职业与健康 ›› 2026, Vol. 42 ›› Issue (7): 955-960.

• 论著 • 上一篇    下一篇

2017—2023年上海市松江区流行性感冒流行趋势和时空特征分析

吴佳玲, 王超, 叶独秋, 李萌, 吕锡宏()   

  1. 上海市松江区疾病预防控制中心(上海市松江区卫生健康监督所)上海 201620
  • 收稿日期:2025-07-07 修回日期:2025-07-29 出版日期:2026-04-01 发布日期:2026-05-14
  • 通信作者: 吕锡宏,E-mail:xihonglv@126.com
  • 作者简介:吴佳玲,女,医师,主要从事急性传染病和病媒生物防制工作。
  • 基金资助:
    上海市公共卫生研究专项面上项目(2024GKM24);上海市松江区加强公共卫生体系建设三年行动计划项目(SJGW6-13);上海市松江区科技攻关项目(2024SJKJ GG040);上海市公共卫生体系建设三年行动计划(2023—2025年)重点学科建设项目“传染病学-方向1:重大传染病早期预警预测、聚集性疫情传播动力学模型、动态监测、干预技术等”(GWVI-11.1-01)

Analysis of epidemiological trend and spatiotemporal distribution characteristics of influenza in Songjiang District of Shanghai City from 2017 to 2023

WU Jialing, WANG Chao, YE Duqiu, LI Meng, LYU Xihong()   

  1. Shanghai Songjiang District Center for Disease Control and Prevention(Shanghai Songjiang District Health Supervision Institute)Shanghai 201620, China
  • Received:2025-07-07 Revised:2025-07-29 Online:2026-04-01 Published:2026-05-14

摘要: 目的 分析上海市松江区流行性感冒病例流行特征及时空分布特征,为科学防控流行性感冒提供依据。方法 收集2017—2023年上海市松江区流行性感冒病例资料,并对数据进行分析。结果 2017—2023年松江区流行性感冒年均发病率为10.38/10万,以0~<10岁为主。2021—2023年松江区流行性感冒发病呈上升趋势(APC=456.65,P<0.05)。2017、2018及2023年病例分布具有空间聚集性,局部自相关分析探测到的高-高聚集区、热点分析探测到热点地区以及时空扫描分析探测到Ⅰ类、Ⅱ类聚集区主要集中在松江区中心城区及其邻居街镇。结论 2017—2023年上海市松江区流行性感冒发病呈上升趋势,且存在时空聚集性,以松江区中心城区及其邻近街镇为重点发病区域,应在高发季节前有针对性地加强重点地区重点人群的防控措施。

关键词: 流行性感冒, 流行特征, Joinpoint回归分析, 趋势分析, 空间自相关分析, 时空扫描

Abstract:

Objective To analyze the epidemiological and spatiotemporal distribution characteristics of influenza cases in Songjiang District of Shanghai City from 2017 to 2023,and provide a basis for scientific prevention and control of influenza. Methods The data of influenza cases in Songjiang District of Shanghai City from 2017 to 2023 were collected and analyzed. Results From 2017 to 2023,the average annual incidence of influenza in Songjiang District was 10.38 per 100 000,mainly aged 0-<10 years. From 2021 to 2023,the incidence of influenza in Songjiang District showed an increasing trend(APC=456.65,P<0.05). The distribution of influenza in Songjiang District exhibited spatial clustering in 2017,2018 and 2023. The high-high aggregation areas were detected by local autocorrelation analysis and the hot spot areas were detected by hot spot analysis. Moreover,the class Ⅰ and Ⅱ aggregation areas detected by spatiotemporal scanning analysis were mainly concentrated in the central urban area of Songjiang District and its surrounding towns. Conclusion From 2017 to 2023,the incidence of influenza cases in Songjiang District illustrates a upward trend,with spatiotemporal clustering,predominantly in the central urban area and surrounding towns. Targeted prevention and control measures should be strengthened for key populations in key areas before the peak influenza season.

Key words: Influenza, Epidemic characteristics, Joinpoint regression analysis, Trend analysis, Spatial autocorrelation analysis, Spatiotemporal scanning

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