Burden of cerebrovascular diseases attributable to metabolic risks in 2011 and 2017 in Nanjing
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摘要:
目的 分析2011年与2017年南京市年龄≥ 25岁人群归因于代谢危险因素的脑血管病疾病负担变化。 方法 利用南京市慢性病监测和全球疾病负担资料，根据人群归因分值，分析2011年和2017年高收缩压、高血糖、高总胆固醇、高体重指数和4种代谢因素合并导致脑血管病患者死亡和寿命损失的变化。 结果 2017年，南京市归因于代谢因素合并的脑血管病死亡数为4 978例，导致期望寿命损失1.43岁。高收缩压是导致南京市脑血管病死亡（35.51%）和过早死亡损失寿命年（years of life lost due to premature mortality，YLL）（38.89%）上升的首位代谢因素。与2011年相比，2017年人群归因于高收缩压、高体重指数的脑血管病标化死亡率和标化YLL率均下降，高血糖则相反。男性各代谢因素导致的脑血管病标化YLL率均高于女性。 结论 代谢危险因素尤其高收缩压是导致南京市脑血管病死亡的重要危险因素。有效控制人群体重、血压、血糖和血脂水平，可降低南京市脑血管病疾病负担。 Abstract:Objective To analyze the burden of cerebrovascular diseases attributable to metabolic risks in Nanjing population aged 25 years and older in 2011 and 2017. Methods The data were extracted from the Nanjing Chronic Disease and Risk Factor Surveillance, the Nanjing Mortality Surveillance and the 2016 Global Burden of Disease Study. Based on population attributable fractions, the deaths and life expectancy losses of cerebrovascular diseases attributable to high systolic blood pressure, high fasting plasma glucose, high total cholesterol, high body mass index and four metabolic risks combination were estimated in 2011 and 2017. Results The number of cerebrovascular diseases death attributable to four metabolic risks combination was 4 978, and resulted in a loss of life expectancy of 1.43 years in 2017. High systolic blood pressure appeared as the major cause on cerebrovascular diseases deaths (35.51%) and years of life lost due to premature mortality (YLL) (38.89%). Compared to 2011, the standardized rate of mortality and YLL on cerebrovascular diseases due to high systolic blood pressure and high body mass index in 2017 showed downward trend, while high fasting plasma glucose showed upward trend. Males appeared to have higher standardized rate of YLL on cerebrovascular diseases than females. Conclusions Metabolic exposures especially high blood pressure were important risk factors attributable to cerebrovascular diseases deaths in Nanjing. Proper management of hypertension, diabetes, dyslipidemia and obesity could remarkably reduce the pervasive burden of cerebrovascular diseases. -
表 1 2011年与2017年南京年龄≥25岁不同性别人群脑血管病死亡和YLL变化
Table 1. Changes of cerebrovascular diseases deaths and YLL in different genders among Nanjing residents aged 25 years or older in 2011 and 2017
指标 死亡数
(例)粗死亡率
(/10万)世标死亡率
(/10万)国标死亡率
(/10万)YLL
(人年)粗YLL率
(/10万)世标YLL率
(/10万)国标YLL率
(/10万)男性 2011年 3 424 154.49 182.97 153.36 60 944 2 749.74 3 002.48 2 770.01 2017年 4 455 172.10 138.27 113.64 68 994 2 665.33 2 179.62 1 992.86 变化率(%) 30.11 11.40 -24.43 -25.90 13.21 -3.07 -27.41 -28.06 女性 2011年 3 285 149.79 140.16 143.35 45 734 2 085.39 1 983.95 2 032.14 2017年 4 534 169.79 113.46 115.81 54 934 2 057.18 1 500.53 1 539.20 变化率(%) 38.02 13.35 -19.05 -19.21 20.12 -1.35 -24.37 -24.26 合计 2011年 6 709 152.15 160.62 148.14 106 678 2 419.32 2 483.96 2 399.75 2017年 8 989 170.93 125.91 114.63 123 928 2 356.53 1 838.76 1 765.67 变化率(%) 33.99 12.34 -21.61 -22.62 16.17 -2.60 -25.97 -26.42 
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表 2 2011年与2017年南京年龄≥25岁不同性别人群4种代谢暴露水平
(x ±s) Table 2. Four metabolic exposure levels of different genders among Nanjing residents aged 25 years or older in 2011 and 2017
(x ±s) 指标 收缩压
(mmHg)空腹血糖
(mmol/L)总胆固醇
(mmol/L)体重指数
