Burden of cancer and associated risk factors in China from 1990 to 2021, with projections to 2050 : a systematic analysis for the Global Burden of Disease Study 2021

Burden of cancer and associated risk factors in China from 1990 to 2021, with projections to 2050 : a systematic analysis for 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 Burden of cancer and associated risk factors in China from 1990 to 2021, with projections to 2050 : a systematic analysis for the Global Burden of Disease Study 2021 Yunsong Liu, Xinying Huang, Wen Tian, Dapeng Wang, Zhe Teng, Linna Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7512162/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background In China, the incidence and mortality rates of cancer have shown a significant upward trajectory from 1980/1990 to 2021, resulting in an escalating public health burden. Identifying key risk factors is critical for improving cancer prevention and management strategies. This study primarily analyzes cancer incidence and mortality data, with a particular focus on understanding the patterns and underlying factors that contribute to these trends. Methods Data from the Global Burden of Disease 2021 study were utilized. A combination of statistical analyses, decomposition analysis, Joinpoint regression analysis, and Bayesian Age-Period-Cohort modeling were employed to examine temporal trends of various cancer types across different sexes and age groups. Additionally, risk factors were identified and projected trends for the five leading cancer types were analyzed. Results In 2021, cancer accounted for 24.07% of all deaths in China. Lung, stomach, esophageal, colorectal, and liver cancers collectively accounted for 71.08% of cancer-related mortality. While age-standardized death rates (ASDR) for most cancers decreased from 1980 to 2021, age-standardized incidence rates (ASIR) significantly increased. Male cancer mortality was nearly 1.8 times higher than that of females, though both sexes shared similar leading cancer types. Notably, breast cancer ranked among the top five causes of cancer-related deaths in women. Mortality peaked in the 70–74 age group for both sexes. The incidence of breast cancer was higher in females at younger ages, while males surpassed females in incidence from age 60 onward. Behavioral and environmental risk factors, particularly tobacco use and air pollution, have the greatest impact on lung cancer. Decomposition analysis revealed that the increase in cancer mortality was predominantly driven by population aging. By 2050, colorectal cancer incidence is expected to rise, while liver cancer is projected to continue its downward trend. Conclusion The cancer profile of China has shifted over the past 30 years. The decline in ASDR indicates improvements in treatment and management, while the rise in ASIR reflects both increased risk exposure and enhanced detection capabilities. In light of aging demographics, economic development, and environmental changes, identifying predominant cancer types and their associated risk factors is essential for developing effective control strategies and targeted interventions. China Cancer GBD Incidence and mortality Risk factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Heart disease and cancer are the leading causes of disease-related deaths worldwide, collectively accounting for over 50% of global mortality[ 1 – 3 ]. Projections indicate that cancer will continue its upward trajectory over the next two decades[ 4 – 6 ]. In mainland China, the accelerating pace of population aging is contributing to the rising burden of chronic diseases, including cancer[ 7 ]. Cancer has become a significant public health challenge in the country, posing severe threats to both public health and the national economy, while also affecting social development[ 6 , 8 – 10 ]. According to estimates from the Global Cancer Observatory (GLOBOCAN) in 2020, China reported more than 4.5 million new cancer cases, a figure that increased to over 4.8 million by 2022[ 11 ]. Simultaneously, over 3 million cancer-related deaths were recorded, highlighting the severity of the cancer burden. Both incidence and mortality rates—whether in absolute numbers or adjusted for age—show significant variation across different cancer types, sexes, and age groups[ 12 , 13 ]. Furthermore, each cancer type is influenced by distinct risk factors. For instance, there is robust evidence linking exposure to PM 2.5 with lung cancer, and an association between nitrogen dioxide (NO2) exposure and breast cancer[ 14 , 15 ]. China is currently undergoing rapid development. As the country continues to progress economically, its earlier model of rapid urbanization and industrialization—often at the expense of environmental quality—is being critically reassessed[ 16 , 17 ]. There is now a growing national commitment to environmental protection and a clear shift toward more sustainable development practices[ 18 ]. In this context, it is essential to comprehensively investigate the influence of the aforementioned cancer-related factors on the Chinese population.[ 10 ]. A detailed understanding of these factors is crucial for shaping the development and implementation of a cancer prevention and control system that aligns with the evolving public health priorities of the country. This study utilizes data from the Global Burden of Disease (GBD) database to systematically analyze the incidence and mortality of all cancer types in China, including a total of 35 neoplasms. In addition to evaluating the national cancer burden, we compare age-standardized incidence and mortality rates for all cancer types in China with those from the global dataset and regions categorized by the Sociodemographic Index (SDI), including high-SDI, high-middle SDI, middle-SDI, low-middle SDI, and low-SDI regions. A major advancement in GBD 2021, compared to GBD 2019, is the extension of cancer mortality estimates back to 1980, offering a richer dataset for assessing long-term trends in cancer burden[ 19 ]. Building upon this foundation, the present study focuses on the five leading cancer types in China in 2021, based on total deaths across all ages and both sexes. Through the use of decomposition analysis, Joinpoint regression, and Bayesian Age-Period-Cohort modeling, we conduct a comprehensive investigation of trends in incidence, mortality, and associated risk factors for these leading cancers. The findings aim to provide robust scientific evidence to inform policy-making and enhance the effectiveness of cancer prevention and control strategies in China. Materials and Methods Data Sources The Global Burden of Disease (GBD) Study 2021 ( https://vizhub.healthdata.org/gbd-results/ ) compiles the most comprehensive catalog of surveys, censuses, vital statistics, and other health-related data worldwide. It encompasses data from 204 countries and 811 subnational regions, covering 88 risk factors, 288 causes of death, and 371 disease types[ 8 , 20 , 21 ]. The study systematically analyzes these data, providing insights into various metrics such as prevalence, incidence and mortality rates and number. The GBD study utilizes the International Classification of Diseases (ICD) framework for systematic coding and categorization of diseases, thereby facilitating standardized comparisons of health outcomes across countries and over time[ 22 ]. The Global Health Data Exchange (GHDx, https://www.healthdata.org/ ) offers an interactive platform that enables researchers and policymakers to access, explore, and analyze comprehensive health data derived from the GBD, supporting evidence-based decision-making in global health. Furthermore, the results are presented through advanced visualization tools, which enhance the capacity for in-depth exploration and interpretation of temporal and spatial patterns in global health trends. Data on 34 types of cancer were initially downloaded, including liver cancer, stomach cancer, and total neoplasms, covering both deaths and incidence. The dataset includes information stratified by sex (both sexes, female, and male) and metric types (number and rate). The dataset encompasses a wide range of age groups, ranging from 0 to 95 years and older, covering the period from 1980 to 2021, where data are available. Data on causes of death and injury were extracted for multiple geographic regions, including China, Global, and regions categorized by the SDI: High-SDI, High-middle SDI, Middle SDI, Low-middle SDI, and Low SDI. Additionally, we downloaded data on risk factors for all cancer types, population statistics for China and globally, stratified by all age groups and all ages combined, as well as the Global Fertility, Mortality, Migration, and Population Forecasts (2017–2100) ( https://www.healthdata.org/data-tools-practices/interactive-visuals/population-forecasting )[ 23 ].In the GBD 2021 study, exposure data for risk factors were estimated using advanced modeling approaches, including spatiotemporal Gaussian process regression and DisMod-MR 2.1—a Bayesian mixed-effects meta-regression tool—with detailed definitions and risk attribution methodologies documented in previously published reports[ 20 ]. The Estimated Annual Percentage Change Analysis We extracted the age-standardized incidence and deaths rates and employed the estimated annual percentage change (EAPC) method to evaluate temporal trends in cancer incidence and mortality. After logarithmic transformation of the age-standardized rates, the geometric mean for each year was calculated and treated as the dependent variable in a linear regression model[ 24 , 25 ]. The EAPC was then computed using the formula 100 × (exp(β) − 1). The corresponding 95% confidence intervals (CIs) were derived from the regression model, allowing assessment of cancer trends across China. Handling of the GBD data Data from mainland China for the year 2021 were selected. All specific cancer types were included, excluding the overall category "Neoplasms". The age group was set to "All ages", with the metric specified as "Number", and the measures selected as both "Deaths" and "Incidence". Data were extracted for all three sex categories: both, female, and male. For each cancer type, the proportion of incidence and deaths relative to the total number of cancer incidence and deaths, respectively, was calculated to determine its percentage contribution to the overall cancer burden. The top five cancer types in terms of deaths number for both sexes and all ages in 2021 were: tracheal, bronchus, and lung cancer, stomach cancer, esophageal cancer, colon and rectum cancer, and liver cancer. All remaining cancer types with a percentage contribution of less than 5% were aggregated into a single category labeled “Other”. A proportional chart was then constructed to visualize the distribution. We further analyzed data on neoplasms for both males and females in China. From 1980 to 2021, we presented the number of deaths and the age-standardized deaths rate; from 1990 to 2021, we reported the number of incidence and the age-standardized incidence rate. Heatmaps were used to visualize the age-standardized deaths rate and age-standardized incidence rate across all cancer types, highlighting temporal trends and patterns. Additionally, for the year 2021, we stratified the number of deaths and incidence for males and females by age groups, and presented the corresponding results. Finally, we extracted data on the age-standardized incidence rate and age-standardized deaths rate for all cancer types from China, the global dataset, and regions classified by the SDI, including High-SDI, High-middle SDI, Middle SDI, Low-middle SDI, and Low SDI. Comparative analysis were performed across sex groups (both, female, and male) to assess variations in cancer burden among different regions. Risk Factors of Cancers and Joinpoint Regression Analysis For risk factor analysis, we utilized Level 1–4 risk classifications provided by the Global Burden of Disease results tool ( https://vizhub.healthdata.org/gbd-results/ ). Specifically, we focused on the top five cancer types in terms of number of deaths for both sexes and all ages in 2021: tracheal, bronchus, and lung cancer; stomach cancer; esophageal cancer; colon and rectum cancer; and liver cancer. For each of these cancer types, we extracted and analyzed the age-standardized death rate data from 1990 to 2021, stratified by sex (both, female, and male). Ultimately, we obtained data on risk factors for each cancer type, including 5 for liver cancer, 4 for esophageal cancer, 11 for colon and rectum cancer, 2 for stomach cancer, and 16 for tracheal, bronchus, and lung cancer. These data were subsequently subjected to downstream Joinpoint regression analysis. The Joinpoint Regression Program (Version 5.3.0, https://surveillance.cancer.gov/joinpoint/ ), developed by the Division of Cancer Control and Population Sciences at the U.S. National Cancer Institute, is a statistical software designed to analyze temporal trends in time series data using joinpoint models[ 26 , 27 ]. This method fits a series of linear segments connected at statistically determined points, known as "joinpoints," dividing the time series into distinct intervals. For each segment, the Annual Percent Change (APC) and its 95% confidence interval (CI) are estimated to quantify the rate of change. The Average Annual Percent Change (AAPC) is then computed to summarize the overall trend, with a statistically significant increase or decrease indicated when the entire 95% CI lies above or below zero, respectively. The model with the least complexity that best fits the data is selected, and statistical significance is assessed using the Monte Carlo permutation method[ 26 ]. In this study, the maximum number of joinpoints was set to five (max_joinpoints = 5). Decomposition Analysis Decomposition analysis provides a robust framework for quantifying the relative contributions of key factors to temporal changes in cancer-related deaths and incidence[ 28 , 29 ]. In this study, changes in mortality and incidence for the selected five cancer types were decomposed into three components: population aging, population growth, and epidemiological changes. This approach enables a comprehensive assessment of how each factor contributes to the dynamics of cancer burden over the study period from 1990 to 2021. By disentangling the effects of demographic shifts from changes in disease risk, this method offers critical insights into the underlying drivers of cancer trends. Such information is essential for informing targeted public health interventions, as it helps determine whether efforts should prioritize risk factor modification, demographic adaptation, or a combination of both. The Bayesian Age-Period-Cohort Analysis Using the Bayesian Age-Period-Cohort (BAPC, Version 0.0.36) model within the Integrated Nested Laplace Approximation (INLA, Version 23.9.9) framework, we projected the incidence and death trends of the five major cancer types in China from 2022 to 2050, with the model integrating empirical data and informative priors to generate statistically robust and reliable estimates that provide critical insights for cancer prevention and public health policy development[ 30 – 32 ]. Statistical Analysis All data used in this study were obtained from the Global Burden of Disease database. A p-value < 0.05 was considered indicative of statistical significance. All statistical analyses and data visualizations were performed using R software (Version 4.3.1). Results Epidemiological Overview of the Cancer Burden in China In 2021, neoplasms accounted for 24.07% (95% uncertainty interval [UI]: 22.74–25.29) of all-cause deaths and 17.70% (95% UI: 15.21–20.20) of total Disability-Adjusted Life Years (DALYs) in both sexes in mainland China. These proportions were substantially higher than those observed globally, where neoplasms contributed to 14.57% (95% UI: 13.65–15.28) of total deaths and 8.80% (95% UI: 7.99–9.67) of total DALYs, respectively[ 19 ]. In contrast, in 2021, the number of incident cases of neoplasms in mainland China reached 13,664,748.50 (95% UI: 11,787,026.26-15,848,005.53), while the number of deaths attributed to neoplasms was 2,817,757.67 (95% UI: 2,347,976.64-3,360,869.47). Compared with the global number of incident neoplasm cases (66,479,607.27; 95% UI: 58,335,731.40–74,980,442.95) and deaths (9,888,413.46; 95% UI: 9,124,879.13-10,585,373.15), the incidence and mortality figures in mainland China were disproportionately high relative to its share of the global population. In 2021, the estimated population of China was 1,422,745,952.85 (95% UI: 1,318,759,193.78-1,530,462,332.89), while the global population was estimated at 7,891,353,300.74 (95% UI: 7,666,733,980.42-8,131,224,517.81). In 2021, the age-standardized death rate (ASDR) and age-standardized incidence rate (ASIR) for neoplasms in China were 137.48 (95% UI: 115.11-163.38) and 790.17 (95% UI: 676.83-926.32) per 100,000 population, respectively (Table 1 ). When compared with the ASDR in 1980, which was 209.52 (95% UI: 179.16-246.87), a substantial decline was observed (Table 1 ). In contrast, the ASIR showed a slight increase from 718.73 (95% UI: 608.70-842.60) in 1990 (Table 1 ). These trends reflect improvements in cancer-related mortality over time, despite a modest increase in incidence. In addition, trends in the ASDR for all cancer types in mainland China from 1980 to 2021 were analyzed. With the exception of tracheal, bronchus, and lung cancer—which rose from 35.64 per 100,000 in 1980 to a peak of 42.02 in 2005, followed by a gradual decline to 38.98 in 2021 (estimated annual percentage change [EAPC]: 0.44; 95% confidence interval [CI]: 0.34 to 0.55)—most major cancer types demonstrated a consistent downward trend over the study period (Table 1 and Figure S1 ). Specifically, stomach cancer decreased substantially from 56.46 to 21.51 (EAPC: -2.31; 95% CI: -2.45 to -2.17), liver cancer declined from 13.70 to 8.35 (EAPC: -0.92; 95% CI: -1.04 to -0.81), esophageal cancer fell from 29.77 to 14.13 (EAPC: -1.88; 95% CI: -2.06 to -1.71), and colon and rectum cancer decreased from 16.69 to 13.64 (EAPC: -0.52; 95% CI: -0.56 to -0.48) (Table 1 and Figure S1 ). From 1990 to 2021, the ASIR for several major cancer types in mainland China exhibited an overall upward trend. Tracheal, bronchus, and lung cancer increased from 33.11 to 44.01 per 100,000, with an estimated annual percentage change (EAPC) of 1.03 (95% confidence interval [CI]: 0.89 to 1.17) (Table 1 and Figure S2 ). Non-melanoma skin cancer showed a pronounced rise from 4.65 to 37.54 (EAPC: 4.60; 95% CI: 3.81 to 5.39), and colon and rectum cancer increased from 19.04 to 31.44 (EAPC: 1.75; 95% CI: 1.64 to 1.86) (Table 1 ). Additionally, breast cancer incidence nearly doubled, rising from 9.08 to 19.36 (EAPC: 2.50; 95% CI: 2.42 to 2.58) (Table 1 ). In contrast, several cancer types showed a declining trend over the same period. Stomach cancer decreased significantly from 48.03 to 29.05 (EAPC: -1.64; 95% CI: -1.81 to -1.47), esophageal cancer declined from 24.80 to 15.04 (EAPC: -1.88; 95% CI: -2.09 to -1.67), and liver cancer showed a slight decrease from 10.58 to 9.52 (EAPC: -0.28; 95% CI: -0.42 to -0.13) (Table 1 ). In 2021, the five leading causes of cancer-related deaths in mainland China were tracheal, bronchus, and lung cancer (814,364 deaths, 28.90% of all cancer deaths), stomach cancer (445,013 deaths, 15.79%), esophageal cancer (296,443 deaths, 10.52%), colon and rectum cancer (275,129 deaths, 9.76%), and liver cancer (172,068 deaths, 6.11%) (Figure S3 ). Collectively, these cancers accounted for approximately 71.08% of total cancer deaths, reflecting their dominant contribution to the cancer mortality burden and highlighting the need for targeted cancer prevention and control interventions. Table 1 All-Age Incidence and Death Cases, Age-Standardized Incidence and Death Rates, and EAPC of ASIR and ASDR in Both Sexes Type of neoplasms All age incidence cases No. (95% UI) 1990 Age-standardized incidence rate per 100,000 (95% UI) 1990 All age incidence cases No. (95% UI) 2021 Age-standardized incidence rate per 100,000 (95% UI) 2021 EAPC of ASIR All age death cases No. (95% UI) 1980 Age-standardized death rate per 100,000 (95% UI) 1980 All age death cases No. (95% UI) 2021 Age-standardized death rate per 100,000 (95% UI) 2021 EAPC of ASDR Neoplasms 7818410.9 (6478326.81, 9545530.11) 718.73 (608.7, 842.6) 13664748.5 (11787026.26, 15848005.53) 790.17 (676.83, 926.32) 0.28(0.25, 0.31) 1304454.05 (1106050.38, 1547919.42) 209.52 (179.16, 246.87) 2817757.67 (2347976.64, 3360869.47) 137.48 (115.11, 163.38) -0.98(-1.05, -0.91) Bladder cancer 35813.04 (25632.01, 42115.49) 4.69 (3.43, 5.46) 105790.52 (83240.8, 136669.9) 5.14 (4.08, 6.62) 0.12(0.02, 0.22) 17298.79 (12734.19, 21344.05) 3.56 (2.69, 4.32) 45113.71 (36262.51, 57335.39) 2.34 (1.89, 2.94) -1.28(-1.4, -1.15) Brain and central nervous system cancer 47364.12 (34332.99, 59067.4) 4.69 (3.42, 5.85) 105540.85 (81400.78, 133527.5) 6.12 (4.76, 7.67) 0.84(0.8, 0.87) 30757.62 (21913.74, 40546.35) 4.04 (2.94, 5.28) 68910.82 (52054.94, 88279.62) 3.63 (2.74, 4.6) -0.27(-0.35, -0.2) Breast cancer 86708.72 (70225.31, 105273.32) 9.08 (7.41, 11.02) 402794.18 (312117.3, 505644.32) 19.36 (15, 24.3) 2.5(2.42, 2.58) 34574.52 (26633.38, 44620.66) 5.28 (4.14, 6.67) 91483.84 (71738.59, 113710.48) 4.4 (3.45, 5.46) -0.45(-0.53, -0.37) Cervical cancer 57843.04 (46321.43, 71401.72) 5.84 (4.71, 7.19) 132787.82 (95959.18, 172599.73) 6.67 (4.8, 8.72) 0.92(0.73, 1.11) 33645.65 (25747.38, 42463.67) 5 (3.88, 6.25) 49841.19 (36878.07, 64386.31) 2.39 (1.77, 3.08) -1.56(-1.75, -1.38) Colon and rectum cancer 158389.3 (135418.51, 182577.3) 19.04 (16.46, 21.81) 658321.36 (531995.02, 798063) 31.44 (25.53, 37.97) 1.75(1.64, 1.86) 96170.98 (71065.72, 121891.35) 16.69 (12.38, 20.95) 275129.23 (223378.58, 330960.39) 13.64 (11.09, 16.31) -0.52(-0.56, -0.48) Esophageal cancer 207494.92 (172673.51, 241458.64) 24.8 (20.71, 28.73) 320805.43 (256102.37, 394756.17) 15.04 (12.04, 18.43) -1.88(-2.09, -1.67) 180283.25 (144651.12, 219978.97) 29.77 (23.77, 36.01) 296443.04 (236647.81, 362831.35) 14.13 (11.36, 17.18) -1.88(-2.06, -1.71) Eye cancer 1496.04 (937.31, 2020.94) 0.16 (0.1, 0.21) 3728.78 (2065.12, 4830.02) 0.28 (0.15, 0.39) 3.14(2.74, 3.53) 602.56 (343.89, 841.1) 0.09 (0.05, 0.11) 693.26 (368.91, 911.98) 0.04 (0.02, 0.05) -1.52(-1.62, -1.42) Gallbladder and biliary tract cancer 17077.45 (13002.87, 