Spectrum of illness in migrants to Canada: sentinel surveillance through CanTravNet
Bibliographic record
Abstract
BACKGROUND: Due to ongoing political instability and conflict in many parts of the world, migrants are increasingly seeking asylum and refuge in Canada. METHODS: We examined demographic and travel correlates of illnesses among migrants to Canada to establish a detailed epidemiologic framework of this population for Canadian practitioners. Data on ill-returned Canadian travellers presenting to a CanTravNet site between 1 January 2015 and 31 December 2015 were analyzed. RESULTS: During the study period, 2415 ill travellers and migrants presented to a CanTravNet site, and of those, 519 (21.5%) travelled for the purpose of migration. Sub-Saharan Africa (n = 160, 30.8%), southeast Asia (n = 84, 16.2%) and south central Asia (n = 75, 14.5%) were the most common source regions for migrants, while the top specific source countries, of 98 represented, were the Philippines (n = 45, 8.7%), China (n = 36, 6.9%) and Vietnam (n = 31, 6.0%). Compared with non-migrant travellers, migrants were more likely to have a pre-existing immunocompromising medical condition, such as HIV or diabetes mellitus (P < 0.0001), and to require inpatient management of their illness (P < 0.0001). Diagnoses such as tuberculosis (n = 263, 50.7%), hepatitis B and C (n = 78, 15%) and HIV (n = 11, 2.1%) were over-represented in the migrant population compared with non-migrant travellers (P < 0.0001). Most cases of tuberculosis in the migrant population (n = 263) were latent (82% [n = 216]); only 18% (n = 47) were active. CONCLUSIONS: Compared with non-migrant travellers, migrants were more likely to present with a communicable infectious disease, such as tuberculosis, potentially complicated by an underlying immunosuppressing condition such as HIV. These differences highlight the divergent healthcare needs in the migrant population, and underscore the importance of surveillance programmes to understand their burden of illness. Intake programming should be adequately resourced to accommodate the medical needs of this vulnerable population of new Canadians.
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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 | 0.001 | 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".