Mortality trend of four major non-communicable diseases in China, 2009-2020
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Background: Non-communicable diseases (NCD) have become the leading cause of death in the world at present. Among them, the most important are cardiovascular diseases (CVD), cancer, chronic respiratory diseases and diabetes. This study aimed to analyze the time trends of mortality rates caused by the four major non-communicable diseases (NCD 4 ) in China between 2009 and 2020, and to provide the evidence basis for policy implementation, information system, and health resource management of chronic NCD in China. Methods Data on resident death was collected from the National Mortality Surveillance data set and used to analyze the crude mortality rates, standardized mortality rates and changing trends of NCD 4 among different genders, urban and rural areas, and geographical regions in China from 2009 to 2020. The Joinpoint Regression Models were fitted by the weighted least squares method. The average annual percent change (AAPC) and its 95% confidence interval (CI) were calculated for the entire time period. Results From 2009 to 2020, the standardized mortality from four major NCD combined in China decreased from 534.51 to 395.84%, with the AAPC value at -2.8% (95% CI [-3.7% to -1.8%]). The standardized mortality from CVD, cancer, and chronic respiratory diseases decreased, but the standardized mortality of diabetes increased, with AAPC values at -2.2%, -1.8%, -8.0% and 1.9% respectively. Conclusions From 2009 to 2020, the mortality rate of chronic NCD is on the decline, but chronic NCD have become the leading cause of death of residents. Close attention needs to be paid on NCD which affecting the health of the labor force population in China. The prevention and treatment of diabetes, male and west region NCD should be enhanced.
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Among them, the most important are cardiovascular diseases (CVD), cancer, chronic respiratory diseases and diabetes. This study aimed to analyze the time trends of mortality rates caused by the four major non-communicable diseases (NCD 4 ) in China between 2009 and 2020, and to provide the evidence basis for policy implementation, information system, and health resource management of chronic NCD in China. Methods Data on resident death was collected from the National Mortality Surveillance data set and used to analyze the crude mortality rates, standardized mortality rates and changing trends of NCD 4 among different genders, urban and rural areas, and geographical regions in China from 2009 to 2020. The Joinpoint Regression Models were fitted by the weighted least squares method. The average annual percent change (AAPC) and its 95% confidence interval (CI) were calculated for the entire time period. Results From 2009 to 2020, the standardized mortality from four major NCD combined in China decreased from 534.51 to 395.84%, with the AAPC value at -2.8% (95% CI [-3.7% to -1.8%]). The standardized mortality from CVD, cancer, and chronic respiratory diseases decreased, but the standardized mortality of diabetes increased, with AAPC values at -2.2%, -1.8%, -8.0% and 1.9% respectively. Conclusions From 2009 to 2020, the mortality rate of chronic NCD is on the decline, but chronic NCD have become the leading cause of death of residents. Close attention needs to be paid on NCD which affecting the health of the labor force population in China. The prevention and treatment of diabetes, male and west region NCD should be enhanced. Non-communicable diseases (NCD) Mortality Average annual percentage changes (AAPC) Trend Introduction Non-communicable diseases (NCD) cause around 41 million deaths annually worldwide, equivalent to 71% of all deaths globally, mainly caused by four major diseases: Cardiovascular disease (CVD) accounts for the majority of NCD deaths, or 17.9 million deaths annually; Cancer-related deaths amounted to 9 million; Chronic respiratory diseases caused by up to 3.8 million people; Diabetes has caused 1.6 million deaths [ 1 ]. In China, NCD comprises the majority of the burden of disease, and causes nearly 90% of deaths in 2019, with CVD being the largest contributor, followed by cancer, with chronic respiratory diseases and diabetes, being the 4th and 6th largest contributors, respectively [ 2 ]. The United Nation’s Sustainable Development Goals for 2030 emphasizes comprehensive medical coverage and prevention and control of non-communicable diseases, while the “Healthy China Action Plan 2019–2030” focuses on disease prevention and health promotion, and specifically planned the prevention and treatment actions of four major non-communicable diseases (NCD 4 , include cancer, CVD, chronic respiration diseases and diseases). We used death certificate data from 2009 to 2020 to further (1) quantify mortality trends in the China population from 2009 to 2020 that is due to the NCD 4 : cancer, CVD, chronic respiratory diseases and diabetes, separately and combined; (2) quantify variation in NCD 4 mortality rates by gender, urban and rural areas and regions (east, central and west). Methods Data sources The population covered by each monitoring point, the total number of deaths from all causes and the number of deaths from NCDin the China Death Cause Monitoring Data Set 2009–2020 were used as the basic data. All categories of causes of death are coded using the International Classification of Diseases 10th Edition (ICD-10), namely cancer (C00-C97), diabetes (E10-E14), cardiovascular diseases (I00-I99) and chronic lung diseases (J30-J98). In 2009 ~ 2012, there were 161 death cause monitoring points, covering 31 provinces (autonomous regions and municipalities directly under the central government), with a population of more than 70 million, accounting for 6% of the country's population (east, central and western region as shown in S1). In 2013 ~ 2020, there were 605 death-cause monitoring points, with a population of more than 300 million, accounting for about 24% of the country's population. All death cases occurring in each jurisdiction, including registered and non-registered Chinese mainland residents, as well as Hong Kong, Macao and Taiwan compatriots and foreign citizens, were monitored. Due to the short start-up time of the national death cause monitoring system, the lack of work experience in the new monitoring points, the weak infrastructure, and the possibility of under-reporting, the mortality rate of less than 3‰ was taken as the elimination criterion for the monitoring points in 2009 ~ 2012. After 2013, the original monitoring points of the Death Cause Monitoring and Statistics System of the Ministry of Health and the National Disease Monitoring System took the mortality rate lower than 4.5‰ as the elimination criterion, while the new monitoring points added in 2013 took the mortality rate lower than 5‰ as the elimination criterion, and some monitoring points that were considered as serious under-reporting and had the potential to affect the overall results were excluded. After elimination, 157, 157, 155, 153, 432, 491, 487, 499, 509, 512, 519 and 522 monitoring points were included respectively from 2009 to 2020. The minimum annual mortality rates at the monitoring points were 3.27‰, 3.27‰, 3.07‰, 3.05‰, 4.51‰, 4.57‰, 4.59‰, 4.59‰, 4.51‰, 4.50‰, 4.50‰ and 4.52‰ respectively. According to the classification method of the National Bureau of Statistics, the monitoring sites are divided into three parts: east, middle and west, including 11, 8 and 12 provinces (autonomous regions and municipalities directly under the Central Government) respectively; Counties (including county-level cities) are defined as rural areas and districts as cities. Statistical analysis Excel 2016 software was used for double entry of death data from cancers, diabetes, CVD and chronic respiratory diseases in China from 2009 to 2020. The Crude Mortality Rate (CMR) of NCD 4 was calculated by stratification according to age (5-year age groups, up to 85 ~ years), gender, urban and rural areas, and regions (east, central and west). We calculated the Standardized Mortality Rate (SMR) per 100,000 with 95% CI for each study year (2009 ~ 2020) using the direct standardization method, based on the population composition of the sixth national population census in 2010, SMR was calculated using the following formula: nPx is the age and population size of the standard population; nMx is the age-specific mortality of the population to be standardized; n is the interval of each age group; x is the starting age of each age group. We performed joinpoint regression analysis using Joinpoint Regression Program from the Surveillance Research Program of the National Cancer Institute Version 4.9.0.0 (Statistical Research and Applications Branch National Cancer Institute, USA). In this study, the model was used to fit the changing trend of NCD 4 mortality rate, and the SMR of gender, urban and rural areas, and regions (east, central and west) were compared within the group to determine whether the change trend curves of the standardized mortality rate were consistent or parallel. The Joinpoint regression model found the turning point with statistical significance ( P 0 means that the rate has increased annually in a certain period time, and APC < 0 means that the rate has decreased annually in a certain period. If there is no joinpoint, then APC = AAPC, which means the rate fluctuated during the total study period. Results Deaths due to four non-communicable diseases in China from 2009 to 2020 Over the 12-year study period from 2009 to 2020, a total of 13, 066, 976 deaths of four major NCDs were recorded in China. CVD were found to be the most common causes among those four major NCDs (7, 419, 141,56.76% of the total), followed by cancer (3, 749, 337, 28.69%), chronic respiratory diseases (1, 556, 869, 11.91%) and diabetes (344, 629, 2.64%). The number of deaths in males was were 30% higher than in females (7, 387, 752 vs. 5, 429, 415, M/F: 1.36), but the deaths in urban areas are almost the same as in rural areas (6, 463, 938 vs. 6, 606, 038, U/R: 0.97). Trend in mortality of four major NCD among different subcategories From 2009 to 2020, the SMR of the NCD 4 , cancer, CVD and chronic respiratory diseases showed a downward trend. It is noteworthy that the SMR of diabetes increased from 11.10 per 100,000 in 2009 to 12.54 per 100,000 in 2020, which was an increase of 12.97% (Table 1). Joinpoint regression analysis indicated that the SMR decrease significantly in CVD, cancer and chronic respiratory diseases, with the AAPC of -2.2%, -1.8%, -8.0%, respectively, showing significant changes, with detectable joinpoint in DM (AAPC=1.9%, 95% CI: 0.4~3.5) (Table 2 and S2A). For diabetes, whereas a decreased trend from 2009 to 2011 (APC = 5.1%) was observed without significance (APC=-2.1%, 95% CI: -10.9~7.6), then an increasing trend from 2011 to 2020 (APC=2.8%, 95% CI: 2.0~3.7) (Table 2). Table 1 Standardized mortality rates of NCD 4 and its subcategory in China (2009-2020) Year NCD 4 Cardiovascular disease Cancer Chronic respiratory diseases Diabetes CMR SMR CMR SMR CMR SMR CMR SMR CMR SMR 2009 459.32 534.51 235.83 281.82 139.27 149.36 74.16 92.23 10.06 11.10 2010 456.22 538.80 238.12 292.36 137.05 146.20 70.80 91.42 10.25 8.82 2011 457.75 500.14 242.69 273.49 136.76 136.55 67.94 79.33 10.36 10.77 2012 477.40 448.20 253.89 238.29 141.23 132.78 71.68 67.19 10.60 9.93 2013 520.70 456.52 284.81 248.94 150.09 132.95 73.97 64.23 11.83 10.40 2014 528.09 450.48 291.00 246.47 155.36 135.32 68.99 57.76 12.74 10.93 2015 532.77 454.88 295.42 250.43 157.34 137.11 66.51 55.75 13.50 11.59 2016 539.73 427.72 301.54 235.55 157.46 130.08 65.97 50.32 14.76 11.77 2017 540.46 425.65 304.11 235.93 158.06 129.93 63.00 47.67 15.29 12.12 2018 551.72 413.22 313.61 230.27 160.17 126.82 62.04 44.11 15.90 12.02 2019 556.01 423.08 317.54 240.43 162.46 125.55 59.37 44.42 16.64 12.68 2020 559.94 395.84 328.84 229.41 161.69 118.40 51.83 35.49 17.58 12.54 Table 2 Standardized mortality rates of NCD 4 by gender, geography and region in China (2009-2020) Year Male Female Urban Rural CMR SMR CMR SMR CMR SMR CMR SMR 2009 523.52 654.76 392.42 432.19 451.1 447.14 463.87 593.45 2010 522.00 688.81 387.72 436.68 451.72 466.13 469.98 596.48 2011 526.38 631.36 386.66 390.83 405.15 464.11 492.61 522.23 2012 546.15 558.46 406.05 358.85 423.95 408.33 512.79 478.68 2013 591.73 565.80 446.95 355.33 503.17 425.84 528.57 490.03 2014 600.49 560.50 452.75 349.58 505.53 433.40 538.69 473.37 2015 603.54 559.99 459.75 356.57 512.71 435.55 542.29 476.53 2016 608.59 529.71 468.32 332.62 506.29 421.71 556.51 453.72 2017 611.00 528.36 467.54 330.02 506.76 418.88 557.64 452.43 2018 623.57 514.31 477.35 318.64 517.53 408.01 569.47 444.16 2019 630.10 524.49 479.57 329.28 519.84 405.34 574.74 463.28 2020 633.01 497.50 484.55 303.36 524.97 386.36 578.84 443.62 Year East region Central region West region CMR SMR CMR SMR CMR SMR 2009 473.87 488.59 471.06 581.96 423.96 567.45 2010 460.25 498.34 478.49 586.09 422.54 586.64 2011 456.5 463.92 475.8 555.39 435.4 561.45 2012 475.27 421.82 493.21 502.17 459.28 505.49 2013 553.42 442.63 502.6 503.92 496.59 498.26 2014 542.88 427.6 521.75 498.26 514.02 499.69 2015 559.53 436.05 525.78 498.14 503.68 492.64 2016 560.02 416.70 535.22 474.79 515.93 475.39 2017 551.57 408.07 541.93 472.99 521.3 490.94 2018 564.68 400.05 551.94 460.72 531.77 483.81 2019 558.57 407.74 566.73 477.94 538.1 490.79 2020 563.86 389.72 571.01 456.82 539.89 468.14 Trend in mortality of four major NCD with different genders From 2009 to 2020, the SMR in both males and females showed a downtrend annually, decreasing from 654.76 per 100,000 people to 497.50 per 100,000 people, and from 432.19 per 100,000 people to 303.36 per 100,000 people, respectively. The declines were slightly greater in males than females (Table 2 and S2B). While a significant decrease in the SMR was observed in both genders during the study period (AAPC -2.8% for males and -3.2% for females, respectively), with two trends. During 2009- 2012, the SMR in both males and females significantly decreased with APC of -5.5% and -6.3% from 2012 to 2020, remarkably decreased trends of SMR of four major NCD 4 in both males (APC=-6.3%, 95%CI: -10.7~-1.8) and females (APC=-2.0%, 95% CI: -3.1~-0.9) were observed (Table 3). Table 3 Joinpoint analysis of trends in standardized mortality rates of NCD 4 in China (2009-2020) Total Study Period AAPC(95% CI) Trend 1 Trend 2 years APC (95% CI) years APC (95% CI) All -2.8 * (-3.7, -1.8) 2009-2012 -5.4 * (-8.7, -2.0) 2012-2020 -1.8 * (-2.6, -0.9) Subcategory Cardiovascular diseases -2.2 * (-3.3, -1.1) 2009-2012 -5.2 * (-9.0, -1.1) 2012-2020 -1.0 * (-2.0, -1.0) Cancer -1.8 * (-3.1, -0.4) 2009-2011 -4.1(-11.7, 4.2) 2011-2020 -1.3 * (-2.1, -0.5) Chronic respiratory diseases -8.0 * (-9.8, -6.2) 2009-2013 -9.8 * (-14.0, -5.5) 2013-2020 -7.0 * (-9.3, -4.5) Diabetes 1.9 * (0.4, 3.5) 2009-2011 -2.1(-10.9, 7.6) 2011-2020 2.8 * (2.0, 3.7) Gender Male -2.8 * (-3.9, -1.7) 2009-2012 -5.5 * (-9.5, -1.3) 2012-2020 -1.8 * (-2.8, -0.8) Female -3.2 * (-4.4, -2.0) 2009-2012 -6.3 * (-10.7, -1.8) 2012-2020 -2.0 * (-3.1, -0.9) Geographic Urban -1.4(-3.5, 0.7) 2009-2018 -1.1(-2.2, 0.0) 2018-2020 -2.9(-14.8, 10.8) Rural -2.9 * (-4.1, -1.7) 2009-2012 -7.3 * (-11.6, -2.8) 2012-2020 -1.2 * (-0.1, -2.5) Region East region -2.2 * (-3.0, -1.3) 2009-2012 -4.2 * (-7.3, -0.9) 2012-2020 -1.4 * (-2.2, -0.6) Central region -2.4 * (-3.2, -1.6) 2009-2012 -5.0 * (-7.8, -2.1) 2012-2020 -1.4 * (-2.1, -0.7) West region -1.9 * (-3.1, -0.7) 2009-2013 -4.0 * (-7.1, -0.9) 2013-2020 -0.7(-2.1, 0.7) * Indicates that the AAPC is significantly different from zero at the alpha = 0.05 level. Trend in mortality of four major NCD among different geographics A downtrend in SMR in both urban and rural areas was observed. The SMR decreased by 13.59% from 447.14 to 386.36 per 100,000 people in urban areas and decreased by 25.24% from 593.45 to 443.62 per 