Antibiotic Resistance associated with the COVID-19 Pandemic: A Rapid Systematic Review
Bibliographic record
Abstract
Abstract Background COVID-19 and antimicrobial resistance (AMR) are two intersecting global public health crises. Objective We aim to describe the impact of the COVID-19 pandemic on AMR across healthcare settings. Data Source A search was conducted in December 2021 in World Health Organization’s COVID-19 Research Database with forward citation searching up to June 2022. Study Eligibility Studies evaluating the impact of COVID-19 on AMR in any population were included and influencing factors were extracted. Methods Pooling was done separately for Gram-negative and Gram-positive organisms. Random effects meta-analysis was performed. Results Of 6036 studies screened, 28 were included and 23 provided sufficient data for meta-analysis. The majority of studies focused on hospital settings (n=25, 89%). The COVID-19 pandemic was not associated with a change in the incidence density (IRR 0.99, 95% CI: 0.67 to 1.47) or proportion (RR 0.91, 95% CI: 0.55 to 1.49) of MRSA or VRE cases. A non-statistically significant increase was noted for resistant Gram-negatives (i.e., ESBL, CRE, MDR or carbapenem-resistant Pseudomonas or Acinetobacter species, IRR 1.64, 95% CI: 0.92 to 2.92; RR 1.08, 95% CI: 0.91 to 1.29). The absence of enhanced IPAC and/or ASP initiatives was associated with an increase in Gram-negative AMR (RR 1.11, 95%CI: 1.03 to 1.20), while studies that did report implementation of these initiatives noted no change in Gram-negative AMR (RR 0.80, 95%CI: 0.38 to 1.70). However, a test for subgroup differences showed no statistically significant difference between these groups (P=0.40) Conclusion The COVID-19 pandemic could play an important role in the emergence and transmission of AMR, particularly for Gram-negative organisms in hospital settings. There is considerable heterogeneity in both the AMR metrics utilized and the rate of resistance reported across studies. These findings reinforce the need for strengthened infection prevention, antimicrobial stewardship, and AMR surveillance in the context of the COVID-19 pandemic. PROSPERO registration: CRD42022325831 This research was carried out as part of routine work, no funding was received Data collection template, data, and analytic code are available upon request.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".