Global trends in travel-related antimicrobial resistance: a systematic review, 2020–2024
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a growing global health threat, with international travel playing a key role in the spread of resistant bacteria. This systematic review examines trends in travel-associated AMR from 2020 to 2024. A search of PubMed, Scopus and Web of Science identified 10 studies involving 359 AMR isolates. Using the Newcastle-Ottawa scale, the study quality was assessed and findings were synthesised to identify patterns in prevalence, diversity and geographic spread. Results revealed a consistent rise in travel-associated AMR, particularly from regions such as Southeast Asia and Africa, which acted as major sources of diverse resistant pathogens. These include extended spectrum beta-lactamase-producing Escherichia coli, multidrug-resistant (MDR) Corynebacterium diphtheriae and colistin-resistant Enterobacterales. The number of MDR strains increased over time, making up 15.3% of cases by 2024. Healthcare exposure during travel emerged as a significant risk factor. Overall, the prevalence and diversity of AMR bacteria linked to travel have risen steadily, highlighting the urgent need for global cooperation. Enhanced surveillance, antimicrobial stewardship, infection control measures and international collaboration are essential to curb the spread of these dangerous pathogens.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".