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JAMA Netw Open
JAMA Netw Open
JAMA Network Open
2574-3805
American Medical Association

37382954
10.1001/jamanetworkopen.2023.21019
zoi230621
Research
Original Investigation
Online Only
Public Health
Health Care Utilization Following Interventions to Improve Social Well-Being
A Systematic Review and Meta-analysis
Health Care Utilization Following Interventions to Improve Social Well-Being
Health Care Utilization Following Interventions to Improve Social Well-Being
HaGani Neta MSW 1 4
Surkalim Daniel L. MPH 1 4
Clare Philip J. PhD 1 2 4
Merom Dafna PhD 3
Smith Ben J. PhD 1 4
Ding Ding PhD 1 4
1 Prevention Research Collaboration, Sydney School of Public Health, The University of Sydney, Sydney, Australia
2 National Drug and Alcohol Research Centre, UNSW Sydney, Sydney, Australia
3 School of Health Science, Western Sydney University, Sydney, Australia
4 Charles Perkins Centre, the University of Sydney, Sydney, Australia
Article Information

Accepted for Publication: May 16, 2023.

Published: June 29, 2023. doi:10.1001/jamanetworkopen.2023.21019

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2023 HaGani N et al. JAMA Network Open.

Corresponding Author: Neta HaGani, MSW, Prevention Research Collaboration, School of Public Health, The Hub Charles Perkins Centre (D17), The University of Sydney, Level 6, Sydney, NSW 2006, Australia (neta.hagani@sydney.edu.au).
Author Contributions: Ms HaGani had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Senior authorship shared: Drs Merom, Smith, and Ding.

Concept and design: HaGani, Merom, Smith, Ding.

Acquisition, analysis, or interpretation of data: HaGani, Surkalim, Clare, Merom, Ding.

Drafting of the manuscript: HaGani, Smith.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: HaGani, Clare.

Obtained funding: HaGani.

Administrative, technical, or material support: HaGani, Smith, Ding.

Supervision: Clare, Merom, Smith, Ding.

Conflict of Interest Disclosures: None reported.

Funding/Support: Prof Ding is funded by the National Health and Medical Research Council Emerging Leader fellowship (award No. 2009254).

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2.

29 6 2023
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Copyright 2023 HaGani N et al. JAMA Network Open.
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the CC-BY License.
jamanetwopen-e2321019.pdf

Key Points

Question

Is there an association between psychosocial interventions and health care service use, and does the association differ by sociodemographic or intervention characteristic?

Findings

This systematic review and meta-analysis including 41 studies and 7842 participants found that psychosocial interventions were associated with decreased health care use in most health services and increased use of outpatient care. The greatest health care decrease was among caregivers and individuals with mental illnesses and in interventions delivered 1-on-1 by health professionals.

Meaning

These findings suggest that psychosocial interventions were associated with reduced health care use.

This systematic review and meta-analysis explores associations between psychosocial interventions and health care utilization.

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.
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pmcIntroduction

Social well-being is an umbrella term that refers to the actual or perceived availability of social resources, such as social networks.1 A lack of social well-being can manifest in problems, such as loneliness and social isolation, that are important public health concerns2,3,4 and can lead to chronic diseases and premature mortality.5,6,7

Another public health challenge faced by many countries is escalating health care costs associated with population aging and increasing prevalence of chronic disease. As this trend is expected to continue,8 decision-makers need to find solutions to reduce health care spending by designing and implementing efficient and equitable health services and minimizing unnecessary health care use. Promising evidence from observational studies has suggested that better social well-being is associated with lower health care utilization,9,10,11,12 indicating that psychosocial interventions to address social well-being may reduce health care demand.13,14

To date, psychosocial interventions have been conducted among a variety of populations15,16 using myriad approaches, such as individual or group therapy, group-based activities, peer support, and outreach and befriending strategies.17,18 Besides positive associations with loneliness, social interaction, and perceptions of support,19,20 some psychosocial interventions have been found to be associated with decreases in unnecessary health service use and costs in some populations, such as older adults, survivors of breast cancer, and people with mental illnesses.17,18,21 For example, peer support interventions and multicomponent psychosocial interventions were associated with decreased use of general practitioners and emergency care among verity of populations.18,22 However, for outpatient care, such as visits to specialists, most studies showed no significant changes.22,23,24

Despite the associations found between psychosocial interventions and health care utilization, evidence on this topic has not been systematically synthesized or quantitatively summarized, to our knowledge.13 Furthermore, we do not yet know what intervention characteristics are associated with lower health care use. To address these gaps, we aimed to synthesize the associations among psychosocial interventions, health care utilization, and social well-being and identify characteristics associated with the variability in effect sizes of these outcomes.

Methods

The reporting of the systematic review and meta-analysis adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. The protocol is registered in the PROSPERO database under record No. CRD42021273388.

