Quantifying the relationship between human Lyme disease and Borrelia burgdorferi exposure in domestic dogs

  • Yan Liu School of Community Health Sciences, University of Nevada, Reno, NV, United States.
  • Shila K. Nordone Department of Molecular and Biomedical Sciences, Comparative Medicine Institute, North Carolina State University, College of Veterinary Medicine, Raleigh, NC, United States.
  • Michael J. Yabsley Southeastern Cooperative Wildlife Disease Study, Department of Population Health, College of Veterinary Medicine, University of Georgia, Athens, GA; Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA, United States.
  • Robert B. Lund School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, United States.
  • Christopher S. McMahan School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, United States.
  • Jenna R. Gettings | Jenna.Gettings@uga.edu Southeastern Cooperative Wildlife Disease Study, Department of Population Health, College of Veterinary Medicine, University of Georgia, Athens, GA; School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, United States.

Abstract

Lyme disease (LD) is the most common vector-borne disease in the United States. Early confirmatory diagnosis remains a challenge, while the disease can be debilitating if left untreated. Further, the decision to test is complicated by under-reporting, low positive predictive values of testing in non-endemic areas and travel, which together exacerbate the difficulty in identification of newly endemic areas or areas of emerging concern. Spatio-temporal analyses at the national scale are critical to establishing a baseline human LD risk assessment tool that would allow for the detection of changes in these areas. A well-established surrogate for human LD incidence is canine LD seroprevalence, making it a strong candidate covariate for use in such analyses. In this paper, Bayesian statistical methods were used to fit a spatio-temporal spline regression model to estimate the relationship between human LD incidence and canine seroprevalence, treating the latter as an explanatory covariate. A strong non-linear monotonically increasing association was found. That is, this analysis suggests that mean incidence in humans increases with canine seroprevalence until the seroprevalence in dogs reaches approximately 30%. This finding reinforces the use of canines as sentinels for human LD risk, especially with respect to identifying geographic areas of concern for potential human exposure.

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Published
2019-05-14
Section
Original Articles
Keywords:
Lyme disease, Canine sentinel, Borrelia burgdorferi, USA
Statistics
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How to Cite
Liu, Y., Nordone, S., Yabsley, M., Lund, R., McMahan, C., & Gettings, J. (2019). Quantifying the relationship between human Lyme disease and Borrelia burgdorferi exposure in domestic dogs. Geospatial Health, 14(1). https://doi.org/10.4081/gh.2019.750