Epidemic characteristics, spatiotemporal pattern, and etiologic factors of liver cancer burden in China from 2010 to 2016: A retrospective analysis

Epidemic characteristics, spatiotemporal pattern, and etiologic factors of liver cancer burden in China from 2010 to 2016: A retrospective analysis | 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 Epidemic characteristics, spatiotemporal pattern, and etiologic factors of liver cancer burden in China from 2010 to 2016: A retrospective analysis Tian Tian, Yangyuna Yang, Jie Wu, Jianzhen Shan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4725208/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background With the rising prevalence of obesity, increasing alcohol consumption and the advances in hepatitis virus treatment, liver cancer epidemiology gradually changes. However, the impact of these changes on liver cancer burden in China remains unclear. This study aimed to assess temporal trends in liver cancer burden across the whole country and 33 province-level administrative regions and the contributions of various liver cancer etiologies in China from 2010 to 2016. Methods The age-standardized incidence/death rate for liver cancer from 2010 to 2016 was evaluated according to sex, age, and etiology using data from the 2016 Global Burden of Disease study. The liver cancer-related age-standardized rates in the 33 province-level administrative regions of China were obtained from the National Central Cancer Registry. Results From 2010 to 2016, there were 25% and 22% increase in liver cancer incidence and death respectively, while the age-standardized incidence/death rate remained stable. South China, especially rural South, had the highest incidence and death rate of liver cancer in the whole country. The proportion of alcohol and non-alcoholic steatohepatitis-associated liver cancer incidence and death increased, whereas that of HBV-associated liver cancer incidence and death decreased from 2010 to 2016. Non-alcoholic steatohepatitis was the only etiology with an increase in liver cancer incidence rate, and alcohol showed the fast-growing incidence of liver cancer in some age groups. Conclusions Urgent measures are required at a national level to tackle the underlying metabolic risk factors and slow down the rising burden of non-alcoholic steatohepatitis -induced liver cancer. Liver cancer burden epidemiology nonalcoholic steatohepatitis alcohol hepatitis virus. Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Liver cancer remains a global public health concern as the sixth-most frequent cancer and the fourth-leading cause of cancer-related death worldwide [ 1 ]. According to GLOBOCAN 2020, the global incidence of liver cancer was estimated to be as high as 0.9 million, with the highest incidence observed in East Asia and Africa [ 1 , 2 ]. China is the region most affected by liver cancer globally, accounting for > 50% of newly diagnosed cases and deaths, despite accounting for only 19% of the global population [ 3 , 4 ]. Recently, because of the control of hepatitis B and C virus (HBV and HCV) infection, a decreasing trend in incidence was observed in several global regions, including China [ 5 ]. However, the incidence of liver cancer due to metabolic and other causes has alarmingly risen in various countries [ 6 – 8 ]. In addition, liver cancer is a lethal malignant disease, considering the disappointing results of systematic treatments and the limitations of new approaches. According to the National Cancer Institute’s Surveillance, Epidemiology, and End Results program, the overall five-year relative survival rate is 20% for liver cancer, 35% for localized stage, 12% for regional stage, and 3% for distant stage [ 9 ]. Over 0.8 million deaths were caused by liver cancer worldwide in 2020 [ 9 ] despite many public health efforts being undertaken to address this problem. The underlying etiological factors of liver cancer have been widely identified, including HBV and HCV infection, alcohol intake, nonalcoholic steatohepatitis (NASH), and other causes [ 10 , 11 ]. The incidence of liver cancer is largely determined by the prevalence of risk factors across different countries and has changed over recent decades. Owing to vaccination coverage for HBV, enactment of the Blood Donation Law, and widespread availability of antiviral therapy, the burden of HBV- or HCV-associated liver cancer has significantly decreased both in China and worldwide, especially among young adults. However, the growing economy has fueled an increase in the global per capita consumption of alcohol, which has increased the burden of alcohol-induced liver cancer [ 12 ]. The prevalence of NASH has also increased along with the surge in obesity and diabetes, producing an increase in NASH-induced liver cancer in various regions, including the United States, Europe, and Asia. However, recent data is lacking from China. The disease burden of liver cancer has decreased in China over the past few decades in terms of incidence, mortality, and disability-adjusted life years (DALYs) [ 1 , 12 , 13 ]. Studies have reported that the incidence rate of liver cancer has decreased by > 2% per year in both males and females over recent decades, especially in the younger generation, particularly for those < 40 years of age, who showed a faster downward trend [ 14 ]. Moreover, the incidence and mortality of liver cancer declined significantly in both urban and rural areas but were more pronounced in rural areas of China [ 15 ]. However, since 1990, studies on liver cancer epidemiology in China have largely focused on certain regions- or etiology-specific data [ 16 , 17 ], and an updated comparative picture of the national liver cancer burden in China remains limited. Herein, we used liver cancer incidence, mortality, and DALYs data from the Global Burden of Disease (GBD) study in the general population according to sex, age, and etiology for the 2010–2016 period at the national level. Additionally, data on liver cancer in 33 provinces of China from the Chinese Center for Disease Control and Prevention were used to obtain an overview of the liver cancer burden in different regions of China. We aimed to report temporal trends in liver cancer burden throughout China and 33 province-level administrative regions, and the contributions of various liver cancer etiologic factors in China from 2010 to 2016. This study provides valuable insights for evidence-based healthcare planning and resource distribution for liver cancer control and prevention in China. 2. Materials and methods 2.1. Data sources Whole country data of this study were obtained from the GBD 2016 study, a systematic effort to estimate the burden of 328 diseases and 84 risk factors in 195 countries/territories. Annual frequencies and age-standardized rates (ASRs) of liver cancer-related incidence, mortality, and DALYs from 1990 to 2016 by sex, age, region, and country were obtained from an online data source, the Global Health Data Exchange (GHDx) query tool ( http://ghdx.healthdat.com/gbd-results-tool ), which is maintained by an ongoing multinational collaboration and coordinated by the Institute for Health Metrics and Evaluation [ 18 ]. To obtain regional-level data for China, 33 province-level administrative regions of China (including two Special Administrative Regions) were analyzed in this study. The 2016 data for two Special Administrative Regions (Hong Kong and Macao) were obtained from the Hong Kong Cancer Registry and Health Bureau of Statistics of Macao. The ASRs of liver cancer-related incidence and mortality in 31 provinces (including autonomous regions and municipalities) were obtained from the National Central Cancer Registry (NCCR), which collected registration data from 2016 from 487 cancer registries (after data quality control), of which 200 registries were from rural areas and 287 were from urban areas. The population covered by these cancer registries was 381,565,422 (193,632,323 males and 187,933,099 females), accounting for 27.6% of the national population as of the end of 2016. 2.2. Estimates of disease burden The general methods used for the GBD 2016 are described in previous studies [ 10 , 18 ]. Briefly, various data from different sources, including the vital registration system and cancer registry incidence data, were transformed to generate cause-specific mortality estimates using the Cause of Death Ensemble Model (CODEm). The incidence was estimated by dividing the mortality estimates by the mortality-to-incidence ratios. DALYs were calculated as the sum of the years of life lost and years lived with disability. All ICD9 and ICD10 codes pertaining to primary liver cancer were included in the estimates. The GBD provides a simple quality assessment from 0 to 5 to assess the quality of the data provided by each country. To determine the proportion of liver cancer cases due to the five etiological groups included in GBD (HBV, HCV, alcohol, NASH, and other causes), a systematic literature search was performed in PubMed. Only population-based studies that provided data on the contribution of liver cancer etiologic factors were included. Search terms used in the systematic review for liver cancer etiology were “liver neoplasmas”[All Fields] OR "HCC"[All Fields] OR "liver cancer"[All Fields] OR "Carcinoma, Hepatocellular"[Mesh]) AND (("hepatitis B"[All Fields] OR "Hepatitis B"[Mesh] OR "Hepatitis B virus"[Mesh] OR"Hepatitis B Antibodies"[Mesh] OR "Hepatitis B Antigens"[Mesh]) OR ("hepatitis C"[All Fields] OR "Hepatitis C"[Mesh] OR "hepatitis C antibodies"[MESH] OR "Hepatitis C Antigens"[Mesh] OR "Hepacivirus"[Mesh]) OR ("alcohol"[All Fields] OR "Alcohol Drinking"[Mesh] OR "Alcohol-Related Disorders"[Mesh] OR "Alcoholism"[Mesh] OR "Alcohol-Induced Disorders"[Mesh])) NOT (animals[MeSH] NOT humans[MeSH]. Cases where the etiology was described as “unknown”, “idiopathic”, or “cryptogenic” were included in the “other causes” group. Other etiologies of liver disease, such as hemochromatosis, Wilson’s disease, autoimmune hepatitis, were also included in the “other causes” category. The proportion data found through the systematic literature review were used as input for five separate Dismod-MR2.1 models to determine the proportion of liver cancer due to the five subgroups for all locations, both sexes, and all age groups. When multiple risk factors were reported in individual patients, they were assigned in proportion to the individual risk factors. These estimated proportions were used to split the overall liver cancer estimates into those for the respective liver cancer etiologies. The proportion models were run independently of each other, and the final proportion models were consequently scaled to sum up to 100% for each age, sex, year, and location by dividing each proportion by the sum of the five models. The methods used for the NCCR data have been described in previous studies [ 19 , 20 ]. Cancer incidence and mortality rates stratified by age (0-, 1–4, 5–84 by 5 years, and 85 + years), sex (male/female), area (urban/rural), and region (seven administrative regions: North, Northeast, East, Central, South, Southwest, and Northwest) were calculated using pooled qualified cancer registry data. A registry was classified as rural when located in a county, and as urban registry was classified when located in a city. Classification of the seven administrative regions was based on the National Bureau of Statistics. 2.3. Statistical analysis Standardization is necessary when comparing several populations with different age structures or the same population over time, where the age profiles change accordingly. The ASRs (per 100,000 population) were calculated by summing the products of the ASR and the number of persons in the same age groups of the chosen reference standard population and then dividing by the sum of the standard population weights. The annual percentage change (APC) in ASRs was estimated to quantify trends within a specific time interval. A 95% uncertainty interval (obtained) for each quantity was exhibited at the same time. The natural logarithm of ASRs is assumed to fit a linear regression model, Y = α + βX + ε, where Y is equal to the natural logarithm of age-standardized rates, α is a constant, β indicates positive or negative changing trends, X refers to the calendar year, and ε is the error. Therefore, estimated APCs = 100 × (e β − 1). An increasing trend was considered when the APC and lower boundary of the 95% confidence interval (CI) were both positive. Conversely, when the APC and upper boundary were negative, a decreasing trend was observed. Otherwise, the ASR was deemed stable over time. All statistical analyses were performed using R, and a p-value of < 0.05 was considered significant. 