21743.83) 2.19 (1.68, 2.79) 51720.39 (35618, 66848.03) 2.49 (1.71, 3.21) 0.5(0.4, 0.6) 13288.11 (9255.02, 18440.96) 2.43 (1.7, 3.28) 37833.49 (26652.59, 49261.82) 1.85 (1.29, 2.4) -0.65(-0.72, -0.58) Hodgkin lymphoma 4745.97 (2015.45, 6560.88) 0.48 (0.2, 0.67) 4211.06 (2542, 5541.5) 0.23 (0.14, 0.3) -2.73(-3, -2.47) 4344.01 (1830.77, 6255.04) 0.61 (0.26, 0.87) 2443.23 (1506.76, 3231.64) 0.13 (0.08, 0.17) -4.26(-4.45, -4.07) Kidney cancer 16232.12 (14234.45, 18286.48) 1.79 (1.58, 2.01) 65799.45 (53687.4, 79742.46) 3.32 (2.73, 3.98) 2.39(2.19, 2.59) 6852.31 (5512.95, 8318.53) 1.13 (0.94, 1.35) 24867.31 (20361.43, 29828.11) 1.25 (1.03, 1.48) 0.35(0.26, 0.45) Larynx cancer 15434.15 (12624.19, 18174.01) 1.82 (1.5, 2.13) 38904.86 (30369.67, 49486.18) 1.79 (1.4, 2.26) 0.04(-0.1, 0.19) 10150.25 (7635.15, 12978.54) 1.68 (1.29, 2.11) 19799.45 (15579.57, 25023.24) 0.94 (0.74, 1.17) -1.58(-1.65, -1.51) Leukemia 76203.78 (58311.79, 90957.96) 7.14 (5.52, 8.58) 105667.19 (75275.71, 132236.91) 7.21 (4.93, 9.05) 0.19(0.06, 0.32) 64275.22 (47458.32, 82514.27) 7.5 (5.65, 9.52) 58903.47 (43625.97, 74038.86) 3.42 (2.51, 4.26) -1.97(-2.06, -1.89) Lip and oral cavity cancer 14687.41 (12390.19, 16908.97) 1.7 (1.45, 1.95) 56359.16 (45178.45, 69804.39) 2.68 (2.15, 3.3) 1.75(1.53, 1.98) 7280.58 (5879.61, 9251.09) 1.21 (0.99, 1.5) 23881.67 (18971.59, 29680.91) 1.15 (0.92, 1.42) -0.12(-0.21, -0.03) Liver cancer 96434.35 (80970.6, 113768.66) 10.58 (8.94, 12.43) 196636.59 (158273.06, 243557.68) 9.52 (7.72, 11.78) -0.28(-0.42, -0.13) 92342.71 (69483.58, 118573.48) 13.7 (10.44, 17.54) 172068.4 (139621.29, 212495.94) 8.35 (6.8, 10.29) -0.92(-1.04, -0.81) Malignant neoplasm of bone and articular cartilage 6382.42 (4177.56, 11227.56) 0.65 (0.42, 1.15) 25937.81 (16243.09, 34274.36) 1.42 (0.9, 1.86) 3.37(2.75, 3.99) 5339.14 (3887.24, 8623.17) 0.76 (0.55, 1.22) 18084.53 (11288.1, 24125.63) 0.93 (0.58, 1.23) 1.59(1.15, 2.04) Malignant skin melanoma 3249.78 (2093.19, 4085.82) 0.36 (0.24, 0.46) 13437.46 (7198.45, 17979.43) 0.68 (0.37, 0.91) 2.27(2.05, 2.48) 1985.43 (1205.39, 2827.26) 0.32 (0.21, 0.44) 5372.5 (2848.58, 7105.75) 0.27 (0.14, 0.36) -0.42(-0.48, -0.35) Mesothelioma 1154.94 (970.24, 1371.76) 0.13 (0.11, 0.15) 3046.26 (2453.8, 3713.59) 0.15 (0.12, 0.18) 0.8(0.55, 1.05) 834.63 (665.02, 1101.05) 0.13 (0.11, 0.17) 3010.48 (2426.53, 3664.77) 0.15 (0.12, 0.18) 0.5(0.35, 0.66) Multiple myeloma 1693.3 (1153.93, 3360.16) 0.2 (0.13, 0.39) 17249.51 (11016.71, 22663.04) 0.81 (0.52, 1.07) 4.05(3.38, 4.73) 1181.89 (724.92, 2300.82) 0.19 (0.12, 0.38) 12984.05 (8447.92, 17113.76) 0.62 (0.4, 0.81) 3.55(3.09, 4) Nasopharynx cancer 44864.15 (38023.29, 51826.95) 4.64 (3.93, 5.36) 65933.78 (53272.37, 81430.46) 3.42 (2.77, 4.23) -1.5(-1.91, -1.09) 30165.88 (23928.58, 36071.42) 4.36 (3.46, 5.19) 31320.96 (25467.27, 38381.2) 1.51 (1.23, 1.84) -3.22(-3.48, -2.96) Neuroblastoma and other peripheral nervous cell tumors 665.32 (457.16, 957.51) 0.06 (0.04, 0.09) 2083.92 (1547.48, 2569.13) 0.15 (0.11, 0.18) 3.29(3.01, 3.57) 198.72 (146.93, 286.54) 0.02 (0.02, 0.03) 1069.84 (791.56, 1298.16) 0.07 (0.05, 0.08) 2.89(2.75, 3.04) Non-Hodgkin lymphoma 31216.05 (26892.76, 37951.45) 3.32 (2.86, 4.03) 110923.55 (86933.89, 135200.41) 5.53 (4.36, 6.68) 1.88(1.59, 2.16) 20943.54 (16648.33, 25760.85) 3.01 (2.41, 3.7) 42856.95 (33553.21, 51712.24) 2.13 (1.68, 2.57) -0.79(-0.91, -0.68) Non-melanoma skin cancer 39491.47 (33276.34, 45761.5) 4.65 (3.99, 5.35) 791867.58 (674907.37, 907771.19) 37.54 (32.4, 42.66) 4.6(3.81, 5.39) 3943.53 (3146.43, 5094.12) 0.77 (0.62, 0.99) 16575.79 (13016.94, 20669.71) 0.87 (0.68, 1.08) 0.7(0.54, 0.86) Other malignant neoplasms 41387 (27526.75, 51719.46) 4.51 (3.05, 5.62) 106746.44 (82738.09, 134939.43) 5.6 (4.35, 6.97) 0.78(0.4, 1.15) 26190.07 (16834.59, 34749.54) 3.96 (2.6, 5.2) 39756.29 (31076.57, 49408.27) 2.04 (1.6, 2.51) -1.9(-2.05, -1.75) Other neoplasms 6007403.75 (4715210.9, 7715300.96) 510.99 (406.4, 635.77) 8335339.62 (6704328.13, 10316551.8) 531.33 (425.7, 661.02) 0.19(0.16, 0.23) 865.53 (395.11, 1785.64) 0.15 (0.06, 0.3) 4777.73 (2821.58, 8796.19) 0.25 (0.15, 0.45) 1.54(1.46, 1.63) Other pharynx cancer 5074.32 (4142.19, 6174.69) 0.58 (0.48, 0.7) 12063.39 (9529.42, 15280.22) 0.56 (0.44, 0.7) -0.48(-0.92, -0.03) 3088.49 (2370.27, 4030.96) 0.49 (0.38, 0.63) 5880.68 (4691.78, 7407.12) 0.28 (0.22, 0.35) -2.02(-2.3, -1.75) Ovarian cancer 19997.6 (14086.43, 26191.07) 2.03 (1.5, 2.63) 41236.26 (30302.39, 54548.52) 2.03 (1.49, 2.69) -0.41(-0.56, -0.27) 8487.12 (5941.49, 12837.92) 1.29 (0.93, 1.9) 25143.85 (18525.7, 32922.74) 1.18 (0.87, 1.55) -0.52(-0.68, -0.35) Pancreatic cancer 37817.66 (31791.43, 44068.33) 4.54 (3.84, 5.29) 118665.43 (94622.75, 144663.08) 5.64 (4.52, 6.84) 0.68(0.64, 0.73) 28824.2 (21257.29, 40081.49) 4.82 (3.62, 6.61) 119601.86 (95653.59, 145218.13) 5.72 (4.59, 6.91) 0.47(0.42, 0.51) Prostate cancer 13753.72 (10153.81, 17772.37) 2.04 (1.52, 2.63) 88601.06 (63194.43, 120964.88) 4.22 (3.01, 5.73) 2.2(2.08, 2.32) 7331.8 (5331.95, 9404.75) 1.77 (1.29, 2.25) 37363.47 (27850.94, 50365.81) 1.99 (1.47, 2.68) 0.28(0.12, 0.45) Soft tissue and other extraosseous sarcomas 5803.83 (4084.97, 7529.94) 0.63 (0.44, 0.82) 9226.99 (6351.77, 13045.46) 0.48 (0.33, 0.69) -1.05(-1.16, -0.94) 3479.53 (2262.75, 4607.99) 0.54 (0.35, 0.7) 4634.82 (3208.18, 6509.71) 0.24 (0.17, 0.34) -2.04(-2.16, -1.92) Stomach cancer 407471.29 (337565.45, 477568.58) 48.03 (40.21, 56.69) 611798.97 (471965.81, 765562.25) 29.05 (22.42, 36.2) -1.64(-1.81, -1.47) 343519.43 (278116.91, 413150.44) 56.46 (46.18, 67.6) 445012.65 (344736.2, 555833.96) 21.51 (16.66, 26.61) -2.31(-2.45, -2.17) Testicular cancer 1839.33 (1521.07, 2183.12) 0.16 (0.13, 0.19) 6695.73 (5181.39, 8656.06) 0.42 (0.32, 0.53) 2.8(2.46, 3.14) 657.53 (500.83, 837.51) 0.09 (0.07, 0.12) 1244.57 (962.24, 1579.64) 0.07 (0.06, 0.09) -1.19(-1.47, -0.91) Thyroid cancer 12157.42 (9714.08, 14406.03) 1.25 (1.01, 1.47) 48104.56 (38694.78, 60068.11) 2.47 (1.99, 3.09) 2.47(2.29, 2.65) 2936.7 (2353.29, 3600.02) 0.52 (0.42, 0.63) 7692.21 (6122.52, 9428.76) 0.39 (0.31, 0.47) -0.67(-0.72, -0.61) Tracheal, bronchus, and lung cancer 274751.96 (234740.75, 315111.78) 33.11 (28.47, 37.79) 934704.06 (750040.14, 1136937.93) 44.01 (35.45, 53.35) 1.03(0.89, 1.17) 212770.3 (170020.39, 268843.89) 35.64 (28.88, 44.65) 814363.76 (652636.22, 987794.68) 38.98 (31.4, 47.06) 0.44(0.34, 0.55) Uterine cancer 26311.18 (18116.14, 33311.82) 2.81 (1.97, 3.53) 72018.5 (53311.86, 99999.63) 3.35 (2.48, 4.65) 0.44(0.13, 0.76) 9844.02 (6061.65, 13643.49) 1.54 (0.99, 2.08) 13598.56 (9925.9, 18595.65) 0.64 (0.47, 0.88) -2.15(-2.38, -1.93) Patterns of Cancer Burden by Age and Sex To further elucidate sex- and age-specific disparities across different cancer types, we conducted stratified analysis by sex and age group. In 2021, among males of all ages in mainland China, the top five cancer types by number of deaths were tracheal, bronchus, and lung cancer (545,962 deaths; 30.16% of total cancer deaths), stomach cancer (314,779 deaths; 17.39%), esophageal cancer (232,754 deaths; 12.86%), colon and rectum cancer (174,400 deaths; 9.63%), and liver cancer (122,463 deaths; 6.76%) (Fig. 1 ). Among females, tracheal, bronchus, and lung cancer also ranked first, with 268,402 deaths, accounting for 26.64% of total cancer deaths (Fig. 1 ). This was followed by stomach cancer (130,234 deaths; 12.93%), colon and rectum cancer (100,729 deaths; 10.00%), breast cancer (88,107 deaths; 8.75%), and esophageal cancer (63,689 deaths; 6.32%) (Fig. 1 ). Liver cancer was the seventh leading cause of cancer death among females, contributing 49,605 deaths (4.92%) (Fig. 1 ). Notably, breast cancer mortality in males was minimal, with only 3,377 deaths in 2021, accounting for just 0.19% of total cancer-related deaths among men in mainland China. This stark contrast with the female burden of breast cancer underscores the strong sex-specific nature of the disease and highlights the need for gender-targeted cancer prevention and control strategies. In 2021, the total number of cancer-related deaths in mainland China was approximately 1,810,252 among males and 1,007,505 among females, indicating that male cancer mortality was about 1.8 times higher than that of females. Excluding other neoplasms, non-melanoma skin cancer ranked among the top three cancer types by incidence in males, with an estimated 443,700 cases, accounting for 7.94% of total male cancer cases in 2021 (Fig. 2 ). Among females, breast cancer was the most common cancer, with 385,838 incident cases, representing 4.78% of all female cancer diagnoses, followed closely by non-melanoma skin cancer, with 348,168 cases (4.31%) (Fig. 2 ). Interestingly, for all neoplasm types, the number of incident cases and the ASIR were consistently higher in females than in males from 1990 to 2021 (Fig. 3 A). In contrast, the ASDR and number of deaths were substantially higher in males compared to females throughout the period from 1980 to 2021 (Fig. 3 B). Despite these sex-specific differences in incidence and mortality, the number of incident cases and deaths showed a continuous and steady increase in both sexes (Fig. 3 A and 3 B). Notably, while ASIR remained relatively stable over time, ASDR showed a marked downward trajectory, decreasing by approximately 29.78% in males and 41.83% in females over the past four decades. This decline suggests considerable advancements in cancer management, early detection, and treatment outcomes in recent years. The temporal trends in ASIR and ASDR for specific cancer types were also illustrated in the corresponding figures, providing a more detailed depiction of their longitudinal patterns over the study period. We further compared the differences in deaths and incidence across different age groups including males and females. In terms of incidence, females showed higher numbers than males in age groups up to 55–59 years. However, starting from the 60–64 age group and continuing through 85–89 years, males exhibited higher incidence numbers than females. The peak incidence for females was observed in the 50–54 age group, whereas for males, it occurred in the 65–69 age group (Fig. 4 A). During the same period, the highest number of cancer-related deaths occurred in the 70–74 age group for both males and females (Fig. 4 B). Except for the 95 + age group, males consistently had higher mortality counts than females across all age categories. Variations in Cancer Burden Across Socio-demographic Index Levels We depicted the temporal changes in age-standardized incidence and death rates for neoplasms across five SDI regions, globally, and in China from 1990 (for incidence) and 1980 (for mortality) to 2021 (Fig. 5 A). The analysis focused on overall neoplasms and six major cancer types: tracheal, bronchus, and lung cancer; stomach cancer; esophageal cancer; colon and rectum cancer; liver cancer; and breast cancer (Fig. 5 A- 5 G). Risk Factors Contributing to Cancer in China Based on the risk factor data across levels 1 to 4 available from the GBD database, we analyzed the associations between risk exposure and the ASDR for the five leading causes of cancer-related mortality in China: tracheal, bronchus, and lung cancer; stomach cancer; esophageal cancer; colon and rectum cancer; and liver cancer. Specifically, we examined the trends from 1990 to 2021 for each of these cancer types across all four risk factor levels, stratified by sex (both sexes, male, and female), using only datasets with complete year coverage. In 2021, for both sexes, tracheal, bronchus, and lung cancer was associated with all three level 1 risk categories—behavioral risks, environmental/occupational risks, and metabolic risks. Among these, behavioral risks had the greatest contribution to the ASDR, reaching 26.10 (95% UI: 20.57–32.97) per 100,000 population. Notably, this was the highest ASDR attributable to behavioral risks among all cancer types analyzed (Fig. 6 A). At the level 2 risk classification, air pollution and tobacco were the primary contributors to the ASDR for tracheal, bronchus, and lung cancer in 2021, with ASDRs of 10.11 (95% UI: 6.32–14.39) and 25.78 (95% UI: 20.19–32.58) per 100,000 population, respectively (Fig. 6 B). Tobacco also played a significant role in esophageal and stomach cancers, contributing ASDRs of 6.62 (95% UI: 4.88–8.66) and 3.10 (95% UI: 2.27–4.30), respectively (Fig. 6 B). For colon and rectum cancer, dietary risks emerged as a major factor, with an associated ASDR of 5.08 (95% UI: 1.79–8.21) (Fig. 6 B). At the level 3 risk category, the leading contributors to the ASDR for tracheal, bronchus, and lung cancer in 2021 were particulate matter pollution and smoking, with ASDRs of 10.11 (95% UI: 6.32–14.39) and 24.53 (95% UI: 19.38–31.09), respectively (Fig. 6 C). At level 4, data were available exclusively for tracheal, bronchus, and lung cancer. Among the detailed risk factors, ambient particulate matter pollution and household air pollution from solid fuels were the two main contributors with complete records from 1990 to 2021 (Figure S4 ). Over this period, the ASDR attributable to ambient particulate matter pollution increased markedly from 3.09 (95% UI: 1.36–5.80) to 8.54 (95% UI: 4.96–12.27) per 100,000 population. In contrast, the ASDR associated with household air pollution from solid fuels showed a substantial decline, dropping from 10.46 (95% UI: 6.70-14.38) to 1.56 (95% UI: 0.22–5.28). Risk Factor Trends Underlying the Burden of the Five Leading Cancers, 1990–2021 We employed a Joinpoint regression model to analyze the ASDR from 1990 to 2021, calculating the APC and the AAPC for each identified time segment, stratified by sex (both sexes, females, and males) (Table S1 and S2). From 1990 to 2021, the analysis of ASDR trends for the top five cancer types revealed distinct patterns in relation to specific risk factors. Among all risk factors analyzed, ambient particulate matter pollution showed the highest AAPC for tracheal, bronchus, and lung cancer in the both sexes group (AAPC: 3.313) (Table S2 ). The most pronounced increase occurred between 1997 and 2003, with an APC of 7.37 (Figure S5 ). Notably, this upward trend was even more evident in females (AAPC: 3.977) (Table S2 ). In contrast, the lowest AAPC for this cancer type was observed for household air pollution from solid fuels, which showed a significant decline in both sexes (AAPC: -6.077) (Table S2 ). The most substantial decreases occurred during 2004–2009 (APC: -7.80) and 2010–2018 (APC: -12.95), with an even more pronounced reduction in males (AAPC: -6.304) (Table S2 and Figure S5 ). For liver cancer, the highest AAPC was linked to high body-mass index in both sexes (3.720), with females (3.807) and males (3.702) showing similar increasing trends. Conversely, smoking was the risk factor with the lowest AAPC (-1.025 in both sexes), reflecting a consistent downward trend, particularly in females (-1.820). In colon and rectum cancer, high body-mass index again accounted for the highest AAPC in both sexes (2.427), with a steeper increase in males (2.901) compared to females (1.852). The lowest AAPC was observed for diet low in fiber (-3.613 in both sexes), with the most rapid decline occurring in females (-4.270). Unlike other cancer types, stomach cancer showed no positive AAPC values among its statistically significant risk factors in both sexes. The greatest decline was linked to diet high in sodium (-2.454). Similarly, esophageal cancer showed no risk factors with increasing AAPC. The most pronounced decline was associated with a diet low in vegetables, with an AAPC of -7.909 in the both sexes group. Notable decreases were observed during 2004–2006 (APC: -13.74) and 2007–2013 (APC: -11.42) (Figure S5 ). Decomposition Analysis of Cancer Burden Dynamics in China We conducted a decomposition analysis to elucidate the driving forces underlying changes in cancer incidence and deaths rates in China from 1990 to 2021. The overall variation was attributed to three key determinants: aging, population growth, and epidemiological changes. The analysis revealed that among the top five cancer types by number of deaths in China in 2021 for both sexes, aging was the most significant positive contributor to the changes in death rates. For four of these cancer types—colon and rectum, esophageal, liver, and stomach cancers—the contribution of epidemiological changes was negative (Fig. 7 A-D). In contrast, tracheal, bronchus, and lung cancer was the only type for which epidemiological changes contributed positively (Fig. 7 E). Specifically, the contributions of aging, population growth, and epidemiological changes were as follows: for colon and rectum cancer, 85.59%, 27.53%, and − 13.12%, respectively; for esophageal cancer, 227.88%, 89.66%, and − 217.54%; for liver cancer, 107.92%, 33.28%, and − 41.20%; for stomach cancer, 461.86%, 157.09%, and − 518.94%; and for tracheal, bronchus, and lung cancer, 58.59%, 19.50%, and 21.90%, respectively (Fig. 7 A-E). The analysis of incidence rate changes showed the following contributions from aging, population growth, and epidemiological changes: for colon and rectum cancer, -92.88%, 83.55%, and 109.33%, respectively; for esophageal cancer, 69.31%, -45.77%, and 76.46%; for liver cancer, 80.55%, 29.96%, and − 10.50%; for stomach cancer, 181.85%, 50.31%, and − 132.15%; and for tracheal, bronchus, and lung cancer, 62.47%, 16.02%, and 21.52%, respectively (Figure S6 A-E). Projected Cancer Incidence and Deaths in China Through 2050 The BAPC projection results based on death numbers and demographic data show that, for both sexes, the age-standardized rates (Agestd. Rate) of esophageal cancer and tracheal, bronchus, and lung cancer are projected to remain relatively stable from 2022 to 2050, and colon and rectum cancer is expected to show a slight upward trend, while stomach cancer and liver cancer are projected to decline significantly (Fig. 8 ). Among males and females, the trends generally align with those observed in both sexes, except for tracheal, bronchus, and lung cancer in females, which shows a slight upward trend. The BAPC projection results based on incidence numbers indicate that stomach cancer is expected to level off in the future, while esophageal cancer is projected to show a slight increase. Tracheal, bronchus, and lung cancer is expected to gradually increase, although the trend remains relatively stable in males. Colon and rectum cancer is projected to rise substantially, whereas liver cancer shows a clear downward trend (Figure S7 ). Discussion Using data from the Global Burden of Disease database, this study systematically analyzed cancer-related deaths and incidence across all cancer types in China, incorporating multiple analytical methods and stratifying by sex, age groups, and a broad spectrum of risk factors. In 2021, neoplasms in mainland China accounted for 24.07% (95% UI: 22.74–25.29) of all-cause deaths—substantially higher than the global rate of 14.57% (95% UI: 13.65–15.28)—with 13.66 million (95% UI: 11.79–15.85 million) new cases and 2.82 million (95% UI: 2.35–3.36 million) deaths reported. The ASDR for neoplasms in China was 137.48 (95% UI: 115.11–163.38) per 100,000 in 2021, reflecting a marked decline from 209.52 (95% UI: 179.16–246.87) in 1980 and indicating improved cancer-related mortality over time. In 2021, tracheal, bronchus, and lung; stomach; esophageal; colorectal; and liver cancers were the top five causes of cancer deaths in mainland China, accounting for 71.08% of total cancer mortality. Furthermore, for the major cancer types, we conducted a decomposition analysis to evaluate the respective contributions of aging, population growth, and epidemiological transitions to cancer trends. By integrating data on risk factors, we assessed the impact of high-risk exposures and highlighted the substantial health burden imposed by cancer in China. Based on BAPC model projections, we also illustrated future trends and underscored the urgent need for targeted interventions to mitigate the growing cancer burden. Sex- and age-specific cancer burdens represent critical areas of concern with important implications for the formulation of effective public health policies and population-targeted interventions[ 33 , 34 ]. In light of the physiological, behavioral, and occupational differences between males and females, we conducted a detailed analysis of sex-specific cancer data, moving general trends in ASDR and ASIR. From 1990 to 2021, females consistently exhibited higher ASIR and numbers of incident cases for all neoplasms, whereas males experienced significantly higher ASDR and cancer-related mortality. Although ASIR remained relatively stable, ASDR declined markedly—by 29.78% in males and 41.83% in females—over the past four decades, with male cancer mortality being 1.8 times that of females in 2021. The top five cancer types by mortality were identical for males and the overall population, while breast cancer accounted for 8.75% of all female cancer deaths. Given the heterogeneity of cancer burden across different age groups, age-specific patterns in incidence and mortality warrant closer examination to inform age-tailored prevention and control strategies[ 35 – 37 ]. Mortality peaked in the 70–74 age group for both sexes, with males showing higher death counts in nearly all age categories except for those aged 95 and above. Incidence was higher among females up to the 55–59 age group, while males exhibited greater incidence from age 60–64 onward. The peak incidence occurred at ages 50–54 in females and 65–69 in males. Over the past three decades, the rapid industrialization and urbanization in China have led to significant advancements, but also resulted in severe environmental pollution—particularly due to high emissions of PM 2.5 and sulfur dioxide—which poses a major threat to the respiratory health of the population[ 38 – 40 ]. Correspondingly, the ASIR of tracheal, bronchus, and lung cancers increased from 33.11 (28.47–37.79) in 1990 to 44.01 (35.45–53.35) in 2021, with an EAPC of 1.03 (0.89–1.17). The ASDR also rose from 35.64 (28.88–44.65) in 1980 to 38.98 (31.40-47.06) in 2021. In 2021, tracheal, bronchus, and lung cancers were responsible for over 800,000 deaths, accounting for 28.9% of all cancer-related fatalities. Additionally, more than 900,000 new cases were reported that year. Prolonged exposure to tobacco and other environmental risk factors has significantly exacerbated the burden of respiratory system cancers[ 41 , 42 ]. In the face of this growing