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

Jay Logan*, A. Hoi, M Sawada, A. Knudby, T Ramsay, J.I. Blanford, N. Ogden, M. Kulkami

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
22 Downloads (Pure)

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
Original languageEnglish
Article numbere0290463
Pages (from-to)e0290463
Number of pages21
JournalPLoS ONE
Volume18
Issue number8
DOIs
Publication statusPublished - 24 Aug 2023

Keywords

  • Lyme disease
  • Ticks
  • GIS
  • Geo-health
  • geohealth
  • spatial analysis
  • geo-health
  • GeoHealth

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