100,000 people in rural areas (Table 2). Joinpoint analysis showed an insignificantly decreasing trend of SMR in urban areas (AAPC=-1.4%, 95%CI: -3.5~0.7). As in rural areas, it decreased significantly with the AAPC of -2.9% (95% CI: -4.1~-1.7 ) (Table 3 and S2C). Trend in mortality of four major NCD among different regions SMR of NCD 4 in the east, central and west regions showed a declining trend, from 2009 to 2020, decreasing from 488.59 per 100,000 people to 389.72 per 100,000 people, from 581.96 per 100,000 people to 456.82 per 100,000 people and from 567.45 per 100,000 people to 468.14 per 100,000 people, respectively (Table 2), and the decline rate of SMR in the central region (APC=-2.2, 95% CI: -3.0~-1.3) was significantly faster than that in the east (APC=-2.4, 95% CI: -3.2~-1.6) and west regions (APC=-1.9, 95% CI: -3.1~-0.7) (Table 2 and S2D). Trend in mortality of Cancer, Diabetes, Cardiovascular disease and Chronic respiratory disease among different subgroups The same cause-specific patterns were observed by gender, geography and region: the highest rates were for CVD followed by cancer, chronic respiratory diseases and diabetes. A downtrend in SMR in both males and females was observed. The SMR was higher in males compared to females for each NCD 4 condition, the greatest difference in SMR between males and females was for CVD (Table 4 and S3A). Joinpoint analysis showed no significant decrease in SMR for diabetes(AAPC=0.7%, 95%CI: -1.1~2.6) and cardiovascular disease (AAPC=-1.3%, 95%CI: -3.9~1.4), there was a significant downward trend in rural areas for each NCD 4 condition (Table 3). In the east, central and west regions, the SMR of chronic respiratory diseases appeared a significant downward trend (AAPC -8.6% for the east region, -8.7% for the central region and -6.9% for the west region, respectively), and it was the highest and the fastest in the west region (Table 4 and S3C). Table 4 Joinpoint analysis of trends in subgroup standardized mortality rates for Cancer, Diabetes, Cardiovascular disease and Chronic respiratory disease in China (2009-2020) Total Study Period AAPC(95%CI) Trend 1 Trend 2 years APC (95%CI) years APC (95%CI) Male Cancer -1.9 * (-2.8, -1.0) 2009-2012 -3.1(-6.3, 0.1) 2012-2020 -1.4 * (-2.2, -0.7) Diabetes -5.6(-13.6, 3.1) 2009-2015 -12.4 * (-22.6, -0.9) 2015-2020 3.3(-13.8, 23.9) CVD -2.6 * (-5.9, -0.1) 2009-2013 -5.3(-11.2, 1.0) 2013-2020 -1.0 * (-3.8, 1.9) Chronic respiratory diseases -5.2 * (-9.4, -0.7) 2009-2018 -4.5 * (-6.6, -2.4) 2018-2020 -8.1(-31.0, 22.4) Female Cancer -1.7 * (-3.2, -0.2) 2009-2011 -4.8(-13.3, 4.6) 2011-2020 -1.0 * (-1.9, -0.1) Diabetes -7.1 * (-13.2, -0.6) 2009-2014 -14.3 * (-24.1, -3.3) 2014-2020 -0.6(-11.1, 11.1) CVD -2.2 * (-3.6, -0.8) 2009-2012 -5.6 * (-10.4, -0.4) 2012-2020 -1.0(-2.2, 0.3) Chronic respiratory diseases -8.9 * (-10.6, -7.2) 2009-2013 -9.9 * (-13.8, -5.8) 2013-2020 -8.3 * (-10.6, -6.0) Urban Cancer -1.7 * (-3.3, -0.2) 2009-2017 -0.5(-1.7, 0.7) 2017-2020 -5.0(-10.6,1.0) Diabetes 0.7(-1.1, 2.6) 2009-2012 -2.1(-8.8, 5.2) 2012-2020 1.8 * (0.2, 3.4) CVD -1.3(-3.9, 1.4) 2009-2017 -0.8(-2.9, 1.3) 2017-2020 -2.5(-12.1, 8.1) Chronic respiratory diseases -7.2 * (-10.7, -3.6) 2009-2018 -5.7 * (-7.3, -4.1) 2018-2020 -13.8(-32.3,9.7) Rural Cancer -1.9 * (-2.7, -1.1) 2009-2012 -3.8 * (-6.8,-0.8) 2012-2020 -1.2 * (-1.9, -0.5) Diabetes 2.0 * (1.0, 2.9) 2009-2012 -2.2(-5.7, 1.5) 2012-2020 3.5 * (2.8, 4.3) CVD -2.8 * (-4.3, -1.3) 2009-2013 -6.2 * (-9.9, -2.4) 2013-2020 -0.8(-2.6, 1.1) Chronic respiratory diseases -8.8 * (-11.8, -5.7) 2009-2012 -17.5 * (-26.7, -7.1) 2012-2020 -5.3 * (-8.5,-2.1) East region Cancer -1.4(-3.2, 0.4) 2009-2016 -0.6(-2.5, 1.4) 2016-2020 -2.9(-7.7, 2.0) Diabetes 0.2(-1.3, 1.7) 2009-2012 0.2(-1.3, 1.7) 2012-2020 1.5 * (0.2, 2.7) CVD -2.4 * (-3.7, -1.1) 2009-2012 -5.3 * (-9.9, -0.6) 2012-2020 -1.2 * (-2.4, -0.1) Chronic respiratory diseases -8.6 * (-11.8, -28.0) 2009-2018 -8.3 * (-9.7, -6.9) 2018-2020 -10.0(-28.0, 12.4) Central region Cancer -2.4 * (-3.2, -1.6) 2009-2013 -4.4 * (-6.3, -2.4) 2013-2020 -1.3 * (-2.2, -0.4) Diabetes 0.5(-0.6, 1.5) 2009-2012 -5.9 * (-9.6, -2.1) 2012-2020 3.0 * (2.1, 7.9) CVD -2.5 * (-3.6, -1.3) 2009-2012 -5.3 * (-9.2, -1.1) 2012-2020 -1.4 * (-2.4, -0.4) Chronic respiratory diseases -8.7 * (-10.5, -6.9) 2009-2013 -11.9 * (-15.9, -7.6) 2013-2020 -6.8 * (-9.3, -4.4) West region Cancer -1.4(-2.9, 0.1) 2009-2018 -0.8(-1.6, 0.0) 2018-2020 -4.1(-12.8, 5.4) Diabetes 4.1 * (1.6, 6.7) 2009-2018 4.5 * (2.9, 6.1) 2018-2020 2.3(-11.9, 18.6) CVD -1.2(-3.0, 0.5) 2009-2013 -3.9(-8.3, 0.7) 2013-2020 0.3(-1.7, 2.3) Chronic respiratory diseases -6.9 * (-9.1, -4.7) 2009-2014 -9.1 * (-12.7, -5.3) 2014-2020 -5.1 * (-8.7, -1.3) Discussion This study confirmed a significant downward trend on standard mortality rates from four major NCD combined from 2009 to 2020 in China. The greatest declines in standard mortality rates were for chronic respiratory diseases (AAPC=-8.0%), followed by CVD (AAPC=-2.2%) and cancer (AAPC=-1.8%). This finding was in line with the global trend (the premature mortality rate decreased from 22.9% in 2000 to 17.8% in 2019). Nevertheless, due to population growth and increased longevity, the total number of deaths attributable to NCD has risen, NCD continued to be the leading cause of ill health worldwide and was responsible for seven of 10 premature deaths in 2019. Inconsistent with data that cancer is the leading cause of death in high-income countries [ 3 ], our study indicated that cardiovascular disease is a major cause of NCD. Previous research reports that ischemic heart disease (IHD), hemorrhagic stroke (HS) and ischemic stroke (IS) were the leading three causes of CVD death [ 4 ]. Multiple factors can contribute occurrence and development of CVD, of which high systolic blood pressure and dietary risk are considered important risk factors. Smoking control was reported as an efficient way to the reduction of early cardiovascular death in males, whilst obesity control can be critical to reducing early cardiovascular death in females [ 5 ]. The control of hypertension has a great impact on the reduction of premature cardiovascular death in the world. For cancer, Joinpoint analysis showed a significant decrease in the standardized mortality rate. But in 2020, there were 19.29 million new cancer cases in the world, among which 4.57 million were new cancer cases in China, accounting for 23.7% of the world's new cancer cases, and the number of new cancer cases far exceeded that in other countries [ 6 ]. In the above-mentioned report, the top ten causes of death for Chinese include lung cancer, liver cancer, stomach cancer, esophageal cancer and other cancers. The mortality rate of lung cancer ranks first, mainly due to tobacco smoking behavior, air pollution and other environmental factors including decoration and cooking oil fume. In general, chronic respiratory diseases mainly include tuberculosis, diffuse pulmonary fibrosis, chronic obstructive pulmonary disease (COPD), bronchiectasis, and bronchial asthma. In low and low-middle Socio-demographic Index (SDI) areas, particulate matter pollution was the main risk factor leading to death from CRDs, while smoking was ranked first among the major risk factors in areas with middle, middle-high, or high SDI [ 7 ]. Previous studies have shown that the prevalence rate of COPD in the Chinese population over the age of 20 years is 8.6%, about 99.9 million patients, but only 2.6% of them are aware of the disease. For COPD [ 8 ], the most effective preventable risk factors are smoking and air pollution. However, both of these two factors have not been overnight cold. The health crisis caused by air pollution and the still high smoking rate pose considerable challenge to the prevention of chronic obstructive pulmonary disease (COPD). However, deaths due to diabetes increased slightly (AAPC = 1.9%) in the same period, which is consistent with the global trend. It is alarming that diabetes causes an increase in premature mortality. Globally, the death toll from diabetes increased by 70.00% from 2000 to 2019 [ 1 ]. Previous studies have found that the mortality due to diabetes in urban areas has been higher than in rural areas for 15 years but the gap between the two gradually narrowed. In addition, the standardized mortality rate of females with diabetes was higher than that of males. Finally, the eastern region had the highest mortality of diabetes, but the western region's mortality grew faster and eventually became the highest in 2020 [ 9 ]. The obesity epidemic caused by changes in people’s lifestyle and diet is considered to be an important reason for the aggravation of the diabetes epidemic in China, and it has been proven to be one of the critical factors leading to the onset of diabetes. Over the past few decades, China’s rapid social and economic development has led to changes in people’s lifestyles, reduced exercise time, longer sitting time, poor dietary structure, and increased life and work pressure, which has led to an increasing prevalence of obesity. Based on Chinese criteria, were 34.3% for overweight and 16.4% for obesity in adults (≥ 18 years) [ 10 ]. Furthermore, there have been persistent disparities in NCD 4 mortality rates across genders, geographics, and regions in China. This is consistent with previously observed trends which have shown that males have had a persistently higher mortality rate for many chronic conditions combined compared to females [ 11 ]. This disparity is partially driven by genetic programming [ 12 ], sex hormones and difference in the response to medicines [ 13 ], in addition, the rates of obesity, smoking and alcohol consumption in males were higher than those in females [ 14 ]. Therefore, efforts should be devoted to diminishing geospatial clustering between rural and urban areas, including the improvement of education and economic income levels, the equalization of medical services, and the universalization of health insurance. At the same time, urban morphology is planned and designed with a focus on spatial distribution, land use and transportation, as they could increase or inhibit chronic non-communicable diseases through their effects on air pollution, physical activity and obesity [ 15 ]. Due to China's vast territory, the differences in climate [ 16 ] and geographical environment have created differences in lifestyles such as diet and physical activity, and also gradually widened the gap in the health level of residents in different areas. Population aging, immutable genetic factors and urbanization [ 17 ] have contributed to this spatial pattern, but it has been recognized that there are also modifiable and unhealthy lifestyle choices such as poor dietary quality and irregular physical activity. Strategies to prevent and manage NCD should integrate the influence of genetic, lifestyle, and environmental factors, focusing on modifiable adverse lifestyle habits such as unhealthy diet, lack of physical activity, obesity, tobacco smoking and alcohol drinking [ 18 ]. The latest national prevalence estimates 34.3% for overweight and 16.40% for obesity in adults (≥ 18 years) for 2015 ~ 2019. Obesity has become not only a chronic disease but also a means of treating other chronic diseases in its own right. The effects of dietary imbalance and physical inactivity interact with risk factors such as genetic susceptibility, psychosocial factors, and exposure early in life [ 19 ]. A large body of evidence suggests that physical activity can reliably prevent and treat obesity and cardiovascular disease. As we all know, a balanced diet is as important as physical exercise, therefore, it is urgent to give scientific and effective nursing interventions and public health measures based on evidence. The healthy diet intervention carried out by Maxwell [ 20 ] and others in Britain aims to enable young people to form the habit of eating vegetables. Findings showed a significant increase in 50 children aged 7 ~ 12 years propensity toward initially disliked vegetables to which they were repeatedly exposed but not to vegetables to which they did not receive repeated exposure. It is necessary to pay attention to environmental risk and protective factors, and the relationship between modifiable environmental factors and NCD. Some scholars suggest that the government should investigate the cost-effectiveness of health-related economic impacts, and shape a healthier city by improving land use measures and building multifunctional and high-quality transportation services and infrastructure (the research results show that walkable parks are related to diabetes with low interest rates) [ 21 ]. It is widely acknowledged that improving early detection mechanism and standardizing early treatment by establishing public health screening and early diagnosis. The incidence of lung cancer declined at an AAPC of -2.0% per year between 2006 and 2015 and at a slightly higher rate of AAPC (-2.2%) between 2011 and 2015, a drop largely attributable to screening with low-dose spiral CT. Moreover, while carrying out regular physical examination of urban workers and cadres, we should gradually spread the knowledge and services of disease screening to rural areas to raise the awareness of people's health screening throughout the country. And introduce foreign advanced screening technology, and at the same time increase national financial support to research and develop domestic advanced screening technology. In addition, it should be noted that gene testing is leading the development of predictive medicine, which can be applied to disease prevention, gene diagnosis, individualized treatment and other aspects to change the shape of the medical and health industry. Strengthening early treatment and management of CVD and chronic respiratory diseases is imperative, as well as appropriate early treatment of acute CVD, acute exacerbations of asthma and chronic obstructive pulmonary disease and acute complications of diabetes in provincial and municipal general and specialist hospitals, improved effective emergency referral systems and high-quality long-term care. There are several limitations to this study. Our results were limited by the quality of the data acquired from the National Mortality Surveillance database, there might be unavoidable miscoding or omission of reporting. However, these data are the most robust and comprehensive estimates of mortality rates available. Another limitation was that as a descriptive study, we can only show the trend and difference in mortality. The internal causes of this phenomenon need to be explored through further researches. In conclusion, from 2009 to 2020, chronic NCD have become the leading cause of death of residents in China, and the mortality rate is on the decline. In the future, the prevention and control of chronic NCD should pay more attention to diabetes, male residents and residents in western regions, so as to further improve the health level of the whole people. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials Ethics approval is not required to use the National Mortality Surveillance data; its use for scientifc purposes is regulated since inception. Data used in this paper are available at the following address: https://ncncd.chinacdc.cn/xzzq_1/202101/t20210111_223706.htm Competing interests The authors declare that they have no competing interests. Funding This study was supported by National Natural Science Foundation of China(Grant No. 20BRK041). Authors’ contributions TW and CS participated in the conception and design of the study, data collection and statistical analysis, drafts of the manuscript. LW participated in the analysis of the literature, contributed to drafts of the manuscript. All authors read and approved the final manuscript. Acknowledgments The authors would like to thank the Chinese Center for Disease Control and Prevention for their contributions to the management of mortality data. References World Health Organization. World health statistics 2020: monitoring health for the SDGs, sustainable development goals. https://www.who.int/publications/i/item/9789240005105 (2020). Accessed 13 May 2020. Zhou MG, Wang HD, Zeng XY. Mortality, morbidity, and risk factors in China and its provinces, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet. 2019; doi: 10.1016/S0140-6736(19)30427-1. Dagenais GR, Leong DP, Rangarajan S. Variations in common diseases, hospital admissions, and deaths in middle-aged adults in 21 countries from five continents (PURE): a prospective cohort study. The Lancet. 