Analyzing the epidemiological trends of cardiovascular diseases in China from 1990 to 2021 and predicting future statistics based on GBD data

Analyzing the epidemiological trends of cardiovascular diseases in China from 1990 to 2021 and predicting future statistics based on GBD data | 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 Analyzing the epidemiological trends of cardiovascular diseases in China from 1990 to 2021 and predicting future statistics based on GBD data Shihong Wang, Zhimei Jin, Yibo Lin, Wenlong Huang, Rudai Cao, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5700163/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 About 330 million people in China suffer from cardiovascular disease(CVD). CVD has become a serious public health problem in China and worldwide. This study aims to analyze the epidemiological trends of cardiovascular diseases in China from 1990 to 2021 and predict future statistics to provide a reference for the prevention and control of CVD. Methods Data were derived from the Global Burden of Disease (GBD) about cardiovascular disease from 1990 to 2021. We searched the GBD database by the key word "Cardiovascular diseases". We included the CVD epidemiological metrics involving incidence rate, prevalence rate, mortality rate and their age-standardized rate. We calculated the percentage change (%) and estimated annual percentage change (EAPC, %) to initially analyze the epidemiological trends of CVD. We used autoregressive integrated moving average (ARIMA) model to further forecast cardiovascular disease statistics from 2022 to 2030. Results The age-standardized incidence rate (ASIR) of CVD was 783.90 per 100,000 in 1990 and increased to 811.81 per 100,000 in 2021 (increased by 3.56%, EAPC = 0.12, P < 0.05). The age-standardized prevalence rate (ASPR) of CVD increased by 9.62%(EAPC = 0.32, P < 0.05) from 6024.24 per 100,000 in 1990 to 6603.72 per 100,000 in 2021. The age-standardized mortality rate (ASMR) of CVD decreased by 31.30% (EAPC=-1.14, P < 0.05) from 1990, reaching 280.11 per 100,000 in 2021. In 2021, male ASIR (847.06 per 100,000), ASPR (6616.82 per 100,000) and ASMR (372.54 per 100,000) were higher than females (772.86 per 100,000, 6587.69 per 100,000, 217.02 per 100,000). The age group over 80 years had the highest incidence rate (9656.94 per 100,000), prevalence rate (57458.84 per 100,000) and mortality rate (7042.60 per 100,000) among all age groups in 2021. The forecasted ASIR of CVD will slowly increase to 812.80 per 100,000 (95% CI : 806.75 ~ 818.84) in 2022 and to 820.26 per 100,000 (95% CI : 762.24 ~ 878.28) in 2030. The forecasted ASPR of CVD will be 6700.67 per 100,000 (95% CI : 6674.52 ~ 6726.81) in 2022 and 7626.55 per 100,000 (95% CI : 6945.36 ~ 8307.75) in 2030. The forecasted ASMR of CVD will decrease to 275.80 per 100,000 (95% CI : 263.61 ~ 288.00) in 2022 and reach 242.42 per 100,000 (95% CI : 179.93 ~ 304.90) in 2030. Conclusions CVD is still a major public health challenge facing China now and in the future. Relevant measures should be taken to prevent and control CVD. Cardiovascular diseases Incidence Prevalence Mortality China Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cardiovascular disease (CVD) is the general term for heart and vascular diseases, including ischemic heart disease (IHD) and stroke. CVD has become one of the main causes of mortality worldwide, especially among old people. The factors affecting CVD may vary by regions [ 1 ]. Despite improvements in initial diagnosis and pharmacological treatment in recent years, the disease burden of CVD is still on the rise worldwide. A previous global burden of disease (GBD) study reported that from 1990 to 2019, prevalent cases of CVD obviously increased (271 million in 1990 vs 523 million in 2019), and the death cases also showed an upward trend (12.1 million in 1990 vs 18.6 million in 2019) [ 2 ]. Currently, CVD has caused a rapid rise in healthcare costs globally and become a major public health problem threatening human health. Due to the poorer economic status and worse health resources, it is well known that low-and middle-income countries (LMICs) usually have a severe disease burden in CVD. Therefore, a focus should be placed on the burden of CVD in LMICs or developing countries. China, a low-middle-income country[ 3 ], also faces a heavy disease burden from the CVD. In the past decades of reform and opening up, while China's urbanization and industrialization have developed rapidly, it has also brought serious air pollution. At the same time, due to the unbalanced economic development and allocation of medical resources in China, access to healthcare is poorer in some remote or rural areas. A study from China indicated that the mortality rate of CVD was higher in rural areas than that in urban areas (8.09 per 1000 person-years vs 3.04 per 1000 person-years) [ 1 ]. Moreover, dietary habits are particular in China. Compared with countries such as Europe and the United States, Chinese people prefer to eat pickled or smoked food. Based on the above several reasons, the epidemiological trend of CVD in China was relatively higher[ 4 – 5 ]. A previous report in 2021 indicated that about 330 million people in China suffer from CVD, and two out of every five deaths were attributed to CVD [ 6 ]. With population aging and an increase in exposure to modifiable risk factors (hypertension, fasting blood glucose, obesity, tobacco use, air pollution, insufficient leisure-time physical activity and etc) [ 2 , 7 ], the incidence, prevalence and mortality of CVD in China will inevitably continue to rise in the future. Implementing effective intervention strategies is crucial for the prevention and control of CVD. Thus, analyzing the epidemiological trends of CVD diseases in China and predicting the future development are beneficial to relevant departments to formulate early prevention and control policies for CVD diseases. However, the latest studies about CVD epidemiological trends in China are still limited. Meanwhile, less studies were conducted to predict the future development of CVD. Global burden of disease (GBD) study is a multinational collaborative study project. It involves data of diseases burden in many countries around the world, with greater reliability, wider coverage, and better comparability across years. Therefore, based on the GBD data, we aim to analyze the epidemiological trends of cardiovascular diseases in China from 1990 to 2021 and predict future statistics to provide a reference for the prevention and control of CVD. Materials and Methods Data sources As a multinational collaborative study project, GBD has estimated the annual disease burden data for every country since the last century. The GBD study produces standard epidemiological metrics including incidence rate, prevalence rate, mortality rates, DALYs rates, YLLs rates and YLLs rate, providing comprehensive guidance for global, regional and national control of disease burden[ 2 , 8 , 9 ]. GBD obtained the data by conducting systematic reviews and opportunistic searches, and utilized data shared by national collaborators and WHO[ 10 ]. The GBD data in China were mainly derived from the CDC disease surveillance system, death cause surveillance system, and other literature reports. All data in GBD were reported separately by year, sex and age. To investigate the epidemiological trends of CVD in China, data from 1990 to 2021 were collected from the GBD in 2021. Complete details about GBD can be accessed from https://vizhub.healthdata.org/gbd-results/ . 1990 and later was an important stage of China's reform and opening up and rapid economic development. At the same time, during this period (after 1900), people's lifestyle and eating habits also began to change significantly, leading higher CVD incidence and mortality. Furthermore, the GBD database was only updated to the 2021 CVD data. Therefore, we selected 1990 to 2021 as the timeframe. We used epidemiological metrics for CVD in this study, including incidence rate (per 100,000), age-standardized incidence rate (ASIR, per 100,000), prevalence rate (per 100,000), age-standardized prevalence rate (ASPR, per 100,000), mortality rate (per 100,000), and age-standardized mortality rate (ASMR, per 100,000). All age-standardized rates were calculated by the 2021 GBD standard population. In order to analyze the incidence, prevalence, and mortality of CVD among age groups, we divided the age into 17 groups (0–4 years old; 5–9 years old; 10–14 years old; 15–19 years old; 20–24 years old; 25–29 years old; 30–34 years old; 35–39 years old; 40–44 years old; 45–49 years old; 50–54 years old; 55–59 years old; 60–64 years old; 65–69 years old; 70–74 years old; 75–79 years old; ≥80 years old). Furthermore, stroke and IDH are common CVD, so we also analyzed the epidemiological trends of stroke and IHD based on GBD. Statistical analyzes To understand the epidemiology of CVD in China, we performed a descriptive analysis among overall population, different gender and each age groups. We calculated the percentage change (%) and Estimated Annual Percentage Change (EAPC, %) to further describe the epidemiological trends of CVD. \(\:\text{P}\text{e}\text{r}\text{c}\text{e}\text{n}\text{t}\text{a}\text{g}\text{e}\:\text{c}\text{h}\text{a}\text{n}\text{g}\text{e}=100\text{\%}\times\:({\text{V}\text{a}\text{l}\text{u}\text{e}}_{2021}-{\text{V}\text{a}\text{l}\text{u}\text{e}}_{1990})/{\text{V}\text{a}\text{l}\text{u}\text{e}}_{1990}\) ; EAPC is commonly used to describe the changing trends of certain epidemiological indicators within a year. When EAPC> 0, the epidemiological indicator showed an increasing trend within a year. Furthermore, EAPC<0 represented the epidemiological indicator showed an decreasing trend within a year. \(\:EAPC=100\%\times\:({e}^{\theta\:}-1)\) , where the parameter e represents the natural constant and parameter \(\:\theta\:\) represents the regression coefficient of the equation \(\:y=\theta\:x+\epsilon\:\) . In above equation, parameter y represents the natural logarithm of metrics of incidence, prevalence, and mortality and parameter x represents years (1990 to 2021). We used t test to evaluate whether EAPC was statistically significant. Statistical analyzes were performed with the R version 4.1.0 software. Based on program packages of “tseries” and “Stats”, we constructed the ARIMA model. We used “adf.test” fuction to evaluate the stationarity of time-series data. Meanwhile, we performed white noise test based on “Box.test” fuction. Furthermore, we used the “forecast” fuction to predict future statistics of CVD. Finally, we used “ts.diag” fuction to evaluate whether the residual was a white noise sequence. ARIMA model Autoregressive integrated moving average (ARIMA) model was used for predicting the incidence, prevalence and mortality of CVD in China from 2022 to 2030. The form of the model is \(\:\text{A}\text{R}\text{I}\text{M}\text{A}(\text{p},\text{d},\text{q})\) . \(\:\text{p}\) represents the order of autoregressive, \(\:\text{d}\) represents the order of difference, and \(\:\text{q}\) represents the order of moving average. R4.1.0 software was used to establish the ARIMA model. We used Akaike information criterion (AIC) and Bayesian information criterion (BIC) to evaluate the reliability of ARIMA model. The lower AIC and BIC value mean that the ARIMA model is better. In addition, the estimation of the confidence intervals (CI) was the more important part of the prediction. A 95% CI indicated a 95% probability of including the true value within this interval range. In this study, we further estimated the 95% CI of prediction results. Results Overall CVD epidemiological trends Table 1 showed the epidemiological trends of CVD from 1990 to 2021. The incidence rate of CVD increased by 112.25% (EAPC = 2.50, P < 0.05) from 1990, reaching 1127.34 per 100,000 in 2021. The ASIR of CVD was 783.90 per 100,000 in 1990 and increased to 811.81 per 100,000 in 2021 (increased by 3.56%, EAPC = 0.12, P < 0.05). Table 1 Incidence, prevalence and mortality of cardiovascular diseases in China from 1990 to2021 Category Years Incidence Prevalence Mortality Incidence Rate ( UI ) ASIR ( UI ) Prevalence rate ( UI ) ASPR ( UI ) Mortality rate ( UI ) ASMR ( UI ) Overall 1990 531.14 (486.85 ~ 582.57) 783.90 (718.42 ~ 857.63) 4451.03 (4108.54 ~ 4754.58) 6024.24 (5599.03 ~ 6393.02) 212.25 (187.56 ~ 236.34) 407.72 (361.40 ~ 452.12) 2021 1127.34 (1015.17 ~ 1249.70) 811.81 (736.14 ~ 892.01) 9407.55 (8694.66 ~ 10157.75) 6603.72 (6121.90 ~ 7087.64) 357.44 (303.01 ~ 414.99) 280.11 (237.90 ~ 323.90) Percentage change (%) 112.25 3.56 111.36 9.62 68.41 -31.30 EAPC (%) 2.50* 0.12* 2.52* 0.32* 1.77* −1.14* Male 1990 512.53 (467.95 ~ 562.96) 800.42 (725.25 ~ 885.47) 4186.16 (3851.47 ~ 4478.72) 5976.81 (5535.16 ~ 6373.11) 208.24 (174.55 ~ 241.81) 466.57 (402.95 ~ 526.73) 2021 1100.39 (983.26 ~ 1226.35) 847.06 (763.94 ~ 932.68) 8888.05 (8165.75 ~ 9676.72) 6616.82 (6114.05 ~ 7152.17) 392.80 (318.74 ~ 482.19) 372.54 (308.34 ~ 447.92) Percentage change (%) 114.70 5.83 112.32 10.71 88.63 -20.15 EAPC (%) 2.65* 0.28* 2.63* 0.43* 2.22* −0.60* Female 1990 550.96 (506.71 ~ 602.07) 766.26 (703.46 ~ 835.55) 4733.21 (4377.56 ~ 5070.63) 6078.83 (5658.66 ~ 6472.64) 216.53 (184.67 ~ 251.42) 369.94 (314.36 ~ 428.11) 2021 1155.58 (1040.73 ~ 1278.40) 772.86 (702.66 ~ 850.06) 9952.07 (9198.79 ~ 10730.47) 6587.69 (6121.03 ~ 7073.13) 320.38 (255.07 ~ 391.94) 217.02 (170.77 ~ 264.96) Percentage change (%) 109.74 0.86 110.26 8.37 47.96 -41.34 EAPC (%) 2.34* −0.06 2.41* 0.21* 1.25* −1.71* Abbreviations: ASIR: age-standardized incidence rate (per 100,000); ASPR: age-standardized prevalence rate (per 100,000); ASMR: age-standardized mortality rate (per 100,000); EAPC: Estimated Annual Percentage Change; UI: Uncertain Interval; * P < 0.05 The prevalence rate of CVD in 2021 (9407.55 per 100,000) was 2.1 times higher than that in 1990 (4451.03 per 100,000). The ASPR of CVD increased by 9.62% (EAPC = 0.32, P < 0.05) from 6024.24 per 100,000 in 1990 to 6603.72 per 100,000 in 2021. Table 1 . In 2021, the mortality rate of CVD was 357.44 per 100,000 and the ASMR was 280.11 per 100,000. From 1990 to 2021, the mortality rate increased by 68.41% (EAPC = 1.77, P < 0.05), but the ASMR decreased by 31.30% (EAPC=-1.14, P < 0.05). Table 1 . Epidemiological trends of CVD by gender From 1990 to 2021, the ASIR of CVD increased by 5.83% (EAPC = 0.28, P 0.05) in female (Table 1 ). In 2021, the ASIR was higher in male (847.06 per 100,000) than in female (772.86 per 100,000). The ASPR of CVD showed an increasing trend among different gender from 1990 to 2021. The ASPR in male was 6616.82 per 100,000 in 2021 and increased by 10.71% (EAPC = 0.43, P < 0.05) from 1990 to 2021. Over the same period, the ASPR in female was 6587.69 per 100,000 in 2021 and increased by 8.37% (EAPC = 0.21, P < 0.05). Table 1 . The ASMR of CVD decreased by 20.15% (EAPC=-0.60, P < 0.05) in male and 41.34% (EAPC=-1.71, P < 0.05) in female from 1990 to 2021. In 2021, male had a higher ASMR (372.54 per 100,000) as compared to female (217.02 per 100,000). Table 1 . Epidemiological