Understanding the Risk of Tickborne Diseases to Public Health

Tickborne diseases reached the highest annual rates ever recorded in the late 2010s, according to public data from the Centers for Disease Control and Prevention (CDC).  

From 2004 to 2016, the number of reported Lyme disease cases more than doubled from 22,000 annually to 48,000, while cases of anaplasmosis and Rocky Mountain spotted fever (RMSF) — other common tickborne diseases — increased by over 16 times and 13 times, respectively.  

These historic levels of disease correspond to changes in climate that have increased the geographic range of ticks in North America. As global temperatures rise, the habitable range of several tick species has expanded, allowing for the spread of zoonotic disease.  

Zoonotic spillover occurs when disease vectors like ticks spread out from their original habitat after environmental changes increase their range. In the United States, certain tick species were previously restricted to eastern and central states, but human activity has encouraged the expansion of the parasites and their diseases.  

Tickborne diseases are considered rare, but the rising rate of several of these pathogens is stressing public health surveillance and affecting the lives of citizens across the country.  

A CDC report from 2016 indicated that Lyme disease was responsible for 82% of tickborne diseases. However, experts estimate that a far larger number of Lyme infections go unreported and that the actual number of cases was somewhere near 350,000 in 2016.  

Lyme disease is often underreported because the early signs resemble a common cold and include symptoms like chills, fever, headache, and achiness. But, when left untreated, Lyme can progress into a severe illness with symptoms like heart palpitations, arthritis, facial palsy, and inflammation of the brain or spinal cord.  

To track the spread of the disease, researchers have begun to leverage data and artificial intelligence tools that analyze patient registries. Artificial intelligence (AI) systems using reported data can predict disease trajectories and identify zoonotic spillover areas as they emerge. AI models have even learned pattern recognition to diagnose Lyme disease based on images of the common Erythema Migrans rash synonymous with the disease.  

While Lyme disease is the single most prevalent tickborne disease, RMSF and anaplasmosis have increased in prevalence since the beginning of the 21st century. Zoonotic spillover is partially responsible for these increases, as both conditions have been detected in new areas of the United States.  

The rise in infections has corresponded with a significant increase in hospitalizations due to tickborne diseases. One study examining 156 hospitals found hospitalizations between 2009 and 2014 were up 15% annually in southern states and 45% per year in western states.  

In addition to zoonotic spillover due to climate change, diseases have risen as growing populations moved into previously wild areas where ticks thrive. Clinicians and researchers are identifying these trends as a threat to public health and suggesting that resources be dedicated to appropriate monitoring of diseases and additional research to create definitive diagnostic tests that detect tickborne disease.