challenge, there is an urgent need for molecular research to better understand and address the lung cancer burden in China. Such research should consider gender-specific differences in smoking behavior, occupational exposures, genetic susceptibility, health awareness, and preventive practices. At the same time, the Chinese government must further strengthen public health campaigns and educational initiatives, coupled with stringent regulatory measures—such as implementing comprehensive smoke-free policies in public places and increasing tobacco taxation. These efforts are crucial to enhancing awareness among younger populations about the harms of smoking, ultimately reducing both smoking prevalence and secondhand smoke exposure. Chronic infections also play a critical role in cancer risk[ 43 , 44 ]. Helicobacter pylori ( H. pylori ) infection is recognized as a major risk factor for gastric cancer[ 45 , 46 ]. Although the ASIR of stomach cancer in China declined significantly from 48.03 (40.21, 56.69) to 29.05 (22.42, 36.20), with an EAPC of -1.64 (-1.81, -1.47), and the ASDR decreased from 56.46 (46.18, 67.60) to 21.51 (16.66, 26.61), with an EAPC of -2.31 (-2.45, -2.17), the overall number of cases and mortality rates remain high. These figures underscore the persistent significance of infection-related cancer risks in China. Chronic hepatitis B virus (HBV) infection has been firmly established as a major contributor to liver cancer in China[ 47 – 49 ]. Furthermore, China bears a disproportionately large share of the global esophageal cancer burden[ 50 ]. Esophageal cancer, which includes esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC), is dominated by ESCC, accounting for approximately 90% of all cases in China[ 51 , 52 ]. The ASIR of esophageal cancer declined from 24.8 (20.71, 28.73) to 15.04 (12.04, 18.43), with an EAPC of -1.88 (-2.09, -1.67), and the ASDR dropped by more than 50%, from 29.77 (23.77, 36.01) to 14.13 (11.36, 17.18). Despite these improvements, more than half of the new esophageal cancer cases and related deaths worldwide occur in China. This disproportionate burden is largely driven by the high prevalence of modifiable risk factors, particularly tobacco use and alcohol consumption. With rapid economic development, significant changes have occurred in lifestyle, dietary patterns, and population aging, all of which may contribute to an increased risk of colorectal cancer[ 53 ]. The rising consumption of high-calorie, high-fat, and high-protein foods, along with a reduction in the intake of fruits, whole grains, and vegetables, has become increasingly common. In addition, behavioral risk factors such as smoking, alcohol consumption, and obesity further exacerbate this risk. This trend is reflected in the epidemiological data: ASIR of colon and rectum cancer in China increased markedly from 19.04 (16.46, 21.81) to 31.44 (25.53, 37.97), with an EAPC of 1.75 (1.64, 1.86). In contrast, the ASDR showed a slight decline, from 16.69 (12.38, 20.95) to 13.64 (11.09, 16.31), indicating a complex but concerning epidemiological shift. In addition, comparisons with global trends and regions at different SDI levels—including High SDI, High-middle SDI, Middle SDI, Low-middle SDI, and Low SDI—can help contextualize the recent progress made by China in cancer prevention and control, as well as highlight areas that require further improvement. Regarding the overall ASIR of neoplasms, there has been no significant change in China or in most SDI regions, except for a notable increase observed in High SDI regions. Interestingly, the ASDR has demonstrated a marked decline globally, as well as in China and in the High, High-middle, and Middle SDI regions. This trend may largely be attributed to advances in medical technologies and improvements in healthcare systems. Moreover, the socioeconomic development of China has led to profound changes in both demographic characteristics—such as population aging and growth—and cancer-related risk factors, including environmental exposures, lifestyle, and behavioral patterns[ 54 ]. Therefore, it is essential to assess the distinct contributions of these risk factors and demographic shifts to the development and incidence trends of different cancer types[ 19 , 55 – 58 ]. The GBD 2021 analysis generated comprehensive, data-driven estimates linking 88 risk factors to 631 health outcomes across multiple demographic and geographic dimensions[ 20 ]. Given this, in addition to analyzing the disease burden of various cancer types in China, we further investigated five major cancers by incorporating data on three major categories of risk factors—behavioral, environmental/occupational, and metabolic risks—as provided by the GBD database[ 20 ]. However, due to limitations in the availability and completeness of cancer-specific risk factor data, our analysis offers only a partial interpretation based on currently accessible information. Using data from the GBD database, this study analyzed the association between risk factors and the ASDR of the five leading cancers in China from 1990 to 2021. Tracheal, bronchus, and lung cancer exhibited the highest ASDR attributable to tobacco use and air pollution. At more granular levels, smoking and ambient particulate matter pollution were identified as the predominant contributors. Although ambient particulate matter pollution-related ASDR increased significantly over time, household air pollution from solid fuels showed a marked decline. For liver and colorectal cancers, high body-mass index demonstrated the most significant upward trend, while in colorectal cancer, dietary risks, particularly low fiber intake, exhibited a consistent decline. In contrast, stomach and esophageal cancers showed no risk factors with increasing trends; their burden declined primarily due to improvements in dietary patterns, including reduced sodium intake and increased vegetable consumption. Joinpoint regression analysis further confirmed these temporal shifts, highlighting the changing risk landscape for major cancers in China and underscoring the need for targeted prevention strategies. Meanwhile, despite the declines in ASIR, ASDR, and EAPC for stomach, liver, and esophageal cancers, the absolute number of cases continues to rise. This increase is likely driven by population aging, which was identified as the most significant positive contributor to the rise in cancer-related mortality among the top five cancer types in China in 2021. Cancer-related deaths peaked in the 70–74 age group for both sexes. Decomposition analysis revealed that the shifting cancer burden in China is primarily influenced by demographic changes; however, different cancer types exhibited distinct epidemiological trends, highlighting the need for more tailored prevention and control strategies. Although population aging significantly influences cancer incidence, a large proportion of cancers remain preventable through prevention strategies, especially in low- and middle-income countries[ 59 ]. This study employed multiple analytical approaches to conduct a comprehensive investigation of cancer-related data in China from the Global Burden of Disease database; however, several aspects warrant further exploration and refinement. First, data incompleteness—particularly in risk factor categories—may introduce estimation bias for certain cancer types or risk burdens. Given the vast geographic and demographic diversity within mainland China, future analysis should aim to disaggregate data into more granular and representative subregions and populations. Second, the integration of multi-omics data—such as genomics, transcriptomics, and proteomics—would allow for a deeper understanding of the molecular mechanisms underlying cancer susceptibility among different groups, facilitating more precise insights into cancer pathogenesis. Finally, as the current dataset extends only through 2021, updated analysis incorporating more recent data are needed to capture ongoing epidemiological trends and inform timely public health interventions. Conclusion In summary, cancer ranks as the second leading cause of disease-related mortality worldwide and represents a major public health challenge in China. In 2021, neoplasms accounted for 24.07% of all-cause deaths across all age groups and both sexes in mainland China. The ASDR declined substantially from 209.52 per 100,000 in 1980 to 137.48 in 2021, while ASIR showed a slight increase from 718.73 per 100,000 in 1990 to 790.17 in 2021. The top five cancer types in terms of number of deaths for both sexes and all ages in 2021 were tracheal, bronchus, and lung cancer; stomach cancer; esophageal cancer; colon and rectum cancer; and liver cancer. As China undergoes a critical phase of socioeconomic transition, understanding the sex-specific and age-related patterns of high-risk cancers, the contribution of key risk factors, and the impact of population aging is essential for developing effective, locally adapted control strategies and targeted interventions. Declarations Competing interests: The authors declare that they have no competing interests. Consent for publication: Not applicable. Clinical trial number: Not applicable. Ethics declaration: As the Global Burden of Disease (GBD) data are de-identified and publicly accessible, the use of these data in the present study does not require approval from an institutional review board. Data availability This study utilized publicly available data from the Global Burden of Disease Study 2021 (https://vizhub.healthdata.org/gbd-results/) and the Global Fertility, Mortality, Migration, and Population Forecasts (2017-2100) (https://www.healthdata.org/data-tools-practices/interactive-visuals/population-forecasting). All data used in this study were publicly available, requiring no ethical approval and intended solely for academic research, as detailed in the Methods section. Author Contributions YSL, and WYD designed and conducted the study; YSL, XYH, WT, DPW, ZT, LNZ, and QGF analyzed and interpreted the data; YSL, XYH, YL, and WYD wrote and revised the manuscript. All authors have read and agreed to the drafted version of the manuscript. Funding This study was supported by Henan Provincial Natural Science Foundation (252300420644). References Kocarnik JM, Compton K, Dean FE, Fu W, Gaw BL, Harvey JD, Henrikson HJ, Lu D, Pennini A, Xu R et al : Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life Years for 29 Cancer Groups From 2010 to 2019: A Systematic Analysis for the Global Burden of Disease Study 2019. JAMA Oncol 2022, 8(3):420-444. Di Cesare M, Perel P, Taylor S, Kabudula C, Bixby H, Gaziano TA, McGhie DV, Mwangi J, Pervan B, Narula J et al : The Heart of the World. Glob Heart 2024, 19(1):11. ReFaey K, Tripathi S, Grewal SS, Bhargav AG, Quinones DJ, Chaichana KL, Antwi SO, Cooper LT, Meyer FB, Dronca RS et al : Cancer Mortality Rates Increasing vs Cardiovascular Disease Mortality Decreasing in the World: Future Implications. Mayo Clin Proc Innov Qual Outcomes 2021, 5(3):645-653. Foreman KJ, Marquez N, Dolgert A, Fukutaki K, Fullman N, McGaughey M, Pletcher MA, Smith AE, Tang K, Yuan CW et al : Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016-40 for 195 countries and territories. Lancet 2018, 392(10159):2052-2090. Bray F, Jemal A, Grey N, Ferlay J, Forman D: Global cancer transitions according to the Human Development Index (2008-2030): a population-based study. Lancet Oncol 2012, 13(8):790-801. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021, 71(3):209-249. Wu H, Wang Y, Zhang H, Yin X, Wang L, Wang L, Wu J: An investigation into the health status of the elderly population in China and the obstacles to achieving healthy aging. Scientific Reports 2024, 14(1):31123. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries 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(10440):2133-2161. Diao X, Guo C, Jin Y, Li B, Gao X, Du X, Chen Z, Jo M, Zeng Y, Ding C et al : Cancer situation in China: an analysis based on the global epidemiological data released in 2024. Cancer Commun (Lond) 2025, 45(2):178-197. Lu J, Li M, He J, Xu Y, Zheng R, Zheng J, Qin G, Qin Y, Chen Y, Tang X et al : Association of social determinants, lifestyle, and metabolic factors with mortality in Chinese adults: A nationwide 10-year prospective cohort study. Cell Rep Med 2024, 5(8):101656. Cao W, Qin K, Li F, Chen W: Socioeconomic inequalities in cancer incidence and mortality: An analysis of GLOBOCAN 2022. Chin Med J (Engl) 2024, 137(12):1407-1413. Xia C, Dong X, Li H, Cao M, Sun D, He S, Yang F, Yan X, Zhang S, Li N et al : Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chin Med J (Engl) 2022, 135(5):584-590. Han B, Zheng R, Zeng H, Wang S, Sun K, Chen R, Li L, Wei W, He J: Cancer incidence and mortality in China, 2022. J Natl Cancer Cent 2024, 4(1):47-53. Wu AH, Wu J, Tseng C, Stram DO, Shariff-Marco S, Larson T, Goldberg D, Fruin S, Jiao A, Inamdar PP et al : Air Pollution and Breast Cancer Incidence in the Multiethnic Cohort Study. J Clin Oncol 2025, 43(3):273-284. Yoon HY, Kim SY, Song JW: Association between high levels of nitrogen dioxide and increased cumulative incidence of lung cancer in patients with idiopathic pulmonary fibrosis. Eur Respir J 2024, 63(5). Saxena V: Water Quality, Air Pollution, and Climate Change: Investigating the Environmental Impacts of Industrialization and Urbanization. Water, Air, & Soil Pollution 2025, 236(2):73. Peng D, Liu XY, Sheng YH, Li SQ, Zhang D, Chen B, Yu P, Li ZY, Li S, Xu RB: Ambient air pollution and the risk of cancer: Evidence from global cohort studies and epigenetic-related causal inference. J Hazard Mater 2025, 489:137619. Zhang S, Chen W, Zhang Q, Krey V, Byers E, Rafaj P, Nguyen B, Awais M, Riahi K: Targeting net-zero emissions while advancing other sustainable development goals in China. Nature Sustainability 2024, 7(9):1107-1119. Wu Z, Xia F, Lin R: Global burden of cancer and associated risk factors in 204 countries and territories, 1980-2021: a systematic analysis for the GBD 2021. J Hematol Oncol 2024, 17(1):119. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024, 403(10440):2162-2203. 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(10440):2100-2132. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020, 396(10258):1204-1222. Vollset SE, Goren E, Yuan CW, Cao J, Smith AE, Hsiao T, Bisignano C, Azhar GS, Castro E, Chalek J et al : Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: a forecasting analysis for the Global Burden of Disease Study. Lancet 2020, 396(10258):1285-1306. Estève J, Benhamou E, Raymond L: Statistical methods in cancer research. Volume IV. Descriptive epidemiology. IARC Sci Publ 1994(128):1-302. Qin Y, Tong X, Fan J, Liu Z, Zhao R, Zhang T, Suo C, Chen X, Zhao G: Global Burden and Trends in Incidence, Mortality, and Disability of Stomach Cancer From 1990 to 2017. Clin Transl Gastroenterol 2021, 12(10):e00406. Kim HJ, Fay MP, Feuer EJ, Midthune DN: Permutation tests for joinpoint regression with applications to cancer rates. Stat Med 2000, 19(3):335-351. Liu B, Kim H-J, Feuer EJ, Graubard BI: Joinpoint Regression Methods of Aggregate Outcomes for Complex Survey Data. Journal of Survey Statistics and Methodology 2023, 11(4):967-989. Zhu J, Li S, Li X, Wang L, Du L, Qiu Y: Impact of population ageing on cancer-related disability-adjusted life years: A global decomposition analysis. J Glob Health 2024, 14:04144. Cheng X, Yang Y, Schwebel DC, Liu Z, Li L, Cheng P, Ning P, Hu G: Population ageing and mortality during 1990-2017: A global decomposition analysis. PLoS Med 2020, 17(6):e1003138. Riebler A, Held L: Projecting the future burden of cancer: Bayesian age-period-cohort analysis with integrated nested Laplace approximations. Biom J 2017, 59(3):531-549. Liu Z, Yin P: Trends in mortality for gastric cancer from 2011 to 2020 with prediction to 2030: a Bayesian age-period-cohort analysis. The Lancet Regional Health – Western Pacific 2025, 55. Martins TG, Simpson D, Lindgren F, Rue H: Bayesian computing with INLA: New features. Computational Statistics & Data Analysis 2013, 67:68-83. Xue M, Guo W, Zhou Y, Meng J, Xi Y, Pan L, Ye Y, Zeng Y, Che Z, Zhang L et al : Age-sex-specific burden of urological cancers attributable to risk factors in China and its provinces, 1990-2021, and forecasts with scenarios simulation: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Reg Health West Pac 2025, 56:101517. Chen W, Xia C, Zheng R, Zhou M, Lin C, Zeng H, Zhang S, Wang L, Yang Z, Sun K et al : Disparities by province, age, and sex in site-specific cancer burden attributable to 23 potentially modifiable risk factors in China: a comparative risk assessment. Lancet Glob Health 2019, 7(2):e257-e269. Gao TY, Tao YT, Li HY, Liu X, Ma YT, Li HJ, Xian-Yu CY, Deng NJ, Leng WD, Luo J et al : Cancer burden and risk in the Chinese population aged 55 years and above: A systematic analysis and comparison with the USA and Western Europe. J Glob Health 2024, 14:04014. Wu Z, Xia F, Wang W, Zhang K, Fan M, Lin R: Worldwide burden of liver cancer across childhood and adolescence, 2000-2021: a systematic analysis of the Global Burden of Disease Study 2021. EClinicalMedicine 2024, 75:102765. Yang M, Du J, Lu H, Xiang F, Mei H, Xiao H: Global trends and age-specific incidence and mortality of cervical cancer from 1990 to 2019: an international comparative study based on the Global Burden of Disease. BMJ Open 2022, 12(7):e055470. Li G, Fang C, Wang S, Sun S: The Effect of Economic Growth, Urbanization, and Industrialization on Fine Particulate Matter (PM(2.5)) Concentrations in China. Environ Sci Technol 2016, 50(21):11452-11459. Shi T, Hu Y, Liu M, Li C, Zhang C, Liu C: How Do Economic Growth, Urbanization, and Industrialization Affect Fine Particulate Matter Concentrations? An Assessment in Liaoning Province, China. Int J Environ Res Public Health 2020, 17(15). Wang Q, Kwan MP, Zhou K, Fan J, Wang Y, Zhan D: The impacts of urbanization on fine particulate matter (PM(2.5)) concentrations: Empirical evidence from 135 countries worldwide. Environ Pollut 2019, 247:989-998. Vineis P, Airoldi L, Veglia F, Olgiati L, Pastorelli R, Autrup H, Dunning A, Garte S, Gormally E, Hainaut P et al : Environmental tobacco smoke and risk of respiratory cancer and chronic obstructive pulmonary disease in former smokers and never smokers in the EPIC prospective study. BMJ 2005, 330(7486):277. Liu X, Yang Q, Pan L, Ye Y, Kuang L, Xu D, Wang L, Hu S, Nie Y, Huang J et al : Burden of respiratory tract cancers in China and its provinces, 1990-2021: a systematic analysis of the Global Burden of Disease Study 2021. Lancet Reg Health West Pac 2025, 55:101485. Ohshima H, Bartsch H: Chronic infections and inflammatory processes as cancer risk factors: possible role of nitric oxide in carcinogenesis. Mutat Res 1994, 305(2):253-264. O'Byrne KJ, Dalgleish AG: Chronic immune activation and inflammation as the cause of malignancy. British Journal of Cancer 2001, 85(4):473-483. Duan Y, Xu Y, Dou Y, Xu D: Helicobacter pylori and gastric cancer: mechanisms and new perspectives. Journal of Hematology & Oncology 2025, 18(1):10. Wroblewski LE, Peek RM, Jr., Wilson KT: Helicobacter pylori and gastric cancer: factors that modulate disease risk. Clin Microbiol Rev 2010, 23(4):713-739. Rizzo GEM, Cabibbo G, Craxì A: Hepatitis B Virus-Associated Hepatocellular Carcinoma. Viruses 2022, 14(5). Levrero M, Zucman-Rossi J: Mechanisms of HBV-induced hepatocellular carcinoma. J Hepatol 2016, 64(1 Suppl):S84-S101. Cao M, Fan J, Lu L, Fan C, Wang Y, Chen T, Zhang S, Yu Y, Xia C, Lu J et al : Long term outcome of prevention of liver cancer by hepatitis B vaccine: Results from an RCT with 37 years. Cancer Lett 2022, 536:215652. Jiang Q, Shu Y, Jiang Z, Zhang Y, Pan S, Jiang W, Liang J, Cheng X, Xu Z: Burdens of stomach and esophageal cancer from 1990 to 2019 and projection to 2030 in China: Findings from the 2019 Global Burden of Disease Study. J Glob Health 2024, 14:04025. Zhang HZ, Jin GF, Shen HB: Epidemiologic differences in esophageal cancer between Asian and Western populations. Chin J Cancer 2012, 31(6):281-286. Liang H, Fan JH, Qiao YL: Epidemiology, etiology, and prevention of esophageal squamous cell carcinoma in China. Cancer Biol Med 2017, 14(1):33-41. Durko L, Malecka-Panas E: Lifestyle Modifications and Colorectal Cancer. Curr Colorectal Cancer Rep 2014, 10(1):45-54. Li M, Hu M, Jiang L, Pei J, Zhu C: Trends in Cancer Incidence and Potential Associated Factors in China. JAMA Netw Open 2024, 7(10):e2440381. Kuang Z, Wang J, Liu K, Wu J, Ge Y, Zhu G, Cao L, Ma X, Li J: Global, regional, and national burden of tracheal, bronchus, and lung cancer and its risk factors from 1990 to 2021: findings from the global burden of disease study 2021. EClinicalMedicine 2024, 75:102804. Jani CT, Kareff SA, Morgenstern-Kaplan D, Salazar AS, Hanbury G, Salciccioli JD, Marshall DC, Shalhoub J, Singh H, Rodriguez E et al : Evolving trends in lung cancer risk factors in the ten most populous countries: an analysis of data from the 2019 Global Burden of Disease Study. EClinicalMedicine 2025, 79:103033. Qin N, Fan Y, Yang T, Yang Z, Fan D: The burden of Gastric Cancer and possible risk factors from 1990 to 2021, and projections until 2035: findings from the Global Burden of Disease Study 2021. Biomark Res 2025, 13(1):5. Li T, Zhang H, Lian M, He Q, Lv M, Zhai L, Zhou J, Wu K, Yi M: Global status and attributable risk factors of breast, cervical, ovarian, and uterine cancers from 1990 to 2021. J Hematol Oncol 2025, 18(1):5. Bray F, Jemal A, Torre LA, Forman D, Vineis P: Long-Term Realism and Cost-Effectiveness: Primary Prevention in Combatting Cancer and Associated Inequalities Worldwide. JNCI: Journal of the National Cancer Institute 2015, 107(12):djv273. Additional Declarations No competing interests reported. Supplementary Files FigureS1.pdf Figure S1: Heatmap of Age-Standardized Death Rates (ASDR) for All Cancer Types in China from 1980 to 2021, Stratified by Sex (Both Sexes (A), Female (B), and Male (C)). FigureS2.pdf Figure S2: Heatmap of Age-Standardized Incidence Rates (ASIR) for All Cancer Types in China from 1990 to 2021, Stratified by Sex (Both Sexes (A), Female (B), and Male (C)). FigureS3.pdf Figure S3 : A pie chart depicting the number and proportion of cancer deaths in 2021 among both sexes and all age groups in China shows that the top five cancer types : tracheal, bronchus, and lung cancer; stomach cancer; esophageal cancer; colon and rectum cancer; and liver cancer. All other cancer types contributing less than 5% were grouped into a single category labeled “Other.” FigureS4.pdf Figure S4 : Time trends of age-standardized death rates (ASDR) from 1990 to 2021 for tracheal, bronchus, and lung cancer, stratified by sex (both sexes, female, and male), and by risk factor hierarchy, including Level 4. FigureS5.pdf Figure S5 : Joinpoint analysis of trends in age-standardized death rates (ASDR) attributable to specific risk factors for tracheal, bronchus, and lung cancer; stomach cancer; esophageal cancer; colon and rectum cancer; and liver cancer from 1990 to 2021, stratified by sex (both sexes, female, and male). The analysis included the following risk factors: Colon and Rectum Cancer (Diet high in processed meat, Diet high in red meat, Diet low in calcium, Diet low in fiber, Diet low in milk, Diet low in whole grains, High alcohol use, High body-mass index, High fasting plasma glucose, Low physical activity, Smoking); Esophageal Cancer (Chewing tobacco, Diet low in vegetables, High alcohol use, Smoking); Liver Cancer (Drug use, High alcohol use, High body-mass index, High fasting plasma glucose, Smoking); Stomach Cancer (Diet high in sodium, Smoking); Tracheal, Bronchus, and Lung Cancer (Ambient particulate matter pollution, Diet low in fruits, High fasting plasma glucose, Household air pollution from solid fuels, Occupational exposure to arsenic, Occupational exposure to asbestos, Occupational exposure to beryllium, Occupational exposure to cadmium, Occupational exposure to chromium, Occupational exposure to diesel engine exhaust, Occupational exposure to nickel, Occupational exposure to polycyclic aromatic hydrocarbons, Occupational exposure to silica, Residential radon, Secondhand smoke, Smoking) FigureS6.pdf Figure S6 : Decomposition analysis of cancer incidence for colon and rectum cancer (A); esophageal cancer (B); liver cancer (C); stomach cancer (D); and tracheal, bronchus, and lung cancer (E) FigureS7.pdf Figure S7 : Projections of cancer burden in terms of incidence from 2022 to 2050 for esophageal cancer (A, B, and C); stomach cancer (D, E, and F); tracheal, bronchus, and lung cancer (G, H, and I); liver cancer (J, K, and L); and colon and rectum cancer (M, N, and O), stratified by gender (male, female, and both sexes). TableS1.csv Table S1 : Joinpoint Analysis of APC Estimates for Risk Factors Associated with Cancer Types, Stratified by Sex (Both Sexes, Female, and Male) TableS2.csv Table S2 : Joinpoint Analysis of AAPC Estimates for Risk Factors Associated with Cancer Types, Stratified by Sex (Both Sexes, Female, and Male) Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 25 Sep, 2025 Editor invited by journal 04 Sep, 2025 Editor assigned by journal 03 Sep, 2025 Submission checks completed at journal 03 Sep, 2025 First submitted to journal 01 Sep, 2025 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. 