose by 102.5%, from 1.5 million (95% UI: 1.2–1.8 million) to 3 million (95% UI: 2.36–3.84 million), with a 99.2% increase in females and a 270.5% rise in males. Age-Period-Cohort (APC) analysis revealed a net annual incidence drift of 2.13% and a mortality decline of -0.80% in females, with the heaviest burden in middle-aged and older groups: incidence peaked at 124.34 per 100,000 in females aged 60–64, and mortality reached 27.35 per 100,000 in the 55–60 age group. Male incidence peaked at 9.76 per 100,000 in the 70–74 age group, reflecting an escalating burden with age, particularly among the elderly. Conclusion From 1990 to 2021, China’s BC burden surged, with rising incidence, prevalence, and DALYs. Female survival improved slightly, with a modest decline in ASMR, while male mortality increased sharply, highlighting a gender gap. Middle-aged and older women face the highest incidence, alongside a notable rise in male cases. Trends show increasing incidence and worsening mortality in the elderly, driven by aging and lifestyle factors. Screening and treatment advances aid women, but male cases reveal awareness gaps. Enhanced prevention, early detection, and tailored interventions, especially for the elderly and men, are critical to ease this growing public health challenge. Global Burden of Disease Study breast cancer incidence prevalence disability adjusted life years age joinpoint regression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cancer remains one of the most formidable chronic diseases threatening global health, with its incidence and mortality posing significant challenges to populations worldwide [ 1 ]. Among the myriad malignancies, breast cancer (BC) stands out as a critical public health concern, particularly due to its prominence among women and its emerging burden in men. According to the Global Cancer Statistics 2020, BC surpassed lung cancer to become the most frequently diagnosed cancer globally, with an estimated 2.3 million new cases and 685,000 deaths, making it the fifth leading cause of cancer-related mortality [ 2 ]. This shift underscores BC’s escalating impact, driven by a complex interplay of demographic changes, lifestyle factors, and advancements in diagnostic capabilities. In China, the world’s most populous nation, BC imposes a substantial disease burden, with nearly 20% of global cases and deaths occurring within its borders despite lower age-standardized rates compared to Western regions like Europe, Oceania, or Northern America [ 3 ]. This paradox highlights the critical need to examine China’s unique BC landscape, where population size amplifies the absolute burden, necessitating tailored prevention and intervention strategies. Over the past three decades, the global rise in BC incidence and mortality has been well-documented across both developing and developed nations [ 4 ]. In China, where this trend is particularly pronounced, studies indicate that the country bears the highest number of breast cancer cases worldwide, with 416,000 new cases reported among women in 2022 [ 5 ]. An epidemiological analysis reported 57,060 BC-related deaths in China in 2020 alone, constituting 8.3% of global BC mortality [ 6 ]. This growing burden is fueled by multiple factors, including rapid urbanization, shifts in reproductive patterns (e.g., delayed childbirth and reduced breastfeeding), and the adoption of lifestyles characterized by increased alcohol and tobacco use, obesity, and sedentary behavior [ 7 ]. Concurrently, China’s demographic structure has undergone a profound transformation, with an accelerating aging population exacerbating the BC challenge. The proportion of individuals aged 65 and older has risen sharply, projected to reach 28% of the population by 2040, amplifying the prevalence of age-related diseases like BC. The United Nations General Assembly’s designation of 2021–2030 as the "Decade of Healthy Aging" reflects global recognition of aging-related health crises, a concern acutely relevant to China, where the elderly population is projected to drive future BC increases [ 8 ]. Projections suggest that by 2034, female BC cases in China could reach 434,744 (a 20.5% rise from 2020), while male cases may climb to 13,105 (a 72.52% increase), with DALYs potentially rising by 32.1% − 79.4% by 2050, reaching 3.80 to 5.16 million person-years [ 9 , 10 ]. This alarming trajectory is further complicated by regional disparities within China, where access to healthcare and screening varies significantly between urban and rural areas, contributing to uneven disease burdens. Urban centers, benefiting from advanced medical infrastructure, report higher detection rates, while rural regions often face underdiagnosis due to limited resources, exacerbating mortality in underserved populations [ 6 ]. Moreover, environmental factors such as air pollution and exposure to endocrine-disrupting chemicals, increasingly prevalent in industrialized zones, have been implicated as emerging risk factors for BC, particularly among younger cohorts [ 11 ]. These regional and environmental dimensions underscore the multifaceted nature of China’s BC epidemic, amplifying the urgency for comprehensive strategies that address not only demographic and lifestyle shifts but also geographic and ecological challenges. Addressing these gaps is essential to mitigate the escalating burden and guide resource allocation in China’s healthcare system, ensuring that interventions are both equitable and effective across diverse demographic groups. Although research on BC is extensive, previous studies have often lacked systematic evaluations utilizing the latest data, such as the GBD 2021, which provide updated metrics on key disease burden indicators. Moreover, existing studies tend to focus predominantly on trends in female breast cancer, offering limited insights into gender differences and age-specific patterns. For instance, women aged 40 and older—particularly those in the 50–59 age group—are experiencing an increasingly heavy breast cancer burden, reflecting age-related shifts in risk profiles [ 12 ]. Meanwhile, male breast cancer (MBC), though less studied, exhibits alarming growth, with incidence rates outpacing those of females in relative terms, yet it remains underexplored in terms of risk factors and outcomes [ 9 ]. These gender disparities, combined with the effects of population aging, underscore the urgent need for a comprehensive analysis encompassing all age groups and genders. Such an approach is critical for identifying high-risk populations and optimizing prevention strategies [ 13 , 14 ]. Addressing these gaps is essential to mitigate the escalating burden and guide resource allocation in China’s healthcare system, ensuring that interventions are both equitable and effective across diverse demographic groups. This study addresses these gaps by systematically analyzing the BC burden in China from 1990 to 2021, utilizing the GBD 2021 dataset to conduct a detailed examination of temporal trends and sex- and age-specific differences. Our objective is to evaluate six key indicators—incidence, prevalence, mortality, DALYs, YLDs, and YLLs—to elucidate the evolving epidemiology of BC. Employing advanced statistical tools, such as the Joinpoint regression model and the Age-Period-Cohort (APC) framework, we characterize trends and disentangle the effects of age, period, and birth cohort, providing a nuanced perspective on how biological, temporal, and generational factors shape BC outcomes. The purpose of this study is to identify high-risk populations and offer evidence-based insights to inform the development of targeted prevention and intervention strategies, ultimately mitigating the growing burden of BC in China. Methods Overview and Source of Data This study utilized data from the GBD 2021, conducted by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington. The GBD 2021 database, accessible via the Global Health Data Exchange (GHDx) website ( https://www.healthdata.org/research-analysis/gbd ), provides comprehensive estimates of incidence, mortality, and DALYs for 371 diseases and injuries across 204 countries and territories from 1990 to 2021. These estimates are derived using standardized analytical methods, adjusted for age and sex, to ensure comparability across regions and time periods. For this analysis, we extracted data specific to breast cancer in China, covering the period from 1990 to 2021. The retrieval strategy for GBD 2021 data was as follows: "GBD estimate"; cause of death or injury; "Measure": incidence, deaths, DALYs; "Metric": number, rate, percentage; "Cause": breast cancer; "Location": China, Global; "Age": all ages, age-standardized, 15–19 years to > 95 years; "Sex": both, female, male; "Year": 1990 to 2021. We adopted a publicly accessible database for secondary analyses in our work, which was exempt from ethical constraints because it did not involve human subjects or animals. Case Definitions In the GBD 2021 framework, breast cancer is classified according to the International Classification of Diseases, 10th edition (ICD − 10), under codes C50 - C50.9, encompassing malignant neoplasms of the breast. Cases are identified based on clinical diagnoses confirmed by histopathological examination or imaging techniques, such as mammography or biopsy, aligning with standardized GBD methodology for cancer burden estimation [ 15 ]. This definition ensures consistency in capturing incident cases across diverse healthcare settings, reflecting both female and male breast cancer occurrences as reported in the database. DALYs, an epidemiologic indicator that assesses the duration of disability in a population of deceased and survivors, is composed of two components: years of life lost due to premature mortality (YLLs) and years lived with disability (YLDs), i.e., DALYs = YLLs + YLDs [ 16 ]. This composite measure quantifies the total health loss attributable to breast cancer, enabling assessment of its burden across age and sex groups in China from 1990 to 2021. Descriptive Analysis Data onreater than China. The United Kingdom recorded an ASDR of 545.7 per 100,000, higher than China. Australia reported an ASDR of 449.6 per 100,000, also above China (see BC in China were processed and analyzed using Microsoft Excel 2021 and R Studio (version 4.2.1). We described the disease burden through key indicators, including incidence (new cases), mortality (deaths), and DALYs, reported as absolute numbers and age-standardized rates (ASRs) per 100,000 population. Age-standardized incidence rates (ASIR), age-standardized mortality rates (ASMR), and age-standardized DALY rates (ASDR) were derived from GBD 2021 estimates, accompanied by 95% uncertainty intervals (UIs), calculated from the 25th and 975th values of 1,000 iterated simulations to quantify estimation uncertainty [ 17 ]. Joinpoint Regression Model The Joinpoint software is an epidemiological statistics software that enables trend analysis. Temporal trends in ASIR, ASMR, and ASDR of BC in China from 1990 to 2021 were assessed using the Joinpoint Regression Program (version 5.2.0),developed by the National Cancer Institute (available at https://surveillance.cancer.gov/joinpoint/ ). The Joinpoint regression model identifies significant changes in trends by fitting a series of straight lines on a logarithmic scale, starting with a minimal number of joinpoints and testing their statistical significance [ 18 ]. We calculated the annual percentage change (APC) for specific time segments and the average annual percentage change (AAPC) for the entire period, with APC > 0 indicating an increasing trend and APC < 0 a decreasing trend. Non-linear regression was employed, and statistical significance was defined as a p-value < 0.05. Age-Period-Cohort Analysis To examine the effects of age, period, and birth cohort on breast cancer (BC) incidence and mortality, we utilized an Age-Period-Cohort (APC) model implemented through the APC Web Tool ( https://analysistools.cancer.gov/apc/ ). This model separates epidemiological data into three independent components: age (reflecting biological aging), period (capturing external factors affecting all age groups simultaneously), and cohort (representing generational differences linked to birth year). For this study, we grouped the data into 5-year intervals covering the period from 1992 to 2021, yielding six complete time periods: 1992–1996, 1997–2001, 2002–2006, 2007–2011, 2012–2016, and 2017–2021. Data from 1990–1991 were excluded to ensure consistency in temporal grouping, as the total study period (1990–2021, 32 years) could not be evenly divided into 5-year segments. Including an incomplete interval, such as 2020–2021 (only 2 years), could destabilize the model and hinder result interpretation. Additionally, the quality and representativeness of data from 1990–1991 were deemed less reliable compared to later years, further justifying their exclusion to enhance the analysis’s robustness. Age-specific rates were analyzed across age groups ranging from 15–19 to over 95 years. Rate ratios (RR) were computed for each period and cohort relative to a reference group, with statistical significance defined at p < 0.05. Statistical Analysis Statistical analyses were performed using R Studio (version 4.2.1) for descriptive statistics and visualization. Joinpoint software (version 5.2.0) was used to compute APC, AAPC, and 95% confidence intervals (CIs) and to model trend shifts in BC burden. The APC Web Tool facilitated the construction of APC models, assessing age-specific RRs for periods and cohorts. All statistical tests were two-sided, with a p-value < 0.05 indicating significance. Results Disease burden of BC in Women in Different Regions of the World in 2021 In 2021, BC affected women variably across global regions, with data from the GBD 2021 study by the Institute for Health Metrics and Evaluation (IHME) detailing age-standardized incidence (ASIR), mortality (ASMR), and DALY rates (ASDR) per 100,000, compared to China (see Supplementary Table 1 and Fig. 1 for regional distribution; global visualization derived from IHME’s interactive interface): China’s ASIR was 37.0 (95% UI: 28.2–46.9), ASMR 8.2 (95% UI: 6.4–10.3), and ASDR 281.5 (95% UI: 216.9–358.1), while the United States recorded a higher ASIR of 97.0 (95% UI: 91.0-101.3), ASMR of 17.2 (95% UI: 15.7–18.1), and ASDR of 522.4 (95% UI: 490.8–554.9); India had a lower ASIR of 25.8 (95% UI: 21.9–30.6) but higher ASMR of 12.4 (95% UI: 10.4–14.8) and ASDR of 401.8 (95% UI: 339.3–480.0); Japan reported an ASIR of 63.8 (95% UI: 58.9–68.0), ASMR of 9.9 (95% UI: 9.2–10.6), and ASDR of 353.0 (95% UI: 326.7–375.6), all exceeding China; South Korea showed an ASIR of 38.7 (95% UI: 31.6–46.0), a lower ASMR of 6.3 (95% UI: 5.0–7.5), and ASDR of 235.0 (95% UI: 192.2–278.0); Brazil had an ASIR of 43.9 (95% UI: 41.1–46.3), ASMR of 16.9 (95% UI: 15.7–18.0), and ASDR of 532.5 (95% UI: 500.4–562.1), all above China; the United Kingdom recorded an ASIR of 89.0 (95% UI: 83.6–93.5), ASMR of 17.5 (95% UI: 16.5–18.5), and ASDR of 545.7 (95% UI: 514.6–574.2), surpassing China; and Australia reported an ASIR of 79.3 (95% UI: 70.1–89.0), ASMR of 15.1 (95% UI: 13.2–16.9), and ASDR of 449.6 (95% UI: 403.2–500.0), also higher than China, highlighting the diverse burden of BC on women globally relative to China. Disease burden of BC in China in 1990 and 2021 From 1990 to 2021, the burden of BC in China increased substantially. In 2021, the total number of incident cases reached 402,794 (95% UI: 312,117–505,644), representing a 364.5% increase from 86,709 cases (95% UI: 70,225 − 105,273) in 1990. The number of deaths also rose significantly, from 41,218 (95% UI: 33,621 − 50,194) in 1990 to 91,484 (95% UI: 71,739 − 113,710) in 2021, reflecting a 122% increase. Despite the rise in absolute mortality, the age-standardized mortality rate (ASMR) showed a slight decline, decreasing by 6.3% from 4.7 per 100,000 population (95% UI: 3.9–5.7) in 1990 to 4.4 per 100,000 (95% UI: 3.5–5.5) in 2021. In addition to increases in incidence and mortality, the prevalence of BC also showed a marked upward trend. The total number of prevalent cases increased from 834,844 (95% UI: 700,844–988,137) in 1990 to 3,893,549 (95% UI: 3,168,427–4,736,669) in 2021. The ASPR rose by 107.6%, from 89.5 per 100,000 (95% UI: 76.1–105.0) to 185.7 per 100,000 (95% UI: 150.8–227.0). Similarly, the number of DALYs attributable to BC increased from 1,495,722 (95% UI: 1,208,227–1,823,680) in 1990 to 3,029,405 (95% UI: 2,360,641–3,844,036) in 2021, reflecting a 102.5% increase. This rise was primarily driven by the increase in female DALYs, which nearly doubled from 1,466,486 (95% UI: 1,177,503–1,798,027) in 1990 to 2,921,096 (95% UI: 2,254,510–3,716,739) in 2021, a 99.2% increase. The ASDR for the total population slightly declined by 3.4%, from 151.5 per 100,000 (95% UI: 123.5–184.4) in 1990 to 146.3 per 100,000 (95% UI: 113.8–185.5) in 2021. Furthermore, both years of life lost (YLLs) and years lived with disability (YLDs) increased significantly, particularly among females, underscoring the growing health burden of BC in China. Disease burden by sex and different years in China From 1990 to 2021, the burden of breast cancer (BC) in China has steadily increased in terms of prevalence, incidence, and disability-adjusted life years (DALYs). The total number of BC cases rose from 834,844 in 1990 (95% UI: 700,844–988,137) to 3,893,549 in 2021 (95% UI: 