3. Results 3.1. Liver cancer burden in China, 2016 In 2016, 189,296 incident cases (95% uncertainty interval [UI] 171,330 − 209,173), 172,587 deaths (95% UI 149,454 − 197,473), and 4.9 million (95% UI 4.2–5.7 million) DALYs occurred in China due to liver cancer (Tables 1 , 2 and S1 ). In 2016, the estimated age-standardized incident rate (ASIR), age-standardized death rate (ASDR), and age-standardized DALYs (ASDALYs) of liver cancer were 10.16 per 100,000 (95% UI 9.24–11.2), 9.41 per 100,000 (95% UI 8.21–10.71), and 262.69 per 100,000 (95% UI 226.36–300.90), respectively (Tables 1 , 2 and S1 ). From 2010 to 2016, liver cancer incidence, liver cancer deaths, and DALYs increased by 25%, 22%, and 18%, respectively. Over this period, the estimated annual percentage changes (APCs) of the ASIR, ASDR and ASDALYs were stable, with APCs of 0.08% (95% CI − 0.12–0.32), 0.01% (95% CI − 0.17–0.22), and 0.03% (95% CI − 0.17–0.25), respectively (Tables 1 , 2 and S1 ). Table 1 Incident cases and age-standardized incidence rates of liver cancer in 2010 and 2016 and the temporal trend of age-standardized incident rates from 2010 to 2016 2010 2016 No. incident cases (95% UI) ASIR per 100,000 (95% UI) No. incident cases (95% UI) ASIR per 100,000 (95% UI) Annual percentage change of ASIR (95% CI) China 151,873 (137,015–168,534) 9.68 (8.76–10.74) 189,296 (171,330 − 209,173) 10.16 (9.24–11.20) 0.08 (-0.12 to 0.32) Etiology Alcohol 11,853 (9,160 − 15,030) 0.76 (0.59–0.96) 16,612 (12,703 − 21,099) 0.88 (0.69–1.11) 0.21 (-0.01 to 0.48) Hepatitis B 101,411 (89,731 − 114,562) 6.24 (5.53–7.02) 123,156 (108,220 − 139,247) 6.46 (5.69–7.28) 0.06 (-0.15 to 0.33) Hepatitis C 23,115 (20,017–26,384) 1.65 (1.43–1.89) 29,692 (25,403 − 33,765) 1.71 (1.47–1.94) 0.06 (-0.11 to 0.25) NASH 6,269 (5,183-7,488) 0.42 (0.35–0.50) 8,691 (7,188 − 10,367) 0.48 (0.40–0.57) 0.20 (0.01 to 0.41) Other causes 9,222 (7,862 − 10,910) 0.61 (0.53–0.72) 11,143 (9,334 − 13,164) 0.63 (0.54–0.74) 0.05 (-0.12 to 0.24) Age 0–9 years 297 (245–369) 0.21 (0.17–0.26) 327 (262–395) 0.22 (0.18–0.27) 0.03 (-0.22 to 0.20) 10–24 years 1,482 (1,302-1,791) 0.47 (0.42–0.57) 1,109 (996-1,283) 0.45 (0.40–0.52) -0.02 (-0.18 to 0.22) 25–29 years 1,377 (1,209-1,664) 1.32 (1.16–1.59) 1,851 (1,661-2,070) 1.42 (1.27–1.59) 0.15 (-0.11 to 0.44) 30–34 years 3,105 (2,706-3,705) 3.09 (2.69–3.69) 3,449 (3,099 − 3,861) 3.13 (2.82–3.51) 0.09 (-0.14 to 0.37) 35–39 years 6,941 (6,111-8,010) 5.68 (5.00-6.56) 6,003 (5,325-6,795) 6.09 (5.40–6.89) 0.18 (-0.08 to 0.50) 40–44 years 12,235 (10,757 − 14,138) 9.49 (8.35–10.97) 12,641 (11,159 − 14,363) 10.95 (9.67–12.44) 0.24 (-0.04 to 0.62) 45–49 years 15,342 (13,322 − 17,716) 14.09 (12.23–16.27) 19,462 (16,920 − 22,192) 15.42 (13.41–17.59) 0.10 (-0.19 to 0.47) 50–54 years 14,958 (13,308 − 16,791) 18.46 (16.43–20.72) 22,427 (19,612 − 25,752) 19.50 (17.05–22.39) 0.12 (-0.13 to 0.44) 55–59 years 20,569 (18,300 − 23,076) 24.76 (22.03–27.78) 20,389 (18,023 − 22,930) 26.27 (23.22–29.55) 0.10 (-0.15 to 0.42) 60–79 years 63,680 (57,785 − 70,272) 39.88 (36.18-44.00) 85,761 (77,994 − 94,249) 41.07 (37.35–45.41) 0.06 (-0.13 to 0.28) 80 + years 11,882 (10,695 − 13,085) 60.90 (54.82–67.06) 15,873 (14,266 − 17,182) 59.69 (53.65–64.62) -0.01 (-0.14 to 0.13) ASIR, age-standardized incidence rate; NASH, nonalcoholic steatohepatitis Table 2 Deaths and age-standardized death rates of liver cancer in 2010 and 2016 and the temporal trend of age-standardized death rates from 2010 to 2016 2010 2016 No. deaths (95% UI) ASDR per 100,000 (95% UI) No. deaths (95% UI) ASDR per 100,000 (95% UI) Annual percentage change of ASDR (95% CI) China 141,706 (128,396 − 157,119) 9.28 (8.42–10.27) 172,587 (149,454 − 197,473) 9.41 (8.21–10.71) 0.01 (-0.17 to 0.22) Etiology Alcohol 11,278 (8,798 − 14,535) 0.74 (0.58–0.95) 15,439 (11,870 − 20,082) 0.83 (0.64–1.06) 0.15 (-0.06 to 0.38) Hepatitis B 92,455 (82,477 − 104,945) 5.79 (5.17–6.57) 109,285 (92,021–127,730) 5.78 (4.88–6.73) -0.01 (-0.21 to 0.23) Hepatitis C 23,364 (20,050 − 26,641) 1.74 (1.51–1.98) 29,452 (24,843 − 34,705) 1.75 (1.48–2.05) 0.01 (-0.14 to 0.18) NASH 6,117 (5,061 − 7,247) 0.43 (0.35–0.51) 8,324 (6,773 − 10,108) 0.47 (0.39–0.57) 0.13 (-0.04 to 0.32) Other causes 8,490 (7,213 − 10,019) 0.58 (0.50–0.67) 10,085 (8,426 − 12,160) 0.58 (0.49–0.68) -0.01 (-0.16 to 0.16) Age 0–9 years 293 (259–337) 0.21 (0.18–0.24) 313 (250–361) 0.21 (0.17–0.24) -0.11 (-0.32 to 0.11) 10–24 years 901 (795-1,082) 0.29 (0.25–0.35) 641 (558–740) 0.26 (0.23–0.30) -0.08 (-0.25 to 0.12) 25–29 years 960 (850-1,149) 0.92 (0.81–1.10) 1,232 (1,073 − 1,402) 0.94 (0.82–1.08) 0.05 (-0.17 to 0.31) 30–34 years 2,408 (2,112-2,869) 2.40 (2.10–2.86) 2,540 (2,205-2,914) 2.31 (2.00-2.65) -0.01 (-0.20 to 0.27) 35–39 years 5,656 (5,015 − 6,558) 4.63 (4.11–5.37) 4,606 (3,910-5,402) 4.67 (3.96–5.48) 0.08 (-0.17 to 0.36) 40–44 years 10,263 (9,071 − 11,796) 7.96 (7.04–9.15) 9,994 (8,393 − 11,725) 8.66 (7.27–10.16) 0.13 (-0.13 to 0.44) 45–49 years 12,899 (11,197 − 14,841) 11.84 (10.28–13.63) 15,976 (13,243 − 19,109) 12.66 (10.49–15.14) 0.03 (-0.24 to 0.35) 50–54 years 12,763 (11,402 − 14,422) 15.75 (14.07–17.80) 18,637 (15,605 − 22,157) 16.20 (13.57–19.26) 0.06 (-0.19 to 0.36) 55–59 years 18,054 (16,149 − 20,256) 21.74 (19.44–24.39) 17,485 (14,666 − 20,504) 22.53 (18.90-26.42) 0.05 (-0.18 to 0.32) 60–79 years 63,203 (57,475 − 69,613) 39.58 (35.99–43.59) 82,599 (72,076–94,387) 39.56 (34.52–45.20) -0.01 (-0.16 to 0.19) 80 + years 14,300 (12,804 − 15,726) 73.29 (65.62–80.59) 18,559 (16,447 − 20,423) 69.80 (61.85–76.81) -0.06 (-0.17 to 0.07) ASDR, age-standardized death rate; NASH, nonalcoholic steatohepatitis 3.2. Burden of liver cancer in seven administrative regions of China in 2016 The estimated frequencies of liver cancer incidence and death rates (ASIRs and ASDRs) in different administrative regions are summarized in Table 3 and shown in Fig. 1 . South China has the highest incidence and death rates of liver cancer in the country. Specifically, South China had the highest ASIR (26.1 per 100,000) of liver cancer, followed by Southwest China (19.2 per 100,000) and Northeast China (18.6 per 100,000) in 2016 (Table 3 and Fig. 1 a). Similarly, South China had the highest ASDR (22.3 per 100,000) for liver cancer, followed by Southwest China (17.1 per 100,000), and Central China (15.7 per 100,000) (Table 3 and Fig. 1 b). North China had the lowest incidence and death rates of liver cancer (12.5 and 10.5 per 100,000 people, respectively) (Fig. 1 c) in the whole country in 2016. Table 3 Age-standardized incident rate (ASIR) and age-standardized death rate (ASDR) of liver cancer by geographic areas covered by 487 cancer, 2010 and 2016 (1/10 5 ) All areas Geographic areas ASIR per 100,000 in 2010 ASIR per 100,000 in 2010 ASIR per 100,000 in 2016 ASDR per 100,000 in 2016 All areas 19.8 17.5 17.7 15.2 North 15.2 13.2 12.5 10.5 Northeast 18.8 16.9 18.6 15.7 East 21.2 18.8 16.6 14.3 Central 22.0 18.7 18.5 15.8 South 26.1 23.5 26.1 22.3 Southwest 22.5 19.8 19.2 17.1 Northwest 18.5 14.5 18.1 14.5 Urban North 11.5 10.5 10.5 9.0 Northeast 16.3 15 15.9 13.4 East 18.5 16.3 15.4 13.1 Central 19.3 17.5 15.9 13.7 South 21.1 21.0 24.1 20.1 Southwest 19.7 17.5 18.0 16.2 Northwest 17.5 11.5 17.3 13.9 Rural North 20.0 17.9 14.6 12.0 Northeast 23.8 22.7 24.8 21.0 East 22.7 20.9 17.7 15.4 Central 22.6 18.9 20.3 17.3 South 40.0 38.0 30.0 26.6 Southwest 29.9 27.8 20.4 17.9 Northwest 25.4 22.8 20.9 16.3 ASIR, Age-standardized incident rate; ASDR: age-standardized death rate 3.3. Burden of liver cancer classified by area (urban and rural) in China, 2016 The ASIR and ASDR of liver cancer in the urban and rural areas of the seven administrative regions are summarized in Table 3 . Generally, the ASIR and ASDR for liver cancer in rural China were higher than those in urban China (19.3 vs 16.3 and 16.6 vs 13.9 per 100,000, respectively) (Table 3 , Fig. 1 d and 1 e). Similarly, the rural areas in each administrative region had higher incidence and death rates than the urban areas in that region. The highest ASIR and ASDR of liver cancer in 2016 occurred in southern rural areas (30.0 and 26.6 per 100,000 people, respectively), followed by northeastern rural areas (24.8 and 21 per 100,000 people, respectively). Notably, southern urban areas also had a high ASIR (24.1 peer 100,000) and ASDR (20.1 per 100,000) for liver cancer in 2016. North China, in both urban and rural areas, had the lowest incidence and death rates of liver cancer in the entire country (10.5 and 9.0 per 100,000, respectively) (Table 3 , Fig. 1 d and 1 e). Liver cancer incidence and death rates in rural and urban areas by sex and age were analyzed simultaneously. Generally, the ASIR and ASDR for liver cancer were higher in males than in females in both rural and urban areas. Specifically, the ASIR by Chinese standard population (ASR China) and World Segi population (ASR World) were 25.3% and 24.88% for males compared with 7.93% and 7.84% for females in urban China, and 29.48% and 28.79% for males compared with 9.80% and 9.72% for females in rural China, respectively. The ASDR by ASR China and ASR World were 21.61% and 21.32% for males compared with 6.70% and 6.61% for females in urban China and 25.5% and 24.97% for males compared with 8.26% and 8.19% for females in rural China. The age-specific incidence and mortality rate of liver cancer usually increased with age and were consistently high among older populations, regardless of the group, men or female, or urban or rural areas. 3.4. Trends in etiology of liver cancer The frequencies of incident cases and deaths, ASIRs, ASDRs, and DALYs according to liver cancer etiology are summarized in Tables 1 , 2 , and S1 . In 2010, 101,411 liver cancer cases were caused by hepatitis B, 23,115 by HCV, 11,853 by alcohol consumption, 9,222 by other causes, and 6,269 by NASH (Fig. 2 a). In 2010, HBV accounted for 67% of liver cancer incidence in China, followed by HCV (15%), alcohol (7.8%), other causes (6.1%), and NASH (4.1%) (Fig. 2 b). In 2016, 123,156 liver cancer cases were caused by hepatitis B, 29,692 by HCV, 16,612 by alcohol consumption, 11,143 by other causes, and 8,691 by NASH (Fig. 2 a). HBV accounted for 65% of the liver cancer incidence, followed by HCV (15%), alcohol (9%), other causes (6%), and NASH (5%) (Fig. 2 b). The proportion of alcohol-, NASH-, and other cause-associated liver cancer incidence increased, whereas the proportion of HBV-associated liver cancer incidence decreased, and the proportion of HCV-associated liver cancer incidence remained stable from 2010 to 2016. Similarly, in 2010, 109,285 hepatitis B-induced liver cancer deaths, 29,452 HCV-related liver cancer deaths, 15,439 alcohol-induced liver cancer deaths, 10,085 other-cause-induced liver cancer deaths, and 8,324 NASH-induced liver cancer deaths were reported (Fig. 2 c). HBV accounted for 65% of liver cancer deaths, followed by HCV (17%), alcohol consumption (8.0%), other causes (6.0%), and NASH (4%). HBV accounted for 63% of liver cancer deaths in 2016, followed by HCV (17%), alcohol (9%), other causes (6%), and NASH (5%) (Fig. 2 d). From 2010 to 2016, the proportion of alcohol- and NASH-associated liver cancer deaths increased, that of HBV-associated liver cancer deaths decreased, and that of HCV- and other cause-associated liver cancer deaths remained stable (Fig. 2 d). The ASIRs, ASDRs, ASDALYs, and APCs in liver cancer rates stratified by etiology from 2010 to 2016 are summarized in Tables 1 , 2 , and S1 , respectively. NASH was the only etiology with an increase in liver cancer ASIRs (APC 0.20%, 95% CI 0.01–0.41) from 2010 to 2016 (Fig. 2 e). ASIRs for alcohol-, HBV-, HCV- and other cause-associated liver cancer remained stable, with APCs of 0.21% (95% CI − 0.01–0.48), 0.06% (95% CI − 0.15–0.33), 0.06% (95% CI − 0.11–0.25), and 0.05% (95% CI − 0.12–0.24), respectively (Fig. 2 e). From 2010 to 2016, the ASDRs for the five etiology-related HCC cases remained stable in China (Table 2 ). Specifically, the ASDRs of liver cancer due to alcohol (APC: 0.15%, 95% CI − 0.06–0.38), NASH (APC: 0.13%, 95% CI − 0.04–0.32), HCV (APC: 0.11%, 95% CI − 0.014–0.18), HBV (APC: -0.01%, 95% CI − 0.21–0.23), and other causes (APC: −0.01%, 95% CI − 0.16–0.16) showed no significant changes from 2010 to 2016 (Table 2 ; Fig. 2 f). Similarly, the liver cancer ASDALYs from 2010 to 2016 in China were statistically stable, specifically reflected in alcohol-related (APC: 0.15%, 95% CI − 0.06–0.40), NASH-related (APC 0.15%, 95% CI − 0.05–0.36), HBV-related (APC: 0.02%, 95% CI − 0.20–0.28), HCV-related (APC: 0.02%, 95% CI − 0.14–0.20) and other cause-related (APC: −0.02%, 95% CI − 0.17 − 0.15) liver cancer (Table S1). 3.5. Trends in the etiology of liver cancer stratified by age The incidence, mortality, and DALY rates of liver cancer according to age are summarized in Tables 1 , 2 , and S1 . In 2016, the incidence rate of liver cancer was < 1.0/100,000 for individuals aged 80 years (59.69 per 100,000 individuals) (Table 1 ). Similar tendencies were observed in the mortality rates, which were consistently high in older populations. Specifically, the death rate of liver cancer was < 1.0/100,000 for those aged 10 per 100,000 population at 45 years, and peaked in the > 80-year age group (69.8 per 100,000 population) (Table 2 ). From 2010 to 2016, the incidence rates of liver cancer due to alcohol consumption and NASH increased in several age groups, with the highest APCs observed in the 40-44-year age group (0.32% and 0.34%, respectively), followed by those in the 35–39-year age group (0.24% and 0.27%, respectively) (Fig. 3 a and b). The incidence of liver cancer due to HBV, HCV, and other causes remained stable in all age groups from 2010 to 2016 (Table S2). The death rates and DALYs rates of liver cancer due to these causes in each age group from 2010 to 2016 are shown in Tables S3 and S4, which indicate no significant differences during this period. Additionally, we investigated the proportion of liver cancer according to etiology in different age groups. As shown in Fig. 4 a, HBV accounted for the highest frequency of liver cancer in each age group in 2016. The incidence rate of HBV-induced liver cancer increased and peaked in the 30–34-year age group (85%) and then decreased with age. However, the proportion of HCV- (37% in the > 80-year age group), alcohol- (11% in the 60–79-year age group), and NASH-induced liver cancer incidence (7.9% in the > 80-year age group) increased with age. Similar trends were observed for liver cancer mortality rate (Fig. 4 b). From 2010 to 2016, the proportion of liver cancer due to HCV and HBV infection declined in each age group, whereas the proportion of liver cancer incidence due to alcohol consumption and NASH increased in each age group. Similar trends were observed in the proportion of liver cancer mortality due to these causes during the study period (Fig. 4 b). The proportion of liver cancer mortality due to alcohol consumption and NASH increased in each age group from 2010 to 2016, whereas the proportion of liver cancer mortality due to HCV and HBV declined in each age group. 