The impact of the COVID-19 pandemic on antimicrobial usage: an international patient-level cohort study
Bibliographic record
Abstract
Background: This study aimed to evaluate the trends in antimicrobial prescription during the first 1.5 years of COVID-19 pandemic. Methods: This was an observational, retrospective cohort study using patient-level data from Bangladesh, Brazil, India, Italy, Malawi, Nigeria, South Korea, Switzerland and Turkey from patients with pneumonia and/or acute respiratory distress syndrome and/or sepsis, regardless of COVID-19 positivity, who were admitted to critical care units or COVID-19 specialized wards. The changes of antimicrobial prescription between pre-pandemic and pandemic were estimated using logistic or linear regression. Pandemic effects on month-wise antimicrobial usage were evaluated using interrupted time series analyses (ITSAs). Results: Antimicrobials for which prescriptions significantly increased during the pandemic were as follows: meropenem in Bangladesh (95% CI: 1.94-4.07) with increased prescribed daily dose (PDD) (95% CI: 1.17-1.58) and Turkey (95% CI: 1.09-1.58), moxifloxacin in Bangladesh (95% CI: 4.11-11.87) with increased days of therapy (DOT) (95% CI: 1.14-2.56), piperacillin/tazobactam in Italy (95% CI: 1.07-1.48) with increased DOT (95% CI: 1.01-1.25) and PDD (95% CI: 1.05-1.21) and azithromycin in Bangladesh (95% CI: 3.36-21.77) and Brazil (95% CI: 2.33-8.42). ITSA showed a significant drop in azithromycin usage in India (95% CI: -8.38 to -3.49 g/100 patients) and South Korea (95% CI: -2.83 to -1.89 g/100 patients) after WHO guidelines v1 release and increased meropenem usage (95% CI: 93.40-126.48 g/100 patients) and moxifloxacin (95% CI: 5.40-13.98 g/100 patients) in Bangladesh and sulfamethoxazole/trimethoprim in India (95% CI: 0.92-9.32 g/100 patients) following the Delta variant emergence. Conclusions: This study reinforces the importance of developing antimicrobial stewardship in the clinical settings during inter-pandemic periods.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".