Global consumption of antimicrobials: impact of the WHO Global Action Plan on Antimicrobial Resistance and 2019 coronavirus pandemic (COVID-19)
Bibliographic record
Abstract
BACKGROUND: Little is known about the effect of the COVID-19 pandemic on antimicrobial consumption worldwide. OBJECTIVES: To describe the impact of the WHO Global Action Plan on Antimicrobial Resistance (GAP-AMR) on antimicrobial consumption pre-pandemic and to evaluate the impact of the COVID-19 pandemic on antimicrobial consumption worldwide. METHODS: A cross-sectional time-series analysis using a dataset of monthly purchases of antimicrobials (antibiotics, antivirals and antifungals) from August 2014 to August 2020. Antimicrobial consumption per 1000 population was assessed pre-pandemic by economic development status using linear regression models. Interventional autoregressive integrated moving average (ARIMA) models tested for significant changes with pandemic declaration (March 2020) and during its first stage from April to August 2020, worldwide and by country development status. RESULTS: Prior to the pandemic, antimicrobial consumption decreased worldwide, with a greater apparent decrease in developed versus developing countries (-8.4%, P = 0.020 versus -1.2%, P = 0.660). Relative to 2019, antimicrobial consumption increased by 11.2%, P < 0.001 in March 2020. The greatest increase was for antivirals in both developed and developing countries (48.2%, P < 0.001; 110.0%, P < 0.001) followed by antibiotics (6.9%, P < 0.001; 5.9%, P = 0.003). From April to August 2020, antimicrobial consumption decreased worldwide by 18.7% (P < 0.001) compared with the previous year. Specifically, antibiotic consumption significantly decreased in both developed and developing countries (-28.0%, P < 0.001; -16.8%, P < 0.001). CONCLUSIONS: The global decrease in antimicrobial consumption pre-pandemic suggests a positive impact of the WHO GAP-AMR. During the pandemic, an initial increase in antimicrobial consumption was followed by a decrease worldwide. AMR plans should specify measures to ensure full implementation of AMR efforts during health crises such as the COVID-19 pandemic.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".