Trends in hospital antibiotic utilization during the coronavirus disease 2019 (COVID-19) pandemic: A multicenter interrupted time-series analysis
Bibliographic record
Abstract
Objective: To describe the evolution of respiratory antibiotic prescribing during the coronavirus disease 2019 (COVID-19) pandemic across 3 large hospitals that maintained antimicrobial stewardship services throughout the pandemic. Design: Retrospective interrupted time-series analysis. Setting: A multicenter study was conducted including medical and intensive care units (ICUs) from 3 hospitals within a Canadian epicenter for COVID-19. Methods: Interrupted time-series analysis was used to analyze rates of respiratory antibiotic utilization measured in days of therapy per 1,000 patient days (DOT/1,000 PD) in medical units and ICUs. Each of the first 3 waves of the pandemic were compared to the baseline. Results: Within the medical units, use of respiratory antibiotics increased during the first wave of the pandemic (rate ratio [RR], 1.76; 95% CI, 1.38-2.25) but returned to the baseline in waves 2 and 3 despite more COVID-19 admissions. In ICU, the use of respiratory antibiotics increased in wave 1 (RR, 1.30; 95% CI, 1.16-1.46) and wave 2 of the pandemic (RR, 1.21; 95% CI, 1.11-1.33) and returned to the baseline in the third wave, which had the most COVID-19 admissions. Conclusions: After an initial surge in respiratory antibiotic prescribing, we observed the normalization of prescribing trends at 3 large hospitals throughout the COVID-19 pandemic. This trend may have been due to the timely generation of new research and guidelines developed with frontline clinicians, allowing for the active application of new research to clinical practice.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".