3,168,427–4,736,669), marking a 366.4% increase. The age-standardized prevalence rate (ASPR) also increased from 89.5 cases per 100,000 people in 1990 (95% UI: 76.1–105.0) to 185.7 per 100,000 in 2021 (95% UI: 150.8–227.0), with a greater increase observed in males (300.4%) compared to females (103.2%). However, females consistently had a significantly higher absolute prevalence rate than males. The incidence of BC in China also grew from 86,709 cases in 1990 (95% UI: 70,225 − 105,273) to 402,794 cases in 2021 (95% UI: 312,117–505,644), representing a 364.5% increase. The age-standardized incidence rate (ASIR) increased from 9.1 cases per 100,000 people in 1990 (95% UI: 7.4–11.0) to 19.4 per 100,000 in 2021 (95% UI: 15.0–24.3), with a greater increase in males (290.3%) than females (107.3%). Despite the higher growth rate in males, the incidence burden remained predominantly in females, who accounted for over 95% of the total cases. The total DALYs due to BC in China increased from 1,495,722 in 1990 (95% UI: 1,208,227–1,828,360) to 3,029,405 in 2021 (95% UI: 2,360,641–3,844,036), reflecting an increase of 102.5%. However, the age-standardized death rate (ASDR) showed different trends between males and females. In males, the ASDR increased steadily from 6.0 per 100,000 people in 1990 (95% UI: 4.2–8.2) to 10.3 per 100,000 in 2021 (95% UI: 4.0–14.8), a 69.9% increase. In contrast, the ASDR in females fluctuated, decreasing from 301.7 per 100,000 people in 1990 (95% UI: 243.2–368.8) to 281.5 per 100,000 in 2021 (95% UI: 216.9–358.1), a 6.7% decrease. The joinpoint regression analysis of the ASMR, ASIR, and ASDR of BC in China from 1990 to 2021 To analyze the trends of ASMR, ASIR, and ASDR of BC in China, Joinpoint regression analysis was conducted separately for males and females. This method identifies significant timepoints where trends change and estimates the annual percent change (APC) and average annual percent change (AAPC). The results are summarized in Table 2 and Fig. 5 . Table 1 All-age cases and age-standardized rate of all measures for breast cancer and percentage changes by gender in China, 1990 and 2021. Measures All-age cases (95% UI) Age-standardized rate, per 100,000 (95% UI) 1990 2021 Change, % 1990 2021 Change, % Deaths Male 861 (592,1174) 3377 (1326,4894) 292.3 (76.9,551.7) 0.2 (0.1,0.3) 0.3 (0.1,0.5) 62.3 (-27,166.6) Female 40357 (32865,49187) 88107 (68163,110341) 118.3 (58.5,204.6) 9.0 (7.4,10.9) 8.2 (6.4,10.3) -8.2 (-33.3,27.6) Total 41218 (33621,50194) 91484 (71739,113710) 122 (61.5,207.8) 4.7 (3.9,5.7) 4.4 (3.5,5.5) -6.3 (-31.2,28.6) Incidence Male 1916 (1311,2627) 16956 (6780,24541) 785.2 (291.1,1349.4) 0.4 (0.3,0.6) 1.6 (0.7,2.3) 290.3 (70.8,545.2) Female 84793 (68417,103213) 385838 (294095,489010) 355 (226.8,543.7) 17.8 (14.5,21.7) 37.0 (28.2,47.0) 107.3 (49.1,193.4) Total 86709 (70225,105273) 402794 (312117,505644) 364.5 (235.7,549.6) 9.1 (7.4,11.0) 19.4 (15.0,24.3) 113.3 (55,197.7) Prevalence Male 15758 (11907,20233) 145449 (68248,200980) 823 (373,1312.1) 3.4 (2.7,4.4) 13.8(6.5,18.8) 300.4 (104.8,501.9) Female 819086 (685350,973902) 3748100 (3037651,4599349) 357.6 (253.4,505.4) 175.0 (147.3,206.2) 355.7 (287.0,437.3) 103.2 (56,169.2) Total 834844 (700844,988137) 3893549 (3168427,4736669) 366.4 (262.8,516.4) 89.5 (76.1,105.0) 185.7 (150.8,227.0) 107.6 (60.3,172.7) DALYs Male 29236 (19458,39968) 108308 (41297,157050) 270.5 (67.7,516.3) 6.0 (4.2,8.2) 10.3 (40,14.8) 69.9 (-24.1,182.1) Female 1466486 (1177503,1798027) 2921096 (2254510,3716739) 99.2 (43.1,181.6) 301.7 (243.2,368.8) 281.5 (216.9,358.1) -6.7 (-32.6,32.2) Total 1495722 (1208227,1828360) 3029405 (2360641,3844036) 102.5 (46.7,183.6) 151.5 (123.5,184.4) 146.3 (113.8,185.5) -3.4 (-29.8,35.1) YLDs Male 1401 (882,2071) 12976 (5408,20484) 825.9 (338.8,1386.9) 0.3 (0.2,0.4) 1.23 (0.52,1.94) 309.4 (92.1,552.8) Female 58881 (39909,83325) 269861 (177500,384526) 358.3 (239.2,525.6) 12.4 (8.4,17.4) 25.9 (17.0,37.0) 109.2 (55.5,185.7) Total 60283 (40952,84969) 282837 (188758,402713) 369.2 (250.5,535.4) 6.3 (4.3,8.8) 13.6 (9.1,19.3) 115.6 (62.3,191.9) YLLs Male 27835 (18308,37869) 95333 (35793,138555) 242.5 (54.4,467.9) 5.7 (3.9,7.8) 9.0(3.4,13.0) 57.3 (-29.8,161.8) Female 1407604 (1131025,1729770) 2651235 (2017494,3365466) 88.4 (34.8,167.3) 289.3(233.6,355.3) 255.7 (194.3,324.9) -11.6 (-36.6,25.6) Total 1435439 (1158047,1756974) 2746568 (2117867,3472596) 91.3 (37.9,169.2) 145.2 (118.0,177.1) 132.7 (102.1,168.0) -8.6 (-34.1,28.3) Abbreviations: UI, Uncertainty interval; DALYs, Disability-adjusted life years; YLDs, Years lived with disability; and YLLs, Years of life lost. Table 2 Results of the joinpoint regression models for trend analysis of age-standardized death, incidence and DALYs rates of breast cancer in China from 1990 to 2021. Trend DAYLs Incidence Death Time interval APC(95% CI) Time interval APC(95% CI) Time interval APC(95% CI) Both Trend 1 1990–2000 0.31 (0.20, 0.41) * 1990–2003 2.22 (2.14, 2.29) * 1990–2000 0.39 (0.27, 0.51) * Trend 2 2000–2011 -0.70 (-0.78, 0.62) 2003–2007 4.09 (3.58, 4.60) * 2000–2011 -0.91 (-1.00, -0.82) * Trend 3 2011–2015 -1.68 (-2.28, -1.08) * 2007–2011 2.29 (1.75, 2.83) * 2011–2015 -2.07 (-2.76, -1.38) * Trend 4 2015–2021 1.51 (1.19, 1.82) * 2011–2016 0.97 (0.54, 1.40) * 2015–2021 1.43 (1.08, 1.79) * Trend 5 2016–2019 4.57 (2.71, 6.47) * Trend 6 2019–2021 2.21 (-0.04, 4.50) AAPC 1990–2021 -0.08 (-0.18, 0.02) 1990–2021 2.49 (2.25, 2.73) * 1990–2021 -0.19 (-0.31, -0.08) * Female Trend 1 1990–2000 0.23 (0.13, 0.33) * 1990–2003 2.20 (2.10, 2.29) * 1990–2000 0.42 (0.30, 0.55) * Trend 2 2000–2004 -0.71 (-1.20, -0.21) * 2003–2007 3.72 (3.07, 4.38) * 2000–2011 -1.12 (-1.21, -1.03) * Trend 3 2004–2011 -1.17 (-1.33, -1.01) * 2007–2011 1.85 (1.15, 2.56) * 2011–2014 -2.82 (-4.16, -1.46) * Trend 4 2011–2015 -1.72 (-2.30, -1.14) * 2011–2015 0.64 (-0.19, 1.49) 2014–2021 1.11 (0.84, 1.38) * Trend 5 2015–2021 1.61 (1.31, 1.91) * 2015–2021 3.62 (3.19, 4.06) * AAPC 1990–2021 -0.20 (-0.31, -0.08) * 1990–2021 2.42 (2.24, 2.60) * 1990–2021 -0.29 (-0.44, -0.14) * Male Trend 1 1990–1998 -1.75 (-1.88, -1.63) * 1990–1997 0.10 (-0.17, 0.37) 1990–1998 -1.54 (-1.65, -1.42) * Trend 2 1998–2001 1.17 (0.15, 2.20) * 1997–2001 2.64 (1.62, 3.67) * 1998–2001 1.17 (0.18, 2.17) * Trend 3 2001–2004 4.38 (3.41, 5.35) * 2001–2004 8.59 (6.76, 10.45) * 2001–2004 4.29 (3.42, 5.17) * Trend 4 2004–2009 10.08 (9.67, 10.50) * 2004–2009 15.35 (14.66, 16.05) * 2004–2009 9.41 (9.06, 9.76) * Trend 5 2009–2012 6.55 (4.78, 8.34) * 2009–2012 9.79 (6.80, 12.87) * 2009–2012 6.44 (4.95, 7.95) * Trend 6 2012–2021 -1.79 (-1.96, -1.62) * 2012–2021 0.16 (-0.13, 0.45) 2012–2021 -2.11 (-2.26, -1.96) * AAPC 1990–2021 1.72 (1.51, 1.94) * 1990–2021 4.51 (4.16, 4.86) * 1990–2021 1.57 (1.38, 1.75)* DALYs, disability-adjusted life years; APC, annual percent change; AAPC, average annual percent change; CI, confidential interval. *P < 0.05. Among females, the ASDR showed an overall decreasing trend, with an AAPC of -0.20% (95% CI: 0.31 to − 0.08) from 1990 to 2021 (Table 2 , Fig. 5 A). In contrast, the ASDR in males exhibited a continuous upward trend with multiple joinpoints. Notably, it increased sharply between 2001 and 2004 (APC: 4.38%, 95% CI: 3.41 to 5.35) and further accelerated from 2004 to 2009 (APC: 10.08%, 95% CI: 9.67 to 10.50), then plateaued. The overall AAPC in males for 1990–2021 was 1.72% (95% CI: 1.51 to 1.94) (Table 2 , Fig. 5 D). The ASIR in females increased significantly from 1990 to 2003 (APC: 2.20%, 95% CI: 2.10 to 2.29), with the most rapid growth observed between 2011 and 2015 (APC: 6.04%, 95% CI: 0.19 to 1.49). In males, ASIR showed a multi-phase rise, peaking during 2004–2009 with the highest APC of 15.33% (95% CI: 14.66 to 16.05). The overall AAPC of ASIR for males was 4.51% (95% CI: 4.16 to 4.86), higher than that for females (Table 2 , Fig. 5 E). For ASMR, females experienced a continuous decline from 1990 to 2011 (APC: − 0.91%, 95% CI: − 1.00 to − 0.82), with an even steeper decline from 2011 to 2015 (APC: − 2.07%, 95% CI: − 2.76 to − 1.38) (Table 2 , Fig. 5 C). Conversely, the overall AAPC for male ASMR during 1990–2021 was 1.57% (95% CI: 1.38 to 1.75), indicating a consistent upward trend (Table 2 , Fig. 5 F). Age-Period-Cohort Analysis To comprehensively assess the temporal trends and generational variations in BC mortality and incidence among Chinese women from 1992 to 2021, we conducted an age-period-cohort (APC) analysis. This method allows us to distinguish the independent effects of age, period, and birth cohort, providing insights into the long-term dynamics of BC burden and potential targets for intervention. In this study, net drift and local drift were calculated to demonstrate the linear trend of incidence and mortality rate after adjusting period and cohort effects. Specifically, net drift represented the trend of all-age incidence and mortality rate, and local drift represented the trend of incidence and mortality rate in each age group.The net drift of the BC incidence was 2.13% per year in China between 1992 and 2021, and the net drift in mortality was − 0.80% per year. From 1992 to 2021, the local drift of BC incidence and mortality among Chinese women exhibited a fluctuating trend, characterized by an initial increase, followed by a gradual decline, a subsequent rise, and a final decrease (Fig. 6 B, F). For mortality, a single peak was observed in the 60–65 age group, with a local drift estimate of -0.2 (95% CI: -0.34 to -0.05). For incidence, two distinct peak age groups were identified: the first peak occurred in the 20–25 age group, with a local drift estimate of 2.74 (95% CI: 1.69 to 3.81), while the second peak was observed in the 60–65 age group, with a local drift estimate of 2.9 (95% CI: 2.71 to 3.08) (Table 3 ). Table 3 Age-Period-Cohort Effect of Breast Cancer Death and Incidence in China for Female, 1992–2021. Measure Age Effect Drifts Period Effect Cohort Effect Age Longitudinal Death Rate,% Local Drifs with Net Drift,% per year Period Period Rate Ratio Cohort Cohort Rate Ratio Death 17.5 0.13(0.1,0.17) -1.39(-4.2,1.51) 1994.5 1.04(1.01,1.07) 1897 1.55(0.79,3.06) 22.5 0.37(0.31,0.44) -0.91(-2.22,0.42) 1999.5 1.04(1.01,1.07) 1902 1.43(1.11,1.84) 27.5 1.2(1.09,1.32) -0.87(-1.5,-0.24) 2004.5 1(1,1) 1907 1.33(1.17,1.52) 32.5 3.5(3.3,3.72) -0.96(-1.31,-0.62) 2009.5 0.94(0.91,0.96) 1912 1.24(1.14,1.35) 37.5 7.63(7.3,7.98) -1.28(-1.53,-1.03) 2014.5 0.86(0.84,0.88) 1917 1.17(1.09,1.24) 42.5 12.62(12.16,13.11) -1.32(-1.51,-1.14) 2019.5 0.89(0.87,0.92) 1922 1.12(1.06,1.17) 47.5 16.21(15.69,16.75) -1.13(-1.28,-0.98) 1927 1.08(1.03,1.12) 52.5 23.19(22.52,23.88) -0.78(-0.92,-0.65) 1932 1.03(1,1.07) 57.5 27.35(26.59,28.13) -0.48(-0.61,-0.34) 1937 1.02(0.99,1.06) 62.5 26.36(25.61,27.14) -0.2(-0.34,-0.05) 1942 1.01(0.98,1.04) 67.5 27.35(26.52,28.2) -0.28(-0.44,-0.13) 1947 1(1,1) 72.5 29.99(29.02,30.99) -0.43(-0.6,-0.25) 1952 1(0.97,1.03) 77.5 34.1(32.76,35.5) -0.58(-0.78,-0.37) 1957 0.98(0.95,1.01) 82.5 39.5(37.71,41.38) -0.78(-1.04,-0.51) 1962 0.88(0.85,0.91) 87.5 48.89(46.15,51.8) -0.99(-1.39,-0.59) 1967 0.83(0.8,0.86) 92.5 59.21(54.42,64.43) -1.14(-1.9,-0.38) 1972 0.77(0.74,0.8) 97.5 70.72(60.39,82.81) -1.33(-3.28,0.66) 1977 0.73(0.69,0.78) 1982 0.71(0.66,0.77) 1987 0.7(0.62,0.78) 1992 0.65(0.53,0.8) 1997 0.6(0.39,0.93) 2002 0.5(0.19,1.32) Incidence 17.5 0.15(0.11,0.2) 2.33(0.12,4.58) 1994.5 0.8(0.78,0.84) 1897 0.66(0.12,3.53) 22.5 0.52(0.44,0.61) 2.74(1.69,3.81) 1999.5 0.89(0.86,0.92) 1902 0.6(0.34,1.08) 27.5 2.25(2.05,2.46) 2.72(2.21,3.23) 2004.5 1(1,1) 1907 0.54(0.42,0.7) 32.5 6.35(5.96,6.76) 2.5(2.19,2.81) 2009.5 1.14(1.11,1.17) 1912 0.54(0.46,0.62) 37.5 15.34(14.62,16.1) 2.09(1.86,2.32) 2014.5 1.2(1.16,1.24) 1917 0.54(0.49,0.6) 42.5 31.97(30.71,33.29) 1.96(1.78,2.14) 2019.5 1.37(1.32,1.43) 1922 0.57(0.53,0.62) 47.5 45.96(44.33,47.64) 2.12(1.96,2.28) 1927 0.61(0.58,0.65) 52.5 62.82(60.76,64.95) 2.46(2.31,2.62) 1932 0.67(0.64,0.7) 57.5 76.27(73.84,78.77) 2.74(2.57,2.9) 1937 0.76(0.73,0.79) 62.5 84.23(81.52,87.03) 2.9(2.71,3.08) 1942 0.86(0.83,0.9) 67.5 91.9(88.76,95.16) 2.64(2.43,2.86) 1947 1(1,1) 72.5 95.51(91.79,99.38) 2.29(2.02,2.55) 1952 1.18(1.14,1.21) 77.5 101.91(96.69,107.42) 1.9(1.55,2.24) 1957 1.36(1.32,1.41) 82.5 105.01(98.24,112.24) 1.42(0.93,1.91) 1962 1.44(1.39,1.49) 87.5 125.87(115.07,137.69) 0.89(0.09,1.7) 1967 1.58(1.52,1.64) 92.5 107.52(91.11,126.9) 0.14(-1.57,1.89) 1972 1.73(1.65,1.81) 97.5 105.72(73.2,152.69) -0.64(-5.39,4.36) 1977 1.97(1.86,2.08) 1982 2.28(2.13,2.45) 1987 2.66(2.42,2.93) 1992 3.02(2.58,3.54) 1997 3.34(2.38,4.68) 2002 3.42(1.67,7.02) Data from Taiwan were not available. The longitudinal age curve of BC mortality in Chinese women from 1992 to 2021 exhibits an overall upward trend. A distinct inflection point is observed in the 55–60 age group, where the mortality rate reaches 27.35 per 100,000 (95% CI: 26.59, 28.13). Among women below 55 years of age, mortality remains at a relatively low level, showing a gradual increase with advancing age. However, after 60 years, the mortality rate rises rapidly with increasing age, indicating an accelerated burden of BC in elderly populations (Fig. 6 A, Table 3 ). The longitudinal age curve for BC incidence in Chinese women demonstrates a steady increase with advancing age. The incidence rate peaks in the 85–90 age group, reaching 125.87 per 100,000 (95% CI: 115.07, 137.69), followed by a slight decline but remaining at a relatively high level (Fig. 6 E, Table 3 ). In China, mortality rate ratios (RR) have steadily decreased over time. The reference period was from 2002 to 2006 (RR = 1.00), with the lowest recorded RR between 2012 and 2016 (mortality RR = 0.86, 95% CI: 0.84–0.88). In contrast, the incidence rate ratios (RR) have gradually increased over time. The reference group was again from 2002 to 2006 (RR = 1.00), with the highest incidence RR observed between 2017 and 2021 (incidence RR = 1.37, 95% CI: 1.32–1.43). These trends are illustrated in Figs. 6 C and 6 G, and detailed in Table 3 . Discussion This study examines trends in the breast cancer burden in China from 1990 to 2021, with a focus on gender and age differences. The findings reveal a significant increase in the breast cancer burden over this period, with substantial rises in incidence and prevalence, particularly a sharp surge in male incidence. Female incidence has steadily increased, with the most notable rise occurring between 2011 and 2015, likely linked to the National Breast and Cervical Cancer Screening Program (launched in 2009), which improved diagnosis rates, while lifestyle changes—such as delayed childbirth and reduced breastfeeding—further drove this trend; male incidence surged, peaking between 2004 and 2009, possibly driven by aging and high-risk diets (e.g., alcohol and red meat) [ 13 , 19 ]. Mortality trends show gender disparities: female mortality slightly declined, with the largest decrease between 2011 and 2015, reflecting improved survival rates due to targeted therapies and multidisciplinary treatment, whereas male mortality generally increased, though it declined from 2012 to 2021, suggesting benefits from adopting female treatment protocols, yet male-specific care remains inadequate [ 14 ]. The disease burden, measured in DALYs, also rose markedly, with significant increases observed in both females and males, alongside rising disability rates in both sexes. Age-period-cohort (APC) analysis highlights significant effects of age, period, and cohort, with incidence peaking in older age groups and mortality accelerating with age. In recent years, incidence has risen while mortality has declined. Globally, China’s incidence rates are lower than those in developed countries, yet its absolute burden remains substantial, consistent with prior studies [ 20 , 21 ]. Joinpoint analysis indicates a persistent increase in female incidence alongside declining mortality, while male incidence has surged dramatically, with mortality trending upward overall. These findings underscore the public health challenges posed by aging populations and the male demographic, emphasizing the urgent need for enhanced early detection and targeted interventions. The age effect analysis of this study indicates that the incidence and mortality rates of BC significantly increase with age, particularly in older populations, a trend consistent with previous long-term studies on the burden of BC in China [ 19 ]. China is undergoing rapid population aging, and the increasing burden of aging may be a key driver of the rising BC incidence. Due to the accelerated aging process, the United Nations General Assembly designated 2021–2030 as the "Decade of Healthy Aging," highlighting the urgency of preventing aging-related diseases.Additionally, the period effect analysis reveals that over time, female BC mortality rates have decreased while incidence rates have increased [ 8 ]. This periodic change is influenced by multiple factors, including socioeconomic development, public health policies, advancements in medical technology, and environmental changes. With economic growth and optimized allocation of medical resources, the coverage of screening programs has continuously expanded, enabling more women to undergo regular BC screening and facilitating early detection [ 20 ]. Advances in screening technologies (e.g., mammography, ultrasound, and MRI) have increased the detection rate of asymptomatic cases, thereby statistically elevating incidence rates. Since the Chinese government launched the national "Two