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

Geospatial Health. 2019;14(1) DOI 10.4081/gh.2019.750

 

Journal Homepage

Journal Title: Geospatial Health

ISSN: 1827-1987 (Print); 1970-7096 (Online)

Publisher: PAGEPress Publications

Society/Institution: GnosisGIS

LCC Subject Category: Geography. Anthropology. Recreation: Geography (General)

Country of publisher: Italy

Language of fulltext: English

Full-text formats available: PDF, XML

 

AUTHORS


Yan Liu (School of Community Health Sciences, University of Nevada, Reno, NV)

Shila K. Nordone (Department of Molecular and Biomedical Sciences, Comparative Medicine Institute, North Carolina State University, College of Veterinary Medicine, Raleigh, NC)

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)

Robert B. Lund (School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC)

Christopher S. McMahan (School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC)

Jenna R. Gettings (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)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 10 weeks

 

Abstract | Full Text

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.