Expatriates ill after travel: Results from the Geosentinel Surveillance Network
Bibliographic record
Abstract
BACKGROUND: Expatriates are a distinct population at unique risk for health problems related to their travel exposure. METHODS: We analyzed GeoSentinel data comparing ill returned expatriates with other travelers for demographics, travel characteristics, and proportionate morbidity (PM) for travel-related illness. RESULTS: Our study included 2,883 expatriates and 11,910 non-expatriates who visited GeoSentinel clinics ill after travel. Expatriates were more likely to be male, do volunteer work, be long-stay travelers (>6 months), and have sought pre-travel advice. Compared to non-expatriates, expatriates returning from Africa had higher proportionate morbidity (PM) for malaria, filariasis, schistosomiasis, and hepatitis E; expatriates from the Asia-Pacific region had higher PM for strongyloidiasis, depression, and anxiety; expatriates returning from Latin America had higher PM for mononucleosis and ingestion-related infections (giardiasis, brucellosis). Expatriates returning from all three regions had higher PM for latent TB, amebiasis, and gastrointestinal infections (other than acute diarrhea) compared to non-expatriates. When the data were stratified by travel reason, business expatriates had higher PM for febrile systemic illness (malaria and dengue) and vaccine-preventable infections (hepatitis A), and volunteer expatriates had higher PM for parasitic infections. Expatriates overall had higher adjusted odds ratios for latent TB and lower odds ratios for acute diarrhea and dermatologic illness. CONCLUSIONS: Ill returned expatriates differ from other travelers in travel characteristics and proportionate morbidity for specific diseases, based on the region of exposure and travel reason. They are more likely to present with more serious illness.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".