ENVIRONMENTAL RISK-FACTORS FOR LYME-DISEASE IDENTIFIED WITH GEOGRAPHIC INFORMATION-SYSTEMS
ENVIRONMENTAL RISK-FACTORS FOR LYME-DISEASE IDENTIFIED WITH GEOGRAPHIC INFORMATION-SYSTEMS
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DOI：
10.2105/ajph.85.7.944
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发表时间：
1995-07-01
影响因子：
12.7
通讯作者：
ISRAEL, E
中科院分区：
文献类型：
--
作者：
GLASS, GE;SCHWARTZ, BS;ISRAEL, E
目标。利用地理信息系统对莱姆病的居住环境风险因素进行识别和定位。获得了从1989年到1990年巴尔的摩县莱姆病患者住所的53个环境变量的数据，并与随机选择的地址的数据进行了比较。将地理信息系统与Logistic回归分析相结合，建立了风险模型。通过将1991年的病例分布与另一组随机选择的地址进行比较，验证了该模型的有效性。在粗略的分析中，11个环境变量与莱姆病有关。在校正分析中，居住在林区(优势比[OR]=3.7，95%可信区间[CI]=1.2，11.8)、特定土壤(OR=2.1，95%CI=1.0，4.4)和两个地区(OR=3.5，95%CI=1.6，7.4)(OR=2.8，95%CI=1.0，7.7)与莱姆病的风险增加有关。经济高度发达地区的居民具有保护性(OR=0.3，95%CI=0.1、1.0)。1991年莱姆病的风险随着1989年至1990年数据中风险类别的确定而增加。将地理信息系统与流行病学方法相结合，可以快速识别大面积人畜共患病的危险因素。
Objective. A geographic information system was used to identify and locate residential environmental risk factors for lyme disease.Methods. Data were obtained for 53 environmental variables at the residences of Lyme disease case patients in Baltimore County from 1989 through 1990 and compared with data for randomly selected addresses. A risk model was generated combining the geographic information system with logistic regression analysis. The model was validated by comparing the distribution of cases in 1991 with another group of randomly selected addresses.Results. In crude analyses, 11 environmental variables were associated with Lyme disease. In adjusted analyses, residence in forested areas (odds ratio [OR] = 3.7, 95% confidence interval [CI] = 1.2, 11.8), on specific soils (OR = 2.1 95% CI = 1.0, 4.4), and in two regions of the county (OR = 3.5, 95% CI = 1.6, 7.4) (OR = 2.8, 95% CI = 1.0, 7.7) was associated with elevated risk of getting Lyme disease. Residence in highly developed regions was protective (OR = 0.3, 95% CI = 0.1, 1.0). The risk of Lyme disease in 1991 increased with risk categories defined from the 1989 through 1990 data.Conclusions. Combining a geographic information system with epidemiologic methods can be used to rapidly identify risk factors of zoonotic disease over large areas.