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Paul B. Tchounwou Subscribe SciFeed Recommended Articles Related Info Links PubMed/Medline Google Scholar More by Authors Links on DOAJ Liu, C. Wu, J. Chang, Z. on Google Scholar Liu, C. Wu, J. Chang, Z. on PubMed Liu, C. Wu, J. Chang, Z. /ajax/scifeed/subscribe Article Views Citations - Table of Contents Altmetric share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up ... Endorse textsms ... Comment Need Help? Support

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Get Information clear JSmol Viewer clear first_page Download PDF settings Order Article Reprints Font Type: Arial Georgia Verdana Font Size: Aa Aa Aa Line Spacing:    Column Width:    Background: Open AccessArticle Trends and Age-Period-Cohort Effects on the Prevalence, Incidence and Mortality of Hepatocellular Carcinoma from 2008 to 2017 in Tianjin, China by Chengyu Liu

Chengyu Liu SciProfiles Scilit Preprints.org Google Scholar 1,2, Jing Wu

Jing Wu SciProfiles Scilit Preprints.org Google Scholar 1,2,* ORCID

and Zheng Chang

Zheng Chang SciProfiles Scilit Preprints.org Google Scholar 3 1 School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, China 2 Center for Social Science Survey and Data, Tianjin University, Tianjin 300072, China 3 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden * Author to whom correspondence should be addressed. Int. J. Environ. Res. Public Health 2021, 18(11), 6034; https://doi.org/10.3390/ijerph18116034 Submission received: 29 April 2021 / Revised: 26 May 2021 / Accepted: 28 May 2021 / Published: 4 June 2021 Download keyboard_arrow_down Download PDF Download PDF with Cover Download XML Download Epub Download Supplementary Material Browse Figures Versions Notes

