Long-term Risk of Infection Among Patients Colonized With Antimicrobial-Resistant Pathogens: A Population-wide Cohort Study
Bibliographic record
Abstract
Abstract Background Antimicrobial-resistant (AMR) pathogens represent an ongoing global health burden. Colonization is often a prerequisite for infection, but the risk of infection after AMR colonization is not well understood. Using population-level health administrative data, we sought to investigate the risk of infection with the same AMR organism after detection of colonization. Methods We conducted a retrospective population-wide cohort study among residents of Ontario, Canada, over a 5-year period to determine the risk of infection after detection of colonization with the following AMR pathogens: methicillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococcus, extended-spectrum β-lactamase–producing Enterobacterales, and carbapenemase-producing Enterobacterales. We also examined the effects of age, sex, and health care setting of colonization detection on subsequent infection risk. Results There were 69 998 individuals with a positive AMR pathogen surveillance test result during the study period, 15.6% of which subsequently developed a sterile or nonsterile site infection within a median 57 days (IQR, 11–228). Infection rates varied among organisms: 18.3% for methicillin-resistant S aureus within a median 57 days (IQR, 10–239), 2.8% for vancomycin-resistant Enterococcus within a median 37 days (IQR, 11–119), 21.5% for extended-spectrum β-lactamase–producing Enterobacterales within a median 71 days (IQR, 18–231), and 20.3% for carbapenemase-producing Enterobacterales within a median 10 days (IQR, 3–42). A positive surveillance test result detected in a hospital was associated with increased infection risk after colonization as compared with the community setting. Conclusions The overall infection rate after colonization with an AMR pathogen was high for most organisms, highlighting the importance of detecting colonization from both an infection control and empiric antibiotic selection perspective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".