Quantifying the relationship between human Lyme disease and Borrelia burgdorferi exposure in domestic dogs
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.
- Abstract views: 3008
- PDF: 1105
- APPENDIX: 167
- HTML: 22
Copyright (c) 2019 Yan Liu, Shila K. Nordone, Michael J. Yabsley, Robert B. Lund, Christopher S. McMahan, Jenna R. Gettings
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.