Currently, Lyme disease is the most commonly reported vector-borne disease in the United States. If untreated, Lyme disease can lead to chronic arthritis and neurological and cardiac disorders (Braukus and Farrar, 1994). More than 80% of cases in the United States are reported in the Northeast. The landscape of this area is changing, with more residential growth occurring in forested areas. Due to these changes, more people are coming in contact with the tick vector, Ixodes scapularis, and becoming infected with Borrelia burgdorferi (CHAART, 2001).

Previous surveillance of Lyme disease was based on physicians reports to the CDC (Center for Disease Control) and was hampered by reporting biases, uneven detection efforts, and lack of proper diagnosis. Additionally, the variable intervals of time between exposure to infected ticks and the appearance of symptoms confounded efforts to determine the exposure location. The practice of using the residences of infected people as exposure locations resulted in mis-reporting of the actual distribution and high risk areas. Therefore, a more accurate way to determine the geographic distribution and high risk transmission areas of Lyme disease would be to map it based on the environmental factors, such as climate and suitable tick habitat (forested areas with sandy soil). Researchers have found the capabilities of GIS extremely useful for mapping of high risk areas for Lyme disease and mapping its distribution in Wisconsin, the state with the highest number of reported cases (Kitron and Kazmierczak, 1997).

A CDC publication defines disease risk as a function of entomological risk and human exposure. Entomological risk is defined as the density per unit area of ticks infested with Borrelia burgdorferi. This could be found through entomological field studies, but due to the availability of resources and the limited seasonal window, this method would be impractical. Instead, the CDC used GIS to combine Borrelia burgdorferi prevalence data, human exposure data, and a vector model into risk classes (MMWR weekly, 1999). To see the Lyme Disease Risk Map created using this method, go to http://www.cdc.gov/mmwr/preview/mmwrhtml/rr4807a2.htm

Finally, extensive research is being conducted in New York by CHAART (The Center for Health Applications of Aerospace Related Technologies), a research group established by NASA's Life Sciences Division. The first comparison uses GIS to map the Lyme disease exposure in dogs based on landscape composition by municipality. They found a significant correlation between Lyme disease antibodies in canines and the proportion of wooded area adjacent to the residential area. The second study compared the landscape characteristics of areas classified as high and low-risk residential properties. Using digitized images in GIS, they were able to discover that high-risk properties were greener, wetter, and contained more broadleaf cover, as opposed to the lower risk areas that were more open and contained non-vegetative cover (CHAART, 2001). Both of these results demonstrate the feasibility of GIS for mapping environmental factors that relate to disease abundance.

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