2020; https://doi.org/10.1016/S0140-6736(19)32007-0. Wang W, Liu Y, Liu J. Mortality and years of life lost of cardiovascular diseases in China, 2005–2020: Empirical evidence from national mortality surveillance system. Int J Cardiol. 2021; doi: 10.1016/j.ijcard.2021.08.034. Connelly PJ, Azizi Z, Alipour P. The Importance of Gender to Understand Sex Differences in Cardiovascular Disease. Can J Cardiol. 2021; doi: 10.1016/j.cjca.2021.02.005. Sung H, Ferlay J, Siegel RL. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021; doi: 10.3322/caac.21660. Safiri S, Carson-Chahhoud K, Noori M. Burden of chronic obstructive pulmonary disease and its attributable risk factors in 204 countries and territories, 1990-2019: results from the Global Burden of Disease Study 2019. BMJ. 2022; doi: 10.1136/bmj-2021-069679. Wang C, Xu J, Yang L. Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study. The Lancet. 2018; doi: 10.1016/S0140-6736(18)30841-9. Wang F, Wang W, Yin P. Mortality and Years of Life Lost in Diabetes Mellitus and Its Subcategories in China and Its Provinces, 2005-2020. Journal of Diabetes Research. 2022; doi: 10.1155/2022/1609267. Wang F, Wang W, Yin P. Mortality and Years of Life Lost in Diabetes Mellitus and Its Subcategories in China and Its Provinces, 2005-2020. Journal of Diabetes Research. 2022; 10.1155/2022/1609267. Mauvais-Jarvis F, Bairey MN, Barnes PJ. Sex and gender: modifiers of health, disease, and medicine. The Lancet. 2020; doi: 10.1016/S0140-6736(20)31561-0. Li Y, Xu A, Jia S. Recent advances in the molecular mechanism of sex disparity in hepatocellular carcinoma. Oncol Lett. 2019; doi: 10.3892/ol.2019.10127. Dennis JM, Henley WE, Weedon MN. Sex and BMI Alter the Benefits and Risks of Sulfonylureas and Thiazolidinediones in Type 2 Diabetes: A Framework for Evaluating Stratification Using Routine Clinical and Individual Trial Data. Diabetes Care. 2018; doi: 10.2337/dc18-0344. Xia S, Du X, Guo L. Sex Differences in Primary and Secondary Prevention of Cardiovascular Disease in China. Circulation. 2020; doi: 10.1161/circulationaha.119.043731. Fazeli DZ, Khatami SM, Ranjbar E. The Associations Between Urban Form and Major Non-communicable Diseases: a Systematic Review. J Urban Health. 2022; doi: 10.1007/s11524-022-00652-4. Zhang Y, Tong M, Wang B. Geographic, Gender, and Seasonal Variation of Diabetes: A Nationwide Study With 1.4 Million Participants. J Clin Endocrinol Metab. 2021; doi: 10.1210/clinem/dgab543. Li C, Zhang L, Gu Q. Spatio-Temporal Differentiation Characteristics and Urbanization Factors of Urban Household Carbon Emissions in China. Int J Environ Res Public Health. 2022; doi: 10.3390/ijerph19084451. Pan XF, Wang L, Pan A. Epidemiology and determinants of obesity in China. Lancet Diabetes Endocrinol. 2021; doi: 10.1016/S2213-8587(21)00045-0. Béland M, Lavoie KL, Briand S. Aerobic exercise alleviates depressive symptoms in patients with a major non-communicable chronic disease: a systematic review and meta-analysis. Br J Sports Med. 2020; doi: 10.1136/bjsports-2018-099360. Maxwell AE, Castillo L, Arce AA. Eating Veggies Is Fun! An Implementation Pilot Study in Partnership With a YMCA in South Los Angeles. Prev Chronic Dis. 2018; doi: 10.5888/pcd15.180150. Frank LD, Adhikari B, White KR. Chronic disease and where you live: Built and natural environment relationships with physical activity, obesity, and diabetes.Environ Int. 2022; doi: 10.1016/j.envint.2021.106959. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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-2417724","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":163510483,"identity":"e2fdfa9a-fcc2-414f-abfc-880dbec7aaca","order_by":0,"name":"tiantian wu","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"tiantian","middleName":"","lastName":"wu","suffix":""},{"id":163510484,"identity":"0a0381a4-8617-4346-9a7c-a1582ea7fe71","order_by":1,"name":"lianke wang","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"lianke","middleName":"","lastName":"wang","suffix":""},{"id":163510485,"identity":"6f792fbb-0cc9-4416-86ab-40275e50aeb8","order_by":2,"name":"bo hu","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"bo","middleName":"","lastName":"hu","suffix":""},{"id":163510486,"identity":"99986c03-131c-4c0d-ada2-1fbcde8c1445","order_by":3,"name":"zihui yao","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"zihui","middleName":"","lastName":"yao","suffix":""},{"id":163510487,"identity":"3a539e23-c53d-4a95-b623-db649a3cd5d5","order_by":4,"name":"yu wang","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"yu","middleName":"","lastName":"wang","suffix":""},{"id":163510488,"identity":"ff50e85b-9aa6-4e5b-932e-a992afdd0eaf","order_by":5,"name":"peijia zhang","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"peijia","middleName":"","lastName":"zhang","suffix":""},{"id":163510489,"identity":"3a40a432-47c6-44ae-8895-4a21986c76d4","order_by":6,"name":"changqing sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYBAC+QYQaWAjx8/MfPABUVoMDoDIgjRjyXa2ZAPitIDJD4cTDc7zmAkQp0Ui9+CHNwbMCcaHGcwYGGpsoglqkZ+Rlyw5x4Atz+wwQ9oDhmNpuQ0E9dzIMWPmMeApBmo5bsDYcJhoLRKJm5sZ2yRI0WKQuIGZmY04LQZn3hgD/ZJgLHGYjdkggRi/yLfnGH548+e/HH//+Y8PPtTYEOEwEOCBMRKIUo6iZRSMglEwCkYBNgAAjo47h4mPZk8AAAAASUVORK5CYII=","orcid":"","institution":"Zhengzhou University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"changqing","middleName":"","lastName":"sun","suffix":""}],"badges":[],"createdAt":"2022-12-27 06:59:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2417724/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2417724/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":31005156,"identity":"806aedb0-fbab-47e8-b030-ab044e469ff5","added_by":"auto","created_at":"2023-01-03 08:26:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":339773,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2417724/v1/785f2d6c-4e3d-46dd-9fe9-b99d896647ba.pdf"},{"id":31005155,"identity":"84b3f396-7de9-410e-a7eb-dd7be628893f","added_by":"auto","created_at":"2023-01-03 08:26:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":339773,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2417724/v1/ce390662-2230-49f3-81f1-fa70d4916fcd.pdf"},{"id":31005133,"identity":"23dbfbb4-88f3-45e7-b1a0-6e802644caa2","added_by":"auto","created_at":"2023-01-03 08:26:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":395074,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-2417724/v1/dd8e7ee273d9e1360d69eccb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mortality trend of four major non-communicable diseases in China, 2009-2020","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNon-communicable diseases (NCD) cause around 41\u0026nbsp;million deaths annually worldwide, equivalent to 71% of all deaths globally, mainly caused by four major diseases: Cardiovascular disease (CVD) accounts for the majority of NCD deaths, or 17.9\u0026nbsp;million deaths annually; Cancer-related deaths amounted to 9\u0026nbsp;million; Chronic respiratory diseases caused by up to 3.8\u0026nbsp;million people; Diabetes has caused 1.6\u0026nbsp;million deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In China, NCD comprises the majority of the burden of disease, and causes nearly 90% of deaths in 2019, with CVD being the largest contributor, followed by cancer, with chronic respiratory diseases and diabetes, being the 4th and 6th largest contributors, respectively [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The United Nation\u0026rsquo;s Sustainable Development Goals for 2030 emphasizes comprehensive medical coverage and prevention and control of non-communicable diseases, while the \u0026ldquo;Healthy China Action Plan 2019\u0026ndash;2030\u0026rdquo; focuses on disease prevention and health promotion, and specifically planned the prevention and treatment actions of four major non-communicable diseases (NCD\u003csub\u003e4\u003c/sub\u003e, include cancer, CVD, chronic respiration diseases and diseases). We used death certificate data from 2009 to 2020 to further (1) quantify mortality trends in the China population from 2009 to 2020 that is due to the NCD\u003csub\u003e4\u003c/sub\u003e: cancer, CVD, chronic respiratory diseases and diabetes, separately and combined; (2) quantify variation in NCD\u003csub\u003e4\u003c/sub\u003e mortality rates by gender, urban and rural areas and regions (east, central and west).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eThe population covered by each monitoring point, the total number of deaths from all causes and the number of deaths from NCDin the China Death Cause Monitoring Data Set 2009\u0026ndash;2020 were used as the basic data. All categories of causes of death are coded using the International Classification of Diseases 10th Edition (ICD-10), namely cancer (C00-C97), diabetes (E10-E14), cardiovascular diseases (I00-I99) and chronic lung diseases (J30-J98).\u003c/p\u003e \u003cp\u003eIn 2009\u0026thinsp;~\u0026thinsp;2012, there were 161 death cause monitoring points, covering 31 provinces (autonomous regions and municipalities directly under the central government), with a population of more than 70\u0026nbsp;million, accounting for 6% of the country's population (east, central and western region as shown in S1). In 2013\u0026thinsp;~\u0026thinsp;2020, there were 605 death-cause monitoring points, with a population of more than 300\u0026nbsp;million, accounting for about 24% of the country's population. All death cases occurring in each jurisdiction, including registered and non-registered Chinese mainland residents, as well as Hong Kong, Macao and Taiwan compatriots and foreign citizens, were monitored. Due to the short start-up time of the national death cause monitoring system, the lack of work experience in the new monitoring points, the weak infrastructure, and the possibility of under-reporting, the mortality rate of less than 3\u0026permil; was taken as the elimination criterion for the monitoring points in 2009\u0026thinsp;~\u0026thinsp;2012. After 2013, the original monitoring points of the Death Cause Monitoring and Statistics System of the Ministry of Health and the National Disease Monitoring System took the mortality rate lower than 4.5\u0026permil; as the elimination criterion, while the new monitoring points added in 2013 took the mortality rate lower than 5\u0026permil; as the elimination criterion, and some monitoring points that were considered as serious under-reporting and had the potential to affect the overall results were excluded. After elimination, 157, 157, 155, 153, 432, 491, 487, 499, 509, 512, 519 and 522 monitoring points were included respectively from 2009 to 2020. The minimum annual mortality rates at the monitoring points were 3.27\u0026permil;, 3.27\u0026permil;, 3.07\u0026permil;, 3.05\u0026permil;, 4.51\u0026permil;, 4.57\u0026permil;, 4.59\u0026permil;, 4.59\u0026permil;, 4.51\u0026permil;, 4.50\u0026permil;, 4.50\u0026permil; and 4.52\u0026permil; respectively.\u003c/p\u003e \u003cp\u003eAccording to the classification method of the National Bureau of Statistics, the monitoring sites are divided into three parts: east, middle and west, including 11, 8 and 12 provinces (autonomous regions and municipalities directly under the Central Government) respectively; Counties (including county-level cities) are defined as rural areas and districts as cities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eExcel 2016 software was used for double entry of death data from cancers, diabetes, CVD and chronic respiratory diseases in China from 2009 to 2020. The Crude Mortality Rate (CMR) of NCD\u003csub\u003e4\u003c/sub\u003e was calculated by stratification according to age (5-year age groups, up to 85\u0026thinsp;~\u0026thinsp;years), gender, urban and rural areas, and regions (east, central and west). We calculated the Standardized Mortality Rate (SMR) per 100,000 with 95% CI for each study year (2009\u0026thinsp;~\u0026thinsp;2020) using the direct standardization method, based on the population composition of the sixth national population census in 2010, SMR was calculated using the following formula:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" height=\"47\" width=\"143\"\u003e\u003c/p\u003e\n \u003cp\u003e \u003cem\u003enPx\u003c/em\u003e is the age and population size of the standard population; \u003cem\u003enMx\u003c/em\u003e is the age-specific mortality of the population to be standardized; \u003cem\u003en\u003c/em\u003e is the interval of each age group; \u003cem\u003ex\u003c/em\u003e is the starting age of each age group.\u003c/p\u003e \u003cp\u003eWe performed joinpoint regression analysis using Joinpoint Regression Program from the Surveillance Research Program of the National Cancer Institute Version 4.9.0.0 (Statistical Research and Applications Branch National Cancer Institute, USA). In this study, the model was used to fit the changing trend of NCD\u003csub\u003e4\u003c/sub\u003e mortality rate, and the SMR of gender, urban and rural areas, and regions (east, central and west) were compared within the group to determine whether the change trend curves of the standardized mortality rate were consistent or parallel. The Joinpoint regression model found the turning point with statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) by using the Monte Carlo substitution test. The trend change indicators were expressed as annual percent change (APC) and average annual percentage change (AAPC). APC\u0026thinsp;\u0026gt;\u0026thinsp;0 means that the rate has increased annually in a certain period time, and APC\u0026thinsp;\u0026lt;\u0026thinsp;0 means that the rate has decreased annually in a certain period. If there is no joinpoint, then APC\u0026thinsp;=\u0026thinsp;AAPC, which means the rate fluctuated during the total study period.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDeaths due to four non-communicable diseases in China from 2009 to 2020\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOver the 12-year study period from 2009 to 2020, a total of 13, 066, 976 deaths of four major NCDs were recorded in China. CVD were found to be the most common causes among those four major NCDs (7, 419, 141,56.76% of the total), followed by cancer (3, 749, 337, 28.69%), chronic respiratory diseases (1, 556, 869, 11.91%) and diabetes (344, 629, 2.64%). The number of deaths in males was were 30% higher than in females (7, 387, 752 vs. 5, 429, 415, M/F: 1.36), but the deaths in urban areas are almost the same as in rural areas (6, 463, 938 vs. 6, 606, 038, U/R: 0.97).