Center for Vector Biology & Zoonotic Diseases, The Connecticut Agricultural Experiment Station, 123 Huntington Street, New Haven, CT 06511, USA; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, 60 College Street, PO Box 208034, New Haven, CO 06520-8034, USA. Electronic address: [Email]
Lyme disease is the most prevalent vector-borne disease in the United States. Ixodes scapularis, commonly referred to as the blacklegged tick, is the primary vector of Lyme disease spirochetes, Borrelia burgdorferi sensu lato (s.l.), in the eastern United States. Connecticut has pervasive populations of I. scapularis and remains a hotspot for Lyme disease. A primary aim of this study was to determine if passively collected data on human-biting I. scapularis ticks in Connecticut could serve as a useful proxy for Lyme disease incidence based on the cases reported by the Connecticut Department of Public Health (CDPH). Data for human-biting I. scapularis ticks submitted to the Tick Testing Laboratory at the Connecticut Agricultural Experiment Station (CAES-TTL), and tested for infection with B. burgdorferi s.l., were used to estimate the rate of submitted nymphs, nymphal infection prevalence, and the rate of submitted infected nymphs. We assessed spatiotemporal patterns in tick-based measures and Lyme disease incidence with generalized linear and spatial models. In conjunction with land cover and household income data, we used generalized linear mixed effects models to examine the association between tick-based risk estimates and Lyme disease incidence. Between 2007 and 2017, the CAES-TTL received 26,116 I. scapularis tick submissions and the CDPH reported 23,423 Lyme disease cases. The rate of submitted nymphs, nymphal infection prevalence, the rate of submitted infected nymphs, and Lyme disease incidence all decreased over time during this eleven-year period. The rate of submitted nymphs, the rate of submitted infected nymphs, and Lyme disease incidence were spatially correlated, but nymphal infection prevalence was not. Using a mixed modeling approach to predict Lyme disease incidence and account for spatiotemporal structuring of the data, we found the best fitting tested model included a strong, positive association with the rate of submitted infected nymphs and a negative association with the percent of developed land for each county. We show that within counties, submissions of B. burgdorferi s.l. infected nymphs were strongly and positively associated with inter-annual variation in reported Lyme disease cases. Tick-based passive surveillance programs may be useful in providing independent measures of entomological risk, particularly in settings where Lyme disease case reporting practices change substantially over time.