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Healthcare (Basel). 2019 Apr 30;7(2). pii: E66. doi: 10.3390/healthcare7020066.

Risk Factors of Lyme Disease: An Intersection of Environmental Ecology and Systems Science.

Author information

1
Department of Population Health Sciences, University of Utah, Salt Lake City, UT 84108, USA. nasser.sharareh@hsc.utah.edu.
2
Department of Chemistry, the State University of New York at Binghamton, Vestal, NY 13902, USA. rbehler1@binghamton.edu.
3
Department of Anthropology, the State University of New York at Binghamton, Vestal, NY 13902, USA. aroome1@binghamton.edu.
4
Department of Biological Sciences, the State University of New York at Binghamton, Vestal, NY 13902, USA. jshepher@binghamton.edu.
5
Department of Anthropology, the State University of New York at Binghamton, Vestal, NY 13902, USA. rgarruto@binghamton.edu.
6
Department of Biological Sciences, the State University of New York at Binghamton, Vestal, NY 13902, USA. rgarruto@binghamton.edu.
7
Department of Systems Science and Industrial Engineering, the State University of New York at Binghamton, Vestal, NY 13902, USA. sabounchi@binghamton.edu.

Abstract

Lyme disease (LD) cases have been on the rise throughout the United States, costing the healthcare system up to $1.3 billion per year, and making LD one of the greatest threats to public health. Factors influencing the number of LD cases range from environmental to system-level variables, but little is known about the influence of vegetation (canopy, understory, and ground cover) and human behavioral risk on LD cases and exposure to infected ticks. We determined the influence of various risk factors on the risk of exposure to infected ticks on 22 different walkways using multinomial logistic regression. The model classifies the walkways into high-risk and low-risk categories with 90% accuracy, in which the understory, human risk, and number of rodents are significant indicators. These factors should be managed to control the risk of transmission of LD to humans.

KEYWORDS:

human behavior; regression; rodents; simulation modeling; ticks; urban planning; vegetation

PMID:
31052225
DOI:
10.3390/healthcare7020066
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