Spatial analysis of the distribution of Lyme disease in Wisconsin

Spatial analysis of the distribution of Lyme disease in Wisconsin
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DOI:
10.1093/oxfordjournals.aje.a009145
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发表时间:
1997-03-15
影响因子:
5
通讯作者:
Kazmierczak, JJ
Kazmierczak, JJ
中科院分区:
医学2区
文献类型:
--
作者:
Kitron, U;Kazmierczak, JJ

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比较了威斯康星州莱姆病人间病例的监测措施,并将其与蜱虫分布和植被覆盖率联系起来。1991-1994年期间,向威斯康星州卫生部门报告的1 759例莱姆病确诊人间病例被指定为居住县,但只有329例(19%)可以确定指定为接触县。按暴露县和居住地分列的病例分布每年往往是一致的。根据研究人员收集的资料、全州范围内对受感染鹿的调查以及公众提交的意见,绘制了威斯康星州72个县中46个县的蜱虫分布地图。利用卫星数据计算各县的归一化植被指数(NDVI)。利用地理信息系统(GIS)绘制人畜莱姆病病例分布、蜱虫分布和植被覆盖度分布图,暴露县人畜莱姆病病例分布与蜱虫分布呈显著相关;两者都与春季和秋季的高NDVI值呈正相关,春季和秋季在卫星图像中可以区分出树木植被和农作物。使用空间自相关测量的空间模式统计分析表明,人类病例和蜱虫最多的县集中在威斯康星州西部的部分地区。根据暴露的人类病例数和蜱虫浓度,绘制了莱姆病传播风险最高的县的分布图。
Surveillance measures for human cases of Lyme disease in Wisconsin were compared and associated with tick distribution and vegetation coverage. During 1991-1994, 1,759 confirmed human cases of Lyme disease reported to the Wisconsin Division of Health were assigned a county of residence, but only 329 (19%) could be assigned with certainty a county of exposure. Distributions of cases by county of exposure and residence were often consistent from year to year. Tick distribution in 46 of 72 Wisconsin counties was mapped based on collections by researchers, statewide surveys of infested deer, and submissions from the public. Satellite data were used to calculate a normalized difference vegetation index (NDVI) for each county. A geographic information system (GIS) was used to map distributions of human Lyme disease cases, ticks, and degree of vegetation cover, Human case distribution by county of exposure was significantly correlated with tick distribution; both were positively correlated with high NDVI Values in spring and fall, when wooded vegetation could be distinguished from agricultural crops in the satellite image. Statistical analysis of spatial patterns using a measure of spatial autocorrelation indicated that counties with most human cases and ticks were clustered in parts of western Wisconsin. A map delineating the counties with highest risk for Lyme disease transmission was generated based on numbers of exposed human cases and tick concentrations.