Lyme disease is an emerging public health threat in Canada. In this context, rapid detection of new risk areas is essential for timely application of prevention and control measures. In Canada, information on Lyme disease risk is collected through three surveillance activities: active tick surveillance, passive tick surveillance, and reported human cases. However, each method has shortcomings that limit its ability to rapidly and reliably identify new risk areas. We investigated the relationships between risk signals provided by human cases, passive and active tick surveillance to assess the performance of tick surveillance for early detection of emerging risk areas. We used regression models to investigate the relationships between the reported human cases, Ixodes scapularis (Say; Acari: Ixodidae) ticks collected on humans through passive surveillance and the density of nymphs collected by active surveillance from 2009 to 2014 in the province of Quebec. We then developed new risk indicators and validated their ability to discriminate risk levels used by provincial public health authorities. While there was a significant positive relationship between the risk signals provided all three surveillance methods, the strongest association was between passive tick surveillance and reported human cases. Passive tick submissions were a reasonable indicator of the abundance of ticks in the environment (sensitivity and specificity [Se and Sp] < 0.70), but were a much better indicator of municipalities with more than three human cases reported over 5 yr (Se = 0.88; Sp = 0.90). These results suggest that passive tick surveillance provides a timely and reliable signal of emerging risk areas for Lyme disease in Canada.
Background:The risk of contracting Lyme disease (LD) can vary spatially because of spatial heterogeneity in risk factors such as social-behavior and exposure to ecological risk factors. Integrating these risk factors to inform decision-making should therefore increase the effectiveness of mitigation interventions.Objectives:The objective of this study was to develop an integrated social-behavioral and ecological risk-mapping approach to identify priority areas for LD interventions.Methods:The study was conducted in the Montérégie region of Southern Quebec, Canada, where LD is a newly endemic disease. Spatial variation in LD knowledge, risk perceptions, and behaviors in the population were measured using web survey data collected in 2012. These data were used as a proxy for the social-behavioral component of risk. Tick vector population densities were measured in the environment during field surveillance from 2007 to 2012 to provide an index of the ecological component of risk. Social-behavioral and ecological components of risk were combined with human population density to create integrated risk maps. Map predictions were validated by testing the association between high-risk areas and the current spatial distribution of human LD cases.Results:Social-behavioral and ecological components of LD risk had markedly different distributions within the study region, suggesting that both factors should be considered for locally adapted interventions. The occurrence of human LD cases in a municipality was positively associated with tick density (p<0.01) but was not significantly associated with social-behavioral risk.Conclusion:This study is an applied demonstration of how integrated social-behavioral and ecological risk maps can be created to assist decision-making. Social survey data are a valuable but underutilized source of information for understanding regional variation in LD exposure, and integrating this information into risk maps provides a novel approach for prioritizing and adapting interventions to the local characteristics of target populations. https://doi.org/10.1289/EHP1943
Since its detection in Canada in the early 1990s, Ixodes scapularis, the primary tick vector of Lyme disease in eastern North America, has continued to expand northward. Estimates of the tick’s broad-scale distribution are useful for tracking the extent of the Lyme disease risk zone; however, tick distribution may vary widely within this zone. Here, we investigated I. scapularis nymph distribution at three spatial scales across the Lyme disease emergence zone in southern Quebec, Canada. We collected ticks and compared the nymph densities among different woodlands and different plots and transects within the same woodland. Hot spot analysis highlighted significant nymph clustering at each spatial scale. In regression models, nymph abundance was associated with litter depth, humidity, and elevation, which contribute to a suitable habitat for ticks, but also with the distance from the trail and the type of trail, which could be linked to host distribution and human disturbance. Accounting for this heterogeneous nymph distribution at a fine spatial scale could help improve Lyme disease management strategies but also help people to understand the risk variation around them and to adopt appropriate behaviors, such as staying on the trail in infested parks to limit their exposure to the vector and associated pathogens.
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