trends of CVD by age groups Figure 1 showed the epidemiological trends of CVD stratified by age groups. From 1990 to 2021, the incidence rate of CVD increased among those aged 40–44 years, 45–49 years, 50–54 years, 55–59 years, 60–64 years, 65–69 years, 70–74 years, 75–79 years and ≥ 80 years. In 2021, the age groups with the highest incidence rate was ≥ 80 years (9656.94 per 100,000), followed by 75–79 years (6106.93 per 100,000) and 70–74 years (4350.37 per 100,000). The prevalence rate of CVD showed an upward trend among those aged 40–44 years, 45–49 years, 50–54 years, 55–59 years, 60–64 years, 65–69 years, 70–74 years, 75–79 years and ≥ 80 years from 1990 to 2021. In 2021, the age groups with the highest prevalence rate was ≥ 80 years (57458.84 per 100,000), followed by 75–79 years (46258.78 per 100,000) and 70–74 years (37061.35 per 100,000). The analysis also showed lower prevalence rate in the younger age groups, with the lowest prevalence rate in the group aged ≤ 4 years (151.54 per 100,000). Figure 1 . From 1990 to 2021, the mortality rate of CVD decreased among all age groups. The data also showed a trend of increasing mortality rate with advancing age. In 2021, the age groups with the highest mortality rate was ≥ 80 years (7042.60 per 100,000), followed by 75–79 years (2337.37 per 100,000) and 70–74 years (1290.30 per 100,000). Figure 1 . Epidemiological trends of stroke and IHD From 1990 to 2021, the ASIR and ASMR of stroke showed a significant downward trend (decreased by 9.78%[EAPC=-0.60, P < 0.05] and 43.01%[EAPC=-1.92, P < 0.05], respectively), but the ASPR of stroke showed a upward trend (increased by 11.48%, EAPC = 0.34, P < 0.05). More importantly, male had higher ASIR, ASPR, and ASMR than female from 1990 and to 2021. Table 2 and Fig. 2 . Table 2 Incidence, prevalence and mortality of stroke and IHD in China from 1990 to 2021 Category Years ASIR ( UI ) ASPR ( UI ) ASMR ( UI ) Stroke 1990 226.94 (202.92 ~ 252.80) 1167.42 (1082.04 ~ 1262.59) 242.18 (213.83 ~ 272.66) 2021 204.75 (181.03 ~ 231.50) 1301.42 (1200.61 ~ 1405.73) 138.03 (116.69 ~ 160.32) Percentage change (%) -9.78 11.48 -43.01 EAPC (%) −0.60* 0.34* −1.92* IHD 1990 315.31 (255.53 ~ 382.49) 2526.44 (2189.97 ~ 2914.97) 94.14 (84.01 ~ 105.89) 2021 365.67 (293.32 ~ 440.07) 3042.35 (2601.68 ~ 3629.87) 110.91 (92.42 ~ 128.56) Percentage change (%) 15.97 20.42 17.81 EAPC (%) 0.66* 0.64* 0.97* Abbreviations: ASIR: age-standardized incidence rate (per 100,000); ASPR: age-standardized prevalence rate (per 100,000); ASMR: age-standardized mortality rate (per 100,000); EAPC: Estimated Annual Percentage Change; IHD: Ischemic Heart Disease; UI: Uncertain Interval; * P < 0.05 .From 1990 to 2021, the ASIR, ASPR and ASMR of IHD increased by 15.97% (EAPC = 0.66, P < 0.05), 20.42% (EAPC = 0.64, P < 0.05) and 17.81% (EAPC = 0.97, P < 0.05), respectively. Moreover, we also observed that the ASIR, ASPR and ASMR of IHD were higher in male than in female. Table 2 and Fig. 2 . Comparison of CVD between China and other regions In 2021, the ASIR of CVD in China (811.81 per 100,000) was higher than those in global (787.04 per 100,000), Asia (806.12 per 100,000), US (673.89 per 100,000) and Japan (424.23 per 100,000). Figure 3 . In 2021, the ASPR of CVD in China (6603.72 per 100,000) was lower than those in global (7178.73 per 100,000), Asia (6782.60 per 100,000) and US (7755.28 per 100,000), but higher than that in Japan (5040.38 per 100,000). Figure 3 . In 2021, the ASMR of CVD in China (280.11 per 100,000) was higher than those in global (235.18 per 100,000), Asia (257.45 per 100,000), US (145.30 per 100,000) and Japan (72.47 per 100,000). Figure 3 . CVD epidemiological trends forecast The forecasted ASIR of CVD in China will slowly increased to 812.80 (95% CI : 806.75 ~ 818.84) in 2022, followed by 813.76 (95% CI : 801.41 ~ 826.11) in 2023, 814.71 (95% CI : 795.67 ~ 833.76) in 2024, 815.65 (95% CI : 789.82 ~ 841.49) in 2025, 816.58 (95% CI : 784.01 ~ 849.16) in 2026, 817.51 (95% CI : 778.32 ~ 856.69) in 2027, 818.43 (95% CI : 772.79 ~ 864.07) in 2028, 819.35 (95% CI : 767.43 ~ 871.26) in 2029 and 820.26 (95% CI : 762.24 ~ 878.28) in 2030. Table 3 and Fig. 4 . The construction of ARIMA model was detailed in the supplementary material. Table 3 Prediction of incidence, prevalence and mortality of cardiovascular diseases in China from 2022 to 2030 Metrics Years Overall (95% CI ) Male (95% CI ) Female (95% CI ) ASIR 2022 812.80 (806.75 ~ 818.84) 838.98 (831.89 ~ 846.07) 781.16 (774.93 ~ 787.39) 2023 813.76 (801.41 ~ 826.11) 831.12 (816.30 ~ 845.95) 789.48 (775.27 ~ 803.69) 2024 814.71 (795.67 ~ 833.76) 823.23 (799.02 ~ 847.45) 797.81 (773.82 ~ 821.79) 2025 815.65 (789.82 ~ 841.49) 815.35 (780.38 ~ 850.32) 806.13 (770.85 ~ 841.41) 2026 816.58 (784.01 ~ 849.16) 807.47 (760.52 ~ 854.42) 814.46 (766.53 ~ 862.38) 2027 817.51 (778.32 ~ 856.69) 799.58 (739.54 ~ 859.62) 822.78 (761.00 ~ 884.56) 2028 818.43 (772.79 ~ 864.07) 791.70 (717.55 ~ 865.85) 831.11 (754.35 ~ 907.86) 2029 819.35 (767.43 ~ 871.26) 783.81 (694.60 ~ 873.03) 839.43 (746.66 ~ 932.20) 2030 820.26 (762.24 ~ 878.28) 775.93 (670.75 ~ 881.11) 847.76 (738.00 ~ 957.51) ASPR 2022 6700.67 (6674.52 ~ 6726.81) 6667.87 (6655.78 ~ 6679.96) 6696.68 (6657.60 ~ 6735.75) 2023 6809.52(6740.95 ~ 6878.10) 6729.74 (6688.73 ~ 6770.74) 6805.67 (6718.15 ~ 6893.19) 2024 6923.44 (6797.73 ~ 7049.14) 6795.79 (6712.86 ~ 6878.73) 6914.66 (6768.10 ~ 7061.22) 2025 7039.49 (6844.30 ~ 7234.69) 6863.48 (6727.90 ~ 6999.06) 7023.65 (6809.02 ~ 7238.28) 2026 7156.46 (6881.16 ~ 7431.77) 6931.79 (6734.52 ~ 7129.07) 7132.64 (6841.96 ~ 7423.32) 2027 7273.82 (6909.03 ~ 7638.61) 7000.35 (6733.51 ~ 7267.19) 7241.63 (6867.66 ~ 7615.60) 2028 7391.34 (6928.63 ~ 7854.05) 7069.01 (6725.58 ~ 7412.44) 7350.62 (6886.70 ~ 7814.53) 2029 7508.93 (6940.57 ~ 8077.30) 7137.70 (6711.28 ~ 7564.12) 7459.61 (6899.55 ~ 8019.67) 2030 7626.55 (6945.36 ~ 8307.75) 7206.41 (6691.08 ~ 7721.73) 7568.60 (6906.57 ~ 8230.63) ASMR 2022 275.80 (263.61 ~ 288.00) 369.17 (350.71 ~ 387.63) 212.20 (201.89 ~ 222.51) 2023 271.41 (247.27 ~ 295.55) 365.97 (332.75 ~ 399.20) 206.88 (186.38 ~ 227.38) 2024 267.16 (233.85 ~ 300.47) 362.86 (316.43 ~ 409.29) 201.63 (173.26 ~ 230.00) 2025 263.03 (223.20 ~ 302.86) 359.78 (301.52 ~ 418.04) 196.58 (162.63 ~ 230.54) 2026 258.94 (214.05 ~ 303.83) 356.72 (287.71 ~ 425.72) 191.67 (153.39 ~ 229.94) 2027 254.83 (205.42 ~ 304.25) 353.66 (274.74 ~ 432.59) 186.76 (144.65 ~ 228.87) 2028 250.70 (196.88 ~ 304.52) 350.61 (262.40 ~ 438.82) 181.82 (135.98 ~ 227.67) 2029 246.56 (188.36 ~ 304.75) 347.57 (250.56 ~ 444.57) 176.86 (127.30 ~ 226.42) 2030 242.42 (179.93 ~ 304.90) 344.52 (239.09 ~ 449.95) 171.89 (118.67 ~ 225.10) Abbreviations: ASIR: age-standardized incidence rate (per 100,000); ASPR: age-standardized prevalence rate (per 100,000); ASMR: age-standardized mortality rate (per 100,000); In 2022, the forecasted ASPR of CVD in China will increased to 6700.67 (95% CI : 6674.52 ~ 6726.81), followed by 6809.52 (95% CI : 6740.95 ~ 6878.10) in 2023, 6923.44 (95% CI : 6797.73 ~ 7049.14) in 2024, 7039.49 (95% CI : 6844.30 ~ 7234.69) in 2025, 7156.46 (95% CI : 6881.16 ~ 7431.77) in 2026, 7273.82 (95% CI : 6909.03 ~ 7638.61) in 2027, 7391.34 (95% CI : 6928.63 ~ 7854.05) in 2028, 7508.93 (95% CI : 6940.57 ~ 8077.30) in 2029 and 7626.55 (95% CI : 6945.36 ~ 8307.75) in 2030. Table 3 and Fig. 4 . The forecasted ASMR of CVD in China will continue to show a downward trend from 2022 to 2030. The forecasted ASMR is 275.80 (95% CI : 263.61 ~ 288.00) in 2022, followed by 271.41 (95% CI : 247.27 ~ 295.55) in 2023, 267.16 (95% CI : 233.85 ~ 300.47) in 2024, 263.03 (95% CI : 223.20 ~ 302.86) in 2025, 258.94 (95% CI : 214.05 ~ 303.83) in 2026, 254.83 (95% CI : 205.42 ~ 304.25) in 2027, 250.70 (95% CI : 196.88 ~ 304.52) in 2028, 246.56 (95% CI : 188.36 ~ 304.75) in 2029 and 242.42 (95% CI : 179.93 ~ 304.90) in 2030. Table 3 and Fig. 4 . Discussion CVD is the main cause of the loss of disability-adjusted life years (DALY) and death [ 4 , 11 ]. In this study, we utilized GBD data to describe and analyze the incidence rate, prevalence rate, mortality rate, ASIR, ASPR and ASMR of CVD in China from 1990 to 2021. Moreover, we made a forecast for the ASIR, ASPR and ASMR of CVD from 2022 to 2030. Our results are of great significance for the development of prevention and control strategies for CVD. In our study, we used the percentage change and EAPC to describe the CVD trend. Both percentage change and EAPC are common epidemiological indicators. They reflect the changing trend of the CVD metrics and have important implications for public health. We observed that the ASIR (increased by 3.56%, EAPC = 0.12, P < 0.05) and ASPR (increased by 9.62%, EAPC = 0.32, P < 0.05) of CVD in China exhibited an upward trend from 1990 to 2021. Our findings were in line with previous similar studies. A previous GBD study indicated that the ASPR of CVD significantly increased by 14.7% in China from 1990 to 2016 [ 5 ]. In addition, a global study also reported the incidence and prevalence of CVD also showed an upward trend [ 12 ]. The possible reasons for the rising ASIR and ASPR are explained as follows: (1) The occurrence of CVD is mainly attributed to the aggravation of air pollution in China in recent years [ 13 ]. Due to the rapid development of industrialization in China, air pollution has become an important topic of great concern. When particulate matter such as PM2.5 from polluted air is inhaled by human body, it can lead to sharply oxidative stress and inflammation in our body, which may ultimately increases the risk of CVD [ 14 ]. (2) The higher prevalence rate of diabetes and hypertension in China may contribute to the occurrence of CVD to some extent [ 15 ]. Chronic diseases including diabetes and hypertension were common risk factors leading to other severe diseases such as CVD. As the prevalence of diabetes and hypertension evidently rose [ 16 – 17 ], the CVD trend will continue to fast increase. (3) The increase in the diagnosis rate of CVD in recent years was also responsible for the increase in the ASIR and ASPR. For example, Wang et al developed cardiac magnetic resonance imaging based on artificial intelligence to diagnose CVD [ 18 ]. The AI-based methods have a higher performance for diagnosis of CVD. Furthermore, a recent study found that microRNAs can be considered as an important biomarker for diagnosing CVD [ 19 ]. (4) Changes in lifestyle and dietary habits in recent years are also important factors for the occurrence of CVD, such as high oil, salt and sugar diets, and staying up late nights. It is important to note that we found the ASMR of CVD in China decreased by 31.30% (EAPC=-1.14, P < 0.05) from 1990 to 2021. Our findings were consistent with previous studies [ 5 , 20 ]. Several reasons may explain this phenomenon: (1) In the past 30 years, the quality of medical care have made remarkable progress in China, especially in cardiovascular technology [ 21 ]. Increasing treatment level is an important reason for curbing CVD mortality. (2) The decrease in ASMR was also related to the promotion of public health interventions or policies in China, such as implementation of basic public health services program [ 22 ] and Urban and Rural Residents' Basic Medical Insurance policy[ 23 ]. With the implementation of public health strategies in China, residents can reduce the economic burden of CVD and elevate the treatment rate and control rate of CVD, which can reduce the death risk of CVD in a degree (3) With the rapid development in China, the life quality of residents has obviously improved and their awareness of disease prevention and control has also been strengthened significantly, so the mortality of CVD showed a downward trend. All in all, the disease burden of CVD is still severe in China. In the future, the prevention and control of CVD should be focused primarily on the strategic and key technological development so as to reduce the disease burden of CVD [ 21 ]. Our findings were inconsistent with several studies. A recent study involved 22 million people from UK found that the incidence of 10 prespecified CVD showed a downward trend (decreased by 19%) during 2000-19 and the overall incidence of CVD has remained a stable state Since the mid-2000s [ 24 ]. The above results indicated that the trend of CVD was relatively controlled in UK. The reason for this difference might be attributed to developed economy and higher level of health care in UK compared to China. Moreover, the decline of CVD mortality was almost a global trend. However, data from the US CDC demonstrated that the mortality of CVD showed an upward trend from 2019 to 2022 [ 25 ], which was inconsistent with our findings. The cause of the increased CVD mortality may be the more severer COVID-19 epidemic in US. In addition, a study from Malaysia found that ASMR of CVD showed an upward trend from 2010 to 2021, which was different from our results [ 26 ]. Reason for the difference may be poorer medical resources in Malaysia than in China. From 1990 to 2021, both ASIR and ASPR in China increased faster in males than in females. Moreover, the current analysis also revealed that male ASIR (847.06 per 100,000), ASPR (6616.82 per 100,000) and ASMR (372.54 per 100,000) were higher than females (772.86 per 100,000, 6587.69 per 100,000, 217.02 per 100,000) in 2021. Our findings were in agreement with early studies. A previous study have shown males have a higher CVD incidence than females among total population and population with certain risk factors (such as hypertension, diabetes, obesity, smoking, etc) [ 27 ]. Moreover, there was also study from China exhibiting that the crude and weighted mortality rate of CVD were higher in males than that in females [ 28 ]. Males appear to have a higher risk of CVD. The possible reasons are as follows: (1) This disparity between males and females may be linked to differences in lifestyle and risk factors, with significantly higher rates of tobacco and alcohol use among males. (2) Compared to females, males are often given more social and financial stress.In China, as the backbone of the family, males have to work harder. Long-term large stress may lead to the occurrence of CVD among males [ 29 ]. (3) Poor sleep quality or insomnia was often an important cause of CVD occurrence and death[ 30 ]. A review reported that females appear to have better sleep quality than males[ 31 ], thus females are at lower risk of CVD than males (4) Estrogen is a direct vasodilator, which plays a key role in reducing the CVD risk among females [ 32 – 33 ]. Therefore, more prevention and control measures should be taken for males. In our study, the incidence, prevalence and mortality of CVD were uncommon in the group aged less than 20 years. With advancing age, the incidence, prevalence and mortality of CVD exhibited a significant increasing trend and peaked in the age group over 80 years. Our findings were in line with previous studies. Qu et al also found that the incidence and mortality of CVD were highest in group aged over 95 than that among other age groups [ 34 ]. Indeed, age was considered as the unmodifiable