20142023年广西北海市流行性感冒流行特征及时空聚集性分析

Analyses of epidemic characteristics and spatiotemporal clustering of influenza in Beihai, Guangxi from 2014 to 2023

  • 摘要:
    目的 分析广西北海市近10年流行性感冒(简称“流感”)流行特征和病原变化,为制定相应防控措施提供科学的依据。
    方法 从中国疾病预防控制信息系统获取2014—2023年北海市流感病例资料、病原监测资料以及人口数据。运用Joinpoint 5.0、DeoDa 1.22和SaTScan 10.1.3软件对流感流行特征及时空聚集性进行分析。
    结果 2014—2023年北海市流感病例年均报告发病率为254.53/10万,年发病率总体呈上升趋势(APC=81.49%,P<0.001)。空间自相关分析和时空扫描结果显示,流感发病主要聚集区为海城区。时空扫描流感发病时间主要在12月—次年1月,其次为6月和3—4月。5~9岁组发病率最高(1 289.66/10万),其次为0~4岁组(991.72/10万)。发病人群以学生为主(44.24%),其次是幼托儿童(26.08%)。流感病毒各亚型阳性构成比最高为A/H3N2(42.01%),其次为A/H1N1(24.96%)和B/Victoria系(23.50%),最低为B/Yamagata系(9.33%)。冬季、春季和夏季流感病毒阳性的风险分别是秋季的4.70倍、4.74倍、2.14倍,差异有统计学意义(P<0.001)。2019、2022和2023年暴发流行年份优势亚型为A/H1N1、A/H3N2。
    结论 2014—2023年北海市流感总体呈上升趋势。应继续加强流感监测。建议相关部门有针对性地开展流感防控知识宣传工作,有计划地对学校、幼托机构等重点场所人群做好流感疫苗接种工作,预防流感的暴发流行。

     

    Abstract:
    Objective To analyze the epidemiological characteristics and the pathogen dynamics of influenza in Beihai over the past ten years, and to provide a scientific basis for formulating targeted prevention and control measures.
    Methods Data for influenza cases, pathogen surveillance, and population statistics from 2014‒2023 were obtained from China Center for Disease Control and Prevention Information System. The analyses of epidemiological characteristics and spatiotemporal clustering of influenza were performed using Joinpoint 5.0, DeoDa 1.22 and SaTScan 10.1.3 software.
    Results The average annual incidence of influenza was 254.53/100 000 in Beihai from 2014 to 2023,displaying an overall upward trend (APC=81.49%, P<0.001). Spatial autocorrelation analysis and spatiotemporal scanning results indicated that the main cluster of influenza cases was mainly located in Haicheng District. Spatiotemporal scanning results revealed that the peak periods of influenza onset were primarily from December to January of the following year, followed by June and March to April. The highest incidence of influenza was observed in 5‒9 years old group (1 289.66/100 000), followed by 0‒4 years old group (991.72/100 000). The majority of cases were students (44.24%), followed by kindergarten children (26.08%). The highest positive subtype proportion of influenza was A/H3N2 (42.01%), followed by A/H1N1 (24.96%) and B/Victoria (23.50%), while the lowest positive subtype proportion of influenza was B/Yamagata (9.33%). The risk of influenza virus positivity in winter, spring and summer was 4.70 times, 4.74 times, and 2.14 times, respectively, compared to that for autumn, with statistically significant differences (P<0.001). The dominant subtypes were A/H1N1 and A/H3N2 in 2019, 2022 and 2023.
    Conclusion The overall incidence of influenza showed an upward trend in Beihai from 2014 to 2023. Continuous enhancement of influenza surveillance is necessary. It is suggested that relevant departments should carry out targeted publicity work to promote knowledge on influenza prevention and control and conduct influenza vaccination for populations in key places, such as schools and childcare institutions to prevent influenza outbreaks and epidemics of influenza.

     

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