Team-based primary care reforms and older adults: a descriptive assessment of sociodemographic trends and prescribing endpoints in two Canadian provinces
Notice bibliographique
Résumé
BACKGROUND: Team-based primary care reforms aim to improve care coordination by involving multiple interdisciplinary health professionals in patient care. Team-based primary care may support improved medication management for older adults with polypharmacy and multiple points of contact with the healthcare system. However, little is known about this association. This study compares sociodemographic and prescribing trends among older adults in team-based vs. traditional primary care models in Ontario and Quebec. METHODS: We constructed two provincial cohorts using population-level health administrative data from 2006-2018. Our primary exposure was enrollment in a team-based model of care. Key endpoints included adverse drug events (ADEs), potentially inappropriate prescriptions (PIPs), and polypharmacy. We plotted prescribing trends across the observation period (stratified by model of care) in each province. We used standardized mean differences to compare characteristics of older adults and providers, as well as prescribing endpoints. RESULTS: Formal patient/physician enrollment increased in both provinces since the time of policy implementation; team-based enrollment among older adults was higher in Quebec (47%) than Ontario (33%) by the end of our observation period. The distribution of sociodemographic characteristics was reasonably comparable between team-based and non-team-based patients in both provinces, aside from a persistently higher share of rural patients in team-based care. Most PIPs assessed either declined or remained relatively steady over time, regardless of model of care and province. Several PIPs were more common among team-based patients than non-team-based patients, particularly in Quebec. We did not detect notable trends in ADEs or polypharmacy in either province. CONCLUSIONS: Our findings offer encouraging evidence that many PIPs are declining over time in this population, regardless of patients' enrollment in team-based care. Rates of decline appear similar across models of care, suggesting these models may not meaningfully influence prescribing endpoints. Additional efforts are needed to understand the impact of team-based care among older adults and improve primary care prescribing practices.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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 ».