Illness in Canadian travellers and migrants from Brazil: CanTravNet surveillance data, 2013–2016
Bibliographic record
Abstract
Background: In light of the 2016 summer Olympic games it is anticipated that Canadian practitioners will require information about common illnesses that may affect travellers returning from Brazil.Objective: To identify the demographic and travel correlates of illness among recent Canadian travellers and migrants from Brazil attending a network of travel health clinics across Canada.Methods: Data was analyzed on returned Canadian travellers and migrants presenting to a CanTravNet site for care of an illness between June 2013 and June 2016.Results: During the study period, 7,707 ill travellers and migrants presented to a CanTravNet site and 89 (0.01%) acquired their illness in Brazil.Tourists were most well represented (n=45, 50.6%), followed by those travelling to "visit friends and relatives" (n=14, 15.7%).The median age was 37 years (range <1-78 years), 49 travellers were men (55.1%) and 40 were women (44.9%).Of the 40 women, 26 (65%) were of childbearing age.Nine percent (n=8) of travellers were diagnosed with arboviruses including dengue (n=6), chikungunya (n=1) and Zika virus (n=1), while another 14.6% (n=13) presented for care of non-specific viral syndrome (n=7), non-specific febrile illness (n=1), peripheral neuropathy (n=1) and non-specific rash (n=4), which are four syndromes that may be indicative of Zika virus infection.Ill returned travellers to Brazil were more likely to present for care of arboviral or Zika-like illness than other ill returned travellers to South America (23.6 per 100 travellers versus 10.5 per 100 travellers, respectively [p=0.0024]).Interpretation: An epidemiologic approach to illness among returned Canadian travellers to Brazil can inform Canadian practitioners encountering both prospective and returned travellers to the Olympic games.Analysis showed that vector-borne illnesses such as dengue are common and even in this small group of travellers, both chikungunya and Zika virus were represented.It is extremely important to educate travellers about mosquito-avoidance measures in advance of travel to Brazil.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".