Variations in antibiotic use for respiratory tract infections
Notice bibliographique
Résumé
ABSTRACT
 ObjectivesAntibiotic resistance is a significant public health issue, driven in large part by selection pressure induced by antibiotic use. Despite knowledge that a reduction in inappropriate antibiotic use is important, significant and sustained behaviour change has remained difficult to achieve. Previous studies have suggested that prescribing decisions vary with patient comorbidities and age, as well as the physician’s age, claim volume, specialty, and continuity of care with the particular patient. Regional level variation has also been reported. The goal of this study is to explore variations in antibiotic prescribing for respiratory tract infections (RTIs) at the levels of patients, physicians, and regions.
 ApproachWe used data on fee-for-service physician visits from the universal Medical Services Plan (MSP) for all residents of British Columbia, Canada from 2002 to 2012. We identified a cohort of patient visits for RTI (ICD-9 codes 460-466, 480, or 487). We derived measures of healthcare use and comorbidities at the patient level, and measures of physician claim volume and frequency of respiratory tract infection management, at the level of the physician. We used data on antibiotics filled by individuals, the number of all medications filled by individuals each year, and counts of prescriptions written by physicians (and filled) by month, from the provincial drug insurance database. We linked antibiotic prescriptions to physician visits by patient and prescriber as antibiotics dispensed within 5 days after the RTI visit. We calculated measures of regional population distributions. We used data on meteorological temperature readings assigned to each region, for each day, and calculated 28-day moving averages. We linked data on patient demographics, physician demographics, and hospitalizations to our dataset. Our analysis will use hierarchical generalized linear mixed models (GLMMs) with logit link to account for the clustering effects of patients among physicians and regions, to model the odds of an antibiotic prescription being dispensed. Measures of variation will be discussed.
 ResultsBetween April 1, 2005 and March 31, 2012, there were over 10.5 million visits by nearly 3 million individuals, served by over 8000 physicians in 88 regions. Antibiotics were prescribed in 37% of all visits.
 ConclusionThese are preliminary results, with full analytic results available in the coming months. These results will have implications for better understanding the extent of variations in antibiotic prescribing, and some of the drivers of these variations, as well as the potential to inform ongoing efforts to improve the appropriateness of antibiotic use.
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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,003 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 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 ».