Cancers" screening program in 2009, BC screening capabilities have significantly improved, with breast ultrasound and mammography widely implemented nationwide[ 13 ].Furthermore, improvements in disease definitions and cancer registry systems have enhanced the completeness of BC data, allowing cases that might have previously gone unrecorded to now be systematically documented [ 21 ]. Despite the rising incidence of BC, advancements in medical technology have significantly improved patient survival rates, reducing mortality. The application of targeted therapies, immunotherapy, personalized endocrine therapy, and multidisciplinary comprehensive treatment has continuously optimized BC treatment outcomes [ 22 ]. Meanwhile, increased public health awareness, the promotion of healthy lifestyles, and the establishment of BC rehabilitation and follow-up systems have further contributed to long-term survival. The cohort effect analysis shows that later birth cohorts exhibit higher BC incidence but lower mortality, a trend likely reflecting the combined impact of widespread screening, medical advancements, and lifestyle changes. Changes in reproductive patterns (e.g., declining fertility rates, delayed age at first birth, and reduced breastfeeding) may contribute to increased BC risk in younger cohorts, while unhealthy lifestyles (e.g., obesity, sedentary behavior, and increased alcohol consumption) further exacerbate this trend. These period and cohort effects reflect the complex interplay between societal development, medical progress, and BC epidemiological trends. In the future, it will be essential to further optimize screening strategies, enhance lifestyle interventions, and advance the application of precision medicine in BC prevention and treatment to continuously reduce the global health burden of BC. Although male breast cancer (MBC) accounts for only 0.6%-1% of all BC cases, its disease burden has risen significantly worldwide, highlighting a health impact disproportionate to their rarity [ 23 , 24 ]. Globally, China led with 145,449 cases in 2021, underscoring its critical role in the global MBC landscape. Our study reveals that from 1990 to 2021, the incidence, mortality, and DALYs of MBC in China increased markedly. Various risk factors have been identified that may contribute to the development of MBC, including modifiable risk factors, such as obesity and physical inactivity, and non-modifiable risk factors, such as age and family history [ 25 ]. In China, rapid urbanization and lifestyle changes have increased red meat and alcohol consumption, exacerbating the MBC burden. Additionally, BRCA1/2 mutations and family history play significant roles among high-risk groups in China [ 26 ]. Compared to female breast cancer, the smaller volume of male breast tissue may facilitate rapid tumor invasion into surrounding skin and chest wall, often presenting as ulceration or fixation, leading to late-stage diagnoses (e.g., T4 stage) and higher mortality rates [ 27 – 29 ]. Moreover, insufficient awareness and emphasis on MBC among the public and healthcare providers may delay diagnosis, as men are less likely to seek medical attention for breast symptoms due to stigma or lack of education [ 30 ]. Regarding future trends, research suggests that global age-standardized MBC incidence and mortality rates are projected to decline by 2050; however, in China, driven by an aging population and evolving lifestyles, the MBC burden may remain elevated in the short term, particularly among older men. Even if standardized rates stabilize, the absolute increase in cases will sustain high medical demand. As a leading MBC-affected country, China could conduct clinical trials in high-burden regions to deepen understanding of etiology and treatment responses, contributing valuable data to MBC prevention and control [ 22 ]. To address this burden, we propose a multi-faceted approach: first, enhance screening and education targeting older men to raise MBC awareness, dispel misconceptions about its rarity, and promote early detection; second, promote lifestyle interventions—such as reducing red meat and alcohol intake and quitting smoking—proven to lower MBC risk, especially in rapidly urbanizing areas; third, draw on experiences from high-income regions (e.g., Western Europe) to advance precision medicine and genetic screening for high-risk individuals (e.g., BRCA mutation carriers); and finally, increase research investment to explore regional MBC characteristics and optimize treatment strategies, mitigating its long-term public health impact. When comparing China to Western countries like the United States and the United Kingdom, differences in screening intensity, cultural factors, and anatomical characteristics emerge as critical. In Western nations, robust screening programs (e.g., annual mammography recommended by the American Cancer Society for women aged 45–54) have long been established, contributing to higher detection rates but also earlier-stage diagnoses, whereas China’s "Two Cancers" program, initiated in 2009, is still expanding coverage and faces challenges in rural areas, potentially leading to delayed diagnoses [ 31 , 32 ]. Culturally, Chinese populations may exhibit greater conservatism, with reluctance to discuss breast health or undergo physical examinations, particularly among men, further compounded by lower health literacy compared to Western counterparts. Additionally, Chinese women, on average, have smaller breast volumes than Western women, which may increase the likelihood of mastectomy over breast-conserving surgery due to tumor-to-breast size ratios, potentially resulting in higher disability rates (e.g., YLDs) from surgical outcomes, as full resection is more common when tumors are proportionally larger. This anatomical difference, alongside less aggressive screening, may contribute to a distinct burden profile in China compared to Europe and North America, where breast conservation is more feasible and disability rates may be lower due to earlier intervention [ 33 , 34 ]. Our findings support enhancing MBC screening, raising public awareness, and implementing precise interventions (e.g., reducing red meat and alcohol intake) in China, particularly for elderly and male populations, to mitigate the long-term burden and address gender health disparities. In conclusion, the burden of BC in China from 1990 to 2021 has reached critical levels, with incidence rising 364.5%, prevalence soaring 366.4%, and DALYs increasing 102.5%, demanding substantial investment in prevention and treatment. Age and gender disparities reveal middle-aged and older women as the primary at-risk group, while MBC incidence surged 785.2%, necessitating tailored approaches. We recommend personalized prevention based on population characteristics, emphasizing lifestyle modifications to curb BC risk. First, maintaining a healthy weight through diet and exercise can reduce postmenopausal BC risk, as obesity is linked to a 20–40% increased incidence [ 1 ]. Second, regular moderate activities like walking or yoga should be encouraged to enhance overall health and lower risk by up to 18%. Third, minimizing alcohol consumption and avoiding prolonged hormone replacement therapy can mitigate risk factors, given their established associations with BC [ 35 ]. Secondary and tertiary measures should include nationwide education campaigns targeting high-incidence regions, promoting early screening (e.g., mammography), and urging timely medical consultation for early detection and intervention. China has prioritized BC control through initiatives like the "Two Cancers" screening program, complemented by the Chinese Anti-Cancer Association Guidelines for BC Diagnosis and Treatment, guiding evidence-based management. Internationally, the World Health Organization advocates BC prevention via lifestyle interventions and early detection, while the American Cancer Society’s guidelines emphasize screening and risk reduction strategies. These concerted efforts provide a robust framework to alleviate BC’s escalating burden in China. This study has several notable strengths. First, we utilized the GBD 2021 database, which spans over 30 years (1990 to 2021), providing a rich and extensive dataset for analyzing BC trends in China. Second, the study population is comprehensive, covering a wide age range from 0 to over 95 years, which enables a detailed understanding of how BC morbidity and mortality affect women across different life stages. Additionally, the use of Age-Period-Cohort (APC) models allowed us to overcome the limitations of traditional descriptive analytical methods, providing a more nuanced view of the individual effects of age, period, and cohort on the disease burden. However, there are several limitations to consider. One key limitation is the lack of detailed data from different regions within China. The GBD database does not provide provincial-level data, meaning we were unable to assess regional variations in BC incidence and mortality, which could be influenced by factors such as healthcare access, lifestyle, and socioeconomic status. Another limitation stems from potential inaccuracies in the data itself. The GBD database, while robust, may still contain gaps in data completeness or quality, leading to possible underestimation of the disease burden, particularly due to issues like under-diagnosis or incomplete data collection. Furthermore, the GBD database does not stratify data by specific BC subtypes, which limits the granularity of our findings and the ability to address subtype-specific trends. Finally, despite the use of multiple correction and adjustment techniques, the inherent limitations of the GBD study, such as potential biases in data collection and modeling, may have introduced inaccuracies that affected the results of this study. Conclusion This study reveals a substantial increase in the BC burden in China over three decades, with a marked rise in incidence and prevalence, alongside a growing impact on disability-adjusted life years. While female survival has slightly improved, reflected in a modest decline in age-standardized mortality, male mortality has surged, highlighting a critical gender disparity. The elderly, particularly middle-aged and older women and aging men, emerge as the most affected groups, driven by an aging population and shifting risk factors such as diet and lifestyle. Advances in screening and treatment have contributed to better outcomes for women, yet the rapid increase in male cases underscores a gap in awareness and tailored interventions. The analysis points to a complex interplay of age, period, and cohort effects, with incidence peaking in later years and mortality escalating among older age groups. To address this escalating public health challenge, enhanced prevention and early detection strategies are essential, including widespread screening starting at middle age for women and increased focus on men’s health. Lifestyle modifications—promoting healthy diets and physical activity—alongside personalized treatment approaches, can mitigate risk and improve outcomes. China’s existing initiatives provide a foundation, but intensified efforts are needed to curb the rising burden, particularly among vulnerable populations. Without proactive measures, the strain on healthcare and society will deepen, emphasizing the urgency of targeted action to reduce BC’s long-term impact. Declarations Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Abbreviations UI, Uncertainty interval; DALYs, Disability-adjusted life years; YLDs, Years lived with disability; and YLLs, Years of life lost. Funding This study was supported by the Senior Medical Talents Program of Chongqing for Young and Middle-aged, the Kuanren Talents Program of the Second Affiliated Hospital of Chongqing Medical University, and Natural Science Foundation of Chongqing (Grant no. CSTB2024NSCQ-MSX0331). Author Contribution T and W contributed equally as co-first authors, primarily responsible for designing experiments, collecting and analyzing data, and drafting the initial manuscript.Y contributed to data organization and analysis and provided critical suggestions for manuscript revisions.D and Y, as co-corresponding authors, oversaw the overall study design, guided experiments, coordinated comprehensive data analysis, and reviewed and finalized the manuscript. All authors participated in discussions and approved the final manuscript for publication. Acknowledgments The authors extend our gratitude to the collaborators of the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 for their outstanding contributions.The original data used in this article is exclusively sourced from GBD 2021 ( http://ghdx.healthdata.org/ ). Data Availability Statement Publicly available datasets were analyzed in this study. This data can be found: https://www.healthdata.org/research-analysis/gbd . References Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;68:394–424.doi: [10.3322/caac.21492] Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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Jama 2019;321:288–300.doi: [10.1001/jama.2018.19323] Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024;403:2100-32.doi: [10.1016/s0140-6736(24)00367-2] Liang J, Wang Y, Yu F, Jiang G, Zhang W, Tian K. Evaluation of the osteoarthritis disease burden in China from 1990 to 2021: based on the Global Burden of Disease Study 2021. Front Public Health 2024;12:1478710.doi: [10.3389/fpubh.2024.1478710] Yin X, Zhang T, Zhang Y, Man J, Yang X, Lu M. The global, regional, and national disease burden of breast cancer attributable to low physical activity from 1990 to 2019: an analysis of the Global Burden of Disease Study 2019. Int J Behav Nutr Phys Act 2022;19:42.doi: [10.1186/s12966-022-01283-3] Vancheri F, Tate AR, Henein M, Backlund L, Donfrancesco C, Palmieri L, et al. Time trends in ischaemic heart disease incidence and mortality over three decades (1990–2019) in 20 Western European countries: systematic analysis of the Global Burden of Disease Study 2019. Eur J Prev Cardiol 2022;29:396–403.doi: [10.1093/eurjpc/zwab134] Yuan J, Li P, Yang M. Long-term trends in the burden of breast cancer in China over three decades: a joinpoint regression and age-period-cohort analysis based on Global Burden of Disease 2021. Eur J Cancer Prev 2024.doi: [10.1097/cej.0000000000000934] Sha R, Kong XM, Li XY, Wang YB. Global burden of breast cancer and attributable risk factors in 204 countries and territories, from 1990 to 2021: results from the Global Burden of Disease Study 2021. Biomark Res 2024;12:87.doi: [10.1186/s40364-024-00631-8] Zhang S, Jin Z, Bao L, Shu P. The global burden of breast cancer in women from 1990 to 2030: assessment and projection based on the global burden of disease study 2019. 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Identification of BRCA1/2 founder mutations in Southern Chinese breast cancer patients using gene sequencing and high resolution DNA melting analysis. PLoS One 2012;7:e43994.doi: [10.1371/journal.pone.0043994] Korde LA, Zujewski JA, Kamin L, Giordano S, Domchek S, Anderson WF, et al. Multidisciplinary meeting on male breast cancer: summary and research recommendations. J Clin Oncol 2010;28:2114-22.doi: [10.1200/jco.2009.25.5729] Aryannejad A, Saeedi Moghaddam S, Mashinchi B, Tabary M, Rezaei N, Shahin S, et al. National and subnational burden of female and male breast cancer and risk factors in Iran from 1990 to 2019: results from the Global Burden of Disease study 2019. Breast Cancer Res 2023;25:47.doi: [10.1186/s13058-023-01633-4] Anderson WF, Jatoi I, Tse J, Rosenberg PS. Male breast cancer: a population-based comparison with female breast cancer. J Clin Oncol 2010;28:232-9.doi: [10.1200/jco.2009.23.8162] Ruddy KJ, Winer EP. Male breast cancer: risk factors, biology, diagnosis, treatment, and survivorship. Ann Oncol 2013;24:1434-43.doi: [10.1093/annonc/mdt025] Oeffinger KC, Fontham ET, Etzioni R, Herzig A, Michaelson JS, Shih YC, et al. Breast Cancer Screening for Women at Average Risk: 2015 Guideline Update From the American Cancer Society. Jama 2015;314:1599 – 614.doi: [10.1001/jama.2015.12783] Huang Y, Dai H, Song F, Li H, Yan Y, Yang Z, et al. Preliminary effectiveness of breast cancer screening among 1.22 million Chinese females and different cancer patterns between urban and rural women. Sci Rep 2016;6:39459.doi: [10.1038/srep39459] Fan L, Strasser-Weippl K, Li JJ, St Louis J, Finkelstein DM, Yu KD, et al. Breast cancer in China. Lancet Oncol 2014;15:e279-89.doi: [10.1016/s1470-2045(13)70567-9] Kwok C, Cant R, Sullivan G. Factors associated with mammographic decisions of Chinese-Australian women. Health Educ Res 2005;20:739 – 47.doi: [10.1093/her/cyh034] Dorling L, Carvalho S, Allen J, González-Neira A, Luccarini C, Wahlström C, et al. Breast Cancer Risk Genes - Association Analysis in More than 113,000 Women. N Engl J Med 2021;384:428–39.doi: [10.1056/NEJMoa1913948] Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6893644","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":484583577,"identity":"19a6aa33-4a02-4aca-a9d4-d723088b9acd","order_by":0,"name":"Yuanfeng Tan","email":"","orcid":"","institution":"Second Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuanfeng","middleName":"","lastName":"Tan","suffix":""},{"id":484583578,"identity":"4d3c3fc9-6f18-45cd-b9af-e78ebad2acbf","order_by":1,"name":"Zhiyu Wang","email":"","orcid":"","institution":"Second Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhiyu","middleName":"","lastName":"Wang","suffix":""},{"id":484583579,"identity":"ace4ef0a-ef2b-4e7d-aaa9-230dec3edb0c","order_by":2,"name":"Xiaoqing Yu","email":"","orcid":"","institution":"Second Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoqing","middleName":"","lastName":"Yu","suffix":""},{"id":484583580,"identity":"abf61c34-d69d-4ae0-b7a1-506311c618ef","order_by":3,"name":"Qin Deng","email":"","orcid":"","institution":"Second Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"Deng","suffix":""},{"id":484583581,"identity":"e4941822-7800-453b-8be9-0c455b464b6b","order_by":4,"name":"Lu Yang","email":"data:image/png;base64,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","orcid":"","institution":"Second Affiliated Hospital of Chongqing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Lu","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-06-14 11:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6893644/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6893644/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86766991,"identity":"630e86d7-2f3f-4c20-89a6-a1c9ba49fe5e","added_by":"auto","created_at":"2025-07-15 11:10:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":925553,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical distribution of age-standardized rate of Death, Incidence and DALY for female breast cancer worldwide in 2021. (A) DALYs; (B) Incidence; (C) Death; DALYs, disability-adjusted life years.