Abstract: Objectives: China is the country most afflicted by hepatocellular carcinoma in the world. However, little is known about the epidemiology of hepatocellular carcinoma in China. This study aimed to examine the trends of the prevalence, incidence, and mortality of hepatocellular carcinoma in China, and to investigate the effects of age, period, and birth cohort on the epidemiological trend. Methods: The data were obtained from the Urban Employee Basic Medical Insurance claims database (2003–2017) in Tianjin, China, which covers 5.95 million individuals. The average annual percentage change of the prevalence, incidence, and mortality were accessed using joinpoint regression. Age-period-cohort models were produced to quantify the effects of age, period, and cohort. Results: The hepatocellular carcinoma prevalence rate increased by 5.13% annually from 20.12/100,000 in 2008 to 30.49/100,000 in 2017, and the incidence rate was almost unchanged, from 13.91/100,000 in 2008 to 14.09/100,000 in 2017, but mortality decreased by 1.80% annually from 8.18/100,000 in 2008 to 7.34/100,000 in 2017. The age-period-cohort analysis revealed that the prevalence rate was remarkably increased from age 25, peaked in age 60, and decreased at age 70 and over. In the period index, the prevalence rate increased gradually from 2008 to 2016, and decreased a little in 2017. In the cohort index, the prevalence rate decreased approximately linearly from the 1925 cohort to the 1990 cohort. The result for the incidence was similar to the prevalence. The mortality rate increased approximately linearly from age 45 to 85, decreased from the 1925 cohort to the 1990 cohort, but it changed a little with the change of period. Conclusions: The findings of this study could inform the necessity of conducting earlier screening for high-risk individuals and improving the treatment of hepatocellular carcinoma, which may also help to predict future changes in hepatocellular carcinoma epidemiology. Keywords: hepatocellular carcinoma; prevalence; incidence; mortality; joinpoint regression analysis; age-period-cohort analysis 1. IntroductionPrimary liver cancer is one of the most prevalent and deadly cancers worldwide, with about 905,667 new cases and 830,180 deaths occurring in 2020 [1]. China is the country most afflicted by liver cancer in the world, and about half of global newly diagnosed cases and deaths occur in China (410,038 new cases and 391,152 deaths in 2020) [1,2]. In patients with primary liver cancer, the major histological type is hepatocellular carcinoma (HCC), comprising about 90%, followed by intrahepatic cholangiocarcinoma and other rare types [3]. As HCC is responsible for a significant incidence and mortality around the world, resulting in a substantial economic burden, the description of the changing HCC epidemiological data is critical for the healthcare system.Previous studies have reported worldwide or national trends on HCC epidemiology. Rich et al. suggested that the highest incidence of HCC in the world was in Asia and Africa, and the HCC incidence may have plateaued or begun to decrease in some Asian countries. Goh et al. also stated that Asia had the highest incidence of HCC worldwide (the age-standardized incidence rate of HCC in males in Eastern Asia was 31.9/100,000), and some Asian countries or regions had success in reducing HCC by introducing hepatitis B virus (HBV) vaccinations, such as Taiwan and Singapore [4]. Yeesoonsang et al. reported that the HCC incidence was expected to decrease among males and stabilize among females in the future [5]. Unlike in Asia, recent studies have reported that the HCC incidence has increased in low and medium incidence areas such as Western Europe and North America [6,7,8,9]. Wallace et al. reported that the age-adjusted incidence rate of HCC among Australian increased from 1.38/100,000 in 1982 to 4.96/100,000 in 2014, and White et al. revealed that the HCC incidence increased from 4.4/100,000 in 2000 to 6.7/100,000 in 2012 in the United States [7,9]. In addition, Bertuccio et al. reported that HCC mortality was observed to reduce in East Asia (e.g., Japan, Hong Kong and Korea) due to the control of HBV and hepatitis C virus (HCV) infections, but they still remained around 10–24/100,000 men and 2–8/100,000 women in Asia, which was 2- to 5-fold higher than those in most European and American countries [10].However, there is no data on the epidemiology of HCC in mainland China, while these have been widely reported in the United States, Australia, Denmark, Thailand and many other counties and areas [4,5,6,7,8,9,10]. Only a few studies have reported the incidence and mortality rates of primary liver cancer in some regions of China, including Shanghai, Guangzhou, Shenzhen, Chongqing, Fuzhou, Nantong, Sihui and other cities [11,12,13,14,15,16,17,18,19,20,21]. However, almost all of the cities mentioned above are located in the south of China; the changes in prevalence, incidence, and mortality of primary liver cancer have not yet been examined in Northern China, and much less for HCC. In addition, the diet and lifestyle for people living in different countries or areas are different, and people in Northern China prefer to drink alcohol, which is an identified risk factor for HCC [22]. Therefore, it is essential to examine the epidemiology of HCC in mainland China, especially in Northern China.Tianjin is one of the four municipalities in China, which is also the largest coastal opening city located in Northern China, ranking the seventh among the total 31 provinces/municipalities in terms of the gross domestic product per capita in mainland China. With the development of society, the living conditions, diet, and lifestyle, as well as the level of disease diagnosis and treatment have changed significantly, which may all affect the epidemiology of HCC. These factors in Tianjin also reflect the changes in many other cities in Northern China. This study aimed to describe the trends in HCC prevalence, incidence, and mortality rates over the past decade in Tianjin, Northern China, and to investigate the effects of age, period, and birth cohort on HCC prevalence, incidence, and mortality. 