Associations Among Self-Reported Tick-Borne Disease Symptoms, Treatments & Diagnoses
In eastern Canada, deer ticks (Ixodes scapularis) carry a variety of bacterial (Borrelia burgdorferi, Anaplasma, Ehrlichia, Rickettsia) and protozoan pathogens (Babesia) that are responsible for more disease in humans than any other arthropod vector. While most known tick-borne diseases (TBD) in Canada are treatable, invasions of new pathogens may go undetected. Misdiagnosed and untreated infections can cause debilitating symptoms, many of which are non-specific and can be mistaken for other diseases. Using quantitative models, the goal of my thesis is to investigate whether tick-borne diseases cause syndromes (e.g., groups of symptoms which consistently occur together). An anonymous cross-sectional survey was disseminated online via the Qualtrics software package to survey age, gender, blood test results, symptom profiles, and chronic health conditions. Recruitment was focused on the Kingston-Ottawa corridor because it is a Lyme disease hotspot in Canada, but inclusion criteria included anyone with a self-reported tick bite. This resulted in 1248 unique submissions, 301 of which self-reported a tick-borne disease. On average, participants who reported a Lyme disease diagnosis along with one or more secondary co-infections presented with more symptoms and a longer time to diagnosis than participants with Lyme disease alone. I used supervised machine learning to model self-reported symptoms while accounting for demographics, clinical tests, and chronic health conditions. A Regularised Discriminant Analysis of 13 binary symptoms correctly classified participants with 98% accuracy into self-reported diagnoses grouped into four categories: Lyme disease, Lyme disease with one or more co-infections, other tick-borne disease, and no diagnosed disease. To model how healthcare practitioners might diagnose disease, I used hierarchical logistic regressions to identify self-reported factors that predict diagnosis. Skin rash and blood tests were predictive of all three diagnosis categories, accounting for 41-61% of the variation in TBD diagnosis predictions. Participants with chronic health conditions (cardiovascular, rheumatological, and central nervous system disorders) were less likely to receive TBD diagnoses, which is consistent with misdiagnosed disease. This research shows that symptom profiles can be used to understand tick-borne diseases. A collaborative research approach between scientists and patients such as this one can improve diagnosis and knowledge translation in the domain of TBD.
URI for this recordhttp://hdl.handle.net/1974/29513
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