Using Exploratory Data Analysis to Identify and Predict Patterns of Human Lyme Disease Case Clustering within a Multistate Region, 2010-2014

The objective of our study was to identify patterns of Lyme disease reporting after multistate inclusion to mitigate potential border effects. County-level human Lyme disease surveillance data were obtained from Kentucky, Maryland, Ohio, Pennsylvania, Virginia, and West Virginia state health departments. Rate smoothing and Local Moran's I was performed to identify clusters of reporting activity and identify spatial outliers. A logistic generalized estimating equation was performed to identify significant associations in disease clustering over time. Resulting analyses identified statistically significant (P=0.05) clusters of high reporting activity and trends over time. High reporting activity aggregated near border counties in high incidence states, while low reporting aggregated near shared county borders in non-high incidence states. Findings highlight the need for exploratory surveillance approaches to describe the extent to which state level reporting affects accurate estimation of Lyme disease progression.
Source: Spatial and Spatio-temporal Epidemiology - Category: Epidemiology Source Type: research