Influenza-Like Illness in Travelers to the Developing World
Bibliographic record
Abstract
Background. International travelers are at risk for development of influenza-like illness (ILI) during travel to less developed regions. While pre-travel counseling and immunization may help in preventing development of ILI during travel, little is known of traveler and trip characteristics most likely to influence development of ILI when traveling to the developing world. Methods. TravMil is a prospective observational study enrolling subjects presenting to 6 military travel clinics. We analyzed pre- and post-travel surveys from travelers visiting regions outside of the continental United States, Western or Northern Europe, Canada or New Zealand between January 2010 and March 2016. ILI was defined as self-reported fever associated with either a sore throat or cough. Characteristics of trip and traveler were analyzed to determine risk factors for development of ILI. Results. A total of 2932 trips were recorded (55% male, median age 45 years [IQR 29–63], 69% white, 51% traveling for vacation, 29% traveling for military purpose, median duration of travel 17 days [IQR 12–29]). Eleven percent were complicated by ILI lasting a median of 5 days (IQR 3–10); 70% and 17% of these reported upper and lower respiratory tract infection, respectively, and 12% reported both. Seven hundred forty included number of self-reported influenza vaccinations (#IV) in the preceding 5 years (median 5, IQR 3–5). On univariate analysis, travelers with ILI were more often female (55% versus 43%, p < 0.01), traveling for vacation (61% versus 50%, p < 0.01), to Asia (44% versus 33%, p < 0.01), with longer duration of travel (21 [IQR 14–40] versus 16 days [IQR 11–28], p < 0.01) and decreased #IV (4 [IQR 2– 5] versus 5 [IQR 3–5], p < 0.01). Those with ILI were less often active duty military (30% versus 39%, p < 0.01), traveling for military purpose (22% versus 30%, p < 0.01), for medical support (4% versus 7%, p < 0.05), or to Africa (25% versus 33%, p < 0.01). However, none remained significant on multivariate analysis; an OR of 0.85 (CI 0.72–1.01, p = 0.07) was seen with #IV. Conclusion. ILI remains common in travelers, regardless of traveler characteristics, purpose of travel, destination, or season of year. In this highly immunized population, a trend toward decreased ILI rates with increased #IV was observed. This remains an important area of emphasis in pre-travel counseling. Disclosures. All authors: No reported disclosures.
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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 | 0.001 |
| 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) | 0.002 | 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".