Summer Peaks in the Incidences of Gram-Negative Bacterial Infection Among Hospitalized Patients
Bibliographic record
Abstract
OBJECTIVE: Recognition of seasonal trends in hospital infections may improve diagnosis, use of empirical therapy, and infection prevention interventions. There are very few data available regarding the seasonal variability of these infections. We quantified the seasonal variation in the incidences of hospital infection caused by common bacterial pathogens and estimated the association between temperature changes and infection rates. METHODS: A cohort of all adult patients admitted to the University of Maryland Medical Center during the period from 1998 through 2005 was analyzed. Time-series analyses were used to estimate the association of the number of infections per month caused by Pseudomonas aeruginosa, Acinetobacter baumannii, Enterobacter cloacae, Escherichia coli, Staphylococcus aureus, and enterococci with season and temperature, while controlling for long-term trends. RESULTS: There were 218,594 admissions to the index hospital, and analysis of 26,624 unique clinical cultures that grew the organisms of interest identified increases in the mean monthly rates of infection caused by P. aeruginosa (28% of isolates recovered; P ! .01), E. cloacae (46%; P ! .01), E. coli (12%; P ! .01), and A. baumannii (21%; Pp.06). For each 10 degrees F increase, we observed a 17% increase in the monthly rates of infection caused by P. aeruginosa (Pp.01) and A. baumanii (Pp.05). CONCLUSION: Significantly higher rates of gram-negative infection were observed during the summer months, compared with other seasons. For some pathogens, higher temperatures were associated with higher infection rates, independent of seasonality. These findings have important implications for infection prevention, such as enhanced surveillance during the warmer months, and for choice of empirical antimicrobial therapy among hospitalized adults. Future, quasi-experimental investigations of gram-negative infection prevention initiatives should control for seasonal variation.
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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 | 0.002 |
| 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) | 0.002 | 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".