4. Discussion China is one of the countries most affected by liver cancer. As shown in previous studies, liver cancer was the only disease that had higher than expected age-standardized DALYs in all provinces of China, indicating the continuing serious impact of liver cancer in this country [ 5 , 21 ]. The present study is the most comprehensive evaluation of the disease burden of liver cancer in China, demarcated by etiology, age, area, and region. Owing to the standardized method for estimating liver cancer metrics used in the GBD study, we could compare the incidence and mortality levels in China with those worldwide or compare the data at the provincial level in China. Using data from the 2016 GBD study, we determined that from 2010 to 2016, the incidence of liver cancer cases and number of liver cancer-related deaths increased by 25% and 22%, respectively, although significant changes occurred in the age-standardized incidence and death rates. The growth and aging of the Chinese population probably contributes to this disconnect in the temporal trends of frequency and age-standardized incidence or death rates. South China, an endemic area for liver cancer [ 22 ], had the highest incidence and death rates of liver cancer in 2016, which was probably attributed to the high hepatitis incidence rate, prevalence of liver fluke infestation, and damp and muggy environment in this region. Moreover, cancer incidence and mortality are higher in rural areas than in urban areas. This disparity was primarily attributed to period effects, mainly referring to the unequal medical levels and resources between urban and rural areas [ 23 , 24 ]. Therefore, improving the medical level and resources in rural areas is essential, including reimbursing treatment for patients with chronic hepatitis, implementing annual cancer surveillance, and upgrading treatment quality in rural areas. Although the ASIR and ASDR due to HBV-associated liver cancer remained stable from 2010 to 2016, the proportion of HBV-associated liver cancer incidence and deaths decreased in each age group. Moreover, a reduction in the ASIR of HBV-associated cirrhosis and other chronic liver diseases was observed in each age group from 2010 to 2016 in China, indicating a decreasing trend in the incidence of HBV-associated liver cancer in the following years. This decrease was likely because of the impact of successful vaccination, antiviral therapy, and aflatoxin reduction programs [ 25 ]. Similarly, no significant changes occurred in the ASIR and ASDR of HCV-associated liver cancer or in the proportion of HCV-associated liver cancer from 2010 to 2016. We observed an increase in the ASIR of HCV-related cirrhosis and other chronic liver diseases in the 25–49-year age group, especially with individuals aged 25–29 years. Despite the implementation of blood donor screening in 1993 and the availability of highly effective oral antiviral therapy [ 26 – 28 ], a high HCV prevalence persists among high-risk populations, including people who inject drugs, patients on hemodialysis, and patients coinfected with human immunodeficiency virus or HBV [ 29 ]. Moreover, major gaps remain in early disease diagnosis and linkage to care [ 30 , 31 ]. Therefore, improvements are needed in infection control, disease screening, and treatment quality to achieve the elimination goal [ 29 , 32 ]. NASH and alcohol consumption were the two etiological factors of liver cancer that increased in incidence and death rates in each age group from 2010 to 2016. NASH was the only etiology with an increase in the liver cancer incidence rate from 2010 to 2016, prominently in the 35–45 age group, driven by rapidly rising obesity rates [ 33 , 34 ]. Moreover, the total burden related to nonalcoholic fatty liver disease (NAFLD) and the incidence of NAFLD-associated cirrhosis and other chronic liver diseases increased from 2010 to 2016, which was primarily observed in the 25–49-year age group, indicating that the incidence of liver cancer due to NASH is projected to increase in the next decade in China [ 35 ]. Therefore, increasing awareness and urgent actions are required to control metabolic risk factors and prevent the rising burden of NASH-related liver cancer in China [ 6 , 34 , 36 ]. Behavioral modifications and changes in diet and exercise habits should be reiterated. Although the ASIR of alcohol-induced liver cancer did not show a significant difference from 2010 to 2016 in China, we observed that the incidence of alcohol-induced liver cancer increased in several age groups, especially in the 35–45 age group. Moreover, alcohol per capita consumption increased from 2010 to 2016 in people aged > 25 years and is projected to increase [ 37 , 38 ]. The incidence of cirrhosis and other chronic diseases due to alcohol use has consistently increased in the past decade, primarily in individuals aged between 25 and 49 years. Implementing policies such as increasing prices or taxation for alcohol and adding cancer warnings to alcohol labels may be considered at the national level to postpone the rising burden of alcohol-related liver cancer in China [ 37 ]. Our study provides an in-depth analysis of the temporal trends in liver cancer and the contributions of various causes using data from the GBD 2016 and the NCCR of China. Our findings validate several previous studies [ 6 , 16 , 34 , 39 ] that reported the increasing impact of NASH and alcohol on the liver cancer burden and provide important information for care providers and policymakers. The current study provides a national overview of the temporal trends in the liver cancer burden and the contributions of various liver cancer etiologic factors in China from 2010 to 2016, unlike most other studies that have focused on a specific region/province or etiology of liver cancer. These data provide a comprehensive impression of the liver cancer burden in China and may help promote future endeavors in liver cancer diagnosis, management, and research. However, it remains critical to keep track of the epidemiology of liver cancer in China over time, particularly given the early signs of potential trends in several regions. 5. Limitations of study Our study had several limitations. First, the data for this study were derived from the GBD 2016, and the general limitations described by the GBD apply to our study. Second, multifactorial liver cancer was not considered in our study because of the scarcity of data in the GBD dataset. Several risk factors play a role in some liver cancer cases. For example, studies have demonstrated that alcohol consumption increases the risk of liver cancer in patients with HBV- and HCV-related cirrhosis [ 40 , 41 ]. Therefore, the temporal trends of multiple etiological factors in liver cancer development should be investigated in future studies. Third, the increasing trend in the liver cancer burden was associated with an increase in the number of cancer registry sites. Underreporting and misdiagnosis are inevitable during registration. Finally, the latest data on different administrative regions in China were obtained in 2016 and were recently published in 2022. For consistency, we used the GBD 2016 data instead of the GBD 2019 data. Therefore, continuing to track the epidemiology of liver cancer over time remains critical. 6. Conclusions The frequency of new cases, deaths, and DALYs due to liver cancer in China increased substantially from 2010 to 2016, although age-standardized incidence and death rates have remained stable at the national level. South China has the highest liver cancer incidence and death rates in the country, and rural areas usually have higher incidence and death rates than urban areas. NASH and alcohol consumption are the two fastest growing etiological factors of age-standardized liver cancer incidence, highlighting the urgent need to implement measures to overcome these growing issues. Declaration of Interest statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Abbreviations APC annual percentage change ASDALYs age-standardized DALYs ASDR age-standardized death rate ASIR age-standardized incident rate ASRs age-standardized rates CI confidence interval CODEm Cause of Death Ensemble Model DALYs Disability-adjusted life-years GBD Global Burden of Disease GHDx GlobalHealth Data Exchange HBV Hepatitis B virus HCV Hepatitis C virus NAFLD Non-alcoholic fatty liver disease NASH non-alcoholic steatohepatitis NCCR National Central Cancer Registry SEER Surveillance, Epidemiology, and End Results UI uncertainty interval Declarations Declaration of Interest statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Fundings This study was funded by the National Natural Science Foundation of China (grant no. NSFC 82100201, 2024C03169, 2017KY393, 2018KY428). Data Availability All data generated or analyzed during this study are included in this published paper. Acknowledgements We would like to thank the GBD Study 2016 and the National Cancer Center of China for providing the original data for this study. Author Contributions Tian Tian: Writing-original draft, writing-review & editing, formal analysis, data curation, resources, conceptualization. Yangyuna Yang: Writing-review & editing, formal analysis, data curation, resources. Jie Wu: Supervision, methodology, writing-review & editing, term, conceptualization, project administration. Jianzhen Shan: Supervision, writing-review & editing, funding acquisition, project administration. References Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49. Global Burden of Disease, Cancer C, Kocarnik JM, Compton K, 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):420–44. Zheng RS, Zhang SW, Sun KX, et al. Cancer statistics in China, 2016. Zhonghua Zhong Liu Za Zhi. 2023;45(3):212–20. Feng R, Su Q, Huang X, et al. Cancer situation in China: what does the China cancer map indicate from the first national death survey to the latest cancer registration? Cancer Commun (Lond). 2023;43(1):75–86. Zhou M, Wang H, Zeng X, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019;394(10204):1145–58. Huang DQ, El-Serag HB, Loomba R. Global epidemiology of NAFLD-related HCC: trends, predictions, risk factors and prevention. Nat Rev Gastroenterol Hepatol. 2021;18(4):223–38. Llovet JM, Kelley RK, Villanueva A, et al. Hepatocellular carcinoma. Nat Rev Dis Primers. 2021;7(1):6. Yao Z, Dai C, Yang J, et al. Time-trends in liver cancer incidence and mortality rates in the U.S. from 1975 to 2017: a study based on the Surveillance, Epidemiology, and End Results database. J Gastrointest Oncol. 2023;14(1):312–24. Miller KD, Nogueira L, Mariotto AB, et al. Cancer treatment and survivorship statistics, 2019. CA Cancer J Clin. 2019;69(5):363–85. Liu Z, Jiang Y, Yuan H, et al. The trends in incidence of primary liver cancer caused by specific etiologies: Results from the Global Burden of Disease Study 2016 and implications for liver cancer prevention. J Hepatol. 2019;70(4):674–83. Global Burden of Disease Liver, Cancer C, Akinyemiju T, Abera S, et al. The Burden of Primary Liver Cancer and Underlying Etiologies From 1990 to 2015 at the Global, Regional, and National Level: Results From the Global Burden of Disease Study 2015. JAMA Oncol. 2017;3(12):1683–91. Diseases GBD, Injuries C. 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):1204–22. Xia C, Basu P, Kramer BS, et al. Cancer screening in China: a steep road from evidence to implementation. Lancet Public Health. 2023;8(12):e996–1005. Zheng R, Qu C, Zhang S, et al. Liver cancer incidence and mortality in China: Temporal trends and projections to 2030. Chin J Cancer Res. 2018;30(6):571–9. Wu J, Yang S, Xu K, et al. Patterns and Trends of Liver Cancer Incidence Rates in Eastern and Southeastern Asian Countries (1983–2007) and Predictions to 2030. Gastroenterology. 2018;154(6):1719–28. Liu Z, Mao X, Jiang Y, et al. Changing trends in the disease burden of primary liver cancer caused by specific etiologies in China. Cancer Med. 2019;8(12):5787–99. Ding C, Fu X, Zhou Y, et al. Disease burden of liver cancer in China from 1997 to 2016: an observational study based on the Global Burden of Diseases. BMJ Open. 2019;9(4):e025613. Disease GBD, Injury I, Prevalence C. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390(10100):1211–59. Wei W, Zeng H, Zheng R, et al. Cancer registration in China and its role in cancer prevention and control. Lancet Oncol. 2020;21(7):e342–9. Bray F, Parkin DM. Evaluation of data quality in the cancer registry: principles and methods. Part I: comparability, validity and timeliness. Eur J Cancer. 2009;45(5):747–55. Wang FS, Fan JG, Zhang Z, et al. The global burden of liver disease: the major impact of China. Hepatology. 2014;60(6):2099–108. Wang H, Men P, Xiao Y, et al. Hepatitis B infection in the general population of China: a systematic review and meta-analysis. BMC Infect Dis. 2019;19(1):811. Sun Y, Wang Y, Li M, et al. Long-term trends of liver cancer mortality by gender in urban and rural areas in China: an age-period-cohort analysis. BMJ Open. 2018;8(2):e020490. Li X, Krumholz HM, Yip W, et al. Quality of primary health care in China: challenges and recommendations. Lancet. 2020;395(10239):1802–12. Cox AL, El-Sayed MH, Kao JH, et al. Progress towards elimination goals for viral hepatitis. Nat Rev Gastroenterol Hepatol. 2020;17(9):533–42. Jia Y, Li L, Cui F, et al. Cost-effectiveness analysis of a hepatitis B vaccination catch-up program among children in Shandong Province, China. Hum Vaccin Immunother. 2014;10(10):2983–91. Zhou Q, Liu A, Wang S, et al. Hepatitis C virus screening reactive among blood donors in mainland China: A systematic review and meta-analysis. Transfus Med. 2023;33(2):147–58. Dang H, Yeo YH, Yasuda S, et al. Cure With Interferon-Free Direct-Acting Antiviral Is Associated With Increased Survival in Patients With Hepatitis C Virus-Related Hepatocellular Carcinoma From Both East and West. Hepatology. 2020;71(6):1910–22. Li M, Zhuang H, Wei L. How would China achieve WHO's target of eliminating HCV by 2030? Expert Rev Anti Infect Ther. 2019;17(10):763–73. Desai N, Rich NE, Jain MK, et al. Randomized Clinical Trial of Inreach With or Without Mailed Outreach to Promote Hepatitis C Screening in a Difficult-to-Reach Patient Population. Am J Gastroenterol. 