Pseudomonas aeruginosa carriage and associated risk factors in healthy individuals and patients from Rotterdam, Rome, and Jakarta
Bibliographic record
Abstract
Pseudomonas aeruginosa may colonize humans, however, epidemiological data are scarce. Here, we determined overall and body site-specific carriage rates and associated risk factors among healthy individuals and newly admitted patients in three major cities. This cross-sectional study was conducted in Rotterdam (The Netherlands), Rome (Italy), and Jakarta (Indonesia) between 2022-2024. Adult healthy individuals and newly admitted patients were asked to provide throat, navel, and rectal/perianal swabs, and to complete a questionnaire. Univariable and multivariable analyses were performed to determine factors associated with P. aeruginosa carriage. Carriage rates differed significantly between cities (p < 0.001), and were lowest in Rome (healthy individuals 4.8%; patients 6.5%), followed by Rotterdam (healthy individuals 12.0%; patients 12.7%), and Jakarta (healthy individuals 28.6%; patients 24.0%). In carriers from Rotterdam, P. aeruginosa was most often detected in perianal swabs, while mostly in throat swabs among carriers from Rome and Jakarta. P. aeruginosa carriage had a seasonal association in patients from Rotterdam (p = 0.014) and Jakarta (p = 0.020). Among patients from Jakarta, female sex (aOR 1.98, 95% CI 1.02-3.84; p = 0.045) was associated with P. aeruginosa carriage. Overall, P. aeruginosa carriage rates and colonized body sites differ between cities and are likely associated with climate differences. Our findings warrant setting-specific adaptations of screening strategies and surveillance programs.
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 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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".