Impact of easing COVID-19 restrictions on antibiotic usage in Eastern China using wastewater-based epidemiology
Bibliographic record
Abstract
Coronavirus Disease 2019 (COVID-19) emerged in December 2019, prompting the implementation of a “zero-COVID” policy in Mainland China. The easing of this policy in December 2022 led to a surge in COVID cases, which was believed to significantly increase antibiotic usage, potentially due to antibiotic misuse or increased coinfections. Our study aimed to compare antibiotic consumption and patterns before and after this policy adjustment. We utilised wastewater-based epidemiology (WBE) to analyse antibiotic levels in samples collected from five wastewater treatment plants in Eastern China during January and February of 2021 and 2023. 27 antibiotics were quantified using ultra-high performance liquid chromatography coupled with triple quadrupole tandem mass spectrometry (UPLC-MS/MS) and analysed via WBE, with the resulting estimates compared with catchment-specific prescription data. 23 antibiotics were detected in wastewater samples, with a substantial increase in usage in 2023 (ranging from 531% to 3734%), consistent with prescription data. Here, we show a significant rise in antibiotic consumption during the COVID-19 surge and this underscores the need for further investigation into the impacts of inappropriate antibiotic use in China. China’s zero-COVID policy was discontinued in December 2022 and was followed by a wave of COVID-19 infections. Here, the authors demonstrate that the end of this policy also coincided with increased antibiotic use, indicating possible inappropriate prescribing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".