737. Geographic Clustering of Travel-acquired Infections in Ontario, Canada, 2008-2020

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)

Background. The prompt recognition and treatment of Plasmodium falciparum is necessary to prevent death. We reviewed data from a cohort of patients presenting with malaria to Kings College Hospital NHS Trust, London.
Methods. Retrospective review of electronic records and drug charts of patients diagnosed with malaria from Jan 2019-March 2021.
Results. 109 cases of malaria were identified representing travellers from 11 Sub-Saharan African countries: Nigeria(38%), Sierra Leone(33%), Ivory Coast(10%). The age range varied from 4 to 76 years with a mean of 44, 66% of the cohort was male. 22 cases occurred during the COVID-19 Pandemic. The commonest symptoms were Fever (97%), Headache (92%) and malaise (72%). P. falciparum was present in 99% cases. A travel history was taken in 94% of cases. Malaria was considered by the first clinician in 82% of cases with the second highest differential being a viral illness. In 6 cases, it took 4 to 11 medical reviews before malaria was considered. 29 patients met the UK criteria for severe malaria. Door to antimalarial time varied from 1 to 128 hours, with a median of 7.4 hours. 46% of the cohort received intravenous Artesunate as their first antimalarial. Extreme delays occurred were clinicians did not consider malaria, patients had negative films or a patient did not declare a travel history when asked. 1 patient died of cerebral malaria with a door to needle time of 2hr 3min. Where a reason for delay is documented, drug availability represented the highest cause with mean delay from prescribing antimalarial to giving antimalarial of 2.7 hours. There was no difference in door to antimalarial administration during the COVID-19 Pandemic, but patients did have a delay in presentation to hospital from onset of symptoms, mean 6.2 days pre-pandemic, 10.5 days during pandemic, this was not statistically significant (P= 0.198). 3 patients presenting during the Pandemic had covid-19 swabs prior to admission and 10 had attended primary care services. Number of days between onset of malaria symptoms and presentation to the Emergency Department Box plot demonstrating that patients were waiting longer post symptom onset to access care in the Emergency Department. 3 patients had covid swabs in the community and 10 accessed care through their primary care physician.
Conclusion. Our data show that malaria is being considered early in the emergency department however there remain significant delays in administration of treatment. In 6 cases where malaria was not considered early there were delays in diagnosis of up to 5 days. An audit cycle will be completed with the aim of reducing door to antimalarial time.
Disclosures. 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. Background. In January-March 2020, the Centers for Disease Control and Prevention (CDC) issued multiple warnings regarding COVID-19 travel-associated risks. We sought to describe US travelers seeking pretravel consultation regarding international travel at US Global TravEpiNet (GTEN) sites before and after the initial COVID-19 travel warnings.

Disclosures. Isaac Bogoch, MD, MSc, BlueDot (Consultant)National Hockey League Players' Association (Consultant) Andrea Boggild, MSc MD DTMH
Methods. We prospectively collected data at 22 GTEN sites pre-COVID-19 (January-December 2019) and 18 GTEN sites during the COVID-19 pandemic (April 2020-March 2021). We excluded travelers evaluated during January-March 2020, when CDC travel guidance was evolving rapidly. Travelers used standardized questionnaires to self-report data regarding demographics and travel-related characteristics. Providers confirmed these data and documented their recommendations during pretravel consultation, which could be performed virtually. We conducted descriptive analyses of differences in demographics, travel-related characteristics, vaccinations, and medications (SAS v9.4;Cary,NC).
Results. Compared with 16,903 pre-COVID-19 consultations, only 1,564 consultations occurred during the COVID-19 pandemic, a 90% reduction (Table). During COVID-19, a greater proportion of travelers were children aged 1-5 years, visiting friends and relatives (VFR), with itineraries ≥ 30 days, and going to Africa; a smaller proportion of travelers were aged > 55 years, or traveling to Southeast Asia or the Western Pacific. During COVID-19, fewer vaccine-eligible travelers received vaccines at the pretravel consultation except for yellow fever, and a greater proportion were referred to another provider for vaccination (Figure). Table. Demographics and travel-related characteristics of international travelers seeking pretravel consultation at Global TravEpiNet sites before and during the COVID-19 pandemic Table continued. Demographics and travel-related characteristics of international travelers seeking pretravel consultation at Global TravEpiNet sites before and during the COVID-19 pandemic Among vaccine-eligible travelers, we summarized those who were vaccinated at the visit (blue) and not vaccinated (orange). We then categorized reasons for nonvaccination into: provider decision (solid), referral to another provider (dots), traveler refusal (striped), or other (hatched). COVID-19 vaccination was not available at GTEN sites during the analysis period; although COVID-19 vaccinations outside of GTEN sites might have affected vaccination recommendations, they were unlikely to have had a large effect given their limited availability in January-March 2021.
Conclusion. Compared with pre-COVID-19, US travelers seeking pretravel consultations at GTEN sites during the pandemic might be at higher risk for travel-related infections given VFR status, traveling for ≥ 30 days, and going to Africa. Fewer vaccine-eligible travelers were vaccinated at pretravel consultations, which could reflect more virtual pretravel consultations. Counseling and vaccination for international travelers continue to be priorities during the COVID-19 pandemic.
Disclosures. Salt Lake County Health Department, Salt Lake City, Utah; 5 Westminster College, Salt Lake City, Utah

Session: P-35. Global Health
Background. Vector borne diseases are responsible for almost one fifth of global infectious disease burden. International travelers are at risk for potentially life-threatening conditions when visiting areas with endemic vector borne disease, but this risk can be mitigated when proper insect precautions are taken. This study sought to evaluate the prevalence of insect precaution use and subsequent insect bites among Utah travelers who have attended pre-travel consultations.

Methods.
A cross-sectional study at the University of Utah and Salt Lake County travel clinics was analyzed. Descriptive statistics and multivariable logistic regression were used to explore factors associated with insect repellant use, and reporting bug bites despite insect repellant use.