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6893644/v1/c7fae6f26f3c7aef98cdcb4a.png"},{"id":86766994,"identity":"80eec6e8-43b6-4938-a492-4ac76991f942","added_by":"auto","created_at":"2025-07-15 11:10:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1538698,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of incidence, prevalence and death of breast cancer according to sex and age group in China in 2021. (A) Prevalence; (B) Incidence; (C) Death.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6893644/v1/7e6e48df8f50c6fab455371e.png"},{"id":86766997,"identity":"34fb4c7a-f120-4b8c-b3ac-e7e783341190","added_by":"auto","created_at":"2025-07-15 11:10:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":7512361,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in breast cancer prevalence, incidence, and death rates by sex and age group in China in 2021. (A) The rate of prevalence; (B) the rate of incidence; (C) the rate of death.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6893644/v1/957cdbc8cf3ec7d4bd6a3442.png"},{"id":86766995,"identity":"c4e7c635-cb9f-43b3-bce7-4ecd5057091e","added_by":"auto","created_at":"2025-07-15 11:10:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4895969,"visible":true,"origin":"","legend":"\u003cp\u003eThe Number of breast cancer prevalence, incidence and DALYs (the bar graph with left Y-axis) and age-standardized prevalence, incidence and DALYs rate (per 100,000) by sex (the line graph with right Y-axis) from 1990 to 2021. (A) Prevalence; (B) incidence; (C) DALYs; DALYs, disability-adjusted life years.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6893644/v1/678d2aba71b095aad65753b7.png"},{"id":86767722,"identity":"7424d3bb-2fc0-46c7-8710-a989a9045dfd","added_by":"auto","created_at":"2025-07-15 11:18:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":11014666,"visible":true,"origin":"","legend":"\u003cp\u003eJoinpoint regression analysis of BC in China, 1990–2021. (A) Age-standardized DALYs rate for females. (B) Age-standardized incidence rate for females. (C) Age-standardized mortality rate for females. (D) Age-standardized DALYs rate for males. (E) Age-standardized incidence rate for males. (F) Age-standardized mortality rate for males. BC, breast cancer.\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6893644/v1/3ad48a74dcba6d5de1581e55.png"},{"id":86768659,"identity":"70f63893-5c23-4734-808a-459dde47afc7","added_by":"auto","created_at":"2025-07-15 11:26:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5611322,"visible":true,"origin":"","legend":"\u003cp\u003eAge-period-cohort effect of breast cancer incidence and mortality in Chinese women, 1990–2021. (A) Longitudinal age curve of mortality. (E) Longitudinal age curve of incidence.(B) Local drift curve of mortality. (F) Local drift curve of incidence. (C) Period RR curve of mortality.(G) Period RR curve of incidence. (D) Cohort RR curve of mortality.(H) Cohort RR curve of incidence. Data from Taiwan were not available. RR: ratio rate.\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6893644/v1/e2807d6e869a99fce0fcca72.png"},{"id":92847262,"identity":"a7652672-31cc-4d5e-88dd-7b2f63d52217","added_by":"auto","created_at":"2025-10-06 09:47:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9744147,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6893644/v1/bedb9491-1501-4279-a457-2d1bd603eec8.pdf"},{"id":86766989,"identity":"870d06d2-711f-497e-b7bd-19ab7d12533e","added_by":"auto","created_at":"2025-07-15 11:10:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18746,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6893644/v1/f35ea8070cf1d4a157d10760.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of the breast cancer disease burden in China from 1990 to 2021: based on the Global Burden of Disease Study 2021","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer remains one of the most formidable chronic diseases threatening global health, with its incidence and mortality posing significant challenges to populations worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among the myriad malignancies, breast cancer (BC) stands out as a critical public health concern, particularly due to its prominence among women and its emerging burden in men. According to the Global Cancer Statistics 2020, BC surpassed lung cancer to become the most frequently diagnosed cancer globally, with an estimated 2.3\u0026nbsp;million new cases and 685,000 deaths, making it the fifth leading cause of cancer-related mortality [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This shift underscores BC\u0026rsquo;s escalating impact, driven by a complex interplay of demographic changes, lifestyle factors, and advancements in diagnostic capabilities. In China, the world\u0026rsquo;s most populous nation, BC imposes a substantial disease burden, with nearly 20% of global cases and deaths occurring within its borders despite lower age-standardized rates compared to Western regions like Europe, Oceania, or Northern America [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This paradox highlights the critical need to examine China\u0026rsquo;s unique BC landscape, where population size amplifies the absolute burden, necessitating tailored prevention and intervention strategies.\u003c/p\u003e\u003cp\u003eOver the past three decades, the global rise in BC incidence and mortality has been well-documented across both developing and developed nations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In China, where this trend is particularly pronounced, studies indicate that the country bears the highest number of breast cancer cases worldwide, with 416,000 new cases reported among women in 2022 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. An epidemiological analysis reported 57,060 BC-related deaths in China in 2020 alone, constituting 8.3% of global BC mortality [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This growing burden is fueled by multiple factors, including rapid urbanization, shifts in reproductive patterns (e.g., delayed childbirth and reduced breastfeeding), and the adoption of lifestyles characterized by increased alcohol and tobacco use, obesity, and sedentary behavior [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Concurrently, China\u0026rsquo;s demographic structure has undergone a profound transformation, with an accelerating aging population exacerbating the BC challenge. The proportion of individuals aged 65 and older has risen sharply, projected to reach 28% of the population by 2040, amplifying the prevalence of age-related diseases like BC. The United Nations General Assembly\u0026rsquo;s designation of 2021\u0026ndash;2030 as the \"Decade of Healthy Aging\" reflects global recognition of aging-related health crises, a concern acutely relevant to China, where the elderly population is projected to drive future BC increases [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Projections suggest that by 2034, female BC cases in China could reach 434,744 (a 20.5% rise from 2020), while male cases may climb to 13,105 (a 72.52% increase), with DALYs potentially rising by 32.1% \u0026minus;\u0026thinsp;79.4% by 2050, reaching 3.80 to 5.16\u0026nbsp;million person-years [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This alarming trajectory is further complicated by regional disparities within China, where access to healthcare and screening varies significantly between urban and rural areas, contributing to uneven disease burdens. Urban centers, benefiting from advanced medical infrastructure, report higher detection rates, while rural regions often face underdiagnosis due to limited resources, exacerbating mortality in underserved populations [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Moreover, environmental factors such as air pollution and exposure to endocrine-disrupting chemicals, increasingly prevalent in industrialized zones, have been implicated as emerging risk factors for BC, particularly among younger cohorts [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These regional and environmental dimensions underscore the multifaceted nature of China\u0026rsquo;s BC epidemic, amplifying the urgency for comprehensive strategies that address not only demographic and lifestyle shifts but also geographic and ecological challenges. Addressing these gaps is essential to mitigate the escalating burden and guide resource allocation in China\u0026rsquo;s healthcare system, ensuring that interventions are both equitable and effective across diverse demographic groups.\u003c/p\u003e\u003cp\u003eAlthough research on BC is extensive, previous studies have often lacked systematic evaluations utilizing the latest data, such as the GBD 2021, which provide updated metrics on key disease burden indicators. Moreover, existing studies tend to focus predominantly on trends in female breast cancer, offering limited insights into gender differences and age-specific patterns. For instance, women aged 40 and older\u0026mdash;particularly those in the 50\u0026ndash;59 age group\u0026mdash;are experiencing an increasingly heavy breast cancer burden, reflecting age-related shifts in risk profiles [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Meanwhile, male breast cancer (MBC), though less studied, exhibits alarming growth, with incidence rates outpacing those of females in relative terms, yet it remains underexplored in terms of risk factors and outcomes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These gender disparities, combined with the effects of population aging, underscore the urgent need for a comprehensive analysis encompassing all age groups and genders. Such an approach is critical for identifying high-risk populations and optimizing prevention strategies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Addressing these gaps is essential to mitigate the escalating burden and guide resource allocation in China\u0026rsquo;s healthcare system, ensuring that interventions are both equitable and effective across diverse demographic groups.\u003c/p\u003e\u003cp\u003eThis study addresses these gaps by systematically analyzing the BC burden in China from 1990 to 2021, utilizing the GBD 2021 dataset to conduct a detailed examination of temporal trends and sex- and age-specific differences. Our objective is to evaluate six key indicators\u0026mdash;incidence, prevalence, mortality, DALYs, YLDs, and YLLs\u0026mdash;to elucidate the evolving epidemiology of BC. Employing advanced statistical tools, such as the Joinpoint regression model and the Age-Period-Cohort (APC) framework, we characterize trends and disentangle the effects of age, period, and birth cohort, providing a nuanced perspective on how biological, temporal, and generational factors shape BC outcomes. The purpose of this study is to identify high-risk populations and offer evidence-based insights to inform the development of targeted prevention and intervention strategies, ultimately mitigating the growing burden of BC in China.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eOverview and Source of Data\u003c/h2\u003e\u003cp\u003eThis study utilized data from the GBD 2021, conducted by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington. The GBD 2021 database, accessible via the Global Health Data Exchange (GHDx) website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.healthdata.org/research-analysis/gbd\u003c/span\u003e\u003cspan address=\"https://www.healthdata.org/research-analysis/gbd\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), provides comprehensive estimates of incidence, mortality, and DALYs for 371 diseases and injuries across 204 countries and territories from 1990 to 2021. These estimates are derived using standardized analytical methods, adjusted for age and sex, to ensure comparability across regions and time periods. For this analysis, we extracted data specific to breast cancer in China, covering the period from 1990 to 2021.\u003c/p\u003e\u003cp\u003eThe retrieval strategy for GBD 2021 data was as follows: \"GBD estimate\"; cause of death or injury; \"Measure\": incidence, deaths, DALYs; \"Metric\": number, rate, percentage; \"Cause\": breast cancer; \"Location\": China, Global; \"Age\": all ages, age-standardized, 15\u0026ndash;19 years to \u0026gt;\u0026thinsp;95 years; \"Sex\": both, female, male; \"Year\": 1990 to 2021.\u003c/p\u003e\u003cp\u003eWe adopted a publicly accessible database for secondary analyses in our work, which was exempt from ethical constraints because it did not involve human subjects or animals.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCase Definitions\u003c/h3\u003e\n\u003cp\u003eIn the GBD 2021 framework, breast cancer is classified according to the International Classification of Diseases, 10th edition (ICD \u0026minus;\u0026thinsp;10), under codes C50 - C50.9, encompassing malignant neoplasms of the breast. Cases are identified based on clinical diagnoses confirmed by histopathological examination or imaging techniques, such as mammography or biopsy, aligning with standardized GBD methodology for cancer burden estimation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This definition ensures consistency in capturing incident cases across diverse healthcare settings, reflecting both female and male breast cancer occurrences as reported in the database.\u003c/p\u003e\u003cp\u003eDALYs, an epidemiologic indicator that assesses the duration of disability in a population of deceased and survivors, is composed of two components: years of life lost due to premature mortality (YLLs) and years lived with disability (YLDs), i.e., DALYs\u0026thinsp;=\u0026thinsp;YLLs\u0026thinsp;+\u0026thinsp;YLDs [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This composite measure quantifies the total health loss attributable to breast cancer, enabling assessment of its burden across age and sex groups in China from 1990 to 2021.