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07:25:10","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":89721,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/ba27687c7dbd4f62e31d19e3.png"},{"id":93013382,"identity":"ed105069-7ccb-4300-8339-2a21ce3118dc","added_by":"auto","created_at":"2025-10-08 07:25:10","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":51640,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/252d35ac84403e7c0355ab2c.png"},{"id":93013381,"identity":"c30d9ed9-e7c1-4d4d-9874-275985feab91","added_by":"auto","created_at":"2025-10-08 07:25:10","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":170659,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/a0187888982a30b90e076ef6.png"},{"id":93014744,"identity":"e2a46e3c-ec45-4620-a847-529d68770211","added_by":"auto","created_at":"2025-10-08 07:41:11","extension":"xml","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":206446,"visible":true,"origin":"","legend":"","description":"","filename":"21b1450608964060a5c500afe54b17f51structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/a479588d0e10e1d703e93a3d.xml"},{"id":93013922,"identity":"a4199d22-b66b-47cb-be61-54de03a50556","added_by":"auto","created_at":"2025-10-08 07:33:11","extension":"html","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":214850,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/1a39fd3ac3bbea9cc67532e6.html"},{"id":93013912,"identity":"804a4440-39e9-4bd8-af62-723e44e1bc62","added_by":"auto","created_at":"2025-10-08 07:33:10","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":137009,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in the Number and Proportion of Cancer Deaths Between Males and Females in China in 2021.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/4f9efca58d65aaa8bf487e52.jpeg"},{"id":93013350,"identity":"cb80d557-2aaf-45fe-8756-56c13e5badc4","added_by":"auto","created_at":"2025-10-08 07:25:09","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":153837,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in the Number and Proportion of Cancer Incidences Between Males and Females in China in 2021.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/8fe63d85dd67440a0cf83f1d.jpeg"},{"id":93013355,"identity":"6b660039-3300-4cd1-911b-0e6c53de3e68","added_by":"auto","created_at":"2025-10-08 07:25:09","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":857569,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in Neoplasms Numbers and Age-Standardized Rates in China. A. Incidence Number and Age-Standardized Rates of Neoplasms from 1990 to 2021. B. Deaths Number and Age-Standardized Rates of Neoplasms from 1980 to 2021.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/6d57ca2bc9a862fb0ad4c37f.jpeg"},{"id":93013361,"identity":"91ec163d-a007-46ef-905a-54aeace02554","added_by":"auto","created_at":"2025-10-08 07:25:10","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":567726,"visible":true,"origin":"","legend":"\u003cp\u003eNeoplasms Incidence and Deaths by Age Group for Males and Females in China in 2021. A. Number of Neoplasms Incidence Cases by Age Group for Males and Females. B. Number of Neoplasms Deaths by Age Group for Males and Females.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/c717774183bc9e24c1bf0033.jpeg"},{"id":93013376,"identity":"3bf6964a-5c5f-4819-ae86-16bf5db0c980","added_by":"auto","created_at":"2025-10-08 07:25:10","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":246272,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in ASDR (1980-2021) and ASIR (1990-2021) for Neoplasms (A) and Six Major Cancer Types—including tracheal, bronchus, and lung cancer (B); liver cancer (C); esophageal cancer (D); stomach cancer (E); colon and rectum cancer (F); and breast cancer (G)—were analyzed across both sexes, females, and males. The analysis covers global trends, China, and regions categorized by the Sociodemographic Index (SDI), including high-SDI, high-middle SDI, middle-SDI, low-middle SDI, and low-SDI regions.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/e5acc2fd821362b890b022e1.jpeg"},{"id":93013913,"identity":"a46a576a-1cf3-4d0c-a35f-3ce3bf807060","added_by":"auto","created_at":"2025-10-08 07:33:10","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1284807,"visible":true,"origin":"","legend":"\u003cp\u003eTime trends of age-standardized death rates (ASDR) from 1990 to 2021 by cancer type (tracheal, bronchus, and lung cancer; stomach cancer; esophageal cancer; colon and rectum cancer; and liver cancer), stratified by sex (both sexes, female, and male), and by risk factor hierarchy, including Level 1 (A), Level 2 (B), and Level 3 (C).\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/25eaf576968cb2f8bd646dae.jpeg"},{"id":93013356,"identity":"c74d699e-eddc-4af7-8eb6-9fde12d106a8","added_by":"auto","created_at":"2025-10-08 07:25:09","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":111170,"visible":true,"origin":"","legend":"\u003cp\u003eDecomposition analysis of cancer-related deaths for colon and rectum cancer (A); esophageal cancer (B); liver cancer (C); stomach cancer (D); and tracheal, bronchus, and lung cancer (E).\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/ba285216e164365d561b6664.jpeg"},{"id":93013920,"identity":"ae70762c-2028-4ff9-9ee2-cd971f68107b","added_by":"auto","created_at":"2025-10-08 07:33:10","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":193255,"visible":true,"origin":"","legend":"\u003cp\u003eProjections of cancer burden in terms of deaths from 2022 to 2050 for esophageal cancer (A, B, and C); stomach cancer (D, E, and F); liver cancer (G, H, and I); tracheal, bronchus, and lung cancer (J, K, and L); and colon and rectum cancer (M, N, and O), stratified by gender (male, female, and both sexes).\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/3e293c601c077727bfa9090a.jpeg"},{"id":93014971,"identity":"f0165b48-a0df-48b6-b748-a59af6449faa","added_by":"auto","created_at":"2025-10-08 07:49:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3249669,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/4ae21100-9f70-419a-81e8-7f20fea8f04a.pdf"},{"id":93013349,"identity":"97c42d5c-f147-4f37-bad6-5968af9d9ca7","added_by":"auto","created_at":"2025-10-08 07:25:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1269377,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1: Heatmap of Age-Standardized Death Rates (ASDR) for All Cancer Types in China from 1980 to 2021, Stratified by Sex (Both Sexes (A), Female (B), and Male (C)).\u003c/p\u003e","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/0949fe2e95d06d22adfedbc1.pdf"},{"id":93013364,"identity":"96418fb3-8185-442f-8e06-9e4da642f942","added_by":"auto","created_at":"2025-10-08 07:25:10","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1084693,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S2: Heatmap of Age-Standardized Incidence Rates (ASIR) for All Cancer Types in China from 1990 to 2021, Stratified by Sex (Both Sexes (A), Female (B), and Male (C)).\u003c/p\u003e","description":"","filename":"FigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/bbb68d39834488aeff0390ae.pdf"},{"id":93013910,"identity":"e8701529-567d-4ab4-8e47-8b8b42080c47","added_by":"auto","created_at":"2025-10-08 07:33:09","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":7368,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S3 : A pie chart depicting the number and proportion of cancer deaths in 2021 among both sexes and all age groups in China shows that the top five cancer types : tracheal, bronchus, and lung cancer; stomach cancer; esophageal cancer; colon and rectum cancer; and liver cancer. All other cancer types contributing less than 5% were grouped into a single category labeled “Other.”\u003c/p\u003e","description":"","filename":"FigureS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/5f13472f390f3ef048f609de.pdf"},{"id":93013353,"identity":"b52d7f4a-2902-456c-9b50-54b566ee6037","added_by":"auto","created_at":"2025-10-08 07:25:09","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":18851,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S4 : Time trends of age-standardized death rates (ASDR) from 1990 to 2021 for tracheal, bronchus, and lung cancer, stratified by sex (both sexes, female, and male), and by risk factor hierarchy, including Level 4.\u003c/p\u003e","description":"","filename":"FigureS4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/31ad14ea8b587905ab767dbb.pdf"},{"id":93013372,"identity":"cf07a11a-1274-4d9b-8574-b7aac1636234","added_by":"auto","created_at":"2025-10-08 07:25:10","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":409238,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S5 : Joinpoint analysis of trends in age-standardized death rates (ASDR) attributable to specific risk factors for tracheal, bronchus, and lung cancer; stomach cancer; esophageal cancer; colon and rectum cancer; and liver cancer from 1990 to 2021, stratified by sex (both sexes, female, and male). The analysis included the following risk factors: Colon and Rectum Cancer (Diet high in processed meat, Diet high in red meat, Diet low in calcium, Diet low in fiber, Diet low in milk, Diet low in whole grains, High alcohol use, High body-mass index, High fasting plasma glucose, Low physical activity, Smoking); Esophageal Cancer (Chewing tobacco, Diet low in vegetables, High alcohol use, Smoking); Liver Cancer (Drug use, High alcohol use, High body-mass index, High fasting plasma glucose, Smoking); Stomach Cancer (Diet high in sodium, Smoking); Tracheal, Bronchus, and Lung Cancer (Ambient particulate matter pollution, Diet low in fruits, High fasting plasma glucose, Household air pollution from solid fuels, Occupational exposure to arsenic, Occupational exposure to asbestos, Occupational exposure to beryllium, Occupational exposure to cadmium, Occupational exposure to chromium, Occupational exposure to diesel engine exhaust, Occupational exposure to nickel, Occupational exposure to polycyclic aromatic hydrocarbons, Occupational exposure to silica, Residential radon, Secondhand smoke, Smoking)\u003c/p\u003e","description":"","filename":"FigureS5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/a8fc0ab99086d2728921d8db.pdf"},{"id":93013915,"identity":"59c444ee-7da1-41c8-860c-bbdbbe30de9e","added_by":"auto","created_at":"2025-10-08 07:33:10","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":6836,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S6 : Decomposition analysis of cancer incidence for colon and rectum cancer (A); esophageal cancer (B); liver cancer (C); stomach cancer (D); and tracheal, bronchus, and lung cancer (E)\u003c/p\u003e","description":"","filename":"FigureS6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/dd3de3d136b515cc8a013310.pdf"},{"id":93013363,"identity":"ae1eb75d-7ccd-412a-954d-8bda81e37128","added_by":"auto","created_at":"2025-10-08 07:25:10","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":126571,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S7 : Projections of cancer burden in terms of incidence from 2022 to 2050 for esophageal cancer (A, B, and C); stomach cancer (D, E, and F); tracheal, bronchus, and lung cancer (G, H, and I); liver cancer (J, K, and L); and colon and rectum cancer (M, N, and O), stratified by gender (male, female, and both sexes).\u003c/p\u003e","description":"","filename":"FigureS7.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/531e82589666c3542600a014.pdf"},{"id":93013384,"identity":"b2406086-9c7f-4415-8351-7ca8d0a966b0","added_by":"auto","created_at":"2025-10-08 07:25:10","extension":"csv","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":64486,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1 : Joinpoint Analysis of APC Estimates for Risk Factors Associated with Cancer Types, Stratified by Sex (Both Sexes, Female, and Male)\u003c/p\u003e","description":"","filename":"TableS1.csv","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/42803fcf9d0b6a96b4c9f0dd.csv"},{"id":93013379,"identity":"8fef3dbc-0e97-4184-a479-57c7e70be18c","added_by":"auto","created_at":"2025-10-08 07:25:10","extension":"csv","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":11831,"visible":true,"origin":"","legend":"\u003cp\u003eTable S2 : Joinpoint Analysis of AAPC Estimates for Risk Factors Associated with Cancer Types, Stratified by Sex (Both Sexes, Female, and Male)\u003c/p\u003e","description":"","filename":"TableS2.csv","url":"https://assets-eu.researchsquare.com/files/rs-7512162/v1/b39610520f2e42d25ca0a117.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Burden of cancer and associated risk factors in China from 1990 to 2021, with projections to 2050 : a systematic analysis for the Global Burden of Disease Study 2021","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeart disease and cancer are the leading causes of disease-related deaths worldwide, collectively accounting for over 50% of global mortality[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Projections indicate that cancer will continue its upward trajectory over the next two decades[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In mainland China, the accelerating pace of population aging is contributing to the rising burden of chronic diseases, including cancer[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Cancer has become a significant public health challenge in the country, posing severe threats to both public health and the national economy, while also affecting social development[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. According to estimates from the Global Cancer Observatory (GLOBOCAN) in 2020, China reported more than 4.5\u0026nbsp;million new cancer cases, a figure that increased to over 4.8\u0026nbsp;million by 2022[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Simultaneously, over 3\u0026nbsp;million cancer-related deaths were recorded, highlighting the severity of the cancer burden. Both incidence and mortality rates\u0026mdash;whether in absolute numbers or adjusted for age\u0026mdash;show significant variation across different cancer types, sexes, and age groups[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Furthermore, each cancer type is influenced by distinct risk factors. For instance, there is robust evidence linking exposure to PM\u003csub\u003e2.5\u003c/sub\u003e with lung cancer, and an association between nitrogen dioxide (NO2) exposure and breast cancer[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eChina is currently undergoing rapid development. As the country continues to progress economically, its earlier model of rapid urbanization and industrialization\u0026mdash;often at the expense of environmental quality\u0026mdash;is being critically reassessed[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. There is now a growing national commitment to environmental protection and a clear shift toward more sustainable development practices[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In this context, it is essential to comprehensively investigate the influence of the aforementioned cancer-related factors on the Chinese population.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A detailed understanding of these factors is crucial for shaping the development and implementation of a cancer prevention and control system that aligns with the evolving public health priorities of the country.\u003c/p\u003e\u003cp\u003eThis study utilizes data from the Global Burden of Disease (GBD) database to systematically analyze the incidence and mortality of all cancer types in China, including a total of 35 neoplasms. In addition to evaluating the national cancer burden, we compare age-standardized incidence and mortality rates for all cancer types in China with those from the global dataset and regions categorized by the Sociodemographic Index (SDI), including high-SDI, high-middle SDI, middle-SDI, low-middle SDI, and low-SDI regions. A major advancement in GBD 2021, compared to GBD 2019, is the extension of cancer mortality estimates back to 1980, offering a richer dataset for assessing long-term trends in cancer burden[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Building upon this foundation, the present study focuses on the five leading cancer types in China in 2021, based on total deaths across all ages and both sexes. Through the use of decomposition analysis, Joinpoint regression, and Bayesian Age-Period-Cohort modeling, we conduct a comprehensive investigation of trends in incidence, mortality, and associated risk factors for these leading cancers. The findings aim to provide robust scientific evidence to inform policy-making and enhance the effectiveness of cancer prevention and control strategies in China.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Sources\u003c/h2\u003e\u003cp\u003eThe Global Burden of Disease (GBD) Study 2021 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd-results/\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd-results/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) compiles the most comprehensive catalog of surveys, censuses, vital statistics, and other health-related data worldwide. It encompasses data from 204 countries and 811 subnational regions, covering 88 risk factors, 288 causes of death, and 371 disease types[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The study systematically analyzes these data, providing insights into various metrics such as prevalence, incidence and mortality rates and number. The GBD study utilizes the International Classification of Diseases (ICD) framework for systematic coding and categorization of diseases, thereby facilitating standardized comparisons of health outcomes across countries and over time[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The Global Health Data Exchange (GHDx, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.healthdata.org/\u003c/span\u003e\u003cspan address=\"https://www.healthdata.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) offers an interactive platform that enables researchers and policymakers to access, explore, and analyze comprehensive health data derived from the GBD, supporting evidence-based decision-making in global health. Furthermore, the results are presented through advanced visualization tools, which enhance the capacity for in-depth exploration and interpretation of temporal and spatial patterns in global health trends.