risk factor for CVD [ 35 ]. Compared with younger adults, the elderly are more likely to develop CVD due to the decline of physical function such as vascular wall thickening, perivascular fibrosis, cardiac myocytes reduction and etc [ 36 ]. Furthermore, due to the lack of expertise, the old people known little about CVD prevention and control, which may cause the higher risk of CVD among the elderly. Our findings provide an important guideline for prevention and treatment of CVD in China. In this study, we compared the disease burden of CVD in China with the global, Asia, US and Japan. It is worth noting that the ASIR and ASMR of CVD in China were higher than US and Japan. This phenomenon may be attributed to changes in modifiable risk factors, income levels and policies. Studies have shown that residents in high-income countries may have a higher awareness of the disease prevention compared with those in low-middle-income countries [ 3 ]. In addition, compared with China, higher medical level and better social welfare in developed countries were an important reason for this phenomenon. Moreover, dietary habit was also one of the main influencing factors. Compared to some developed countries such as Japan, the Chinese prefered pickled or spicy foods, so the incidence of CVD was relatively higher in China. In addition, we also observed that the ASIR and ASMR were higher than asian and global levels. This suggested that the CVD epidemic situation was more serious in China. Relevant strategies should be pushed to address the severe CVD trends in China. In this study, we used ARIMA model to forecast the cardiovascular disease statistics from 2022 to 2030. We found that the forecasted ASIR of CVD will slowly increase to 812.80 per 100,000 in 2022 and to 820.26 per 100,000 in 2030. The increase in ASIR of CVD is mainly attributed to an aging population in China. A previous study indicated that by 2050, China will have 400 million elderly people over 65 years old, of which 150 million are over 80 years old [ 37 ]. Beside, increasing exposure levels of risk factors can also contribute to a higher risk of CVD such as obesity, large economic stress and bad lifestyle in the future. Moreover, we also observed that The forecasted ASPR of CVD will be 6700.67 per 100,000 in 2022 and 7626.55 per 100,000 in 2030. The increasing life expectancy in the Chinese population may be responsible for the increasing ASPR of CVD. A study reported that the life expectancy in China will reach 81.3 years by 2035 [ 38 ]. In addition, the continuous improvement in CVD treatment techniques in the future may reduce the risk of death among CVD patients, which will increase the prevalence of CVD to some extent. Interestingly, the forecasted ASMR of CVD will decrease to 275.80 per 100,000 in 2022 and reach 242.42 per 100,000 in 2030. We speculate that the decrease in ASMR is due to the improved level of CVD treatment in the future. Meanwhile, further implementation of CVD health policies in the future may also help to reduce the risk of CVD death. Finally, ARIMA model has several strengths. On the one hand, ARIMA model can better predict the future trend of the data and have high fitting performance [ 39 ]. On the other hand, compared with short-term prediction models such as gray model (GM), the ARIMA model can perform medium or long term predictions. Moreover, in contrast to other prediction models such as Neural network autoregressive (NNAR) model and prophet model, ARIMA models are more interpretable and easier to understand. However, there are also some limitations for ARIMA model. For example, ARIMA model is difficult to fit the data with nonlinear changes. Besides, ARIMA model do not consider the risk factors for CVD, as compared to other models such as linear regression models. Our study has important policy implications: (1) The total CVD burden is currently relatively severe in China. Thus, the government and relevant departments should deeply implement medical reform and accurately allocate health resources to reduce the burden of CVD in China. (2) Male and older adults are at higher risk of CVD. Therefore, more early CVD screening and intervention strategies should focus on the above two high-risk groups (3) The ASIR and ASPR of CVD in China will show an increasing trend in the future. Based on this, the health department can further explore and develop relevant strategies for the prevention and control of future CVD. There are several strengths in our study: (1) GBD data can better reflect the epidemic trends of CVD in China, providing important information for public health policy making. (2) The ARIMA model has a higher fitting performance and can accurately predict CVD statistics from 2022 to 2030. Our study also has several limitations: (1) The ARIMA model did not consider some important risk factors for CVD, such as dietary factors, lifestyle, genetics, hormones and etc. In the future study, we will further include risk factors for analysis. (2) The GBD data were estimated by statistical and mathematical models. Therefore, the results of GBD may have some differences from the actual situation. Our results still need to be further compared with monitoring CVD data from China CDC. (3) GBD did not provide epidemiological data of CVD for different ethnic groups in China. In future study, we will further explore the epidemiological trends of CVD among different ethnic groups in China. Conclusions In this study, we found that the disease burden of CVD in China was still severe. Moreover, the ASIR and ASPR of CVD will continue to increase from 2022 to 2030. Prevention and control of CVD is still a large public health challenge in China. Therefore, the health department should further implement the public health policy of CVD. For example, conducting early screening programs for population at high risk for CVD (such as male, advanced age or individuals with underlying chronic diseases). Meanwhile, regular follow-up and interventions were given for positive CVD individuals to reduce the risk for CVD. Finally, the factors affecting the CVD are complex and diverse. In the future, more studies should be conducted to further explore the influencing factors leading to the incidence and mortality of CVD. Declarations Funding 1.The State Key Laboratory of Causes and Prevention of High Incidence Disease in Central Asia jointly built in 2024-Guangdong Workstation Joint Fund project (SKL-HIDCA-2024-GD7B) 2.2022 Dongguan Social Development Science and Technology (Key) Project (20221800905642) 3.2022 Guangdong Basic and Applied Basic Research Foundation Natural Science Foundation project (2022A1515012407) 4.Undergraduate Innovation and Entrepreneurship Education Base Project of Guangdong Medical University 5.2022 Dongguan Science and Technology Commissioner Project (20221800500342) 6.Guangdong Provincial Undergraduate Online Open Curriculum Steering Committee (key) research topic (2022ZXKC182) 7.The 14th Five-Year Plan Guangdong Higher Education research topic in 2022 (22GYB10) 8.2020 Guangdong Province Graduate Education Innovation Plan Project (Research on Degree and Graduate Education Reform) (2020JGXM057) Consent for publication All authors agree to the publication of this manuscript. Availability of data and materials The data used for the analysis in the study are publicly available at https://vizhub.healthdata.org/gbd-results/. Ethics approval and consent to participate Not applicable for that section Competing interests The authors do not have any possible conflicts of interest. Author contributions Conceptualization: Wang S. Data curation: Wang S, Jin Z, Lin Y, Huang W. Formal analysis: Wang S, Cao R, Lai F, Wu S. Funding acquisition: Yu H, Hu Y, Wang X. Project administration: Yu H, Hu Y, Wang X. Visualization: Wang S. Writing-original draft: Wang S, Jin Z, Lin Y. Writing-review & editing: Wang S, Yu H. Acknowledgements Not applicable for that section References Li S, Liu Z, Joseph P, et al. Modifiable risk factors associated with cardiovascular disease and mortality in China: a PURE substudy. Eur Heart J. 2022, 43(30):2852-2863. doi: 10.1093/eurheartj/ehac268. Roth GA, Mensah GA, Johnson CO, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020, 76(25):2982-3021. doi: 10.1016/j.jacc.2020.11.010. Erratum in: J Am Coll Cardiol. 2021, 77(15):1958-1959. doi: 10.1016/j.jacc.2021.02.039. Chow CK, Teo KK, Rangarajan S, et al. Prevalence, awareness, treatment, and control of hypertension in rural and urban communities in high-, middle-, and low-income countries. JAMA. 2013,310(9):959-968. doi: 10.1001/jama.2013.184182. Purroy F, Montalà N. Epidemiology of stroke in the last decade: a systematic review. Rev Neurol. 2021 Nov 1;73(9):321-336. doi: 10.33588/rn.7309.2021138. Liu S, Li Y, Zeng X, et al. Burden of Cardiovascular Diseases in China, 1990-2016: Findings From the 2016 Global Burden of Disease Study. JAMA Cardiol. 2019, 4(4):342-352. doi: 10.1001/jamacardio.2019.0295. In China TWCOTROCHAD, Hu SS. Report on cardiovascular health and diseases in China 2021: an updated summary. J Geriatr Cardiol. 2023, 20(6):399-430. doi: 10.26599/1671-5411.2023.06.001. Kaminsky LA, German C, Imboden M, et al. The importance of healthy lifestyle behaviors in the prevention of cardiovascular disease. Prog Cardiovasc Dis. 2022 Jan-Feb;70:8-15. doi: 10.1016/j.pcad.2021.12.001. Lababidi H, Salerno PR, Wass SY, et al. The Global Burden of premature cardiovascular disease, 1990-2019. Int J Cardiol Cardiovasc Risk Prev. 2023 Sep 23;19:200212. doi: 10.1016/j.ijcrp.2023.200212. Al Zomia AS, Al Zehefa IAM, Lahiq LA, et al. Tracking the epidemiological trends of female breast cancer in Saudi Arabia since 1990 and forecasting future statistics using global burden of disease data, time-series analysis. BMC Public Health. 2024, 24(1):1953. doi: 10.1186/s12889-024-19377-x. GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023, 402(10397):203-234. doi: 10.1016/S0140-6736(23)01301-6. Erratum in: Lancet. 2023, 402(10408):1132. doi: 10.1016/S0140-6736(23)02044-5. Deng P, Fu Y, Chen M, et al. Temporal trends in inequalities of the burden of cardiovascular disease across 186 countries and territories. Int J Equity Health. 2023, 22(1):164. doi: 10.1186/s12939-023-01988-2. Sun J, Qiao Y, Zhao M, et al. Global, regional, and national burden of cardiovascular diseases in youths and young adults aged 15-39 years in 204 countries/territories, 1990-2019: a systematic analysis of Global Burden of Disease Study 2019. BMC Med. 2023, 21(1):222. doi: 10.1186/s12916-023-02925-4. Khoshakhlagh AH, Mohammadzadeh M, Gruszecka-Kosowska A, et al. Burden of cardiovascular disease attributed to air pollution: a systematic review. Global Health. 2024, 20(1):37. doi: 10.1186/s12992-024-01040-0. Bhatnagar A. Cardiovascular Effects of Particulate Air Pollution. Annu Rev Med. 2022, 73:393-406. doi: 10.1146/annurev-med-042220-011549. Zhao D. Epidemiological Features of Cardiovascular Disease in Asia. JACC Asia. 2021, 1(1):1-13. doi: 10.1016/j.jacasi.2021.04.007. Xu Y, Lu J, Li M, et al. Diabetes in China part 1: epidemiology and risk factors. Lancet Public Health. 2024, 9(12):e1089-e1097. doi: 10.1016/S2468-2667(24)00250-0. Du J, Zhu G, Yue Y, et al. Blood pressure and hypertension prevalence among oldest-old in China for 16 year: based on CLHLS. BMC Geriatr. 2019, 19(1):248. doi: 10.1186/s12877-019-1262-4. Wang YJ, Yang K, Wen Y, et al. Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging. Nat Med. 2024, 30(5):1471-1480. doi: 10.1038/s41591-024-02971-2. Kramna D, Riedlova P, Jirik V. MicroRNAs as a Potential Biomarker in the Diagnosis of Cardiovascular Diseases. Medicina (Kaunas). 2023, 59(7):1329. doi: 10.3390/medicina59071329. Nedkoff L, Briffa T, Zemedikun D, et al. Global Trends in Atherosclerotic Cardiovascular Disease. Clin Ther. 2023, 45(11):1087-1091. doi: 10.1016/j.clinthera.2023.09.020. The WCOTROCHADIC. Report on Cardiovascular Health and Diseases in China 2022: an Updated Summary. Biomed Environ Sci. 2023, 36(8):669-701. doi: 10.3967/bes2023.106. Fang G, Yang D, Wang L, et al. Experiences and Challenges of Implementing Universal Health Coverage With China's National Basic Public Health Service Program: Literature Review, Regression Analysis, and Insider Interviews. JMIR Public Health Surveill. 2022, 8(7):e31289. doi: 10.2196/31289. Fan X, Su M, Zhao Y, et al. Effect of Health Insurance Policy on the Health Outcomes of the Middle-Aged and Elderly: Progress Toward Universal Health Coverage. Front Public Health. 2022, 10:889377. doi: 10.3389/fpubh.2022.889377. Conrad N, Molenberghs G, Verbeke G, et al. Trends in cardiovascular disease incidence among 22 million people in the UK over 20 years: population based study. BMJ. 2024, 385:e078523. doi: 10.1136/bmj-2023-078523. Erratum in: BMJ. 2024, 387:q2381. doi: 10.1136/bmj.q2381. Woodruff RC, Tong X, Khan SS, et al. Trends in Cardiovascular Disease Mortality Rates and Excess Deaths, 2010-2022. Am J Prev Med. 2024, 66(4):582-589. doi: 10.1016/j.amepre.2023.11.009. Hasani WSR, Musa KI, Cheng KY, et al. Exploring the trend of age-standardized mortality rates from cardiovascular disease in Malaysia: a joinpoint analysis (2010-2021). BMC Public Health. 2024, 24(1):2519. doi: 10.1186/s12889-024-19103-7. Woodward M. Cardiovascular Disease and the Female Disadvantage. Int J Environ Res Public Health. 2019, 16(7):1165. doi: 10.3390/ijerph16071165. Wang W, Wang D, Liu H, et al. Trend of declining stroke mortality in China: reasons and analysis. Stroke Vasc Neurol. 2017, 2(3):132-139. doi: 10.1136/svn-2017-000098. Li J, Loerbroks A, Bosma H, et al. Work stress and cardiovascular disease: a life course perspective. J Occup Health. 2016, 58(2):216-219. doi: 10.1539/joh.15-0326-OP. Miller MA, Howarth NE. Sleep and cardiovascular disease. Emerg Top Life Sci. 2023, 7(5):457-466. doi: 10.1042/ETLS20230111. Mong JA, Cusmano DM. Sex differences in sleep: impact of biological sex and sex steroids. Philos Trans R Soc Lond B Biol Sci. 2016, 371(1688):20150110. doi: 10.1098/rstb.2015.0110. Ueda K, Fukuma N, Adachi Y, et al. Sex Differences and Regulatory Actions of Estrogen in Cardiovascular System. Front Physiol. 2021 Sep 28;12:738218. doi: 10.3389/fphys.2021.738218. Oneglia A, Nelson MD, Merz CNB. Sex Differences in Cardiovascular Aging and Heart Failure. Curr Heart Fail Rep. 2020, 17(6):409-423. doi: 10.1007/s11897-020-00487-7. Qu C, Liao S, Zhang J, et al. Burden of cardiovascular disease among elderly: based on the Global Burden of Disease Study 2019. Eur Heart J Qual Care Clin Outcomes. 2024,10(2):143-153. doi: 10.1093/ehjqcco/qcad033. Dhingra R, Vasan RS. Age as a risk factor. Med Clin North Am. 2012, 96(1):87-91. doi: 10.1016/j.mcna.2011.11.003. Costantino S, Paneni F, Cosentino F. Ageing, metabolism and cardiovascular disease. J Physiol. 2016, 594(8):2061-2073. doi: 10.1113/JP270538. Fang EF, Scheibye-Knudsen M, Jahn HJ, et al. A research agenda for aging in China in the 21st century. Ageing Res Rev. 2015, 24(Pt B):197-205. doi: 10.1016/j.arr.2015.08.003. Bai R, Liu Y, Zhang L, et al. Projections of future life expectancy in China up to 2035: a modelling study. Lancet Public Health. 