中国血吸虫病防治杂志 ›› 2023, Vol. 35 ›› Issue (4): 349-357.

• 论著 • 上一篇    下一篇

2020—2022年湖北省钉螺扩散空间分布特征分析

陈艳艳,肖瑛,魏凤华,杨军晶,代凌峰,钟晨晖*,刘建兵*   

  1. 湖北省疾病预防控制中心(湖北 武汉 430079)
  • 出版日期:2023-08-15 发布日期:2023-10-12
  • 作者简介:陈艳艳,女,主任医师。研究方向:血吸虫病流行病学
  • 基金资助:
    湖北省自然科学基金(2019CFB114);湖北省卫生计生科研基金资助项目(WJ2018H251)

Spatial distribution of Oncomelania hupensis spread in Hubei Province from 2020 to 2022

CHEN Yanyan, XIAO Ying, WEI Fenghua, YANG Junjing, DAI Lingfeng, ZHONG Chenhui*, LIU Jianbing*   

  1. Hubei Center for Disease Control and Prevention, Wuhan, Hubei 430079, China
  • Online:2023-08-15 Published:2023-10-12

摘要: 目的 分析湖北省钉螺扩散空间分布规律,为全省精准控制钉螺提供科学依据。方法 收集2020—2022年湖北省新发和复现钉螺资料,构建钉螺扩散空间数据库。分别采用全局和局部空间自相关分析识别钉螺扩散空间聚集性;采用核密度分析法探索钉螺扩散热点区域;采用近邻分析和Spearman相关分析探索钉螺扩散环境与主要水系长江之间距离的关系。结果 2020—2022年,湖北省钉螺扩散主要分布在长江沿线及江汉平原,累计扩散面积4 320.63 hm2,其中新发钉螺面积1 230.77 hm2、复现钉螺面积3 089.87 hm2。全局空间自相关分析发现,2020年和2021年湖北省钉螺扩散面积分布存在空间自相关,呈空间聚集模式(Moran’s I = 0.003 593、0.060 973,P均< 0.05)。2020年,湖北省扩散钉螺平均密度呈空间聚集现象(Moran’s I = 0.512 856,P < 0.05)。局部空间自相关分析发现,2020—2022年湖北省扩散钉螺面积呈高值(high⁃high,H⁃H)聚集区域主要分布在武穴市、洪湖市和黄州区等10个县(市、区)的50处环境;扩散钉螺平均密度呈H⁃H聚集区域主要分布在江陵县、洪湖市、阳新县和公安县等4个县(市)的219处钉螺环境。核密度分析发现,2020—2022年湖北省钉螺扩散面积存在高、次高和中密度等3类热点区,分别分布在荆州区、武穴市、洪湖市和黄州区。扩散钉螺平均密度存在高、中密度等2类热点区,分别分布在江陵县、洪湖市和阳新县。湖北省钉螺扩散面积与环境距长江距离呈负相关(r = – 0.108 9,P < 0.05)。结论 湖北省钉螺扩散具有空间聚集性,需对聚集区域尤其是垸内环境加强钉螺监测与控制力度,防止发生血吸虫病疫情反弹。

关键词: 血吸虫病, 钉螺, 空间自相关分析, 核密度分析, 湖北省

Abstract: Objective To identify the spatial distribution pattern of Oncomelania hupensis spread in Hubei Province, so as to provide insights into precision O. hupensis snail control in the province. Methods Data pertaining to emerging and reemerging snails were collected from Hubei Province from 2020 to 2022 to build a spatial database of O. hupensis snail spread. The spatial clustering of O. hupensis snail spread was identified using global and local spatial autocorrelation analyses, and the hot spots of snail spread were identified using kernel density estimation. In addition, the correlation between environments with snail spread and the distance from the Yangtze River was evaluated using nearest⁃neighbor analysis and Spearman correlation analysis. Results O. hupensis snail spread mainly occurred along the Yangtze River and Jianghan Plain in Hubei Province from 2020 to 2022, with a total spread area of 4 320.63 hm2, including 1 230.77 hm2 emerging snail habitats and 3 089.87 hm2 reemerging snail habitats. Global spatial autocorrelation analysis showed spatial autocorrelation in the O. hupensis snail spread in Hubei Province in 2020 and 2021, appearing a spatial clustering pattern (Moran’s I = 0.003 593 and 0.060 973, both P values < 0.05), and the mean density of spread snails showed spatial aggregation in Hubei Province in 2020 (Moran’s I = 0.512 856, P < 0.05). Local spatial autocorrelation analysis showed that the high⁃high clustering areas of spread snails were mainly distributed in 50 settings of 10 counties (districts) in Hubei Province from 2020 to 2022, and the high⁃high clustering areas of the mean density of spread snails were predominantly found in 219 snail habitats in four counties of Jiangling, Honghu, Yangxin and Gong’an. Kernel density estimation showed that there were high⁃, secondary high⁃ and medium⁃density hot spots in snail spread areas in Hubei Province from 2020 to 2022, which were distributed in Jingzhou District, Wuxue District, Honghu County and Huangzhou District, respectively. There were high⁃ and medium⁃density hot spots in the mean density of spread snails, which were located in Jiangling County, Honghu County and Yangxin County, respectively. In addition, the snail spread areas negatively correlated with the distance from the Yangtze River (r = –0.1089, P < 0.05). Conclusions There was spatial clustering of O. hupensis snail spread in Hubei Province from 2020 to 2022. The monitoring and control of O. hupensis snails require to be reinforced in the clustering areas, notably in inner embankments to prevent reemerging schistosomiasis.

Key words: Schistosomiasis, Oncomelania hupensis, Spatial autocorrelation analysis, Kernel density estimation, Hubei Province

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