\u003c/p\u003e\u003cp\u003eData on 34 types of cancer were initially downloaded, including liver cancer, stomach cancer, and total neoplasms, covering both deaths and incidence. The dataset includes information stratified by sex (both sexes, female, and male) and metric types (number and rate). The dataset encompasses a wide range of age groups, ranging from 0 to 95 years and older, covering the period from 1980 to 2021, where data are available. Data on causes of death and injury were extracted for multiple geographic regions, including China, Global, and regions categorized by the SDI: High-SDI, High-middle SDI, Middle SDI, Low-middle SDI, and Low SDI. Additionally, we downloaded data on risk factors for all cancer types, population statistics for China and globally, stratified by all age groups and all ages combined, as well as the Global Fertility, Mortality, Migration, and Population Forecasts (2017\u0026ndash;2100) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.healthdata.org/data-tools-practices/interactive-visuals/population-forecasting\u003c/span\u003e\u003cspan address=\"https://www.healthdata.org/data-tools-practices/interactive-visuals/population-forecasting\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].In the GBD 2021 study, exposure data for risk factors were estimated using advanced modeling approaches, including spatiotemporal Gaussian process regression and DisMod-MR 2.1\u0026mdash;a Bayesian mixed-effects meta-regression tool\u0026mdash;with detailed definitions and risk attribution methodologies documented in previously published reports[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eThe Estimated Annual Percentage Change Analysis\u003c/h3\u003e\n\u003cp\u003eWe extracted the age-standardized incidence and deaths rates and employed the estimated annual percentage change (EAPC) method to evaluate temporal trends in cancer incidence and mortality. After logarithmic transformation of the age-standardized rates, the geometric mean for each year was calculated and treated as the dependent variable in a linear regression model[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The EAPC was then computed using the formula 100 \u0026times; (exp(β)\u0026thinsp;\u0026minus;\u0026thinsp;1). The corresponding 95% confidence intervals (CIs) were derived from the regression model, allowing assessment of cancer trends across China.\u003c/p\u003e\n\u003ch3\u003eHandling of the GBD data\u003c/h3\u003e\n\u003cp\u003eData from mainland China for the year 2021 were selected. All specific cancer types were included, excluding the overall category \"Neoplasms\". The age group was set to \"All ages\", with the metric specified as \"Number\", and the measures selected as both \"Deaths\" and \"Incidence\". Data were extracted for all three sex categories: both, female, and male. For each cancer type, the proportion of incidence and deaths relative to the total number of cancer incidence and deaths, respectively, was calculated to determine its percentage contribution to the overall cancer burden. The top five cancer types in terms of deaths number for both sexes and all ages in 2021 were: tracheal, bronchus, and lung cancer, stomach cancer, esophageal cancer, colon and rectum cancer, and liver cancer. All remaining cancer types with a percentage contribution of less than 5% were aggregated into a single category labeled \u0026ldquo;Other\u0026rdquo;. A proportional chart was then constructed to visualize the distribution. We further analyzed data on neoplasms for both males and females in China. From 1980 to 2021, we presented the number of deaths and the age-standardized deaths rate; from 1990 to 2021, we reported the number of incidence and the age-standardized incidence rate. Heatmaps were used to visualize the age-standardized deaths rate and age-standardized incidence rate across all cancer types, highlighting temporal trends and patterns. Additionally, for the year 2021, we stratified the number of deaths and incidence for males and females by age groups, and presented the corresponding results. Finally, we extracted data on the age-standardized incidence rate and age-standardized deaths rate for all cancer types from China, the global dataset, and regions classified by the SDI, including High-SDI, High-middle SDI, Middle SDI, Low-middle SDI, and Low SDI. Comparative analysis were performed across sex groups (both, female, and male) to assess variations in cancer burden among different regions.\u003c/p\u003e\n\u003ch3\u003eRisk Factors of Cancers and Joinpoint Regression Analysis\u003c/h3\u003e\n\u003cp\u003eFor risk factor analysis, we utilized Level 1\u0026ndash;4 risk classifications provided by the Global Burden of Disease results tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd-results/\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd-results/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Specifically, we focused on the top five cancer types in terms of number of deaths for both sexes and all ages in 2021: tracheal, bronchus, and lung cancer; stomach cancer; esophageal cancer; colon and rectum cancer; and liver cancer. For each of these cancer types, we extracted and analyzed the age-standardized death rate data from 1990 to 2021, stratified by sex (both, female, and male). Ultimately, we obtained data on risk factors for each cancer type, including 5 for liver cancer, 4 for esophageal cancer, 11 for colon and rectum cancer, 2 for stomach cancer, and 16 for tracheal, bronchus, and lung cancer. These data were subsequently subjected to downstream Joinpoint regression analysis.\u003c/p\u003e\u003cp\u003eThe Joinpoint Regression Program (Version 5.3.0, \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), developed by the Division of Cancer Control and Population Sciences at the U.S. National Cancer Institute, is a statistical software designed to analyze temporal trends in time series data using joinpoint models[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This method fits a series of linear segments connected at statistically determined points, known as \"joinpoints,\" dividing the time series into distinct intervals. For each segment, the Annual Percent Change (APC) and its 95% confidence interval (CI) are estimated to quantify the rate of change. The Average Annual Percent Change (AAPC) is then computed to summarize the overall trend, with a statistically significant increase or decrease indicated when the entire 95% CI lies above or below zero, respectively. The model with the least complexity that best fits the data is selected, and statistical significance is assessed using the Monte Carlo permutation method[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In this study, the maximum number of joinpoints was set to five (max_joinpoints\u0026thinsp;=\u0026thinsp;5).\u003c/p\u003e\n\u003ch3\u003eDecomposition Analysis\u003c/h3\u003e\n\u003cp\u003eDecomposition analysis provides a robust framework for quantifying the relative contributions of key factors to temporal changes in cancer-related deaths and incidence[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In this study, changes in mortality and incidence for the selected five cancer types were decomposed into three components: population aging, population growth, and epidemiological changes. This approach enables a comprehensive assessment of how each factor contributes to the dynamics of cancer burden over the study period from 1990 to 2021. By disentangling the effects of demographic shifts from changes in disease risk, this method offers critical insights into the underlying drivers of cancer trends. Such information is essential for informing targeted public health interventions, as it helps determine whether efforts should prioritize risk factor modification, demographic adaptation, or a combination of both.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eThe Bayesian Age-Period-Cohort Analysis\u003c/h2\u003e\u003cp\u003eUsing the Bayesian Age-Period-Cohort (BAPC, Version 0.0.36) model within the Integrated Nested Laplace Approximation (INLA, Version 23.9.9) framework, we projected the incidence and death trends of the five major cancer types in China from 2022 to 2050, with the model integrating empirical data and informative priors to generate statistically robust and reliable estimates that provide critical insights for cancer prevention and public health policy development[\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll data used in this study were obtained from the Global Burden of Disease database. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered indicative of statistical significance. All statistical analyses and data visualizations were performed using R software (Version 4.3.1).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eEpidemiological Overview of the Cancer Burden in China\u003c/h2\u003e\u003cp\u003eIn 2021, neoplasms accounted for 24.07% (95% uncertainty interval [UI]: 22.74\u0026ndash;25.29) of all-cause deaths and 17.70% (95% UI: 15.21\u0026ndash;20.20) of total Disability-Adjusted Life Years (DALYs) in both sexes in mainland China. These proportions were substantially higher than those observed globally, where neoplasms contributed to 14.57% (95% UI: 13.65\u0026ndash;15.28) of total deaths and 8.80% (95% UI: 7.99\u0026ndash;9.67) of total DALYs, respectively[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In contrast, in 2021, the number of incident cases of neoplasms in mainland China reached 13,664,748.50 (95% UI: 11,787,026.26-15,848,005.53), while the number of deaths attributed to neoplasms was 2,817,757.67 (95% UI: 2,347,976.64-3,360,869.47). Compared with the global number of incident neoplasm cases (66,479,607.27; 95% UI: 58,335,731.40\u0026ndash;74,980,442.95) and deaths (9,888,413.46; 95% UI: 9,124,879.13-10,585,373.15), the incidence and mortality figures in mainland China were disproportionately high relative to its share of the global population. In 2021, the estimated population of China was 1,422,745,952.85 (95% UI: 1,318,759,193.78-1,530,462,332.89), while the global population was estimated at 7,891,353,300.74 (95% UI: 7,666,733,980.42-8,131,224,517.81). In 2021, the age-standardized death rate (ASDR) and age-standardized incidence rate (ASIR) for neoplasms in China were 137.48 (95% UI: 115.11-163.38) and 790.17 (95% UI: 676.83-926.32) per 100,000 population, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). When compared with the ASDR in 1980, which was 209.52 (95% UI: 179.16-246.87), a substantial decline was observed (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In contrast, the ASIR showed a slight increase from 718.73 (95% UI: 608.70-842.60) in 1990 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These trends reflect improvements in cancer-related mortality over time, despite a modest increase in incidence.\u003c/p\u003e\u003cp\u003eIn addition, trends in the ASDR for all cancer types in mainland China from 1980 to 2021 were analyzed. With the exception of tracheal, bronchus, and lung cancer\u0026mdash;which rose from 35.64 per 100,000 in 1980 to a peak of 42.02 in 2005, followed by a gradual decline to 38.98 in 2021 (estimated annual percentage change [EAPC]: 0.44; 95% confidence interval [CI]: 0.34 to 0.55)\u0026mdash;most major cancer types demonstrated a consistent downward trend over the study period (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Specifically, stomach cancer decreased substantially from 56.46 to 21.51 (EAPC: -2.31; 95% CI: -2.45 to -2.17), liver cancer declined from 13.70 to 8.35 (EAPC: -0.92; 95% CI: -1.04 to -0.81), esophageal cancer fell from 29.77 to 14.13 (EAPC: -1.88; 95% CI: -2.06 to -1.71), and colon and rectum cancer decreased from 16.69 to 13.64 (EAPC: -0.52; 95% CI: -0.56 to -0.48) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). From 1990 to 2021, the ASIR for several major cancer types in mainland China exhibited an overall upward trend. Tracheal, bronchus, and lung cancer increased from 33.11 to 44.01 per 100,000, with an estimated annual percentage change (EAPC) of 1.03 (95% confidence interval [CI]: 0.89 to 1.17) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Non-melanoma skin cancer showed a pronounced rise from 4.65 to 37.54 (EAPC: 4.60; 95% CI: 3.81 to 5.39), and colon and rectum cancer increased from 19.04 to 31.44 (EAPC: 1.75; 95% CI: 1.64 to 1.86) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, breast cancer incidence nearly doubled, rising from 9.08 to 19.36 (EAPC: 2.50; 95% CI: 2.42 to 2.58) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In contrast, several cancer types showed a declining trend over the same period. Stomach cancer decreased significantly from 48.03 to 29.05 (EAPC: -1.64; 95% CI: -1.81 to -1.47), esophageal cancer declined from 24.80 to 15.04 (EAPC: -1.88; 95% CI: -2.09 to -1.67), and liver cancer showed a slight decrease from 10.58 to 9.52 (EAPC: -0.28; 95% CI: -0.42 to -0.13) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In 2021, the five leading causes of cancer-related deaths in mainland China were tracheal, bronchus, and lung cancer (814,364 deaths, 28.90% of all cancer deaths), stomach cancer (445,013 deaths, 15.79%), esophageal cancer (296,443 deaths, 10.52%), colon and rectum cancer (275,129 deaths, 9.76%), and liver cancer (172,068 deaths, 6.11%) (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Collectively, these cancers accounted for approximately 71.08% of total cancer deaths, reflecting their dominant contribution to the cancer mortality burden and highlighting the need for targeted cancer prevention and control interventions.\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 Incidence and Death Cases, Age-Standardized Incidence and Death Rates, and EAPC of ASIR and ASDR in Both Sexes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType of neoplasms\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll age incidence cases No. (95% UI) 1990\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge-standardized incidence rate per 100,000 (95% UI) 1990\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAll age incidence cases No. (95% UI) 2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAge-standardized incidence rate per 100,000 (95% UI) 2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEAPC of ASIR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAll age death cases No. (95% UI) 1980\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAge-standardized death rate per 100,000 (95% UI) 1980\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAll age death cases No. (95% UI) 2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eAge-standardized death rate per 100,000 (95% UI) 2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eEAPC of ASDR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeoplasms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7818410.9 (6478326.81, 9545530.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e718.73 (608.7, 842.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13664748.5 (11787026.26, 15848005.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e790.17 (676.83, 926.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.28(0.25, 0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1304454.05 (1106050.38, 1547919.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e209.52 (179.16, 246.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2817757.67 (2347976.64, 3360869.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e137.48 (115.11, 163.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.98(-1.05, -0.91)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBladder cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35813.04 (25632.01, 42115.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.69 (3.43, 5.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e105790.52 (83240.8, 136669.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.14 (4.08, 6.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.12(0.02, 0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e17298.79 (12734.19, 21344.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.56 (2.69, 4.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e45113.71 (36262.51, 57335.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.34 (1.89, 2.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-1.28(-1.4, -1.15)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrain and central nervous system cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47364.12 (34332.99, 59067.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.69 (3.42, 5.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e105540.85 (81400.78, 133527.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.12 (4.76, 7.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.84(0.8, 0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e30757.62 (21913.74, 40546.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.04 (2.94, 5.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e68910.82 (52054.94, 88279.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e3.63 (2.74, 4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.27(-0.35, -0.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreast cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e86708.72 (70225.31, 105273.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.08 (7.41, 11.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e402794.18 (312117.3, 505644.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19.36 (15, 24.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.5(2.42, 2.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e34574.52 (26633.38, 44620.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5.28 (4.14, 6.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e91483.84 (71738.59, 113710.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e4.4 (3.45, 5.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.45(-0.53, -0.37)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCervical cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57843.04 (46321.43, 71401.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.84 (4.71, 7.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e132787.82 (95959.18, 172599.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.67 (4.8, 8.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.92(0.73, 1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e33645.65 (25747.38, 42463.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5 (3.88, 6.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e49841.19 (36878.07, 64386.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.39 (1.77, 3.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-1.56(-1.75, -1.38)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eColon and rectum cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e158389.3 (135418.51, 182577.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.04 (16.46, 21.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e658321.36 (531995.02, 798063)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e31.44 (25.53, 37.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.75(1.64, 1.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e96170.98 (71065.72, 121891.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e16.69 (12.38, 20.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e275129.23 (223378.58, 330960.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e13.64 (11.09, 16.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.52(-0.56, -0.48)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEsophageal cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e207494.92 (172673.51, 241458.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.8 (20.71, 28.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e320805.43 (256102.37, 394756.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.04 (12.04, 18.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.88(-2.09, -1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e180283.25 (144651.12, 219978.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e29.77 (23.77, 36.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e296443.04 (236647.81, 362831.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e14.13 (11.36, 17.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-1.88(-2.06, -1.71)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEye cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1496.04 (937.31, 2020.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.16 (0.1, 0.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3728.78 (2065.12, 4830.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.28 (0.15, 0.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.14(2.74, 3.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e602.56 (343.89, 841.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.09 (0.05, 0.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e693.26 (368.91, 911.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.04 (0.02, 0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-1.52(-1.62, -1.42)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGallbladder and biliary tract cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17077.45 (13002.87, 21743.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.19 (1.68, 2.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51720.39 (35618, 66848.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.49 (1.71, 3.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.5(0.4, 0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e13288.11 (9255.02, 18440.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.43 (1.7, 3.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e37833.49 (26652.59, 49261.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.85 (1.29, 2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.65(-0.72, -0.58)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHodgkin lymphoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4745.97 (2015.45, 6560.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.48 (0.2, 0.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4211.06 (2542, 5541.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.23 (0.14, 0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-2.73(-3, -2.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4344.01 (1830.77, 6255.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.61 (0.26, 0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2443.23 (1506.76, 3231.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.13 (0.08, 0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-4.26(-4.45, -4.07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKidney cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16232.12 (14234.45, 18286.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.79 (1.58, 2.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65799.45 (53687.4, 79742.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.32 (2.73, 3.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.39(2.19, 2.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6852.31 (5512.95, 8318.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.13 (0.94, 1.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e24867.31 (20361.43, 29828.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.25 (1.03, 1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.35(0.26, 0.45)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLarynx cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15434.15 (12624.19, 18174.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.82 (1.5, 2.