Effect of the COVID-19 pandemic on antibiotic consumption: A systematic review comparing 2019 and 2020 data
Bibliographic record
Abstract
Background: The coronavirus disease 2019 (COVID-19) pandemic has influenced antibiotic consumption over a long period, with variability in trends among studies. We conducted this systematic review to explore and compare the effect of the pandemic on overall and individual antibiotic consumption in 2020 with that in 2019. Methods: This systematic literature review was conducted using PubMed, EMBASE, and Web of Science databases. Data on antibiotic consumption in Japan was sourced from the Japan Surveillance of Antimicrobial Consumption. Results: A total of 1,442 articles and reports were screened, and 16 eligible articles were reviewed. The included studies were conducted in Jordan, Australia, Canada, UK, Japan, Brazil, India, China, and the EU. There was no study from African and Southeast Asian Countries. Overall, antibiotic consumption in the community consistently reduced in 2020. Studies from Australia, Canada, Portugal, Spain, the UK, Japan, and the European Union reported both decreases in overall and selected individual antibiotics consumption. In contrast, hospital-based studies reported both increases and decreases. Hospital-based studies in Lebanon, Spain, Italy, India, and the UK reported an increase in antibiotic consumption in 2020. Studies reporting an interruption of antibiotic stewardship programs during the pandemic also reported increases in antibiotic consumption for hospitalized patients in 2020 compared with that in 2019. Conclusion: Our results showed a different trend between communities and hospitals in antibiotic consumption during 2020 compared to 2019. The continuity of the antibiotic stewardship program might have influenced the antibiotic consumption trend variability among hospitals in 2020. Alongside this, the lack of information on antibiotic consumption from low-income countries and limited reports from middle-income countries revealed gaps that need to be urgently filled.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".