Predominant risk factors for tick-borne co-infections in hunting dogs from the USA

Abstract

Background

Both incidence and geographical range of tick-borne disease has increased across the USA. Similar to people, dogs are hosts for Anaplasma spp., Babesia spp., Ehrlichia spp. and Borrelia burgdorferi. Dogs also share our homes and beds, making them both a sentinel for the ticks in our backyards but also increasing our exposure to ticks. Measures to better track, prevent, and/or treat tick-borne diseases in companion animals can lead to better control and prevention of human tick-borne disease. This study identifies demographic and co-infection risk factors for canine seropositivity to tick-borne infections in a cohort of hunting dogs across the USA.

Results

Human patterns of tick-borne disease co-infection in the USA have been predominantly driven by the geographical distribution of the tick vector. Dogs who tested seropositive for Anaplasma spp. were 1.40 times more likely ( P = 0.0242) to also test seropositive for Babesia spp. and vice versa (1.60 times more likely, P = 0.0014). Dogs living in the West had 5% lower risk ( P = 0.0001) for Ehrlichia spp. seropositivity compared to other regions. Controlling for age and Anaplasma spp. seroprevalence, dogs in all three other regions were 2.30 times more likely ( P = 0.0216) to test seropositive for B. burgdorferi than dogs in the West. Dogs seropositive for B. burgdorferi were 1.60 times more likely ( P = 0.0473) to be seropositive for Anaplasma spp.

Conclusions

Tick geographical distributions have a prominent impact on the regional distribution of hunting dog exposure to tick-borne diseases. Education concerning regional tick prevalence and disease risk is important for everyone, but particularly dog owners, regarding ticks in their region and protection from infection and co-infection of tick-borne pathogens as they travel or move with their dogs. Dogs are sentinel species for human exposure to ticks, and as such surveillance of canine tick-borne infections and understanding the probability that these infections might be seen together as co-infections helps predict emerging areas where people are more likely to be exposed as well.