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基于分布滞后非线性模型探讨上海市诺如病毒感染性腹泻的发病与气象因素的关联

周士夏, 张海洋, 王丽萍, 刘玮, 方立群, 国家科技重大专项腹泻症候群监测团队. 基于分布滞后非线性模型探讨上海市诺如病毒感染性腹泻的发病与气象因素的关联[J]. 中华疾病控制杂志, 2021, 25(10): 1180-1185, 1193. doi: 10.16462/j.cnki.zhjbkz.2021.10.012
引用本文: 周士夏, 张海洋, 王丽萍, 刘玮, 方立群, 国家科技重大专项腹泻症候群监测团队. 基于分布滞后非线性模型探讨上海市诺如病毒感染性腹泻的发病与气象因素的关联[J]. 中华疾病控制杂志, 2021, 25(10): 1180-1185, 1193. doi: 10.16462/j.cnki.zhjbkz.2021.10.012
ZHOU Shi-xia, ZHANG Hai-yang, WANG Li-ping, LIU Wei, FANG Li-qun, National Science and Technology Major Project Etiology of Diarrhea Surveillance Study Team. Exploration of the association between meteorological factors and positive rate of norovirus infectious diarrhea based on the distributed lag non-linear model in Shanghai[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(10): 1180-1185, 1193. doi: 10.16462/j.cnki.zhjbkz.2021.10.012
Citation: ZHOU Shi-xia, ZHANG Hai-yang, WANG Li-ping, LIU Wei, FANG Li-qun, National Science and Technology Major Project Etiology of Diarrhea Surveillance Study Team. Exploration of the association between meteorological factors and positive rate of norovirus infectious diarrhea based on the distributed lag non-linear model in Shanghai[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(10): 1180-1185, 1193. doi: 10.16462/j.cnki.zhjbkz.2021.10.012

基于分布滞后非线性模型探讨上海市诺如病毒感染性腹泻的发病与气象因素的关联

doi: 10.16462/j.cnki.zhjbkz.2021.10.012
基金项目: 

国家科技重大专项 2018ZX10713001

国家科技重大专项 2018ZX10713002

国家科技重大专项 2018ZX10201001

详细信息
    通讯作者:

    方立群,E-mail: fang_lq@163.com

  • 中图分类号: R183.4

Exploration of the association between meteorological factors and positive rate of norovirus infectious diarrhea based on the distributed lag non-linear model in Shanghai

Funds: 

National Science and Technology Major Project 2018ZX10713001

National Science and Technology Major Project 2018ZX10713002

National Science and Technology Major Project 2018ZX10201001

More Information
  • 摘要:   目的  探讨上海市腹泻患者中诺如病毒感染与气象因素的关联,为诺如病毒感染性腹泻的防控提供科学依据。  方法  收集上海市2012―2019年腹泻病例与上海市各气象监测点逐日气象资料,利用分布滞后非线性模型(distributed lag non-linear model, DLNM)分析诺如病毒感染性腹泻的发病与气象因素的关联。  结果  在21 148名腹泻入院的就诊患者中,诺如病毒检出率为18.75%。其中45~<60岁人群的检出率最高(22.72%)。DLNM结果显示,相对于日平均气温18 ℃(P50),当日平均气温为-6 ℃时对诺如病毒感染的影响最大(RR=3.06, 95% CI: 1.49~6.30);日平均气温在滞后2 d时对诺如病毒感染产生最大影响(RR=1.15, 95% CI: 1.02~1.29)。相对于日降水量0 mm(P50),当日降水量195 mm时出现最大负效应(RR=0.31, 95% CI: 0.06~1.66)。  结论  上海市腹泻入院就诊患者的诺如病毒感染率较高,低温增加诺如病毒感染性腹泻发病风险,应根据诺如病毒流行特征针对性地开展防控工作。
  • 图  1  2012―2019年上海市25家哨点医院诺如病毒感染病例检出率分布

    Figure  1.  Distribution of norovirus cases and positive rate in 25 sentinel hospitals in Shanghai from 2012 to 2019

    图  2  (A) RR随日平均气温与滞后时间的变化趋势3D图(B)特定日平均气温不同滞后天数对诺如病毒感染的效应(C)诺如病毒感染与日平均气温的总体累积效应图

    Figure  2.  (A) 3D plot for variation trend of RR with average daily temperature and lag time (B) Lag-response association at specific daily average temperature of norovirus infection (C) Overall cumulative exposure-response association between norovirus infection and daily average temperature

    图  3  (A) RR随日降水量与滞后时间的变化趋势3D图(B)特定日降水量不同滞后天数对诺如病毒感染的效应(C)诺如病毒感染与日降水量的总体累积效应图

    Figure  3.  (A) 3D plot for variation trend of relative risk with daily precipitation and lag time (B) Lag-response association at specific daily precipitation of norovirus infection (C) Overall cumulative exposure-response association between norovirus infection and daily precipitation

    图  4  (A) 改变最长滞后时间,日平均气温模型总效应的变化(B)改变最长滞后时间,日降水量模型总效应的变化

    Figure  4.  (A) The overall effects of the daily average temperature model by changing maximum lag time (B) The overall effects of the daily precipitation model by changing maximum lag time

    表  1  2012―2019年上海市诺如病毒感染性腹泻基本特征[n(%)]

    Table  1.   Characteristics of norovirus infectious diarrhea in Shanghai from 2012 to 2019 [n(%)]

    病例数 诺如病毒检出率 P GI组检出率 P GII组检出率 P
    总计(例) 21 148(100.00) 3 966(18.75) 335(1.58) 3 500(16.55)
    性别 0.03 0.14 0.11
      男 11 211(53.01) 2 165(19.31) 191(1.70) 1 898(16.93)
      女 9 937(46.99) 1 801(18.12) 144(1.45) 1 602(16.12)
    年龄组(岁) <0.01 <0.01 <0.01
      <1 2 049(9.69) 208(10.15) 5(0.24) 201(9.81)
      1~<5 3 046(14.40) 480(15.76) 11(0.36) 464(15.23)
      5~<18 1 240(5.86) 174(14.03) 12(0.97) 155(12.50)
      18~<45 7 377(34.88) 1 591(21.57) 183(2.48) 1 332(18.06)
      45~<60 3 424(16.19) 778(22.72) 62(1.81) 693(20.24)
      ≥60 4 012(18.97) 735(18.32) 62(1.55) 655(16.33)
    季节 <0.01 <0.01 <0.01
      春季 4 849(22.93) 1 061(21.88) 141(2.91) 846(17.45)
      夏季 5 992(28.33) 507(8.46) 56(0.93) 440(7.34)
      秋季 5 523(26.12) 1 282(23.21) 46(0.83) 1 227(22.22)
      冬季 4 784(22.62) 1 116(23.33) 92(1.92) 987(20.63)
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  • 收稿日期:  2021-03-02
  • 修回日期:  2021-05-04
  • 网络出版日期:  2021-11-17
  • 刊出日期:  2021-10-10

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