Evaluating the Relationship Between Hospital Antibiotic Use and Antibiotic Resistance in Common Nosocomial Pathogens
Bibliographic record
Abstract
OBJECTIVE The relationship between hospital antibiotic use and antibiotic resistance is poorly understood. We evaluated the association between antibiotic utilization and resistance in academic and community hospitals in Ontario, Canada. METHODS We conducted a multicenter observational ecological study of 37 hospitals in 2014. Hospital antibiotic purchasing data were used as an indicator of antibiotic use, whereas antibiotic resistance data were extracted from hospital indexes of resistance. Multivariate regression was performed, with antibiotic susceptibility as the primary outcome, antibiotic consumption as the main predictor, and additional covariates of interest (ie, hospital type, laboratory standards, and patient days). RESULTS With resistance data representing more than 90,000 isolates, we found the increased antibiotic consumption in defined daily doses per 1,000 patient days (DDDs/1,000 PD) was associated with decreased antibiotic susceptibility for Pseudomonas aeruginosa (-0.162% per DDD/1,000 PD; P=.119). However, increased antibiotic consumption predicted increased antibiotic susceptibility significantly for Escherichia coli (0.173% per DDD/1,000 PD; P=.005), Klebsiella spp (0.124% per DDD/1,000 PD; P=.004), Enterobacter spp (0.194% per DDD/1,000 PD; P=.003), and Enterococcus spp (0.309% per DDD/1,000 PD; P=.001), and nonsignificantly for Staphylococcus aureus (0.012% per DDD/1,000 PD; P=.878). Hospital type (P=.797) and laboratory standard (P=.394) did not significantly predict antibiotic susceptibility, while increased hospital patient days generally predicted increased organism susceptibility (0.728% per 10,000 PD; P<.001). CONCLUSIONS We found that hospital-specific antibiotic usage was generally associated with increased, rather than decreased hospital antibiotic susceptibility. These findings may be explained by community origins for many hospital-diagnosed infections and practitioners choosing agents based on local antibiotic resistance patterns. Infect Control Hosp Epidemiol 2017;38:1457-1463.
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".