Effect of a pharmacy comprehensive chronic diseases care plan on use of lipid-lowering drugs among patients with hypertension
Notice bibliographique
Résumé
BACKGROUND: The management of chronic diseases is a continuing challenge for health care systems and patients. OBJECTIVE: To assess the effect of a pharmacist-specific chronic diseases management incentive plan (the Comprehensive Annual Care Plan [CACP]) implemented by the government of Alberta (Canada) on adherence to lipid-lowering drugs (LLD) among patients with hypertension. METHODS: We conducted a cohort study of patients with hypertension who received the CACP between 2012 and 2015, using administrative health data. Patients who qualified to receive the CACP but did not receive it were selected as controls. Adherence was assessed 1 year before and after the CACP as the proportion of days covered (PDC) by any LLD. We conducted 2 distinct logistic regressions to assess the likelihood of an increase of the post-CACP PDC by 0.20 among patients with poor pre-CACP adherence (i.e., pre-CACP PDC < 0.80), and the post-CACP PDC decrease by 0.20 among those with previous good adherence. RESULTS: Data for 12,763 CACP patients and 14,555 controls were analysed. CACP patients who had a pre-CACP PDC < 0.80 were more likely to increase their PDC compared with controls (44.7% vs. 37.8%; adjusted odds ratio [aOR] = 1.34; 95% CI = 1.22-1.46). Conversely, CACP and control patients with a pre-CACP PDC ≥ 0.80 had similar likelihood to decrease their PDC (13.4% vs. 14.1%; aOR = 0.96; 95% CI = 0.88-1.04). CONCLUSIONS: The pharmacy CACP was associated with a modest improvement of adherence to LLD. The incentive system for improved care seemed more effective among patients who had low baseline adherence rates with minimal effect in those with previous good adherence. DISCLOSURES: This work was supported by a grant from the Institute of Health Economics, with funding from Alberta Innovates and Eli Lilly Canada. The sponsor had no role in the study design, data acquisition, analysis, interpretation of the results, and the decision to publish. The authors have no conflicts of interest to disclose. This study is based on data provided by Alberta Health. The interpretation and conclusions contained herein are those of the researchers and do not necessarily represent the views of the government of Alberta nor the funder (Institute of Health Economics). Neither the government nor Alberta Health nor the Institute of Health Economics express any opinion in relation to this study.
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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) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 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 ».