职业与健康 ›› 2023, Vol. 39 ›› Issue (4): 492-496.

• 论著 • 上一篇    下一篇

2017—2020年上海市松江区其他感染性腹泻流行特征分析

李萌a, 乔雪飞b, 吕锡宏a, 孙中兴, 高桂玲c   

  1. 上海市松江区疾病预防控制中心a传染病防制科,b卫生物检验科,c学校卫生科,上海 201600
  • 收稿日期:2022-05-30 修回日期:2022-06-06 发布日期:2026-03-02
  • 通信作者: 高桂玲,主管医师,E-mail:superling906@126.com
  • 作者简介:李萌,女,主管医师,主要从事急性传染病防控工作。
  • 基金资助:
    上海市松江区公共卫生体系建设三年行动计划项目(20GWTX02)

Analysis on epidemiological characteristics of other infectious diarrhea in Songjiang District of Shanghai City from 2017-2020

LI Menga, QIAO Xue-feib, LYU Xi-honga, SUN Zhong-xing, GAO Gui-lingc   

  1. a Infections Disease Prevention and Control Department,b Microbiological laboratory,c Department of School Health,Songjiang District Center for Disease Control and Prevention,Shanghai 201600,China
  • Received:2022-05-30 Revised:2022-06-06 Published:2026-03-02
  • Contact: GAO Gui-ling,Physician in charge,E-mail:superling906@126.com

摘要: 目的 了解2017—2020年上海市松江区其他感染性腹泻的流行特征,为制订科学防控措施提供依据。方法 自中国疾病预防控制信息系统收集2017—2020年上海市松江区其他感染性腹泻监测资料,自上海市松江区聚集性疫情信息系统收集聚集性及暴发疫情资料,自上海市腹泻病综合监测系统收集病原学数据,并对数据进行分析。结果 2017—2020年,上海市松江区共报告其他感染性腹泻病例3 635例,年平均发病率为51.56/10万,各年发病水平比较差异有统计学意义(χ2=543.072,P<0.01);病例以<5岁(55.90%)和20~<40岁(14.97%)年龄组最为多见,总性别比为1.25∶1;发病呈现双峰分布,主高峰为12月—次年3月,次高峰为8—9月。各街镇均有病例报告,主要集中于方松街道(22.72%)、岳阳街道(9.71%)和永丰街道(9.11%)。全区共报告聚集性疫情79起,暴发疫情14起,诺如病毒为最常见的病原体,11—12月为疫情高峰,主要发生于幼儿园(48.61%)和小学(23.61%)。成人腹泻病综合监测中共监测病例6 757例,开展病原监测者879例,检出阳性病例355例。5—9月以细菌性感染为主,以副溶血性弧菌和弯曲菌最为常见;10月—次年4月以病毒性感染为主,以诺如病毒和轮状病毒最为常见。结论 2017—2020年上海市松江区散居儿童与青壮年为其他感染性腹泻高发人群,夏季细菌感染高峰与冬季病毒感染高峰明显,应在不同季节对高发人群与聚集性疫情高发场所采取针对性强的防控措施。

关键词: 其他感染性腹泻, 流行特征, 病原学, 聚集性疫情

Abstract: Objective To understand the epidemiological characteristics of other infectious diarrhea in Songjiang District of Shanghai City from 2017-2020,provide a basis for the formulation of scientific prevention and control measures. Methods The surveillance data of other infectious diarrhea in Songjiang District of Shanghai from 2017-2020 were collected from China information system for disease control and prevention. The clusters and outbreaks data were collected from Shanghai Songjiang aggregation epidemic information system. The etiological data were collected from Shanghai diarrhea comprehensive surveillance system. And the data were analyzed. Results From 2017 to 2020,a total of 3 635 cases of other infectious diarrhea were reported in Songjiang District of Shanghai with an average annual incidence rate of 51.56/100 000,and the difference in the incidence rate among different years was statistically significant(χ2=543.072,P<0.01). The vast majority occurred in age group of <5(55.90%) and 20-<40(14.97%) years old,with a total sex ratio of 1.25∶1. The incidence presented a bimodal distribution,with the major peak between December and March and the minor peak between August and September. The cases were reported in all streets and towns,mainly concentrated in Fangsong Street(22.72%),Yueyang Street(9.71%) and Yongfeng Street(9.11%). A total of 79 clusters and 14 outbreaks were reported. Norovirus was the most common pathogen,with a peak in November-December,mainly occurred in kindergartens(48.61%) and elementary school(23.61%). A total of 6 757 cases were monitored in the adult diarrhea comprehensive surveillance. The pathogen monitoring was performed among 879 cases,and 355 cases were positive for bacteria or virus. The bacteria infection was commonly seen from May to September,which was mainly caused by Vibrio parahaemolyticus and Campylobacter. The virus infection was commonly seen from October to April,which was mainly caused by norovirus and rotavirus. Conclusion The majority of other infectious diarrhea are scattered children and young adults in Songjiang District of Shanghai from 2017-2020,the obvious seasonal characteristics are showed with the bacteria peak in summer and virus peak in winter,and highly targeted prevention and control measures should be taken in different seasons for high-risk populations and places with a high incidence of clusters or outbreaks.

Key words: Other infectious diarrhea, Epidemiological characteristics, Etiological, Clusters

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