Significant Regional Differences in Antibiotic Use Across 576 US Hospitals and 11,701,326 Million Admissions, 2016–2017
Notice bibliographique
Résumé
Background: Reducing inappropriate antibiotic use is critical for fighting antibiotic resistance. Quantifying the amount and diversity of antibiotic use in US hospitals is foundational to these efforts but hampered by limited national surveillance. The current study aims to address this knowledge gap by examining adult inpatient antibiotic usage, including regional, facility, and case-mix differences, across 576 hospitals and nearly 12 million encounters in 2016–2017. Methods: We conducted a retrospective cohort study of patients aged ≥18 years discharged from hospitals in the Premier Healthcare Database, a repository of nearly 1 of every 4 annual US hospitalizations, between January 1, 2016, and December 31, 2017. Detailed hospital- and patient-level data were extracted for each admission. Facilities were classified geographically by census division. Using daily antibiotic charge data, we mapped antibiotics to 18 mutually exclusive classes and to categories based upon spectrum of activity. Patient-level data were transformed into hospital case-mix variables (eg, hospital mean patient age), and relationships between antibiotic days of therapy (DOTs), and these and other facility-level variables were evaluated in negative binomial regression models. Results: The study included 11,701,326 adult admissions, totaling 64,064,632 patient days across 576 US hospitals. Overall, antibiotics were used in 65% of all hospitalizations, at a rate of 870 DOTs per 1,000 patient days. The most commonly used classes per patient days were β-lactam/β-lactamase inhibitor combinations (206 DOTs), third- and fourth-generation cephalosporins (128 DOTs), and glycopeptides (113 DOTs) (Fig. 1). By spectrum of activity, antipseudomonal agents (245 DOTs) were the most common. Crude usage rates varied by geographic region (Fig. 2). In multivariable analyses, teaching hospitals, and/or larger bed sizes were independently associated with lower use across a range of antibiotic classes (adjusted IRR ranges, 0.90–0.94 and 0.96–0.98, respectively). Significant regional differences also persisted. Compared to the South Atlantic region (chosen as the reference category because it had the largest representation in the cohort), rates of total antibiotic use were 6%, 15%, and 18% lower on average in the Pacific, New England, and the Middle Atlantic regions, respectively. By class, carbapenems reflected the most geographic variability. Conclusions: In a large, diverse cohort of US hospitals, adult inpatients received antibiotics at a rate similar to, but higher than, previously published estimates. In adjusted models, lower antibiotic use was frequently associated with facilities likely to have robust antibiotic stewardship programs—those with teaching status and larger bed size. Further research to understand other reasons for regional differences in antibiotic use such as different rates of resistance is needed. Funding: This work was supported by Funding: from the Agency for Healthcare Research and Quality (AHRQ) (R01-HS026205 to A.D.H.). Disclosures: None
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».