2. Method 2.1. Data SourceThis population-based study was conducted using data obtained from the Urban Employee Basic Medical Insurance (UEBMI) claims database (2003–2017) in Tianjin, China. China has almost achieved the universal coverage of medical insurance through two systems: UEBMI and Urban and Rural Resident Basic Medical Insurance (URRBMI), which covers 1354.36 million inhabitants, accounting for 96.7% of the total Chinese population in 2019 [23]. The URRBMI program covers children, students and other unemployed adult residents living in urban and rural areas, and UEBMI enrollees represent all adult (at least 18 years) employees and retirees of the public and private sectors. Due to the different reimbursement benefits between the two types of medical insurance systems (the URRBMI program covers fewer healthcare items and pays less than UEBMI), inhabitants who are enrolled in the URRBMI program always have a lower utilization rate of healthcare resources than those enrolled in the UEBMI plan [24]. The evidence from the UEBMI claims database could more truly reflect the level of diagnosis and treatment, as well as the epidemiology data of a city. In Tianjin, there were about 11.37 million enrollees (UEBMI: 5.95 million; URRBMI: 5.42 million) in 2019, of which UEBMI enrollees accounted for over half [25]. The analytical sample in this study was thirty percent of the UEBMI enrollees randomly sampled based on their unique identification number. The UEBMI database consisted of inpatient, outpatient, and pharmacy service claims (the database from 2003 to 2007 included only the inpatient claims). The enrollment history, patient demographics (age, sex, working status), dates of service, diagnoses, medical prescription, and procedure information, as well as the related costs, were included in this database. Both the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) codes and medical records were used to identify the disease diagnoses. In addition, the all-cause death information was included in a separate dataset, which could be linked by patients’ unique identification number. This study was exempted from the application for ethical approval by the Safety and Ethics Committee of the School of Pharmaceutical Science and Technology in Tianjin University. 2.2. Target Population and Case IdentificationThe target population were the enrollees of UEBMI in the analytical sample during each calendar year from 2008 to 2017. The definition and identification of prevalent cases, incident cases and death cases are introduced as follows. The prevalent cases were patients with a diagnosis of HCC (ICD-10: C22.0 supplemented with Chinese descriptions) through the inpatient and outpatient claims, who were identified by the calendar year during 2008–2017 and included newly diagnosed patients and previously diagnosed cases. The incident cases (i.e., newly diagnosed patients) were identified from the prevalent cases. The year of the initial HCC diagnosis for each prevalent case was identified. The patients who had any diagnosis of HCC between 1 January 2003 and 31 December 2007 were excluded. Death cases (i.e., patients with HCC who died in each year) were also identified from the prevalent cases. The individual identification numbers were used to identify the death cases from a separate dataset mentioned above. 2.3. Statistical AnalysisThe crude rate and age-standardized rate (ASR) were calculated. The crude prevalence, incidence, and mortality rate were defined as the number of prevalent cases, incident cases and death cases divided by the target population size, respectively. The crude rates were also calculated by sex (male/female), age groups (divided according to Segi’s World Standard Population, i.e., 20–24, 25–29, and 30–80 by 5 years, ≥85) and birth cohorts (1915–1919, 1920–1990 by 5 years). The ASR was calculated by summing up the products of the age-specific rates (ai, where i denotes the ith age class) and the number of persons (or weight, wi) in the same age subgroup i of the chosen standard population, then dividing the sum of the standard population [2] (or weights): ASR = ∑ i = 1 A a i w i ∑ i = 1 A w i × 100,000 Segi’s World Standard Population was used to standardize the prevalence, incidence, and mortality in this study [26]. 2.3.1. Joinpoint Regression AnalysisThe joinpoint regression analysis was established to estimate the annual percent change (APC) for each segment and the average annual percent change (AAPC) over the entire period to quantify the trends of the age-standardized HCC prevalence, incidence, and mortality. This analysis was composed of a few continuous linear segments, which are always used to describe the trends in the outcome, and are also named as piecewise regression, segmented regression, broken line regression and multi-phase regression [27]. A general form of this model for observations ( x 1 , y 1 ) , ( x 2 , y 2 ) , … … , ( x n − 1 , y n − 1 ) , and x n , y n could be written as follows: E y | x = β 0 + β 1 x + δ 1 x − τ 1 + + … … + δ k x − τ k + where τ 1 , τ 2 , … … , τ k − 1 , τ k are the unknown joinpoints and x − τ k + = x − τ k for x − τ k > 0 , and 0 otherwise [27]. The analysis starts with the zero joinpoint, representing a straight line, and tests whether more joinpoints are statistically significant and should be added to the model. The significant joinpoints were identified by a Monte Carlo Permutation method [27]. In the final model, each joinpoint denotes a significant change in the trend of the line segment separated by this joinpoint. The annual percent change for each line segment and the corresponding 95% confidence intervals (CI) are reported in the final model. 2.3.2. Age-Period-Cohort AnalysisThe age-period-cohort analysis was performed to evaluate the net effects of age, period, and cohort on HCC prevalence, incidence, and mortality simultaneously. Both age (from 20 to 85 years old) and period (from 2008 to 2017) were subdivided by 1-year intervals, and the birth cohort was calculated by subtracting the age from the period. The age-period-cohort models in this study were based on a Poisson log-linear model with an intrinsic estimator (IE), which was widely used to avoid linear dependency (i.e., period = age + cohort) and to disentangle the three effects of age, period, and cohort [28]. The IE approaches the estimator of the age-period-cohort model by applying the estimable functions and the singular value decomposition of the matrices, and it generates the coefficients of the effects, which are exponentially expressed as rate ratios [28]. The model could be generally expressed as: l o g Y j = μ + α a g e j + β p e r i o d j + γ c o h o r t j + ε where the Y denotes the HCC prevalence, incidence, and mortality of the corresponding age group j; the α, β and γ denote the corresponding age, period, and cohort effects; μ is the intercept item; and ε is the random error. A full age-period-cohort model fitted better than any combination of age, period, and cohort factors (Supplementary Table S1).The statistical analyses were performed using Joinpoint Regression Program V.4.7.0.0 and Stata V.13.0. The significant level was defined as two-sided alpha = 0.05. 3. ResultsOver the study period from 2008 to 2017, a total of 3811 men and 1834 women with HCC were identified, of which 3563 men and 1742 women were new cases, and 2163 men and 722 women died during the follow-up. The prevalence, incidence, and mortality of males were about twice those of females (Figure 1). The number of patients with HCC increased from 447 (crude prevalence rate: 38.07/100,000; ASR: 20.12/100,000) in 2008 to 1141 (crude prevalence rate: 71.37/100,000; ASR: 30.49/100,000) in 2017. There were 334 new cases (crude incidence rate: 26.65/100,000; ASR: 13.91/100,000) in 2008 and 524 new cases (crude incidence rate: 32.78/100,000; ASR: 14.09/100,000) in 2017. The number of deaths increased from 196 (crude mortality rate: 15.64/100,000; ASR: 8.18/100,000) in 2008 to 281 (crude mortality rate: 17.58/100,000; ASR: 7.34/100,000) in 2017 (Table 1). Figure 2 shows the age-specific prevalence, incidence, and mortality rates of HCC from 2008 to 2017. The prevalence rate of HCC was lower among individuals under the age of 30 ( Figure 1. Trends of the age-standardized prevalence, incidence, and mortality rates of HCC from 2008 to 2017. Ijerph 18 06034 g001