\u003c/p\u003e\n\u003ch3\u003eDescriptive Analysis\u003c/h3\u003e\n\u003cp\u003eData onreater than China. The United Kingdom recorded an ASDR of 545.7 per 100,000, higher than China. Australia reported an ASDR of 449.6 per 100,000, also above China (see BC in China were processed and analyzed using Microsoft Excel 2021 and R Studio (version 4.2.1). We described the disease burden through key indicators, including incidence (new cases), mortality (deaths), and DALYs, reported as absolute numbers and age-standardized rates (ASRs) per 100,000 population. Age-standardized incidence rates (ASIR), age-standardized mortality rates (ASMR), and age-standardized DALY rates (ASDR) were derived from GBD 2021 estimates, accompanied by 95% uncertainty intervals (UIs), calculated from the 25th and 975th values of 1,000 iterated simulations to quantify estimation uncertainty [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eJoinpoint Regression Model\u003c/h3\u003e\n\u003cp\u003eThe Joinpoint software is an epidemiological statistics software that enables trend analysis. Temporal trends in ASIR, ASMR, and ASDR of BC in China from 1990 to 2021 were assessed using the Joinpoint Regression Program (version 5.2.0),developed by the National Cancer Institute (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://surveillance.cancer.gov/joinpoint/\u003c/span\u003e\u003cspan address=\"https://surveillance.cancer.gov/joinpoint/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The Joinpoint regression model identifies significant changes in trends by fitting a series of straight lines on a logarithmic scale, starting with a minimal number of joinpoints and testing their statistical significance [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. We calculated the annual percentage change (APC) for specific time segments and the average annual percentage change (AAPC) for the entire period, with APC\u0026thinsp;\u0026gt;\u0026thinsp;0 indicating an increasing trend and APC\u0026thinsp;\u0026lt;\u0026thinsp;0 a decreasing trend. Non-linear regression was employed, and statistical significance was defined as a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003ch3\u003eAge-Period-Cohort Analysis\u003c/h3\u003e\n\u003cp\u003eTo examine the effects of age, period, and birth cohort on breast cancer (BC) incidence and mortality, we utilized an Age-Period-Cohort (APC) model implemented through the APC Web Tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://analysistools.cancer.gov/apc/\u003c/span\u003e\u003cspan address=\"https://analysistools.cancer.gov/apc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This model separates epidemiological data into three independent components: age (reflecting biological aging), period (capturing external factors affecting all age groups simultaneously), and cohort (representing generational differences linked to birth year). For this study, we grouped the data into 5-year intervals covering the period from 1992 to 2021, yielding six complete time periods: 1992\u0026ndash;1996, 1997\u0026ndash;2001, 2002\u0026ndash;2006, 2007\u0026ndash;2011, 2012\u0026ndash;2016, and 2017\u0026ndash;2021. Data from 1990\u0026ndash;1991 were excluded to ensure consistency in temporal grouping, as the total study period (1990\u0026ndash;2021, 32 years) could not be evenly divided into 5-year segments. Including an incomplete interval, such as 2020\u0026ndash;2021 (only 2 years), could destabilize the model and hinder result interpretation. Additionally, the quality and representativeness of data from 1990\u0026ndash;1991 were deemed less reliable compared to later years, further justifying their exclusion to enhance the analysis\u0026rsquo;s robustness. Age-specific rates were analyzed across age groups ranging from 15\u0026ndash;19 to over 95 years. Rate ratios (RR) were computed for each period and cohort relative to a reference group, with statistical significance defined at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using R Studio (version 4.2.1) for descriptive statistics and visualization. Joinpoint software (version 5.2.0) was used to compute APC, AAPC, and 95% confidence intervals (CIs) and to model trend shifts in BC burden. The APC Web Tool facilitated the construction of APC models, assessing age-specific RRs for periods and cohorts. All statistical tests were two-sided, with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating significance.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eDisease burden of BC in Women in Different Regions of the World in 2021\u003c/h2\u003e\u003cp\u003eIn 2021, BC affected women variably across global regions, with data from the GBD 2021 study by the Institute for Health Metrics and Evaluation (IHME) detailing age-standardized incidence (ASIR), mortality (ASMR), and DALY rates (ASDR) per 100,000, compared to China (see Supplementary Table\u0026nbsp;1 and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for regional distribution; global visualization derived from IHME\u0026rsquo;s interactive interface): China\u0026rsquo;s ASIR was 37.0 (95% UI: 28.2\u0026ndash;46.9), ASMR 8.2 (95% UI: 6.4\u0026ndash;10.3), and ASDR 281.5 (95% UI: 216.9\u0026ndash;358.1), while the United States recorded a higher ASIR of 97.0 (95% UI: 91.0-101.3), ASMR of 17.2 (95% UI: 15.7\u0026ndash;18.1), and ASDR of 522.4 (95% UI: 490.8\u0026ndash;554.9); India had a lower ASIR of 25.8 (95% UI: 21.9\u0026ndash;30.6) but higher ASMR of 12.4 (95% UI: 10.4\u0026ndash;14.8) and ASDR of 401.8 (95% UI: 339.3\u0026ndash;480.0); Japan reported an ASIR of 63.8 (95% UI: 58.9\u0026ndash;68.0), ASMR of 9.9 (95% UI: 9.2\u0026ndash;10.6), and ASDR of 353.0 (95% UI: 326.7\u0026ndash;375.6), all exceeding China; South Korea showed an ASIR of 38.7 (95% UI: 31.6\u0026ndash;46.0), a lower ASMR of 6.3 (95% UI: 5.0\u0026ndash;7.5), and ASDR of 235.0 (95% UI: 192.2\u0026ndash;278.0); Brazil had an ASIR of 43.9 (95% UI: 41.1\u0026ndash;46.3), ASMR of 16.9 (95% UI: 15.7\u0026ndash;18.0), and ASDR of 532.5 (95% UI: 500.4\u0026ndash;562.1), all above China; the United Kingdom recorded an ASIR of 89.0 (95% UI: 83.6\u0026ndash;93.5), ASMR of 17.5 (95% UI: 16.5\u0026ndash;18.5), and ASDR of 545.7 (95% UI: 514.6\u0026ndash;574.2), surpassing China; and Australia reported an ASIR of 79.3 (95% UI: 70.1\u0026ndash;89.0), ASMR of 15.1 (95% UI: 13.2\u0026ndash;16.9), and ASDR of 449.6 (95% UI: 403.2\u0026ndash;500.0), also higher than China, highlighting the diverse burden of BC on women globally relative to China.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDisease burden of BC in China in 1990 and 2021\u003c/h2\u003e\u003cp\u003eFrom 1990 to 2021, the burden of BC in China increased substantially. In 2021, the total number of incident cases reached 402,794 (95% UI: 312,117\u0026ndash;505,644), representing a 364.5% increase from 86,709 cases (95% UI: 70,225\u0026thinsp;\u0026minus;\u0026thinsp;105,273) in 1990. The number of deaths also rose significantly, from 41,218 (95% UI: 33,621\u0026thinsp;\u0026minus;\u0026thinsp;50,194) in 1990 to 91,484 (95% UI: 71,739\u0026thinsp;\u0026minus;\u0026thinsp;113,710) in 2021, reflecting a 122% increase. Despite the rise in absolute mortality, the age-standardized mortality rate (ASMR) showed a slight decline, decreasing by 6.3% from 4.7 per 100,000 population (95% UI: 3.9\u0026ndash;5.7) in 1990 to 4.4 per 100,000 (95% UI: 3.5\u0026ndash;5.5) in 2021.\u003c/p\u003e\u003cp\u003eIn addition to increases in incidence and mortality, the prevalence of BC also showed a marked upward trend. The total number of prevalent cases increased from 834,844 (95% UI: 700,844\u0026ndash;988,137) in 1990 to 3,893,549 (95% UI: 3,168,427\u0026ndash;4,736,669) in 2021. The ASPR rose by 107.6%, from 89.5 per 100,000 (95% UI: 76.1\u0026ndash;105.0) to 185.7 per 100,000 (95% UI: 150.8\u0026ndash;227.0).\u003c/p\u003e\u003cp\u003eSimilarly, the number of DALYs attributable to BC increased from 1,495,722 (95% UI: 1,208,227\u0026ndash;1,823,680) in 1990 to 3,029,405 (95% UI: 2,360,641\u0026ndash;3,844,036) in 2021, reflecting a 102.5% increase. This rise was primarily driven by the increase in female DALYs, which nearly doubled from 1,466,486 (95% UI: 1,177,503\u0026ndash;1,798,027) in 1990 to 2,921,096 (95% UI: 2,254,510\u0026ndash;3,716,739) in 2021, a 99.2% increase. The ASDR for the total population slightly declined by 3.4%, from 151.5 per 100,000 (95% UI: 123.5\u0026ndash;184.4) in 1990 to 146.3 per 100,000 (95% UI: 113.8\u0026ndash;185.5) in 2021. Furthermore, both years of life lost (YLLs) and years lived with disability (YLDs) increased significantly, particularly among females, underscoring the growing health burden of BC in China.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDisease burden by sex and different years in China\u003c/h2\u003e\u003cp\u003eFrom 1990 to 2021, the burden of breast cancer (BC) in China has steadily increased in terms of prevalence, incidence, and disability-adjusted life years (DALYs). The total number of BC cases rose from 834,844 in 1990 (95% UI: 700,844\u0026ndash;988,137) to 3,893,549 in 2021 (95% UI: 3,168,427\u0026ndash;4,736,669), marking a 366.4% increase. The age-standardized prevalence rate (ASPR) also increased from 89.5 cases per 100,000 people in 1990 (95% UI: 76.1\u0026ndash;105.0) to 185.7 per 100,000 in 2021 (95% UI: 150.8\u0026ndash;227.0), with a greater increase observed in males (300.4%) compared to females (103.2%). However, females consistently had a significantly higher absolute prevalence rate than males. The incidence of BC in China also grew from 86,709 cases in 1990 (95% UI: 70,225\u0026thinsp;\u0026minus;\u0026thinsp;105,273) to 402,794 cases in 2021 (95% UI: 312,117\u0026ndash;505,644), representing a 364.5% increase. The age-standardized incidence rate (ASIR) increased from 9.1 cases per 100,000 people in 1990 (95% UI: 7.4\u0026ndash;11.0) to 19.4 per 100,000 in 2021 (95% UI: 15.0\u0026ndash;24.3), with a greater increase in males (290.3%) than females (107.3%). Despite the higher growth rate in males, the incidence burden remained predominantly in females, who accounted for over 95% of the total cases. The total DALYs due to BC in China increased from 1,495,722 in 1990 (95% UI: 1,208,227\u0026ndash;1,828,360) to 3,029,405 in 2021 (95% UI: 2,360,641\u0026ndash;3,844,036), reflecting an increase of 102.5%. However, the age-standardized death rate (ASDR) showed different trends between males and females. In males, the ASDR increased steadily from 6.0 per 100,000 people in 1990 (95% UI: 4.2\u0026ndash;8.2) to 10.3 per 100,000 in 2021 (95% UI: 4.0\u0026ndash;14.8), a 69.9% increase. In contrast, the ASDR in females fluctuated, decreasing from 301.7 per 100,000 people in 1990 (95% UI: 243.2\u0026ndash;368.8) to 281.5 per 100,000 in 2021 (95% UI: 216.9\u0026ndash;358.1), a 6.7% decrease.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe joinpoint regression analysis of the ASMR, ASIR, and ASDR of BC in China from 1990 to 2021\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo analyze the trends of ASMR, ASIR, and ASDR of BC in China, Joinpoint regression analysis was conducted separately for males and females. This method identifies significant timepoints where trends change and estimates the annual percent change (APC) and average annual percent change (AAPC). The results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAll-age cases and age-standardized rate of all measures for breast cancer and percentage changes by gender in China, 1990 and 2021.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMeasures\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eAll-age cases (95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eAge-standardized rate, per 100,000 (95% UI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1990\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChange, %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1990\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eChange, %\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e861 (592,1174)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3377 (1326,4894)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e292.3 (76.9,551.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2 (0.1,0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3 (0.1,0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e62.3 (-27,166.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40357 (32865,49187)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88107 (68163,110341)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e118.3 (58.5,204.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.0 (7.4,10.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.2 (6.4,10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-8.2 (-33.3,27.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41218 (33621,50194)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91484 (71739,113710)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e122 (61.5,207.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.7 (3.9,5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.4 (3.5,5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-6.3 (-31.2,28.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncidence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1916 (1311,2627)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16956 (6780,24541)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e785.2 (291.1,1349.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4 (0.3,0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.6 (0.7,2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e290.3 (70.8,545.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84793 (68417,103213)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e385838 (294095,489010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e355 (226.8,543.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.8 (14.5,21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37.0 (28.2,47.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e107.3 (49.1,193.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86709 (70225,105273)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e402794 (312117,505644)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e364.5 (235.7,549.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.1 (7.4,11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.4 (15.0,24.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e113.3 (55,197.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevalence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15758 (11907,20233)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e145449 (68248,200980)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e823 (373,1312.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.4 (2.7,4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.8(6.5,18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e300.4 (104.8,501.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e819086 (685350,973902)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3748100 (3037651,4599349)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e357.6 (253.4,505.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e175.0 (147.3,206.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e355.7 (287.0,437.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e103.2 (56,169.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e834844 (700844,988137)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3893549 (3168427,4736669)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e366.4 (262.8,516.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89.5 (76.1,105.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e185.7 (150.8,227.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e107.6 (60.3,172.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29236 (19458,39968)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108308 (41297,157050)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e270.5 (67.7,516.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.0 (4.2,8.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.3 (40,14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e69.9 (-24.1,182.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1466486 (1177503,1798027)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2921096 (2254510,3716739)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99.2 (43.1,181.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e301.7 (243.2,368.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e281.5 (216.9,358.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-6.7 (-32.6,32.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1495722 (1208227,1828360)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3029405 (2360641,3844036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e102.5 (46.7,183.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e151.5 (123.5,184.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e146.3 (113.8,185.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-3.4 (-29.8,35.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYLDs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1401 (882,2071)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12976 (5408,20484)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e825.9 (338.8,1386.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3 (0.2,0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.23 (0.52,1.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e309.4 (92.1,552.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58881 (39909,83325)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e269861 (177500,384526)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e358.3 (239.2,525.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.4 (8.4,17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25.9 (17.0,37.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e109.2 (55.5,185.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60283 (40952,84969)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e282837 (188758,402713)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e369.2 (250.5,535.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.3 (4.3,8.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.6 (9.1,19.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e115.6 (62.3,191.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYLLs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27835 (18308,37869)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95333 (35793,138555)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e242.5 (54.4,467.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.7 (3.9,7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.0(3.4,13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57.3 (-29.8,161.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1407604 (1131025,1729770)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2651235 (2017494,3365466)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.4 (34.8,167.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e289.3(233.6,355.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e255.7 (194.3,324.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-11.6 (-36.6,25.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1435439 (1158047,1756974)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2746568 (2117867,3472596)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.3 (37.9,169.