COVID-19 vaccination and use of antibiotics in COVID-19 patients: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Vaccinations are considered one of the most effective medical interventions. Among other benefits, certain vaccinations help reduce antimicrobial resistance by decreasing antibiotic use. Considering reports of increased antimicrobial resistance during the COVID-19 pandemic, this study aimed to explore the relationship between COVID-19 vaccination status and antibiotic use in COVID-19 patients. Methods: A systematic literature search was conducted in PubMed, Scopus, Web of Science, Embase, and Google Scholar between January 1, 2021, and November 6, 2024. The included studies were assessed for risk of bias using the Newcastle-Ottawa tool. Narrative synthesis and random-effects meta-analysis were employed to synthesize the evidence. Results: Eight studies were included in this systematic review and meta-analysis (134,022 participants). COVID-19 vaccination was significantly associated with a 34% reduction in the odds of antibiotic use (OR: 0.662; 95% CI: 0.540-0.811) in COVID-19 patients. These findings were supported by the sensitivity analyses. In the subgroup analysis, a significant negative association was observed between COVID-19 vaccination and antibiotic use among COVID-19 patients across all study designs. A major limitation of this study is that most of the included studies did not adjust for confounders. Conclusions: COVID-19 vaccination was associated with a significant reduction in antibiotic use among COVID-19 patients. COVID-19 vaccination status may have influenced healthcare providers' decisions regarding antibiotic use in this group. Further large-scale cohort studies are needed to confirm these findings. Other: The study protocol is registered with PROSPERO (ID: CRD42023449625). No funding was provided for this study. The APCs were covered by the Karolinska Institute.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.044 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".