Travel-acquired infections in Canada: CanTravNet 2011—2012
Bibliographic record
Abstract
BACKGROUND: Important gaps remain in our knowledge of the infectious diseases people acquire while travelling and the impact of pathogens imported by Canadian travellers. OBJECTIVE: To provide a surveillance update of illness in a cohort of returned Canadian travellers and new immigrants. METHODS: Data on returning Canadian travellers and new immigrants presenting to a CanTravNet site between September 2011 and September 2012 were extracted and analyzed by destination, presenting symptoms, common and emerging infectious diseases and disease severity. RESULTS: During the study period, 2283 travellers and immigrants presented to a CanTravNet site, 88% (N=2004) of whom were assigned a travel-related diagnosis. Top three destinations for non-immigrant travellers were India (N=132), Mexico (N=103) and Cuba (N=89). Fifty-one cases of malaria were imported by ill returned travellers during the study period, 60% (N=30) of which were Plasmodium falciparum infections. Individuals travelling to visit friends and relatives accounted for 83% of enteric fever cases (15/18) and 41% of malaria cases (21/51). The requirement for inpatient management was over-represented among those with malaria compared to those without malaria (25% versus 2.8%; p<0.0001) and those travelling to visit friends and relatives versus those travelling for other reasons (12.1% versus 2.4%; p<0.0001). Nine new cases of HIV were diagnosed among the cohort, as well as one case of acute hepatitis B. Emerging infections among travellers included hepatitis E virus (N=6), chikungunya fever (N=4) and cutaneous leishmaniasis (N=16). Common chief complaints included gastrointestinal (N=804), dermatologic (N=440) and fever (N=287). Common specific causes of chief complaint of fever in the cohort were malaria (N=47/51 total cases), dengue fever (14/18 total cases), enteric fever (14/17 total cases) and influenza and influenza-like illness (15/21 total cases). Animal bites were the tenth most common diagnosis among tourist travellers. INTERPRETATION: Our analysis of surveillance data on ill returned Canadian travellers provides a recent update to the spectrum of imported illness among travelling Canadians. Preventable travel-acquired illnesses and injuries in the cohort include malaria, enteric fever, HIV, hepatitis B, hepatitis A, influenza and animal bites. Strategies to improve uptake of preventive interventions such as malaria chemoprophylaxis, immunizations and arthropod/animal avoidance may be warranted.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".