Geospatial Factors for Lyme Disease Risk in Wisconsin
University of Minnesota
Twin Cities
Campbell CC, Schotthoefer AM PhD
Integrated Research and Development Laboratory
Research area: Infectious Diseases
Background: Over the past twelve years, the quantity of human Lyme disease cases in Wisconsin has greatly increased, particularly in the north-eastern counties of the state. The pattern of infections and recent surge in cases has not been extensively studied. To better understand the geographic risk of Lyme disease, we analyzed environmental factors to determine conditions associated with infections.
Methods: We identified 4573 laboratory-confirmed cases of Lyme disease in the Marshfield Clinic Health Care System in Wisconsin from 2000 through 2011; of these, 3296 had addresses that could be mapped in ArcGIS 10.1. Publically available environmental layers were evaluated in our analysis. We used 2010 U.S. Census data to calculate incidence rates and generate controls. The cases and controls associated with the layers were analyzed with logistic regression in SAS 9.3.
Results: Fragmentation factors (e.g. road density) tested had little or no effect, accounting for ~10% of total variation in cases. Persons living beyond 1 km from a highway and in wooded regions were 1.59 (95% Confidence Interval: 1.38, 1.84) times more likely to be tested positive for Lyme disease than those in wooded areas and within 1 km of a highway. Land cover, bedrock type, soil temperature, and soil order were successful at determining geographic risk, correctly modeling ~70% of cases. Notably, forest edge and open-to-low development were associated with high risk, and highly developed regions were associated with lower risk. Frigid soils and the orders spodosols and alfisols also conferred higher risk.
Conclusions: This study confirmed that living within the edges of forest substantially increases risk of Lyme disease, as suggested by other studies. Additionally, this study shows that features outside of the immediate home environment (e.g., >1 km) may strongly influence risk.