The 131st Annual Meeting (November 15-19, 2003) of APHA

Abstract #73378

Sergio Recuenco, MD MPH, School of Public Health, State University of New York, Albany, One University Place, Rensselaer, Albany, NY 12144, 518 427 0595, sxr03@health.state.ny.us

Lyme disease (LD) has been a reportable disease in New York State since 1986. The Hudson Valley, comprising 10 counties, is the most hyperendemic area for LD in the state. Epidemiological and environmental data are available for this area but they were not previously used together to study Lyme disease in this region. This study used spatial analysis methods and GIS to construct risk maps based on epidemiological and environmental information.

Surveillance reports of LD human cases in the 10 counties of the Hudson Valley from the years 1997 to 2001 were geocoded and prepared for analysis. These data were analyzed for spatial clustering with a scan statistic. Kernel density grids were also created. Deer population rates were mapped for the study area and period. A GIS was developed to allow spatial correlation among clustering, kernel densities, EPA ecoregions, and deer population for each year. Four strata of risk were constructed and presented in a consolidated map.

During the study period the risk areas for LD expanded to the northeast and southwest sides of the Hudson River in both the cluster analysis and the Kernel density maps. Dutchess County, Columbia County and Orange County contain the areas of highest risk. For the EPA ecoregions the area of highest risk is a continuous strip along the transition between the Northeastern Highlands, the Eastern Great Lakes and Hudson Lowlands, and Ridge and Valley. These analyses are useful to forecast the pattern of the future spread of LD and to focus preventive interventions.

Learning Objectives:

• At the conclusion of the presentation, the participant should be able to:
• 1. Understand how the environmental data can be used to predict the spread of Lyme disease.
• 2. Recognize the spatial analysis methods that allow combining surveillance data with other information to discriminate levels of risk for emerging diseases.

Keywords: Emerging Diseases,

Presenting author's disclosure statement:
I do not have any significant financial interest/arrangement or affiliation with any organization/institution whose products or services are being discussed in this session.