Malaria in travellers returning or migrating to Canada: surveillance report from CanTravNet surveillance data, 2004-2014
Bibliographic record
Abstract
Background: 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. Methods: 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. Results: 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 Plasmodium falciparum (n = 282, 61.8%). People travelling to visit friends and relatives were most well-represented (n = 169, 38.7%), followed by business travellers (n = 71, 16.2%). Sub-Saharan Africa was the most common source region, accounting for 341 (74.8%) malaria diagnoses, followed by South Central Asia (n = 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 Plasmodium vivax. Of 456 malaria diagnoses, 26 (5.7%) were severe. Of 377 nonimmigrant travellers with malaria, 19.9% (n = 75) travelled for less than 2 weeks, and 7.2% (n = 27) travelled for less than 1 week. Interpretation: 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 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 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".