Global consumption of antimicrobials: impact of the WHO Global Action Plan on Antimicrobial Resistance and 2019 coronavirus pandemic (COVID-19)—authors’ response
Bibliographic record
Abstract
We thank Drs Sulis, Pai and Gandra for their interest in our paper and their knowledgeable comments.1 We agree with the authors that the global decrease in antibiotic consumption during the COVID-19 pandemic is only suggestive of continued antimicrobial resistance (AMR) efforts. Despite our reporting of decreased total antibiotic consumption worldwide, including in developing countries, we would like to continue to emphasize that AMR plans should specify measures to ensure full implementation of AMR efforts during health crises such as the COVID-19 pandemic.2 For this reason, our analysis focused on country-level antimicrobial consumption both during the COVID-19 pandemic and prior to the pandemic (2015–19). Country-level variations in antibiotic consumption are expected as we displayed in our analysis (Figure 4 and Table S3).2 Our analysis presented aggregated data on total antimicrobial consumption worldwide and in developing and developed countries. Unfortunately, as highlighted in our limitations, our data are unable to adjust for country-specific factors. Additionally, our population-level data are not limited to patients with COVID-19. Therefore, we are unable to associate an antibiotic with a diagnosis (i.e. azithromycin for COVID-19 treatment). However, assessing aggregated antimicrobial rates, as we calculated, is essential to antimicrobial stewardship efforts,3,4 especially in low-resourced settings where granular data is unavailable (such as economically developing countries).5 In fact, focusing exclusively on one condition may be misleading.3 Our study filled a gap in our knowledge of total antimicrobial rates and the impact of the WHO Global Action Plan (GAP)-AMR initiative. In our sample of countries with national AMR plans, the majority decreased antibiotic consumption rates from 2015 through 2019 (pre-pandemic) and April through August 2020 (during the pandemic).2 Class-specific and AWaRE classification results were previously published for 76 countries at the population-level using the same dataset.6,7 However, we recognize that more granular geographic areas (city/state/province) and analyses at the facility/clinic-level are needed to more directly inform stewardship efforts at the local scale. Although not within the scope of our study, we agree that it is important to evaluate misuse of antibiotics during the pandemic as well as assess the appropriateness of treatments suggested for COVID-19, such as azithromycin, hydroxychloroquine and ivermectin. Motivated by Sulis and colleagues letter, we include two additional descriptive analyses not included in our original manuscript. First, we assessed purchases of the top 5 antibiotic classes that increased in developing and developed countries in March 2020 compared with March 2019 (Table 1). Except for J1G9 (other fluoroquinolones), it appears that increases were similar regardless of the AWaRE classification. Second, we evaluated azithromycin consumption. Comparing March 2020 with March 2019, there was a 25.2% increase in azithromycin consumption globally (from 24.0 units per 1000 population in March 2019 to 30.0 units per 1000 population in March 2020) and in developed (from 48.7 units per 1000 population in March 2019 to 74.9 units per 1000 population in 2020, a 53.6% increase) and developing countries (from 19.1 units per 1000 population in March 2019 to 21.2 units per 1000 population in March 2020, an 11.0% increase). The increase in azithromycin consumption follows the trend of increases in total antibiotic consumption during this period, but may be due to inappropriate treatment for COVID-19 or stockpiling, which our other analysis has reported.8 After initially increasing in March 2020, globally, azithromycin consumption decreased from 98.8 units per 1000 population in April–August 2019 to 98.2 units per 1000 population in April–August 2020 and in developed countries from 154.8 units per 1000 population in April–August 2019 to 99.9 units per 1000 population in April–August 2020 (0.5% and 35.5% decrease respectively). The decrease in azithromycin consumption in developed countries follows the overall decrease in total antibiotic consumption during this period and could be due to dissemination of evidence recommending against the use of azithromycin for COVID-19 treatment. In developing countries, there was an increase in azithromycin consumption from 88.2 units per 1000 population in April–August 2019 to 98.6 units per 1000 population in April–August 2020 (11.7% increase). Although suggestive, we cannot directly attribute this increase in azithromycin use to treatment of COVID-19 in developing countries during this period of the pandemic. Antibiotics with the greatest increase in consumption (% change March 2020 compared with March 2019) according to the AWaRE classification in developed and developing countriesa The UN’s 2020 World Economic Situation Prospectus was used to group MIDAS regions into ‘developed’ (N = 33) and ‘developing’ (N = 35) areas. Economies in transition were included in the developing category. ATC, Anatomic Therapeutic Chemical. Antibiotics with the greatest increase in consumption (% change March 2020 compared with March 2019) according to the AWaRE classification in developed and developing countriesa The UN’s 2020 World Economic Situation Prospectus was used to group MIDAS regions into ‘developed’ (N = 33) and ‘developing’ (N = 35) areas. Economies in transition were included in the developing category. ATC, Anatomic Therapeutic Chemical. In closing, we acknowledge the challenge economically developing countries face with accessing antibiotics while limiting excess use. Assessing country-specific data is important to develop measures to combat AMR customized to the needs and resources available in each country. Additionally, similar data sources are needed to allow direct country-to-country comparison of antibiotic consumption levels and AMR efforts. Lastly, we would like to re-iterate our call to action regarding the need for global coordination to ensure future AMR responses are adequate. The authors have no financial conflicts of interest to declare. This study was carried out as part of our routine work. All authors had full access to all data (including statistical reports and tables) in the study and take responsibility for the integrity of the data and accuracy of the data analysis. The statements, findings, conclusions, views, and opinions contained and expressed in this publication are based in part on data obtained under license from IQVIA as part of the IQVIA Institute’s Human Data Science Research Collaborative. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Department of Veterans Affairs, the U.S. government, or of IQVIA or any of its affiliated entities.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.041 | 0.033 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".