Travel-associated multidrug-resistant organism acquisition and risk factors among US military personnel
Bibliographic record
Abstract
BACKGROUND: International travel is a risk factor for incident colonization with extended spectrum beta-lactamase (ESBL)-producing organisms. These and other multidrug-resistant (MDR) bacteria are major pathogens in combat casualties. We evaluated risk factors for colonization with MDR bacteria in US military personnel travelling internationally for official duty. METHODS: TravMil is a prospective observational study enrolling subjects presenting to military travel clinics. We analysed surveys, antimicrobial use data, and pre- and post-travel perirectal swabs in military travellers to regions outside the continental USA, Canada, Western or Northern Europe, or New Zealand, presenting to one clinic from 12/2015 to 12/2017. Recovered Gram-negative isolates underwent identification and susceptibility testing (BD Phoenix). Characteristics of trip and traveller were analysed to determine risk factors for MDR organism colonization. RESULTS: 110 trips were planned by 99 travellers (74% male, median age 38 years [IQR 31, 47.25]); 72 trips with returned pre- and post-travel swabs were completed by 64 travellers. Median duration was 21 days (IQR 12.75, 79.5). 17% travelled to Mexico/Caribbean/Central America, 15% to Asia, 57% to Africa and 10% to South America; 56% stayed in hotels and 50% in dormitories/barracks. Travellers used doxycycline (15%) for malaria prophylaxis, 11% took an antibiotic for travellers' diarrhoea (TD) treatment (fluoroquinolone 7%, azithromycin 4%). Incident MDR organism colonization occurred in 8 travellers (incidence density 3.5/1000 travel days; cumulative incidence 11% of trips [95% CI: 4-19%]), all ESBL-producing Escherichia coli. A higher incidence of ESBL-producing E. coli acquisition was associated with travel to Asia (36% vs 7%, P = 0.02) but not with travel to other regions, TD or use of antimicrobials. No relationship was seen between fluoroquinolone or doxycycline exposure and resistance to those antimicrobials. CONCLUSIONS: Incident colonization with MDR organisms occurs at a lower rate in this military population compared with civilian travellers, with no identified modifiable risk factors, with highest incidence of ESBL acquisition observed after South Asia travel.
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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) | 0.003 | 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".