Interventions for improving outcomes in patients with multimorbidity in primary care and community settings
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".