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38904.86 (30369.67, 49486.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.79 (1.4, 2.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.04(-0.1, 0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10150.25 (7635.15, 12978.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.68 (1.29, 2.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e19799.45 (15579.57, 25023.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.94 (0.74, 1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-1.58(-1.65, -1.51)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeukemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e76203.78 (58311.79, 90957.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.14 (5.52, 8.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e105667.19 (75275.71, 132236.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.21 (4.93, 9.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.19(0.06, 0.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e64275.22 (47458.32, 82514.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.5 (5.65, 9.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e58903.47 (43625.97, 74038.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e3.42 (2.51, 4.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-1.97(-2.06, -1.89)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLip and oral cavity cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14687.41 (12390.19, 16908.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.7 (1.45, 1.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56359.16 (45178.45, 69804.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.68 (2.15, 3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.75(1.53, 1.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7280.58 (5879.61, 9251.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.21 (0.99, 1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e23881.67 (18971.59, 29680.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.15 (0.92, 1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.12(-0.21, -0.03)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e96434.35 (80970.6, 113768.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.58 (8.94, 12.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e196636.59 (158273.06, 243557.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.52 (7.72, 11.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.28(-0.42, -0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e92342.71 (69483.58, 118573.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e13.7 (10.44, 17.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e172068.4 (139621.29, 212495.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e8.35 (6.8, 10.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.92(-1.04, -0.81)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalignant neoplasm of bone and articular cartilage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6382.42 (4177.56, 11227.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.65 (0.42, 1.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25937.81 (16243.09, 34274.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42 (0.9, 1.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.37(2.75, 3.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5339.14 (3887.24, 8623.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.76 (0.55, 1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e18084.53 (11288.1, 24125.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.93 (0.58, 1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.59(1.15, 2.04)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalignant skin melanoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3249.78 (2093.19, 4085.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.36 (0.24, 0.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13437.46 (7198.45, 17979.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.68 (0.37, 0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.27(2.05, 2.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1985.43 (1205.39, 2827.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.32 (0.21, 0.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5372.5 (2848.58, 7105.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.27 (0.14, 0.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.42(-0.48, -0.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMesothelioma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1154.94 (970.24, 1371.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.13 (0.11, 0.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3046.26 (2453.8, 3713.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.15 (0.12, 0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.8(0.55, 1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e834.63 (665.02, 1101.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.13 (0.11, 0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3010.48 (2426.53, 3664.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.15 (0.12, 0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.5(0.35, 0.66)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultiple myeloma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1693.3 (1153.93, 3360.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2 (0.13, 0.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17249.51 (11016.71, 22663.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.81 (0.52, 1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.05(3.38, 4.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1181.89 (724.92, 2300.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.19 (0.12, 0.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e12984.05 (8447.92, 17113.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.62 (0.4, 0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3.55(3.09, 4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNasopharynx cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44864.15 (38023.29, 51826.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.64 (3.93, 5.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65933.78 (53272.37, 81430.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.42 (2.77, 4.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.5(-1.91, -1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e30165.88 (23928.58, 36071.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.36 (3.46, 5.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e31320.96 (25467.27, 38381.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.51 (1.23, 1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-3.22(-3.48, -2.96)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeuroblastoma and other peripheral nervous cell tumors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e665.32 (457.16, 957.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06 (0.04, 0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2083.92 (1547.48, 2569.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.15 (0.11, 0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.29(3.01, 3.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e198.72 (146.93, 286.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.02 (0.02, 0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1069.84 (791.56, 1298.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.07 (0.05, 0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2.89(2.75, 3.04)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hodgkin lymphoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31216.05 (26892.76, 37951.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.32 (2.86, 4.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e110923.55 (86933.89, 135200.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.53 (4.36, 6.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.88(1.59, 2.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e20943.54 (16648.33, 25760.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.01 (2.41, 3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e42856.95 (33553.21, 51712.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.13 (1.68, 2.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.79(-0.91, -0.68)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-melanoma skin cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39491.47 (33276.34, 45761.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.65 (3.99, 5.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e791867.58 (674907.37, 907771.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37.54 (32.4, 42.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.6(3.81, 5.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3943.53 (3146.43, 5094.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.77 (0.62, 0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e16575.79 (13016.94, 20669.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.87 (0.68, 1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.7(0.54, 0.86)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther malignant neoplasms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41387 (27526.75, 51719.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.51 (3.05, 5.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e106746.44 (82738.09, 134939.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.6 (4.35, 6.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.78(0.4, 1.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e26190.07 (16834.59, 34749.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.96 (2.6, 5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e39756.29 (31076.57, 49408.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.04 (1.6, 2.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-1.9(-2.05, -1.75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther neoplasms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6007403.75 (4715210.9, 7715300.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e510.99 (406.4, 635.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8335339.62 (6704328.13, 10316551.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e531.33 (425.7, 661.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.19(0.16, 0.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e865.53 (395.11, 1785.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.15 (0.06, 0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4777.73 (2821.58, 8796.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.25 (0.15, 0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.54(1.46, 1.63)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther pharynx cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5074.32 (4142.19, 6174.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.58 (0.48, 0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12063.39 (9529.42, 15280.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.56 (0.44, 0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.48(-0.92, -0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3088.49 (2370.27, 4030.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.49 (0.38, 0.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5880.68 (4691.78, 7407.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.28 (0.22, 0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-2.02(-2.3, -1.75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOvarian cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19997.6 (14086.43, 26191.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.03 (1.5, 2.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41236.26 (30302.39, 54548.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.03 (1.49, 2.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.41(-0.56, -0.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8487.12 (5941.49, 12837.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.29 (0.93, 1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e25143.85 (18525.7, 32922.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.18 (0.87, 1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.52(-0.68, -0.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePancreatic cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37817.66 (31791.43, 44068.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.54 (3.84, 5.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e118665.43 (94622.75, 144663.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.64 (4.52, 6.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.68(0.64, 0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e28824.2 (21257.29, 40081.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.82 (3.62, 6.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e119601.86 (95653.59, 145218.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e5.72 (4.59, 6.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.47(0.42, 0.51)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProstate cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13753.72 (10153.81, 17772.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.04 (1.52, 2.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e88601.06 (63194.43, 120964.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.22 (3.01, 5.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.2(2.08, 2.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7331.8 (5331.95, 9404.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.77 (1.29, 2.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e37363.47 (27850.94, 50365.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.99 (1.47, 2.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.28(0.12, 0.45)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoft tissue and other extraosseous sarcomas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5803.83 (4084.97, 7529.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.63 (0.44, 0.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9226.99 (6351.77, 13045.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.48 (0.33, 0.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.05(-1.16, -0.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3479.53 (2262.75, 4607.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.54 (0.35, 0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4634.82 (3208.18, 6509.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.24 (0.17, 0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-2.04(-2.16, -1.92)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStomach cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e407471.29 (337565.45, 477568.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.03 (40.21, 56.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e611798.97 (471965.81, 765562.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29.05 (22.42, 36.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.64(-1.81, -1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e343519.43 (278116.91, 413150.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e56.46 (46.18, 67.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e445012.65 (344736.2, 555833.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e21.51 (16.66, 26.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-2.31(-2.45, -2.17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTesticular cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1839.33 (1521.07, 2183.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.16 (0.13, 0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6695.73 (5181.39, 8656.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.42 (0.32, 0.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.8(2.46, 3.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e657.53 (500.83, 837.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.09 (0.07, 0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1244.57 (962.24, 1579.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.07 (0.06, 0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-1.19(-1.47, -0.91)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThyroid cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12157.42 (9714.08, 14406.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.25 (1.01, 1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48104.56 (38694.78, 60068.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.47 (1.99, 3.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.47(2.29, 2.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2936.7 (2353.29, 3600.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.52 (0.42, 0.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e7692.21 (6122.52, 9428.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.39 (0.31, 0.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.67(-0.72, -0.61)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTracheal, bronchus, and lung cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e274751.96 (234740.75, 315111.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.11 (28.47, 37.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e934704.06 (750040.14, 1136937.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e44.01 (35.45, 53.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.03(0.89, 1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e212770.3 (170020.39, 268843.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e35.64 (28.88, 44.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e814363.76 (652636.22, 987794.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e38.98 (31.4, 47.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.44(0.34, 0.55)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUterine cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26311.18 (18116.14, 33311.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.81 (1.97, 3.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e72018.5 (53311.86, 99999.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.35 (2.48, 4.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.44(0.13, 0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e9844.02 (6061.65, 13643.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.54 (0.99, 2.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e13598.56 (9925.9, 18595.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.64 (0.47, 0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-2.15(-2.38, -1.93)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePatterns of Cancer Burden by Age and Sex\u003c/h2\u003e\u003cp\u003eTo further elucidate sex- and age-specific disparities across different cancer types, we conducted stratified analysis by sex and age group. In 2021, among males of all ages in mainland China, the top five cancer types by number of deaths were tracheal, bronchus, and lung cancer (545,962 deaths; 30.16% of total cancer deaths), stomach cancer (314,779 deaths; 17.39%), esophageal cancer (232,754 deaths; 12.86%), colon and rectum cancer (174,400 deaths; 9.63%), and liver cancer (122,463 deaths; 6.76%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among females, tracheal, bronchus, and lung cancer also ranked first, with 268,402 deaths, accounting for 26.64% of total cancer deaths (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This was followed by stomach cancer (130,234 deaths; 12.93%), colon and rectum cancer (100,729 deaths; 10.00%), breast cancer (88,107 deaths; 8.75%), and esophageal cancer (63,689 deaths; 6.32%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Liver cancer was the seventh leading cause of cancer death among females, contributing 49,605 deaths (4.92%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Notably, breast cancer mortality in males was minimal, with only 3,377 deaths in 2021, accounting for just 0.19% of total cancer-related deaths among men in mainland China. This stark contrast with the female burden of breast cancer underscores the strong sex-specific nature of the disease and highlights the need for gender-targeted cancer prevention and control strategies. In 2021, the total number of cancer-related deaths in mainland China was approximately 1,810,252 among males and 1,007,505 among females, indicating that male cancer mortality was about 1.8 times higher than that of females. Excluding other neoplasms, non-melanoma skin cancer ranked among the top three cancer types by incidence in males, with an estimated 443,700 cases, accounting for 7.94% of total male cancer cases in 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among females, breast cancer was the most common cancer, with 385,838 incident cases, representing 4.78% of all female cancer diagnoses, followed closely by non-melanoma skin cancer, with 348,168 cases (4.31%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInterestingly, for all neoplasm types, the number of incident cases and the ASIR were consistently higher in females than in males from 1990 to 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In contrast, the ASDR and number of deaths were substantially higher in males compared to females throughout the period from 1980 to 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Despite these sex-specific differences in incidence and mortality, the number of incident cases and deaths showed a continuous and steady increase in both sexes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Notably, while ASIR remained relatively stable over time, ASDR showed a marked downward trajectory, decreasing by approximately 29.78% in males and 41.83% in females over the past four decades. This decline suggests considerable advancements in cancer management, early detection, and treatment outcomes in recent years. The temporal trends in ASIR and ASDR for specific cancer types were also illustrated in the corresponding figures, providing a more detailed depiction of their longitudinal patterns over the study period.\u003c/p\u003e\u003cp\u003eWe further compared the differences in deaths and incidence across different age groups including males and females. In terms of incidence, females showed higher numbers than males in age groups up to 55\u0026ndash;59 years. However, starting from the 60\u0026ndash;64 age group and continuing through 85\u0026ndash;89 years, males exhibited higher incidence numbers than females. The peak incidence for females was observed in the 50\u0026ndash;54 age group, whereas for males, it occurred in the 65\u0026ndash;69 age group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). During the same period, the highest number of cancer-related deaths occurred in the 70\u0026ndash;74 age group for both males and females (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Except for the 95\u0026thinsp;+\u0026thinsp;age group, males consistently had higher mortality counts than females across all age categories.