热带病与寄生虫学 ›› 2025, Vol. 23 ›› Issue (4): 211-215.doi: 10.20199/j.issn.1672-2302.2025.04.004

• 登革热防控专题 • 上一篇    下一篇

2011—2023年成都市登革热流行特征和时间趋势分析

严可(), 冯静, 周蓉, 杜训波, 刘辉, 王瑶()   

  1. 成都市疾病预防控制中心(成都市卫生监督所)四川成都 610041
  • 收稿日期:2024-09-24 出版日期:2025-08-20 发布日期:2025-09-19
  • 通信作者: 王瑶,E-mail: 330146703@qq.com
  • 作者简介:严可,女,硕士,医师,研究方向:传染病控制。E-mail: cherylkeyan@163.com
  • 基金资助:
    成都市疾病预防控制中心青年项目(20240201)

Epidemiological characteristics and temporal trend of dengue fever in Chengdu City, 2011-2023

YAN Ke(), FENG Jing, ZHOU Rong, DU Xunbo, LIU Hui, WANG Yao()   

  1. Chengdu Center for Disease Control and Prevention (Chengdu Institute of Health Supervision), Chengdu 610041, Sichuan Province, China
  • Received:2024-09-24 Online:2025-08-20 Published:2025-09-19
  • Contact: WANG Yao, E-mail: 330146703@qq.com

摘要:

目的 分析成都市登革热流行特征和时间趋势,为登革热输入和本地传播的有效防控提供参考。方法 通过中国疾病预防控制信息系统以及流行病学调查,获取并整理2011—2023年成都市登革热病例资料,采用描述流行病学方法分析其流行特征,采用Joinpoint回归模型分析其时间趋势。结果 2011—2023年成都市共报告登革热病例269例,其中本地病例1例、境外输入197例、境内输入71例。输入病例数居前3位的区(市、县)为新都区(28例)、金牛区(27例)、青羊区(24例)。输入病例中,发病年龄主要集中在21~50岁(225例,83.96%);男性150例、女性118例,性别比为1.27∶1;职业分布以商业服务人员(60例,22.39%)、家务及待业(59例,22.01%)和工人(47例,17.54%)为主。境外输入登革热病例以东南亚地区输入为主(169例,占85.79%),境内输入来源为云南省(69例,占97.18%)和广东省(2例,占2.82%)。全年均有境外输入病例报告,发病高峰为6—10月(占69.54%,137/197);境内输入病例仅在8—11月报告;本地病例报告时间为10月。2011—2023年成都市登革热病例数呈“缓升-升-降-升”的趋势(AAPC=39.26%,P<0.05)。2011—2018年登革热病例数缓慢上升(APC=28.53%,P<0.05),2018—2019年病例数迅速增加(APC=322.90%,P<0.05),2019—2021年病例数急剧下降(APC=-99.58%,P<0.05),2021—2023年病例数大幅上升(APC=2 746.79%,P<0.05)。结论 成都市登革热疫情主要受东南亚地区和国内邻近省份病例输入影响,夏秋季为防控的重点时间,应采取有效措施应对输入病例造成的疫情扩散及其引发本地疫情的风险。

关键词: 登革热, 流行病学特征, Joinpoint回归模型, 成都市

Abstract:

Objective To analyze the epidemiological characteristics and temporal trend of dengue fever in Chengdu area for scientific evidences to effectively control epidemic import and local transmission. Methods The case data of dengue fever reported in Chengdu area from 2011 to 2023 were collected from the Chinese Disease Prevention and Control Information System as well as epidemiological investigation. Descriptive epidemiology was used to analyze the epidemiological characteristics, and Joinpoint regression model was used to analyze time trends. Results In total, 269 cases of dengue were reported in Chengdu area from 2011 to 2023, including 1 indigenous case, 197 imported cases from overseas, and 71 imported cases from other provinces within China. The top three districts/counties with the higher number of imported cases were Xindu District (28 cases), Jinniu District (27 cases), and Qingyang District (24 cases). Of the imported cases, the onset age was dominant in 21-50 years (225 cases, 83.96%). One hundred and fifty were males, and 118 females, with a sex ration of 1.27∶1. The occupational distribution was led by commerce/services (60 cases, 22.39%), homemakers/unemployed (59 cases, 22.01%), and workers (47 cases, 17.54%). Overseas imported cases originated primarily from Southeast Asia (169 cases, 85.79%), and domestic imported cases came from Yunnan Province (69 cases, 97.18%) and Guangdong Province (2 cases, 2.82%). Overseas imported cases occurred year-round, and the incidence peaked from June to October (69.54%, 137/197). Domestic imported cases were reported exclusively between August and November. The locally acquired case occurred in October. Overall, the incidence of dengue fever in Chengdu area exhibited a “slow rise-rise-decline-rise” trend from 2011 to 2023 (AAPC=39.26%, P<0.05). The case number was slowly increased from 2011 to 2018 (APC=28.53%, P<0.05), shot rapidly from 2018 to 2019 (APC=322.90%, P<0.05), sharply decreased from 2019 to 2021 (APC=-99.58%, P<0.05), and greatly went up from 2021 to 2023 (APC=2 746.79%, P<0.05). Conclusion The dengue fever epidemic in Chengdu area is primarily influenced by imported cases from Southeast Asian regions and neighboring domestic provinces. Summer and autumn seasons represent critical periods for prevention and control dengue fever in Chengdu area. Effective measures should be implemented to address the risk of epidemic spread caused by imported cases and potential local outbreaks.

Key words: Dengue fever, Epidemiological characteristics, Joinpoint regression model, Chengdu City

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