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景观破碎化和气候对美国东北部莱姆病发病率的影响。

Effects of landscape fragmentation and climate on Lyme disease incidence in the northeastern United States.

作者信息

Tran Phoebe Minh, Waller Lance

机构信息

College of Arts and Sciences, Emory University, Atlanta, GA, USA,

出版信息

Ecohealth. 2013 Dec;10(4):394-404. doi: 10.1007/s10393-013-0890-y. Epub 2014 Jan 14.

Abstract

Lyme disease is the most frequently reported vector borne illness in the United States, and incidences are increasing steadily year after year. This study explores the influence of landscape (e.g., land use pattern and landscape fragmentation) and climatic factors (e.g., temperature and precipitation) at a regional scale on Lyme disease incidence. The study area includes thirteen states in the Northeastern United States. Lyme disease incidence at county level for the period of 2002-2006 was linked with several key landscape and climatic variables in a negative binomial regression model. Results show that Lyme disease incidence has a relatively clear connection with regional landscape fragmentation and temperature. For example, more fragmentation between forests and residential areas results in higher local Lyme disease incidence. This study also indicates that, for the same landscape, some landscape variables derived at a particular scale show a clearer connection to Lyme disease than do others. In general, the study sheds more light on connections between Lyme disease incidence and climate and landscape patterns at the regional scale. Integrating findings of this regional study with studies at a local scale will further refine understanding of the pattern of Lyme disease as well as increase our ability to predict, prevent, and respond to disease.

摘要

莱姆病是美国报告最多的媒介传播疾病,且发病率逐年稳步上升。本研究探讨了区域尺度上景观(如土地利用模式和景观破碎化)和气候因素(如温度和降水)对莱姆病发病率的影响。研究区域包括美国东北部的13个州。在负二项回归模型中,将2002 - 2006年县级莱姆病发病率与几个关键的景观和气候变量相关联。结果表明,莱姆病发病率与区域景观破碎化和温度有较为明显的关联。例如,森林与居民区之间的破碎化程度越高,当地莱姆病发病率就越高。本研究还表明,对于相同的景观,在特定尺度上得出的一些景观变量与莱姆病的联系比其他变量更明显。总体而言,该研究进一步揭示了区域尺度上莱姆病发病率与气候和景观模式之间的联系。将该区域研究的结果与局部尺度的研究相结合,将进一步完善对莱姆病模式的理解,并提高我们预测、预防和应对该疾病的能力。

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