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e145.2 (118.0,177.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e132.7 (102.1,168.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-8.6 (-34.1,28.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003eUI, Uncertainty interval; DALYs, Disability-adjusted life years; YLDs, Years lived with disability; and YLLs, Years of life lost.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of the joinpoint regression models for trend analysis of age-standardized death, incidence and DALYs rates of breast cancer in China from 1990 to 2021.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTrend\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDAYLs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eIncidence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eDeath\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTime interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAPC(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTime interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAPC(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTime interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAPC(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\u003e\u003cb\u003eBoth\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrend 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1990\u0026ndash;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.31 (0.20, 0.41) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1990\u0026ndash;2003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.22 (2.14, 2.29) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1990\u0026ndash;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.39 (0.27, 0.51) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrend 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2000\u0026ndash;2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.70 (-0.78, 0.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2003\u0026ndash;2007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.09 (3.58, 4.60) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2000\u0026ndash;2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.91 (-1.00, -0.82) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrend 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2011\u0026ndash;2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.68 (-2.28, -1.08) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2007\u0026ndash;2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.29 (1.75, 2.83) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2011\u0026ndash;2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.07 (-2.76, -1.38) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrend 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.51 (1.19, 1.82) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2011\u0026ndash;2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97 (0.54, 1.40) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2015\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.43 (1.08, 1.79) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrend 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2016\u0026ndash;2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.57 (2.71, 6.47) *\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrend 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2019\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.21 (-0.04, 4.50)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAAPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.08 (-0.18, 0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.49 (2.25, 2.73) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.19 (-0.31, -0.08) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrend 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1990\u0026ndash;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.23 (0.13, 0.33) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1990\u0026ndash;2003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.20 (2.10, 2.29) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1990\u0026ndash;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.42 (0.30, 0.55) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrend 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2000\u0026ndash;2004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.71 (-1.20, -0.21) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2003\u0026ndash;2007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.72 (3.07, 4.38) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2000\u0026ndash;2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.12 (-1.21, -1.03) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrend 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2004\u0026ndash;2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.17 (-1.33, -1.01) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2007\u0026ndash;2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.85 (1.15, 2.56) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2011\u0026ndash;2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.82 (-4.16, -1.46) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrend 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2011\u0026ndash;2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.72 (-2.30, -1.14) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2011\u0026ndash;2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.64 (-0.19, 1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2014\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.11 (0.84, 1.38) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrend 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.61 (1.31, 1.91) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2015\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.62 (3.19, 4.06) *\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAAPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.20 (-0.31, -0.08) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.42 (2.24, 2.60) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.29 (-0.44, -0.14) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrend 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1990\u0026ndash;1998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.75 (-1.88, -1.63) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1990\u0026ndash;1997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.10 (-0.17, 0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1990\u0026ndash;1998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.54 (-1.65, -1.42) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrend 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1998\u0026ndash;2001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.17 (0.15, 2.20) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1997\u0026ndash;2001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.64 (1.62, 3.67) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1998\u0026ndash;2001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.17 (0.18, 2.17) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrend 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2001\u0026ndash;2004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.38 (3.41, 5.35) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2001\u0026ndash;2004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.59 (6.76, 10.45) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2001\u0026ndash;2004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.29 (3.42, 5.17) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrend 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2004\u0026ndash;2009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.08 (9.67, 10.50) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2004\u0026ndash;2009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.35 (14.66, 16.05) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2004\u0026ndash;2009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.41 (9.06, 9.76) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrend 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2009\u0026ndash;2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.55 (4.78, 8.34) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2009\u0026ndash;2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.79 (6.80, 12.87) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2009\u0026ndash;2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.44 (4.95, 7.95) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrend 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2012\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.79 (-1.96, -1.62) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2012\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.16 (-0.13, 0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2012\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.11 (-2.26, -1.96) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAAPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.72 (1.51, 1.94) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.51 (4.16, 4.86) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.57 (1.38, 1.75)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eDALYs, disability-adjusted life years; APC, annual percent change; AAPC, average annual percent change; CI, confidential interval. *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAmong females, the ASDR showed an overall decreasing trend, with an AAPC of -0.20% (95% CI: 0.31 to \u0026minus;\u0026thinsp;0.08) from 1990 to 2021 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In contrast, the ASDR in males exhibited a continuous upward trend with multiple joinpoints. Notably, it increased sharply between 2001 and 2004 (APC: 4.38%, 95% CI: 3.41 to 5.35) and further accelerated from 2004 to 2009 (APC: 10.08%, 95% CI: 9.67 to 10.50), then plateaued. The overall AAPC in males for 1990\u0026ndash;2021 was 1.72% (95% CI: 1.51 to 1.94) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The ASIR in females increased significantly from 1990 to 2003 (APC: 2.20%, 95% CI: 2.10 to 2.29), with the most rapid growth observed between 2011 and 2015 (APC: 6.04%, 95% CI: 0.19 to 1.49). In males, ASIR showed a multi-phase rise, peaking during 2004\u0026ndash;2009 with the highest APC of 15.33% (95% CI: 14.66 to 16.05). The overall AAPC of ASIR for males was 4.51% (95% CI: 4.16 to 4.86), higher than that for females (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). For ASMR, females experienced a continuous decline from 1990 to 2011 (APC: \u0026minus;\u0026thinsp;0.91%, 95% CI: \u0026minus;\u0026thinsp;1.00 to \u0026minus;\u0026thinsp;0.82), with an even steeper decline from 2011 to 2015 (APC: \u0026minus;\u0026thinsp;2.07%, 95% CI: \u0026minus;\u0026thinsp;2.76 to \u0026minus;\u0026thinsp;1.38) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Conversely, the overall AAPC for male ASMR during 1990\u0026ndash;2021 was 1.57% (95% CI: 1.38 to 1.75), indicating a consistent upward trend (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eAge-Period-Cohort Analysis\u003c/h2\u003e\u003cp\u003eTo comprehensively assess the temporal trends and generational variations in BC mortality and incidence among Chinese women from 1992 to 2021, we conducted an age-period-cohort (APC) analysis. This method allows us to distinguish the independent effects of age, period, and birth cohort, providing insights into the long-term dynamics of BC burden and potential targets for intervention. In this study, net drift and local drift were calculated to demonstrate the linear trend of incidence and mortality rate after adjusting period and cohort effects. Specifically, net drift represented the trend of all-age incidence and mortality rate, and local drift represented the trend of incidence and mortality rate in each age group.The net drift of the BC incidence was 2.13% per year in China between 1992 and 2021, and the net drift in mortality was \u0026minus;\u0026thinsp;0.80% per year. From 1992 to 2021, the local drift of BC incidence and mortality among Chinese women exhibited a fluctuating trend, characterized by an initial increase, followed by a gradual decline, a subsequent rise, and a final decrease (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, F). For mortality, a single peak was observed in the 60\u0026ndash;65 age group, with a local drift estimate of -0.2 (95% CI: -0.34 to -0.05). For incidence, two distinct peak age groups were identified: the first peak occurred in the 20\u0026ndash;25 age group, with a local drift estimate of 2.74 (95% CI: 1.69 to 3.81), while the second peak was observed in the 60\u0026ndash;65 age group, with a local drift estimate of 2.9 (95% CI: 2.71 to 3.08) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAge-Period-Cohort Effect of Breast Cancer Death and Incidence in China for Female, 1992\u0026ndash;2021.\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=\"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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eAge Effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDrifts\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003ePeriod Effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eCohort Effect\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLongitudinal Death Rate,%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLocal Drifs with Net Drift,% per year\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePeriod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePeriod Rate Ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCohort\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCohort Rate Ratio\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDeath\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.13(0.1,0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.39(-4.2,1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1994.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.04(1.01,1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.55(0.79,3.06)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.37(0.31,0.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.91(-2.22,0.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1999.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.04(1.01,1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.43(1.11,1.84)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.2(1.09,1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.87(-1.5,-0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2004.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1(1,1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.33(1.17,1.52)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.5(3.3,3.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.96(-1.31,-0.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2009.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.94(0.91,0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1912\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.24(1.14,1.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.63(7.3,7.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.28(-1.53,-1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2014.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.86(0.84,0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.17(1.09,1.24)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.62(12.16,13.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.32(-1.51,-1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2019.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.89(0.87,0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.12(1.06,1.17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.21(15.69,16.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.13(-1.28,-0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.08(1.03,1.12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.19(22.52,23.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.78(-0.92,-0.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.03(1,1.07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.35(26.59,28.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.48(-0.61,-0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.02(0.99,1.06)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.36(25.61,27.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.2(-0.34,-0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.01(0.98,1.04)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.35(26.52,28.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.28(-0.44,-0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1947\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1(1,1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e72.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.99(29.02,30.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.43(-0.6,-0.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1(0.97,1.03)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e77.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34.1(32.76,35.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.58(-0.78,-0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.98(0.95,1.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39.5(37.71,41.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.78(-1.04,-0.