Figure 2. Age-specific prevalence, incidence, and mortality rates of HCC from 2008 to 2017. Figure 2. Age-specific prevalence, incidence, and mortality rates of HCC from 2008 to 2017. Ijerph 18 06034 g002

Figure 3. Temporal trends of the prevalence, incidence, and mortality rates of HCC by age group from 2008 to 2017. Note: As there was no apparent trend among individuals aged 75−79, 80−84, and ≥85, these three age groups were not shown to enhance the clarity and readability of this figure. Figure 3. Temporal trends of the prevalence, incidence, and mortality rates of HCC by age group from 2008 to 2017. Note: As there was no apparent trend among individuals aged 75−79, 80−84, and ≥85, these three age groups were not shown to enhance the clarity and readability of this figure. Ijerph 18 06034 g003

Figure 4. Cohort-specific prevalence, incidence, and mortality rates of HCC by age group. Figure 4. Cohort-specific prevalence, incidence, and mortality rates of HCC by age group. Ijerph 18 06034 g004

Figure 5. The age, period, and cohort effects on the prevalence, incidence, and mortality of HCC. Note: Some categories of the age and cohort factors are not shown in the figure due to the lack of space. Please refer to Supplementary Table S2 for the detailed statistics. Figure 5. The age, period, and cohort effects on the prevalence, incidence, and mortality of HCC. Note: Some categories of the age and cohort factors are not shown in the figure due to the lack of space. Please refer to Supplementary Table S2 for the detailed statistics. Ijerph 18 06034 g005

