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周利慧, 彭琴, 王媛, 芦文丽. 中国居民1993 — 2017年房颤发病及死亡趋势年龄–时期–队列模型分析[J]. 中国公共卫生, 2021, 37(11): 1672-1676. DOI: 10.11847/zgggws1131433
引用本文: 周利慧, 彭琴, 王媛, 芦文丽. 中国居民1993 — 2017年房颤发病及死亡趋势年龄–时期–队列模型分析[J]. 中国公共卫生, 2021, 37(11): 1672-1676. DOI: 10.11847/zgggws1131433
ZHOU Li-hui, PENG Qin, WANG Yuan, . Incidence and mortality of atrial fibrillation in China, 1993 – 2017: an age-period-cohort analysis[J]. Chinese Journal of Public Health, 2021, 37(11): 1672-1676. DOI: 10.11847/zgggws1131433
Citation: ZHOU Li-hui, PENG Qin, WANG Yuan, . Incidence and mortality of atrial fibrillation in China, 1993 – 2017: an age-period-cohort analysis[J]. Chinese Journal of Public Health, 2021, 37(11): 1672-1676. DOI: 10.11847/zgggws1131433

中国居民1993 — 2017年房颤发病及死亡趋势年龄–时期–队列模型分析

Incidence and mortality of atrial fibrillation in China, 1993 – 2017: an age-period-cohort analysis

  • 摘要:
      目的  了解中国居民1993 — 2017年房颤发病和死亡趋势以及年龄、时期和出生队列因素对其的影响,为房颤的预防和控制策略的制定提供科学依据。
      方法  收集2017年全球疾病负担研究(GBD 2017)中1993 — 2017年中国居民房颤发病率和死亡率相关数据,应用美国国家癌症研究所研发的基于R语言的年龄 – 时期 – 队列(APC)模型网络工具进行统计分析,评估年龄、时期和出生队列效应。
      结果  中国居民1993 — 2017年房颤发病和死亡的粗率和年龄标化率均呈上升趋势,男性和女性居民的房颤发病粗率分别由1993年的20.61/10万和24.90/10万上升至2017年的42.30/10万和49.26/10万,年龄标化发病率分别由1993年的30.35/10万和31.95/10万上升至2017年的32.67/10万和34.18/10万;男性和女性居民的房颤死亡粗率分别由1993年的0.79/10万和1.89/10万上升至2017年的2.32/10万和4.64/10万,年龄标化死亡率分别由1993年的2.46/10万和3.78/10万上升至2017年的2.78/10万和3.86/10万。APC模型结果显示,不同性别人群房颤发病率和死亡率的净变化值均 > 0(发病率:男性16.595 %、女性16.527 %,死亡率:男性28.570 %、女性29.026 %);中国不同性别人群房颤发病和死亡率均呈“J”字型上升,在85~89岁年龄组达到最高值;2003 — 2007年作为时期对照,房颤发病风险在1993 — 2012年显著上升,2013 — 2017年有下降趋势,房颤死亡风险随时期呈上升趋势,且在2008 — 2017年显著上升;1976 — 1985年出生队列的房颤发病风险和死亡风险(RR值)均呈“J”字型指数上升。
      结论  由于年龄、时期和队列效应的影响,中国居民1993 — 2017年房颤发病率和死亡率均呈持续上升趋势,需进一步降低房颤的疾病负担。

     

    Abstract:
      Objective   To examine long-time trend in incidence and mortality of atrial fibrillation (AF) and influences of age, period and birth cohort on the incidence and mortality among Chinese population and to provide evidences for developing AF prevention and control strategies.
      Methods   The data on AF incidence and mortality from 1993 through 2017 in China were collected from the results of the Global Burden of Diseases Study 2017 (GBD 2017). An R-based web tool from the United States National Cancer Institute for age-period-cohort (APC) analysis was used in this study. The data were arranged to conduct APC analysis for assessing effects of age, period and cohort on AF incidence and mortality.
      Results  Increasing trend in the crude and age standardized AF incidence and mortality were observed during the 25-year period in China. The increments during the period of 1993 – 2017 for the crude rate (1/100 000) and age-standardized rate (1/100 000) of AF incidence and mortality for male and female populations were as following: (1) crude AF incidence: 20.61 vs. 42.30 and 24.90 vs. 42.30; (2) age-standardized AF incidence: 30.35 vs. 32.67 and 31.95 vs. 34.18; (3) crude AF mortality: 0.79 vs. 2.32 and 1.89 vs. 4.64; and (4) age-standardized AF mortality: 2.46 vs. 2.78 and 3.78 vs. 3.86. The results of APC analysis demonstrated that the net drift of AF incidence and mortality for male and female populations were all above 0 (AF incidence: 16.595% and 16.527%, AF mortality: 28.570% and 29.026%). Both the incidence and mortality risk of AF increased sharply with the increment of age in a 'J' shaped pattern and the highest AF incidence and mortality risk were observed in populations aged 80 – 89 years. Compared to those during the period of 2003 – 2007, the risk of AF incidence was higher for the periods of 1993 – 2002 and 2008 – 2012 but lower for the period of 2013 – 2017. The risk of AF mortality increased yearly during the 25-year period, and the increase was more dramatic for the years of 2008 – 2017. The risk of AF incidence and mortality for people born between 1976 and 1985 increase with their year of birth in a 'J' shaped exponential pattern.
      Conclusion  The incidence and mortality of atrial fibrillation increased persistently during 1993 – 2017 for Chinese population due to impacts of age, period and birth cohort effect.

     

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