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eVariations in Cancer Burden Across Socio-demographic Index Levels\u003c/h2\u003e\u003cp\u003eWe depicted the temporal changes in age-standardized incidence and death rates for neoplasms across five SDI regions, globally, and in China from 1990 (for incidence) and 1980 (for mortality) to 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The analysis focused on overall neoplasms and six major cancer types: tracheal, bronchus, and lung cancer; stomach cancer; esophageal cancer; colon and rectum cancer; liver cancer; and breast cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eG).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eRisk Factors Contributing to Cancer in China\u003c/h2\u003e\u003cp\u003eBased on the risk factor data across levels 1 to 4 available from the GBD database, we analyzed the associations between risk exposure and the ASDR for the five leading causes of cancer-related mortality in China: tracheal, bronchus, and lung cancer; stomach cancer; esophageal cancer; colon and rectum cancer; and liver cancer. Specifically, we examined the trends from 1990 to 2021 for each of these cancer types across all four risk factor levels, stratified by sex (both sexes, male, and female), using only datasets with complete year coverage. In 2021, for both sexes, tracheal, bronchus, and lung cancer was associated with all three level 1 risk categories\u0026mdash;behavioral risks, environmental/occupational risks, and metabolic risks. Among these, behavioral risks had the greatest contribution to the ASDR, reaching 26.10 (95% UI: 20.57\u0026ndash;32.97) per 100,000 population. Notably, this was the highest ASDR attributable to behavioral risks among all cancer types analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). At the level 2 risk classification, air pollution and tobacco were the primary contributors to the ASDR for tracheal, bronchus, and lung cancer in 2021, with ASDRs of 10.11 (95% UI: 6.32\u0026ndash;14.39) and 25.78 (95% UI: 20.19\u0026ndash;32.58) per 100,000 population, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Tobacco also played a significant role in esophageal and stomach cancers, contributing ASDRs of 6.62 (95% UI: 4.88\u0026ndash;8.66) and 3.10 (95% UI: 2.27\u0026ndash;4.30), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). For colon and rectum cancer, dietary risks emerged as a major factor, with an associated ASDR of 5.08 (95% UI: 1.79\u0026ndash;8.21) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). At the level 3 risk category, the leading contributors to the ASDR for tracheal, bronchus, and lung cancer in 2021 were particulate matter pollution and smoking, with ASDRs of 10.11 (95% UI: 6.32\u0026ndash;14.39) and 24.53 (95% UI: 19.38\u0026ndash;31.09), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). At level 4, data were available exclusively for tracheal, bronchus, and lung cancer. Among the detailed risk factors, ambient particulate matter pollution and household air pollution from solid fuels were the two main contributors with complete records from 1990 to 2021 (Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Over this period, the ASDR attributable to ambient particulate matter pollution increased markedly from 3.09 (95% UI: 1.36\u0026ndash;5.80) to 8.54 (95% UI: 4.96\u0026ndash;12.27) per 100,000 population. In contrast, the ASDR associated with household air pollution from solid fuels showed a substantial decline, dropping from 10.46 (95% UI: 6.70-14.38) to 1.56 (95% UI: 0.22\u0026ndash;5.28).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eRisk Factor Trends Underlying the Burden of the Five Leading Cancers, 1990\u0026ndash;2021\u003c/h2\u003e\u003cp\u003eWe employed a Joinpoint regression model to analyze the ASDR from 1990 to 2021, calculating the APC and the AAPC for each identified time segment, stratified by sex (both sexes, females, and males) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2). From 1990 to 2021, the analysis of ASDR trends for the top five cancer types revealed distinct patterns in relation to specific risk factors. Among all risk factors analyzed, ambient particulate matter pollution showed the highest AAPC for tracheal, bronchus, and lung cancer in the both sexes group (AAPC: 3.313) (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The most pronounced increase occurred between 1997 and 2003, with an APC of 7.37 (Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). Notably, this upward trend was even more evident in females (AAPC: 3.977) (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). In contrast, the lowest AAPC for this cancer type was observed for household air pollution from solid fuels, which showed a significant decline in both sexes (AAPC: -6.077) (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The most substantial decreases occurred during 2004\u0026ndash;2009 (APC: -7.80) and 2010\u0026ndash;2018 (APC: -12.95), with an even more pronounced reduction in males (AAPC: -6.304) (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e and Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). For liver cancer, the highest AAPC was linked to high body-mass index in both sexes (3.720), with females (3.807) and males (3.702) showing similar increasing trends. Conversely, smoking was the risk factor with the lowest AAPC (-1.025 in both sexes), reflecting a consistent downward trend, particularly in females (-1.820). In colon and rectum cancer, high body-mass index again accounted for the highest AAPC in both sexes (2.427), with a steeper increase in males (2.901) compared to females (1.852). The lowest AAPC was observed for diet low in fiber (-3.613 in both sexes), with the most rapid decline occurring in females (-4.270). Unlike other cancer types, stomach cancer showed no positive AAPC values among its statistically significant risk factors in both sexes. The greatest decline was linked to diet high in sodium (-2.454). Similarly, esophageal cancer showed no risk factors with increasing AAPC. The most pronounced decline was associated with a diet low in vegetables, with an AAPC of -7.909 in the both sexes group. Notable decreases were observed during 2004\u0026ndash;2006 (APC: -13.74) and 2007\u0026ndash;2013 (APC: -11.42) (Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eDecomposition Analysis of Cancer Burden Dynamics in China\u003c/h2\u003e\u003cp\u003eWe conducted a decomposition analysis to elucidate the driving forces underlying changes in cancer incidence and deaths rates in China from 1990 to 2021. The overall variation was attributed to three key determinants: aging, population growth, and epidemiological changes. The analysis revealed that among the top five cancer types by number of deaths in China in 2021 for both sexes, aging was the most significant positive contributor to the changes in death rates. For four of these cancer types\u0026mdash;colon and rectum, esophageal, liver, and stomach cancers\u0026mdash;the contribution of epidemiological changes was negative (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-D). In contrast, tracheal, bronchus, and lung cancer was the only type for which epidemiological changes contributed positively (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). Specifically, the contributions of aging, population growth, and epidemiological changes were as follows: for colon and rectum cancer, 85.59%, 27.53%, and \u0026minus;\u0026thinsp;13.12%, respectively; for esophageal cancer, 227.88%, 89.66%, and \u0026minus;\u0026thinsp;217.54%; for liver cancer, 107.92%, 33.28%, and \u0026minus;\u0026thinsp;41.20%; for stomach cancer, 461.86%, 157.09%, and \u0026minus;\u0026thinsp;518.94%; and for tracheal, bronchus, and lung cancer, 58.59%, 19.50%, and 21.90%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-E). The analysis of incidence rate changes showed the following contributions from aging, population growth, and epidemiological changes: for colon and rectum cancer, -92.88%, 83.55%, and 109.33%, respectively; for esophageal cancer, 69.31%, -45.77%, and 76.46%; for liver cancer, 80.55%, 29.96%, and \u0026minus;\u0026thinsp;10.50%; for stomach cancer, 181.85%, 50.31%, and \u0026minus;\u0026thinsp;132.15%; and for tracheal, bronchus, and lung cancer, 62.47%, 16.02%, and 21.52%, respectively (Figure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eA-E).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eProjected Cancer Incidence and Deaths in China Through 2050\u003c/h2\u003e\u003cp\u003eThe BAPC projection results based on death numbers and demographic data show that, for both sexes, the age-standardized rates (Agestd. Rate) of esophageal cancer and tracheal, bronchus, and lung cancer are projected to remain relatively stable from 2022 to 2050, and colon and rectum cancer is expected to show a slight upward trend, while stomach cancer and liver cancer are projected to decline significantly (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Among males and females, the trends generally align with those observed in both sexes, except for tracheal, bronchus, and lung cancer in females, which shows a slight upward trend. The BAPC projection results based on incidence numbers indicate that stomach cancer is expected to level off in the future, while esophageal cancer is projected to show a slight increase. Tracheal, bronchus, and lung cancer is expected to gradually increase, although the trend remains relatively stable in males. Colon and rectum cancer is projected to rise substantially, whereas liver cancer shows a clear downward trend (Figure \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing data from the Global Burden of Disease database, this study systematically analyzed cancer-related deaths and incidence across all cancer types in China, incorporating multiple analytical methods and stratifying by sex, age groups, and a broad spectrum of risk factors. In 2021, neoplasms in mainland China accounted for 24.07% (95% UI: 22.74\u0026ndash;25.29) of all-cause deaths\u0026mdash;substantially higher than the global rate of 14.57% (95% UI: 13.65\u0026ndash;15.28)\u0026mdash;with 13.66\u0026nbsp;million (95% UI: 11.79\u0026ndash;15.85\u0026nbsp;million) new cases and 2.82\u0026nbsp;million (95% UI: 2.35\u0026ndash;3.36\u0026nbsp;million) deaths reported. The ASDR for neoplasms in China was 137.48 (95% UI: 115.11\u0026ndash;163.38) per 100,000 in 2021, reflecting a marked decline from 209.52 (95% UI: 179.16\u0026ndash;246.87) in 1980 and indicating improved cancer-related mortality over time. In 2021, tracheal, bronchus, and lung; stomach; esophageal; colorectal; and liver cancers were the top five causes of cancer deaths in mainland China, accounting for 71.08% of total cancer mortality. Furthermore, for the major cancer types, we conducted a decomposition analysis to evaluate the respective contributions of aging, population growth, and epidemiological transitions to cancer trends. By integrating data on risk factors, we assessed the impact of high-risk exposures and highlighted the substantial health burden imposed by cancer in China. Based on BAPC model projections, we also illustrated future trends and underscored the urgent need for targeted interventions to mitigate the growing cancer burden.\u003c/p\u003e\u003cp\u003eSex- and age-specific cancer burdens represent critical areas of concern with important implications for the formulation of effective public health policies and population-targeted interventions[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In light of the physiological, behavioral, and occupational differences between males and females, we conducted a detailed analysis of sex-specific cancer data, moving general trends in ASDR and ASIR. From 1990 to 2021, females consistently exhibited higher ASIR and numbers of incident cases for all neoplasms, whereas males experienced significantly higher ASDR and cancer-related mortality. Although ASIR remained relatively stable, ASDR declined markedly\u0026mdash;by 29.78% in males and 41.83% in females\u0026mdash;over the past four decades, with male cancer mortality being 1.8 times that of females in 2021. The top five cancer types by mortality were identical for males and the overall population, while breast cancer accounted for 8.75% of all female cancer deaths. Given the heterogeneity of cancer burden across different age groups, age-specific patterns in incidence and mortality warrant closer examination to inform age-tailored prevention and control strategies[\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Mortality peaked in the 70\u0026ndash;74 age group for both sexes, with males showing higher death counts in nearly all age categories except for those aged 95 and above. Incidence was higher among females up to the 55\u0026ndash;59 age group, while males exhibited greater incidence from age 60\u0026ndash;64 onward. The peak incidence occurred at ages 50\u0026ndash;54 in females and 65\u0026ndash;69 in males.\u003c/p\u003e\u003cp\u003eOver the past three decades, the rapid industrialization and urbanization in China have led to significant advancements, but also resulted in severe environmental pollution\u0026mdash;particularly due to high emissions of PM\u003csub\u003e2.5\u003c/sub\u003e and sulfur dioxide\u0026mdash;which poses a major threat to the respiratory health of the population[\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Correspondingly, the ASIR of tracheal, bronchus, and lung cancers increased from 33.11 (28.47\u0026ndash;37.79) in 1990 to 44.01 (35.45\u0026ndash;53.35) in 2021, with an EAPC of 1.03 (0.89\u0026ndash;1.17). The ASDR also rose from 35.64 (28.88\u0026ndash;44.65) in 1980 to 38.98 (31.40-47.06) in 2021. In 2021, tracheal, bronchus, and lung cancers were responsible for over 800,000 deaths, accounting for 28.9% of all cancer-related fatalities. Additionally, more than 900,000 new cases were reported that year. Prolonged exposure to tobacco and other environmental risk factors has significantly exacerbated the burden of respiratory system cancers[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In the face of this growing challenge, there is an urgent need for molecular research to better understand and address the lung cancer burden in China. Such research should consider gender-specific differences in smoking behavior, occupational exposures, genetic susceptibility, health awareness, and preventive practices. At the same time, the Chinese government must further strengthen public health campaigns and educational initiatives, coupled with stringent regulatory measures\u0026mdash;such as implementing comprehensive smoke-free policies in public places and increasing tobacco taxation. These efforts are crucial to enhancing awareness among younger populations about the harms of smoking, ultimately reducing both smoking prevalence and secondhand smoke exposure.\u003c/p\u003e\u003cp\u003eChronic infections also play a critical role in cancer risk[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. \u003cem\u003eHelicobacter pylori\u003c/em\u003e (\u003cem\u003eH. pylori\u003c/em\u003e) infection is recognized as a major risk factor for gastric cancer[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Although the ASIR of stomach cancer in China declined significantly from 48.03 (40.21, 56.69) to 29.05 (22.42, 36.20), with an EAPC of -1.64 (-1.81, -1.47), and the ASDR decreased from 56.46 (46.18, 67.60) to 21.51 (16.66, 26.61), with an EAPC of -2.31 (-2.45, -2.17), the overall number of cases and mortality rates remain high. These figures underscore the persistent significance of infection-related cancer risks in China. Chronic hepatitis B virus (HBV) infection has been firmly established as a major contributor to liver cancer in China[\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Furthermore, China bears a disproportionately large share of the global esophageal cancer burden[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Esophageal cancer, which includes esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC), is dominated by ESCC, accounting for approximately 90% of all cases in China[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The ASIR of esophageal cancer declined from 24.8 (20.71, 28.73) to 15.04 (12.04, 18.43), with an EAPC of -1.88 (-2.09, -1.67), and the ASDR dropped by more than 50%, from 29.77 (23.77, 36.01) to 14.13 (11.36, 17.18). Despite these improvements, more than half of the new esophageal cancer cases and related deaths worldwide occur in China. This disproportionate burden is largely driven by the high prevalence of modifiable risk factors, particularly tobacco use and alcohol consumption.\u003c/p\u003e\u003cp\u003eWith rapid economic development, significant changes have occurred in lifestyle, dietary patterns, and population aging, all of which may contribute to an increased risk of colorectal cancer[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The rising consumption of high-calorie, high-fat, and high-protein foods, along with a reduction in the intake of fruits, whole grains, and vegetables, has become increasingly common. In addition, behavioral risk factors such as smoking, alcohol consumption, and obesity further exacerbate this risk. This trend is reflected in the epidemiological data: ASIR of colon and rectum cancer in China increased markedly from 19.04 (16.46, 21.81) to 31.44 (25.53, 37.97), with an EAPC of 1.75 (1.64, 1.86). In contrast, the ASDR showed a slight decline, from 16.69 (12.38, 20.95) to 13.64 (11.09, 16.31), indicating a complex but concerning epidemiological shift.\u003c/p\u003e\u003cp\u003eIn addition, comparisons with global trends and regions at different SDI levels\u0026mdash;including High SDI, High-middle SDI, Middle SDI, Low-middle SDI, and Low SDI\u0026mdash;can help contextualize the recent progress made by China in cancer prevention and control, as well as highlight areas that require further improvement. Regarding the overall ASIR of neoplasms, there has been no significant change in China or in most SDI regions, except for a notable increase observed in High SDI regions. Interestingly, the ASDR has demonstrated a marked decline globally, as well as in China and in the High, High-middle, and Middle SDI regions. This trend may largely be attributed to advances in medical technologies and improvements in healthcare systems.\u003c/p\u003e\u003cp\u003eMoreover, the socioeconomic development of China has led to profound changes in both demographic characteristics\u0026mdash;such as population aging and growth\u0026mdash;and cancer-related risk factors, including environmental exposures, lifestyle, and behavioral patterns[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Therefore, it is essential to assess the distinct contributions of these risk factors and demographic shifts to the development and incidence trends of different cancer types[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR56 CR57\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The GBD 2021 analysis generated comprehensive, data-driven estimates linking 88 risk factors to 631 health outcomes across multiple demographic and geographic dimensions[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Given this, in addition to analyzing the disease burden of various cancer types in China, we further investigated five major cancers by incorporating data on three major categories of risk factors\u0026mdash;behavioral, environmental/occupational, and metabolic risks\u0026mdash;as provided by the GBD database[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, due to limitations in the availability and completeness of cancer-specific risk factor data, our analysis offers only a partial interpretation based on currently accessible information. Using data from the GBD database, this study analyzed the association between risk factors and the ASDR of the five leading cancers in China from 1990 to 2021. Tracheal, bronchus, and lung cancer exhibited the highest ASDR attributable to tobacco use and air pollution. At more granular levels, smoking and ambient particulate matter pollution were identified as the predominant contributors. Although ambient particulate matter pollution-related ASDR increased significantly over time, household air pollution from solid fuels showed a marked decline. For liver and colorectal cancers, high body-mass index demonstrated the most significant upward trend, while in colorectal cancer, dietary risks, particularly low fiber intake, exhibited a consistent decline. In contrast, stomach and esophageal cancers showed no risk factors with increasing trends; their burden declined primarily due to improvements in dietary patterns, including reduced sodium intake and increased vegetable consumption. Joinpoint regression analysis further confirmed these temporal shifts, highlighting the changing risk landscape for major cancers in China and underscoring the need for targeted prevention strategies. Meanwhile, despite the declines in ASIR, ASDR, and EAPC for stomach, liver, and esophageal cancers, the absolute number of cases continues to rise. This increase is likely driven by population aging, which was identified as the most significant positive contributor to the rise in cancer-related mortality among the top five cancer types in China in 2021. Cancer-related deaths peaked in the 70\u0026ndash;74 age group for both sexes. Decomposition analysis revealed that the shifting cancer burden in China is primarily influenced by demographic changes; however, different cancer types exhibited distinct epidemiological trends, highlighting the need for more tailored prevention and control strategies. Although population aging significantly influences cancer incidence, a large proportion of cancers remain preventable through prevention strategies, especially in low- and middle-income countries[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study employed multiple analytical approaches to conduct a comprehensive investigation of cancer-related data in China from the Global Burden of Disease database; however, several aspects warrant further exploration and refinement. First, data incompleteness\u0026mdash;particularly in risk factor categories\u0026mdash;may introduce estimation bias for certain cancer types or risk burdens. Given the vast geographic and demographic diversity within mainland China, future analysis should aim to disaggregate data into more granular and representative subregions and populations. Second, the integration of multi-omics data\u0026mdash;such as genomics, transcriptomics, and proteomics\u0026mdash;would allow for a deeper understanding of the molecular mechanisms underlying cancer susceptibility among different groups, facilitating more precise insights into cancer pathogenesis. Finally, as the current dataset extends only through 2021, updated analysis incorporating more recent data are needed to capture ongoing epidemiological trends and inform timely public health interventions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, cancer ranks as the second leading cause of disease-related mortality worldwide and represents a major public health challenge in China. In 2021, neoplasms accounted for 24.07% of all-cause deaths across all age groups and both sexes in mainland China. The ASDR declined substantially from 209.52 per 100,000 in 1980 to 137.48 in 2021, while ASIR showed a slight increase from 718.73 per 100,000 in 1990 to 790.17 in 2021. The top five cancer types in terms of number of deaths for both sexes and all ages in 2021 were tracheal, bronchus, and lung cancer; stomach cancer; esophageal cancer; colon and rectum cancer; and liver cancer. As China undergoes a critical phase of socioeconomic transition, understanding the sex-specific and age-related patterns of high-risk cancers, the contribution of key risk factors, and the impact of population aging is essential for developing effective, locally adapted control strategies and targeted interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration:\u0026nbsp;\u003c/strong\u003eAs the Global Burden of Disease (GBD) data are de-identified and publicly accessible, the use of these data in the present study does not require approval from an institutional review board.