Patterns of Outpatient Antibiotic Prescribing in Older Adults by Social Determinants of Health Before and During COVID-19
Notice bibliographique
Résumé
Background: Identifying patterns and predictors of antibiotic prescribing can begin to inform interventions aimed at improving antibiotic use and slowing the emergence of antimicrobial resistance. As it stands, patterns of prescribing among older adults by social determinants of health (SDOH) remain poorly described. We sought to examine associations between SDOH variables and antibiotic prescribing among older community-dwelling adults over time and identify variations in these associations before and during the COVID-19 pandemic. Methods: We conducted a retrospective, population-based cohort study of community-dwelling older adults (≥66 years of age) in Ontario between March 2018-March 2020 (pre-pandemic period) and March 2020-March 2022 (pandemic period). We used multivariable Fine-Gray subdistribution hazard models to evaluate associations between SDOH variables (neighbourhood income, neighbourhood proportion of racially minoritized groups, and immigration status) and incident antibiotic prescriptions (overall and for respiratory infections), accounting for mortality as a competing risk. We used interaction terms and stratification to assess for potential effect modification by the COVID-19 pandemic period. Results: After exclusions, 2,567,382 outpatients were identified in the pre-pandemic period, and 2,744,337 in the pandemic period. In both study periods, antibiotic prescribing was higher among residents in highest income neighbourhoods (vs. lowest) overall (subdistribution hazard ratio [sHR] 1.03, 95% CI 1.02-1.04 and sHR 1.02, 95% CI 1.01-1.03, respectively), with a similar pattern for prescriptions for respiratory infections (sHR 1.06, 95% CI 1.05-1.07 and sHR 1.05, 1.04-1.06, respectively). Antibiotic prescribing was higher among recent immigrants (vs. long-term residents) in both periods, with a more pronounced increase during the pandemic than pre-pandemic period in overall prescriptions (sHR 1.21, 95% CI 1.18-1.25 vs. sHR 1.12, 95% CI 1.09-1.16, p=0.049) and in prescriptions for respiratory infections (sHR 1.27, 95% CI 1.23-1.32 vs. sHR 1.15, 95% CI 1.11-1.18, p<0.001). Overall antibiotic prescribing was lower among residents in neighbourhoods with the highest proportion racially minoritized (vs. lowest) in both periods, with a more pronounced decrease during the pandemic than pre-pandemic period (sHR 0.81, 95% CI 0.80-0.82 vs. sHR 0.92, 95% CI 0.91-0.93, p<0.001); similarly, there was a more pronounced decrease in prescriptions for respiratory infections during the pandemic than prepandemic period (sHR 0.93, 95% CI 0.92-0.94 vs. sHR 1.07, 95% CI 1.05-1.08, p<0.001). Conclusion: In this cohort of older outpatients, SDOH variables were associated with outpatient antibiotic prescribing during the two years before and the first two years of the COVID-19 pandemic, with some of these associations being modified during the COVID-19 pandemic period. These findings suggest that patient sociodemographic characteristics are important for identifying populations who could be at risk of disproportionate antibiotic use in the outpatient setting.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».