737. Geographic Clustering of Travel-acquired Infections in Ontario, Canada, 2008-2020
Bibliographic record
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)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".