Eligibility Criteria

Psychosocial interventions that used a randomized clinical trial design to improve social well-being were included. To be eligible, studies needed to report on at least 1 health care utilization–related outcome and at least 1 social well-being–related outcome. Health care utilization refers to using primary care (visits to the general practitioner or nurse), emergency care (visits to the emergency department), inpatient care (eg, number of hospitalizations and readmissions, length of stay), and outpatient care (visits to a specialist, such as a cardiologist, obstetrician, or psychiatrist) services. Social well-being covers multiple domains, including social support, social participation, social relationships, community support, and loneliness. The population, intervention, control, and outcomes data are presented in eTable 1 in Supplement 1.

Study Selection and Data Extraction

A comprehensive search was conducted from inception until May 31, 2021, and an update search until November 31, 2022, in the following databases: Medline, Embase, PsycInfo, Cumulated Index to Nursing and Allied Health Literature, Cochrane, and Scopus. Search terms on the concepts of health care utilization, social well-being, and psychosocial interventions were combined with AND (specific search terms and search results are presented in eTables 2-8 in Supplement 1). Google Scholar, reference lists of the included studies, and relevant systematic reviews10,13,14,25 were manually searched for additional potential studies. All records were imported to Covidence reference management software (Veritas Health Innovation). After duplicates were removed, titles and abstracts were assessed according to the eligibility criteria. The full-text review was conducted by 2 reviewers (N.H. and D.S.) independently. Disagreements were resolved by a third reviewer (D.D., D.M., or B.J.S.). Data were extracted by 1 reviewer (N.H.) and a sample of the extracted data (approximately 30%) was reviewed by a second reviewer (D.D.). Where means and SDs were missing, they were estimated from other measures of effect26 or imputed from other studies,22,26,27 as suggested by Cochrane.28

For the main outcome of health care utilization, 2 types of data were extracted for the intervention and control groups: raw means and SDs to calculate standardized mean differences (SMDs) and numbers and percentages of health care users to calculate odds ratios (ORs). Some studies included multiple follow-ups after their intervention. Data were extracted and analyzed from each study for the first follow-up immediately after the intervention and for the last follow-up after the intervention, regardless of specific durations of the follow-ups.

Risk of Bias

All studies were assessed for risk of bias (ROB) using the Cochrane Handbook for Systematic Reviews of Interventions, Version 5.1.0.29 Assessment was done using Covidence and RevMan version 5.4 (Cochrane) tools. Studies were assessed by 2 independent reviewers (N.H. and D.L.S.). Conflicts were resolved by consultations with a third reviewer (D.M., B.J.S., or D.D.). Each study was assessed on 7 different domains: random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective reporting, and other biases. Each domain received a judgement of low, high, or unclear ROB.

Publication Bias

Publication bias was examined using funnel plots and an extension of Egger regression test was used to quantify the funnel plot asymmetry.30 This included using a measure of effect size precision as a predictor in a meta-regression.31 P < .05 indicated a substantial asymmetry in the funnel plot that could have been caused by publication bias.

Assessing the Certainty of the Evidence

Certainty of evidence was assessed for each of the overall outcomes using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach.32,33 The assessment was performed using the GRADEpro GDT software (McMaster University and Evidence Prime). GRADE domains were assessed according to the level of uncertainty (ie, not serious, serious, or very serious). The overall certainty was categorized as very low, low, moderate, or high. Certainty was downgraded 1 level per limitation, starting from high certainty.33

Statistical Analysis

Results were combined and analyzed using random-effects models. Separate meta-analytic estimates were calculated for studies that reported on SMDs and ORs. To account for multiple dependent effect sizes,34 we performed a 3-level meta-analysis. The effect sizes of the overall and different types of health care utilization were calculated for the immediate and sustained outcomes after interventions. Subgroup analyses were used to identify differences according to participant characteristics, such as age, gender, and population groups, and intervention characteristics, including type (individual vs group), duration (number of months), and delivery personnel (health professionals, peers, and activity coordinators). All analyses were performed using the meta and metafor libraries of R software version 4.1.3 (R Project for Statistical Computing). Bayes factors (BFs) were calculated to examine the strength of evidence using an online calculator.35 BFs with a value between 3 and 10 indicate moderate relative evidence for the alternative hypothesis; BFs between 0.3 and 3, inconclusive evidence; and BFs less than 0.3, moderate evidence for the null hypothesis.36 Expected effect sizes were estimated based on previous meta-analyses on related topics.14,37 For the SMD random-effects model, the expected SMD was set to −0.3. Similarly, the expected OR was set to 0.70 (30% reduction). For the alternative hypothesis of the social support measure and outpatient care use, we used an SMD of 0.3 and an OR of 1.5, since they were associated positively with intervention effect size.

P values were 2-sided, and statistical significance was set at P < .05. The last analysis update was conducted between January 12 and 19, 2023.

