Overprescription of antibiotics for treating hospitalized COVID-19 patients: A systematic review & meta-analysis
Bibliographic record
Abstract
Background Empirical use of antibiotics was reported throughout the coronavirus disease of 2019 (COVID-19) pandemic; however, evidence of bacterial coinfection or secondary bacterial infection among COVID-19 patients was sparse. Antibiotic overprescription for COVID-19 patients without confirmed bacterial coinfection can increase antimicrobial resistance (AMR). The objective of this study is to assess the appropriateness of antibiotic use during COVID-19 by summarizing the frequency of antibiotic use among hospitalized COVID-19 and the frequency of antibiotic use in patients with COVID-19. Methods A systematic search was conducted of the Embase, Medline, Web of Science, and Cochrane Library databases by generating search terms using the concepts of "COVID-19," "Bacterial Coinfection," "Secondary bacterial infection," and "Antimicrobial resistance" to identify studies reporting antibiotic prescription for hospitalized COVID-19 patients with or without bacterial coinfection. We excluded studies on outpatients, studies informed infection due to mechanical ventilation, and randomized controlled trials. The pooled estimate of the percentage of the total and confirmed appropriate antibiotic prescriptions provided to hospitalized COVID-19 patients was generated using a random effect meta-analysis with inverse variance weighting. The study protocol registration DOI is osf.io/d3fpm. Results Of 157,623 participants from 29 studies (11 countries, 45 % women) included in our review, antibiotics were prescribed to 67 % of participants (CI 64 %–71 %, P < 0·001), of which 80 % (CI 76 %–83 %, P < 0·001) of prescriptions were for COVID-19 patients without confirmed bacterial coinfections. Antibiotic overprescription varied during different periods of the pandemic and between High-Income and Upper and Lower Middle-Income Countries. We found heterogeneity among the studies ( I 2 = 100 %). The risk of bias analysis showed that 100 % of the included studies had the proper sample framing, and we are at low risk of bias due to sampling. Discussion We find greater than expected use of antibiotics to treat hospitalized COVID-19 patients without bacterial coinfections, which may contribute to AMR globally. Concrete guidelines for using antibiotics to treat COVID-19 patients, strict monitoring, and administering Antimicrobial Stewardship are needed to prevent overprescription.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".