2021;116(5):976–83. Thomas DL. Global Elimination of Chronic Hepatitis. N Engl J Med. 2019;380(21):2041–50. Heffernan A, Cooke GS, Nayagam S, et al. Scaling up prevention and treatment towards the elimination of hepatitis C: a global mathematical model. Lancet. 2019;393(10178):1319–29. Xiao J, Wang F, Wong NK, et al. Global liver disease burdens and research trends: Analysis from a Chinese perspective. J Hepatol. 2019;71(1):212–21. Zhou F, Zhou J, Wang W, et al. Unexpected Rapid Increase in the Burden of NAFLD in China From 2008 to 2018: A Systematic Review and Meta-Analysis. Hepatology. 2019;70(4):1119–33. Estes C, Anstee QM, Arias-Loste MT, et al. Modeling NAFLD disease burden in China, France, Germany, Italy, Japan, Spain, United Kingdom, and United States for the period 2016–2030. J Hepatol. 2018;69(4):896–904. Tan DJH, Ng CH, Lin SY, et al. Clinical characteristics, surveillance, treatment allocation, and outcomes of non-alcoholic fatty liver disease-related hepatocellular carcinoma: a systematic review and meta-analysis. Lancet Oncol. 2022;23(4):521–30. Rumgay H, Shield K, Charvat H, et al. Global burden of cancer in 2020 attributable to alcohol consumption: a population-based study. Lancet Oncol. 2021;22(8):1071–80. Fan JG. Epidemiology of alcoholic and nonalcoholic fatty liver disease in China. J Gastroenterol Hepatol. 2013;28(Suppl 1):11–7. Zou H, Ge Y, Lei Q, et al. Epidemiology and disease burden of non-alcoholic steatohepatitis in greater China: a systematic review. Hepatol Int. 2022;16(1):27–37. Paik JM, Golabi P, Younossi Y, et al. Changes in the Global Burden of Chronic Liver Diseases From 2012 to 2017: The Growing Impact of NAFLD. Hepatology. 2020;72(5):1605–16. Llovet JM, Kelley RK, Villanueva A, et al. Hepatocellular carcinoma. Nat Rev Dis Primers. 2021;7(1):6. Additional Declarations No competing interests reported. Supplementary Files TableS1S4.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4725208","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":326926073,"identity":"3b0560ae-e87e-48d4-8364-5b7a37314739","order_by":0,"name":"Tian Tian","email":"","orcid":"","institution":"First Affiliated Hospital Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Tian","middleName":"","lastName":"Tian","suffix":""},{"id":326926075,"identity":"cf5e5f55-319a-49c0-8ce1-386be3a395ca","order_by":1,"name":"Yangyuna Yang","email":"","orcid":"","institution":"University of Nebraska Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Yangyuna","middleName":"","lastName":"Yang","suffix":""},{"id":326926078,"identity":"ba9903c8-d451-4f76-92fa-ee6debdcccf5","order_by":2,"name":"Jie Wu","email":"","orcid":"","institution":"First Affiliated Hospital Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Wu","suffix":""},{"id":326926080,"identity":"8d74bb44-f269-4d3d-8262-bf580a76fd90","order_by":3,"name":"Jianzhen Shan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYDACCTC2YWBsADJ4SNCSRqoWBobDEA5RWuRnNz97YPHnvD3zjATGB2/bGOTNCWkxuHPM3ECC5zYz44wEZsO5bQyGOxsIaZFIMJOQkLjNBtTCJs3bxpBgcICQw2akf5OQMDjHA9TC/psoLQw3coC2JByQANnCTJQWgxs5ZRISB5INGHseNkvOOSdhuIEIh22TlvhjZ2/Ynnzww5syG3nCDgMCZlDcGDaAI1OCCPVAwPgBZB1xakfBKBgFo2AkAgDeBjjiSq6JZAAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital Zhejiang University","correspondingAuthor":true,"prefix":"","firstName":"Jianzhen","middleName":"","lastName":"Shan","suffix":""}],"badges":[],"createdAt":"2024-07-11 15:36:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4725208/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4725208/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61878048,"identity":"684d1778-d213-4d9c-8b53-7b9f70a96559","added_by":"auto","created_at":"2024-08-06 14:45:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":279603,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated age-standardized rates from liver cancer in 2016 by seven administrative regions. (a) Age-standardized incidence rate. (b) Age-standardized death rate. (c) Age-standardized incidence and death rate. (d) Age-standardized incidence rate by geographic areas. (e) Age-standardized death rate by geographic areas.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4725208/v1/c50e77569ead89b5167cde82.png"},{"id":61878050,"identity":"bf2b282d-f261-4328-9f48-a7d475e39912","added_by":"auto","created_at":"2024-08-06 14:45:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":178695,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal trends in the etiology of liver cancer from 2010 to 2016. (a) Frequency of liver cancer cases in 2010 versus 2016 by etiology. (b) Contribution to frequency of liver cancer cases by etiology in 2010 versus 2016. (c) Frequency of liver cancer deaths in 2010 versus 2016 by etiology. (d) Contribution to frequency of liver cancer deaths by etiology in 2010 versus 2016. (e) Estimated age-standardized incidence rates of liver cancer in 2010 versus 2016 by etiology. (f) Estimated age-standardized death rates of liver cancer in 2010 versus 2016 by etiology. *, statistical difference with p\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4725208/v1/46ec9449983ab41b27353f64.png"},{"id":61878049,"identity":"04630029-f000-4dd2-a4af-6db2dc054702","added_by":"auto","created_at":"2024-08-06 14:45:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":97065,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated age-standardized incidence rates of liver cancer in 2010 versus 2016 by etiology and age. (a) Age-standardized incidence rates of alcohol-induced liver cancer by age. (b) Age-standardized incidence rates of NASH-induced liver cancer by age. *, statistical difference with p\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4725208/v1/dca413ebe1acb1c33bfe24d3.png"},{"id":61878606,"identity":"17f5761c-2ed8-4427-9102-003acbbe7c88","added_by":"auto","created_at":"2024-08-06 14:53:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":390451,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal trends in the etiology of liver cancer by age from 2010 to 2016. (a) Constituent ratio of liver cancer cases by etiology in different age group in 2010 versus 2016. (b) Constituent ratio of liver cancer deaths by etiology in different age group in 2010 versus 2016.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4725208/v1/f08de55e3d24bc9c24cddbc7.png"},{"id":79666733,"identity":"1b00dc6f-8e00-4800-83db-e1a61f661864","added_by":"auto","created_at":"2025-04-01 10:23:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2149548,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4725208/v1/7f74626e-d09f-4078-a51a-a96ff8302176.pdf"},{"id":61878047,"identity":"45052e3a-174f-4d41-b46a-723fe65a6c5a","added_by":"auto","created_at":"2024-08-06 14:45:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":54166,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1S4.docx","url":"https://assets-eu.researchsquare.com/files/rs-4725208/v1/c8ad886ccdbd0c939895054a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epidemic characteristics, spatiotemporal pattern, and etiologic factors of liver cancer burden in China from 2010 to 2016: A retrospective analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLiver cancer remains a global public health concern as the sixth-most frequent cancer and the fourth-leading cause of cancer-related death worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to GLOBOCAN 2020, the global incidence of liver cancer was estimated to be as high as 0.9\u0026nbsp;million, with the highest incidence observed in East Asia and Africa [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. China is the region most affected by liver cancer globally, accounting for \u0026gt;\u0026thinsp;50% of newly diagnosed cases and deaths, despite accounting for only 19% of the global population [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Recently, because of the control of hepatitis B and C virus (HBV and HCV) infection, a decreasing trend in incidence was observed in several global regions, including China [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the incidence of liver cancer due to metabolic and other causes has alarmingly risen in various countries [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition, liver cancer is a lethal malignant disease, considering the disappointing results of systematic treatments and the limitations of new approaches. According to the National Cancer Institute\u0026rsquo;s Surveillance, Epidemiology, and End Results program, the overall five-year relative survival rate is 20% for liver cancer, 35% for localized stage, 12% for regional stage, and 3% for distant stage [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Over 0.8\u0026nbsp;million deaths were caused by liver cancer worldwide in 2020 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] despite many public health efforts being undertaken to address this problem.\u003c/p\u003e \u003cp\u003eThe underlying etiological factors of liver cancer have been widely identified, including HBV and HCV infection, alcohol intake, nonalcoholic steatohepatitis (NASH), and other causes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The incidence of liver cancer is largely determined by the prevalence of risk factors across different countries and has changed over recent decades. Owing to vaccination coverage for HBV, enactment of the Blood Donation Law, and widespread availability of antiviral therapy, the burden of HBV- or HCV-associated liver cancer has significantly decreased both in China and worldwide, especially among young adults. However, the growing economy has fueled an increase in the global per capita consumption of alcohol, which has increased the burden of alcohol-induced liver cancer [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The prevalence of NASH has also increased along with the surge in obesity and diabetes, producing an increase in NASH-induced liver cancer in various regions, including the United States, Europe, and Asia. However, recent data is lacking from China.\u003c/p\u003e \u003cp\u003eThe disease burden of liver cancer has decreased in China over the past few decades in terms of incidence, mortality, and disability-adjusted life years (DALYs) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Studies have reported that the incidence rate of liver cancer has decreased by \u0026gt;\u0026thinsp;2% per year in both males and females over recent decades, especially in the younger generation, particularly for those\u0026thinsp;\u0026lt;\u0026thinsp;40 years of age, who showed a faster downward trend [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Moreover, the incidence and mortality of liver cancer declined significantly in both urban and rural areas but were more pronounced in rural areas of China [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, since 1990, studies on liver cancer epidemiology in China have largely focused on certain regions- or etiology-specific data [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and an updated comparative picture of the national liver cancer burden in China remains limited. Herein, we used liver cancer incidence, mortality, and DALYs data from the Global Burden of Disease (GBD) study in the general population according to sex, age, and etiology for the 2010\u0026ndash;2016 period at the national level. Additionally, data on liver cancer in 33 provinces of China from the Chinese Center for Disease Control and Prevention were used to obtain an overview of the liver cancer burden in different regions of China. We aimed to report temporal trends in liver cancer burden throughout China and 33 province-level administrative regions, and the contributions of various liver cancer etiologic factors in China from 2010 to 2016. This study provides valuable insights for evidence-based healthcare planning and resource distribution for liver cancer control and prevention in China.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data sources\u003c/h2\u003e \u003cp\u003eWhole country data of this study were obtained from the GBD 2016 study, a systematic effort to estimate the burden of 328 diseases and 84 risk factors in 195 countries/territories. Annual frequencies and age-standardized rates (ASRs) of liver cancer-related incidence, mortality, and DALYs from 1990 to 2016 by sex, age, region, and country were obtained from an online data source, the Global Health Data Exchange (GHDx) query tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ghdx.healthdat.com/gbd-results-tool\u003c/span\u003e\u003cspan address=\"http://ghdx.healthdat.com/gbd-results-tool\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which is maintained by an ongoing multinational collaboration and coordinated by the Institute for Health Metrics and Evaluation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. To obtain regional-level data for China, 33 province-level administrative regions of China (including two Special Administrative Regions) were analyzed in this study. The 2016 data for two Special Administrative Regions (Hong Kong and Macao) were obtained from the Hong Kong Cancer Registry and Health Bureau of Statistics of Macao. The ASRs of liver cancer-related incidence and mortality in 31 provinces (including autonomous regions and municipalities) were obtained from the National Central Cancer Registry (NCCR), which collected registration data from 2016 from 487 cancer registries (after data quality control), of which 200 registries were from rural areas and 287 were from urban areas. The population covered by these cancer registries was 381,565,422 (193,632,323 males and 187,933,099 females), accounting for 27.6% of the national population as of the end of 2016.