Evidence map›Paper›PMID 37382954›Full record

SynthesisJAMA network open2023

Health Care Utilization Following Interventions to Improve Social Well-Being: A Systematic Review and Meta-analysis.

Neta HaGani, Daniel L Surkalim, Philip J Clare, Dafna Merom, Ben J Smith, Ding Ding

Open access · goldAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in JAMA network open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
3.0field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 11 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Neta HaGaniPrevention Research Collaboration, Sydney School of Public Health, The University of Sydney, Sydney, Australia.
Daniel L SurkalimPrevention Research Collaboration, Sydney School of Public Health, The University of Sydney, Sydney, Australia.
Philip J ClarePrevention Research Collaboration, Sydney School of Public Health, The University of Sydney, Sydney, Australia.
Dafna MeromSchool of Health Science, Western Sydney University, Sydney, Australia.
Ben J SmithPrevention Research Collaboration, Sydney School of Public Health, The University of Sydney, Sydney, Australia.
Ding DingPrevention Research Collaboration, Sydney School of Public Health, The University of Sydney, Sydney, Australia.
University of Sydney · AUUNSW Sydney · AUWestern Sydney University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: It has been suggested that interventions that aim to improve social well-being may contribute to decreased health care use; however, such evidence has not been fully systematically synthesized. Objective: To systematically review and meta-analyze available evidence on the associations between psychosocial interventions and health care utilization. Data Sources: Medline, Embase, PsycInfo, Cumulated Index to Nursing and Allied Health Literature, Cochrane, Scopus, Google Scholar, and reference lists of systematic reviews were searched from inception until November 31, 2022. Study Selection: Included studies were randomized clinical trials reporting on both health care utilization and social well-being outcomes. Data Extraction and Synthesis: The reporting of the systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Full-text and quality assessments were conducted by 2 reviewers independently. Multilevel random-effects meta-analyses were used to synthesize the data. Subgroup analyses were conducted to examine the characteristics associated with decreased health care use. Main Outcomes and Measures: The outcome of interest was health care utilization, including primary, emergency, inpatient, and outpatient care services. Social well-being was measured as social support, social participation, social relationships, community support, social integration, or loneliness. Results: A total of 41 studies were retrieved from 18 969 citations; 37 studies were eligible for meta-analysis. Data were analyzed for 7842 participants, including 2745 older adults, 1579 young women considered to be at risk of social and mental health disadvantages, 1118 people with chronic illnesses, 1597 people with mental illnesses, and 803 caregivers. The odds ratio (OR) random-effects model showed an overall reduction in health care use (OR, 0.75; 95% CI, 0.59 to 0.97), but the standardized mean difference (SMD) random effect model showed no association. An improvement in health care utilization was observed in association with social support interventions (SMD, 0.25; 95% CI, 0.04 to 0.45) but not in loneliness interventions. Subgroup analysis indicated a reduced length of inpatient visits (SMD, -0.35; 95% CI, -0.61 to -0.09) and number of emergency care visits (OR, 0.64; 95% CI, 0.43 to 0.96) following the intervention. However, an increase in outpatient care associated with psychosocial interventions was observed (SMD, 0.34; 95% CI, 0.05 to 0.62). The largest reductions in health care use were associated with interventions among caregivers (OR, 0.23; 95% CI, 0.07 to 0.71) and individuals with mental illnesses (OR, 0.31; 95% CI, 0.13 to 0.74). Conclusions: These findings suggest that psychosocial interventions were associated with most measures of health care utilization. As the association differed by participant and intervention delivery characteristics, these characteristics should be considered in the design of future interventions.

Indexed as

Emergency Medical ServicesPatient Acceptance of Health CareAgedAmbulatory CareFemaleHumansInpatientsInterpersonal Relations

Identifiers

PMID37382954
PMCPMC10311391
OpenAlexW4382502836

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.