Results

Systematic Review

Of 18 968 records identified, 268 full texts articles were assessed for eligibility, 41 studies met the inclusion criteria for the systematic review.18,21,22,23,24,26,27,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71 Of these, 21 studies22,23,24,27,40,41,44,45,46,49,50,53,54,58,59,63,65,66,67,68,69 were included in the OR health care utilization meta-analysis, 18 studies18,23,24,26,38,39,40,41,42,43,48,51,55,58,60,64,67,70 were included in the SMD health care utilization meta-analysis, 27 studies18,22,23,24,26,27,38,39,40,42,43,44,46,51,52,54,55,58,59,60,61,62,63,64,67,68,71 were included in the social support SMD meta-analysis, and 3 studies26,40,70 were included in the loneliness SMD meta-analysis (Figure 1). Data were analyzed for 7842 participants, including 2745 older adults, 1579 young women considered at risk of social and mental health disadvantages (1 study among women in a shelter for family violence survivors, 1 study among women at risk for postpartum depression and 1 study among women from disadvantage living areas), 1118 people with chronic illnesses, 1597 people with mental illnesses, and 803 caregivers.

Figure 1. Flowchart of Identification of Studies Via Databases and Registers

CINAHL indicates Cumulated Index to Nursing and Allied Health Literature; OR, odds ratio; RCT, randomized clinical trial; SMD, standardized mean difference.

eTable 9 in Supplement 1 presents the characteristics of the included studies. The included studies were published between 1995 and 2022. Most were conducted in the UK (10 studies21,23,38,39,41,51,61,67,69,71) or other European countries (8 studies18,26,42,48,49,65,66,70); 6 studies43,44,54,55,59,60 were undertaken in China, 7 studies22,24,27,46,47,52,64 were undertaken in Canada, 6 studies38,40,45,50,58,68 were undertaken in the US, and 1 study each was conducted in Singapore,62 Columbia,63 Australia,53 and Zambia.57 Fourteen studies24,38,39,40,41,42,43,44,45,46,47,48,49,72 had sample sizes of fewer than 100 participants, 18 studies18,22,27,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64 had samples of 100 to 200 participants, 7 studies23,26,65,66,67,68,69 included 200 to 1000 participants, and 2 studies70,71 had sample sizes of more than 1000 participants. Studied populations included 7 studies43,47,49,50,53,54,55 of caregivers, 9 studies of people with physical illnesses (6 on heart disease,22,41,44,48,51,62 1 on HIV,66 1 on stroke,57 and 1 on multimorbidity42), 11 studies21,27,45,56,58,59,60,61,63,68,69 of people with mental illnesses, 7 studies18,26,39,46,52,65,70 of older adults, 6 studies23,24,40,64,67,71 of postnatal young women, and 1 study38 of women at a domestic violence shelter. Interventions were delivered by health professionals (28 studies18,23,26,38,40,42,43,44,46,47,48,49,50,52,53,54,56,57,58,59,60,62,63,64,65,66,70,71), peer volunteers (8 studies21,22,24,27,45,55,68,69 with individuals with lived experience of health challenges or services), or a coordinator on behalf of the intervention team (5 studies39,41,51,61,67). Sixteen interventions18,26,27,38,39,41,42,49,51,55,60,63,64,65,68,71 were group-based, 18 interventions18,21,22,23,24,40,43,44,46,47,52,53,54,56,61,62,66,67,69 were delivered to individuals 1-on-1, and 7 interventions45,48,50,57,58,59,70 included both individual and group components. Most of the control groups received standard care (31 studies18,21,22,23,24,27,40,41,43,44,45,46,47,48,49,52,53,54,55,56,58,59,60,61,62,63,65,66,67,69,70). Two studies26,42 used a waitlist control, whereas 8 studies38,39,50,51,57,64,68,71 had a minimal intervention as a control condition. Seventeen interventions18,40,41,43,44,46,47,49,51,52,55,60,65,66,67,68,70 were 6 to 12 months long, and the remainder18,21,22,23,24,26,27,38,39,42,45,48,50,53,54,56,57,58,59,61,62,63,64,69,71 were 4 months or shorter.

Risk of Bias

Sixteen studies18,21,40,42,46,50,52,54,55,56,60,62,64,66,67,69 were classified as low risk of bias; 6 studies,23,26,44,47,49,57 medium risk; and 19 studies22,24,27,38,39,41,43,45,48,51,53,58,59,61,63,65,68,70,71 high risk. Most interventions were classified as low risk for randomization (31 interventions18,21,22,23,26,27,38,39,40,41,44,46,48,49,50,52,53,54,55,56,57,60,61,62,64,65,67,69,71 [76%]), allocation concealment (27 interventions21,22,23,24,26,38,40,41,42,43,44,46,47,49,50,52,54,55,56,57,60,62,64,65,66,67,69 [66%]) and attrition bias (30 interventions18,21,22,24,27,40,41,42,43,44,46,47,48,49,50,52,53,54,55,56,58,60,62,63,64,65,67,68,69,70 [73%]). There were 23 interventions18,21,23,24,26,39,40,42,46,48,53,54,55,56,57,60,62,63,64,66,67,69,70 (56%) considered low risk for reporting bias. Only 2 studies18,21,23,24,26,39,40,42,46,48,53,54,55,56,57,60,62,63,64,66,67,69,70 (5%) had low risk in the blinding of participants and personnel domain, and 19 studies18,21,23,24,27,44,46,50,52,54,55,58,60,62,63,64,66,68,69 (46%) had low risk in the blinding of outcome data domain (eFigure 1 in Supplement 1).