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrend in mortality of four major NCD among different subcategories\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 2009 to 2020, the SMR of the NCD\u003csub\u003e4\u003c/sub\u003e, cancer, CVD and chronic respiratory diseases showed a downward trend. It is noteworthy that the SMR of diabetes increased from\u0026nbsp;11.10\u0026nbsp;per 100,000 in 2009 to\u0026nbsp;12.54\u0026nbsp;per 100,000 in 2020, which was an increase of 12.97% (Table 1). Joinpoint regression analysis indicated that the SMR decrease significantly in CVD, cancer and chronic respiratory diseases, with the AAPC of -2.2%, -1.8%, -8.0%, respectively, showing significant changes, with detectable joinpoint in DM (AAPC=1.9%, 95% CI: 0.4~3.5) (Table 2 and S2A). For diabetes, whereas a decreased trend from 2009 to 2011 (APC = 5.1%) was observed without significance (APC=-2.1%, 95% CI: -10.9~7.6), then an increasing trend from 2011 to 2020 (APC=2.8%, 95% CI: 2.0~3.7) (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Standardized mortality rates of NCD\u003csub\u003e4\u003c/sub\u003e and its subcategory in China (2009-2020)\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"638\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6.9659%;\" width=\"7.053291536050157%\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19.232%;\" width=\"17.86833855799373%\"\u003e\n \u003cp\u003eNCD\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 18.9292%;\" width=\"17.398119122257054%\"\u003e\n \u003cp\u003eCardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 18.6263%;\" width=\"17.398119122257054%\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16.0519%;\" width=\"15.047021943573668%\"\u003e\n \u003cp\u003eChronic respiratory diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14.3862%;\" width=\"15.203761755485893%\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.8432%;\" width=\"9.830508474576272%\"\u003e\n \u003cp\u003eCMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"9.322033898305085%\"\u003e\n \u003cp\u003eSMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"9.322033898305085%\"\u003e\n \u003cp\u003eCMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"9.322033898305085%\"\u003e\n \u003cp\u003eSMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"9.322033898305085%\"\u003e\n \u003cp\u003eCMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"9.322033898305085%\"\u003e\n \u003cp\u003eSMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8745%;\" width=\"7.966101694915254%\"\u003e\n \u003cp\u003eCMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"8.305084745762711%\"\u003e\n \u003cp\u003eSMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"8.305084745762711%\"\u003e\n \u003cp\u003eCMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2088%;\" width=\"8.135593220338983%\"\u003e\n \u003cp\u003eSMR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.9659%;\" width=\"7.086614173228346%\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8432%;\" width=\"9.133858267716535%\"\u003e\n \u003cp\u003e459.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e534.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e235.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e281.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e139.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e149.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8745%;\" width=\"7.4015748031496065%\"\u003e\n \u003cp\u003e74.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e92.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e10.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2088%;\" width=\"7.559055118110236%\"\u003e\n \u003cp\u003e11.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.9659%;\" width=\"7.086614173228346%\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8432%;\" width=\"9.133858267716535%\"\u003e\n \u003cp\u003e456.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e538.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e238.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e292.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e137.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e146.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8745%;\" width=\"7.4015748031496065%\"\u003e\n \u003cp\u003e70.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e91.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e10.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2088%;\" width=\"7.559055118110236%\"\u003e\n \u003cp\u003e8.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.9659%;\" width=\"7.086614173228346%\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8432%;\" width=\"9.133858267716535%\"\u003e\n \u003cp\u003e457.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e500.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e242.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e273.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e136.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e136.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8745%;\" width=\"7.4015748031496065%\"\u003e\n \u003cp\u003e67.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e79.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e10.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2088%;\" width=\"7.559055118110236%\"\u003e\n \u003cp\u003e10.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.9659%;\" width=\"7.086614173228346%\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8432%;\" width=\"9.133858267716535%\"\u003e\n \u003cp\u003e477.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e448.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e253.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e238.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e141.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e132.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8745%;\" width=\"7.4015748031496065%\"\u003e\n \u003cp\u003e71.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e67.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e10.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2088%;\" width=\"7.559055118110236%\"\u003e\n \u003cp\u003e9.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.9659%;\" width=\"7.086614173228346%\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8432%;\" width=\"9.133858267716535%\"\u003e\n \u003cp\u003e520.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e456.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e284.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e248.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e150.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e132.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8745%;\" width=\"7.4015748031496065%\"\u003e\n \u003cp\u003e73.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e64.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e11.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2088%;\" width=\"7.559055118110236%\"\u003e\n \u003cp\u003e10.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.9659%;\" width=\"7.086614173228346%\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8432%;\" width=\"9.133858267716535%\"\u003e\n \u003cp\u003e528.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e450.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e291.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e246.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e155.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e135.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8745%;\" width=\"7.4015748031496065%\"\u003e\n \u003cp\u003e68.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e57.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e12.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2088%;\" width=\"7.559055118110236%\"\u003e\n \u003cp\u003e10.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.9659%;\" width=\"7.086614173228346%\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8432%;\" width=\"9.133858267716535%\"\u003e\n \u003cp\u003e532.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e454.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e295.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e250.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e157.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e137.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8745%;\" width=\"7.4015748031496065%\"\u003e\n \u003cp\u003e66.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e55.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e13.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2088%;\" width=\"7.559055118110236%\"\u003e\n \u003cp\u003e11.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.9659%;\" width=\"7.086614173228346%\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8432%;\" width=\"9.133858267716535%\"\u003e\n \u003cp\u003e539.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e427.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e301.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e235.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e157.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e130.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8745%;\" width=\"7.4015748031496065%\"\u003e\n \u003cp\u003e65.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e50.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e14.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2088%;\" width=\"7.559055118110236%\"\u003e\n \u003cp\u003e11.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.9659%;\" width=\"7.086614173228346%\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8432%;\" width=\"9.133858267716535%\"\u003e\n \u003cp\u003e540.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e425.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e304.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e235.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e158.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e129.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8745%;\" width=\"7.4015748031496065%\"\u003e\n \u003cp\u003e63.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e47.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e15.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2088%;\" width=\"7.559055118110236%\"\u003e\n \u003cp\u003e12.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.9659%;\" width=\"7.086614173228346%\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8432%;\" width=\"9.133858267716535%\"\u003e\n \u003cp\u003e551.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e413.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e313.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e230.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e160.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e126.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8745%;\" width=\"7.4015748031496065%\"\u003e\n \u003cp\u003e62.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e44.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e15.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2088%;\" width=\"7.559055118110236%\"\u003e\n \u003cp\u003e12.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.9659%;\" width=\"7.086614173228346%\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8432%;\" width=\"9.133858267716535%\"\u003e\n \u003cp\u003e556.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e423.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e317.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e240.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e162.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e125.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8745%;\" width=\"7.4015748031496065%\"\u003e\n \u003cp\u003e59.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e44.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e16.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2088%;\" width=\"7.559055118110236%\"\u003e\n \u003cp\u003e12.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.9659%;\" width=\"7.086614173228346%\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8432%;\" width=\"9.133858267716535%\"\u003e\n \u003cp\u003e559.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e395.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e328.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3889%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e229.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e161.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2374%;\" width=\"8.661417322834646%\"\u003e\n \u003cp\u003e118.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8745%;\" width=\"7.4015748031496065%\"\u003e\n \u003cp\u003e51.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e35.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1774%;\" width=\"7.716535433070866%\"\u003e\n \u003cp\u003e17.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2088%;\" width=\"7.559055118110236%\"\u003e\n \u003cp\u003e12.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Standardized mortality rates of NCD\u003csub\u003e4\u003c/sub\u003e by gender, geography and region in China (2009-2020)\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"8.050089445438283%\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"20.39355992844365%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.862254025044723%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"22.54025044722719%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.862254025044723%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"19.677996422182467%\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.862254025044723%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"20.75134168157424%\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.306042884990253%\"\u003e\n \u003cp\u003eCMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"10.721247563352826%\"\u003e\n \u003cp\u003eSMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.1189083820662766%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.721247563352826%\"\u003e\n \u003cp\u003eCMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.840155945419104%\"\u003e\n \u003cp\u003eSMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.1189083820662766%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.721247563352826%\"\u003e\n \u003cp\u003eCMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.721247563352826%\"\u003e\n \u003cp\u003eSMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.1189083820662766%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.306042884990253%\"\u003e\n \u003cp\u003eCMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.306042884990253%\"\u003e\n \u003cp\u003eSMR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.064516129032258%\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e523.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.85663082437276%\"\u003e\n \u003cp\u003e654.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e392.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724014336917563%\"\u003e\n \u003cp\u003e432.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e451.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e447.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"10.394265232974911%\"\u003e\n \u003cp\u003e463.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e593.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.064516129032258%\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e522.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.85663082437276%\"\u003e\n \u003cp\u003e688.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e387.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724014336917563%\"\u003e\n \u003cp\u003e436.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e451.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e466.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"10.394265232974911%\"\u003e\n \u003cp\u003e469.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e596.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.064516129032258%\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e526.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.85663082437276%\"\u003e\n \u003cp\u003e631.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e386.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724014336917563%\"\u003e\n \u003cp\u003e390.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e405.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e464.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"10.394265232974911%\"\u003e\n \u003cp\u003e492.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e522.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.064516129032258%\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e546.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.85663082437276%\"\u003e\n \u003cp\u003e558.