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Estimates of disease burden\u003c/h2\u003e \u003cp\u003eThe general methods used for the GBD 2016 are described in previous studies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Briefly, various data from different sources, including the vital registration system and cancer registry incidence data, were transformed to generate cause-specific mortality estimates using the Cause of Death Ensemble Model (CODEm). The incidence was estimated by dividing the mortality estimates by the mortality-to-incidence ratios. DALYs were calculated as the sum of the years of life lost and years lived with disability. All ICD9 and ICD10 codes pertaining to primary liver cancer were included in the estimates. The GBD provides a simple quality assessment from 0 to 5 to assess the quality of the data provided by each country.\u003c/p\u003e \u003cp\u003eTo determine the proportion of liver cancer cases due to the five etiological groups included in GBD (HBV, HCV, alcohol, NASH, and other causes), a systematic literature search was performed in PubMed. Only population-based studies that provided data on the contribution of liver cancer etiologic factors were included. Search terms used in the systematic review for liver cancer etiology were \u0026ldquo;liver neoplasmas\u0026rdquo;[All Fields] OR \"HCC\"[All Fields] OR \"liver cancer\"[All Fields] OR \"Carcinoma, Hepatocellular\"[Mesh]) AND ((\"hepatitis B\"[All Fields] OR \"Hepatitis B\"[Mesh] OR \"Hepatitis B virus\"[Mesh] OR\"Hepatitis B Antibodies\"[Mesh] OR \"Hepatitis B Antigens\"[Mesh]) OR (\"hepatitis C\"[All Fields] OR \"Hepatitis C\"[Mesh] OR \"hepatitis C antibodies\"[MESH] OR \"Hepatitis C Antigens\"[Mesh] OR \"Hepacivirus\"[Mesh]) OR (\"alcohol\"[All Fields] OR \"Alcohol Drinking\"[Mesh] OR \"Alcohol-Related Disorders\"[Mesh] OR \"Alcoholism\"[Mesh] OR \"Alcohol-Induced Disorders\"[Mesh])) NOT (animals[MeSH] NOT humans[MeSH]. Cases where the etiology was described as \u0026ldquo;unknown\u0026rdquo;, \u0026ldquo;idiopathic\u0026rdquo;, or \u0026ldquo;cryptogenic\u0026rdquo; were included in the \u0026ldquo;other causes\u0026rdquo; group. Other etiologies of liver disease, such as hemochromatosis, Wilson\u0026rsquo;s disease, autoimmune hepatitis, were also included in the \u0026ldquo;other causes\u0026rdquo; category. The proportion data found through the systematic literature review were used as input for five separate Dismod-MR2.1 models to determine the proportion of liver cancer due to the five subgroups for all locations, both sexes, and all age groups. When multiple risk factors were reported in individual patients, they were assigned in proportion to the individual risk factors. These estimated proportions were used to split the overall liver cancer estimates into those for the respective liver cancer etiologies. The proportion models were run independently of each other, and the final proportion models were consequently scaled to sum up to 100% for each age, sex, year, and location by dividing each proportion by the sum of the five models.\u003c/p\u003e \u003cp\u003eThe methods used for the NCCR data have been described in previous studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Cancer incidence and mortality rates stratified by age (0-, 1\u0026ndash;4, 5\u0026ndash;84 by 5 years, and 85\u0026thinsp;+\u0026thinsp;years), sex (male/female), area (urban/rural), and region (seven administrative regions: North, Northeast, East, Central, South, Southwest, and Northwest) were calculated using pooled qualified cancer registry data. A registry was classified as rural when located in a county, and as urban registry was classified when located in a city. Classification of the seven administrative regions was based on the National Bureau of Statistics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Statistical analysis\u003c/h2\u003e \u003cp\u003eStandardization is necessary when comparing several populations with different age structures or the same population over time, where the age profiles change accordingly. The ASRs (per 100,000 population) were calculated by summing the products of the ASR and the number of persons in the same age groups of the chosen reference standard population and then dividing by the sum of the standard population weights. The annual percentage change (APC) in ASRs was estimated to quantify trends within a specific time interval. A 95% uncertainty interval (obtained) for each quantity was exhibited at the same time. The natural logarithm of ASRs is assumed to fit a linear regression model, Y\u0026thinsp;=\u0026thinsp;α\u0026thinsp;+\u0026thinsp;βX\u0026thinsp;+\u0026thinsp;ε, where Y is equal to the natural logarithm of age-standardized rates, α is a constant, β indicates positive or negative changing trends, X refers to the calendar year, and ε is the error. Therefore, estimated APCs\u0026thinsp;=\u0026thinsp;100 \u0026times; (e\u003csup\u003eβ\u003c/sup\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1). An increasing trend was considered when the APC and lower boundary of the 95% confidence interval (CI) were both positive. Conversely, when the APC and upper boundary were negative, a decreasing trend was observed. Otherwise, the ASR was deemed stable over time. All statistical analyses were performed using R, and a p-value of \u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Liver cancer burden in China, 2016\u003c/h2\u003e\n \u003cp\u003eIn 2016, 189,296 incident cases (95% uncertainty interval [UI] 171,330\u0026thinsp;\u0026minus;\u0026thinsp;209,173), 172,587 deaths (95% UI 149,454\u0026thinsp;\u0026minus;\u0026thinsp;197,473), and 4.9 million (95% UI 4.2\u0026ndash;5.7 million) DALYs occurred in China due to liver cancer (Tables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). In 2016, the estimated age-standardized incident rate (ASIR), age-standardized death rate (ASDR), and age-standardized DALYs (ASDALYs) of liver cancer were 10.16 per 100,000 (95% UI 9.24\u0026ndash;11.2), 9.41 per 100,000 (95% UI 8.21\u0026ndash;10.71), and 262.69 per 100,000 (95% UI 226.36\u0026ndash;300.90), respectively (Tables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). From 2010 to 2016, liver cancer incidence, liver cancer deaths, and DALYs increased by 25%, 22%, and 18%, respectively. Over this period, the estimated annual percentage changes (APCs) of the ASIR, ASDR and ASDALYs were stable, with APCs of 0.08% (95% CI \u0026minus;\u0026thinsp;0.12\u0026ndash;0.32), 0.01% (95% CI \u0026minus;\u0026thinsp;0.17\u0026ndash;0.22), and 0.03% (95% CI \u0026minus;\u0026thinsp;0.17\u0026ndash;0.25), respectively (Tables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIncident cases and age-standardized incidence rates of liver cancer in 2010 and 2016 and the temporal trend of age-standardized incident rates from 2010 to 2016\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. incident cases\u003c/p\u003e\n \u003cp\u003e(95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eASIR per 100,000 (95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. incident cases\u003c/p\u003e\n \u003cp\u003e(95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eASIR per 100,000 (95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnnual percentage change of ASIR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChina\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151,873\u003c/p\u003e\n \u003cp\u003e(137,015\u0026ndash;168,534)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.68\u003c/p\u003e\n \u003cp\u003e(8.76\u0026ndash;10.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189,296\u003c/p\u003e\n \u003cp\u003e(171,330\u0026thinsp;\u0026minus;\u0026thinsp;209,173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.16\u003c/p\u003e\n \u003cp\u003e(9.24\u0026ndash;11.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003cp\u003e(-0.12 to 0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eEtiology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,853\u003c/p\u003e\n \u003cp\u003e(9,160\u0026thinsp;\u0026minus;\u0026thinsp;15,030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003cp\u003e(0.59\u0026ndash;0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16,612\u003c/p\u003e\n \u003cp\u003e(12,703\u0026thinsp;\u0026minus;\u0026thinsp;21,099)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003cp\u003e(0.69\u0026ndash;1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003cp\u003e(-0.01 to 0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHepatitis B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101,411\u003c/p\u003e\n \u003cp\u003e(89,731\u0026thinsp;\u0026minus;\u0026thinsp;114,562)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.24\u003c/p\u003e\n \u003cp\u003e(5.53\u0026ndash;7.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123,156\u003c/p\u003e\n \u003cp\u003e(108,220\u0026thinsp;\u0026minus;\u0026thinsp;139,247)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.46\u003c/p\u003e\n \u003cp\u003e(5.69\u0026ndash;7.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003cp\u003e(-0.15 to 0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHepatitis C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23,115\u003c/p\u003e\n \u003cp\u003e(20,017\u0026ndash;26,384)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003cp\u003e(1.43\u0026ndash;1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29,692\u003c/p\u003e\n \u003cp\u003e(25,403\u0026thinsp;\u0026minus;\u0026thinsp;33,765)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003cp\u003e(1.47\u0026ndash;1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003cp\u003e(-0.11 to 0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNASH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,269\u003c/p\u003e\n \u003cp\u003e(5,183-7,488)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003cp\u003e(0.35\u0026ndash;0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,691\u003c/p\u003e\n \u003cp\u003e(7,188\u0026thinsp;\u0026minus;\u0026thinsp;10,367)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003cp\u003e(0.40\u0026ndash;0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003cp\u003e(0.01 to 0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther causes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,222\u003c/p\u003e\n \u003cp\u003e(7,862\u0026thinsp;\u0026minus;\u0026thinsp;10,910)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003cp\u003e(0.53\u0026ndash;0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,143\u003c/p\u003e\n \u003cp\u003e(9,334\u0026thinsp;\u0026minus;\u0026thinsp;13,164)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003cp\u003e(0.54\u0026ndash;0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003cp\u003e(-0.12 to 0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;9 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e297\u003c/p\u003e\n \u003cp\u003e(245\u0026ndash;369)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003cp\u003e(0.17\u0026ndash;0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e327\u003c/p\u003e\n \u003cp\u003e(262\u0026ndash;395)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003cp\u003e(0.18\u0026ndash;0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003cp\u003e(-0.22 to 0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026ndash;24 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,482\u003c/p\u003e\n \u003cp\u003e(1,302-1,791)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003cp\u003e(0.42\u0026ndash;0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,109\u003c/p\u003e\n \u003cp\u003e(996-1,283)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003cp\u003e(0.40\u0026ndash;0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003cp\u003e(-0.18 to 0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;29 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,377\u003c/p\u003e\n \u003cp\u003e(1,209-1,664)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003cp\u003e(1.16\u0026ndash;1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,851\u003c/p\u003e\n \u003cp\u003e(1,661-2,070)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003cp\u003e(1.27\u0026ndash;1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003cp\u003e(-0.11 to 0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;34 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,105\u003c/p\u003e\n \u003cp\u003e(2,706-3,705)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003cp\u003e(2.69\u0026ndash;3.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,449\u003c/p\u003e\n \u003cp\u003e(3,099\u0026thinsp;\u0026minus;\u0026thinsp;3,861)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.13\u003c/p\u003e\n \u003cp\u003e(2.82\u0026ndash;3.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003cp\u003e(-0.14 to 0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u0026ndash;39 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,941\u003c/p\u003e\n \u003cp\u003e(6,111-8,010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.68\u003c/p\u003e\n \u003cp\u003e(5.00-6.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,003\u003c/p\u003e\n \u003cp\u003e(5,325-6,795)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.09\u003c/p\u003e\n \u003cp\u003e(5.40\u0026ndash;6.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003cp\u003e(-0.08 to 0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;44 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,235\u003c/p\u003e\n \u003cp\u003e(10,757\u0026thinsp;\u0026minus;\u0026thinsp;14,138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.49\u003c/p\u003e\n \u003cp\u003e(8.35\u0026ndash;10.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,641\u003c/p\u003e\n \u003cp\u003e(11,159\u0026thinsp;\u0026minus;\u0026thinsp;14,363)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.95\u003c/p\u003e\n \u003cp\u003e(9.67\u0026ndash;12.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003cp\u003e(-0.04 to 0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u0026ndash;49 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,342\u003c/p\u003e\n \u003cp\u003e(13,322\u0026thinsp;\u0026minus;\u0026thinsp;17,716)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.09\u003c/p\u003e\n \u003cp\u003e(12.23\u0026ndash;16.