Risk factors for Lyme disease resulting from residential exposure amidst emerging Ixodes scapularis populations: A neighbourhood-level analysis of Ottawa, Ontario

Abstract

Lyme disease is an emerging health threat in Canada due to the continued northward expansion of the main tick vector, Ixodes scapularis. It is of particular concern to populations living in expanding peri-urban areas where residential development and municipal climate change response impact neighbourhood structure and composition. The objective of this study was to estimate associations of socio-ecological characteristics with residential Lyme disease risk at the neighbourhood scale. We used Lyme disease case data for 2017-2020 reported for Ottawa, Ontario to determine where patients' residential property, or elsewhere within their neighbourhood, was the suspected site of tick exposure. Cases meeting this exposure definition (n = 118) were aggregated and linked to neighbourhood boundaries. We calculated landscape characteristics from composited and classified August 2018 PlanetScope satellite imagery. Negative binomial generalized linear models guided by a priori hypothesized relationships explored the association between hypothesized interactions of landscape structure and the outcome. Increases in median household income, the number of forest patches, the proportion of forested area, forest edge density, and mean forest patch size were associated with higher residential Lyme disease incidence at the neighbourhood scale, while increases in forest shape complexity and average distance to forest edge were associated with reduced incidence (P<0.001). Among Ottawa neighbourhoods, the combined effect of forest shape complexity and average forest patch size was associated with higher residential Lyme disease incidence (P<0.001). These findings suggest that Lyme disease risk in residential settings is associated with urban design elements. This is particularly relevant in urban centres where local ecological changes may impact the presence of emerging tick populations and how residents interact with tick habitat. Further research into the mechanistic underpinnings of these associations would be an asset to both urban development planning and public health management.

Fig 1. Neighbourhood boundaries (n = 112) with population densities for Ottawa, Ontario.

Locations of the Greenbelt and neighbourhoods with no population figures are highlighted.

Fig 2. Forest patches of comparable size demonstrating differences in forest shape complexity.

Figure was produced with ArcGIS Pro 2.7.2 software and includes the area, perimeter, and edge-to-area ratio of each forest patch depicted.

Fig 3. Residential Lyme disease case counts by Ottawa neighbourhoods (2017–2020).

Fig 4. Interaction plots where vertical height represents the incidence rate ratio for the combined effect from changes, in standard deviation units, to proportion of forest cover and mean forest patch size (A), forest edge-to-area ratio and mean forest patch size (B), and median household income and forest edge-to-area ratio (C) in negative binomial GLMs with spatial filtering.

The slope along each edge gives each variable’s marginal effect.