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized publicly available data from the Global Burden of Disease Study 2021 (https://vizhub.healthdata.org/gbd-results/) and the Global Fertility, Mortality, Migration, and Population Forecasts (2017-2100) (https://www.healthdata.org/data-tools-practices/interactive-visuals/population-forecasting). All data used in this study were publicly available, requiring no ethical approval and intended solely for academic research, as detailed in the Methods section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYSL, and WYD designed and conducted the study; YSL, XYH, WT, DPW, ZT, LNZ, and QGF analyzed and interpreted the data; YSL, XYH, YL, and WYD wrote and revised the manuscript. All authors have read and agreed to the drafted version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Henan Provincial Natural Science Foundation (252300420644).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKocarnik JM, Compton K, Dean FE, Fu W, Gaw BL, Harvey JD, Henrikson HJ, Lu D, Pennini A, Xu R\u003cem\u003e et al\u003c/em\u003e: Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life Years for 29 Cancer Groups From 2010 to 2019: A Systematic Analysis for the Global Burden of Disease Study 2019. \u003cem\u003eJAMA Oncol \u003c/em\u003e2022, 8(3):420-444.\u003c/li\u003e\n\u003cli\u003eDi Cesare M, Perel P, Taylor S, Kabudula C, Bixby H, Gaziano TA, McGhie DV, Mwangi J, Pervan B, Narula J\u003cem\u003e et al\u003c/em\u003e: The Heart of the World. \u003cem\u003eGlob Heart \u003c/em\u003e2024, 19(1):11.\u003c/li\u003e\n\u003cli\u003eReFaey K, Tripathi S, Grewal SS, Bhargav AG, Quinones DJ, Chaichana KL, Antwi SO, Cooper LT, Meyer FB, Dronca RS\u003cem\u003e et al\u003c/em\u003e: Cancer Mortality Rates Increasing vs Cardiovascular Disease Mortality Decreasing in the World: Future Implications. \u003cem\u003eMayo Clin Proc Innov Qual Outcomes \u003c/em\u003e2021, 5(3):645-653.\u003c/li\u003e\n\u003cli\u003eForeman KJ, Marquez N, Dolgert A, Fukutaki K, Fullman N, McGaughey M, Pletcher MA, Smith AE, Tang K, Yuan CW\u003cem\u003e et al\u003c/em\u003e: Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016-40 for 195 countries and territories. \u003cem\u003eLancet \u003c/em\u003e2018, 392(10159):2052-2090.\u003c/li\u003e\n\u003cli\u003eBray F, Jemal A, Grey N, Ferlay J, Forman D: Global cancer transitions according to the Human Development Index (2008-2030): a population-based study. \u003cem\u003eLancet Oncol \u003c/em\u003e2012, 13(8):790-801.\u003c/li\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2021, 71(3):209-249.\u003c/li\u003e\n\u003cli\u003eWu H, Wang Y, Zhang H, Yin X, Wang L, Wang L, Wu J: An investigation into the health status of the elderly population in China and the obstacles to achieving healthy aging. \u003cem\u003eScientific Reports \u003c/em\u003e2024, 14(1):31123.\u003c/li\u003e\n\u003cli\u003eGlobal incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. \u003cem\u003eLancet \u003c/em\u003e2024, 403(10440):2133-2161.\u003c/li\u003e\n\u003cli\u003eDiao X, Guo C, Jin Y, Li B, Gao X, Du X, Chen Z, Jo M, Zeng Y, Ding C\u003cem\u003e et al\u003c/em\u003e: Cancer situation in China: an analysis based on the global epidemiological data released in 2024. \u003cem\u003eCancer Commun (Lond) \u003c/em\u003e2025, 45(2):178-197.\u003c/li\u003e\n\u003cli\u003eLu J, Li M, He J, Xu Y, Zheng R, Zheng J, Qin G, Qin Y, Chen Y, Tang X\u003cem\u003e et al\u003c/em\u003e: Association of social determinants, lifestyle, and metabolic factors with mortality in Chinese adults: A nationwide 10-year prospective cohort study. \u003cem\u003eCell Rep Med \u003c/em\u003e2024, 5(8):101656.\u003c/li\u003e\n\u003cli\u003eCao W, Qin K, Li F, Chen W: Socioeconomic inequalities in cancer incidence and mortality: An analysis of GLOBOCAN 2022. \u003cem\u003eChin Med J (Engl) \u003c/em\u003e2024, 137(12):1407-1413.\u003c/li\u003e\n\u003cli\u003eXia C, Dong X, Li H, Cao M, Sun D, He S, Yang F, Yan X, Zhang S, Li N\u003cem\u003e et al\u003c/em\u003e: Cancer statistics in China and United States, 2022: profiles, trends, and determinants. \u003cem\u003eChin Med J (Engl) \u003c/em\u003e2022, 135(5):584-590.\u003c/li\u003e\n\u003cli\u003eHan B, Zheng R, Zeng H, Wang S, Sun K, Chen R, Li L, Wei W, He J: Cancer incidence and mortality in China, 2022. \u003cem\u003eJ Natl Cancer Cent \u003c/em\u003e2024, 4(1):47-53.\u003c/li\u003e\n\u003cli\u003eWu AH, Wu J, Tseng C, Stram DO, Shariff-Marco S, Larson T, Goldberg D, Fruin S, Jiao A, Inamdar PP\u003cem\u003e et al\u003c/em\u003e: Air Pollution and Breast Cancer Incidence in the Multiethnic Cohort Study. \u003cem\u003eJ Clin Oncol \u003c/em\u003e2025, 43(3):273-284.\u003c/li\u003e\n\u003cli\u003eYoon HY, Kim SY, Song JW: Association between high levels of nitrogen dioxide and increased cumulative incidence of lung cancer in patients with idiopathic pulmonary fibrosis. \u003cem\u003eEur Respir J \u003c/em\u003e2024, 63(5).\u003c/li\u003e\n\u003cli\u003eSaxena V: Water Quality, Air Pollution, and Climate Change: Investigating the Environmental Impacts of Industrialization and Urbanization. \u003cem\u003eWater, Air, \u0026amp; Soil Pollution \u003c/em\u003e2025, 236(2):73.\u003c/li\u003e\n\u003cli\u003ePeng D, Liu XY, Sheng YH, Li SQ, Zhang D, Chen B, Yu P, Li ZY, Li S, Xu RB: Ambient air pollution and the risk of cancer: Evidence from global cohort studies and epigenetic-related causal inference. \u003cem\u003eJ Hazard Mater \u003c/em\u003e2025, 489:137619.\u003c/li\u003e\n\u003cli\u003eZhang S, Chen W, Zhang Q, Krey V, Byers E, Rafaj P, Nguyen B, Awais M, Riahi K: Targeting net-zero emissions while advancing other sustainable development goals in China. \u003cem\u003eNature Sustainability \u003c/em\u003e2024, 7(9):1107-1119.\u003c/li\u003e\n\u003cli\u003eWu Z, Xia F, Lin R: Global burden of cancer and associated risk factors in 204 countries and territories, 1980-2021: a systematic analysis for the GBD 2021. \u003cem\u003eJ Hematol Oncol \u003c/em\u003e2024, 17(1):119.\u003c/li\u003e\n\u003cli\u003eGlobal burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. \u003cem\u003eLancet \u003c/em\u003e2024, 403(10440):2162-2203.\u003c/li\u003e\n\u003cli\u003eGlobal 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. \u003cem\u003eLancet \u003c/em\u003e2024, 403(10440):2100-2132.\u003c/li\u003e\n\u003cli\u003eGlobal burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. \u003cem\u003eLancet \u003c/em\u003e2020, 396(10258):1204-1222.\u003c/li\u003e\n\u003cli\u003eVollset SE, Goren E, Yuan CW, Cao J, Smith AE, Hsiao T, Bisignano C, Azhar GS, Castro E, Chalek J\u003cem\u003e et al\u003c/em\u003e: Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: a forecasting analysis for the Global Burden of Disease Study. \u003cem\u003eLancet \u003c/em\u003e2020, 396(10258):1285-1306.\u003c/li\u003e\n\u003cli\u003eEst\u0026egrave;ve J, Benhamou E, Raymond L: Statistical methods in cancer research. Volume IV. Descriptive epidemiology. \u003cem\u003eIARC Sci Publ \u003c/em\u003e1994(128):1-302.\u003c/li\u003e\n\u003cli\u003eQin Y, Tong X, Fan J, Liu Z, Zhao R, Zhang T, Suo C, Chen X, Zhao G: Global Burden and Trends in Incidence, Mortality, and Disability of Stomach Cancer From 1990 to 2017. \u003cem\u003eClin Transl Gastroenterol \u003c/em\u003e2021, 12(10):e00406.\u003c/li\u003e\n\u003cli\u003eKim HJ, Fay MP, Feuer EJ, Midthune DN: Permutation tests for joinpoint regression with applications to cancer rates. \u003cem\u003eStat Med \u003c/em\u003e2000, 19(3):335-351.\u003c/li\u003e\n\u003cli\u003eLiu B, Kim H-J, Feuer EJ, Graubard BI: Joinpoint Regression Methods of Aggregate Outcomes for Complex Survey Data. \u003cem\u003eJournal of Survey Statistics and Methodology \u003c/em\u003e2023, 11(4):967-989.\u003c/li\u003e\n\u003cli\u003eZhu J, Li S, Li X, Wang L, Du L, Qiu Y: Impact of population ageing on cancer-related disability-adjusted life years: A global decomposition analysis. \u003cem\u003eJ Glob Health \u003c/em\u003e2024, 14:04144.\u003c/li\u003e\n\u003cli\u003eCheng X, Yang Y, Schwebel DC, Liu Z, Li L, Cheng P, Ning P, Hu G: Population ageing and mortality during 1990-2017: A global decomposition analysis. \u003cem\u003ePLoS Med \u003c/em\u003e2020, 17(6):e1003138.\u003c/li\u003e\n\u003cli\u003eRiebler A, Held L: Projecting the future burden of cancer: Bayesian age-period-cohort analysis with integrated nested Laplace approximations. \u003cem\u003eBiom J \u003c/em\u003e2017, 59(3):531-549.\u003c/li\u003e\n\u003cli\u003eLiu Z, Yin P: Trends in mortality for gastric cancer from 2011 to 2020 with prediction to 2030: a Bayesian age-period-cohort analysis. \u003cem\u003eThe Lancet Regional Health \u0026ndash; Western Pacific \u003c/em\u003e2025, 55.\u003c/li\u003e\n\u003cli\u003eMartins TG, Simpson D, Lindgren F, Rue H: Bayesian computing with INLA: New features. \u003cem\u003eComputational Statistics \u0026amp; Data Analysis \u003c/em\u003e2013, 67:68-83.\u003c/li\u003e\n\u003cli\u003eXue M, Guo W, Zhou Y, Meng J, Xi Y, Pan L, Ye Y, Zeng Y, Che Z, Zhang L\u003cem\u003e et al\u003c/em\u003e: Age-sex-specific burden of urological cancers attributable to risk factors in China and its provinces, 1990-2021, and forecasts with scenarios simulation: a systematic analysis for the Global Burden of Disease Study 2021. \u003cem\u003eLancet Reg Health West Pac \u003c/em\u003e2025, 56:101517.\u003c/li\u003e\n\u003cli\u003eChen W, Xia C, Zheng R, Zhou M, Lin C, Zeng H, Zhang S, Wang L, Yang Z, Sun K\u003cem\u003e et al\u003c/em\u003e: Disparities by province, age, and sex in site-specific cancer burden attributable to 23 potentially modifiable risk factors in China: a comparative risk assessment. \u003cem\u003eLancet Glob Health \u003c/em\u003e2019, 7(2):e257-e269.\u003c/li\u003e\n\u003cli\u003eGao TY, Tao YT, Li HY, Liu X, Ma YT, Li HJ, Xian-Yu CY, Deng NJ, Leng WD, Luo J\u003cem\u003e et al\u003c/em\u003e: Cancer burden and risk in the Chinese population aged 55 years and above: A systematic analysis and comparison with the USA and Western Europe. \u003cem\u003eJ Glob Health \u003c/em\u003e2024, 14:04014.\u003c/li\u003e\n\u003cli\u003eWu Z, Xia F, Wang W, Zhang K, Fan M, Lin R: Worldwide burden of liver cancer across childhood and adolescence, 2000-2021: a systematic analysis of the Global Burden of Disease Study 2021. \u003cem\u003eEClinicalMedicine \u003c/em\u003e2024, 75:102765.\u003c/li\u003e\n\u003cli\u003eYang M, Du J, Lu H, Xiang F, Mei H, Xiao H: Global trends and age-specific incidence and mortality of cervical cancer from 1990 to 2019: an international comparative study based on the Global Burden of Disease. \u003cem\u003eBMJ Open \u003c/em\u003e2022, 12(7):e055470.\u003c/li\u003e\n\u003cli\u003eLi G, Fang C, Wang S, Sun S: The Effect of Economic Growth, Urbanization, and Industrialization on Fine Particulate Matter (PM(2.5)) Concentrations in China. \u003cem\u003eEnviron Sci Technol \u003c/em\u003e2016, 50(21):11452-11459.\u003c/li\u003e\n\u003cli\u003eShi T, Hu Y, Liu M, Li C, Zhang C, Liu C: How Do Economic Growth, Urbanization, and Industrialization Affect Fine Particulate Matter Concentrations? An Assessment in Liaoning Province, China. \u003cem\u003eInt J Environ Res Public Health \u003c/em\u003e2020, 17(15).\u003c/li\u003e\n\u003cli\u003eWang Q, Kwan MP, Zhou K, Fan J, Wang Y, Zhan D: The impacts of urbanization on fine particulate matter (PM(2.5)) concentrations: Empirical evidence from 135 countries worldwide. \u003cem\u003eEnviron Pollut \u003c/em\u003e2019, 247:989-998.\u003c/li\u003e\n\u003cli\u003eVineis P, Airoldi L, Veglia F, Olgiati L, Pastorelli R, Autrup H, Dunning A, Garte S, Gormally E, Hainaut P\u003cem\u003e et al\u003c/em\u003e: Environmental tobacco smoke and risk of respiratory cancer and chronic obstructive pulmonary disease in former smokers and never smokers in the EPIC prospective study. \u003cem\u003eBMJ \u003c/em\u003e2005, 330(7486):277.\u003c/li\u003e\n\u003cli\u003eLiu X, Yang Q, Pan L, Ye Y, Kuang L, Xu D, Wang L, Hu S, Nie Y, Huang J\u003cem\u003e et al\u003c/em\u003e: Burden of respiratory tract cancers in China and its provinces, 1990-2021: a systematic analysis of the Global Burden of Disease Study 2021. \u003cem\u003eLancet Reg Health West Pac \u003c/em\u003e2025, 55:101485.\u003c/li\u003e\n\u003cli\u003eOhshima H, Bartsch H: Chronic infections and inflammatory processes as cancer risk factors: possible role of nitric oxide in carcinogenesis. \u003cem\u003eMutat Res \u003c/em\u003e1994, 305(2):253-264.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Byrne KJ, Dalgleish AG: Chronic immune activation and inflammation as the cause of malignancy. \u003cem\u003eBritish Journal of Cancer \u003c/em\u003e2001, 85(4):473-483.\u003c/li\u003e\n\u003cli\u003eDuan Y, Xu Y, Dou Y, Xu D: Helicobacter pylori and gastric cancer: mechanisms and new perspectives. \u003cem\u003eJournal of Hematology \u0026amp; Oncology \u003c/em\u003e2025, 18(1):10.\u003c/li\u003e\n\u003cli\u003eWroblewski LE, Peek RM, Jr., Wilson KT: Helicobacter pylori and gastric cancer: factors that modulate disease risk. \u003cem\u003eClin Microbiol Rev \u003c/em\u003e2010, 23(4):713-739.\u003c/li\u003e\n\u003cli\u003eRizzo GEM, Cabibbo G, Crax\u0026igrave; A: Hepatitis B Virus-Associated Hepatocellular Carcinoma. \u003cem\u003eViruses \u003c/em\u003e2022, 14(5).\u003c/li\u003e\n\u003cli\u003eLevrero M, Zucman-Rossi J: Mechanisms of HBV-induced hepatocellular carcinoma. \u003cem\u003eJ Hepatol \u003c/em\u003e2016, 64(1 Suppl):S84-S101.\u003c/li\u003e\n\u003cli\u003eCao M, Fan J, Lu L, Fan C, Wang Y, Chen T, Zhang S, Yu Y, Xia C, Lu J\u003cem\u003e et al\u003c/em\u003e: Long term outcome of prevention of liver cancer by hepatitis B vaccine: Results from an RCT with 37 years. \u003cem\u003eCancer Lett \u003c/em\u003e2022, 536:215652.\u003c/li\u003e\n\u003cli\u003eJiang Q, Shu Y, Jiang Z, Zhang Y, Pan S, Jiang W, Liang J, Cheng X, Xu Z: Burdens of stomach and esophageal cancer from 1990 to 2019 and projection to 2030 in China: Findings from the 2019 Global Burden of Disease Study. \u003cem\u003eJ Glob Health \u003c/em\u003e2024, 14:04025.\u003c/li\u003e\n\u003cli\u003eZhang HZ, Jin GF, Shen HB: Epidemiologic differences in esophageal cancer between Asian and Western populations. \u003cem\u003eChin J Cancer \u003c/em\u003e2012, 31(6):281-286.\u003c/li\u003e\n\u003cli\u003eLiang H, Fan JH, Qiao YL: Epidemiology, etiology, and prevention of esophageal squamous cell carcinoma in China. \u003cem\u003eCancer Biol Med \u003c/em\u003e2017, 14(1):33-41.\u003c/li\u003e\n\u003cli\u003eDurko L, Malecka-Panas E: Lifestyle Modifications and Colorectal Cancer. \u003cem\u003eCurr Colorectal Cancer Rep \u003c/em\u003e2014, 10(1):45-54.\u003c/li\u003e\n\u003cli\u003eLi M, Hu M, Jiang L, Pei J, Zhu C: Trends in Cancer Incidence and Potential Associated Factors in China. \u003cem\u003eJAMA Netw Open \u003c/em\u003e2024, 7(10):e2440381.\u003c/li\u003e\n\u003cli\u003eKuang Z, Wang J, Liu K, Wu J, Ge Y, Zhu G, Cao L, Ma X, Li J: Global, regional, and national burden of tracheal, bronchus, and lung cancer and its risk factors from 1990 to 2021: findings from the global burden of disease study 2021. \u003cem\u003eEClinicalMedicine \u003c/em\u003e2024, 75:102804.\u003c/li\u003e\n\u003cli\u003eJani CT, Kareff SA, Morgenstern-Kaplan D, Salazar AS, Hanbury G, Salciccioli JD, Marshall DC, Shalhoub J, Singh H, Rodriguez E\u003cem\u003e et al\u003c/em\u003e: Evolving trends in lung cancer risk factors in the ten most populous countries: an analysis of data from the 2019 Global Burden of Disease Study. \u003cem\u003eEClinicalMedicine \u003c/em\u003e2025, 79:103033.\u003c/li\u003e\n\u003cli\u003eQin N, Fan Y, Yang T, Yang Z, Fan D: The burden of Gastric Cancer and possible risk factors from 1990 to 2021, and projections until 2035: findings from the Global Burden of Disease Study 2021. \u003cem\u003eBiomark Res \u003c/em\u003e2025, 13(1):5.\u003c/li\u003e\n\u003cli\u003eLi T, Zhang H, Lian M, He Q, Lv M, Zhai L, Zhou J, Wu K, Yi M: Global status and attributable risk factors of breast, cervical, ovarian, and uterine cancers from 1990 to 2021. \u003cem\u003eJ Hematol Oncol \u003c/em\u003e2025, 18(1):5.\u003c/li\u003e\n\u003cli\u003eBray F, Jemal A, Torre LA, Forman D, Vineis P: Long-Term Realism and Cost-Effectiveness: Primary Prevention in Combatting Cancer and Associated Inequalities Worldwide. \u003cem\u003eJNCI: Journal of the National Cancer Institute \u003c/em\u003e2015, 107(12):djv273.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"China, Cancer, GBD, Incidence and mortality, Risk factors","lastPublishedDoi":"10.21203/rs.3.rs-7512162/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7512162/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eIn China, the incidence and mortality rates of cancer have shown a significant upward trajectory from 1980/1990 to 2021, resulting in an escalating public health burden. Identifying key risk factors is critical for improving cancer prevention and management strategies. This study primarily analyzes cancer incidence and mortality data, with a particular focus on understanding the patterns and underlying factors that contribute to these trends.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eData from the Global Burden of Disease 2021 study were utilized. A combination of statistical analyses, decomposition analysis, Joinpoint regression analysis, and Bayesian Age-Period-Cohort modeling were employed to examine temporal trends of various cancer types across different sexes and age groups. Additionally, risk factors were identified and projected trends for the five leading cancer types were analyzed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn 2021, cancer accounted for 24.07% of all deaths in China. Lung, stomach, esophageal, colorectal, and liver cancers collectively accounted for 71.08% of cancer-related mortality. While age-standardized death rates (ASDR) for most cancers decreased from 1980 to 2021, age-standardized incidence rates (ASIR) significantly increased. Male cancer mortality was nearly 1.8 times higher than that of females, though both sexes shared similar leading cancer types. Notably, breast cancer ranked among the top five causes of cancer-related deaths in women. Mortality peaked in the 70\u0026ndash;74 age group for both sexes. The incidence of breast cancer was higher in females at younger ages, while males surpassed females in incidence from age 60 onward. Behavioral and environmental risk factors, particularly tobacco use and air pollution, have the greatest impact on lung cancer. Decomposition analysis revealed that the increase in cancer mortality was predominantly driven by population aging. By 2050, colorectal cancer incidence is expected to rise, while liver cancer is projected to continue its downward trend.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe cancer profile of China has shifted over the past 30 years. The decline in ASDR indicates improvements in treatment and management, while the rise in ASIR reflects both increased risk exposure and enhanced detection capabilities. In light of aging demographics, economic development, and environmental changes, identifying predominant cancer types and their associated risk factors is essential for developing effective control strategies and targeted interventions.\u003c/p\u003e","manuscriptTitle":"Burden of cancer and associated risk factors in China from 1990 to 2021, with projections to 2050 : a systematic analysis for the Global Burden of Disease Study 2021","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 07:25:04","doi":"10.21203/rs.3.rs-7512162/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-09-25T15:05:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-04T09:44:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-03T04:37:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-03T04:36:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-09-02T01:18:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e7009d2c-9afb-4664-9320-598b44e55134","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-08T07:25:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 07:25:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7512162","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7512162","identity":"rs-7512162","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}