(kg/m2)男性 2011年 126.04±14.80 5.29±1.29 4.58±2.16 23.74±2.98 2017年 126.64±15.56 5.43±1.62 4.61±1.09 24.49±3.02 t值 3.957 9.545 1.516 25.037 P值 < 0.001 < 0.001 0.058 < 0.001 女性 2011年 123.01±16.87 5.31±1.32 4.55±1.58 23.47±3.33 2017年 122.19±20.53 5.26±1.38 4.71±1.11 23.32±3.33 t值 4.793 3.683 11.377 4.586 P值 < 0.001 < 0.001 < 0.001 < 0.001 合计 2011年 124.38±16.04 5.30±1.31 4.57±1.86 23.59±3.18 2017年 124.35±18.42 5.34±1.50 4.66±1.10 23.89±3.24 t值 0.213 4.254 9.533 13.844 P值 0.799 < 0.001 < 0.001 < 0.001
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表 3 2011年与2017年南京年龄≥25岁人群代谢危险因素导致脑血管病死亡负担
Table 3. Burden of cerebrovascular disease death caused by metabolic risk factors in different genders among Nanjing residents aged 25 years or older in 2011 and 2017
代谢危险因素 2017年 2011年 死亡数
(例)世标死亡率
(/10万)国标死亡率
(/10万)YLL
(人年)世标YLL率
(/10万)国标YLL率
(/10万)死亡数
(例)世标死亡率
(/10万)国标死亡率
(/10万)YLL
(人年)世标YLL率
(/10万)国标YLL率
(/10万)男性 高收缩压 1 667 51.95 43.91 28 110 892.72 837.04 1 217 63.50 54.83 23 400 1 131.99 1 067.97 高血糖 749 23.35 19.41 11 969 378.60 348.53 327 16.73 14.77 6 459 309.83 295.15 高总胆固醇 413 12.76 10.19 6 135 192.44 174.21 248 13.27 10.95 4 181 205.55 187.98 高体重指数 439 14.08 13.45 10 511 347.69 349.73 329 15.60 15.15 8 723 397.08 402.39 合并 2 543 79.35 66.82 42 611 1 356.43 1 268.06 1 739 90.26 78.32 33 966 1 635.91 1 550.08 女性 高收缩压 1 525 39.21 40.19 20 086 564.76 582.13 1 155 49.38 50.57 16 704 725.58 744.17 高血糖 681 17.43 17.86 8 650 238.57 244.78 362 15.48 15.90 5 532 241.10 247.13 高总胆固醇 551 13.29 13.54 5 986 155.52 159.63 269 11.31 11.64 3 514 149.94 154.92 高体重指数 306 8.88 9.08 5 879 183.05 187.70 251 11.13 11.33 5 127 230.41 234.92 合并 2 435 62.19 63.63 31 519 880.11 904.86 1 674 71.62 73.36 24 781 1 079.15 1 106.66 合计 高收缩压 3 192 45.43 42.06 48 196 727.22 709.43 2 372 56.41 52.64 40 104 928.79 906.01 高血糖 1 430 20.26 18.66 20 619 307.57 296.67 689 16.21 15.33 11 991 276.77 271.20 高总胆固醇 964 13.20 11.79 12 120 175.26 166.30 517 12.29 11.28 7 695 177.56 171.36 高体重指数 745 11.47 11.26 16 390 265.18 268.20 580 13.44 13.25 13 850 315.27 319.23 合并 4 978 70.68 65.19 74 130 1 117.01 1 085.82 3 413 80.95 75.77 58 747 1 358.21 1 328.45
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表 4 2011年与2017年南京年龄≥25岁不同性别人群代谢危险因素导致脑血管病期望寿命损失
(岁) Table 4. Loss of life expectancy in cerebrovascular diseases caused by metabolic risk factors of different genders among Nanjing residents aged 25 years or older in 2011 and 2017
(years) 代谢危险因素 2017年 2011年 缺血性
脑卒中出血性
脑卒中脑血
管病缺血性
脑卒中出血性
脑卒中脑血
管病男性 高收缩压 0.53 0.26 0.81 0.28 0.33 0.61 高血糖 0.25 0.10 0.35 0.08 0.08 0.16 高总胆固醇a 0.19 - - 0.11 - - 高体重指数 0.10 0.10 0.21 0.06 0.12 0.18 合并 0.89 0.36 1.29 0.43 0.44 0.89 女性 高收缩压 0.68 0.22 0.92 0.38 0.31 0.71 高血糖 0.31 0.08 0.39 0.12 0.09 0.21 高总胆固醇a 0.32 - - 0.15 - - 高体重指数 0.10 0.07 0.17 0.07 0.09 0.16 合并 1.20 0.30 1.56 0.60 0.41 1.05 合计 高收缩压 0.60 0.24 0.87 0.33 0.32 0.67 高血糖 0.28 0.09 0.37 0.10 0.08 0.19 高总胆固醇a 0.25 - - 0.13 - - 高体重指数 0.11 0.09 0.19 0.06 0.11 0.17 合并 1.04 0.33 1.43 0.52 0.43 0.98 注：a GBD 2016未提供总胆固醇与出血性脑卒中相关的RR值。