GRADE Certainty of Evidence Results

The evidence for the immediate postintervention health care utilization and social support outcomes using ORs was evaluated with a moderate level of certainty. The evidence for the sustained health care utilization and loneliness outcomes was assessed with low levels of certainty. Evidence for both immediate and sustained health care utilization outcomes using SMDs was assessed with a very low level of certainty (eTable 10 in Supplement 1).

Meta-Analyses of Immediate Postintervention Health Care Utilization Outcomes

According to the random-effects model of studies reporting ORs, the intervention group had significant lower odds of using health care compared with the control group (OR, 0.75; 95% CI, 0.59 to 0.97). However, the SMD random-effects model was not significant. When looking at different services, the OR model showed sizeable and significant reductions in the odds of emergency care use (OR, 0.64; 95% CI, 0.43-0.96) among the intervention groups. The SMD model showed significant increase in outpatient care (SMD, 0.34; 95% CI, 0.05-0.62), a decrease in length of inpatient care (SMD, −0.35; 95% CI, −0.61 to −0.09) by the intervention group. Heterogeneity was significant for both the OR and SMD random-effects models, suggesting that the amount of between-study variability was greater than would be expected by chance (Figure 2 and Figure 3).

Figure 2. Pooled 95% CI Odds Ratio of the Association of Psychosocial Interventions With Health Care Utilization

Since multilevel meta-analyses included several effect estimates from each study, studies may be listed more than once for each analysis. Dots indicate estimates; whiskers, 95% CIs; diamond, summary estimate.

Figure 3. Pooled 95% CI Standardized Mean Difference of the Association of Psychosocial Interventions With Health Care Utilization

Since multilevel meta-analyses included several effect estimates from each study, studies may be listed more than once for each analysis. Dots indicate estimates; whiskers, 95% CIs; diamond, summary estimate; SMD, standardized mean difference.

Bayes Factors

The strength of evidence was moderate for the overall OR random-effects model (BF = 5.63) and was inconclusive for the overall SMD random-effects model (BF = 0.84). Strong relative evidence for the alternative hypothesis was found in the SMD model for inpatient length (BF > 10) and moderate evidence was found for inpatient and outpatient services utilization (BF, 3-10). In the OR models, we found moderate evidence for the alternative hypothesis for primary care, inpatient, and emergency care. We also found moderate evidence for the alternative hypothesis for social support in the SMD model (BF = 7.21) (eTable 11 in Supplement 1).

Subgroup Analysis

In both the OR and SMD models, we observed significant decreases in health care use after interventions in studies conducted in China compared with other countries.43,44,45,46,47,48,49,50,51,52,53,54,55,59,60 In the OR model only, we observed significant decreases in health care use after interventions in studies conducted in the US and Australia.40,45,50,53,58,68 In the OR model, we also observed a decrease in health care use in studies that included participants aged 30 to 60 years.27,44,45,50,53,54,59,68,69 Reductions in health care use were observed in studies among caregivers49,50,53,54 and individuals with mental illnesses27,45,58,59,63,68,69 compared with other population groups, but only in the OR model. Single-component interventions that were based on the individual level, shorter (1-4 months), and delivered by health professionals were associated with significant decreases in health care use as well. However, in the SMD model, longer interventions were significantly associated with lower health care use after the interventions (eTable 12 in Supplement 1).

Meta-Analyses of Sustained Postintervention Health Care Utilization Outcomes

Measures of effect were calculated for the last follow-up measure in a subset of 5 studies22,23,54,59,67 that included ORs and 5 studies23,61,66,70,73 that included 13 SMDs that included more than 1 follow-up (eFigure 3 and eFigure 4 in Supplement 1). The OR random-effects model showed a significant decrease only in inpatient care (OR, 0.52; 95% CI, 0.28 to 0.98) and the SMD model showed a lower length of inpatient care (SMD, −1.21; 95% CI, −1.67 to −0.75).

Meta-Analyses of Social Support and Loneliness Outcomes

Social support and loneliness were the primary domains of social well-being reported, and these were examined as secondary outcomes only in an SMD meta-analysis (Figure 4 and Figure 5). There was a statistically significant postintervention increase in social support but not in loneliness. Subgroup analyses of participants’ characteristics revealed a significant increase in social support following interventions conducted in China,43,44,54,55,59,60 among participants aged 30 to 60 years,27,38,43,44,54,55,59,61,64,68 men,22,27,44,51,55,60,62,63,68 and people with chronic illnesses.22,42,44,51,62 Analyses by intervention characteristics found improvements in social support following longer interventions, 1-on-1 interventions, and interventions delivered by health professionals.

Figure 4. Pooled 95% CI Standardized Mean Difference of the Association of Psychosocial Interventions With Social Support

Since multilevel meta-analyses included several effect estimates from each study, studies may be listed more than once for each analysis. Dots indicate estimates; whiskers, 95% CIs; diamond, summary estimate; SMD, standardized mean difference.

