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

Submitted: 6 November 2018
Accepted: 20 January 2019
Published: 14 May 2019
Abstract Views: 5390
PDF: 1755
APPENDIX: 253
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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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Supporting Agencies

National Science Foundation Grant DMS 1407480, Grant R01 AI121351 from the National Institutes of Health, The Boehringer Ingelheim Vetmedica-CAPC Infectious Disease Postdoctoral Fellowship

How to Cite

Liu, Y., Nordone, S. K., Yabsley, M. J., Lund, R. B., McMahan, C. S., & Gettings, J. R. (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

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