Malaria in travellers returning or migrating to Canada: surveillance report from CanTravNet surveillance data, 2004-2014
Bibliographic record
Abstract
<h3>Background:</h3> Malaria remains the most common specific cause of fever in returned travellers and can be life-threatening. We examined demographic and travel correlates of malaria among Canadian travellers and immigrants to identify groups for targeted pretravel intervention. <h3>Methods:</h3> Descriptive data on ill returned Canadian travellers and immigrants presenting to a CanTravNet site between 2004 and 2014 with a diagnosis of malaria were analyzed. Data were collected using the GeoSentinel data platform. This network comprises 63 specialized travel and tropical medicine clinics, including 7 Canadian sites (Vancouver, Calgary, Toronto, Ottawa, Winnipeg and Montréal), that contribute anonymous, delinked, clinician- and questionnaire-based travel surveillance data on all ill travellers examined to a centralized Structure Query Language database. <h3>Results:</h3> During the study period, 20 345 travellers and immigrants were evaluated, and 93% had a travel-related diagnosis. Of these, 437 (2.1%) patients received 456 malaria diagnoses, the most common species being <i>Plasmodium falciparum</i> (<i>n</i> = 282, 61.8%). People travelling to visit friends and relatives were most well-represented (<i>n</i> = 169, 38.7%), followed by business travellers (<i>n</i> = 71, 16.2%). Sub-Saharan Africa was the most common source region, accounting for 341 (74.8%) malaria diagnoses, followed by South Central Asia (<i>n</i> = 55, 12%). Nigeria was the most well-represented source country, accounting for 41 cases (9.0%). India, a high-volume destination for Canadians, accounted for 40 cases (8.8%), 36 of which were caused by <i>Plasmodium vivax</i>. Of 456 malaria diagnoses, 26 (5.7%) were severe. Of 377 nonimmigrant travellers with malaria, 19.9% (<i>n</i> = 75) travelled for less than 2 weeks, and 7.2% (<i>n</i> = 27) travelled for less than 1 week. <h3>Interpretation:</h3> This analysis provides an epidemiologic framework for Canadian practitioners encountering prospective and returned travellers. It confirms the importance of preventive measures and surveillance associated with travel to sub-Saharan Africa and India, particularly by travellers visiting friends or relatives. Short-duration travel confers important malaria risk.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 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".