Figure 5. Pooled 95% CI Standardized Mean Difference of the Association of Psychosocial Interventions With Loneliness

Since multilevel meta-analyses included several effect estimates from each study, studies may be listed more than once for each analysis. Dots indicate estimates; whiskers, 95% CIs; diamond, summary estimate; SMD, standardized mean difference.

Assessment of Publication Bias

All the OR and SMD random-effects models were tested for publication bias, except for the model for loneliness due to the small number of studies (eFigure 2 in Supplement 1). No publication bias was found for the meta-analyses of ORs in either the immediate or sustained effect models. For the meta-analysis of SMD, the regression test for asymmetry reached statistical significance in both the immediate and sustained effect models, indicating significant publication bias. There was no bias found in the funnel plot of the SMD social support model (eFigure 2 in Supplement 1).

Discussion

This systematic review and meta-analysis found that psychosocial interventions were associated with improved health care utilization. Models showed strong to moderate strength of evidence by BFs for number of inpatient visits and length of inpatients visits associated with social support. Emergency and primary care utilization also showed moderate strength of evidence for the OR model. Additionally, these interventions were associated with improvements in social support but not loneliness. Considering the need to reduce unnecessary health care spending in many countries, our findings have highlighted the potential for psychosocial interventions to reduce health care utilization. The largest reduction in health care use occurred in inpatient visit numbers and length of stay, emergency admissions, and primary care services. The reduction in inpatient and emergency care utilization could be due to an instrumental support system that may have prevented a health condition from deteriorating73 by direct care or by encouraging patients to seek treatment earlier.14 In addition, the support provided could also prevent or lower stress and anxiety associated with different health conditions,74 which may have contributed to reduction in unnecessary visits to the hospital.75,76 The decrease in health care use could also be a result of an improvement in overall health.77,78 These findings suggest that the emotional, informational, and instrumental support provided by psychosocial interventions may decrease the need to seek medical care excessively and may contribute to reduced health care expenses and improved service efficiency.52,79,80,81 Such outcomes may last over longer periods, although the current evidence on sustained outcomes is limited by the small number of studies with sustained follow-up.

Outpatient care was the only outcome that showed an increase. This supports previous findings of a positive association between social well-being and outpatient visits.10,82 Outpatient care often supports tertiary-level prevention, which can sustain recovery and reduce the need for urgent care.83 Therefore, our findings suggest that psychosocial interventions may lead to improved preventive care in the outpatient context, which in turn, may lead to less emergency and inpatient care utilization.84,85

Our subgroup analysis found a significant decrease in health care use among caregivers, suggesting the importance of including caregivers in psychosocial interventions. A significant decrease in health care use was also found among people with mental illness. As people with mental illnesses can experience poor social well-being due to isolation and perceived stigma,86,87,88 psychosocial interventions may provide much-needed social support for this group. Our subgroup analysis also found that interventions conducted in China and the US showed significant decreases in postintervention health care use, possibly due to differences in measurements or sample sizes.89 However, some of these differences may reflect country-specific variations in standards of care, service availability, health professional capacity, and cultural differences in health-seeking practices.90,91,92 We found that in interventions delivered by health professionals, there was a significant decrease in postintervention health care use. Health care professionals are often perceived as trusted sources of information and support, which may have contributed to more favorable outcomes. This finding echoes previous observations regarding implementing social care within health care settings93,94 and including health professionals in the delivery of psychosocial interventions.85 Finally, the decrease in health care use was more pronounced in individual-based interventions that included 1-on-1 activities. This finding highlights the importance of tailoring psychosocial interventions according to patients’ needs, as applied in case and disease management models.85,95

Our analysis also found a significant increase in postintervention social support. Previous studies found that loneliness and low social support were associated with more health care utilization,10,96,97 although a rapid review did not find a consistent association between psychosocial interventions and improvement in loneliness.13 Likewise, our study found no association of psychosocial interventions with loneliness, which is most likely due to the inconsistency in the results and the lack of power. These findings highlight the complex and measure-specific nature of the association between social well-being and health care utilization.10

Limitations

This systematic review and meta-analysis has several limitations. First, the number of studies included in the analysis and the small sample sizes limited the certainty about the effect sizes and our ability to conduct multivariate analyses. Second, the use of ORs and SMDs is prone to measurement bias and therefore may have contributed to bias in the pooled effect size. Furthermore, most studies included in the SMD analysis reported means and SDs despite the underlying distributions being nonparametric. However, assuming that the SMD distribution shape remained the same, the SMD should provide a consistent estimate of the mean difference. In addition, synthesizing both ORs and SMDs should provide stronger and more complete evidence than relying on 1 measure of effect only. Third, the high level of heterogeneity among the studies may have contributed to the variability in the outcomes. We addressed this by conducting subgroup analyses. Fourth, the regression test for asymmetry indicated significant publication bias in the SMD meta-analysis, but since this analysis showed no association, the inclusion of unpublished data is unlikely to change our results. Fifth, relying on English abstracts may have led to the exclusion of eligible studies published in other languages.