2023, 8(12):e915-e922. doi: 10.1016/S2468-2667(22)00338-3. da Cunha VP, Botelho GM, de Oliveira AHM, et al. Application of the ARIMA Model to Predict Under-Reporting of New Cases of Hansen's Disease during the COVID-19 Pandemic in a Municipality of the Amazon Region. Int J Environ Res Public Health. 2021, 19(1):415. doi: 10.3390/ijerph19010415. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.pdf 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5700163","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":443935237,"identity":"faca9f69-0d0a-4bc1-9998-bf3faa9f9e6c","order_by":0,"name":"Shihong Wang","email":"","orcid":"","institution":"School of Public Health, Guangdong Medical University, Dongguan","correspondingAuthor":false,"prefix":"","firstName":"Shihong","middleName":"","lastName":"Wang","suffix":""},{"id":443935239,"identity":"a32dc4f2-313b-4ada-a3cc-bfb9d317ed29","order_by":1,"name":"Zhimei Jin","email":"","orcid":"","institution":"School of Public Health, Guangdong Medical University, Dongguan","correspondingAuthor":false,"prefix":"","firstName":"Zhimei","middleName":"","lastName":"Jin","suffix":""},{"id":443935240,"identity":"144291ba-bd35-4e17-836e-b815a27aab5b","order_by":2,"name":"Yibo Lin","email":"","orcid":"","institution":"School of Public Health, Guangdong Medical University, Dongguan","correspondingAuthor":false,"prefix":"","firstName":"Yibo","middleName":"","lastName":"Lin","suffix":""},{"id":443935241,"identity":"36c17962-acfa-4aa3-9e67-48becdc59232","order_by":3,"name":"Wenlong Huang","email":"","orcid":"","institution":"School of Public Health, Guangdong Medical University, Dongguan","correspondingAuthor":false,"prefix":"","firstName":"Wenlong","middleName":"","lastName":"Huang","suffix":""},{"id":443935242,"identity":"8deb3d1f-a18d-4eae-bd88-dcdc8c87c5cf","order_by":4,"name":"Rudai Cao","email":"","orcid":"","institution":"School of Public Health, Guangdong Medical University, Dongguan","correspondingAuthor":false,"prefix":"","firstName":"Rudai","middleName":"","lastName":"Cao","suffix":""},{"id":443935243,"identity":"bb2422d9-02aa-4307-bec3-466d2e97b479","order_by":5,"name":"Fengxia Lai","email":"","orcid":"","institution":"School of Public Health, Guangdong Medical University, Dongguan","correspondingAuthor":false,"prefix":"","firstName":"Fengxia","middleName":"","lastName":"Lai","suffix":""},{"id":443935244,"identity":"b59fce68-9b42-48df-8aee-ede865553829","order_by":6,"name":"Shufa Wu","email":"","orcid":"","institution":"School of Public Health, Guangdong Medical University, Dongguan","correspondingAuthor":false,"prefix":"","firstName":"Shufa","middleName":"","lastName":"Wu","suffix":""},{"id":443935245,"identity":"6f85d0b6-6d9a-4916-a7d8-b93858c7412f","order_by":7,"name":"Zhuguo Wu","email":"","orcid":"","institution":"The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan","correspondingAuthor":false,"prefix":"","firstName":"Zhuguo","middleName":"","lastName":"Wu","suffix":""},{"id":443935246,"identity":"3bbf0ec1-05f7-4ae6-ad85-65beeac855c8","order_by":8,"name":"Xin Wang","email":"","orcid":"","institution":"Shenzhen Baoan Central Hospital, Affiliated Hospital Group of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Wang","suffix":""},{"id":443935248,"identity":"db0b08fa-9ced-41d6-b231-88503dfaddd9","order_by":9,"name":"Yuehua Hu","email":"","orcid":"","institution":"Office of Epidemiology, Chinese Center for Disease Control and Prevention, Beijing","correspondingAuthor":false,"prefix":"","firstName":"Yuehua","middleName":"","lastName":"Hu","suffix":""},{"id":443935250,"identity":"91d44d9f-1b78-46ab-b981-770933a3db21","order_by":10,"name":"Haibing Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACAwkGxgcJFTZy/OyNjQ8/EKmF2eDBmTRjyZ7DzcYSRGphE3zYcihxw430NgEeYrSYS/eYMSQ2HDCWnPmwjUGCwU5Ot4GAFss5Z8weJO64I8cvndj2oIAh2djsACGH3cgxN0g888xYcnZiO9CVBxK3EaHFTCKx7XDihpsH2yR4SNNyg5FYLXeOFRskgAM5ERjIBsT45Xbzxoc/wFF5/OHDDxV2cgS1MDBwGCCbQFA5CLA/IErZKBgFo2AUjGAAAD3jTHNFD3GOAAAAAElFTkSuQmCC","orcid":"","institution":"The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan","correspondingAuthor":true,"prefix":"","firstName":"Haibing","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2024-12-23 13:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5700163/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5700163/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80794162,"identity":"9c045ba0-f8de-4b99-adae-b1b41971baca","added_by":"auto","created_at":"2025-04-17 07:18:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":262042,"visible":true,"origin":"","legend":"\u003cp\u003eIncidence, prevalence and mortality of cardiovascular diseases among all age groups in China from 1990 to2021\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5700163/v1/dfffd70c99959f6eb3774698.jpg"},{"id":80792825,"identity":"2503e4d6-9ac7-4c0b-a01b-c36c94b0e38f","added_by":"auto","created_at":"2025-04-17 07:02:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":93000,"visible":true,"origin":"","legend":"\u003cp\u003eIncidence, prevalence and mortality of stroke and IHD in China from 1990 to2021\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5700163/v1/1ee6f8b4b81492a1d26f0db5.jpg"},{"id":80793692,"identity":"7b986772-6237-405c-bd2e-4b3169e500e6","added_by":"auto","created_at":"2025-04-17 07:10:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51805,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of CVD incidence, prevalence and mortality between China and other regions\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5700163/v1/723318b2025764558432e1b9.jpg"},{"id":80793694,"identity":"89b677b8-ae48-4294-97cb-c9b96f228985","added_by":"auto","created_at":"2025-04-17 07:10:19","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":429320,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction of incidence, prevalence and mortality of cardiovascular diseases in China from 2022 to 2030\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5700163/v1/6adf9c7e5343f7380b4e8bea.jpg"},{"id":81606590,"identity":"167831e5-61fd-4103-95bf-0f882d54c543","added_by":"auto","created_at":"2025-04-29 06:03:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1947838,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5700163/v1/8a09731a-44b7-410f-834f-0c96849420e1.pdf"},{"id":80792827,"identity":"abdef997-085f-4355-9bb5-6aa23f98fd5c","added_by":"auto","created_at":"2025-04-17 07:02:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1558692,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5700163/v1/74d54f6665071237476b7774.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analyzing the epidemiological trends of cardiovascular diseases in China from 1990 to 2021 and predicting future statistics based on GBD data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular disease (CVD) is the general term for heart and vascular diseases, including ischemic heart disease (IHD) and stroke. CVD has become one of the main causes of mortality worldwide, especially among old people. The factors affecting CVD may vary by regions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite improvements in initial diagnosis and pharmacological treatment in recent years, the disease burden of CVD is still on the rise worldwide. A previous global burden of disease (GBD) study reported that from 1990 to 2019, prevalent cases of CVD obviously increased (271\u0026nbsp;million in 1990 vs 523\u0026nbsp;million in 2019), and the death cases also showed an upward trend (12.1\u0026nbsp;million in 1990 vs 18.6\u0026nbsp;million in 2019) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Currently, CVD has caused a rapid rise in healthcare costs globally and become a major public health problem threatening human health. Due to the poorer economic status and worse health resources, it is well known that low-and middle-income countries (LMICs) usually have a severe disease burden in CVD. Therefore, a focus should be placed on the burden of CVD in LMICs or developing countries.\u003c/p\u003e \u003cp\u003eChina, a low-middle-income country[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], also faces a heavy disease burden from the CVD. In the past decades of reform and opening up, while China's urbanization and industrialization have developed rapidly, it has also brought serious air pollution. At the same time, due to the unbalanced economic development and allocation of medical resources in China, access to healthcare is poorer in some remote or rural areas. A study from China indicated that the mortality rate of CVD was higher in rural areas than that in urban areas (8.09 per 1000 person-years vs 3.04 per 1000 person-years) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Moreover, dietary habits are particular in China. Compared with countries such as Europe and the United States, Chinese people prefer to eat pickled or smoked food. Based on the above several reasons, the epidemiological trend of CVD in China was relatively higher[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A previous report in 2021 indicated that about 330\u0026nbsp;million people in China suffer from CVD, and two out of every five deaths were attributed to CVD [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. With population aging and an increase in exposure to modifiable risk factors (hypertension, fasting blood glucose, obesity, tobacco use, air pollution, insufficient leisure-time physical activity and etc) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], the incidence, prevalence and mortality of CVD in China will inevitably continue to rise in the future. Implementing effective intervention strategies is crucial for the prevention and control of CVD. Thus, analyzing the epidemiological trends of CVD diseases in China and predicting the future development are beneficial to relevant departments to formulate early prevention and control policies for CVD diseases.\u003c/p\u003e \u003cp\u003eHowever, the latest studies about CVD epidemiological trends in China are still limited. Meanwhile, less studies were conducted to predict the future development of CVD. Global burden of disease (GBD) study is a multinational collaborative study project. It involves data of diseases burden in many countries around the world, with greater reliability, wider coverage, and better comparability across years. Therefore, based on the GBD data, we aim to analyze the epidemiological trends of cardiovascular diseases in China from 1990 to 2021 and predict future statistics to provide a reference for the prevention and control of CVD.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eAs a multinational collaborative study project, GBD has estimated the annual disease burden data for every country since the last century. The GBD study produces standard epidemiological metrics including incidence rate, prevalence rate, mortality rates, DALYs rates, YLLs rates and YLLs rate, providing comprehensive guidance for global, regional and national control of disease burden[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. GBD obtained the data by conducting systematic reviews and opportunistic searches, and utilized data shared by national collaborators and WHO[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The GBD data in China were mainly derived from the CDC disease surveillance system, death cause surveillance system, and other literature reports. All data in GBD were reported separately by year, sex and age. To investigate the epidemiological trends of CVD in China, data from 1990 to 2021 were collected from the GBD in 2021. Complete details about GBD can be accessed from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd-results/\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd-results/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e1990 and later was an important stage of China's reform and opening up and rapid economic development. At the same time, during this period (after 1900), people's lifestyle and eating habits also began to change significantly, leading higher CVD incidence and mortality. Furthermore, the GBD database was only updated to the 2021 CVD data. Therefore, we selected 1990 to 2021 as the timeframe.\u003c/p\u003e \u003cp\u003eWe used epidemiological metrics for CVD in this study, including incidence rate (per 100,000), age-standardized incidence rate (ASIR, per 100,000), prevalence rate (per 100,000), age-standardized prevalence rate (ASPR, per 100,000), mortality rate (per 100,000), and age-standardized mortality rate (ASMR, per 100,000). All age-standardized rates were calculated by the 2021 GBD standard population. In order to analyze the incidence, prevalence, and mortality of CVD among age groups, we divided the age into 17 groups (0\u0026ndash;4 years old; 5\u0026ndash;9 years old; 10\u0026ndash;14 years old; 15\u0026ndash;19 years old; 20\u0026ndash;24 years old; 25\u0026ndash;29 years old; 30\u0026ndash;34 years old; 35\u0026ndash;39 years old; 40\u0026ndash;44 years old; 45\u0026ndash;49 years old; 50\u0026ndash;54 years old; 55\u0026ndash;59 years old; 60\u0026ndash;64 years old; 65\u0026ndash;69 years old; 70\u0026ndash;74 years old; 75\u0026ndash;79 years old; \u0026ge;80 years old). Furthermore, stroke and IDH are common CVD, so we also analyzed the epidemiological trends of stroke and IHD based on GBD.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical analyzes\u003c/h3\u003e\n\u003cp\u003eTo understand the epidemiology of CVD in China, we performed a descriptive analysis among overall population, different gender and each age groups. We calculated the percentage change (%) and Estimated Annual Percentage Change (EAPC, %) to further describe the epidemiological trends of CVD. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{P}\\text{e}\\text{r}\\text{c}\\text{e}\\text{n}\\text{t}\\text{a}\\text{g}\\text{e}\\:\\text{c}\\text{h}\\text{a}\\text{n}\\text{g}\\text{e}=100\\text{\\%}\\times\\:({\\text{V}\\text{a}\\text{l}\\text{u}\\text{e}}_{2021}-{\\text{V}\\text{a}\\text{l}\\text{u}\\text{e}}_{1990})/{\\text{V}\\text{a}\\text{l}\\text{u}\\text{e}}_{1990}\\)\u003c/span\u003e\u003c/span\u003e; EAPC is commonly used to describe the changing trends of certain epidemiological indicators within a year. When EAPC\u0026gt; 0, the epidemiological indicator showed an increasing trend within a year. Furthermore, EAPC\u0026lt;0 represented the epidemiological indicator showed an decreasing trend within a year. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:EAPC=100\\%\\times\\:({e}^{\\theta\\:}-1)\\)\u003c/span\u003e\u003c/span\u003e, where the parameter \u003cem\u003ee\u003c/em\u003e represents the natural constant and parameter \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\theta\\:\\)\u003c/span\u003e\u003c/span\u003e represents the regression coefficient of the equation \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:y=\\theta\\:x+\\epsilon\\:\\)\u003c/span\u003e\u003c/span\u003e. In above equation, parameter \u003cem\u003ey\u003c/em\u003e represents the natural logarithm of metrics of incidence, prevalence, and mortality and parameter \u003cem\u003ex\u003c/em\u003e represents years (1990 to 2021). We used \u003cem\u003et\u003c/em\u003e test to evaluate whether EAPC was statistically significant. Statistical analyzes were performed with the R version 4.1.0 software. Based on program packages of \u0026ldquo;tseries\u0026rdquo; and \u0026ldquo;Stats\u0026rdquo;, we constructed the ARIMA model. We used \u0026ldquo;adf.test\u0026rdquo; fuction to evaluate the stationarity of time-series data. Meanwhile, we performed white noise test based on \u0026ldquo;Box.test\u0026rdquo; fuction. Furthermore, we used the \u0026ldquo;forecast\u0026rdquo; fuction to predict future statistics of CVD. Finally, we used \u0026ldquo;ts.diag\u0026rdquo; fuction to evaluate whether the residual was a white noise sequence.\u003c/p\u003e\n\u003ch3\u003eARIMA model\u003c/h3\u003e\n\u003cp\u003eAutoregressive integrated moving average (ARIMA) model was used for predicting the incidence, prevalence and mortality of CVD in China from 2022 to 2030. The form of the model is \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{A}\\text{R}\\text{I}\\text{M}\\text{A}(\\text{p},\\text{d},\\text{q})\\)\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\)\u003c/span\u003e\u003c/span\u003e represents the order of autoregressive, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{d}\\)\u003c/span\u003e\u003c/span\u003e represents the order of difference, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{q}\\)\u003c/span\u003e\u003c/span\u003e represents the order of moving average. R4.1.0 software was used to establish the ARIMA model. We used Akaike information criterion (AIC) and Bayesian information criterion (BIC) to evaluate the reliability of ARIMA model. The lower AIC and BIC value mean that the ARIMA model is better. In addition, the estimation of the confidence intervals (CI) was the more important part of the prediction. A 95%\u003cem\u003eCI\u003c/em\u003e indicated a 95% probability of including the true value within this interval range. In this study, we further estimated the 95%\u003cem\u003eCI\u003c/em\u003e of prediction results.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eOverall CVD epidemiological trends\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed the epidemiological trends of CVD from 1990 to 2021. The incidence rate of CVD increased by 112.25% (EAPC\u0026thinsp;=\u0026thinsp;2.50, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from 1990, reaching 1127.34 per 100,000 in 2021. The ASIR of CVD was 783.90 per 100,000 in 1990 and increased to 811.81 per 100,000 in 2021 (increased by 3.56%, EAPC\u0026thinsp;=\u0026thinsp;0.12, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIncidence, prevalence and mortality of cardiovascular diseases in China from 1990 to2021\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eIncidence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ePrevalence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eMortality\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIncidence\u003c/p\u003e \u003cp\u003eRate (\u003cem\u003eUI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eASIR (\u003cem\u003eUI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrevalence\u003c/p\u003e \u003cp\u003erate (\u003cem\u003eUI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eASPR (\u003cem\u003eUI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMortality\u003c/p\u003e \u003cp\u003erate (\u003cem\u003eUI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eASMR (\u003cem\u003eUI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e531.14\u003c/p\u003e \u003cp\u003e(486.85\u0026thinsp;~\u0026thinsp;582.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e783.90\u003c/p\u003e \u003cp\u003e(718.42\u0026thinsp;~\u0026thinsp;857.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4451.03\u003c/p\u003e \u003cp\u003e(4108.54\u0026thinsp;~\u0026thinsp;4754.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6024.24\u003c/p\u003e \u003cp\u003e(5599.03\u0026thinsp;~\u0026thinsp;6393.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e212.25\u003c/p\u003e \u003cp\u003e(187.56\u0026thinsp;~\u0026thinsp;236.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e407.72\u003c/p\u003e \u003cp\u003e(361.40\u0026thinsp;~\u0026thinsp;452.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1127.34\u003c/p\u003e \u003cp\u003e(1015.17\u0026thinsp;~\u0026thinsp;1249.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e811.81\u003c/p\u003e \u003cp\u003e(736.14\u0026thinsp;~\u0026thinsp;892.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9407.55\u003c/p\u003e \u003cp\u003e(8694.66\u0026thinsp;~\u0026thinsp;10157.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6603.72\u003c/p\u003e \u003cp\u003e(6121.90\u0026thinsp;~\u0026thinsp;7087.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e357.44\u003c/p\u003e \u003cp\u003e(303.01\u0026thinsp;~\u0026thinsp;414.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e280.11\u003c/p\u003e \u003cp\u003e(237.90\u0026thinsp;~\u0026thinsp;323.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage change (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e111.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e68.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-31.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEAPC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.50*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.52*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.77*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;1.14*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e512.53\u003c/p\u003e \u003cp\u003e(467.95\u0026thinsp;~\u0026thinsp;562.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e800.42\u003c/p\u003e \u003cp\u003e(725.25\u0026thinsp;~\u0026thinsp;885.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4186.16\u003c/p\u003e \u003cp\u003e(3851.47\u0026thinsp;~\u0026thinsp;4478.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5976.81\u003c/p\u003e \u003cp\u003e(5535.16\u0026thinsp;~\u0026thinsp;6373.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e208.24\u003c/p\u003e \u003cp\u003e(174.55\u0026thinsp;~\u0026thinsp;241.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e466.57\u003c/p\u003e \u003cp\u003e(402.95\u0026thinsp;~\u0026thinsp;526.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1100.39\u003c/p\u003e \u003cp\u003e(983.26\u0026thinsp;~\u0026thinsp;1226.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e847.06\u003c/p\u003e \u003cp\u003e(763.94\u0026thinsp;~\u0026thinsp;932.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8888.05\u003c/p\u003e \u003cp\u003e(8165.75\u0026thinsp;~\u0026thinsp;9676.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6616.82\u003c/p\u003e \u003cp\u003e(6114.05\u0026thinsp;~\u0026thinsp;7152.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e392.80\u003c/p\u003e \u003cp\u003e(318.74\u0026thinsp;~\u0026thinsp;482.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e372.54\u003c/p\u003e \u003cp\u003e(308.34\u0026thinsp;~\u0026thinsp;447.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage change (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e112.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e88.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-20.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEAPC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.65*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.63*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.43*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.22*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.60*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e550.96\u003c/p\u003e \u003cp\u003e(506.71\u0026thinsp;~\u0026thinsp;602.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e766.26\u003c/p\u003e \u003cp\u003e(703.46\u0026thinsp;~\u0026thinsp;835.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4733.21\u003c/p\u003e \u003cp\u003e(4377.56\u0026thinsp;~\u0026thinsp;5070.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6078.83\u003c/p\u003e \u003cp\u003e(5658.66\u0026thinsp;~\u0026thinsp;6472.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e216.53\u003c/p\u003e \u003cp\u003e(184.67\u0026thinsp;~\u0026thinsp;251.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e369.94\u003c/p\u003e \u003cp\u003e(314.36\u0026thinsp;~\u0026thinsp;428.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1155.58\u003c/p\u003e \u003cp\u003e(1040.73\u0026thinsp;~\u0026thinsp;1278.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e772.86\u003c/p\u003e \u003cp\u003e(702.66\u0026thinsp;~\u0026thinsp;850.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9952.07\u003c/p\u003e \u003cp\u003e(9198.79\u0026thinsp;~\u0026thinsp;10730.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6587.69\u003c/p\u003e \u003cp\u003e(6121.03\u0026thinsp;~\u0026thinsp;7073.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e320.38\u003c/p\u003e \u003cp\u003e(255.07\u0026thinsp;~\u0026thinsp;391.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e217.02\u003c/p\u003e \u003cp\u003e(170.77\u0026thinsp;~\u0026thinsp;264.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage change (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e47.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-41.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEAPC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.34*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.41*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.21*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.25*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;1.71*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eAbbreviations: ASIR: age-standardized incidence rate (per 100,000); ASPR: age-standardized prevalence rate (per 100,000); ASMR: age-standardized mortality rate (per 100,000); EAPC: Estimated Annual Percentage Change; UI: Uncertain Interval; *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe prevalence rate of CVD in 2021 (9407.55 per 100,000) was 2.1 times higher than that in 1990 (4451.03 per 100,000). The ASPR of CVD increased by 9.62% (EAPC\u0026thinsp;=\u0026thinsp;0.32, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from 6024.24 per 100,000 in 1990 to 6603.72 per 100,000 in 2021. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn 2021, the mortality rate of CVD was 357.44 per 100,000 and the ASMR was 280.11 per 100,000. From 1990 to 2021, the mortality rate increased by 68.41% (EAPC\u0026thinsp;=\u0026thinsp;1.77, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but the ASMR decreased by 31.30% (EAPC=-1.14, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEpidemiological trends of CVD by gender\u003c/h2\u003e \u003cp\u003eFrom 1990 to 2021, the ASIR of CVD increased by 5.83% (EAPC\u0026thinsp;=\u0026thinsp;0.28, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in male and 0.86% (EAPC=-0.06, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in female (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In 2021, the ASIR was higher in male (847.06 per 100,000) than in female (772.86 per 100,000).\u003c/p\u003e \u003cp\u003eThe ASPR of CVD showed an increasing trend among different gender from 1990 to 2021. The ASPR in male was 6616.82 per 100,000 in 2021 and increased by 10.71% (EAPC\u0026thinsp;=\u0026thinsp;0.43, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from 1990 to 2021. Over the same period, the ASPR in female was 6587.69 per 100,000 in 2021 and increased by 8.37% (EAPC\u0026thinsp;=\u0026thinsp;0.21, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe ASMR of CVD decreased by 20.15% (EAPC=-0.60, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in male and 41.34% (EAPC=-1.71, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in female from 1990 to 2021. In 2021, male had a higher ASMR (372.54 per 100,000) as compared to female (217.02 per 100,000). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEpidemiological trends of CVD by age groups\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed the epidemiological trends of CVD stratified by age groups. From 1990 to 2021, the incidence rate of CVD increased among those aged 40\u0026ndash;44 years, 45\u0026ndash;49 years, 50\u0026ndash;54 years, 55\u0026ndash;59 years, 60\u0026ndash;64 years, 65\u0026ndash;69 years, 70\u0026ndash;74 years, 75\u0026ndash;79 years and \u0026ge;\u0026thinsp;80 years. In 2021, the age groups with the highest incidence rate was \u0026ge;\u0026thinsp;80 years (9656.94 per 100,000), followed by 75\u0026ndash;79 years (6106.93 per 100,000) and 70\u0026ndash;74 years (4350.37 per 100,000).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe prevalence rate of CVD showed an upward trend among those aged 40\u0026ndash;44 years, 45\u0026ndash;49 years, 50\u0026ndash;54 years, 55\u0026ndash;59 years, 60\u0026ndash;64 years, 65\u0026ndash;69 years, 70\u0026ndash;74 years, 75\u0026ndash;79 years and \u0026ge;\u0026thinsp;80 years from 1990 to 2021. In 2021, the age groups with the highest prevalence rate was \u0026ge;\u0026thinsp;80 years (57458.84 per 100,000), followed by 75\u0026ndash;79 years (46258.78 per 100,000) and 70\u0026ndash;74 years (37061.35 per 100,000). The analysis also showed lower prevalence rate in the younger age groups, with the lowest prevalence rate in the group aged\u0026thinsp;\u0026le;\u0026thinsp;4 years (151.54 per 100,000). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFrom 1990 to 2021, the mortality rate of CVD decreased among all age groups. The data also showed a trend of increasing mortality rate with advancing age. In 2021, the age groups with the highest mortality rate was \u0026ge;\u0026thinsp;80 years (7042.60 per 100,000), followed by 75\u0026ndash;79 years (2337.37 per 100,000) and 70\u0026ndash;74 years (1290.30 per 100,000). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eEpidemiological trends of stroke and IHD\u003c/h3\u003e\n\u003cp\u003eFrom 1990 to 2021, the ASIR and ASMR of stroke showed a significant downward trend (decreased by 9.78%[EAPC=-0.60, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05] and 43.01%[EAPC=-1.92, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05], respectively), but the ASPR of stroke showed a upward trend (increased by 11.48%, EAPC\u0026thinsp;=\u0026thinsp;0.34, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). More importantly, male had higher ASIR, ASPR, and ASMR than female from 1990 and to 2021. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIncidence, prevalence and mortality of stroke and IHD in China from 1990 to 2021\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eASIR (\u003cem\u003eUI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASPR (\u003cem\u003eUI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eASMR (\u003cem\u003eUI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e226.94\u003c/p\u003e \u003cp\u003e(202.92\u0026thinsp;~\u0026thinsp;252.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1167.42\u003c/p\u003e \u003cp\u003e(1082.04\u0026thinsp;~\u0026thinsp;1262.