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.88(0.85,0.91)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e87.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.89(46.15,51.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.99(-1.39,-0.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.83(0.8,0.86)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e92.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59.21(54.42,64.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.14(-1.9,-0.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.77(0.74,0.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e97.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70.72(60.39,82.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.33(-3.28,0.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.73(0.69,0.78)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\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=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.71(0.66,0.77)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\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=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.7(0.62,0.78)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\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=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.65(0.53,0.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\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=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.6(0.39,0.93)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\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=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.5(0.19,1.32)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIncidence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.15(0.11,0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.33(0.12,4.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1994.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8(0.78,0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.66(0.12,3.53)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.52(0.44,0.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.74(1.69,3.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1999.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.89(0.86,0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.6(0.34,1.08)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.25(2.05,2.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.72(2.21,3.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2004.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1(1,1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.54(0.42,0.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.35(5.96,6.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.5(2.19,2.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2009.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.14(1.11,1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1912\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.54(0.46,0.62)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.34(14.62,16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.09(1.86,2.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2014.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.2(1.16,1.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.54(0.49,0.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.97(30.71,33.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.96(1.78,2.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2019.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.37(1.32,1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.57(0.53,0.62)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.96(44.33,47.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.12(1.96,2.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.61(0.58,0.65)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62.82(60.76,64.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.46(2.31,2.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.67(0.64,0.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76.27(73.84,78.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.74(2.57,2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.76(0.73,0.79)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84.23(81.52,87.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.9(2.71,3.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.86(0.83,0.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91.9(88.76,95.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.64(2.43,2.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1947\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1(1,1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e72.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95.51(91.79,99.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.29(2.02,2.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.18(1.14,1.21)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e77.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e101.91(96.69,107.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.9(1.55,2.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.36(1.32,1.41)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e105.01(98.24,112.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.42(0.93,1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.44(1.39,1.49)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e87.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e125.87(115.07,137.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89(0.09,1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.58(1.52,1.64)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e92.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107.52(91.11,126.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.14(-1.57,1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.73(1.65,1.81)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e97.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e105.72(73.2,152.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.64(-5.39,4.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.97(1.86,2.08)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\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=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.28(2.13,2.45)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\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=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.66(2.42,2.93)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\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=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.02(2.58,3.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\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=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.34(2.38,4.68)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\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=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.42(1.67,7.02)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eData from Taiwan were not available.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe longitudinal age curve of BC mortality in Chinese women from 1992 to 2021 exhibits an overall upward trend. A distinct inflection point is observed in the 55\u0026ndash;60 age group, where the mortality rate reaches 27.35 per 100,000 (95% CI: 26.59, 28.13). Among women below 55 years of age, mortality remains at a relatively low level, showing a gradual increase with advancing age. However, after 60 years, the mortality rate rises rapidly with increasing age, indicating an accelerated burden of BC in elderly populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The longitudinal age curve for BC incidence in Chinese women demonstrates a steady increase with advancing age. The incidence rate peaks in the 85\u0026ndash;90 age group, reaching 125.87 per 100,000 (95% CI: 115.07, 137.69), followed by a slight decline but remaining at a relatively high level (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn China, mortality rate ratios (RR) have steadily decreased over time. The reference period was from 2002 to 2006 (RR\u0026thinsp;=\u0026thinsp;1.00), with the lowest recorded RR between 2012 and 2016 (mortality RR\u0026thinsp;=\u0026thinsp;0.86, 95% CI: 0.84\u0026ndash;0.88). In contrast, the incidence rate ratios (RR) have gradually increased over time. The reference group was again from 2002 to 2006 (RR\u0026thinsp;=\u0026thinsp;1.00), with the highest incidence RR observed between 2017 and 2021 (incidence RR\u0026thinsp;=\u0026thinsp;1.37, 95% CI: 1.32\u0026ndash;1.43). These trends are illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG, and detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examines trends in the breast cancer burden in China from 1990 to 2021, with a focus on gender and age differences. The findings reveal a significant increase in the breast cancer burden over this period, with substantial rises in incidence and prevalence, particularly a sharp surge in male incidence. Female incidence has steadily increased, with the most notable rise occurring between 2011 and 2015, likely linked to the National Breast and Cervical Cancer Screening Program (launched in 2009), which improved diagnosis rates, while lifestyle changes\u0026mdash;such as delayed childbirth and reduced breastfeeding\u0026mdash;further drove this trend; male incidence surged, peaking between 2004 and 2009, possibly driven by aging and high-risk diets (e.g., alcohol and red meat) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Mortality trends show gender disparities: female mortality slightly declined, with the largest decrease between 2011 and 2015, reflecting improved survival rates due to targeted therapies and multidisciplinary treatment, whereas male mortality generally increased, though it declined from 2012 to 2021, suggesting benefits from adopting female treatment protocols, yet male-specific care remains inadequate [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The disease burden, measured in DALYs, also rose markedly, with significant increases observed in both females and males, alongside rising disability rates in both sexes. Age-period-cohort (APC) analysis highlights significant effects of age, period, and cohort, with incidence peaking in older age groups and mortality accelerating with age. In recent years, incidence has risen while mortality has declined. Globally, China\u0026rsquo;s incidence rates are lower than those in developed countries, yet its absolute burden remains substantial, consistent with prior studies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Joinpoint analysis indicates a persistent increase in female incidence alongside declining mortality, while male incidence has surged dramatically, with mortality trending upward overall. These findings underscore the public health challenges posed by aging populations and the male demographic, emphasizing the urgent need for enhanced early detection and targeted interventions.\u003c/p\u003e\u003cp\u003eThe age effect analysis of this study indicates that the incidence and mortality rates of BC significantly increase with age, particularly in older populations, a trend consistent with previous long-term studies on the burden of BC in China [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. China is undergoing rapid population aging, and the increasing burden of aging may be a key driver of the rising BC incidence. Due to the accelerated aging process, the United Nations General Assembly designated 2021\u0026ndash;2030 as the \"Decade of Healthy Aging,\" highlighting the urgency of preventing aging-related diseases.Additionally, the period effect analysis reveals that over time, female BC mortality rates have decreased while incidence rates have increased [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This periodic change is influenced by multiple factors, including socioeconomic development, public health policies, advancements in medical technology, and environmental changes. With economic growth and optimized allocation of medical resources, the coverage of screening programs has continuously expanded, enabling more women to undergo regular BC screening and facilitating early detection [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Advances in screening technologies (e.g., mammography, ultrasound, and MRI) have increased the detection rate of asymptomatic cases, thereby statistically elevating incidence rates. Since the Chinese government launched the national \"Two Cancers\" screening program in 2009, BC screening capabilities have significantly improved, with breast ultrasound and mammography widely implemented nationwide[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].Furthermore, improvements in disease definitions and cancer registry systems have enhanced the completeness of BC data, allowing cases that might have previously gone unrecorded to now be systematically documented [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Despite the rising incidence of BC, advancements in medical technology have significantly improved patient survival rates, reducing mortality. The application of targeted therapies, immunotherapy, personalized endocrine therapy, and multidisciplinary comprehensive treatment has continuously optimized BC treatment outcomes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Meanwhile, increased public health awareness, the promotion of healthy lifestyles, and the establishment of BC rehabilitation and follow-up systems have further contributed to long-term survival. The cohort effect analysis shows that later birth cohorts exhibit higher BC incidence but lower mortality, a trend likely reflecting the combined impact of widespread screening, medical advancements, and lifestyle changes. Changes in reproductive patterns (e.g., declining fertility rates, delayed age at first birth, and reduced breastfeeding) may contribute to increased BC risk in younger cohorts, while unhealthy lifestyles (e.g., obesity, sedentary behavior, and increased alcohol consumption) further exacerbate this trend. These period and cohort effects reflect the complex interplay between societal development, medical progress, and BC epidemiological trends. In the future, it will be essential to further optimize screening strategies, enhance lifestyle interventions, and advance the application of precision medicine in BC prevention and treatment to continuously reduce the global health burden of BC.\u003c/p\u003e\u003cp\u003eAlthough male breast cancer (MBC) accounts for only 0.6%-1% of all BC cases, its disease burden has risen significantly worldwide, highlighting a health impact disproportionate to their rarity [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Globally, China led with 145,449 cases in 2021, underscoring its critical role in the global MBC landscape. Our study reveals that from 1990 to 2021, the incidence, mortality, and DALYs of MBC in China increased markedly. Various risk factors have been identified that may contribute to the development of MBC, including modifiable risk factors, such as obesity and physical inactivity, and non-modifiable risk factors, such as age and family history [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In China, rapid urbanization and lifestyle changes have increased red meat and alcohol consumption, exacerbating the MBC burden. Additionally, BRCA1/2 mutations and family history play significant roles among high-risk groups in China [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Compared to female breast cancer, the smaller volume of male breast tissue may facilitate rapid tumor invasion into surrounding skin and chest wall, often presenting as ulceration or fixation, leading to late-stage diagnoses (e.g., T4 stage) and higher mortality rates [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Moreover, insufficient awareness and emphasis on MBC among the public and healthcare providers may delay diagnosis, as men are less likely to seek medical attention for breast symptoms due to stigma or lack of education [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Regarding future trends, research suggests that global age-standardized MBC incidence and mortality rates are projected to decline by 2050; however, in China, driven by an aging population and evolving lifestyles, the MBC burden may remain elevated in the short term, particularly among older men. Even if standardized rates stabilize, the absolute increase in cases will sustain high medical demand. As a leading MBC-affected country, China could conduct clinical trials in high-burden regions to deepen understanding of etiology and treatment responses, contributing valuable data to MBC prevention and control [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. To address this burden, we propose a multi-faceted approach: first, enhance screening and education targeting older men to raise MBC awareness, dispel misconceptions about its rarity, and promote early detection; second, promote lifestyle interventions\u0026mdash;such as reducing red meat and alcohol intake and quitting smoking\u0026mdash;proven to lower MBC risk, especially in rapidly urbanizing areas; third, draw on experiences from high-income regions (e.g., Western Europe) to advance precision medicine and genetic screening for high-risk individuals (e.g., BRCA mutation carriers); and finally, increase research investment to explore regional MBC characteristics and optimize treatment strategies, mitigating its long-term public health impact.