Interventions to optimize the use of antibiotics in China: A scoping review of evidence from humans, animals, and the environment from a One Health perspective
Bibliographic record
Abstract
Objectives: The overuse and misuse of antibiotics has accelerated the rapid emergence of antibiotic resistance. The aim of the study was to review interventions conducted in China to optimize use of antibiotics in humans, animals, and the environment from a One Health perspective. Methods: The literature review for this study was limited to English and Chinese articles published from January 1985 to May 2021. Literature review searches were conducted using Web of Science, Scopus, PubMed and three biomedical databases from China (the Chinese Scientific Journals database, the Wanfang Database, and China National Knowledge Infrastructure). We used Arksey and O'Malley's step-wise methodological framework as the basis for our scoping review. Results: A total of 53 studies met our inclusion criteria, of which 51 (96%) were from human healthcare settings, one from environment health that pertained to rural ponds, and no studies were found that met our criteria on interventions used to improve antibiotic use in animals. For human health, the majority of the research was related to antibiotic intervention programs performed in public institutions, and only one policy assessment study included private institutions. Interventions were classified into four broad categories: 1) Knowledge interventions; 2) decision support; 3) financial incentives; and 4) organizational/management systems. Our findings indicated that combinations of multiple interventions were more effective in promoting the rational use of antibiotics in China. Conclusions: China has made major efforts on improving rational use of antibiotics in the past decades. Most policies or interventions, however, focused mainly on the human health aspect, less effort targeted toward the environment and animal health sectors. For further optimizing use of antibiotics, the cross-disciplinary and coordinated multi-faceted interventions guided by the One Health perspective should be developed and implemented. Meanwhile, the cross-departmental collaborative mechanism leading by the Chinese central government should be further strengthened to play a greater and more active role in fighting against antibiotic resistance wholly.
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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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".