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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

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 (p0.001). Using a dichotomized measure of model-predicted habitat suitability, the incidence rate ratio - the ratio of ticks per m

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.