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[1] Zhou M, Wang H, Zeng X, et al. Mortality, morbidity, and risk factors in China and its Provinces, 1990-2017:a systematic analysis for the Global Burden of Disease Study 2017[J]. Lancet, 2019, 394(10204):1145-1158. DOI: 10.1016/S0140-6736(19)30427-1. [2] 国家卫生计生委疾病预防控制局.中国居民营养与慢性病状况报告(2015年)[M].北京:人民卫生出版社, 2015.National Commission of Health and Family Planning Bureau of disease control and Prevention. Report on China national nutrition and chronic disease survey (2015)[M]. Beijing:People's Medical Publishing House, 2015. [3] 中共中央国务院.中共中央国务院印发《"健康中国2030"规划纲要》[EB/OL]. (2016-10-25)[2019-12-21].http://news.xinhuanet.com/politics/2016-10/25/c_1119785867.htm.CPC Central Committee, State Council. The plan for "Healthy China 2030"[EB/OL]. (2016-10-25)[2019-12-21]. http://news.xinhuanet.com/politics/2016-10/25/c_1119785867.htm. [4] 国务院办公厅.国务院办公厅关于印发中国防治慢性病中长期规划(2017-2025年)的通知[EB/OL]. (2017-02-14)[2019-12-21]. http://www.gov.cn/zhengce/content/2017-02/14/content_5167886.htm.Office of The State Council of China. Notice of the General Office of the State Council on printing and distributing China's Medium and Long-term Plan for Prevention and Treatment of Chronic Diseases (2017-2025).[EB/OL]. (2017-02-14)[2019-12-21]. http://www.gov.cn/zhengce/content/2017-02/14/content_5167886.htm. [5] 南京市疾病预防控制中心.南京市慢性病及其危险因素监测报告(2011)[M].南京:江苏科学技术出版社, 2014.Nanjing Municipal Center for Disease Control and Prevention. Report on Chronic Disease Risk Factor Surveillance in Nanjing, 2011[M]. Nanjing:Jiangsu Science and Technology Press, 2014. [6] GBD 2016 Risk Factors Collaborators. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2016:a systematic analysis for the Global Burden of Disease Study 2016[J]. Lancet, 2017, 390(10100):1345-1422. DOI: 10.1016/S0140-6736(17)32366-8. [7] Devleesschauwer B, Havelaar AH, Maertens de Noordhout C, et al. Calculating disability-adjusted life years to quantify burden of disease[J]. Int J Public Health, 2014, 59(3):565-569. DOI: 10.1007/s00038-014-0552-z. [8] Ahmad OB, Boschi-Pinto C, Lopez AD, et al. Age standardization of rates: A new WHO standard[EB/OL]. (2017-05-20)[2019-12-21]. http://www.who.int/healthinfo/paper31.pdf. [9] 蔡玥, 周脉耕, 李小洪, 等. 2013年中国居民预期寿命和去死因预期寿命分析[J].中华流行病学杂志, 2017, 38(8):1001-1004. DOI: 10.3760/cma.j.issn.0254-6450.2017.08.001.Cai Y, Zhou MG, Li XH, et al. Life expectancy and influence on disease in China, 2013[J]. Chin J Epidemiol, 2017, 38(8):1001-1004. DOI: 10.3760/cma.j.issn.0254-6450.2017.08.001. [10] 杨华凤, 陈旭鹏, 洪忻, 等. 2011-2014年南京市户籍人口疾病死亡趋势变化对期望寿命的影响[J].江苏预防医学, 2017, 28(5):512-514. DOI: 10.13668/j.issn.1006-9070.2017.05.10.Yang HF, Chen XP, Hong X, et al. Influence of epidemiological trend of disease mortality on life expectancy among residents in Nanjing from 2011 to 2014[J]. Jiangsu J Prev Med, 2017, 28(5):512-514. DOI: 10.13668/j.issn.1006-9070.2017.05.10. [11] GBD 2017 Risk Factor Collaborators. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017:a systematic analysis for the Global Burden of Disease Study 2017[J]. Lancet, 2018, 392(10159):1923-1994. DOI: 10.1016/S0140-6736(18)32225-6. [12] 曾新颖, 李镒冲, 刘江美, 等.危险因素控制对2030年中国慢性病死亡、期望寿命和劳动力损失的影响估计[J].中华预防医学杂志, 2017, 51(12):1079-1085. DOI: 10.3760/cma.j.issn.0253-9624.2017.12.006.Zeng XY, Li YC, Liu JM, et al. Estimation of the impact of risk factors control on non-communicable diseases mortality, life expectancy and the labor force lost in China in 2030[J]. Chin J Pre Med, 2017, 51(12):1079-1085. DOI: 10.3760/cma.j.issn.0253-9624.2017.12.006. [13] 杨华凤, 陈旭鹏, 洪忻, 等.南京市2011-2016年主要慢性病早死概率变化趋势分析[J].中国卫生统计, 2018, 35(5):769-771. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgwstj201805035Yang HF, Chen XP, Hong X, et al. Trend of premature death caused by main chronic diseases among residents in Nanjing, 2011-2016[J].Chinese J Health Statistics, 2018, 35(5):769-771. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgwstj201805035 -