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e242.18\u003c/p\u003e \u003cp\u003e(213.83\u0026thinsp;~\u0026thinsp;272.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204.75\u003c/p\u003e \u003cp\u003e(181.03\u0026thinsp;~\u0026thinsp;231.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1301.42\u003c/p\u003e \u003cp\u003e(1200.61\u0026thinsp;~\u0026thinsp;1405.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e138.03\u003c/p\u003e \u003cp\u003e(116.69\u0026thinsp;~\u0026thinsp;160.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage change (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-43.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEAPC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.60*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;1.92*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e315.31\u003c/p\u003e \u003cp\u003e(255.53\u0026thinsp;~\u0026thinsp;382.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2526.44\u003c/p\u003e \u003cp\u003e(2189.97\u0026thinsp;~\u0026thinsp;2914.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94.14\u003c/p\u003e \u003cp\u003e(84.01\u0026thinsp;~\u0026thinsp;105.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e365.67\u003c/p\u003e \u003cp\u003e(293.32\u0026thinsp;~\u0026thinsp;440.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3042.35\u003c/p\u003e \u003cp\u003e(2601.68\u0026thinsp;~\u0026thinsp;3629.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e110.91\u003c/p\u003e \u003cp\u003e(92.42\u0026thinsp;~\u0026thinsp;128.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage change (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEAPC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.64*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAbbreviations: ASIR: age-standardized incidence rate (per 100,000); ASPR: age-standardized prevalence rate (per 100,000); ASMR: age-standardized mortality rate (per 100,000); EAPC: Estimated Annual Percentage Change; IHD: Ischemic Heart Disease; UI: Uncertain Interval; *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e.From 1990 to 2021, the ASIR, ASPR and ASMR of IHD increased by 15.97% (EAPC\u0026thinsp;=\u0026thinsp;0.66, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), 20.42% (EAPC\u0026thinsp;=\u0026thinsp;0.64, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and 17.81% (EAPC\u0026thinsp;=\u0026thinsp;0.97, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), respectively. Moreover, we also observed that the ASIR, ASPR and ASMR of IHD were higher in male than in female. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComparison of CVD between China and other regions\u003c/h2\u003e \u003cp\u003eIn 2021, the ASIR of CVD in China (811.81 per 100,000) was higher than those in global (787.04 per 100,000), Asia (806.12 per 100,000), US (673.89 per 100,000) and Japan (424.23 per 100,000). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn 2021, the ASPR of CVD in China (6603.72 per 100,000) was lower than those in global (7178.73 per 100,000), Asia (6782.60 per 100,000) and US (7755.28 per 100,000), but higher than that in Japan (5040.38 per 100,000). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn 2021, the ASMR of CVD in China (280.11 per 100,000) was higher than those in global (235.18 per 100,000), Asia (257.45 per 100,000), US (145.30 per 100,000) and Japan (72.47 per 100,000). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCVD epidemiological trends forecast\u003c/h2\u003e \u003cp\u003eThe forecasted ASIR of CVD in China will slowly increased to 812.80 (95%\u003cem\u003eCI\u003c/em\u003e: 806.75\u0026thinsp;~\u0026thinsp;818.84) in 2022, followed by 813.76 (95%\u003cem\u003eCI\u003c/em\u003e: 801.41\u0026thinsp;~\u0026thinsp;826.11) in 2023, 814.71 (95%\u003cem\u003eCI\u003c/em\u003e: 795.67\u0026thinsp;~\u0026thinsp;833.76) in 2024, 815.65 (95%\u003cem\u003eCI\u003c/em\u003e: 789.82\u0026thinsp;~\u0026thinsp;841.49) in 2025, 816.58 (95%\u003cem\u003eCI\u003c/em\u003e: 784.01\u0026thinsp;~\u0026thinsp;849.16) in 2026, 817.51 (95%\u003cem\u003eCI\u003c/em\u003e: 778.32\u0026thinsp;~\u0026thinsp;856.69) in 2027, 818.43 (95%\u003cem\u003eCI\u003c/em\u003e: 772.79\u0026thinsp;~\u0026thinsp;864.07) in 2028, 819.35 (95%\u003cem\u003eCI\u003c/em\u003e: 767.43\u0026thinsp;~\u0026thinsp;871.26) in 2029 and 820.26 (95%\u003cem\u003eCI\u003c/em\u003e: 762.24\u0026thinsp;~\u0026thinsp;878.28) in 2030. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The construction of ARIMA model was detailed in the supplementary material.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrediction of incidence, prevalence and mortality of cardiovascular diseases in China from 2022 to 2030\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetrics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall (95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMale (95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFemale (95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e812.80 (806.75\u0026thinsp;~\u0026thinsp;818.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e838.98 (831.89\u0026thinsp;~\u0026thinsp;846.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e781.16 (774.93\u0026thinsp;~\u0026thinsp;787.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e813.76 (801.41\u0026thinsp;~\u0026thinsp;826.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e831.12 (816.30\u0026thinsp;~\u0026thinsp;845.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e789.48 (775.27\u0026thinsp;~\u0026thinsp;803.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e814.71 (795.67\u0026thinsp;~\u0026thinsp;833.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e823.23 (799.02\u0026thinsp;~\u0026thinsp;847.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e797.81 (773.82\u0026thinsp;~\u0026thinsp;821.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e815.65 (789.82\u0026thinsp;~\u0026thinsp;841.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e815.35 (780.38\u0026thinsp;~\u0026thinsp;850.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e806.13 (770.85\u0026thinsp;~\u0026thinsp;841.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e816.58 (784.01\u0026thinsp;~\u0026thinsp;849.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e807.47 (760.52\u0026thinsp;~\u0026thinsp;854.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e814.46 (766.53\u0026thinsp;~\u0026thinsp;862.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e817.51 (778.32\u0026thinsp;~\u0026thinsp;856.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e799.58 (739.54\u0026thinsp;~\u0026thinsp;859.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e822.78 (761.00\u0026thinsp;~\u0026thinsp;884.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e818.43 (772.79\u0026thinsp;~\u0026thinsp;864.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e791.70 (717.55\u0026thinsp;~\u0026thinsp;865.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e831.11 (754.35\u0026thinsp;~\u0026thinsp;907.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e819.35 (767.43\u0026thinsp;~\u0026thinsp;871.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e783.81 (694.60\u0026thinsp;~\u0026thinsp;873.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e839.43 (746.66\u0026thinsp;~\u0026thinsp;932.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e820.26 (762.24\u0026thinsp;~\u0026thinsp;878.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e775.93 (670.75\u0026thinsp;~\u0026thinsp;881.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e847.76 (738.00\u0026thinsp;~\u0026thinsp;957.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6700.67 (6674.52\u0026thinsp;~\u0026thinsp;6726.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6667.87 (6655.78\u0026thinsp;~\u0026thinsp;6679.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6696.68 (6657.60\u0026thinsp;~\u0026thinsp;6735.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6809.52(6740.95\u0026thinsp;~\u0026thinsp;6878.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6729.74 (6688.73\u0026thinsp;~\u0026thinsp;6770.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6805.67 (6718.15\u0026thinsp;~\u0026thinsp;6893.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6923.44 (6797.73\u0026thinsp;~\u0026thinsp;7049.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6795.79 (6712.86\u0026thinsp;~\u0026thinsp;6878.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6914.66 (6768.10\u0026thinsp;~\u0026thinsp;7061.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7039.49 (6844.30\u0026thinsp;~\u0026thinsp;7234.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6863.48 (6727.90\u0026thinsp;~\u0026thinsp;6999.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7023.65 (6809.02\u0026thinsp;~\u0026thinsp;7238.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7156.46 (6881.16\u0026thinsp;~\u0026thinsp;7431.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6931.79 (6734.52\u0026thinsp;~\u0026thinsp;7129.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7132.64 (6841.96\u0026thinsp;~\u0026thinsp;7423.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7273.82 (6909.03\u0026thinsp;~\u0026thinsp;7638.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7000.35 (6733.51\u0026thinsp;~\u0026thinsp;7267.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7241.63 (6867.66\u0026thinsp;~\u0026thinsp;7615.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7391.34 (6928.63\u0026thinsp;~\u0026thinsp;7854.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7069.01 (6725.58\u0026thinsp;~\u0026thinsp;7412.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7350.62 (6886.70\u0026thinsp;~\u0026thinsp;7814.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7508.93 (6940.57\u0026thinsp;~\u0026thinsp;8077.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7137.70 (6711.28\u0026thinsp;~\u0026thinsp;7564.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7459.61 (6899.55\u0026thinsp;~\u0026thinsp;8019.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7626.55 (6945.36\u0026thinsp;~\u0026thinsp;8307.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7206.41 (6691.08\u0026thinsp;~\u0026thinsp;7721.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7568.60 (6906.57\u0026thinsp;~\u0026thinsp;8230.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e275.80 (263.61\u0026thinsp;~\u0026thinsp;288.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e369.17 (350.71\u0026thinsp;~\u0026thinsp;387.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e212.20 (201.89\u0026thinsp;~\u0026thinsp;222.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e271.41 (247.27\u0026thinsp;~\u0026thinsp;295.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e365.97 (332.75\u0026thinsp;~\u0026thinsp;399.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e206.88 (186.38\u0026thinsp;~\u0026thinsp;227.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e267.16 (233.85\u0026thinsp;~\u0026thinsp;300.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e362.86 (316.43\u0026thinsp;~\u0026thinsp;409.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e201.63 (173.26\u0026thinsp;~\u0026thinsp;230.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e263.03 (223.20\u0026thinsp;~\u0026thinsp;302.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e359.78 (301.52\u0026thinsp;~\u0026thinsp;418.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e196.58 (162.63\u0026thinsp;~\u0026thinsp;230.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e258.94 (214.05\u0026thinsp;~\u0026thinsp;303.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e356.72 (287.71\u0026thinsp;~\u0026thinsp;425.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e191.67 (153.39\u0026thinsp;~\u0026thinsp;229.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e254.83 (205.42\u0026thinsp;~\u0026thinsp;304.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e353.66 (274.74\u0026thinsp;~\u0026thinsp;432.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e186.76 (144.65\u0026thinsp;~\u0026thinsp;228.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250.70 (196.88\u0026thinsp;~\u0026thinsp;304.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350.61 (262.40\u0026thinsp;~\u0026thinsp;438.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e181.82 (135.98\u0026thinsp;~\u0026thinsp;227.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e246.56 (188.36\u0026thinsp;~\u0026thinsp;304.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e347.57 (250.56\u0026thinsp;~\u0026thinsp;444.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e176.86 (127.30\u0026thinsp;~\u0026thinsp;226.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e242.42 (179.93\u0026thinsp;~\u0026thinsp;304.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e344.52 (239.09\u0026thinsp;~\u0026thinsp;449.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e171.89 (118.67\u0026thinsp;~\u0026thinsp;225.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAbbreviations: ASIR: age-standardized incidence rate (per 100,000); ASPR: age-standardized prevalence rate (per 100,000); ASMR: age-standardized mortality rate (per 100,000);\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn 2022, the forecasted ASPR of CVD in China will increased to 6700.67 (95%\u003cem\u003eCI\u003c/em\u003e: 6674.52\u0026thinsp;~\u0026thinsp;6726.81), followed by 6809.52 (95%\u003cem\u003eCI\u003c/em\u003e: 6740.95\u0026thinsp;~\u0026thinsp;6878.10) in 2023, 6923.44 (95%\u003cem\u003eCI\u003c/em\u003e: 6797.73\u0026thinsp;~\u0026thinsp;7049.14) in 2024, 7039.49 (95%\u003cem\u003eCI\u003c/em\u003e: 6844.30\u0026thinsp;~\u0026thinsp;7234.69) in 2025, 7156.46 (95%\u003cem\u003eCI\u003c/em\u003e: 6881.16\u0026thinsp;~\u0026thinsp;7431.77) in 2026, 7273.82 (95%\u003cem\u003eCI\u003c/em\u003e: 6909.03\u0026thinsp;~\u0026thinsp;7638.61) in 2027, 7391.34 (95%\u003cem\u003eCI\u003c/em\u003e: 6928.63\u0026thinsp;~\u0026thinsp;7854.05) in 2028, 7508.93 (95%\u003cem\u003eCI\u003c/em\u003e: 6940.57\u0026thinsp;~\u0026thinsp;8077.30) in 2029 and 7626.55 (95%\u003cem\u003eCI\u003c/em\u003e: 6945.36\u0026thinsp;~\u0026thinsp;8307.75) in 2030. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe forecasted ASMR of CVD in China will continue to show a downward trend from 2022 to 2030. The forecasted ASMR is 275.80 (95%\u003cem\u003eCI\u003c/em\u003e: 263.61\u0026thinsp;~\u0026thinsp;288.00) in 2022, followed by 271.41 (95%\u003cem\u003eCI\u003c/em\u003e: 247.27\u0026thinsp;~\u0026thinsp;295.55) in 2023, 267.16 (95%\u003cem\u003eCI\u003c/em\u003e: 233.85\u0026thinsp;~\u0026thinsp;300.47) in 2024, 263.03 (95%\u003cem\u003eCI\u003c/em\u003e: 223.20\u0026thinsp;~\u0026thinsp;302.86) in 2025, 258.94 (95%\u003cem\u003eCI\u003c/em\u003e: 214.05\u0026thinsp;~\u0026thinsp;303.83) in 2026, 254.83 (95%\u003cem\u003eCI\u003c/em\u003e: 205.42\u0026thinsp;~\u0026thinsp;304.25) in 2027, 250.70 (95%\u003cem\u003eCI\u003c/em\u003e: 196.88\u0026thinsp;~\u0026thinsp;304.52) in 2028, 246.56 (95%\u003cem\u003eCI\u003c/em\u003e: 188.36\u0026thinsp;~\u0026thinsp;304.75) in 2029 and 242.42 (95%\u003cem\u003eCI\u003c/em\u003e: 179.93\u0026thinsp;~\u0026thinsp;304.90) in 2030. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCVD is the main cause of the loss of disability-adjusted life years (DALY) and death [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In this study, we utilized GBD data to describe and analyze the incidence rate, prevalence rate, mortality rate, ASIR, ASPR and ASMR of CVD in China from 1990 to 2021. Moreover, we made a forecast for the ASIR, ASPR and ASMR of CVD from 2022 to 2030. Our results are of great significance for the development of prevention and control strategies for CVD.\u003c/p\u003e \u003cp\u003eIn our study, we used the percentage change and EAPC to describe the CVD trend. Both percentage change and EAPC are common epidemiological indicators. They reflect the changing trend of the CVD metrics and have important implications for public health. We observed that the ASIR (increased by 3.56%, EAPC\u0026thinsp;=\u0026thinsp;0.12, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and ASPR (increased by 9.62%, EAPC\u0026thinsp;=\u0026thinsp;0.32, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) of CVD in China exhibited an upward trend from 1990 to 2021. Our findings were in line with previous similar studies. A previous GBD study indicated that the ASPR of CVD significantly increased by 14.7% in China from 1990 to 2016 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In addition, a global study also reported the incidence and prevalence of CVD also showed an upward trend [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The possible reasons for the rising ASIR and ASPR are explained as follows: (1) The occurrence of CVD is mainly attributed to the aggravation of air pollution in China in recent years [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Due to the rapid development of industrialization in China, air pollution has become an important topic of great concern. When particulate matter such as PM2.5 from polluted air is inhaled by human body, it can lead to sharply oxidative stress and inflammation in our body, which may ultimately increases the risk of CVD [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. (2) The higher prevalence rate of diabetes and hypertension in China may contribute to the occurrence of CVD to some extent [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Chronic diseases including diabetes and hypertension were common risk factors leading to other severe diseases such as CVD. As the prevalence of diabetes and hypertension evidently rose [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], the CVD trend will continue to fast increase. (3) The increase in the diagnosis rate of CVD in recent years was also responsible for the increase in the ASIR and ASPR. For example, Wang et al developed cardiac magnetic resonance imaging based on artificial intelligence to diagnose CVD [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The AI-based methods have a higher performance for diagnosis of CVD. Furthermore, a recent study found that microRNAs can be considered as an important biomarker for diagnosing CVD [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. (4) Changes in lifestyle and dietary habits in recent years are also important factors for the occurrence of CVD, such as high oil, salt and sugar diets, and staying up late nights. It is important to note that we found the ASMR of CVD in China decreased by 31.30% (EAPC=-1.14, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from 1990 to 2021. Our findings were consistent with previous studies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Several reasons may explain this phenomenon: (1) In the past 30 years, the quality of medical care have made remarkable progress in China, especially in cardiovascular technology [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Increasing treatment level is an important reason for curbing CVD mortality. (2) The decrease in ASMR was also related to the promotion of public health interventions or policies in China, such as implementation of basic public health services program [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and Urban and Rural Residents' Basic Medical Insurance policy[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. With the implementation of public health strategies in China, residents can reduce the economic burden of CVD and elevate the treatment rate and control rate of CVD, which can reduce the death risk of CVD in a degree (3) With the rapid development in China, the life quality of residents has obviously improved and their awareness of disease prevention and control has also been strengthened significantly, so the mortality of CVD showed a downward trend. All in all, the disease burden of CVD is still severe in China. In the future, the prevention and control of CVD should be focused primarily on the strategic and key technological development so as to reduce the disease burden of CVD [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur findings were inconsistent with several studies. A recent study involved 22\u0026nbsp;million people from UK found that the incidence of 10 prespecified CVD showed a downward trend (decreased by 19%) during 2000-19 and the overall incidence of CVD has remained a stable state Since the mid-2000s [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The above results indicated that the trend of CVD was relatively controlled in UK. The reason for this difference might be attributed to developed economy and higher level of health care in UK compared to China. Moreover, the decline of CVD mortality was almost a global trend. However, data from the US CDC demonstrated that the mortality of CVD showed an upward trend from 2019 to 2022 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], which was inconsistent with our findings. The cause of the increased CVD mortality may be the more severer COVID-19 epidemic in US. In addition, a study from Malaysia found that ASMR of CVD showed an upward trend from 2010 to 2021, which was different from our results [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Reason for the difference may be poorer medical resources in Malaysia than in China.\u003c/p\u003e \u003cp\u003eFrom 1990 to 2021, both ASIR and ASPR in China increased faster in males than in females. Moreover, the current analysis also revealed that male ASIR (847.06 per 100,000), ASPR (6616.82 per 100,000) and ASMR (372.54 per 100,000) were higher than females (772.86 per 100,000, 6587.69 per 100,000, 217.02 per 100,000) in 2021. Our findings were in agreement with early studies. A previous study have shown males have a higher CVD incidence than females among total population and population with certain risk factors (such as hypertension, diabetes, obesity, smoking, etc) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Moreover, there was also study from China exhibiting that the crude and weighted mortality rate of CVD were higher in males than that in females [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Males appear to have a higher risk of CVD. The possible reasons are as follows: (1) This disparity between males and females may be linked to differences in lifestyle and risk factors, with significantly higher rates of tobacco and alcohol use among males. (2) Compared to females, males are often given more social and financial stress.In China, as the backbone of the family, males have to work harder. Long-term large stress may lead to the occurrence of CVD among males [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. (3) Poor sleep quality or insomnia was often an important cause of CVD occurrence and death[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A review reported that females appear to have better sleep quality than males[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], thus females are at lower risk of CVD than males (4) Estrogen is a direct vasodilator, which plays a key role in reducing the CVD risk among females [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Therefore, more prevention and control measures should be taken for males.\u003c/p\u003e \u003cp\u003eIn our study, the incidence, prevalence and mortality of CVD were uncommon in the group aged less than 20 years. With advancing age, the incidence, prevalence and mortality of CVD exhibited a significant increasing trend and peaked in the age group over 80 years. Our findings were in line with previous studies. Qu et al also found that the incidence and mortality of CVD were highest in group aged over 95 than that among other age groups [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Indeed, age was considered as the unmodifiable risk factor for CVD [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Compared with younger adults, the elderly are more likely to develop CVD due to the decline of physical function such as vascular wall thickening, perivascular fibrosis, cardiac myocytes reduction and etc [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Furthermore, due to the lack of expertise, the old people known little about CVD prevention and control, which may cause the higher risk of CVD among the elderly. Our findings provide an important guideline for prevention and treatment of CVD in China.\u003c/p\u003e \u003cp\u003eIn this study, we compared the disease burden of CVD in China with the global, Asia, US and Japan. It is worth noting that the ASIR and ASMR of CVD in China were higher than US and Japan. This phenomenon may be attributed to changes in modifiable risk factors, income levels and policies. Studies have shown that residents in high-income countries may have a higher awareness of the disease prevention compared with those in low-middle-income countries [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition, compared with China, higher medical level and better social welfare in developed countries were an important reason for this phenomenon. Moreover, dietary habit was also one of the main influencing factors. Compared to some developed countries such as Japan, the Chinese prefered pickled or spicy foods, so the incidence of CVD was relatively higher in China. In addition, we also observed that the ASIR and ASMR were higher than asian and global levels. This suggested that the CVD epidemic situation was more serious in China. Relevant strategies should be pushed to address the severe CVD trends in China.\u003c/p\u003e \u003cp\u003eIn this study, we used ARIMA model to forecast the cardiovascular disease statistics from 2022 to 2030. We found that the forecasted ASIR of CVD will slowly increase to 812.80 per 100,000 in 2022 and to 820.26 per 100,000 in 2030. The increase in ASIR of CVD is mainly attributed to an aging population in China. A previous study indicated that by 2050, China will have 400\u0026nbsp;million elderly people over 65 years old, of which 150\u0026nbsp;million are over 80 years old [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Beside, increasing exposure levels of risk factors can also contribute to a higher risk of CVD such as obesity, large economic stress and bad lifestyle in the future. Moreover, we also observed that The forecasted ASPR of CVD will be 6700.67 per 100,000 in 2022 and 7626.55 per 100,000 in 2030. The increasing life expectancy in the Chinese population may be responsible for the increasing ASPR of CVD. A study reported that the life expectancy in China will reach 81.3 years by 2035 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In addition, the continuous improvement in CVD treatment techniques in the future may reduce the risk of death among CVD patients, which will increase the prevalence of CVD to some extent. Interestingly, the forecasted ASMR of CVD will decrease to 275.80 per 100,000 in 2022 and reach 242.42 per 100,000 in 2030. We speculate that the decrease in ASMR is due to the improved level of CVD treatment in the future. Meanwhile, further implementation of CVD health policies in the future may also help to reduce the risk of CVD death. Finally, ARIMA model has several strengths. On the one hand, ARIMA model can better predict the future trend of the data and have high fitting performance [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. On the other hand, compared with short-term prediction models such as gray model (GM), the ARIMA model can perform medium or long term predictions. Moreover, in contrast to other prediction models such as Neural network autoregressive (NNAR) model and prophet model, ARIMA models are more interpretable and easier to understand. However, there are also some limitations for ARIMA model. For example, ARIMA model is difficult to fit the data with nonlinear changes. Besides, ARIMA model do not consider the risk factors for CVD, as compared to other models such as linear regression models.\u003c/p\u003e \u003cp\u003eOur study has important policy implications: (1) The total CVD burden is currently relatively severe in China. Thus, the government and relevant departments should deeply implement medical reform and accurately allocate health resources to reduce the burden of CVD in China. (2) Male and older adults are at higher risk of CVD. Therefore, more early CVD screening and intervention strategies should focus on the above two high-risk groups (3) The ASIR and ASPR of CVD in China will show an increasing trend in the future. Based on this, the health department can further explore and develop relevant strategies for the prevention and control of future CVD.\u003c/p\u003e \u003cp\u003eThere are several strengths in our study: (1) GBD data can better reflect the epidemic trends of CVD in China, providing important information for public health policy making. (2) The ARIMA model has a higher fitting performance and can accurately predict CVD statistics from 2022 to 2030. Our study also has several limitations: (1) The ARIMA model did not consider some important risk factors for CVD, such as dietary factors, lifestyle, genetics, hormones and etc. In the future study, we will further include risk factors for analysis. (2) The GBD data were estimated by statistical and mathematical models. Therefore, the results of GBD may have some differences from the actual situation. Our results still need to be further compared with monitoring CVD data from China CDC. (3) GBD did not provide epidemiological data of CVD for different ethnic groups in China. In future study, we will further explore the epidemiological trends of CVD among different ethnic groups in China.