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19,462\u003c/p\u003e\n \u003cp\u003e(16,920\u0026thinsp;\u0026minus;\u0026thinsp;22,192)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.42\u003c/p\u003e\n \u003cp\u003e(13.41\u0026ndash;17.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003cp\u003e(-0.19 to 0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;54 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,958\u003c/p\u003e\n \u003cp\u003e(13,308\u0026thinsp;\u0026minus;\u0026thinsp;16,791)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.46\u003c/p\u003e\n \u003cp\u003e(16.43\u0026ndash;20.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22,427\u003c/p\u003e\n \u003cp\u003e(19,612\u0026thinsp;\u0026minus;\u0026thinsp;25,752)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.50\u003c/p\u003e\n \u003cp\u003e(17.05\u0026ndash;22.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003cp\u003e(-0.13 to 0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u0026ndash;59 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20,569\u003c/p\u003e\n \u003cp\u003e(18,300\u0026thinsp;\u0026minus;\u0026thinsp;23,076)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.76\u003c/p\u003e\n \u003cp\u003e(22.03\u0026ndash;27.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20,389\u003c/p\u003e\n \u003cp\u003e(18,023\u0026thinsp;\u0026minus;\u0026thinsp;22,930)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.27\u003c/p\u003e\n \u003cp\u003e(23.22\u0026ndash;29.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003cp\u003e(-0.15 to 0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;79 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63,680\u003c/p\u003e\n \u003cp\u003e(57,785\u0026thinsp;\u0026minus;\u0026thinsp;70,272)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.88\u003c/p\u003e\n \u003cp\u003e(36.18-44.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85,761\u003c/p\u003e\n \u003cp\u003e(77,994\u0026thinsp;\u0026minus;\u0026thinsp;94,249)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.07\u003c/p\u003e\n \u003cp\u003e(37.35\u0026ndash;45.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003cp\u003e(-0.13 to 0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,882\u003c/p\u003e\n \u003cp\u003e(10,695\u0026thinsp;\u0026minus;\u0026thinsp;13,085)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.90\u003c/p\u003e\n \u003cp\u003e(54.82\u0026ndash;67.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,873\u003c/p\u003e\n \u003cp\u003e(14,266\u0026thinsp;\u0026minus;\u0026thinsp;17,182)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.69\u003c/p\u003e\n \u003cp\u003e(53.65\u0026ndash;64.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003cp\u003e(-0.14 to 0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eASIR, age-standardized incidence rate; NASH, nonalcoholic steatohepatitis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDeaths and age-standardized death rates of liver cancer in 2010 and 2016 and the temporal trend of age-standardized death rates from 2010 to 2016\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. deaths\u003c/p\u003e\n \u003cp\u003e(95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eASDR per 100,000 (95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. deaths\u003c/p\u003e\n \u003cp\u003e(95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eASDR per 100,000 (95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnnual percentage change of ASDR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChina\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141,706\u003c/p\u003e\n \u003cp\u003e(128,396\u0026thinsp;\u0026minus;\u0026thinsp;157,119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.28\u003c/p\u003e\n \u003cp\u003e(8.42\u0026ndash;10.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172,587\u003c/p\u003e\n \u003cp\u003e(149,454\u0026thinsp;\u0026minus;\u0026thinsp;197,473)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.41\u003c/p\u003e\n \u003cp\u003e(8.21\u0026ndash;10.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003cp\u003e(-0.17 to 0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eEtiology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,278\u003c/p\u003e\n \u003cp\u003e(8,798\u0026thinsp;\u0026minus;\u0026thinsp;14,535)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003cp\u003e(0.58\u0026ndash;0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,439\u003c/p\u003e\n \u003cp\u003e(11,870\u0026thinsp;\u0026minus;\u0026thinsp;20,082)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003cp\u003e(0.64\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003cp\u003e(-0.06 to 0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHepatitis B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92,455\u003c/p\u003e\n \u003cp\u003e(82,477\u0026thinsp;\u0026minus;\u0026thinsp;104,945)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.79\u003c/p\u003e\n \u003cp\u003e(5.17\u0026ndash;6.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109,285\u003c/p\u003e\n \u003cp\u003e(92,021\u0026ndash;127,730)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.78\u003c/p\u003e\n \u003cp\u003e(4.88\u0026ndash;6.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003cp\u003e(-0.21 to 0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHepatitis C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23,364\u003c/p\u003e\n \u003cp\u003e(20,050\u0026thinsp;\u0026minus;\u0026thinsp;26,641)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003cp\u003e(1.51\u0026ndash;1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29,452\u003c/p\u003e\n \u003cp\u003e(24,843\u0026thinsp;\u0026minus;\u0026thinsp;34,705)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003cp\u003e(1.48\u0026ndash;2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003cp\u003e(-0.14 to 0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNASH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,117\u003c/p\u003e\n \u003cp\u003e(5,061\u0026thinsp;\u0026minus;\u0026thinsp;7,247)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003cp\u003e(0.35\u0026ndash;0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,324\u003c/p\u003e\n \u003cp\u003e(6,773\u0026thinsp;\u0026minus;\u0026thinsp;10,108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003cp\u003e(0.39\u0026ndash;0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003cp\u003e(-0.04 to 0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther causes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,490\u003c/p\u003e\n \u003cp\u003e(7,213\u0026thinsp;\u0026minus;\u0026thinsp;10,019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003cp\u003e(0.50\u0026ndash;0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10,085\u003c/p\u003e\n \u003cp\u003e(8,426\u0026thinsp;\u0026minus;\u0026thinsp;12,160)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003cp\u003e(0.49\u0026ndash;0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003cp\u003e(-0.16 to 0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;9 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003cp\u003e(259\u0026ndash;337)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003cp\u003e(0.18\u0026ndash;0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e313\u003c/p\u003e\n \u003cp\u003e(250\u0026ndash;361)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003cp\u003e(0.17\u0026ndash;0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003cp\u003e(-0.32 to 0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026ndash;24 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e901\u003c/p\u003e\n \u003cp\u003e(795-1,082)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003cp\u003e(0.25\u0026ndash;0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e641\u003c/p\u003e\n \u003cp\u003e(558\u0026ndash;740)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003cp\u003e(0.23\u0026ndash;0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003cp\u003e(-0.25 to 0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;29 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e960\u003c/p\u003e\n \u003cp\u003e(850-1,149)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003cp\u003e(0.81\u0026ndash;1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,232\u003c/p\u003e\n \u003cp\u003e(1,073\u0026thinsp;\u0026minus;\u0026thinsp;1,402)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003cp\u003e(0.82\u0026ndash;1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003cp\u003e(-0.17 to 0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;34 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,408\u003c/p\u003e\n \u003cp\u003e(2,112-2,869)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003cp\u003e(2.10\u0026ndash;2.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,540\u003c/p\u003e\n \u003cp\u003e(2,205-2,914)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003cp\u003e(2.00-2.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003cp\u003e(-0.20 to 0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u0026ndash;39 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,656\u003c/p\u003e\n \u003cp\u003e(5,015\u0026thinsp;\u0026minus;\u0026thinsp;6,558)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.63\u003c/p\u003e\n \u003cp\u003e(4.11\u0026ndash;5.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,606\u003c/p\u003e\n \u003cp\u003e(3,910-5,402)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.67\u003c/p\u003e\n \u003cp\u003e(3.96\u0026ndash;5.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003cp\u003e(-0.17 to 0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;44 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10,263\u003c/p\u003e\n \u003cp\u003e(9,071\u0026thinsp;\u0026minus;\u0026thinsp;11,796)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.96\u003c/p\u003e\n \u003cp\u003e(7.04\u0026ndash;9.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,994\u003c/p\u003e\n \u003cp\u003e(8,393\u0026thinsp;\u0026minus;\u0026thinsp;11,725)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.66\u003c/p\u003e\n \u003cp\u003e(7.27\u0026ndash;10.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003cp\u003e(-0.13 to 0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u0026ndash;49 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,899\u003c/p\u003e\n \u003cp\u003e(11,197\u0026thinsp;\u0026minus;\u0026thinsp;14,841)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.84\u003c/p\u003e\n \u003cp\u003e(10.28\u0026ndash;13.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,976\u003c/p\u003e\n \u003cp\u003e(13,243\u0026thinsp;\u0026minus;\u0026thinsp;19,109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.66\u003c/p\u003e\n \u003cp\u003e(10.49\u0026ndash;15.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003cp\u003e(-0.24 to 0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;54 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,763\u003c/p\u003e\n \u003cp\u003e(11,402\u0026thinsp;\u0026minus;\u0026thinsp;14,422)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.75\u003c/p\u003e\n \u003cp\u003e(14.07\u0026ndash;17.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18,637\u003c/p\u003e\n \u003cp\u003e(15,605\u0026thinsp;\u0026minus;\u0026thinsp;22,157)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.20\u003c/p\u003e\n \u003cp\u003e(13.57\u0026ndash;19.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003cp\u003e(-0.19 to 0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u0026ndash;59 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18,054\u003c/p\u003e\n \u003cp\u003e(16,149\u0026thinsp;\u0026minus;\u0026thinsp;20,256)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.74\u003c/p\u003e\n \u003cp\u003e(19.44\u0026ndash;24.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17,485\u003c/p\u003e\n \u003cp\u003e(14,666\u0026thinsp;\u0026minus;\u0026thinsp;20,504)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.53\u003c/p\u003e\n \u003cp\u003e(18.90-26.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003cp\u003e(-0.18 to 0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;79 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63,203\u003c/p\u003e\n \u003cp\u003e(57,475\u0026thinsp;\u0026minus;\u0026thinsp;69,613)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.58\u003c/p\u003e\n \u003cp\u003e(35.99\u0026ndash;43.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82,599\u003c/p\u003e\n \u003cp\u003e(72,076\u0026ndash;94,387)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.56\u003c/p\u003e\n \u003cp\u003e(34.52\u0026ndash;45.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003cp\u003e(-0.16 to 0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,300\u003c/p\u003e\n \u003cp\u003e(12,804\u0026thinsp;\u0026minus;\u0026thinsp;15,726)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.29\u003c/p\u003e\n \u003cp\u003e(65.62\u0026ndash;80.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18,559\u003c/p\u003e\n \u003cp\u003e(16,447\u0026thinsp;\u0026minus;\u0026thinsp;20,423)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.80\u003c/p\u003e\n \u003cp\u003e(61.85\u0026ndash;76.