Conclusions

The findings of this systematic review and meta-analysis suggest that psychosocial interventions were associated with improvements in patients’ social support and health care use, possibly through increasing outpatient care while reducing the use of emergency and inpatient care. Our results also suggest that interventions should be tailored to the needs of patients and include health professionals as the deliverers. In addition, our findings about individual- and intervention-level characteristics may inform the design and implementation of future psychosocial interventions by providing information on the variables associated with intervention success. More randomized clinical trials with longer-term follow-ups are needed to better understand the sustained postintervention outcomes of health care use associated with psychosocial interventions.

Supplement 1. eTable 1. Population, Intervention, Comparison, and Outcome (PICO) Table of Study Eligibility Criteria

eTable 2. Study Search Strategy

eTable 3. CINAHL Search Results

eTable 4. Cochrane Search Results

eTable 5. Embase Search Results

eTable 6. Medline Search Results

eTable 7. PsycInfo Search Results

eTable 8. Scopus Search Results

eTable 9. Summary Description of Randomized Clinical Trials Included in the Systematic Review (N = 41)

eTable 10. Grading of Recommendations Assessment, Development and Evaluation (GRADE) Evidence Profile

eTable 11. Strength of Evidence (Bayes Factors) in the OR and SMD According to Health Care Service

eTable 12. Subgroup Analyses of Postintervention Health Care Utilization According to Participant- and Intervention-Level Factors

eFigure 1. Cochrane Risk-of-Bias Summary and Author Judgments of Low, High and Unclear Risk of Bias Across All Included Studies (N = 41)

eFigure 2. Publication Bias Funnel Plots for Standardized Mean Difference (SMD) and Log Odds Ratio (OR) Random Effect Models of Health Care Utilization Outcomes and Social Support

eFigure 3. Pooled 95% CI Odds Ratio of the Sustained Association of Psychosocial Interventions With Health Care Utilization

eFigure 4. Pooled 95% CI Standardized Mean Difference of the Sustained Association of Psychosocial Interventions With Health Care Utilization

Click here for additional data file.

