Determining the impact of the COVID-19 pandemic on the consumption of antibiotics in Shaanxi province, China: an interrupted time-series analysis
Bibliographic record
Abstract
Background The COVID-19 pandemic, as well as associated prevention and control measures, have impacted the entire healthcare system, including the use patterns of medicine. However, the impact of COVID-19 on the use of antibiotics has not yet been extensively evaluated in China. This study aimed to evaluate the impact of the pandemic on the consumption and expenditure of antibiotics in public healthcare institutions in Shaanxi Province, China. Methods We used longitudinal data from the Shaanxi provincial drug procurement database in this study. An interrupted time-series analysis was performed to evaluate the effects of COVID-19 on consumption and expenditures of antibiotics in public healthcare institutions in Shaanxi from 1 January 2017 to 31 December 2020. Antibiotic consumption was expressed as the number of defined daily doses (DDDs) per 1,000 residents per day (DIDs), based on the population of Shaanxi Province at the end of each year from the National Bureau of Statistics. The pre-pandemic period was from January 2017 to January 2020, and the post-pandemic period was from February 2020 to December 2020. Results A declining trend in antibiotic consumption was observed immediately following the COVID-19 pandemic (β2 = −4.099; p < 0.001). Regarding the route of administration, a larger decrease in the consumption of oral antibiotics was observed compared to parenteral (β2 = −3.056; p < 0.001). The decrease in antibiotic consumption in the Watch category (β2 = −2.164; p < 0.001) was larger than in the Access category (β2 = −1.773; p < 0.001), and penicillins (J01C) (β2 = −1.261; p < 0.001) showed a higher decline than cephalosporins (J01D) (β2 = −1.147; p < 0.001). An increasing trend of broad-spectrum antibiotic consumption was observed after the onset of the pandemic (β3 = 0.021; p = 0.015). Expenditures on and consumption of antibiotics follow essentially the same trend. Conclusion Despite an initial decline in antibiotic consumption at the start of the COVID-19 pandemic, it had returned to prior levels by the end of 2020. Findings underscore the continued importance of antibiotic stewardship initiatives.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".