COVID-19 impact on AMR: a rapid scoping review, equity analysis and evidence gap map study
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic is expected to have impacted many drivers of antimicrobial resistance (AMR) and compounded existing societal and health inequities. This rapid scoping review examined how three selected healthcare system factors, which we have called 'drivers'-antimicrobial use, infection prevention and control and health system use-were affected by COVID-19 and how they have impacted resistance. METHODS: Peer-reviewed searches were performed in MEDLINE, Embase and Cochrane on 19 December 2022 and updated on 25 February 2023 and 1 September 2023. Results of these searches were integrated with an initial search run on 19 October 2022, using the WHO COVID-19 Research Database. References of included studies were also searched to identify any additional relevant studies. Data on the three drivers from included studies were assessed to determine whether they influenced the emergence, spread or number of resistant infections due to antimicrobial-resistant organisms. Studies were then mapped to identify literature gaps and assessed for equity considerations and quality of evidence. RESULTS: 63 studies were analysed. Reported COVID-19 changes to antimicrobial use were associated with increased AMR burden in hospital settings. Conversely, the infection prevention and control measures implemented to reduce COVID spread may have decreased resistance in community settings. Differences in health system use during the COVID-19 pandemic may have increased resistance, although we identified knowledge gaps on COVID-19-related changes in health system use. Few studies considered equity in their analyses and no studies directly mentioned equity. All included studies had a moderate to high risk of bias. CONCLUSIONS: COVID-19 led to mixed effects on AMR, which depended on the setting and context. There is a need for more rigorous studies that examine how COVID-19 impacted the health system as well as socioeconomic determinants to provide evidence for future pandemics or health crises. Our findings also underscore the importance of integrating antimicrobial stewardship, robust infection prevention and equity-focused surveillance into pandemic preparedness to mitigate AMR risks in future public health emergencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.087 | 0.243 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.065 | 0.047 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".