2010―2023年杭州市猩红热流行病学特征及时空聚集性分析

王乙; 王哲; 金新叶; 陶明勇; 段晓健; 朱一; 贺兆锴; 孙昼

doi:10.16462/j.cnki.zhjbkz.2024.08.013

2010―2023年杭州市猩红热流行病学特征及时空聚集性分析

doi: 10.16462/j.cnki.zhjbkz.2024.08.013

杭州市疾病预防控制中心(杭州市卫生监督所)传染病防制所，杭州 310021

基金项目:

浙江省医药卫生科技计划项目 2024KY1414

杭州市医药卫生科技计划项目 A20200444

详细信息

通讯作者:
孙昼. E-mail:hzcdc@qq.com

中图分类号: R181.3;R515.1
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出版历程
- 收稿日期: 2024-03-14
- 修回日期: 2024-06-18
- 网络出版日期: 2024-09-29
- 刊出日期: 2024-08-10

Epidemiological characteristics and spatial-temporal clustering of scarlet fever in Hangzhou from 2010 to 2023

Institute of Infectious Disease Control and Prevention, Hangzhou Center for Disease Controland Prevention (Hangzhou Health Supervision Institution), Hangzhou 310021, China

Funds:

Zhejiang Provincial Medical and Health Science and Technology Project 2024KY1414

Hangzhou Medical and Health Technology Project A20200444

More Information

Corresponding author: SUN Zhou，E-mail:hzcdc@qq.com

摘要

摘要: 目的探索2010―2023年杭州市猩红热的流行病学特征及其时空分布特点，为杭州市猩红热防控策略的制定和合理配置卫生资源提供科学依据。方法 2010―2023年杭州市猩红热发病数据来源于中国疾病预防控制信息系统，采用Joinpoint5.1.0软件对猩红热发病数据进行趋势分析，运用ArcGIS 10.8软件开展空间自相关分析，使用SaTScan 10.1.2软件进行时空扫描分析。结果 2010―2023年杭州市猩红热的年均发病率为5.06/10万；Joinpoint回归分析结果显示，2015―2023年杭州市各年的猩红热发病率呈下降趋势。病例分布呈季节性，春末夏初(4―6月)和秋末冬初(11月―次年1月)为发病高峰期。病例主要为3~＜9岁组(85.79%)、男性(61.00%)和幼托儿童(47.09%)。拱墅区和上城区是杭州市猩红热的高发区域，占所有报告病例的35.73%(2 459/6 882)。全局自相关分析显示，除2011年、2013年和2014年外，其余各年猩红热发病率均存在空间相关性(均P＜0.05)；局部空间自相关分析显示，2010―2023年杭州市猩红热发病率均有热点区域(“高-高”聚集)出现，且相对集中分布于东北部和中部区域。时空扫描分析共探测到3个聚集区，其中一类聚集区主要位于上城区、拱墅区的13个街道，聚集时间为2014年3月―2016年6月。结论 2010―2023年，杭州市猩红热疫情先呈上升趋势，自2015年后呈下降趋势，全市存在时空聚集性，主要集中于杭州市东北部和中部区域，应加强高发地区和人群的健康教育以及疾病监测工作。
- 猩红热 /
- 流行病学特征 /
- 空间自相关 /
- 时空聚集性
Abstract: Objective This study aims to explore the epidemiology and spatial-temporal clustering characteristics of scarlet fever in Hangzhou from 2010 to 2023, providing a scientific basis for the formulation of prevention and control strategies for scarlet fever as well as the rational allocation of health resources in Hangzhou. Methods The incidence data of scarlet fever in Hangzhou from 2010 to 2023 came from China Disease Prevention and Control Information System. Joinpoint 5.1.0 software was used to analyze the trend of scarlet fever incidence data, and ArcGIS 10.8 software was used to carry out spatial autocorrelation analysis. SaTScan 10.1.2 was used to perform spatio-temporal scan analysis. Results The average annual incidence of scarlet fever in Hangzhou from 2010 to 2023 was 5.06 per 100 000 population. Joinpoint regression analysis revealed a decreasing trend in the incidence of scarlet fever in Hangzhou from 2015 to 2023. The distribution of cases exhibited distinct seasonal patterns, peaking in late spring to early summer (April to June) and late autumn to early winter (November to January of the following year). The majority of cases were among children aged 3~9 years (85.79%), males (61.00%), and preschool children (47.09%). Gongshu District and Shangcheng District were identified as high-incidence areas, accounting for 35.73% (2 459/6 882) of all reported cases. The global autocorrelation analysis showed that the incidence of scarlet fever was spatially correlated in all years except 2011, 2013 and 2014 (P<0.05). Local spatial autocorrelation analysis indicated the presence of hotspots ("high-high" clusters) of scarlet fever incidence in Hangzhou from 2010 to 2023, relatively concentrated in the northeastern and central regions. Spatiotemporal scan analysis detected three clusters, of which the primary cluster encompassed 13 neighborhoods in Shangcheng and Gongshu Districts, spanning from March 2014 to June 2016. Conclusions From 2010 to 2023, the epidemic of scarlet fever in Hangzhou initially shows a fluctuating upward trend, followed by a fluctuating downward trend after 2015. There is a significant spatial-temporal clustering in the city, primarily concentrated in the northeastern and central regions of Hangzhou. Enhanced health education and disease surveillance efforts should be implemented in high-incidence areas and populations.
- Scarlet fever /
- Epidemiological characteristics /
- Spatial autocorrelation /
- Spatial-temporal clustering

