Interventions for improving outcomes in patients with multimorbidity in primary care and community settings
Notice bibliographique
Résumé
BACKGROUND: Many people with chronic disease have more than one chronic condition, which is referred to as multimorbidity. While this is not a new phenomenon, there is greater recognition of its impact and the importance of improving outcomes for individuals affected. Research in the area to date has focused mainly on descriptive epidemiology and impact assessment. There has been limited exploration of the effectiveness of interventions for multimorbidity. OBJECTIVES: To determine the effectiveness of interventions designed to improve outcomes in patients with multimorbidity in primary care and community settings. Multimorbidity was defined as two or more chronic conditions in the same individual. SEARCH METHODS: We searched MEDLINE, EMBASE, CINAHL, CAB Health, AMED, HealthStar, The Cochrane Central Register of Controlled Trials (CENTRAL), the EPOC Register and the Database of Abstracts of Reviews of Effectiveness (DARE), and the EPOC Register in April 2011. SELECTION CRITERIA: We considered randomised controlled trials (RCTs), controlled clinical trials (CCTs), controlled before and after studies (CBAs), and interrupted time series analyses (ITS) reporting on interventions to improve outcomes for people with multimorbidity in primary care and community settings. The outcomes included any validated measure of physical or mental health, psychosocial status including quality of life outcomes, well-being, and measures of disability or functional status. We also included measures of patient and provider behaviour including measures of medication adherence, utilisation of health services, and acceptability of services and costs. DATA COLLECTION AND ANALYSIS: Two review authors independently assessed studies for eligibility, extracted data, and assessed study quality. Meta-analysis of results was not possible so we carried out a narrative synthesis of the results from the included studies. MAIN RESULTS: Ten studies examining a range of complex interventions for patients with multimorbidity were identified. All were RCTs and there was low risk of bias. Two of the nine studies focused on specific co-morbidities. The remaining studies focused on multimorbidity, generally in older patients. All studies involved complex interventions with multiple elements. In six of the ten studies, the predominant intervention element was a change to the organisation of care delivery, usually through case management or enhanced multidisciplinary team work. In the remaining four studies, the interventions were predominantly patient oriented. Overall the results were mixed with a trend towards improved prescribing and medication adherence. The results indicate that it is difficult to improve outcomes in this population but that interventions focusing on particular risk factors or functional difficulties in patients with co-morbid conditions or multimorbidity may be more effective. Cost data were limited with no economic analyses included, though the improvements in prescribing and risk factor management in some studies provided potentially significant cost savings. AUTHORS' CONCLUSIONS: This review highlights the paucity of research into interventions to improve outcomes for multimorbidity with the focus to date being on co-morbid conditions or multimorbidity in older patients. The limited results suggest that interventions to date have had mixed effects but have shown a tendency to improve prescribing and medication adherence, particularly if interventions can be targeted at risk factors or specific functional difficulties in people with co-morbid conditions or multimorbidity. There is a need for clear definitions of participants, consideration of appropriate outcomes, and further pragmatic studies based in primary care settings.
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 | 0,014 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,013 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 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; 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 ».