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003cp\u003e(-0.17 to 0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eASDR, age-standardized death rate; NASH, nonalcoholic steatohepatitis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Burden of liver cancer in seven administrative regions of China in 2016\u003c/h2\u003e\n \u003cp\u003eThe estimated frequencies of liver cancer incidence and death rates (ASIRs and ASDRs) in different administrative regions are summarized in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. South China has the highest incidence and death rates of liver cancer in the country. Specifically, South China had the highest ASIR (26.1 per 100,000) of liver cancer, followed by Southwest China (19.2 per 100,000) and Northeast China (18.6 per 100,000) in 2016 (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea). Similarly, South China had the highest ASDR (22.3 per 100,000) for liver cancer, followed by Southwest China (17.1 per 100,000), and Central China (15.7 per 100,000) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb). North China had the lowest incidence and death rates of liver cancer (12.5 and 10.5 per 100,000 people, respectively) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec) in the whole country in 2016.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAge-standardized incident rate (ASIR) and age-standardized death rate (ASDR) of liver cancer by geographic areas covered by 487 cancer, 2010 and 2016 (1/10\u003csup\u003e5\u003c/sup\u003e)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eAll areas\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeographic areas\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eASIR per 100,000 in 2010\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eASIR per 100,000 in 2010\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eASIR per 100,000 in 2016\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eASDR per 100,000 in 2016\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNortheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthwest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorthwest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNortheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthwest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorthwest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eRural\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNortheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthwest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorthwest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eASIR, Age-standardized incident rate; ASDR: age-standardized death rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Burden of liver cancer classified by area (urban and rural) in China, 2016\u003c/h2\u003e\n \u003cp\u003eThe ASIR and ASDR of liver cancer in the urban and rural areas of the seven administrative regions are summarized in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Generally, the ASIR and ASDR for liver cancer in rural China were higher than those in urban China (19.3 vs 16.3 and 16.6 vs 13.9 per 100,000, respectively) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ed and \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ee). Similarly, the rural areas in each administrative region had higher incidence and death rates than the urban areas in that region.\u003c/p\u003e\n \u003cp\u003eThe highest ASIR and ASDR of liver cancer in 2016 occurred in southern rural areas (30.0 and 26.6 per 100,000 people, respectively), followed by northeastern rural areas (24.8 and 21 per 100,000 people, respectively). Notably, southern urban areas also had a high ASIR (24.1 peer 100,000) and ASDR (20.1 per 100,000) for liver cancer in 2016. North China, in both urban and rural areas, had the lowest incidence and death rates of liver cancer in the entire country (10.5 and 9.0 per 100,000, respectively) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ed and \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ee).\u003c/p\u003e\n \u003cp\u003eLiver cancer incidence and death rates in rural and urban areas by sex and age were analyzed simultaneously. Generally, the ASIR and ASDR for liver cancer were higher in males than in females in both rural and urban areas. Specifically, the ASIR by Chinese standard population (ASR China) and World Segi population (ASR World) were 25.3% and 24.88% for males compared with 7.93% and 7.84% for females in urban China, and 29.48% and 28.79% for males compared with 9.80% and 9.72% for females in rural China, respectively. The ASDR by ASR China and ASR World were 21.61% and 21.32% for males compared with 6.70% and 6.61% for females in urban China and 25.5% and 24.97% for males compared with 8.26% and 8.19% for females in rural China. The age-specific incidence and mortality rate of liver cancer usually increased with age and were consistently high among older populations, regardless of the group, men or female, or urban or rural areas.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Trends in etiology of liver cancer\u003c/h2\u003e\n \u003cp\u003eThe frequencies of incident cases and deaths, ASIRs, ASDRs, and DALYs according to liver cancer etiology are summarized in Tables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, and \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e. In 2010, 101,411 liver cancer cases were caused by hepatitis B, 23,115 by HCV, 11,853 by alcohol consumption, 9,222 by other causes, and 6,269 by NASH (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). In 2010, HBV accounted for 67% of liver cancer incidence in China, followed by HCV (15%), alcohol (7.8%), other causes (6.1%), and NASH (4.1%) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb). In 2016, 123,156 liver cancer cases were caused by hepatitis B, 29,692 by HCV, 16,612 by alcohol consumption, 11,143 by other causes, and 8,691 by NASH (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). HBV accounted for 65% of the liver cancer incidence, followed by HCV (15%), alcohol (9%), other causes (6%), and NASH (5%) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb). The proportion of alcohol-, NASH-, and other cause-associated liver cancer incidence increased, whereas the proportion of HBV-associated liver cancer incidence decreased, and the proportion of HCV-associated liver cancer incidence remained stable from 2010 to 2016. Similarly, in 2010, 109,285 hepatitis B-induced liver cancer deaths, 29,452 HCV-related liver cancer deaths, 15,439 alcohol-induced liver cancer deaths, 10,085 other-cause-induced liver cancer deaths, and 8,324 NASH-induced liver cancer deaths were reported (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec). HBV accounted for 65% of liver cancer deaths, followed by HCV (17%), alcohol consumption (8.0%), other causes (6.0%), and NASH (4%). HBV accounted for 63% of liver cancer deaths in 2016, followed by HCV (17%), alcohol (9%), other causes (6%), and NASH (5%) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed). From 2010 to 2016, the proportion of alcohol- and NASH-associated liver cancer deaths increased, that of HBV-associated liver cancer deaths decreased, and that of HCV- and other cause-associated liver cancer deaths remained stable (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e\n \u003cp\u003eThe ASIRs, ASDRs, ASDALYs, and APCs in liver cancer rates stratified by etiology from 2010 to 2016 are summarized in Tables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, and \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e, respectively. NASH was the only etiology with an increase in liver cancer ASIRs (APC 0.20%, 95% CI 0.01\u0026ndash;0.41) from 2010 to 2016 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ee). ASIRs for alcohol-, HBV-, HCV- and other cause-associated liver cancer remained stable, with APCs of 0.21% (95% CI \u0026minus;\u0026thinsp;0.01\u0026ndash;0.48), 0.06% (95% CI \u0026minus;\u0026thinsp;0.15\u0026ndash;0.33), 0.06% (95% CI \u0026minus;\u0026thinsp;0.11\u0026ndash;0.25), and 0.05% (95% CI \u0026minus;\u0026thinsp;0.12\u0026ndash;0.24), respectively (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ee). From 2010 to 2016, the ASDRs for the five etiology-related HCC cases remained stable in China (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, the ASDRs of liver cancer due to alcohol (APC: 0.15%, 95% CI \u0026minus;\u0026thinsp;0.06\u0026ndash;0.38), NASH (APC: 0.13%, 95% CI \u0026minus;\u0026thinsp;0.04\u0026ndash;0.32), HCV (APC: 0.11%, 95% CI \u0026minus;\u0026thinsp;0.014\u0026ndash;0.18), HBV (APC: -0.01%, 95% CI \u0026minus;\u0026thinsp;0.21\u0026ndash;0.23), and other causes (APC: \u0026minus;0.01%, 95% CI \u0026minus;\u0026thinsp;0.16\u0026ndash;0.16) showed no significant changes from 2010 to 2016 (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ef). Similarly, the liver cancer ASDALYs from 2010 to 2016 in China were statistically stable, specifically reflected in alcohol-related (APC: 0.15%, 95% CI \u0026minus;\u0026thinsp;0.06\u0026ndash;0.40), NASH-related (APC 0.15%, 95% CI \u0026minus;\u0026thinsp;0.05\u0026ndash;0.36), HBV-related (APC: 0.02%, 95% CI \u0026minus;\u0026thinsp;0.20\u0026ndash;0.28), HCV-related (APC: 0.02%, 95% CI \u0026minus;\u0026thinsp;0.14\u0026ndash;0.20) and other cause-related (APC: \u0026minus;0.02%, 95% CI \u0026minus;\u0026thinsp;0.17\u0026thinsp;\u0026minus;\u0026thinsp;0.15) liver cancer (Table S1).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Trends in the etiology of liver cancer stratified by age\u003c/h2\u003e\n \u003cp\u003eThe incidence, mortality, and DALY rates of liver cancer according to age are summarized in Tables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, and \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e. In 2016, the incidence rate of liver cancer was \u0026lt;\u0026thinsp;1.0/100,000 for individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;25 years and increased with age. The highest incidence of liver cancer was observed in individuals aged\u0026thinsp;\u0026gt;\u0026thinsp;80 years (59.69 per 100,000 individuals) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Similar tendencies were observed in the mortality rates, which were consistently high in older populations. Specifically, the death rate of liver cancer was \u0026lt;\u0026thinsp;1.0/100,000 for those aged\u0026thinsp;\u0026lt;\u0026thinsp;30 years, increased to \u0026gt;\u0026thinsp;10 per 100,000 population at 45 years, and peaked in the \u0026gt;\u0026thinsp;80-year age group (69.8 per 100,000 population) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). From 2010 to 2016, the incidence rates of liver cancer due to alcohol consumption and NASH increased in several age groups, with the highest APCs observed in the 40-44-year age group (0.32% and 0.34%, respectively), followed by those in the 35\u0026ndash;39-year age group (0.24% and 0.27%, respectively) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea and b). The incidence of liver cancer due to HBV, HCV, and other causes remained stable in all age groups from 2010 to 2016 (Table S2). The death rates and DALYs rates of liver cancer due to these causes in each age group from 2010 to 2016 are shown in Tables S3 and S4, which indicate no significant differences during this period.\u003c/p\u003e\n \u003cp\u003eAdditionally, we investigated the proportion of liver cancer according to etiology in different age groups. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea, HBV accounted for the highest frequency of liver cancer in each age group in 2016. The incidence rate of HBV-induced liver cancer increased and peaked in the 30\u0026ndash;34-year age group (85%) and then decreased with age. However, the proportion of HCV- (37% in the \u0026gt;\u0026thinsp;80-year age group), alcohol- (11% in the 60\u0026ndash;79-year age group), and NASH-induced liver cancer incidence (7.9% in the \u0026gt;\u0026thinsp;80-year age group) increased with age. Similar trends were observed for liver cancer mortality rate (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). From 2010 to 2016, the proportion of liver cancer due to HCV and HBV infection declined in each age group, whereas the proportion of liver cancer incidence due to alcohol consumption and NASH increased in each age group. Similar trends were observed in the proportion of liver cancer mortality due to these causes during the study period (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). The proportion of liver cancer mortality due to alcohol consumption and NASH increased in each age group from 2010 to 2016, whereas the proportion of liver cancer mortality due to HCV and HBV declined in each age group.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eChina is one of the countries most affected by liver cancer. As shown in previous studies, liver cancer was the only disease that had higher than expected age-standardized DALYs in all provinces of China, indicating the continuing serious impact of liver cancer in this country [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The present study is the most comprehensive evaluation of the disease burden of liver cancer in China, demarcated by etiology, age, area, and region. Owing to the standardized method for estimating liver cancer metrics used in the GBD study, we could compare the incidence and mortality levels in China with those worldwide or compare the data at the provincial level in China. Using data from the 2016 GBD study, we determined that from 2010 to 2016, the incidence of liver cancer cases and number of liver cancer-related deaths increased by 25% and 22%, respectively, although significant changes occurred in the age-standardized incidence and death rates. The growth and aging of the Chinese population probably contributes to this disconnect in the temporal trends of frequency and age-standardized incidence or death rates. South China, an endemic area for liver cancer [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], had the highest incidence and death rates of liver cancer in 2016, which was probably attributed to the high hepatitis incidence rate, prevalence of liver fluke infestation, and damp and muggy environment in this region. Moreover, cancer incidence and mortality are higher in rural areas than in urban areas. This disparity was primarily attributed to period effects, mainly referring to the unequal medical levels and resources between urban and rural areas [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Therefore, improving the medical level and resources in rural areas is essential, including reimbursing treatment for patients with chronic hepatitis, implementing annual cancer surveillance, and upgrading treatment quality in rural areas.