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e406.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724014336917563%\"\u003e\n \u003cp\u003e358.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e423.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e408.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"10.394265232974911%\"\u003e\n \u003cp\u003e512.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e478.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.064516129032258%\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e591.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.85663082437276%\"\u003e\n \u003cp\u003e565.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e446.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724014336917563%\"\u003e\n \u003cp\u003e355.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e503.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e425.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"10.394265232974911%\"\u003e\n \u003cp\u003e528.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e490.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.064516129032258%\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e600.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.85663082437276%\"\u003e\n \u003cp\u003e560.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e452.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724014336917563%\"\u003e\n \u003cp\u003e349.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e505.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e433.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"10.394265232974911%\"\u003e\n \u003cp\u003e538.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e473.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.064516129032258%\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e603.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.85663082437276%\"\u003e\n \u003cp\u003e559.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e459.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724014336917563%\"\u003e\n \u003cp\u003e356.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e512.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e435.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"10.394265232974911%\"\u003e\n \u003cp\u003e542.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e476.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.064516129032258%\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e608.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.85663082437276%\"\u003e\n \u003cp\u003e529.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e468.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724014336917563%\"\u003e\n \u003cp\u003e332.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e506.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e421.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"10.394265232974911%\"\u003e\n \u003cp\u003e556.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e453.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.064516129032258%\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e611.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.85663082437276%\"\u003e\n \u003cp\u003e528.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e467.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724014336917563%\"\u003e\n \u003cp\u003e330.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e506.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e418.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"10.394265232974911%\"\u003e\n \u003cp\u003e557.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e452.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.064516129032258%\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e623.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.85663082437276%\"\u003e\n \u003cp\u003e514.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e477.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724014336917563%\"\u003e\n \u003cp\u003e318.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e517.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e408.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"10.394265232974911%\"\u003e\n \u003cp\u003e569.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e444.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.064516129032258%\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e630.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.85663082437276%\"\u003e\n \u003cp\u003e524.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e479.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724014336917563%\"\u003e\n \u003cp\u003e329.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e519.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e405.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"10.394265232974911%\"\u003e\n \u003cp\u003e574.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e463.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.064516129032258%\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e633.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.85663082437276%\"\u003e\n \u003cp\u003e497.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e484.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724014336917563%\"\u003e\n \u003cp\u003e303.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e524.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e386.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.867383512544803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"10.394265232974911%\"\u003e\n \u003cp\u003e578.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394265232974911%\"\u003e\n \u003cp\u003e443.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"8.064516129032258%\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" width=\"23.29749103942652%\"\u003e\n \u003cp\u003eEast region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"25.268817204301076%\"\u003e\n \u003cp\u003eCentral region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.85663082437276%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" width=\"23.655913978494624%\"\u003e\n \u003cp\u003eWest region\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" width=\"12.256809338521402%\"\u003e\n \u003cp\u003eCMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"13.035019455252918%\"\u003e\n \u003cp\u003eSMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.700389105058365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813229571984436%\"\u003e\n \u003cp\u003eCMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"13.813229571984436%\"\u003e\n \u003cp\u003eSMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.700389105058365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.062256809338521%\"\u003e\n \u003cp\u003eCMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"13.618677042801556%\"\u003e\n \u003cp\u003eSMR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.050089445438283%\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.270125223613595%\"\u003e\n \u003cp\u003e473.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.985688729874777%\"\u003e\n \u003cp\u003e488.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\"\u003e\n \u003cp\u003e471.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.701252236135957%\"\u003e\n \u003cp\u003e581.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.091234347048301%\"\u003e\n \u003cp\u003e423.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.522361359570661%\"\u003e\n \u003cp\u003e567.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.050089445438283%\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.270125223613595%\"\u003e\n \u003cp\u003e460.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.985688729874777%\"\u003e\n \u003cp\u003e498.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\"\u003e\n \u003cp\u003e478.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.701252236135957%\"\u003e\n \u003cp\u003e586.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.091234347048301%\"\u003e\n \u003cp\u003e422.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.522361359570661%\"\u003e\n \u003cp\u003e586.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.050089445438283%\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.270125223613595%\"\u003e\n \u003cp\u003e456.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.985688729874777%\"\u003e\n \u003cp\u003e463.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\"\u003e\n \u003cp\u003e475.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.701252236135957%\"\u003e\n \u003cp\u003e555.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.091234347048301%\"\u003e\n \u003cp\u003e435.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.522361359570661%\"\u003e\n \u003cp\u003e561.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.050089445438283%\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.270125223613595%\"\u003e\n \u003cp\u003e475.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.985688729874777%\"\u003e\n \u003cp\u003e421.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\"\u003e\n \u003cp\u003e493.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.701252236135957%\"\u003e\n \u003cp\u003e502.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.091234347048301%\"\u003e\n \u003cp\u003e459.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.522361359570661%\"\u003e\n \u003cp\u003e505.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.050089445438283%\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.270125223613595%\"\u003e\n \u003cp\u003e553.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.985688729874777%\"\u003e\n \u003cp\u003e442.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\"\u003e\n \u003cp\u003e502.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.701252236135957%\"\u003e\n \u003cp\u003e503.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.091234347048301%\"\u003e\n \u003cp\u003e496.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.522361359570661%\"\u003e\n \u003cp\u003e498.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.050089445438283%\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.270125223613595%\"\u003e\n \u003cp\u003e542.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.985688729874777%\"\u003e\n \u003cp\u003e427.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\"\u003e\n \u003cp\u003e521.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.701252236135957%\"\u003e\n \u003cp\u003e498.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.091234347048301%\"\u003e\n \u003cp\u003e514.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.522361359570661%\"\u003e\n \u003cp\u003e499.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.050089445438283%\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.270125223613595%\"\u003e\n \u003cp\u003e559.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.985688729874777%\"\u003e\n \u003cp\u003e436.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\"\u003e\n \u003cp\u003e525.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.701252236135957%\"\u003e\n \u003cp\u003e498.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.091234347048301%\"\u003e\n \u003cp\u003e503.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.522361359570661%\"\u003e\n \u003cp\u003e492.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.050089445438283%\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.270125223613595%\"\u003e\n \u003cp\u003e560.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.985688729874777%\"\u003e\n \u003cp\u003e416.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\"\u003e\n \u003cp\u003e535.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.701252236135957%\"\u003e\n \u003cp\u003e474.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.091234347048301%\"\u003e\n \u003cp\u003e515.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.522361359570661%\"\u003e\n \u003cp\u003e475.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.050089445438283%\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.270125223613595%\"\u003e\n \u003cp\u003e551.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.985688729874777%\"\u003e\n \u003cp\u003e408.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\"\u003e\n \u003cp\u003e541.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.701252236135957%\"\u003e\n \u003cp\u003e472.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.091234347048301%\"\u003e\n \u003cp\u003e521.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.522361359570661%\"\u003e\n \u003cp\u003e490.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.050089445438283%\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.270125223613595%\"\u003e\n \u003cp\u003e564.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.985688729874777%\"\u003e\n \u003cp\u003e400.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\"\u003e\n \u003cp\u003e551.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.701252236135957%\"\u003e\n \u003cp\u003e460.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.091234347048301%\"\u003e\n \u003cp\u003e531.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.522361359570661%\"\u003e\n \u003cp\u003e483.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.050089445438283%\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.270125223613595%\"\u003e\n \u003cp\u003e558.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.985688729874777%\"\u003e\n \u003cp\u003e407.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\"\u003e\n \u003cp\u003e566.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.701252236135957%\"\u003e\n \u003cp\u003e477.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.091234347048301%\"\u003e\n \u003cp\u003e538.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.522361359570661%\"\u003e\n \u003cp\u003e490.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.050089445438283%\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.270125223613595%\"\u003e\n \u003cp\u003e563.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.985688729874777%\"\u003e\n \u003cp\u003e389.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\"\u003e\n \u003cp\u003e571.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.701252236135957%\"\u003e\n \u003cp\u003e456.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.838998211091234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.091234347048301%\"\u003e\n \u003cp\u003e539.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.522361359570661%\"\u003e\n \u003cp\u003e468.