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图 1 2010―2023年杭州市猩红热报告病例数及发病率

Figure 1. Number of cases and incidence of scarlet fever in Hangzhou, 2010-2023

下载: 全尺寸图片幻灯片

图 2 2010―2023年杭州市猩红热发病率变化趋势Jointpoint回归分析

APC：年度变化百分比；a: P＜0.05。

Figure 2. Jointpoint regression analysis of scarlet fever incidence in Hangzhou from 2010 to 2023

annual percent change; a: P<0.05.

下载: 全尺寸图片幻灯片

图 3 2010―2023年杭州市猩红热病例发病时间分布

Figure 3. Time distribution of scarlet fever cases in Hangzhou from 2010 to 2023

下载: 全尺寸图片幻灯片

表 1 2010―2023年杭州市猩红热发病率全局空间自相关分析

Table 1. Global spatial autocorrelation analysis of incidence of scarlet fever in Hangzhou from 2010 to 2023

年份 Year	Moran′s I	Z 值 value	P 值 value
2010	0.12	2.06	0.039
2011	0.01	1.11	0.266
2012	0.05	2.40	0.016
2013	0.06	1.94	0.053
2014	0.03	1.59	0.111
2015	0.05	3.22	0.001
2016	0.19	6.07	＜0.001
2017	0.17	6.48	＜0.001
2018	0.11	3.23	0.001
2019	0.07	3.58	＜0.001
2020	0.19	3.87	＜0.001
2021	0.50	10.68	＜0.001
2022	0.33	14.88	＜0.001
2023	0.48	9.45	＜0.001

下载: 导出CSV

表 2 2010―2023年杭州市猩红热病例时空扫描结果

Table 2. Spatial-temporal scan results of scarlet fever cases in Hangzhou from 2010 to 2023

聚集区类型 Type of clusters	实际病例数 Number of actual cases	期望病例数 Number of expected cases	半径 Radiu/km	聚集时间/年-月 Cluster time/year-month	涉及街道(乡镇)数量 Number of towns and streets involved	RR 值value	LLR 值value	P 值 value
一类聚集区 Class one clusters	739	129	6.28	2014.03―2016.06	13	6.28	706.38	＜0.001
二类聚集区1 Class two clusters 1	168	18	13.65	2021.05―2022.07	9	9.29	222.93	＜0.001
二类聚集区2 Class two clusters 2	209	70	22.69	2015.01―2015.06	38	3.01	89.46	＜0.001

下载: 导出CSV

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