\u003c/p\u003e \u003cp\u003eAlthough the ASIR and ASDR due to HBV-associated liver cancer remained stable from 2010 to 2016, the proportion of HBV-associated liver cancer incidence and deaths decreased in each age group. Moreover, a reduction in the ASIR of HBV-associated cirrhosis and other chronic liver diseases was observed in each age group from 2010 to 2016 in China, indicating a decreasing trend in the incidence of HBV-associated liver cancer in the following years. This decrease was likely because of the impact of successful vaccination, antiviral therapy, and aflatoxin reduction programs [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Similarly, no significant changes occurred in the ASIR and ASDR of HCV-associated liver cancer or in the proportion of HCV-associated liver cancer from 2010 to 2016. We observed an increase in the ASIR of HCV-related cirrhosis and other chronic liver diseases in the 25\u0026ndash;49-year age group, especially with individuals aged 25\u0026ndash;29 years. Despite the implementation of blood donor screening in 1993 and the availability of highly effective oral antiviral therapy [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], a high HCV prevalence persists among high-risk populations, including people who inject drugs, patients on hemodialysis, and patients coinfected with human immunodeficiency virus or HBV [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Moreover, major gaps remain in early disease diagnosis and linkage to care [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Therefore, improvements are needed in infection control, disease screening, and treatment quality to achieve the elimination goal [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNASH and alcohol consumption were the two etiological factors of liver cancer that increased in incidence and death rates in each age group from 2010 to 2016. NASH was the only etiology with an increase in the liver cancer incidence rate from 2010 to 2016, prominently in the 35\u0026ndash;45 age group, driven by rapidly rising obesity rates [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Moreover, the total burden related to nonalcoholic fatty liver disease (NAFLD) and the incidence of NAFLD-associated cirrhosis and other chronic liver diseases increased from 2010 to 2016, which was primarily observed in the 25\u0026ndash;49-year age group, indicating that the incidence of liver cancer due to NASH is projected to increase in the next decade in China [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Therefore, increasing awareness and urgent actions are required to control metabolic risk factors and prevent the rising burden of NASH-related liver cancer in China [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Behavioral modifications and changes in diet and exercise habits should be reiterated. Although the ASIR of alcohol-induced liver cancer did not show a significant difference from 2010 to 2016 in China, we observed that the incidence of alcohol-induced liver cancer increased in several age groups, especially in the 35\u0026ndash;45 age group. Moreover, alcohol per capita consumption increased from 2010 to 2016 in people aged\u0026thinsp;\u0026gt;\u0026thinsp;25 years and is projected to increase [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The incidence of cirrhosis and other chronic diseases due to alcohol use has consistently increased in the past decade, primarily in individuals aged between 25 and 49 years. Implementing policies such as increasing prices or taxation for alcohol and adding cancer warnings to alcohol labels may be considered at the national level to postpone the rising burden of alcohol-related liver cancer in China [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study provides an in-depth analysis of the temporal trends in liver cancer and the contributions of various causes using data from the GBD 2016 and the NCCR of China. Our findings validate several previous studies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] that reported the increasing impact of NASH and alcohol on the liver cancer burden and provide important information for care providers and policymakers. The current study provides a national overview of the temporal trends in the liver cancer burden and the contributions of various liver cancer etiologic factors in China from 2010 to 2016, unlike most other studies that have focused on a specific region/province or etiology of liver cancer. These data provide a comprehensive impression of the liver cancer burden in China and may help promote future endeavors in liver cancer diagnosis, management, and research. However, it remains critical to keep track of the epidemiology of liver cancer in China over time, particularly given the early signs of potential trends in several regions.\u003c/p\u003e"},{"header":"5. Limitations of study","content":"\u003cp\u003eOur study had several limitations. First, the data for this study were derived from the GBD 2016, and the general limitations described by the GBD apply to our study. Second, multifactorial liver cancer was not considered in our study because of the scarcity of data in the GBD dataset. Several risk factors play a role in some liver cancer cases. For example, studies have demonstrated that alcohol consumption increases the risk of liver cancer in patients with HBV- and HCV-related cirrhosis [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Therefore, the temporal trends of multiple etiological factors in liver cancer development should be investigated in future studies. Third, the increasing trend in the liver cancer burden was associated with an increase in the number of cancer registry sites. Underreporting and misdiagnosis are inevitable during registration. Finally, the latest data on different administrative regions in China were obtained in 2016 and were recently published in 2022. For consistency, we used the GBD 2016 data instead of the GBD 2019 data. Therefore, continuing to track the epidemiology of liver cancer over time remains critical.\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eThe frequency of new cases, deaths, and DALYs due to liver cancer in China increased substantially from 2010 to 2016, although age-standardized incidence and death rates have remained stable at the national level. South China has the highest liver cancer incidence and death rates in the country, and rural areas usually have higher incidence and death rates than urban areas. NASH and alcohol consumption are the two fastest growing etiological factors of age-standardized liver cancer incidence, highlighting the urgent need to implement measures to overcome these growing issues.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDeclaration of Interest\u003c/strong\u003e \u003cp\u003e \u003cb\u003estatement\u003c/b\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eannual percentage change\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASDALYs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eage-standardized DALYs\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eage-standardized death rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASIR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eage-standardized incident rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASRs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eage-standardized rates\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCODEm\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCause of Death Ensemble Model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDALYs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDisability-adjusted life-years\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGBD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlobal Burden of Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGHDx\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlobalHealth Data Exchange\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHBV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHepatitis B virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHCV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHepatitis C virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNAFLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon-alcoholic fatty liver disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNASH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-alcoholic steatohepatitis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Central Cancer Registry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSEER\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSurveillance, Epidemiology, and End Results\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003euncertainty interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of Interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the National Natural Science Foundation of China (grant no. NSFC 82100201, 2024C03169, 2017KY393, 2018KY428).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank\u0026nbsp;the GBD Study 2016 and\u0026nbsp;the National Cancer Center of China for providing the original data for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTian Tian: Writing-original draft, writing-review \u0026amp; editing, formal analysis, data curation, resources, conceptualization.\u003c/p\u003e\n\u003cp\u003eYangyuna Yang: Writing-review \u0026amp; editing, formal analysis, data curation, resources.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJie Wu: Supervision, methodology, writing-review \u0026amp; editing, term, conceptualization, project administration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJianzhen Shan: Supervision, writing-review \u0026amp; editing, funding acquisition, project administration.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, et al. 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Lancet Oncol. 2022;23(4):521\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRumgay H, Shield K, Charvat H, et al. Global burden of cancer in 2020 attributable to alcohol consumption: a population-based study. Lancet Oncol. 2021;22(8):1071\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan JG. Epidemiology of alcoholic and nonalcoholic fatty liver disease in China. J Gastroenterol Hepatol. 2013;28(Suppl 1):11\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou H, Ge Y, Lei Q, et al. Epidemiology and disease burden of non-alcoholic steatohepatitis in greater China: a systematic review. Hepatol Int. 2022;16(1):27\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaik JM, Golabi P, Younossi Y, et al. Changes in the Global Burden of Chronic Liver Diseases From 2012 to 2017: The Growing Impact of NAFLD. Hepatology. 2020;72(5):1605\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLlovet JM, Kelley RK, Villanueva A, et al. Hepatocellular carcinoma. Nat Rev Dis Primers. 2021;7(1):6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Liver cancer burden, epidemiology, nonalcoholic steatohepatitis, alcohol, hepatitis virus.","lastPublishedDoi":"10.21203/rs.3.rs-4725208/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4725208/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWith the rising prevalence of obesity, increasing alcohol consumption and the advances in hepatitis virus treatment, liver cancer epidemiology gradually changes. However, the impact of these changes on liver cancer burden in China remains unclear. This study aimed to assess temporal trends in liver cancer burden across the whole country and 33 province-level administrative regions and the contributions of various liver cancer etiologies in China from 2010 to 2016.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe age-standardized incidence/death rate for liver cancer from 2010 to 2016 was evaluated according to sex, age, and etiology using data from the 2016 Global Burden of Disease study. The liver cancer-related age-standardized rates in the 33 province-level administrative regions of China were obtained from the National Central Cancer Registry.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFrom 2010 to 2016, there were 25% and 22% increase in liver cancer incidence and death respectively, while the age-standardized incidence/death rate remained stable. South China, especially rural South, had the highest incidence and death rate of liver cancer in the whole country. The proportion of alcohol and non-alcoholic steatohepatitis-associated liver cancer incidence and death increased, whereas that of HBV-associated liver cancer incidence and death decreased from 2010 to 2016. Non-alcoholic steatohepatitis was the only etiology with an increase in liver cancer incidence rate, and alcohol showed the fast-growing incidence of liver cancer in some age groups.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eUrgent measures are required at a national level to tackle the underlying metabolic risk factors and slow down the rising burden of non-alcoholic steatohepatitis -induced liver cancer.\u003c/p\u003e","manuscriptTitle":"Epidemic characteristics, spatiotemporal pattern, and etiologic factors of liver cancer burden in China from 2010 to 2016: A retrospective analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-06 14:44:58","doi":"10.21203/rs.3.rs-4725208/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d071d027-e4ae-4761-b34a-5e93f598edb3","owner":[],"postedDate":"August 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-01T10:23:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-06 14:44:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4725208","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4725208","identity":"rs-4725208","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}