热带病与寄生虫学

热带病与寄生虫学 ›› 2025, Vol. 23 ›› Issue (3): 171-175,182.doi: 10.20199/j.issn.1672-2302.2025.03.008

• 防治研究 • 上一篇    下一篇

2017—2023年成都市猩红热流行特征及时空聚集性分析

李永盛1(), 杜训波1, 龙露1, 魏荣杰2, 王瑶1(), 王亮1()   

  1. 1.成都市疾病预防控制中心，四川成都 610041
    2.四川省疾病预防控制中心
  • 收稿日期:2024-11-09 出版日期:2025-06-20 发布日期:2025-08-08
  • 通信作者: 王瑶，E-mail: 330146703@qq.com；王亮，E-mail: 363686849@qq.com
  • 作者简介:李永盛，男，硕士，主管医师，研究方向：传染病控制。E-mail: 946678483@qq.com
  • 基金资助:
    四川省科技计划项目(2022YFS0052);成都市医学科研课题(2025038);成都市科技重点研发支持计划重大及应用示范项目(2021-YF09-00061-SN);重庆市技术创新与应用发展专项重点项目(stc2020isc-evhX0003)

Epidemiological characteristics and spatiotemporal distribution of scarlet fever in Chengdu City, 2017-2023

LI Yongsheng1(), DU Xunbo1, LONG Lu1, WEI Rongjie2, WANG Yao1(), WANG Liang1()   

  1. 1. Chengdu Center for Disease Control and Prevention, Chengdu 610041, Sichuan Province, China
    2. Sichuan Center for Disease Control and Prevention
  • Received:2024-11-09 Online:2025-06-20 Published:2025-08-08
  • Contact: WANG Yao, E-mail: 330146703@qq.com; WANG Liang, E-mail: 363686849@qq.com

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摘要：

目的 分析成都市猩红热的流行特征和时空聚集性，为猩红热的科学防控提供参考。方法 通过中国疾病预防控制信息系统获取2017—2023年成都市各区（市、县）街道/乡镇的猩红热监测数据，描述发病概况及三间分布特征，采用空间自相关分析和时空扫描分析方法分析时空聚集性。结果 2017—2023年成都市累计报告猩红热病例4 433例；报告发病率为1.41/10万~7.50/10万，整体呈波动下降趋势（χ2趋势=1 089.79，P<0.01）。4—6月（1 655例，占37.33%）和11月—次年1月（1 336例，占30.14%）为发病高峰期。3~8岁为高发年龄段（3 837例，占86.56%），男、女报告病例数分别为2 669例和1 764例，男性年均报告发病率（2.08/10万）高于女性（1.38/10万）。各地区中，邛崃市年均报告发病率最高（13.68/10万），新都区报告病例数最多（1 034例，占23.33%）。空间自相关分析发现，2017—2019年和2021—2023年成都市猩红热发病率呈空间正相关（Moran’s I均>0，Z均>1.960，P均<0.05），热点区域为新都区、邛崃市、双流区、龙泉驿区等区（市、县）的部分街道/乡镇；时空扫描分析共探测到1个一类聚集区和6个二类聚集区，一类聚集区为新都区，聚集时间为2017年1月—2019年1月。结论 成都市猩红热疫情整体呈波动下降趋势，具有明显的时空聚集性。应在高发季节加强对重点区域、重点人群的监测预警和宣传教育，减少疾病危害。

关键词: 猩红热, 流行特征, 空间自相关, 时空扫描, 成都市

Abstract:

Objective To investigate the epidemiological characteristics and spatiotemporal clustering of scarlet fever in Chengdu area for evidences to formulate scientific prevention and control measures for this acute respiratory infection. Methods Surveillance data of scarlet fever reported at each subdistrict/township of urban areas, counties, communities in Chengdu City from 2017 to 2023 were obtained from the Chinese Disease Prevention and Control Information System. The epidemiological profile and temporal, spatial and population distribution were characterized, and spatial autocorrelation and spatiotemporal scan analyses were performed to identify clustering patterns. Results Between 2017 and 2023, a total of 4 433 scarlet fever cases were reported in Chengdu area. The annual incidence ranged from 1.41/100 000 to 7.50/100 000, and overall, the prevalence tended to decline (χ2trend=1 089.79, P<0.01). Seasonal peaks occurred in April-June (n=1 655; 37.33%) and November-January (n=1 336; 30.14%). The incidence was dominated by children aged 3-8 years (n=3 837; 86.56%). The reported cases were 2 669 for males and 1 764 for females, and the annually reported cases were higher in males than in females (n=2 669; 2.08/100 000 vs. n=1 764; 1.38/100 000). Qionglai City had the highest average annual incidence (13.68/100 000), whereas the most cases were from Xindu District (n=1 034; 23.33%). Spatial autocorrelation analysis revealed a significant spatial positive correlation in 2017-2019 and 2021-2023 (Moran’s I>0, Z>1.960, P<0.05), and that the hotspots were involved in the subdistricts/townships of Xindu, Qionglai, Shuangliu, and Longquanyi. Spatiotemporal scan analysis identified one primary cluster (Xindu District, January 2017-January 2019) and six secondary clusters. Conclusion The incidence of scarlet fever in Chengdu City exhibited a fluctuating downward trend with notable spatiotemporal clustering. Our findings suggest that surveillance, early warning, publicity and education should be intensified in key areas and key populations during the higher prevalence seasons to mitigate the disease burden.

Key words: Scarlet fever, Epidemic characteristics, Spatial autocorrelation, Spatiotemporal scan, Chengdu City

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