Illness among US resident student travellers after return to the USA: a GeoSentinel analysis, 2007–17
Bibliographic record
Abstract
Background: The number of US students studying abroad more than tripled during the past 20 years. As study abroad programmes' destinations diversify, students increasingly travel to resource-limited countries, placing them at risk for infectious diseases. Data describing infections acquired by US students while travelling internationally are limited. We describe illnesses among students who returned from international travel and suggest how to prevent illness among these travellers. Methods: GeoSentinel is a global surveillance network of travel and tropical medicine providers that monitors travel-related morbidity. This study included the records of US resident student international travellers, 17-24 years old, who returned to the USA, had a confirmed travel-related illness at one of 15 US GeoSentinel sites during 2007-17 and had a documented exposure region. Records were analysed to describe demographic and travel characteristics and diagnoses. Results: The study included 432 students. The median age was 21 years; 69% were female. More than 70% had a pre-travel consultation with a healthcare provider. The most common exposure region was sub-Saharan Africa (112; 26%). Students were most commonly exposed in India (44; 11%), Ecuador (28; 7%), Ghana (25; 6%) and China (24; 6%). The median duration of travel abroad was 40 days (range: 1-469) and presented to a GeoSentinel site a median of 8 days (range: 0-181) after travel; 98% were outpatients. Of 581 confirmed diagnoses, the most common diagnosis category was gastrointestinal (45%). Acute diarrhoea was the most common gastrointestinal diagnosis (113 of 261; 43%). Thirty-one (7%) students had vector-borne diseases [14 (41%) malaria and 11 (32%) dengue]. Three had vaccine-preventable diseases (two typhoid; one hepatitis A); two had acute human immunodeficiency virus infection. Conclusions: Students experienced travel-related infections, despite the majority having a pre-travel consultation. US students should receive pre-travel advice, vaccinations and chemoprophylaxis to prevent gastrointestinal, vector-borne, sexually transmitted and vaccine-preventable infections.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".