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTrend in mortality of four major NCD with different genders\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 2009 to 2020, the SMR in both males and females showed a downtrend annually, decreasing from\u0026nbsp;654.76 per 100,000 people to 497.50 per 100,000 people, and from 432.19 per 100,000 people to 303.36 per 100,000 people, respectively. The declines were slightly greater in males than females (Table 2 and S2B). While a significant decrease in the SMR was observed in both genders during the study period (AAPC -2.8% for males and -3.2% for females, respectively), with two trends. During 2009- 2012, the SMR in both males and females significantly decreased with APC of -5.5% and -6.3% from 2012 to 2020, remarkably decreased trends of SMR of four major NCD\u003csub\u003e4\u003c/sub\u003e in both males (APC=-6.3%, 95%CI: -10.7~-1.8) and females (APC=-2.0%, 95% CI: -3.1~-0.9) were observed (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Joinpoint analysis of trends in standardized mortality rates of NCD\u003csub\u003e4\u0026nbsp;\u003c/sub\u003ein China (2009-2020)\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"741\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 24.2329%;\" width=\"26.450742240215924%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14.8917%;\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003eTotal Study Period\u003c/p\u003e\n \u003cp\u003eAAPC(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 39.3953%;\" valign=\"top\" width=\"25.641025641025642%\"\u003e\n \u003cp\u003eTrend 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 18.2762%;\" valign=\"top\" width=\"29.959514170040485%\"\u003e\n \u003cp\u003eTrend 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.2455%;\" valign=\"top\" width=\"18.457943925233646%\"\u003e\n \u003cp\u003eyears\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1498%;\" valign=\"top\" width=\"25.934579439252335%\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.935%;\" valign=\"top\" width=\"17.523364485981308%\"\u003e\n \u003cp\u003eyears\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3412%;\" valign=\"top\" width=\"34.345794392523366%\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.2329%;\" valign=\"top\" width=\"26.450742240215924%\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8917%;\" valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e-2.8\u003csup\u003e*\u003c/sup\u003e(-3.7, -1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2455%;\" valign=\"top\" width=\"10.661268556005398%\"\u003e\n \u003cp\u003e2009-2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1498%;\" valign=\"top\" width=\"14.979757085020243%\"\u003e\n \u003cp\u003e-5.4\u003csup\u003e*\u003c/sup\u003e(-8.7, -2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.935%;\" valign=\"top\" width=\"10.121457489878543%\"\u003e\n \u003cp\u003e2012-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3412%;\" valign=\"top\" width=\"19.838056680161944%\"\u003e\n \u003cp\u003e-1.8\u003csup\u003e*\u003c/sup\u003e(-2.6, -0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.2329%;\" valign=\"top\" width=\"26.450742240215924%\"\u003e\n \u003cp\u003eSubcategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8917%;\" valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2455%;\" valign=\"top\" width=\"10.661268556005398%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1498%;\" valign=\"top\" width=\"14.979757085020243%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.935%;\" valign=\"top\" width=\"10.121457489878543%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3412%;\" valign=\"top\" width=\"19.838056680161944%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.2329%;\" valign=\"top\" width=\"26.450742240215924%\"\u003e\n \u003cp\u003eCardiovascular diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8917%;\" valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e-2.2\u003csup\u003e*\u003c/sup\u003e(-3.3, -1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2455%;\" valign=\"top\" width=\"10.661268556005398%\"\u003e\n \u003cp\u003e2009-2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1498%;\" valign=\"top\" width=\"14.979757085020243%\"\u003e\n \u003cp\u003e-5.2\u003csup\u003e*\u003c/sup\u003e(-9.0, -1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.935%;\" valign=\"top\" width=\"10.121457489878543%\"\u003e\n \u003cp\u003e2012-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3412%;\" valign=\"top\" width=\"19.838056680161944%\"\u003e\n \u003cp\u003e-1.0\u003csup\u003e*\u003c/sup\u003e(-2.0, -1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.2329%;\" valign=\"top\" width=\"26.450742240215924%\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8917%;\" valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e-1.8\u003csup\u003e*\u003c/sup\u003e(-3.1, -0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2455%;\" valign=\"top\" width=\"10.661268556005398%\"\u003e\n \u003cp\u003e2009-2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1498%;\" valign=\"top\" width=\"14.979757085020243%\"\u003e\n \u003cp\u003e-4.1(-11.7, 4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.935%;\" valign=\"top\" width=\"10.121457489878543%\"\u003e\n \u003cp\u003e2011-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3412%;\" valign=\"top\" width=\"19.838056680161944%\"\u003e\n \u003cp\u003e-1.3\u003csup\u003e*\u003c/sup\u003e(-2.1, -0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.2329%;\" valign=\"top\" width=\"26.450742240215924%\"\u003e\n \u003cp\u003eChronic respiratory diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8917%;\" valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e-8.0\u003csup\u003e*\u003c/sup\u003e(-9.8, -6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2455%;\" valign=\"top\" width=\"10.661268556005398%\"\u003e\n \u003cp\u003e2009-2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1498%;\" valign=\"top\" width=\"14.979757085020243%\"\u003e\n \u003cp\u003e-9.8\u003csup\u003e*\u003c/sup\u003e(-14.0, -5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.935%;\" valign=\"top\" width=\"10.121457489878543%\"\u003e\n \u003cp\u003e2013-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3412%;\" valign=\"top\" width=\"19.838056680161944%\"\u003e\n \u003cp\u003e-7.0\u003csup\u003e*\u003c/sup\u003e(-9.3, -4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.2329%;\" valign=\"top\" width=\"26.450742240215924%\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8917%;\" valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e1.9\u003csup\u003e*\u003c/sup\u003e(0.4, 3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2455%;\" valign=\"top\" width=\"10.661268556005398%\"\u003e\n \u003cp\u003e2009-2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1498%;\" valign=\"top\" width=\"14.979757085020243%\"\u003e\n \u003cp\u003e-2.1(-10.9, 7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.935%;\" valign=\"top\" width=\"10.121457489878543%\"\u003e\n \u003cp\u003e2011-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3412%;\" valign=\"top\" width=\"19.838056680161944%\"\u003e\n \u003cp\u003e2.8\u003csup\u003e*\u003c/sup\u003e(2.0, 3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.2329%;\" valign=\"top\" width=\"26.450742240215924%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8917%;\" valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2455%;\" valign=\"top\" width=\"10.661268556005398%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1498%;\" valign=\"top\" width=\"14.979757085020243%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.935%;\" valign=\"top\" width=\"10.121457489878543%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3412%;\" valign=\"top\" width=\"19.838056680161944%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.2329%;\" valign=\"top\" width=\"26.450742240215924%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8917%;\" valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e-2.8\u003csup\u003e*\u003c/sup\u003e(-3.9, -1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2455%;\" valign=\"top\" width=\"10.661268556005398%\"\u003e\n \u003cp\u003e2009-2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1498%;\" valign=\"top\" width=\"14.979757085020243%\"\u003e\n \u003cp\u003e-5.5\u003csup\u003e*\u003c/sup\u003e(-9.5, -1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.935%;\" valign=\"top\" width=\"10.121457489878543%\"\u003e\n \u003cp\u003e2012-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3412%;\" valign=\"top\" width=\"19.838056680161944%\"\u003e\n \u003cp\u003e-1.8\u003csup\u003e*\u003c/sup\u003e(-2.8, -0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.2329%;\" valign=\"top\" width=\"26.450742240215924%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8917%;\" valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e-3.2\u003csup\u003e*\u003c/sup\u003e(-4.4, -2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2455%;\" valign=\"top\" width=\"10.661268556005398%\"\u003e\n \u003cp\u003e2009-2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1498%;\" valign=\"top\" width=\"14.979757085020243%\"\u003e\n \u003cp\u003e-6.3\u003csup\u003e*\u003c/sup\u003e(-10.7, -1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.935%;\" valign=\"top\" width=\"10.121457489878543%\"\u003e\n \u003cp\u003e2012-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3412%;\" valign=\"top\" width=\"19.838056680161944%\"\u003e\n \u003cp\u003e-2.0\u003csup\u003e*\u003c/sup\u003e(-3.1, -0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.2329%;\" valign=\"top\" width=\"26.450742240215924%\"\u003e\n \u003cp\u003eGeographic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8917%;\" valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2455%;\" valign=\"top\" width=\"10.661268556005398%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1498%;\" valign=\"top\" width=\"14.979757085020243%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.935%;\" valign=\"top\" width=\"10.121457489878543%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3412%;\" valign=\"top\" width=\"19.838056680161944%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.2329%;\" valign=\"top\" width=\"26.450742240215924%\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8917%;\" valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e-1.4(-3.5, 0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2455%;\" valign=\"top\" width=\"10.661268556005398%\"\u003e\n \u003cp\u003e2009-2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1498%;\" valign=\"top\" width=\"14.979757085020243%\"\u003e\n \u003cp\u003e-1.1(-2.2, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.935%;\" valign=\"top\" width=\"10.121457489878543%\"\u003e\n \u003cp\u003e2018-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3412%;\" valign=\"top\" width=\"19.838056680161944%\"\u003e\n \u003cp\u003e-2.9(-14.8, 10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.2329%;\" valign=\"top\" width=\"26.450742240215924%\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8917%;\" valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e-2.9\u003csup\u003e*\u003c/sup\u003e(-4.1, -1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2455%;\" valign=\"top\" width=\"10.661268556005398%\"\u003e\n \u003cp\u003e2009-2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1498%;\" valign=\"top\" width=\"14.979757085020243%\"\u003e\n \u003cp\u003e-7.3\u003csup\u003e*\u003c/sup\u003e(-11.6, -2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.935%;\" valign=\"top\" width=\"10.121457489878543%\"\u003e\n \u003cp\u003e2012-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3412%;\" valign=\"top\" width=\"19.838056680161944%\"\u003e\n \u003cp\u003e-1.2\u003csup\u003e*\u003c/sup\u003e(-0.1, -2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.2329%;\" valign=\"top\" width=\"26.450742240215924%\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8917%;\" valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2455%;\" valign=\"top\" width=\"10.661268556005398%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1498%;\" valign=\"top\" width=\"14.979757085020243%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.935%;\" valign=\"top\" width=\"10.121457489878543%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3412%;\" valign=\"top\" width=\"19.838056680161944%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.2329%;\" valign=\"top\" width=\"26.450742240215924%\"\u003e\n \u003cp\u003eEast region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8917%;\" valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e-2.2\u003csup\u003e*\u003c/sup\u003e(-3.0, -1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2455%;\" valign=\"top\" width=\"10.661268556005398%\"\u003e\n \u003cp\u003e2009-2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1498%;\" valign=\"top\" width=\"14.979757085020243%\"\u003e\n \u003cp\u003e-4.2\u003csup\u003e*\u003c/sup\u003e(-7.3, -0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.935%;\" valign=\"top\" width=\"10.121457489878543%\"\u003e\n \u003cp\u003e2012-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3412%;\" valign=\"top\" width=\"19.838056680161944%\"\u003e\n \u003cp\u003e-1.4\u003csup\u003e*\u003c/sup\u003e(-2.2, -0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.2329%;\" valign=\"top\" width=\"26.450742240215924%\"\u003e\n \u003cp\u003eCentral region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8917%;\" valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e-2.4\u003csup\u003e*\u003c/sup\u003e(-3.2, -1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2455%;\" valign=\"top\" width=\"10.661268556005398%\"\u003e\n \u003cp\u003e2009-2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1498%;\" valign=\"top\" width=\"14.979757085020243%\"\u003e\n \u003cp\u003e-5.0\u003csup\u003e*\u003c/sup\u003e(-7.8, -2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.935%;\" valign=\"top\" width=\"10.121457489878543%\"\u003e\n \u003cp\u003e2012-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3412%;\" valign=\"top\" width=\"19.838056680161944%\"\u003e\n \u003cp\u003e-1.4\u003csup\u003e*\u003c/sup\u003e(-2.1, -0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.2329%;\" valign=\"top\" width=\"26.450742240215924%\"\u003e\n \u003cp\u003eWest region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8917%;\" valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e-1.9\u003csup\u003e*\u003c/sup\u003e(-3.1, -0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2455%;\" valign=\"top\" width=\"10.661268556005398%\"\u003e\n \u003cp\u003e2009-2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1498%;\" valign=\"top\" width=\"14.979757085020243%\"\u003e\n \u003cp\u003e-4.0\u003csup\u003e*\u003c/sup\u003e(-7.1, -0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.935%;\" valign=\"top\" width=\"10.121457489878543%\"\u003e\n \u003cp\u003e2013-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3412%;\" valign=\"top\" width=\"19.838056680161944%\"\u003e\n \u003cp\u003e-0.7(-2.1, 0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e Indicates that the AAPC is significantly different from zero at the alpha = 0.05 level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrend in mortality of four major NCD among different geographics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA downtrend in SMR in both urban and rural areas was observed. The SMR decreased by 13.59% from\u0026nbsp;447.14\u0026nbsp;to\u0026nbsp;386.36\u0026nbsp;per 100,000 people in urban areas and decreased by 25.24% from 593.45 to\u0026nbsp;443.62\u0026nbsp;per 100,000 people in rural areas (Table 2). Joinpoint analysis showed an insignificantly decreasing trend of SMR in urban areas (AAPC=-1.4%, 95%CI:\u0026nbsp;-3.5~0.7). As in rural areas, it decreased significantly with the AAPC of -2.9% (95% CI: -4.1~-1.7 ) (Table 3 and S2C).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrend in mortality of four major NCD among different regions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSMR of NCD\u003csub\u003e4\u003c/sub\u003e in the east, central and west regions showed a declining trend, from 2009 to 2020, decreasing from\u0026nbsp;488.59 per 100,000 people to 389.72 per 100,000 people, from 581.96 per 100,000 people to\u0026nbsp;456.82 per 100,000 people and from 567.45 per 100,000 people to 468.14 per 100,000 people, respectively\u0026nbsp;(Table 2),\u0026nbsp;and the decline rate of SMR in the central region (APC=-2.2, 95% CI: -3.0~-1.3) was significantly faster than that in the east (APC=-2.4, 95% CI: -3.2~-1.6) and west regions (APC=-1.9, 95% CI: -3.1~-0.7) (Table 2 and S2D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrend in mortality of Cancer, Diabetes, Cardiovascular disease and Chronic respiratory disease among different subgroups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe same cause-specific patterns were observed by gender, geography and region: the highest rates were for CVD followed by cancer, chronic respiratory diseases and diabetes. A downtrend in SMR in both males and females was observed. The SMR was higher in males compared to females for each NCD\u003csub\u003e4\u003c/sub\u003e condition, the greatest difference in SMR between males and females was for CVD (Table 4 and S3A). Joinpoint analysis showed no significant decrease in SMR for diabetes(AAPC=0.7%, 95%CI:\u0026nbsp;-1.1~2.6) and cardiovascular disease (AAPC=-1.3%, 95%CI:\u0026nbsp;-3.9~1.4), there was a significant downward trend in rural areas for each NCD\u003csub\u003e4\u003c/sub\u003e condition (Table 3). In the east, central and west regions, the SMR of chronic respiratory diseases appeared a significant downward trend (AAPC -8.6% for the east region, -8.7% for the central region and -6.9% for the west region, respectively), and it was the highest and the fastest in the west region (Table 4 and S3C).