Consumer-oriented interventions for evidence-based prescribing and medicines use: an overview of systematic reviews
Notice bibliographique
Résumé
BACKGROUND: Numerous systematic reviews exist on interventions to improve consumers' medicines use, but this research is distributed across diseases, populations and settings. The scope and focus of reviews on consumers' medicines use also varies widely. Such differences create challenges for decision makers seeking review-level evidence to inform decisions about medicines use. OBJECTIVES: To synthesise the evidence from systematic reviews on the effects of interventions which target healthcare consumers to promote evidence-based prescribing for, and medicines use, by consumers. We sought evidence on the effects on health and other outcomes for healthcare consumers, professionals and services. METHODS: We included systematic reviews published on the Cochrane Database of Systematic Reviews and the Database of Abstracts of Reviews of Effects. We identified relevant reviews by handsearching both databases from start date to Issue 3 2008. We screened and ranked reviews based on relevance to consumers' medicines use, using criteria developed for this overview. Standardised forms were used to extract data, and reviews were assessed for methodological quality using the AMSTAR instrument. We used standardised language to summarise results within and across reviews; and a further synthesis step was used to give bottom-line statements about intervention effectiveness. Two review authors selected reviews, extracted and analysed data. We used a taxonomy of interventions to categorise reviews. MAIN RESULTS: We included 37 reviews (18 Cochrane, 19 non-Cochrane), of varied methodological quality.Reviews assessed interventions with diverse aims including support for behaviour change, risk minimisation, skills acquisition and information provision. No reviews aimed to promote systems-level consumer participation in medicines-related activities. Medicines adherence was the most commonly reported outcome, but others such as clinical (health and wellbeing), service use and knowledge outcomes were also reported. Reviews rarely reported adverse events or harms, and the evidence was sparse for several populations, including children and young people, carers, and people with multimorbidity.Promising interventions to improve adherence and other key medicines use outcomes (eg adverse events, knowledge) included self-monitoring and self-management, simplified dosing and interventions directly involving pharmacists. Other strategies showed promise in relation to adherence but their effects were less consistent. These included reminders; education combined with self-management skills training, counselling or support; financial incentives; and lay health worker interventions.No interventions were effective to improve all medicines use outcomes across all diseases, populations or settings. For some interventions, such as information or education provided alone, the evidence suggests ineffectiveness; for many others there is insufficient evidence to determine effects on medicines use outcomes. AUTHORS' CONCLUSIONS: Systematically assembling the evidence across reviews allows identification of effective or promising interventions to improve consumers' medicines use, as well as those for which the evidence indicates ineffectiveness or uncertainty.Decision makers faced with implementing interventions to improve consumers' medicines use can use this overview to inform these decisions and also to consider the range of interventions available; while researchers and funders can use this overview to determine where research is needed. However, the limitations of the literature relating to the lack of evidence for important outcomes and specific populations, such as people with multimorbidity, should also be considered.
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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 | 0,048 | 0,114 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,044 | 0,006 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».