热带病与寄生虫学 ›› 2024, Vol. 22 ›› Issue (2): 68-75.doi: 10.3969/j.issn.1672-2302.2024.02.002

• 虫媒传染病专题 • 上一篇    下一篇

2017—2022年中国内脏利什曼病流行特征及时空聚集性分析

王奇1,3(), 师悦2, 秦瑶1,3, 马会来1, 张丽杰1, 孙军玲2(), 刘慧慧1()   

  1. 1.中国疾病预防控制中心现场流行病学培训项目,北京 100050
    2.中国疾病预防控制中心传染病管理处
    3.四川省疾病预防控制中心
  • 收稿日期:2024-03-25 出版日期:2024-04-20 发布日期:2024-04-30
  • 通信作者: 孙军玲,E-mail: sunjl@chinacdc.cn;刘慧慧,E-mail: liuhh@chinacdc.cn
  • 作者简介:王奇,男,本科,助理研究员,研究方向:寄生虫病防治。E-mail: wangq9111@163.com
  • 基金资助:
    中国现场流行病学培训项目;中美疾控中心合作项目(6NU2GGH002164-05-07);公共卫生应急反应机制运行项目(102393220020010000017)

Epidemiological characteristics and spatial-temporal cluster of visceral leishmaniosis in China, 2017-2022

WANG Qi1,3(), SHI Yue2, QIN Yao1,3, MA Huilai1, ZHANG Lijie1, SUN Junling2(), LIU Huihui1()   

  1. 1. Chinese Field Epidemiology Training Program of Education Division, Chinese Center for Disease Control and Prevention, Beijing 100050, China
    2. Division of infectious disease, Chinese Center for Disease Control and Prevention
    3. Sichuan Provincial Center for Disease Control and Prevention
  • Received:2024-03-25 Online:2024-04-20 Published:2024-04-30
  • Contact: SUN Junling, E-mail: sunjl@chinacdc.cn; LIU Huihui, E-mail: liuhh@chinacdc.cn

摘要:

目的 分析2017—2022年中国内脏利什曼病的流行特征和时空变化趋势,为有针对性地制定防控策略和措施提供科学依据。方法 通过中国疾病预防控制信息系统收集2017—2022年全国内脏利什曼病病例数据,进行人群分布特征和时间变化趋势的描述性分析;使用ArcGIS 10.7软件绘制病例分布地图,计算莫兰指数(Moran’s I)和局部莫兰指数(local Moran’s I),分析空间自相关性和聚集类型;采用SaTScan 10.1软件进行时空扫描,分析时空聚集特征。结果 2017—2022年,全国22个省份的296个县(市、区)共报告内脏利什曼病病例1 251例,其中犬源型流行区病例数891例(占71.22%),且病例数逐年上升;人源型和野生动物源型流行区病例数分别为47例(占3.76%)和36例(占2.88%),持续处于较低水平。4月的报告病例数最多(142例);男女性别比为2.18∶1;病例职业以农牧民和学龄前儿童为主,分别占43.88%(549/1 251)和27.50%(344/1 251);不同年龄组中,0~4岁的报告病例数最多,为325例(占25.98%),其次是50~64岁,为300例(占23.98%)。病例主要分布于山西(449例)、甘肃(257例)、陕西(188例)、新疆(88例)、四川(84例)、河南(70例)和河北(41例)等7个省份,占总报告病例数的94.08%(1 177/1 251)。犬源型、人源型和野生动物源型流行区累计报告病例数最多的县分别是平定县(112例)、喀什市(11例)和伽师县(11例)。空间自相关分析显示,病例在空间分布上存在聚集性(Moran’s I均>0,Z均>2.58,P均<0.01),高-高聚集区的县数由2017年的38个增加至2022年的48个,且由西部逐年向东转移;2022年河北、河南和北京出现新增高-高聚集的县。时空扫描分析显示,2017—2022年共发现3个聚集区,其中以阳泉市城区为中心,覆盖8个县(市、区)的一类聚集区发病风险最高(RR=46.76,LLR=704.79,P<0.01),聚集时间为2020年1月—2022年9月,发病数256例。结论 2017—2022年中国内脏利什曼病整体呈上升趋势,重点流行区域由新疆野生动物源型区域向山西、陕西、河南和河北等中北部的犬源型流行区转移,需针对重点地区、重点人群加强防控工作,遏制疫情上升势头。

关键词: 内脏利什曼病, 流行特征, 空间自相关, 莫兰指数, 时空聚类分析, 中国

Abstract:

Objective To analyze the epidemiological characteristics and spatial-temporal trends of visceral leishmaniosis (VL) in China from 2017 to 2022 for scientific evidence to formulate targeted prevention and control strategies and measures. Methods Individual data of VL cases in China from 2017 to 2022 were collected from the China Information System for Disease Control and Prevention, and descriptively analyzed for the population characteristics and time trends. ArcGIS 10.7 software was used to map the case distribution, and Moran’s I and local Moran’s I were calculated to analyze the spatial autocorrelation and clustering type. SaTScan 10.1 software was used to scan the spatial-temporal characteristics and analyze the clustering. Results Between 2017 and 2022, a total of 1 251 VL cases were reported in 296 counties of 22 provinces in China. Among these cases, 891 (71.22%) were from canine-derived endemic areas, with increase trend year by year. Cases from anthropogenic and wildlife-derived endemic areas accounted for 47 (3.76%) and 36 (2.88%), respectively, which remained at a low level. The number of VL cases was highest in April (142 cases), and the case ratio of male-to-female was 2.18∶1. Farmers and herdsmen as well as preschool children dominated the cases (43.88%, 549/1 251; 27.50%, 344/1 251, respectively). By the age group, 0-4 years old were the most reported (325 cases, 25.98%), followed by 50-64 years old (300 cases, 23.98%). The cases were primarily distributed in Shanxi (449 cases), Gansu (257 cases), Shaanxi (188 cases), Xinjiang (88 cases), Sichuan (84 cases), Henan (70 cases) and Hebei (41 cases), which accounted for 94.08% (1 177/1 251) of the total cases. The canine-derived in Pingding County (112 cases), the anthropogenic in Kashgar City (11 cases) and the wildlife-derived in Jiashi County (11 cases) were the highest cumulative number of cases. Spatial correlation analysis showed a spatial clustering (Moran’s I>0, all Z>2.58, all P<0.01), and the number of counties in high-high clustering area increased from 38 in 2017 to 48 in 2022, displacing from west to east. High-high clustering occurred in Hebei, Henan and Beijing in 2022. Spatio-temporal scanning showed three-level aggregation areas from 2017 to 2022, and the highest incidence was found in the first-level aggregation area, covering 8 counties and centering on the urban area of Yangquan City (RR=46.76, LLR=704.79, P<0.01). The aggregation occurred between January 2020 to September 2022, during which 256 cases were reported. Conclusion The VL reported in China from 2017 to 2022 presented a rising trend. The key epidemic areas shifted from Xinjiang with wildlife-derived to Shanxi, Shaanxi, Henan and Hebei with canine-derived in the north-central of China. The findings suggest that it is necessary to intensify prevention and control efforts targeting key areas and populations to restrain the epidemic.

Key words: Visceral leishmaniosis, Epidemiological characteristics, Spatial autocorrelation, Moran’s I, Temporal-spatial clustering analysis, China

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