Supplement 2. Data Sharing Statement

Click here for additional data file.
==== Refs
References

1 National Academies of Sciences, Engineering, and Medicine. Social Isolation and Loneliness in Older Adults: Opportunities for the Health Care System. National Academies Press; 2020. doi:10.17226/25663
2 Centers for Disease Control and Prevention. Loneliness and social isolation linked to serious health conditions. Accessed August 30, 2022. https://www.cdc.gov/aging/publications/features/lonely-older-adults.html
3 Killgore WDS, Cloonan SA, Taylor EC, Dailey NS. Loneliness: a signature mental health concern in the era of COVID-19. Psychiatry Res. 2020;290 :113117. doi:10.1016/j.psychres.2020.113117 32480121
4 Surkalim DL, Luo M, Eres R, . The prevalence of loneliness across 113 countries: systematic review and meta-analysis. BMJ. 2022;376 :e067068. doi:10.1136/bmj-2021-067068 35140066
5 Lin FH, Yih DN, Shih FM, Chu CM. Effect of social support and health education on depression scale scores of chronic stroke patients. Medicine (Baltimore). 2019;98 (44 ):e17667. doi:10.1097/MD.0000000000017667 31689780
6 Courtin E, Knapp M. Social isolation, loneliness and health in old age: a scoping review. Health Soc Care Community. 2017;25 (3 ):799-812. doi:10.1111/hsc.12311 26712585
7 Bell CM, Ridley JA, Overholser JC, . The role of perceived burden and social support in suicide and depression. Suicide Life Threat Behav. 2018;48 (1 ):87-94. doi:10.1111/sltb.12327 28093796
8 GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396 (10258 ):1204-1222. doi:10.1016/S0140-6736(20)30925-9 33069326
9 Prang KH, Berecki-Gisolf J, Newnam S. The influence of social support on healthcare service use following transport-related musculoskeletal injury. BMC Health Serv Res. 2016;16 :310. doi:10.1186/s12913-016-1582-4 27464878
10 Valtorta NK, Moore DC, Barron L, Stow D, Hanratty B. Older adults’ social relationships and health care utilization: a systematic review. Am J Public Health. 2018;108 (4 ):e1-e10. doi:10.2105/AJPH.2017.304256 29470115
11 Lim KK, Chan A. Association of loneliness and healthcare utilization among older adults in Singapore. Geriatr Gerontol Int. 2017;17 (11 ):1789-1798. doi:10.1111/ggi.12962 28060447
12 Shaw JG, Farid M, Noel-Miller C, . Social isolation and Medicare spending: among older adults, objective social isolation increases expenditures while loneliness does not. J Aging Health. 2017;29 (7 ):1119-1143. doi:10.1177/0898264317703559 29545676
13 Veazie S, Gilbert J, Winchell K, Paynter R, Guise JM. Addressing Social Isolation to Improve the Health of Older Adults: A Rapid Review. Agency for Healthcare Research and Quality; 2019. doi:10.23970/AHRQEPC-RAPIDISOLATION
14 Spiers G, Matthews FE, Moffatt S, . Impact of social care supply on healthcare utilisation by older adults: a systematic review and meta-analysis. Age Ageing. 2019;48 (1 ):57-66. doi:10.1093/ageing/afy147 30247573
15 Forsman AK, Nordmyr J, Wahlbeck K. Psychosocial interventions for the promotion of mental health and the prevention of depression among older adults. Health Promot Int. 2011;26 (suppl 1 ):i85-i107. doi:10.1093/heapro/dar074 22079938
16 Barbui C, Purgato M, Abdulmalik J, . Efficacy of psychosocial interventions for mental health outcomes in low-income and middle-income countries: an umbrella review. Lancet Psychiatry. 2020;7 (2 ):162-172. doi:10.1016/S2215-0366(19)30511-5 31948935
17 Pitkala KH, Routasalo P, Kautiainen H, Tilvis RS. Effects of psychosocial group rehabilitation on health, use of health care services, and mortality of older persons suffering from loneliness: a randomized, controlled trial. J Gerontol A Biol Sci Med Sci. 2009;64 (7 ):792-800. doi:10.1093/gerona/glp011 19223606
18 Tarazona-Santabalbina FJ, Gómez-Cabrera MC, Pérez-Ros P, . A multicomponent exercise intervention that reverses frailty and improves cognition, emotion, and social networking in the community-dwelling frail elderly: a randomized clinical trial. J Am Med Dir Assoc. 2016;17 (5 ):426-433. doi:10.1016/j.jamda.2016.01.019 26947059
19 Alaviani M, Khosravan S, Alami A, Moshki M. The effect of a multi-strategy program on developing social behaviors based on Pender’s Health Promotion model to prevent loneliness of old women referred to Gonabad urban health centers. Int J Community Based Nurs Midwifery. 2015;3 (2 ):132-140.26005693
20 Hemingway A, Jack E. Reducing social isolation and promoting well being in older people. J Quality in Ageing Older Adults. 2013;14 (1 ):25-35. doi:10.1108/14717791311311085
21 Simpson JS, Carlson LE, Trew ME. Effect of group therapy for breast cancer on healthcare utilization. Cancer Pract. 2001;9 (1 ):19-26. doi:10.1046/j.1523-5394.2001.91005.x 11879269
22 Colella TJ, King-Shier K. The effect of a peer support intervention on early recovery outcomes in men recovering from coronary bypass surgery: a randomized controlled trial. Eur J Cardiovasc Nurs. 2018;17 (5 ):408-417. doi:10.1177/1474515117725521 28805455
23 Morrell CJ, Spiby H, Stewart P, Walters S, Morgan A. Costs and benefits of community postnatal support workers: a randomised controlled trial. Health Technol Assess. 2000;4 (6 ):1-100. doi:10.3310/hta4060 10858637
24 Chyzzy B. Mobile Phone-Based Peer Support in the Prevention of Postpartum Depression Among Adolescent Mothers: A Pilot Randomized Controlled Trial. Dissertation. University of Toronto; 2019. Accessed May 26, 2023. https://tspace.library.utoronto.ca/handle/1807/95775
25 Aminzadeh F, Dalziel WB. Older adults in the emergency department: a systematic review of patterns of use, adverse outcomes, and effectiveness of interventions. Ann Emerg Med. 2002;39 (3 ):238-247. doi:10.1067/mem.2002.121523 11867975
