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安徽池州市晚期血吸虫病流行状况的空间分析
引用本文:巩六兵,;邹武庆,;解卫平,;冯晓明,;吴明耀,;王忠明,;李石柱,;高风华. 安徽池州市晚期血吸虫病流行状况的空间分析[J]. 热带病与寄生虫学, 2014, 12(2): 63-66. DOI: 10.3969/j.issn.1672-2302.2014.02.001
作者姓名:巩六兵,  邹武庆,  解卫平,  冯晓明,  吴明耀,  王忠明,  李石柱,  高风华
作者单位:247000 安徽池州市,池州市疾病预防控制中心(巩六兵、邹武庆),东至县血吸虫病防治站(解卫平),贵池区血吸虫病防治站(冯晓明),石台县疾病预防控制中心(吴明耀),青阳县血吸虫病防治站(王忠明),中国疾病预防控制中心寄生虫病预防控制所(李石柱),安徽省血吸虫病防治研究所(高风华);* 通讯作者
基金项目:国家传染病重大专项(2012ZXl0004-201),安徽省血防科研基金(1401)
摘    要:目的 探讨池州市晚期血吸虫病(晚血)的流行状况和空间分布特征。方法 以寄生虫病信息管理系统2013 年报告的池州市晚血患者为研究对象,分别建立晚血个案数据库与基于乡镇的空间数据库。在描述分析晚血分布基本特征的基础上,基于Arcgis10.2,运用全局空间自相关(Moran's I)、局部空间自相关(Local Moran's I,LISA)与(Getis-Ord Gi*)分析乡级尺度上晚血空间分布特征。结果 2013年池州市共有晚血患者1 855例,患病率为0.11%;患者年龄12~93岁,多为55岁以上,占76.0%(1 410/1 855);男性占51.3%(952/1 855),女性占48.7%(903/1 855),男女比为1.05∶1;患者的职业以农民为主,占86.6%(1 607/1 855);文化程度以文盲和小学程度为主,分别占48.4%(898/1 855)和35.6%(661/1 855);晚血分型以腹水型83.9%(1 557/1 885)为主;首次确诊年份在2000~2009 年的占61.0%(1 131/1 855)。全局空间自相关结果显示,总体研究区域上晚血患病率存在空间自相关(Moran's I=0.215 6,P<0.01);局部空间自相关结果显示,LISA 分析与Getis-Ord Gi*分析均探测出相同的5 个晚血患病率高值聚集且具有统计学意义乡镇(P<0.05),这些乡镇均位于池州市东至县。结论 池州市晚血以中老年的农民为主,晚血分型以腹水型为主。晚血分布存在空间自相关,有5 个高值聚集乡镇,该些乡镇均为东至县的疫情较重乡镇。分析结果为池州市晚血的控制提供了线索与依据。

关 键 词:晚期血吸虫病  地理信息系统  空间自相关分析  

Study on spatial distribution of advanced schistosomiasis in Chizhou City based on geographic information system
Gong Liubing,Zou Wuqing,Xie Weiping,Fong Xiaoming,Wu Mingyao,Wang Zhongming,Li Shizhu,Gao Fenghua. Study on spatial distribution of advanced schistosomiasis in Chizhou City based on geographic information system[J]. Journal of Tropical Diseases and Parasitology, 2014, 12(2): 63-66. DOI: 10.3969/j.issn.1672-2302.2014.02.001
Authors:Gong Liubing  Zou Wuqing  Xie Weiping  Fong Xiaoming  Wu Mingyao  Wang Zhongming  Li Shizhu  Gao Fenghua
Affiliation:1. Chizhou Center for Disease Control and Prevention,Chizhou 247000,China. 2.Dongzhi Station of Schistosomiasis Control. 3. Guichi Station of Schistosomiasis Sontrol. 4. Qingyang Station of Schistosomiasis Control. 5. Shitai Center for Disease Control and Prevention. 6. National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention. 7. Anhui Provincial Institute of Schistosomiasis Control. *Corresponding author.
Abstract:Objective To understand the prevalence situation and spatial autocorrelation features of advanced schistosomiasis in Chizhou. Methods The data of advanced schistosomiasis in Chizhou were obtained from the National Surveillance System of Parasite Diseases in 2013. The geographic database of advanced schistosomiasis at the township level and the case database were set up. Statistical analyses were performed to describe the epidemiological features (distribution by age, gender, occupation, and type) of advanced schistosomiasis. The global clustering method( Moran's I) and local clustering methods including local Moran's I and Getis-Ord Gi indices were applied to analyze the spatial distribution of advanced schistosomiasis on Arcgis 10.2. Results A total of 1 855 cases (51.3% were male, 47.7% were female) were reported in Chizhou in 2013. The age ranged between l2 and 93 years old, and 76.0%were over 55 years old. 86.6%of the cases were farmers, 48.4%were illiteracy and 35.6%received primary education. 83.9%of the cases were ascitic type, and 61.0% were first diagnosed as advanced schistosomiasis in 2000-2009. The global Moran's I of prevalence rate of advanced schistosomiasis was 0.2156 (P〈0.05) and there was spatial auto-correlation as a whole.The local Moran's I index and Getis-Ord Gi statistics both found 5 townships, where were located in Dongzhi county, showed High- High positive spatial association with statistical significance ( P〈0.05). Conclusions The majority of patients with advance schistosomiasis were middle-aged and elderly farmers and most of the patients were ascitic type in Chizhou.A positive spatial correlation of advanced schistosomiasis was found, and 5 clustering townships were identified in Dongzhi County. The findings provided clues useful for disease surveillance and control in Chizhou.
Keywords:Advanced schistosomiasis  Geographic information system(GIS)  Spatial autocorrelation analysis
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