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Joinpoint analysis of trends in subgroup standardized mortality rates for Cancer, Diabetes, Cardiovascular disease and Chronic respiratory disease in China (2009-2020)\u003c/p\u003e\n \u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"718\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 22.2524%;\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 15.8744%;\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003eTotal Study Period\u003c/p\u003e\n \u003cp\u003eAAPC(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 40.3946%;\" valign=\"top\" width=\"27.99442896935933%\"\u003e\n \u003cp\u003eTrend 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19.9847%;\" valign=\"top\" width=\"27.01949860724234%\"\u003e\n \u003cp\u003eTrend 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"18.978102189781023%\"\u003e\n \u003cp\u003eyears\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"29.927007299270073%\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"18.491484184914842%\"\u003e\n \u003cp\u003eyears\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"28.710462287104622%\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-1.9\u003csup\u003e*\u003c/sup\u003e(-2.8, -1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-3.1(-6.3, 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2012-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e-1.4\u003csup\u003e*\u003c/sup\u003e(-2.2, -0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-5.6(-13.6, 3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-12.4\u003csup\u003e*\u003c/sup\u003e(-22.6, -0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2015-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e3.3(-13.8, 23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-2.6\u003csup\u003e*\u003c/sup\u003e(-5.9, -0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-5.3(-11.2, 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2013-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e-1.0\u003csup\u003e*\u003c/sup\u003e(-3.8, 1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eChronic respiratory diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-5.2\u003csup\u003e*\u003c/sup\u003e(-9.4, -0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-4.5\u003csup\u003e*\u003c/sup\u003e(-6.6, -2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2018-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e-8.1(-31.0, 22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-1.7\u003csup\u003e*\u003c/sup\u003e(-3.2, -0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-4.8(-13.3, 4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2011-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e-1.0\u003csup\u003e*\u003c/sup\u003e(-1.9, -0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-7.1\u003csup\u003e*\u003c/sup\u003e(-13.2, -0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-14.3\u003csup\u003e*\u003c/sup\u003e(-24.1, -3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2014-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e-0.6(-11.1, 11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-2.2\u003csup\u003e*\u003c/sup\u003e(-3.6, -0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-5.6\u003csup\u003e*\u003c/sup\u003e(-10.4, -0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2012-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e-1.0(-2.2, 0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eChronic respiratory diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-8.9\u003csup\u003e*\u003c/sup\u003e(-10.6, -7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-9.9\u003csup\u003e*\u003c/sup\u003e(-13.8, -5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2013-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e-8.3\u003csup\u003e*\u003c/sup\u003e(-10.6, -6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-1.7\u003csup\u003e*\u003c/sup\u003e(-3.3, -0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-0.5(-1.7, 0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2017-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e-5.0(-10.6,1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e0.7(-1.1, 2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-2.1(-8.8, 5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2012-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e1.8\u003csup\u003e*\u003c/sup\u003e(0.2, 3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-1.3(-3.9, 1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-0.8(-2.9, 1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2017-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e-2.5(-12.1, 8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eChronic respiratory diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-7.2\u003csup\u003e*\u003c/sup\u003e(-10.7, -3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-5.7\u003csup\u003e*\u003c/sup\u003e(-7.3, -4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2018-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e-13.8(-32.3,9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-1.9\u003csup\u003e*\u003c/sup\u003e(-2.7, -1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-3.8\u003csup\u003e*\u003c/sup\u003e(-6.8,-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2012-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e-1.2\u003csup\u003e*\u003c/sup\u003e(-1.9, -0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e2.0\u003csup\u003e*\u003c/sup\u003e(1.0, 2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-2.2(-5.7, 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2012-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e3.5\u003csup\u003e*\u003c/sup\u003e(2.8, 4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-2.8\u003csup\u003e*\u003c/sup\u003e(-4.3, -1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-6.2\u003csup\u003e*\u003c/sup\u003e(-9.9, -2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2013-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e-0.8(-2.6, 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eChronic respiratory diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-8.8\u003csup\u003e*\u003c/sup\u003e(-11.8, -5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-17.5\u003csup\u003e*\u003c/sup\u003e(-26.7, -7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2012-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e-5.3\u003csup\u003e*\u003c/sup\u003e(-8.5,-2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eEast region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-1.4(-3.2, 0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-0.6(-2.5, 1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2016-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e-2.9(-7.7, 2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e0.2(-1.3, 1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e0.2(-1.3, 1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2012-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e1.5\u003csup\u003e*\u003c/sup\u003e(0.2, 2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-2.4\u003csup\u003e*\u003c/sup\u003e(-3.7, -1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-5.3\u003csup\u003e*\u003c/sup\u003e(-9.9, -0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2012-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e-1.2\u003csup\u003e*\u003c/sup\u003e(-2.4, -0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eChronic respiratory diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-8.6\u003csup\u003e*\u003c/sup\u003e(-11.8, -28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-8.3\u003csup\u003e*\u003c/sup\u003e(-9.7, -6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2018-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e-10.0(-28.0, 12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eCentral region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-2.4\u003csup\u003e*\u003c/sup\u003e(-3.2, -1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-4.4\u003csup\u003e*\u003c/sup\u003e(-6.3, -2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2013-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e-1.3\u003csup\u003e*\u003c/sup\u003e(-2.2, -0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e0.5(-0.6, 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-5.9\u003csup\u003e*\u003c/sup\u003e(-9.6, -2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2012-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e3.0\u003csup\u003e*\u003c/sup\u003e(2.1, 7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-2.5\u003csup\u003e*\u003c/sup\u003e(-3.6, -1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-5.3\u003csup\u003e*\u003c/sup\u003e(-9.2, -1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2012-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e-1.4\u003csup\u003e*\u003c/sup\u003e(-2.4, -0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eChronic respiratory diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-8.7\u003csup\u003e*\u003c/sup\u003e(-10.5, -6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-11.9\u003csup\u003e*\u003c/sup\u003e(-15.9, -7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2013-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e-6.8\u003csup\u003e*\u003c/sup\u003e(-9.3, -4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eWest region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-1.4(-2.9, 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-0.8(-1.6, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2018-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e-4.1(-12.8, 5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e4.1\u003csup\u003e*\u003c/sup\u003e(1.6, 6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e4.5\u003csup\u003e*\u003c/sup\u003e(2.9, 6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2018-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e2.3(-11.9, 18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-1.2(-3.0, 0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-3.9(-8.3, 0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2013-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e0.3(-1.7, 2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.2524%;\" valign=\"top\" width=\"26.462395543175486%\"\u003e\n \u003cp\u003eChronic respiratory diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8744%;\" valign=\"top\" width=\"16.295264623955433%\"\u003e\n \u003cp\u003e-6.9\u003csup\u003e*\u003c/sup\u003e(-9.1, -4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5909%;\" valign=\"top\" width=\"10.86350974930362%\"\u003e\n \u003cp\u003e2009-2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.8037%;\" valign=\"top\" width=\"17.13091922005571%\"\u003e\n \u003cp\u003e-9.1\u003csup\u003e*\u003c/sup\u003e(-12.7, -5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.3859%;\" valign=\"top\" width=\"10.584958217270195%\"\u003e\n \u003cp\u003e2014-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.457%;\" valign=\"top\" width=\"16.434540389972145%\"\u003e\n \u003cp\u003e-5.1\u003csup\u003e*\u003c/sup\u003e(-8.7, -1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study confirmed a significant downward trend on standard mortality rates from four major NCD combined from 2009 to 2020 in China. The greatest declines in standard mortality rates were for chronic respiratory diseases (AAPC=-8.0%), followed by CVD (AAPC=-2.2%) and cancer (AAPC=-1.8%). This finding was in line with the global trend (the premature mortality rate decreased from 22.9% in 2000 to 17.8% in 2019). Nevertheless, due to population growth and increased longevity, the total number of deaths attributable to NCD has risen, NCD continued to be the leading cause of ill health worldwide and was responsible for seven of 10 premature deaths in 2019.\u003c/p\u003e \u003cp\u003eInconsistent with data that cancer is the leading cause of death in high-income countries [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], our study indicated that cardiovascular disease is a major cause of NCD. Previous research reports that ischemic heart disease (IHD), hemorrhagic stroke (HS) and ischemic stroke (IS) were the leading three causes of CVD death [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Multiple factors can contribute occurrence and development of CVD, of which high systolic blood pressure and dietary risk are considered important risk factors. Smoking control was reported as an efficient way to the reduction of early cardiovascular death in males, whilst obesity control can be critical to reducing early cardiovascular death in females [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The control of hypertension has a great impact on the reduction of premature cardiovascular death in the world.\u003c/p\u003e \u003cp\u003eFor cancer, Joinpoint analysis showed a significant decrease in the standardized mortality rate. But in 2020, there were 19.29\u0026nbsp;million new cancer cases in the world, among which 4.57\u0026nbsp;million were new cancer cases in China, accounting for 23.7% of the world's new cancer cases, and the number of new cancer cases far exceeded that in other countries [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In the above-mentioned report, the top ten causes of death for Chinese include lung cancer, liver cancer, stomach cancer, esophageal cancer and other cancers. The mortality rate of lung cancer ranks first, mainly due to tobacco smoking behavior, air pollution and other environmental factors including decoration and cooking oil fume.\u003c/p\u003e \u003cp\u003eIn general, chronic respiratory diseases mainly include tuberculosis, diffuse pulmonary fibrosis, chronic obstructive pulmonary disease (COPD), bronchiectasis, and bronchial asthma. In low and low-middle Socio-demographic Index (SDI) areas, particulate matter pollution was the main risk factor leading to death from CRDs, while smoking was ranked first among the major risk factors in areas with middle, middle-high, or high SDI [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Previous studies have shown that the prevalence rate of COPD in the Chinese population over the age of 20 years is 8.6%, about 99.9\u0026nbsp;million patients, but only 2.6% of them are aware of the disease. For COPD [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], the most effective preventable risk factors are smoking and air pollution. However, both of these two factors have not been overnight cold. The health crisis caused by air pollution and the still high smoking rate pose considerable challenge to the prevention of chronic obstructive pulmonary disease (COPD).