\u003c/p\u003e\u003cp\u003eWhen comparing China to Western countries like the United States and the United Kingdom, differences in screening intensity, cultural factors, and anatomical characteristics emerge as critical. In Western nations, robust screening programs (e.g., annual mammography recommended by the American Cancer Society for women aged 45\u0026ndash;54) have long been established, contributing to higher detection rates but also earlier-stage diagnoses, whereas China\u0026rsquo;s \"Two Cancers\" program, initiated in 2009, is still expanding coverage and faces challenges in rural areas, potentially leading to delayed diagnoses [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Culturally, Chinese populations may exhibit greater conservatism, with reluctance to discuss breast health or undergo physical examinations, particularly among men, further compounded by lower health literacy compared to Western counterparts. Additionally, Chinese women, on average, have smaller breast volumes than Western women, which may increase the likelihood of mastectomy over breast-conserving surgery due to tumor-to-breast size ratios, potentially resulting in higher disability rates (e.g., YLDs) from surgical outcomes, as full resection is more common when tumors are proportionally larger. This anatomical difference, alongside less aggressive screening, may contribute to a distinct burden profile in China compared to Europe and North America, where breast conservation is more feasible and disability rates may be lower due to earlier intervention [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Our findings support enhancing MBC screening, raising public awareness, and implementing precise interventions (e.g., reducing red meat and alcohol intake) in China, particularly for elderly and male populations, to mitigate the long-term burden and address gender health disparities.\u003c/p\u003e\u003cp\u003eIn conclusion, the burden of BC in China from 1990 to 2021 has reached critical levels, with incidence rising 364.5%, prevalence soaring 366.4%, and DALYs increasing 102.5%, demanding substantial investment in prevention and treatment. Age and gender disparities reveal middle-aged and older women as the primary at-risk group, while MBC incidence surged 785.2%, necessitating tailored approaches. We recommend personalized prevention based on population characteristics, emphasizing lifestyle modifications to curb BC risk. First, maintaining a healthy weight through diet and exercise can reduce postmenopausal BC risk, as obesity is linked to a 20\u0026ndash;40% increased incidence [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Second, regular moderate activities like walking or yoga should be encouraged to enhance overall health and lower risk by up to 18%. Third, minimizing alcohol consumption and avoiding prolonged hormone replacement therapy can mitigate risk factors, given their established associations with BC [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Secondary and tertiary measures should include nationwide education campaigns targeting high-incidence regions, promoting early screening (e.g., mammography), and urging timely medical consultation for early detection and intervention. China has prioritized BC control through initiatives like the \"Two Cancers\" screening program, complemented by the Chinese Anti-Cancer Association Guidelines for BC Diagnosis and Treatment, guiding evidence-based management. Internationally, the World Health Organization advocates BC prevention via lifestyle interventions and early detection, while the American Cancer Society\u0026rsquo;s guidelines emphasize screening and risk reduction strategies. These concerted efforts provide a robust framework to alleviate BC\u0026rsquo;s escalating burden in China.\u003c/p\u003e\u003cp\u003eThis study has several notable strengths. First, we utilized the GBD 2021 database, which spans over 30 years (1990 to 2021), providing a rich and extensive dataset for analyzing BC trends in China. Second, the study population is comprehensive, covering a wide age range from 0 to over 95 years, which enables a detailed understanding of how BC morbidity and mortality affect women across different life stages. Additionally, the use of Age-Period-Cohort (APC) models allowed us to overcome the limitations of traditional descriptive analytical methods, providing a more nuanced view of the individual effects of age, period, and cohort on the disease burden. However, there are several limitations to consider. One key limitation is the lack of detailed data from different regions within China. The GBD database does not provide provincial-level data, meaning we were unable to assess regional variations in BC incidence and mortality, which could be influenced by factors such as healthcare access, lifestyle, and socioeconomic status. Another limitation stems from potential inaccuracies in the data itself. The GBD database, while robust, may still contain gaps in data completeness or quality, leading to possible underestimation of the disease burden, particularly due to issues like under-diagnosis or incomplete data collection. Furthermore, the GBD database does not stratify data by specific BC subtypes, which limits the granularity of our findings and the ability to address subtype-specific trends. Finally, despite the use of multiple correction and adjustment techniques, the inherent limitations of the GBD study, such as potential biases in data collection and modeling, may have introduced inaccuracies that affected the results of this study.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reveals a substantial increase in the BC burden in China over three decades, with a marked rise in incidence and prevalence, alongside a growing impact on disability-adjusted life years. While female survival has slightly improved, reflected in a modest decline in age-standardized mortality, male mortality has surged, highlighting a critical gender disparity. The elderly, particularly middle-aged and older women and aging men, emerge as the most affected groups, driven by an aging population and shifting risk factors such as diet and lifestyle. Advances in screening and treatment have contributed to better outcomes for women, yet the rapid increase in male cases underscores a gap in awareness and tailored interventions. The analysis points to a complex interplay of age, period, and cohort effects, with incidence peaking in later years and mortality escalating among older age groups. To address this escalating public health challenge, enhanced prevention and early detection strategies are essential, including widespread screening starting at middle age for women and increased focus on men\u0026rsquo;s health. Lifestyle modifications\u0026mdash;promoting healthy diets and physical activity\u0026mdash;alongside personalized treatment approaches, can mitigate risk and improve outcomes. China\u0026rsquo;s existing initiatives provide a foundation, but intensified efforts are needed to curb the rising burden, particularly among vulnerable populations. Without proactive measures, the strain on healthcare and society will deepen, emphasizing the urgency of targeted action to reduce BC\u0026rsquo;s long-term impact.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of interest\u003c/h2\u003e\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eAbbreviations\u003c/h2\u003e\u003cp\u003eUI, Uncertainty interval; DALYs, Disability-adjusted life years; YLDs, Years lived with disability; and YLLs, Years of life lost.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was supported by the Senior Medical Talents Program of Chongqing for Young and Middle-aged, the Kuanren Talents Program of the Second Affiliated Hospital of Chongqing Medical University, and Natural Science Foundation of Chongqing (Grant no. CSTB2024NSCQ-MSX0331).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eT and W contributed equally as co-first authors, primarily responsible for designing experiments, collecting and analyzing data, and drafting the initial manuscript.Y contributed to data organization and analysis and provided critical suggestions for manuscript revisions.D and Y, as co-corresponding authors, oversaw the overall study design, guided experiments, coordinated comprehensive data analysis, and reviewed and finalized the manuscript. All authors participated in discussions and approved the final manuscript for publication.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThe authors extend our gratitude to the collaborators of the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 for their outstanding contributions.The original data used in this article is exclusively sourced from GBD 2021 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ghdx.healthdata.org/\u003c/span\u003e\u003cspan address=\"http://ghdx.healthdata.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e\u003cp\u003ePublicly available datasets were analyzed in this study. This data can be found: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.healthdata.org/research-analysis/gbd\u003c/span\u003e\u003cspan address=\"https://www.healthdata.org/research-analysis/gbd\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;68:394\u0026ndash;424.doi: [10.3322/caac.21492]\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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EClinicalMedicine 2025;80:103027.doi: [10.1016/j.eclinm.2024.103027]\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang F, Shu X, Meszoely I, Pal T, Mayer IA, Yu Z, et al. Overall Mortality After Diagnosis of Breast Cancer in Men vs Women. JAMA Oncol 2019;5:1589-96.doi: [10.1001/jamaoncol.2019.2803]\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiao H, Verkooijen HM, Chia KS, Bouchardy C, Pukkala E, Lar\u0026oslash;nningen S, et al. Incidence and outcome of male breast cancer: an international population-based study. J Clin Oncol 2011;29:4381-6.doi: [10.1200/jco.2011.36.8902]\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBhardwaj PV, Gupta S, Elyash A, Teplinsky E. Male Breast Cancer: a Review on Diagnosis, Treatment, and Survivorship. Curr Oncol Rep 2024;26:34\u0026ndash;45.doi: [10.1007/s11912-023-01489-z]\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKwong A, Ng EK, Wong CL, Law FB, Au T, Wong HN, et al. Identification of BRCA1/2 founder mutations in Southern Chinese breast cancer patients using gene sequencing and high resolution DNA melting analysis. PLoS One 2012;7:e43994.doi: [10.1371/journal.pone.0043994]\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKorde LA, Zujewski JA, Kamin L, Giordano S, Domchek S, Anderson WF, et al. Multidisciplinary meeting on male breast cancer: summary and research recommendations. J Clin Oncol 2010;28:2114-22.doi: [10.1200/jco.2009.25.5729]\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAryannejad A, Saeedi Moghaddam S, Mashinchi B, Tabary M, Rezaei N, Shahin S, et al. National and subnational burden of female and male breast cancer and risk factors in Iran from 1990 to 2019: results from the Global Burden of Disease study 2019. Breast Cancer Res 2023;25:47.doi: [10.1186/s13058-023-01633-4]\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnderson WF, Jatoi I, Tse J, Rosenberg PS. Male breast cancer: a population-based comparison with female breast cancer. J Clin Oncol 2010;28:232-9.doi: [10.1200/jco.2009.23.8162]\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRuddy KJ, Winer EP. Male breast cancer: risk factors, biology, diagnosis, treatment, and survivorship. Ann Oncol 2013;24:1434-43.doi: [10.1093/annonc/mdt025]\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOeffinger KC, Fontham ET, Etzioni R, Herzig A, Michaelson JS, Shih YC, et al. Breast Cancer Screening for Women at Average Risk: 2015 Guideline Update From the American Cancer Society. Jama 2015;314:1599\u0026thinsp;\u0026ndash;\u0026thinsp;614.doi: [10.1001/jama.2015.12783]\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang Y, Dai H, Song F, Li H, Yan Y, Yang Z, et al. Preliminary effectiveness of breast cancer screening among 1.22 million Chinese females and different cancer patterns between urban and rural women. Sci Rep 2016;6:39459.doi: [10.1038/srep39459]\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFan L, Strasser-Weippl K, Li JJ, St Louis J, Finkelstein DM, Yu KD, et al. Breast cancer in China. Lancet Oncol 2014;15:e279-89.doi: [10.1016/s1470-2045(13)70567-9]\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKwok C, Cant R, Sullivan G. Factors associated with mammographic decisions of Chinese-Australian women. Health Educ Res 2005;20:739\u0026thinsp;\u0026ndash;\u0026thinsp;47.doi: [10.1093/her/cyh034]\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDorling L, Carvalho S, Allen J, Gonz\u0026aacute;lez-Neira A, Luccarini C, Wahlstr\u0026ouml;m C, et al. Breast Cancer Risk Genes - Association Analysis in More than 113,000 Women. N Engl J Med 2021;384:428\u0026ndash;39.doi: [10.1056/NEJMoa1913948]\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Global Burden of Disease Study, breast cancer, incidence, prevalence, disability adjusted life years, age, joinpoint regression","lastPublishedDoi":"10.21203/rs.3.rs-6893644/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6893644/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study aims to assess the disease burden of breast cancer (BC) in China from 1990 to 2021, identifying key population groups at increased risk and provide reference data for improving breast cancer prevention and treatment strategies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eData from the Global Burden of Disease Study 2021 (GBD 2021) were analyzed, focusing on six key indicators: mortality, prevalence, incidence, disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost (YLLs). Temporal trends were characterized via the Joinpoint regression model and Age-Period-Cohort (APC) model, with age-standardized rates calculated using the global age structure as a reference.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eFrom 1990 to 2021, the incidence of BC in China increased by 364.5%, increaseing from 86,709 cases (95% UI: 70,225\u0026thinsp;\u0026minus;\u0026thinsp;105,273) to 402,794 cases (95% UI: 312,117\u0026ndash;505,644). Among females, incidence increased by 355% (from 84,793 to 385,838 cases), while male incidence saw a striking 785.2% rise (from 1,916 to 16,956 cases), highlighting a significant gender disparity. The age-standardized incidence rate (ASIR) grew by 113.3%, from 9.1 per 100,000 (95% UI: 7.4\u0026ndash;11.0) in 1990 to 19.4 per 100,000 (95% UI: 15.0\u0026ndash;24.3) in 2021. Mortality rose by 122%, from 41,218 deaths (95% UI: 33,621\u0026thinsp;\u0026minus;\u0026thinsp;50,194) to 91,484 deaths (95% UI: 71,739\u0026thinsp;\u0026minus;\u0026thinsp;113,710), with female mortality increasing by 118.3% and male mortality surging by 292.3%. However, the age-standardized mortality rate (ASMR) declined slightly by 6.3%, driven by an 8.2% decrease in females, while male ASMR increased by 62.3%, underscoring divergent survival trends. Disability-adjusted life years (DALYs) rose by 102.5%, from 1.5\u0026nbsp;million (95% UI: 1.2\u0026ndash;1.8\u0026nbsp;million) to 3\u0026nbsp;million (95% UI: 2.36\u0026ndash;3.84\u0026nbsp;million), with a 99.2% increase in females and a 270.5% rise in males. Age-Period-Cohort (APC) analysis revealed a net annual incidence drift of 2.13% and a mortality decline of -0.80% in females, with the heaviest burden in middle-aged and older groups: incidence peaked at 124.34 per 100,000 in females aged 60\u0026ndash;64, and mortality reached 27.35 per 100,000 in the 55\u0026ndash;60 age group. Male incidence peaked at 9.76 per 100,000 in the 70\u0026ndash;74 age group, reflecting an escalating burden with age, particularly among the elderly.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eFrom 1990 to 2021, China\u0026rsquo;s BC burden surged, with rising incidence, prevalence, and DALYs. Female survival improved slightly, with a modest decline in ASMR, while male mortality increased sharply, highlighting a gender gap. Middle-aged and older women face the highest incidence, alongside a notable rise in male cases. Trends show increasing incidence and worsening mortality in the elderly, driven by aging and lifestyle factors. Screening and treatment advances aid women, but male cases reveal awareness gaps. Enhanced prevention, early detection, and tailored interventions, especially for the elderly and men, are critical to ease this growing public health challenge.\u003c/p\u003e","manuscriptTitle":"Evaluation of the breast cancer disease burden in China from 1990 to 2021: based on the Global Burden of Disease Study 2021","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 11:10:22","doi":"10.21203/rs.3.rs-6893644/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"079ebcf6-6eef-4adf-8a16-59728e0372e0","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-06T09:38:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-15 11:10:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6893644","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6893644","identity":"rs-6893644","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}