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we found that the disease burden of CVD in China was still severe. Moreover, the ASIR and ASPR of CVD will continue to increase from 2022 to 2030. Prevention and control of CVD is still a large public health challenge in China. Therefore, the health department should further implement the public health policy of CVD. For example, conducting early screening programs for population at high risk for CVD (such as male, advanced age or individuals with underlying chronic diseases). Meanwhile, regular follow-up and interventions were given for positive CVD individuals to reduce the risk for CVD. Finally, the factors affecting the CVD are complex and diverse. In the future, more studies should be conducted to further explore the influencing factors leading to the incidence and mortality of CVD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.The State Key Laboratory of Causes and Prevention of High Incidence Disease in Central Asia jointly built in 2024-Guangdong Workstation Joint Fund project (SKL-HIDCA-2024-GD7B)\u003c/p\u003e\n\u003cp\u003e2.2022 Dongguan Social Development Science and Technology (Key) Project (20221800905642)\u003c/p\u003e\n\u003cp\u003e3.2022 Guangdong Basic and Applied Basic Research Foundation Natural Science Foundation project (2022A1515012407)\u003c/p\u003e\n\u003cp\u003e4.Undergraduate Innovation and Entrepreneurship Education Base Project of Guangdong Medical University\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e5.2022 Dongguan Science and Technology Commissioner Project (20221800500342)\u003c/p\u003e\n\u003cp\u003e6.Guangdong Provincial Undergraduate Online Open Curriculum Steering Committee (key) research topic (2022ZXKC182)\u003c/p\u003e\n\u003cp\u003e7.The 14th Five-Year Plan Guangdong Higher Education research topic in 2022 (22GYB10)\u003c/p\u003e\n\u003cp\u003e8.2020 Guangdong Province Graduate Education Innovation Plan Project (Research on Degree and Graduate Education Reform) (2020JGXM057)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors agree to the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used for the analysis in the study are publicly available at https://vizhub.healthdata.org/gbd-results/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable for that section\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors do not have any possible conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Wang S. Data curation: Wang S, Jin Z, Lin Y, Huang W. Formal analysis: Wang S, Cao R, Lai F, Wu S. Funding acquisition: Yu H, Hu Y, Wang X. Project administration: Yu H, Hu Y, Wang X. Visualization: Wang S. Writing-original draft: Wang S, Jin Z, Lin Y. Writing-review \u0026amp; editing: Wang S, Yu H.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable for that section\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi S, Liu Z, Joseph P, et al. Modifiable risk factors associated with cardiovascular disease and mortality in China: a PURE substudy. Eur Heart J. 2022, 43(30):2852-2863. doi: 10.1093/eurheartj/ehac268. \u003c/li\u003e\n\u003cli\u003eRoth GA, Mensah GA, Johnson CO, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020, 76(25):2982-3021. doi: 10.1016/j.jacc.2020.11.010. Erratum in: J Am Coll Cardiol. 2021, 77(15):1958-1959. doi: 10.1016/j.jacc.2021.02.039. \u003c/li\u003e\n\u003cli\u003eChow CK, Teo KK, Rangarajan S, et al. Prevalence, awareness, treatment, and control of hypertension in rural and urban communities in high-, middle-, and low-income countries. JAMA. 2013,310(9):959-968. doi: 10.1001/jama.2013.184182.\u003c/li\u003e\n\u003cli\u003ePurroy F, Montal\u0026agrave; N. Epidemiology of stroke in the last decade: a systematic review. Rev Neurol. 2021 Nov 1;73(9):321-336. doi: 10.33588/rn.7309.2021138. \u003c/li\u003e\n\u003cli\u003eLiu S, Li Y, Zeng X, et al. Burden of Cardiovascular Diseases in China, 1990-2016: Findings From the 2016 Global Burden of Disease Study. JAMA Cardiol. 2019, 4(4):342-352. doi: 10.1001/jamacardio.2019.0295.\u003c/li\u003e\n\u003cli\u003eIn China TWCOTROCHAD, Hu SS. Report on cardiovascular health and diseases in China 2021: an updated summary. J Geriatr Cardiol. 2023, 20(6):399-430. doi: 10.26599/1671-5411.2023.06.001. \u003c/li\u003e\n\u003cli\u003eKaminsky LA, German C, Imboden M, et al. The importance of healthy lifestyle behaviors in the prevention of cardiovascular disease. Prog Cardiovasc Dis. 2022 Jan-Feb;70:8-15. doi: 10.1016/j.pcad.2021.12.001. \u003c/li\u003e\n\u003cli\u003eLababidi H, Salerno PR, Wass SY, et al. The Global Burden of premature cardiovascular disease, 1990-2019. Int J Cardiol Cardiovasc Risk Prev. 2023 Sep 23;19:200212. doi: 10.1016/j.ijcrp.2023.200212. \u003c/li\u003e\n\u003cli\u003eAl Zomia AS, Al Zehefa IAM, Lahiq LA, et al. Tracking the epidemiological trends of female breast cancer in Saudi Arabia since 1990 and forecasting future statistics using global burden of disease data, time-series analysis. BMC Public Health. 2024, 24(1):1953. doi: 10.1186/s12889-024-19377-x. \u003c/li\u003e\n\u003cli\u003eGBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023, 402(10397):203-234. doi: 10.1016/S0140-6736(23)01301-6. Erratum in: Lancet. 2023, 402(10408):1132. doi: 10.1016/S0140-6736(23)02044-5.\u003c/li\u003e\n\u003cli\u003eDeng P, Fu Y, Chen M, et al. Temporal trends in inequalities of the burden of cardiovascular disease across 186 countries and territories. Int J Equity Health. 2023, 22(1):164. doi: 10.1186/s12939-023-01988-2. \u003c/li\u003e\n\u003cli\u003eSun J, Qiao Y, Zhao M, et al. Global, regional, and national burden of cardiovascular diseases in youths and young adults aged 15-39 years in 204 countries/territories, 1990-2019: a systematic analysis of Global Burden of Disease Study 2019. BMC Med. 2023, 21(1):222. doi: 10.1186/s12916-023-02925-4.\u003c/li\u003e\n\u003cli\u003eKhoshakhlagh AH, Mohammadzadeh M, Gruszecka-Kosowska A, et al. Burden of cardiovascular disease attributed to air pollution: a systematic review. Global Health. 2024, 20(1):37. doi: 10.1186/s12992-024-01040-0. \u003c/li\u003e\n\u003cli\u003eBhatnagar A. Cardiovascular Effects of Particulate Air Pollution. Annu Rev Med. 2022, 73:393-406. doi: 10.1146/annurev-med-042220-011549.\u003c/li\u003e\n\u003cli\u003eZhao D. Epidemiological Features of Cardiovascular Disease in Asia. JACC Asia. 2021, 1(1):1-13. doi: 10.1016/j.jacasi.2021.04.007. \u003c/li\u003e\n\u003cli\u003eXu Y, Lu J, Li M, et al. Diabetes in China part 1: epidemiology and risk factors. Lancet Public Health. 2024, 9(12):e1089-e1097. doi: 10.1016/S2468-2667(24)00250-0.\u003c/li\u003e\n\u003cli\u003eDu J, Zhu G, Yue Y, et al. Blood pressure and hypertension prevalence among oldest-old in China for 16\u0026thinsp;year: based on CLHLS. BMC Geriatr. 2019, 19(1):248. doi: 10.1186/s12877-019-1262-4.\u003c/li\u003e\n\u003cli\u003eWang YJ, Yang K, Wen Y, et al. Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging. Nat Med. 2024, 30(5):1471-1480. doi: 10.1038/s41591-024-02971-2. \u003c/li\u003e\n\u003cli\u003eKramna D, Riedlova P, Jirik V. MicroRNAs as a Potential Biomarker in the Diagnosis of Cardiovascular Diseases. Medicina (Kaunas). 2023, 59(7):1329. doi: 10.3390/medicina59071329. \u003c/li\u003e\n\u003cli\u003eNedkoff L, Briffa T, Zemedikun D, et al. Global Trends in Atherosclerotic Cardiovascular Disease. Clin Ther. 2023, 45(11):1087-1091. doi: 10.1016/j.clinthera.2023.09.020. \u003c/li\u003e\n\u003cli\u003eThe WCOTROCHADIC. Report on Cardiovascular Health and Diseases in China 2022: an Updated Summary. Biomed Environ Sci. 2023, 36(8):669-701. doi: 10.3967/bes2023.106.\u003c/li\u003e\n\u003cli\u003eFang G, Yang D, Wang L, et al. Experiences and Challenges of Implementing Universal Health Coverage With China\u0026apos;s National Basic Public Health Service Program: Literature Review, Regression Analysis, and Insider Interviews. JMIR Public Health Surveill. 2022, 8(7):e31289. doi: 10.2196/31289. \u003c/li\u003e\n\u003cli\u003eFan X, Su M, Zhao Y, et al. Effect of Health Insurance Policy on the Health Outcomes of the Middle-Aged and Elderly: Progress Toward Universal Health Coverage. Front Public Health. 2022, 10:889377. doi: 10.3389/fpubh.2022.889377.\u003c/li\u003e\n\u003cli\u003eConrad N, Molenberghs G, Verbeke G, et al. Trends in cardiovascular disease incidence among 22 million people in the UK over 20 years: population based study. BMJ. 2024, 385:e078523. doi: 10.1136/bmj-2023-078523. Erratum in: BMJ. 2024, 387:q2381. doi: 10.1136/bmj.q2381.\u003c/li\u003e\n\u003cli\u003eWoodruff RC, Tong X, Khan SS, et al. Trends in Cardiovascular Disease Mortality Rates and Excess Deaths, 2010-2022. Am J Prev Med. 2024, 66(4):582-589. doi: 10.1016/j.amepre.2023.11.009. \u003c/li\u003e\n\u003cli\u003eHasani WSR, Musa KI, Cheng KY, et al. Exploring the trend of age-standardized mortality rates from cardiovascular disease in Malaysia: a joinpoint analysis (2010-2021). BMC Public Health. 2024, 24(1):2519. doi: 10.1186/s12889-024-19103-7.\u003c/li\u003e\n\u003cli\u003eWoodward M. Cardiovascular Disease and the Female Disadvantage. Int J Environ Res Public Health. 2019, 16(7):1165. doi: 10.3390/ijerph16071165. \u003c/li\u003e\n\u003cli\u003eWang W, Wang D, Liu H, et al. Trend of declining stroke mortality in China: reasons and analysis. Stroke Vasc Neurol. 2017, 2(3):132-139. doi: 10.1136/svn-2017-000098.\u003c/li\u003e\n\u003cli\u003eLi J, Loerbroks A, Bosma H, et al. Work stress and cardiovascular disease: a life course perspective. J Occup Health. 2016, 58(2):216-219. doi: 10.1539/joh.15-0326-OP. \u003c/li\u003e\n\u003cli\u003eMiller MA, Howarth NE. Sleep and cardiovascular disease. Emerg Top Life Sci. 2023, 7(5):457-466. doi: 10.1042/ETLS20230111. \u003c/li\u003e\n\u003cli\u003eMong JA, Cusmano DM. Sex differences in sleep: impact of biological sex and sex steroids. Philos Trans R Soc Lond B Biol Sci. 2016, 371(1688):20150110. doi: 10.1098/rstb.2015.0110.\u003c/li\u003e\n\u003cli\u003eUeda K, Fukuma N, Adachi Y, et al. Sex Differences and Regulatory Actions of Estrogen in Cardiovascular System. Front Physiol. 2021 Sep 28;12:738218. doi: 10.3389/fphys.2021.738218. \u003c/li\u003e\n\u003cli\u003eOneglia A, Nelson MD, Merz CNB. Sex Differences in Cardiovascular Aging and Heart Failure. Curr Heart Fail Rep. 2020, 17(6):409-423. doi: 10.1007/s11897-020-00487-7. \u003c/li\u003e\n\u003cli\u003eQu C, Liao S, Zhang J, et al. Burden of cardiovascular disease among elderly: based on the Global Burden of Disease Study 2019. Eur Heart J Qual Care Clin Outcomes. 2024,10(2):143-153. doi: 10.1093/ehjqcco/qcad033.\u003c/li\u003e\n\u003cli\u003eDhingra R, Vasan RS. Age as a risk factor. Med Clin North Am. 2012, 96(1):87-91. doi: 10.1016/j.mcna.2011.11.003. \u003c/li\u003e\n\u003cli\u003eCostantino S, Paneni F, Cosentino F. Ageing, metabolism and cardiovascular disease. J Physiol. 2016, 594(8):2061-2073. doi: 10.1113/JP270538. \u003c/li\u003e\n\u003cli\u003eFang EF, Scheibye-Knudsen M, Jahn HJ, et al. A research agenda for aging in China in the 21st century. Ageing Res Rev. 2015, 24(Pt B):197-205. doi: 10.1016/j.arr.2015.08.003. \u003c/li\u003e\n\u003cli\u003eBai R, Liu Y, Zhang L, et al. Projections of future life expectancy in China up to 2035: a modelling study. Lancet Public Health. 2023, 8(12):e915-e922. doi: 10.1016/S2468-2667(22)00338-3. \u003c/li\u003e\n\u003cli\u003eda Cunha VP, Botelho GM, de Oliveira AHM, et al. Application of the ARIMA Model to Predict Under-Reporting of New Cases of Hansen\u0026apos;s Disease during the COVID-19 Pandemic in a Municipality of the Amazon Region. Int J Environ Res Public Health. 2021, 19(1):415. doi: 10.3390/ijerph19010415. \u003c/li\u003e\n\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":"Cardiovascular diseases, Incidence, Prevalence, Mortality, China","lastPublishedDoi":"10.21203/rs.3.rs-5700163/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5700163/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAbout 330\u0026nbsp;million people in China suffer from cardiovascular disease(CVD). CVD has become a serious public health problem in China and worldwide. This study aims to analyze the epidemiological trends of cardiovascular diseases in China from 1990 to 2021 and predict future statistics to provide a reference for the prevention and control of CVD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were derived from the Global Burden of Disease (GBD) about cardiovascular disease from 1990 to 2021. We searched the GBD database by the key word \"Cardiovascular diseases\". We included the CVD epidemiological metrics involving incidence rate, prevalence rate, mortality rate and their age-standardized rate. We calculated the percentage change (%) and estimated annual percentage change (EAPC, %) to initially analyze the epidemiological trends of CVD. We used autoregressive integrated moving average (ARIMA) model to further forecast cardiovascular disease statistics from 2022 to 2030.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe age-standardized incidence rate (ASIR) of CVD was 783.90 per 100,000 in 1990 and increased to 811.81 per 100,000 in 2021 (increased by 3.56%, EAPC\u0026thinsp;=\u0026thinsp;0.12, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The age-standardized prevalence rate (ASPR) of CVD increased by 9.62%(EAPC\u0026thinsp;=\u0026thinsp;0.32, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from 6024.24 per 100,000 in 1990 to 6603.72 per 100,000 in 2021. The age-standardized mortality rate (ASMR) of CVD decreased by 31.30% (EAPC=-1.14, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from 1990, reaching 280.11 per 100,000 in 2021. In 2021, male ASIR (847.06 per 100,000), ASPR (6616.82 per 100,000) and ASMR (372.54 per 100,000) were higher than females (772.86 per 100,000, 6587.69 per 100,000, 217.02 per 100,000). The age group over 80 years had the highest incidence rate (9656.94 per 100,000), prevalence rate (57458.84 per 100,000) and mortality rate (7042.60 per 100,000) among all age groups in 2021. The forecasted ASIR of CVD will slowly increase to 812.80 per 100,000 (95%\u003cem\u003eCI\u003c/em\u003e: 806.75\u0026thinsp;~\u0026thinsp;818.84) in 2022 and to 820.26 per 100,000 (95%\u003cem\u003eCI\u003c/em\u003e: 762.24\u0026thinsp;~\u0026thinsp;878.28) in 2030. The forecasted ASPR of CVD will be 6700.67 per 100,000 (95%\u003cem\u003eCI\u003c/em\u003e: 6674.52\u0026thinsp;~\u0026thinsp;6726.81) in 2022 and 7626.55 per 100,000 (95%\u003cem\u003eCI\u003c/em\u003e: 6945.36\u0026thinsp;~\u0026thinsp;8307.75) in 2030. The forecasted ASMR of CVD will decrease to 275.80 per 100,000 (95%\u003cem\u003eCI\u003c/em\u003e: 263.61\u0026thinsp;~\u0026thinsp;288.00) in 2022 and reach 242.42 per 100,000 (95%\u003cem\u003eCI\u003c/em\u003e: 179.93\u0026thinsp;~\u0026thinsp;304.90) in 2030.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCVD is still a major public health challenge facing China now and in the future. Relevant measures should be taken to prevent and control CVD.\u003c/p\u003e","manuscriptTitle":"Analyzing the epidemiological trends of cardiovascular diseases in China from 1990 to 2021 and predicting future statistics based on GBD data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-17 07:02:14","doi":"10.21203/rs.3.rs-5700163/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":"b9fee47f-0c86-4553-8973-fce3269a4a4f","owner":[],"postedDate":"April 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-29T05:38:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-17 07:02:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5700163","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5700163","identity":"rs-5700163","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}