Spatio-Temporal Dynamics of Tick-Borne Diseases in North-Central Wisconsin from 2000–2016

Austin Rau, Claudia Munoz-Zanzi, Anna M. Schotthoefer, Jonathan D. Oliver and Jesse D. Berman
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Austin Rau: Division of Environmental Health Sciences, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
Claudia Munoz-Zanzi: Division of Environmental Health Sciences, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
Anna M. Schotthoefer: Marshfield Clinic Research Institute, Marshfield, WI 54449, USA
Jonathan D. Oliver: Division of Environmental Health Sciences, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
Jesse D. Berman: Division of Environmental Health Sciences, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA

IJERPH, 2020, vol. 17, issue 14, 1-20

Abstract: Lyme disease is a well-recognized public health problem in the USA, however, other tick-borne diseases also have major public health impacts. Yet, limited research has evaluated changes in the spatial and temporal patterns of non-Lyme tick-borne diseases within endemic regions. Using laboratory data from a large healthcare system in north-central Wisconsin from 2000–2016, we applied a Kulldorf’s scan statistic to analyze spatial, temporal and seasonal clusters of laboratory-positive cases of human granulocytic anaplasmosis (HGA), babesiosis, and ehrlichiosis at the county level. Older males were identified as the subpopulation at greatest risk for non-Lyme tick-borne diseases and we observed a statistically significant spatial and temporal clustering of cases ( p < 0.05). HGA risk shifted from west to east over time (2000–2016) with a relative risk (RR) ranging from 3.30 to 11.85, whereas babesiosis risk shifted from south to north and west over time (2004–2016) with an RR ranging from 4.33 to 4.81. Our study highlights the occurrence of non-Lyme tick-borne diseases, and identifies at-risk subpopulations and shifting spatial and temporal heterogeneities in disease risk. Our findings can be used by healthcare providers and public health practitioners to increase public awareness and improve case detection.

Keywords: spatial epidemiology; geographic information systems (GIS); SatScan; spatial analysis; tick-borne diseases (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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