\u003c/p\u003e \u003cp\u003eHowever, deaths due to diabetes increased slightly (AAPC\u0026thinsp;=\u0026thinsp;1.9%) in the same period, which is consistent with the global trend. It is alarming that diabetes causes an increase in premature mortality. Globally, the death toll from diabetes increased by 70.00% from 2000 to 2019 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Previous studies have found that the mortality due to diabetes in urban areas has been higher than in rural areas for 15 years but the gap between the two gradually narrowed. In addition, the standardized mortality rate of females with diabetes was higher than that of males. Finally, the eastern region had the highest mortality of diabetes, but the western region's mortality grew faster and eventually became the highest in 2020 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The obesity epidemic caused by changes in people\u0026rsquo;s lifestyle and diet is considered to be an important reason for the aggravation of the diabetes epidemic in China, and it has been proven to be one of the critical factors leading to the onset of diabetes. Over the past few decades, China\u0026rsquo;s rapid social and economic development has led to changes in people\u0026rsquo;s lifestyles, reduced exercise time, longer sitting time, poor dietary structure, and increased life and work pressure, which has led to an increasing prevalence of obesity. Based on Chinese criteria, were 34.3% for overweight and 16.4% for obesity in adults (\u0026ge;\u0026thinsp;18 years) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, there have been persistent disparities in NCD\u003csub\u003e4\u003c/sub\u003e mortality rates across genders, geographics, and regions in China. This is consistent with previously observed trends which have shown that males have had a persistently higher mortality rate for many chronic conditions combined compared to females [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This disparity is partially driven by genetic programming [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], sex hormones and difference in the response to medicines [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], in addition, the rates of obesity, smoking and alcohol consumption in males were higher than those in females [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, efforts should be devoted to diminishing geospatial clustering between rural and urban areas, including the improvement of education and economic income levels, the equalization of medical services, and the universalization of health insurance. At the same time, urban morphology is planned and designed with a focus on spatial distribution, land use and transportation, as they could increase or inhibit chronic non-communicable diseases through their effects on air pollution, physical activity and obesity [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Due to China's vast territory, the differences in climate [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and geographical environment have created differences in lifestyles such as diet and physical activity, and also gradually widened the gap in the health level of residents in different areas. Population aging, immutable genetic factors and urbanization [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] have contributed to this spatial pattern, but it has been recognized that there are also modifiable and unhealthy lifestyle choices such as poor dietary quality and irregular physical activity.\u003c/p\u003e \u003cp\u003eStrategies to prevent and manage NCD should integrate the influence of genetic, lifestyle, and environmental factors, focusing on modifiable adverse lifestyle habits such as unhealthy diet, lack of physical activity, obesity, tobacco smoking and alcohol drinking [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The latest national prevalence estimates 34.3% for overweight and 16.40% for obesity in adults (\u0026ge;\u0026thinsp;18 years) for 2015\u0026thinsp;~\u0026thinsp;2019. Obesity has become not only a chronic disease but also a means of treating other chronic diseases in its own right. The effects of dietary imbalance and physical inactivity interact with risk factors such as genetic susceptibility, psychosocial factors, and exposure early in life [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A large body of evidence suggests that physical activity can reliably prevent and treat obesity and cardiovascular disease. As we all know, a balanced diet is as important as physical exercise, therefore, it is urgent to give scientific and effective nursing interventions and public health measures based on evidence. The healthy diet intervention carried out by Maxwell [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and others in Britain aims to enable young people to form the habit of eating vegetables. Findings showed a significant increase in 50 children aged 7\u0026thinsp;~\u0026thinsp;12 years propensity toward initially disliked vegetables to which they were repeatedly exposed but not to vegetables to which they did not receive repeated exposure. It is necessary to pay attention to environmental risk and protective factors, and the relationship between modifiable environmental factors and NCD. Some scholars suggest that the government should investigate the cost-effectiveness of health-related economic impacts, and shape a healthier city by improving land use measures and building multifunctional and high-quality transportation services and infrastructure (the research results show that walkable parks are related to diabetes with low interest rates) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is widely acknowledged that improving early detection mechanism and standardizing early treatment by establishing public health screening and early diagnosis. The incidence of lung cancer declined at an AAPC of -2.0% per year between 2006 and 2015 and at a slightly higher rate of AAPC (-2.2%) between 2011 and 2015, a drop largely attributable to screening with low-dose spiral CT. Moreover, while carrying out regular physical examination of urban workers and cadres, we should gradually spread the knowledge and services of disease screening to rural areas to raise the awareness of people's health screening throughout the country. And introduce foreign advanced screening technology, and at the same time increase national financial support to research and develop domestic advanced screening technology. In addition, it should be noted that gene testing is leading the development of predictive medicine, which can be applied to disease prevention, gene diagnosis, individualized treatment and other aspects to change the shape of the medical and health industry.\u003c/p\u003e \u003cp\u003eStrengthening early treatment and management of CVD and chronic respiratory diseases is imperative, as well as appropriate early treatment of acute CVD, acute exacerbations of asthma and chronic obstructive pulmonary disease and acute complications of diabetes in provincial and municipal general and specialist hospitals, improved effective emergency referral systems and high-quality long-term care.\u003c/p\u003e \u003cp\u003eThere are several limitations to this study. Our results were limited by the quality of the data acquired from the National Mortality Surveillance database, there might be unavoidable miscoding or omission of reporting. However, these data are the most robust and comprehensive estimates of mortality rates available. Another limitation was that as a descriptive study, we can only show the trend and difference in mortality. The internal causes of this phenomenon need to be explored through further researches.\u003c/p\u003e \u003cp\u003eIn conclusion, from 2009 to 2020, chronic NCD have become the leading cause of death of residents in China, and the mortality rate is on the decline. In the future, the prevention and control of chronic NCD should pay more attention to diabetes, male residents and residents in western regions, so as to further improve the health level of the whole people.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval is not required to use the National Mortality Surveillance data; its use for scientifc purposes is regulated since inception. Data used in this paper are available at the following address:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehttps://ncncd.chinacdc.cn/xzzq_1/202101/t20210111_223706.htm\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by National Natural Science Foundation of China(Grant No. 20BRK041).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTW and CS participated in the conception and design of the study, data collection and statistical analysis, drafts of the manuscript. LW participated in the analysis of the literature, contributed to drafts of the manuscript. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Chinese Center for Disease Control and Prevention for their contributions to the management of mortality data.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. World health statistics 2020: monitoring health for the SDGs, sustainable development goals. https://www.who.int/publications/i/item/9789240005105 (2020). Accessed 13 May 2020. \u003c/li\u003e\n\u003cli\u003eZhou MG, Wang HD, Zeng XY. Mortality, morbidity, and risk factors in China and its provinces, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet. 2019; doi: 10.1016/S0140-6736(19)30427-1. \u003c/li\u003e\n\u003cli\u003eDagenais GR, Leong DP, Rangarajan S. Variations in common diseases, hospital admissions, and deaths in middle-aged adults in 21 countries from five continents (PURE): a prospective cohort study. The Lancet. 2020; https://doi.org/10.1016/S0140-6736(19)32007-0.\u003c/li\u003e\n\u003cli\u003eWang W, Liu Y, Liu J. Mortality and years of life lost of cardiovascular diseases in China, 2005\u0026ndash;2020: Empirical evidence from national mortality surveillance system. Int J Cardiol. 2021; doi: 10.1016/j.ijcard.2021.08.034. \u003c/li\u003e\n\u003cli\u003eConnelly PJ, Azizi Z, Alipour P. The Importance of Gender to Understand Sex Differences in Cardiovascular Disease. Can J Cardiol. 2021; doi: 10.1016/j.cjca.2021.02.005. \u003c/li\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021; doi: 10.3322/caac.21660. \u003c/li\u003e\n\u003cli\u003eSafiri S, Carson-Chahhoud K, Noori M. Burden of chronic obstructive pulmonary disease and its attributable risk factors in 204 countries and territories, 1990-2019: results from the Global Burden of Disease Study 2019. BMJ. 2022; doi: 10.1136/bmj-2021-069679. \u003c/li\u003e\n\u003cli\u003eWang C, Xu J, Yang L. Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study. The Lancet. 2018; doi: 10.1016/S0140-6736(18)30841-9. \u003c/li\u003e\n\u003cli\u003eWang F, Wang W, Yin P. Mortality and Years of Life Lost in Diabetes Mellitus and Its Subcategories in China and Its Provinces, 2005-2020. Journal of Diabetes Research. 2022; doi: 10.1155/2022/1609267. \u003c/li\u003e\n\u003cli\u003eWang F, Wang W, Yin P. Mortality and Years of Life Lost in Diabetes Mellitus and Its Subcategories in China and Its Provinces, 2005-2020. Journal of Diabetes Research. 2022; 10.1155/2022/1609267.\u003c/li\u003e\n\u003cli\u003eMauvais-Jarvis F, Bairey MN, Barnes PJ. Sex and gender: modifiers of health, disease, and medicine. The Lancet. 2020; doi: 10.1016/S0140-6736(20)31561-0. \u003c/li\u003e\n\u003cli\u003eLi Y, Xu A, Jia S. Recent advances in the molecular mechanism of sex disparity in hepatocellular carcinoma. Oncol Lett. 2019; doi: 10.3892/ol.2019.10127. \u003c/li\u003e\n\u003cli\u003eDennis JM, Henley WE, Weedon MN. Sex and BMI Alter the Benefits and Risks of Sulfonylureas and Thiazolidinediones in Type 2 Diabetes: A Framework for Evaluating Stratification Using Routine Clinical and Individual Trial Data. Diabetes Care. 2018; doi: 10.2337/dc18-0344. \u003c/li\u003e\n\u003cli\u003eXia S, Du X, Guo L. Sex Differences in Primary and Secondary Prevention of Cardiovascular Disease in China. Circulation. 2020; doi: 10.1161/circulationaha.119.043731. \u003c/li\u003e\n\u003cli\u003eFazeli DZ, Khatami SM, Ranjbar E. The Associations Between Urban Form and Major Non-communicable Diseases: a Systematic Review. J Urban Health. 2022; doi: 10.1007/s11524-022-00652-4. \u003c/li\u003e\n\u003cli\u003eZhang Y, Tong M, Wang B. Geographic, Gender, and Seasonal Variation of Diabetes: A Nationwide Study With 1.4 Million Participants. J Clin Endocrinol Metab. 2021; doi: 10.1210/clinem/dgab543. \u003c/li\u003e\n\u003cli\u003eLi C, Zhang L, Gu Q. Spatio-Temporal Differentiation Characteristics and Urbanization Factors of Urban Household Carbon Emissions in China. Int J Environ Res Public Health. 2022; doi: 10.3390/ijerph19084451. \u003c/li\u003e\n\u003cli\u003ePan XF, Wang L, Pan A. Epidemiology and determinants of obesity in China. Lancet Diabetes Endocrinol. 2021; doi: 10.1016/S2213-8587(21)00045-0. \u003c/li\u003e\n\u003cli\u003eB\u0026eacute;land M, Lavoie KL, Briand S. Aerobic exercise alleviates depressive symptoms in patients with a major non-communicable chronic disease: a systematic review and meta-analysis. Br J Sports Med. 2020; doi: 10.1136/bjsports-2018-099360. \u003c/li\u003e\n\u003cli\u003eMaxwell AE, Castillo L, Arce AA. Eating Veggies Is Fun! An Implementation Pilot Study in Partnership With a YMCA in South Los Angeles. Prev Chronic Dis. 2018; doi: 10.5888/pcd15.180150. \u003c/li\u003e\n\u003cli\u003eFrank LD, Adhikari B, White KR. Chronic disease and where you live: Built and natural environment relationships with physical activity, obesity, and diabetes.Environ Int. 2022; doi: 10.1016/j.envint.2021.106959. \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":"Non-communicable diseases (NCD), Mortality, Average annual percentage changes (AAPC), Trend","lastPublishedDoi":"10.21203/rs.3.rs-2417724/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2417724/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNon-communicable diseases (NCD) have become the leading cause of death in the world at present. Among them, the most important are cardiovascular diseases (CVD), cancer, chronic respiratory diseases and diabetes. This study aimed to analyze the time trends of mortality rates caused by the four major non-communicable diseases (NCD\u003csub\u003e4\u003c/sub\u003e) in China between 2009 and 2020, and to provide the evidence basis for policy implementation, information system, and health resource management of chronic NCD in China.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData on resident death was collected from the National Mortality Surveillance data set and used to analyze the crude mortality rates, standardized mortality rates and changing trends of NCD\u003csub\u003e4\u003c/sub\u003e among different genders, urban and rural areas, and geographical regions in China from 2009 to 2020. The Joinpoint Regression Models were fitted by the weighted least squares method. The average annual percent change (AAPC) and its 95% confidence interval (CI) were calculated for the entire time period.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFrom 2009 to 2020, the standardized mortality from four major NCD combined in China decreased from 534.51 to 395.84%, with the AAPC value at -2.8% (95% CI [-3.7% to -1.8%]). The standardized mortality from CVD, cancer, and chronic respiratory diseases decreased, but the standardized mortality of diabetes increased, with AAPC values at -2.2%, -1.8%, -8.0% and 1.9% respectively.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFrom 2009 to 2020, the mortality rate of chronic NCD is on the decline, but chronic NCD have become the leading cause of death of residents. Close attention needs to be paid on NCD which affecting the health of the labor force population in China. The prevention and treatment of diabetes, male and west region NCD should be enhanced.\u003c/p\u003e","manuscriptTitle":"Mortality trend of four major non-communicable diseases in China, 2009-2020","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-01-03 08:25:55","doi":"10.21203/rs.3.rs-2417724/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":"a5d82f64-4411-4897-9159-12ebb3c45c2b","owner":[],"postedDate":"January 3rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-01-03T08:25:57+00:00","versionOfRecord":[],"versionCreatedAt":"2023-01-03 08:25:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2417724","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2417724","identity":"rs-2417724","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}
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