737. Geographic Clustering of Travel-acquired Infections in Ontario, Canada, 2008-2020. (4th December 2021)

• Record Type: Journal Article
• Authors: Harish, Vinyas; Buajitti, Emmalin; Burrows, Holly; Posen, Joshua; Bogoch, Isaac; Gubbay, Jonathan; Boggild, Andrea; Boggild, Andrea; Rosella, Laura; Morris, Shaun
• Is Part Of: Open forum infectious diseases. Volume 8(2021)Supplement 1
• Page Start: S466
• Page End: S467
• Publication Date: 2021-12-04
• Subjects: Communicable diseases -- Periodicals; Medical microbiology -- Periodicals; Infection -- Periodicals; 616.9
• DOI: 10.1093/ofid/ofab466.934
• Languages: English
• ISSNs: 2328-8957

Abstract

Abstract: Background: As rates of international travel increase, more individuals are at risk of travel-acquired infections (TAIs). We aimed to review all microbiologically confirmed cases of malaria, dengue, chikungunya, and enteric fever ( Salmonella enterica serovar Typhi/Paratyphi) in Ontario, Canada between 2008-2020 to identify high-resolution geographical clusters that could be targeted for pre-travel prevention. Methods: Retrospective cohort study of over 174, 000 unique tests for the four above TAIs from Public Health Ontario Laboratories. Test-level data were processed to calculate annual case counts and crude population-standardized incidence ratios (SIRs) at the forward sortation area (FSA) level. Moran's I statistic was used to test for global spatial autocorrelation. Smoothed SIRs and 95% posterior credible intervals (CIs) were estimated using a spatial Bayesian hierarchical model, which accounts for statistical instability and uncertainty in small-area incidence. Posterior CIs were used to identify high- and low-risk areas, which were described using sociodemographic data from the 2016 Census. Finally, a second model was used to estimate the association between drivetime to the nearest travel clinic and risk of TAI within high-risk areas. Results: There were 5962 cases of the four TAIs across Ontario over the study period. Smoothed FSA-level SIRs are shown in Figure 1a, with an inset for the Greater Toronto Area (GTA) in 1b. There was spatial clustering of TAIs (Moran's I=0.61, p< 2.2e-16). Identified high- and low-risk areas are shown in panels c and d. Compared to low-risk areas, high-risk areas were significantly more likely to have higher proportions of immigrants (p< 0.0001), lower household after-tax income (p=0.04), more university education (p< 0.0001), and were less knowledgeable of English/French (p< 0.0001). In the high-risk GTA, each minute increase in drivetime to the closest travel clinic was associated with a 4% reduction in TAI risk (95% CI 2 - 6%). Bayesian hierarchical model (BHM) smoothed standardized incidence ratios (SIRs) for travel-acquired infections (TAIs) and estimated risk levels (a and c) with insets for the Greater Toronto Area (b and d). High-risk areas are defined as those with smoothed SIR 95% CIs greater than 2, and low-risk areas with smoothed SIR 95% CIs less than 0.25. Conclusion: Urban neighbourhoods in the GTA had elevated risks of becoming ill with TAIs. However, geographic proximity to a travel clinic was not associated with an area-level risk reduction in TAI, suggesting other barriers to seeking and adhering to pre-travel advice. Disclosures: Isaac Bogoch, MD, MSc, BlueDot (Consultant)National Hockey League Players' Association (Consultant) Andrea Boggild, MSc MD DTMH FRCPC, Nothing to disclose Shaun Morris, MD, MPH, DTM&H, FRCPC, FAAP, GSK (Speaker's Bureau)Pfizer (Advisor or Review Panel member)Pfizer (Grant/Research Support)