Business travel-associated illness: a GeoSentinel analysis†
Bibliographic record
Abstract
Background: Analysis of a large cohort of business travelers will help clinicians focus on frequent and serious illnesses. We aimed to describe travel-related health problems in business travelers. Methods: GeoSentinel Surveillance Network consists of 64 travel and tropical medicine clinics in 29 countries; descriptive analysis was performed on ill business travelers, defined as persons traveling for work, evaluated after international travel 1 January 1997 through 31 December 2014. Results: Among 12 203 business travelers seen 1997-2014 (14 045 eligible diagnoses), the majority (97%) were adults aged 20-64 years; most (74%) reported from Western Europe or North America; two-thirds were male. Most (86%) were outpatients. Fewer than half (45%) reported a pre-travel healthcare encounter. Frequent regions of exposure were sub-Saharan Africa (37%), Southeast Asia (15%) and South Central Asia (14%). The most frequent diagnoses were malaria (9%), acute unspecified diarrhea (8%), viral syndrome (6%), acute bacterial diarrhea (5%) and chronic diarrhea (4%). Species was reported for 973 (90%) of 1079 patients with malaria, predominantly Plasmodium falciparum acquired in sub-Saharan Africa. Of 584 (54%) with malaria chemoprophylaxis information, 92% took none or incomplete courses. Thirteen deaths were reported, over half of which were due to malaria; others succumbed to pneumonia, typhoid fever, rabies, melioidosis and pyogenic abscess. Conclusions: Diarrheal illness was a major cause of morbidity. Malaria contributed substantial morbidity and mortality, particularly among business travelers to sub-Saharan Africa. Underuse or non-use of chemoprophylaxis contributed to malaria cases. Deaths in business travelers could be reduced by improving adherence to malaria chemoprophylaxis and targeted vaccination for vaccine-preventable diseases. Pre-travel advice is indicated for business travelers and is currently under-utilized and needs improvement.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".