Rurality, Socioeconomic Status, and Residence in Environmental Risk Areas Associated with Increased Lyme Disease Incidence in Ontario, Canada: A Case-Control Study.

Background: Lyme disease (LD) is the most common tick-borne illness in North America. LD is acquired through exposure to the tick vector, Ixodes scapularis, known as the blacklegged tick. In Canada, LD is rapidly emerging, with the establishment of I. scapularis in many newly endemic regions posing a growing risk to local communities. In the Canadian context, many environmental and socioeconomic risk factors for human LD infection are yet to be ascertained and the degree of risk associated with residential and community exposure to ticks is not well known. Methods: We conducted a matched case-control study in southeastern Ontario, using LD patient data from provincial laboratory databases and uninfected population controls from 2014 to 2018. We aimed to identify area-level risk factors for LD and associations with residence in environmental risk areas, defined as areas with high model-predicted probability of I. scapularis occurrence, using the neighborhood dissemination area as the unit of analysis. Results: Using multivariable conditional logistic regression analysis, we identified that patients with LD had higher odds (odds ratio, OR; 95% confidence interval, CI) of living in neighborhoods with high probability of tick occurrence in the environment (OR = 2.2; 95% CI: 2.0-2.5), low walkability (OR = 1.6; 95% CI: 1.2-2.1), low material deprivation (OR = 1.4; 95% CI: 1.2-1.7), and low ethnic concentration (OR = 8.1; 95% CI: 6.7-9.9). We also found that the odds of LD infection for individuals residing in environmental risk areas was highest for those living in public health units (PHUs) with <250,000 population (OR = 3.0; 95% CI: 2.4-3.9) compared to those living in PHUs with >1,000,000 population (OR = 1.5; 95% CI: 1.1-2.1). Conclusion: This study shows that odds of human LD infection in Ontario, Canada is higher in less urbanized areas with higher socioeconomic status and indicates that exposure to ticks around the home residence or neighborhood is linked to increased odds of LD.

The utility of a maximum entropy species distribution model for Ixodes scapularis in predicting the public health risk of Lyme disease in Ontario, Canada.

Lyme disease is an emerging public health threat in Ontario, Canada due to ongoing range expansion of the tick vector, Ixodes scapularis. Tick density is an important predictor of human Lyme disease risk and is typically measured using active tick surveillance via drag sampling, which is time and resource-intensive. New cost-effective tools are needed to augment current surveillance activities. Our objective was to evaluate the ability of a maximum entropy (Maxent) species distribution model to predict I. scapularis density in three regions of Ontario - Ottawa, Kingston, and southern Ontario - in order to determine its utility in predicting the public health risk of Lyme disease. Ticks were collected via drag sampling at 60 sites across the three regions. Model-predicted habitat suitability was calculated from a previously constructed Maxent model as the mean predicted habitat suitability within a 1-km radius of each site. Spearman's correlation coefficient was used to quantify the continuous relationship between model-predicted habitat suitability and tick density, and negative binomial regression was used to quantify the relationship between tick density and model-predicated habitat suitability. Spearman's correlation coefficients for the full study area, Kingston region, and Ottawa region were 0.517, 0.707, and 0.537, respectively, indicating a moderate positive relationship and ability of the model to predict tick density. Regression analysis further demonstrated a significant positive association between tick density and model-predicted habitat suitability (p< 0.001). Using a dichotomized measure of model-predicted habitat suitability, the incidence rate ratio - the ratio of ticks per m2 in sites predicted to have a 'suitable' habitat compared to those predicted to have 'not suitable' habitat - was 33.95, indicating that tick density was significantly higher at sites situated in areas with predicted suitable habitat. Given that tick density is an important component of Lyme disease risk, the ability to predict high tick density locations using the Maxent model may make it a cost-effective tool for identifying geographic areas that pose elevated public health risk of Lyme disease.

Spatiotemporal trends and socioecological factors associated with Lyme disease in eastern Ontario, Canada from 2010-2017.

Currently, there is limited knowledge about socioeconomic, neighbourhood, and local ecological factors that contribute to the growing Lyme disease incidence in the province of Ontario, Canada. In this study, we sought to identify these factors that play an important role at the local scale, where people are encountering ticks in their communities. We used reported human Lyme disease case data and tick surveillance data submitted by the public from 2010-2017 to analyze trends in tick exposure, spatiotemporal clusters of infection using the spatial scan statistic and Local Moran's I statistic, and socioecological risk factors for Lyme disease using a multivariable negative binomial regression model. Data were analyzed at the smallest geographic unit, consisting of 400-700 individuals, for which census data are disseminated in Canada. We found significant heterogeneity in tick exposure patterns based on location of residence, with 65.2% of Lyme disease patients from the city of Ottawa reporting tick exposures outside their health unit of residence, compared to 86.1%-98.1% of patients from other, largely rural, health units, reporting peri-domestic exposures. We detected eight spatiotemporal clusters of human Lyme disease incidence in eastern Ontario, overlapping with three clusters of Borrelia burgdorferi-infected ticks. When adjusting for population counts, Lyme disease case counts increased with larger numbers of Borrelia burgdorferi-infected ticks submitted by the public, higher proportion of treed landcover, lower neighbourhood walkability due to fewer intersections, dwellings, and points of interest, as well as with regions of higher residential instability and lower ethnic concentration (Relative Risk [RR] = 1.25, 1.02, 0.67-0.04, 1.34, and 0.57, respectively, p < .0001). Our study shows that there are regional differences in tick exposure patterns in eastern Ontario and that multiple socioecological factors contribute to Lyme disease risk in this region.

