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Enregistrement W4292411934 · doi:10.1093/jac/dkac274

Global consumption of antimicrobials: impact of the WHO Global Action Plan on Antimicrobial Resistance and 2019 coronavirus pandemic (COVID-19)—authors’ response

2022· letter· en· W4292411934 sur OpenAlexaff
Tumader Khouja, Mina Tadrous, Katie J. Suda

Notice bibliographique

RevueJournal of Antimicrobial Chemotherapy · 2022
Typeletter
Langueen
DomaineImmunology and Microbiology
ThématiqueAntibiotic Use and Resistance
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésPandemicCoronavirus disease 2019 (COVID-19)Antimicrobial2019-20 coronavirus outbreakCoronavirusSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Action planAntibiotic resistanceMedicineConsumption (sociology)Action (physics)Anti-Infective AgentsVirologyMicrobiologyBiologyAntibioticsInfectious disease (medical specialty)OutbreakInternal medicine

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,020
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,041
Score d'incertitude au seuil0,023

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,020
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0020,002
Communication savante0,0030,002
Science ouverte0,0010,002
Intégrité de la recherche0,0410,033
Charge utile insuffisante (le modèle a refusé de juger)0,0060,004

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,051
Tête enseignante GPT0,345
Écart entre enseignants0,293 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2022
Routes d'admission1
Résumé présentnon

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