Maps Show How Dog Blood Samples Can Predict Lyme Disease Risks

In the Northeastern United States, the beginning of summer is both a relief from a painful winter, and the beginning of tick season — prompting CDC warnings, as well as guides for how to spot them on, say, a lemon poppy seed muffin. If you’re wondering whether your locale might play host to the tiny blood sucking creatures, a paper published May 14 in Geospatial Health suggests that the blood of local dogs may hold a clue.

The most famous reason ticks are on the CDC’s radar is that they can carry a bacterium called Borrelia burgdorferi — which can cause Lyme disease. If a human gets bitten by a tick or feels the symptoms of Lyme, whether a headache or the characteristic skin rash, they can be tested for the disease. But in this paper, a team of scientists shows that we can monitor where humans are at risk for Lyme disease by looking for the signs of it in canine blood samples collected during routine vet checkups.

Even if humans haven’t yet reported cases, Michael Yabsley, Ph.D., a professor at The University of Georgia’s College of Veterinary Medicine, tells Inverse that canine blood samples may hold important clues for where the disease is getting more common.

“Dogs can be used as sentinels for where people may have Lyme disease,” Yabsley says. “They are also useful for determining if Lyme disease is becoming more common in an area. Dogs are often outside, in tick habitat and a great sentinel for where ticks may be active.”

Yabsley and a team of veterinarians and statisticians at The University of Georgia and Clemson University show that dog blood serum levels of B. burgdorferi are strongly correlated with human cases of lyme disease. This paper was published in conjunction with the Companion Animal Parasite Council, a non-profit organization. As part of the partnership, CAPC boiled the data down into an interactive map, which displays dog blood test data on a county by county level — as well as the implications for humans.

But in the paper, the team makes note of these “leading edges” of endemic (red) regions, shown in the map below, where the disease isn’t extremely common yet, but have seen light increases in cases.

These edges, for instance, include the northwest and southwest corners of Indiana and Michigan. They also point to “smaller foci,” or places where ticks may not be as prevalent as the Northeast, but still may bear watching. They include the southern tip of Florida, the border between Kentucky and Indiana, Northern California, and southern Oregon.

There’s one important caveat to consider, which the team found during its statistical analysis. The relationship between dog and human Lyme disease seems to “plateau” after more than 30 percent of dogs in a specific region test positive for the infection, suggesting that they are not as closely tied after that mark is reached. Still, Yabsley says that this doesn’t discount the strength of the relationship.

“It is a rapid increase and tight association up to that plateau,” he says. “The reason it plateaus is that 30 percent is a very high prevalence and is rarely noted, so it is hard to continue to see an increase in risk.”

Based off an analysis of canine blood samples, Yabsley’s team points to a number of states where human cases are particularly high (10 cases per 100,000 people): Minnesota, Wisconsin, Maine, New Hampshire, Vermont, Massachusetts, New York, Connecticut, Rhode Island, New Jersey, Pennsylvania, Maryland, Delaware, and Virginia. Many of these are already classic Lyme hotspots in the Northeast and Midwest, which fits with the CDC’s maps showing rates of human Lyme disease cases.

The fact that this dog-based model lines up with the CDC’s human data suggests that this model may actually hold promise for detecting future Lyme outbreaks.

If we can pick up signs of Lyme during out pets checkups, we may be able to create an early warning system for humans too.

Abstract:

Lyme disease 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 base- line 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.