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A 24-year exploratory spatial data analysis of Lyme disease incidence rate in Connecticut, USA

Abolfazl Mollalo, Jason K. Blackburn, Lillian R. Morris, Gregory E. Glass
  • Abolfazl Mollalo
    Department of Geography, University of Florida, Gainesville, FL, United States | abolfazl@ufl.edu
  • Jason K. Blackburn
    Department of Geography, University of Florida, Gainesville, FL; Spatial Epidemiology and Ecology Research Laboratory, Department of Geography, University of Florida, Gainesville, FL; Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States
  • Lillian R. Morris
    Department of Geography, University of Florida, Gainesville, FL; Spatial Epidemiology and Ecology Research Laboratory, Department of Geography, University of Florida, Gainesville, FL; Emerging Pathogens Institute, University of Florida, Gainesville, FL; Environmental Epidemiology Section, Office of Environmental Public Health Sciences, Washington State Department of Health, Olympia WA, United States
  • Gregory E. Glass
    Department of Geography, University of Florida, Gainesville, FL; Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States

Abstract

Despite efforts to control Lyme disease in Connecticut, USA, it remains endemic in many towns, posing a heavy burden. We examined changes in the spatial distribution of significant spatial clusters of Lyme disease incidence rates at the town level from 1991 to 2014 as an approach for targeted interventions. Lyme disease data were grouped into four discrete time periods and incidence rates were smoothed with Empirical Bayes estimation in GeoDa. Local clustering was measured using a local indicator of spatial autocorrelation (LISA). Elliptic spatial scan statistics (SSS) in different shapes and directions were also performed in SaTScan. The accuracy of these two cluster detection methods was assessed and compared for sensitivity, specificity, and overall accuracy. There was significant clustering during each period and significant clusters persisted predominantly in western and eastern parts of the state. Generally, the SSS method was more sensitive, while LISA was more specific with higher overall accuracy in identifying clusters. Even though the location of clusters changed over time, some towns were persistently (across all four periods) identified as clusters in LISA and their neighbouring towns (three of four periods) in SSS suggesting these regions should be prioritized for targeted interventions.

Keywords

Accuracy assessment; Cluster detection; Exploratory spatial data analysis; GIS; Lyme disease

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Submitted: 2017-05-26 21:04:05
Published: 2017-11-08 09:15:56
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