Species distribution models for the eastern blacklegged tick, Ixodes scapularis, and the Lyme disease pathogen, Borrelia burgdorferi, in Ontario, Canada.

The blacklegged tick, Ixodes scapularis, is established in several regions of Ontario, Canada, and continues to spread into new geographic areas across the province at a rapid rate. This poses a significant public health risk since I. scapularis transmits the Lyme disease-causing bacterium, Borrelia burgdorferi, and other pathogens of potential public health concern. The objective of this study was to develop species distribution models for I. scapularis and B. burgdorferi to predict and compare the potential distributions of the tick vector and the Lyme disease pathogen as well as the ecological factors most important for species establishment. Ticks were collected via tick dragging at 120 sites across southern, central, and eastern Ontario between 2015 and 2018 and tested for tick-borne pathogens. A maximum entropy (Maxent) approach was used to model the potential distributions of I. scapularis and B. burgdorferi. Two independent datasets derived from tick dragging at 25 new sites in 2019 and ticks submitted by the public to local health units between 2015 and 2017 were used to validate the predictive accuracy of the models. The model for I. scapularis showed high suitability for blacklegged ticks in eastern Ontario and some regions along the shorelines of the Great Lakes, and moderate suitability near Algonquin Provincial Park and the Georgian Bay with good predictive accuracy (tick dragging 2019: AUC = 0.898; ticks from public: AUC = 0.727). The model for B. burgdorferi showed a similar predicted distribution but was more constrained to eastern Ontario, particularly between Ottawa and Kingston, and along Lake Ontario, with similarly good predictive accuracy (tick dragging 2019: AUC = 0.958; ticks from public: AUC = 0.863. The ecological variables most important for predicting the distributions of I. scapularis and B. burgdorferi included elevation, distance to deciduous and coniferous forest, proportions of agricultural land, water, and infrastructure, mean summer/spring temperature, and cumulative annual degree days above 0°C. Our study presents a novel application of species distribution modelling for I. scapularis and B. burgdorferi in Ontario, Canada, and provides an up to date projection of their potential distributions for public health knowledge users.

Lyme Disease Emergence after Invasion of the Blacklegged Tick, Ixodes scapularis, Ontario, Canada, 2010-2016.

Analysis of surveillance data for 2010-2016 in eastern Ontario, Canada, demonstrates the rapid northward spread of Ixodes scapularis ticks and Borrelia burgdorferi, followed by increasing human Lyme disease incidence. Most spread occurred during 2011-2013. Continued monitoring is essential to identify emerging risk areas in this region.

Synergism between Female Gender and High Levels of Daily Stress Associated with Migraine Headaches in Ontario, Canada.

BACKGROUND: Migraines affect women more than men and originate from interactions of genetic and environmental factors. This study assessed the prevalence of migraines in Ontario, Canada and the effect of gender and stress on migraines. METHODS: Our analysis was based on data from 42,282 persons 12 years or older who participated in the 2013-2014 Canadian Community Health Survey. Multivariate log-binomial model was used to calculate adjusted prevalence ratios for migraines associated with individual and joint exposures of female gender and stress. We used relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (S index) to measure additive interaction. RESULTS: The prevalence of migraines was 10.7%. The adjusted prevalence ratios were 2.37 (95% CI 2.13-2.63) for female versus male, 1.63 (95% CI 1.39-1.90) for persons with high versus low levels of stress, and 3.38 (95% CI 3.00-3.80) for women with high stress versus men with low stress. The RERI estimate was 0.38 (95% CI 0.04-0.73), the AP estimate was 0.11 (95% CI 0.02-0.21), and the S index was 1.19 (95% CI 1.01-1.41). CONCLUSION: We report 10.7% prevalence of migraines and synergism between female gender and stress on risk of migraine, suggesting health interventions targeting women under stress may be beneficial.

High-Resolution Ecological Niche Modeling of Ixodes scapularis Ticks Based on Passive Surveillance Data at the Northern Frontier of Lyme Disease Emergence in North America.

BACKGROUND: Lyme disease (LD) is a bacterial infection transmitted by the black-legged tick (Ixodes scapularis) in eastern North America. It is an emerging disease in Canada due to the expanding range of its tick vector. Environmental risk maps for LD, based on the distribution of the black-legged tick, have focused on coarse determinants such as climate. However, climatic factors vary little within individual health units, the level at which local public health decision-making takes place. We hypothesize that high-resolution environmental data and routinely collected passive surveillance data can be used to develop valid models for tick occurrence and provide insight into ecological processes affecting tick presence at fine scales. METHODS: We used a maximum entropy algorithm (MaxEnt) to build a habitat suitability model for I. scapularis in Ottawa, Ontario, Canada using georeferenced occurrence points from passive surveillance data collected between 2013 and 2016 and high-resolution land cover and elevation data. We evaluated our model using an independent tick presence/absence dataset collected through active surveillance at 17 field sites during the summer of 2017. RESULTS: Our model showed a good ability to discriminate positive sites from negative sites for tick presence (AUC = 0.878 ± 0.019, classification accuracy = 0.835 ± 0.020). Heavily forested suburban and rural areas in the west and southwest of Ottawa had higher predicted suitability than the more agricultural eastern areas. CONCLUSIONS: This study demonstrates the value of passive surveillance data to model local-scale environmental risk for the tick vector of LD at sites of interest to public health. Given the rising incidence of LD and other emerging vector-borne diseases in Canada, our findings support the ongoing collection of these data and collaboration with researchers to provide a timely and accurate portrait of evolving public health risk.