Geographic Information Systems and Applied Spatial Statistics Are Efficient Tools to Study Hansen's Disease (Leprosy) and to Determine Areas of Greater Risk of Disease
Geographic Information Systems and Applied Spatial Statistics Are Efficient Tools to Study Hansen's Disease (Leprosy) and to Determine Areas of Greater Risk of Disease
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DOI：
10.4269/ajtmh.2010.08-0675
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发表时间：
2010-02-01
影响因子：
3.3
通讯作者：
Jeronimo, Selma M. B.
中科院分区：
文献类型：
--
作者：
Queiroz, Jose Wilton;Dias, Gutemberg H.;Jeronimo, Selma M. B.
应用空间统计学与地理信息系统（GIS）相结合，为疾病监测提供了有效的工具。这里.利用这些工具，我们分析了巴西一个地方病流行区的汉森病的空间分布。从1，293例病例中选择的808例样本在巴西北格兰德河的莫索罗进行了地理编码。汉森病病例在社区内并不是随机分布的，在人口较多的地区发现的检出率较高。聚类分析确定了两个高风险区域，一个相对风险为5.9（P = 0.001），另一个为6.5（P = 0.001）。据观察，疾病的地理分布与表明贫穷的社会经济变量之间存在着重要关系。我们的研究表明，地理信息系统和空间分析相结合，可以确定聚集的传染病，如汉森病，指出干预措施，可以有针对性地控制疾病的领域。
Applied Spatial Statistics used in Conjunction with geographic information systems (GIS) provide ail efficient tool for the surveillance of diseases. Here. using these tools we analyzed the spatial distribution of Hansen's disease in an endemic area in Brazil. A sample of 808 selected from a Universe of 1,293 cases was geocoded in Mossoro, Rio Grande do Norte, Brazil. Hansen's disease cases were not distributed randomly within the neighborhoods, with higher detection rates found in more populated districts. Cluster analysis identified two areas of high risk, one with a relative risk of 5.9 (P = 0.001) and the other 6.5 (P = 0.001). A significant relationship between the geographic distribution of disease and the social economic variables indicative of poverty was observed. Our study shows that the combination of G IS and spatial analysis can identify clustering of transmissible disease, such as Hansen's disease, pointing to areas where intervention efforts can be targeted to control disease.