Table 1. Estimated prevalence, incidence, and mortality of HCC from 2008 to 2017. Table 1. Estimated prevalence, incidence, and mortality of HCC from 2008 to 2017. YearOverallMaleFemaleCasesCrude Rate(1/105)ASR †(1/105)CasesCrude Rate(1/105)ASR †(1/105)CasesCrude Rate(1/105)ASR †(1/105)Prevalence 200847738.0720.1238660.2230.979114.878.34200964946.2224.8546362.8133.0118627.8815.80201071546.8625.7351963.5634.6719627.6415.60201187460.3631.1958676.6539.7828842.1321.85201289458.6830.2463278.8440.9826236.3018.66201398261.4031.4168481.1941.8229839.3720.232014107570.3333.7674393.1645.3133245.4221.652015110265.4532.2876386.1143.4133942.5120.422016117672.0032.8882796.8045.6434944.8019.402017114171.3730.4980997.3042.5033243.2717.89Incidence 200833426.6513.9126441.1920.867011.436.33200944331.5516.9330541.3821.7813820.6911.48201044529.1715.9131037.9620.4913519.0410.67201155738.4719.9834645.2623.6621130.8716.06201255736.5618.8737546.7824.3718225.2112.96201359237.0119.2437944.9923.5521328.1414.65201461240.0419.5039349.2824.2821929.9614.43201561136.2918.3641046.2723.9420125.2012.32201663038.5717.8843050.3324.1620025.6711.27201752432.7814.0935142.2118.4317322.559.56Mortality ‡ 200819615.648.1817126.6813.40254.082.28200922616.098.6317223.3312.00548.094.75201021313.967.4716520.2110.65486.773.94201123516.238.2817322.6311.42629.074.77201224015.757.6717521.8310.60659.004.43201327217.018.5220223.9812.12709.254.63201425316.557.7918823.5711.19658.894.12201524614.616.8617920.209.49678.404.04201626115.987.0219422.7110.29678.603.58201728117.587.3421625.9811.14658.473.37 †ASR—age-standardized rate, and Segi’s world population was used for ASR. ‡ The death cases used to calculate the mortality rate in a particular year were defined as the patients who had claims related to HCC in that year, and they do not include the patients with a diagnosis of HCC in the early year but who did not see a doctor in that year. Table

Table 2. Results of the joinpoint regression on the age-adjusted prevalence, incidence, and mortality rates of the HCC from 2008 to 2017. Table 2. Results of the joinpoint regression on the age-adjusted prevalence, incidence, and mortality rates of the HCC from 2008 to 2017. GroupJoinpointYearsAPC (95%CI)p-ValueAAPC (95%CI)p-ValuePrevalence Overall12008–201114.97 (1.83, 29.81)0.032 *5.13 (1.56, 8.83)0.005 *2011–20170.53 (−2.52, 3.68)0.678Male12008–20146.38 (3.69, 9.14)0.002 *3.62 (1.54, 5.76)0.001 *2014–2017−1.68 (−7.55, 4.57)0.512Female12008–201127.65 (−5.29, 72.05)0.0907.01 (−1.52, 16.27)0.1102011–2017−2.03 (−8.32, 4.70)0.463Incidence Overall12008–20145.05 (−0.47, 10.88)0.066−0.31 (−4.77, 4.35)0.8932014–2017−10.24 (−22.27, 3.66)0.112Male12008–20152.71 (0.48, 4.98)0.026 *−0.82 (−3.62, 2.07)0.5762015–2017−12.22 (−24.51, 2.07)0.077Female12008–201129.10 (−8.30, 81.75)0.1133.98 (−5.60, 14.53)0.4292011–2017−6.69 (−14.13, 1.41)0.085Mortality Overall02008–2017−1.80 (−3.37, −0.20)0.032 *−1.80 (−3.37, −0.20)0.032 *Male02008–2017−0.10 (−5.85, 5.99)0.969−0.10 (−5.85, 5.99)0.969Female02008–2017−12.19 (−22.32, −0.75)0.040 *−12.19 (−22.32, −0.75)0.040 * * p < 0.05. APC: annual percentage change; AAPC: average annual percentage change; 95% CI: 95% confidence interval. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Share and Cite MDPI and ACS Style

Liu, C.; Wu, J.; Chang, Z. Trends and Age-Period-Cohort Effects on the Prevalence, Incidence and Mortality of Hepatocellular Carcinoma from 2008 to 2017 in Tianjin, China. Int. J. Environ. Res. Public Health 2021, 18, 6034. https://doi.org/10.3390/ijerph18116034

AMA Style

Liu C, Wu J, Chang Z. Trends and Age-Period-Cohort Effects on the Prevalence, Incidence and Mortality of Hepatocellular Carcinoma from 2008 to 2017 in Tianjin, China. International Journal of Environmental Research and Public Health. 2021; 18(11):6034. https://doi.org/10.3390/ijerph18116034

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Liu, Chengyu, Jing Wu, and Zheng Chang. 2021. "Trends and Age-Period-Cohort Effects on the Prevalence, Incidence and Mortality of Hepatocellular Carcinoma from 2008 to 2017 in Tianjin, China" International Journal of Environmental Research and Public Health 18, no. 11: 6034. https://doi.org/10.3390/ijerph18116034

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