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Record W2899832880 · doi:10.1093/ofid/ofy210.1005

1172. Travel-Associated Multidrug-Resistant Organism Acquisition and Risk Factors Among US Military Personnel

2018· article· en· W2899832880 on OpenAlexaboutno aff
Gregory Buchek, Katrin Mende, Kalyani Telu, Susan J Kaiser, David R. Tribble, Jamie Fraser, Indrani Mitra, Tahaniyat Lalani, Heather C. Yun

Bibliographic record

VenueOpen Forum Infectious Diseases · 2018
Typearticle
Languageen
FieldMedicine
TopicTravel-related health issues
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIncidence (geometry)Antibiotic resistanceColonizationDemographyEnvironmental healthAntibioticsMicrobiologyBiology

Abstract

fetched live from OpenAlex

Abstract Background International travel is a risk factor for incident colonization with extended spectrum β-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 traveling internationally for official duty. Methods TravMil is a prospective observational study enrolling subjects presenting to military travel clinics. We analyzed surveys, antimicrobial use data, and pre- and post-travel self-collected perirectal swabs in military travelers to regions outside the continental United States, Canada, Western or Northern Europe, or New Zealand presenting to one clinic from December 2015 to December 2017. Gram-negative isolates recovered from swabs underwent real-time identification and susceptibility testing (BD Phoenix). Characteristics of trip and traveler were analyzed to determine risk factors for MDR organism colonization. Results One hundred ten trips were planned by 99 travelers (74% male, median age 38 years [IQR 31, 47.25]); 72 trips were completed by 64 travelers. Median trip duration was 21 days (IQR 12.75, 79.5). Of those with trips completed, 17% traveled 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. Travelers used doxycycline (15%) for malaria prophylaxis, 11% took an antibiotic for travelers’ diarrhea (TD) treatment (fluoroquinolone 7%, azithromycin 4%). Incident MDR organism colonization occurred in eight travelers (incidence density 3.5/1,000 travel days; cumulative incidence 11% of trips [95% CI: 4%–19%]), all ESBL-producing E. 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. Conclusion Consistent with other studies of US military personnel travelers, incident colonization with MDR organisms following official travel occurs at a lower rate in this population compared with civilian travelers, with no identified modifiable risk factors. The highest incidence of ESBL acquisition was observed during travel to Asia. Disclosures All authors: No reported disclosures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.010
GPT teacher head0.272
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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