RESUMEN
Background: Lyme disease (LD) emerged in southern Québec at the start of the century, with many municipalities now endemic. A coordinated active surveillance programme has been in place in the province of Québec since 2014, including a limited number of sentinel field sites resampled each year and a larger set of accessory field sites that change yearly according to the LD surveillance signal. We aimed to evaluate whether a sentinel approach to active surveillance was more representative of LD risk to human populations, compared to risk-based surveillance. Methods: We compared enzootic hazard measures (average nymph densities) from sentinel and accessory sites with LD risk (number of human LD cases) across the study area between 2015 and 2019 using local bivariate Moran's I analysis. Results: Hazard measures from sentinel sites captured spatial risk significantly better than data from accessory sites (χ2=20.473, p<0.001). In addition, sentinel sites successfully tracked the interannual trend in LD case numbers, whereas accessory sites showed no association despite the larger sample size. Conclusion: Where surveillance aims to document changes in tick-borne disease risk over time and space, we suggest that repeated sampling of carefully selected field sites may be most effective, while risk-based surveillance may be more usefully applied to confirm the presence of emerging disease risk in a specific region of interest or to identify suitable sites for long-term monitoring as LD and other tick-borne diseases continue to emerge.
RESUMEN
Lyme disease (LD) is a tick-borne disease which has been emerging in temperate areas in North America, Europe, and Asia. In Quebec, Canada, the number of human LD cases is increasing rapidly and thus surveillance of LD risk is a public health priority. In this study, we aimed to evaluate the ability of active sentinel surveillance to track spatiotemporal trends in LD risk. Using drag flannel data from 2015-2019, we calculated density of nymphal ticks (DON), an index of enzootic hazard, across the study region (southern Quebec). A Poisson regression model was used to explore the association between the enzootic hazard and LD risk (annual number of human cases) at the municipal level. Predictions from models were able to track both spatial and interannual variation in risk. Furthermore, a risk map produced by using model predictions closely matched the official risk map published by provincial public health authorities, which requires the use of complex criteria-based risk assessment. Our study shows that active sentinel surveillance in Quebec provides a sustainable system to follow spatiotemporal trends in LD risk. Such a network can support public health authorities in informing the public about LD risk within their region or municipality and this method could be extended to support Lyme disease risk assessment at the national level in Canada.