Illness in Long-Term Travelers Visiting GeoSentinel Clinics
Bibliographic record
Abstract
Length of travel appears to be associated with health risks.GeoSentinel Surveillance Network data for 4,039 long-term travelers (trip duration >6 months) seen after travel during June 1, 1996, through December 31, 2008, were compared with data for 24,807 short-term travelers (trip duration <1 month).Long-term travelers traveled more often than short-term travelers for volunteer activities (39.7% vs. 7.0%) and business (25.2%vs. 13.8%).More long-term travelers were men (57.2% vs. 50.1%)and expatriates (54.0% vs. 8.9%); most had pretravel medical advice (70.3% vs. 48.9%).Per 1,000 travelers, long-term travelers more often experienced chronic diarrhea, giardiasis, Plasmodium falciparum and P. vivax malaria, irritable bowel syndrome (postinfectious), fatigue >1 month, eosinophilia, cutaneous leishmaniasis, schistosomiasis, and Entamoeba histolytica diarrhea.Areas of concern for long-term travelers were vector-borne diseases, contact-transmitted diseases, and psychological problems.Our results can help prioritize screening for and diagnosis of illness in long-term travelers and provide evidence-based pretravel advice.T ravelers have many reasons for long durations of trav- el, including diplomatic work, education and research, missionary work, Peace Corps volunteer (PCV) work, military operations, backpacking trips, and corporate expatriate assignments (1-7).Longer trips often are assumed to be associated with increased risk for some health problems, but few studies have compared the types and causes of illness in travelers on the basis of duration of travel.Previous studies suggested that long-term travelers are more likely than short-term travelers to acquire malaria (8) and that recommendations should be tailored individually (9).Other illness also might be more common in long-term than in short-term travelers.To evaluate the effect of trip duration on illness, we compared illnesses by duration of travel for travelers seeking treatment at GeoSentinel Surveillance Network sites.We also characterized long-term travelers' demographics, travel patterns, and travel-related illnesses. MethodsGeoSentinel Surveillance Network (www.istm.org/geosentinel/main.html)sites are clinics on 6 continents that specialize in travel or tropical medicine and contribute data on travel-related illnesses and trip information.Our study comprised data from ill travelers visiting GeoSentinel sites from June 1, 1996, through December 31, 2008. Inclusion CriteriaPersons in our study must have crossed an international border within the past 10 years and then sought treatment or medical advice at a GeoSentinel site for a presumed travel-related illness.Only travelers with confi rmed and probable diagnoses (including a healthy screening result) were included (Figure 1), and >1 diagnosis per patient was possible.Final diagnoses were assigned by a clinician.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".