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Record W2888528917 · doi:10.1503/cmaj.171391

Effectiveness of interventions for managing multiple high-burden chronic diseases in older adults: a systematic review and meta-analysis

2018· review· en· W2888528917 on OpenAlexaffvenue
Monika Kastner, Roberta Cardoso, Yonda Lai, Victoria Treister, Jemila S. Hamid, Leigh Hayden, Geoff Wong, Noah Ivers, Barbara Liu, Sharon Marr, Jayna Holroyd‐Leduc, Sharon E. Straus

Bibliographic record

VenueCanadian Medical Association Journal · 2018
Typereview
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsPublic Health OntarioCalgary General HospitalFoothills Medical CentreMcMaster UniversityMcMaster Children's HospitalWomen's College HospitalHamilton Health SciencesUniversity of TorontoSunnybrook Health Science CentreSt. Michael's Hospital
Fundersnot available
KeywordsMedicinePsychological interventionMeta-analysisCOPDDepression (economics)Randomized controlled trialConfidence intervalRelative riskDementiaComorbidityMEDLINEInternal medicineDiabetes mellitusSystematic reviewPhysical therapyGerontologyDiseasePsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: More than half of older adults (age ≥ 65 yr) have 2 or more high-burden multimorbidity conditions (i.e., highly prevalent chronic diseases, which are associated with increased health care utilization; these include diabetes [DM], dementia, depression, chronic obstructive pulmonary disease [COPD], cardiovascular disease [CVD], arthritis, and heart failure [HF]), yet most existing interventions for managing chronic disease focus on a single disease or do not respond to the specialized needs of older adults. We conducted a systematic review and meta-analysis to identify effective multimorbidity interventions compared with a control or usual care strategy for older adults. METHODS: We searched bibliometric databases for randomized controlled trials (RCTs) evaluating interventions for managing multiple chronic diseases in any language from 1990 to December 2017. The primary outcome was any outcome specific to managing multiple chronic diseases as reported by studies. Reviewer pairs independently screened citations and full-text articles, extracted data and assessed risk of bias. We assessed statistical and methodological heterogeneity and performed a meta-analysis of RCTs with similar interventions and components. RESULTS: We included 25 studies (including 15 RCTs and 6 cluster RCTs) (12 579 older adults; mean age 67.3 yr). In patients with [depression + COPD] or [CVD + DM], care-coordination strategies significantly improved depressive symptoms (standardized mean difference −0.41; 95% confidence interval [CI] −0.59 to −0.22; I2 = 0%) and reduced glycosylated hemoglobin (HbA1c) levels (mean difference −0.51; 95% CI −0.90 to −0.11; I2 = 0%), but not mortality (relative risk [RR] 0.79; 95% CI 0.53 to 1.17; I2 = 0%). Among secondary outcomes, care-coordination strategies reduced functional impairment in patients with [arthritis + depression] (between-group difference −0.82; 95% CI −1.17 to −0.47) or [DM + depression] (between-group difference 3.21; 95% CI 1.78 to 4.63); improved cognitive functioning in patients with [DM + depression] (between-group difference 2.44; 95% CI 0.79 to 4.09) or [HF + COPD] (p = 0.006); and increased use of mental health services in those with [DM + (CVD or depression)] (RR 2.57; 95% CI 1.90 to 3.49; I2 = 0%). INTERPRETATION: Subgroup analyses showed that older adults with diabetes and either depression or cardiovascular disease, or with coexistence of chronic obstructive pulmonary disease and heart failure, can benefit from care-coordination strategies with or without education to lower HbA1c, reduce depressive symptoms, improve health-related functional status, and increase the use of mental health services. Protocol registration: PROSPERO-CRD42014014489

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.023
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.052
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0230.043
Bibliometrics0.0090.008
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.038
GPT teacher head0.360
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations168
Published2018
Admission routes2
Has abstractyes

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