26 Blancafort Alias S, Monteserín Nadal R, Moral I, Roqué Fígols M, Rojano I Luque X, Coll-Planas L. Promoting social capital, self-management and health literacy in older adults through a group-based intervention delivered in low-income urban areas: results of the randomized trial AEQUALIS. BMC Public Health. 2021;21 (1 ):84. doi:10.1186/s12889-020-10094-9 33413233
27 Kidd SA, Mutschler C, Lichtenstein S, . Randomized trial of a brief peer support intervention for individuals with schizophrenia transitioning from hospital to community. Schizophr Res. 2021;231 :214-220. doi:10.1016/j.schres.2021.03.019 33895598
28 Higgins JPT, Thomas J, Chandler J, , eds. Cochrane Handbook for Systematic Reviews of Interventions. Version 6.3. Cochrane; 2022.
29 Higgins JPT, Green S, eds. Cochrane Handbook for Systematic Reviews of Interventions. Version 5.1.0. The Cochrane Collaboration; 2011.
30 Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315 (7109 ):629-634. doi:10.1136/bmj.315.7109.629 9310563
31 Rodgers MA, Pustejovsky JE. Evaluating meta-analytic methods to detect selective reporting in the presence of dependent effect sizes. Psychol Methods. 2020;26 (2 ):141-160. doi:10.1037/met0000300 32673040
32 Guyatt GH, Oxman AD, Vist GE, ; GRADE Working Group. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008;336 (7650 ):924-926. doi:10.1136/bmj.39489.470347.AD 18436948
33 Atkins D, Best D, Briss PA, ; GRADE Working Group. Grading quality of evidence and strength of recommendations. BMJ. 2004;328 (7454 ):1490. doi:10.1136/bmj.328.7454.1490 15205295
34 Harrer M, Cuijpers P, Furukawa TA, Ebert DD. Doing Meta-Analysis With R: A Hands-On Guide. Chapman & Hall/CRC Press; 2021. doi:10.1201/9781003107347
35 Dienes Z. Making the most of your data with Bayes. Accessed May 26, 2023. http://www.lifesci.sussex.ac.uk/home/Zoltan_Dienes/inference/Bayes.htm
36 Scott RB, Samaha J, Chrisley R, Dienes Z. Prevailing theories of consciousness are challenged by novel cross-modal associations acquired between subliminal stimuli. Cognition. 2018;175 :169-185. doi:10.1016/j.cognition.2018.02.008 29544152
37 Chronister J, Chou CC, Frain M. Cardoso EdS: the relationship between social support and rehabilitation related outcomes: a meta-analysis. J Rehabil. 2008;74 (2 ):16-32.
38 Constantino R, Kim Y, Crane PA. Effects of a social support intervention on health outcomes in residents of a domestic violence shelter: a pilot study. Issues Ment Health Nurs. 2005;26 (6 ):575-590. doi:10.1080/01612840590959416 16020071
39 Gleibs IH, Haslam C, Haslam SA, Jones JM. Water clubs in residential care: is it the water or the club that enhances health and well-being? Psychol Health. 2011;26 (10 ):1361-1377. doi:10.1080/08870446.2010.529140 21598183
40 Hudson DB, Campbell-Grossman C, Hertzog M. Effects of an internet intervention on mothers’ psychological, parenting, and health care utilization outcomes. Issues Compr Pediatr Nurs. 2012;35 (3-4 ):176-193. doi:10.3109/01460862.2012.734211 23140413
41 Asbury EA, Webb CM, Collins P. Group support to improve psychosocial well-being and primary-care demands among women with cardiac syndrome X. Climacteric. 2011;14 (1 ):100-104. doi:10.3109/13697137.2010.499181 20642328
42 Garvey J, Connolly D, Boland F, Smith SM. OPTIMAL, an occupational therapy led self-management support programme for people with multimorbidity in primary care: a randomized controlled trial. BMC Fam Pract. 2015;16 :59. doi:10.1186/s12875-015-0267-0 25962515
43 Chien WT, Lee IY. Randomized controlled trial of a dementia care programme for families of home-resided older people with dementia. J Adv Nurs. 2011;67 (4 ):774-787. doi:10.1111/j.1365-2648.2010.05537.x 21198803
44 Cui X, Zhou X, Ma LL, . A nurse-led structured education program improves self-management skills and reduces hospital readmissions in patients with chronic heart failure: a randomized and controlled trial in China. Rural Remote Health. 2019;19 (2 ):5270. doi:10.22605/RRH5270 31113205
45 Goldberg RW, Dickerson F, Lucksted A, . Living well: an intervention to improve self-management of medical illness for individuals with serious mental illness. Psychiatr Serv. 2013;64 (1 ):51-57. doi:10.1176/appi.ps.201200034 23070062
46 Markle-Reid M, McAiney C, Fisher K, . Effectiveness of a nurse-led hospital-to-home transitional care intervention for older adults with multimorbidity and depressive symptoms: a pragmatic randomized controlled trial. PLoS One. 2021;16 (7 ):e0254573. doi:10.1371/journal.pone.0254573 34310640
47 Roberts J, Browne G, Milne C, . Problem-solving counseling for caregivers of the cognitively impaired: effective for whom? Nurs Res. 1999;48 (3 ):162-172. doi:10.1097/00006199-199905000-00006 10337847
48 Roncella A, Pristipino C, Cianfrocca C, . One-year results of the randomized, controlled, short-term psychotherapy in acute myocardial infarction (STEP-IN-AMI) trial. Int J Cardiol. 2013;170 (2 ):132-139. doi:10.1016/j.ijcard.2013.08.094 24239154
49 Buchkremer G, Mönking HS, Holle R, Hornung W. The impact of therapeutic relatives’ groups on the course of illness of schizophrenic patients. Eur Psychiatry. 1995;10 (1 ):17-27. doi:10.1016/0767-399X(96)80071-X 19698311
50 Gaugler JE, Reese M, Mittelman MS. Effects of the NYU caregiver intervention-adult child on residential care placement. Gerontologist. 2013;53 (6 ):985-997. doi:10.1093/geront/gns193 23339050
51 Lindsay S, Smith S, Bellaby P, Baker R. The health impact of an online heart disease support group: a comparison of moderated versus unmoderated support. Health Educ Res. 2009;24 (4 ):646-654. doi:10.1093/her/cyp001 19251770
52 Markle-Reid M, Weir R, Browne G, Henderson S, Roberts CJ, Gafni A. Frail Elderly Homecare Clients: The Costs and Effects of Adding Nursing Health Promotion and Preventive Care to Personal Support Services. Dissertation. McMaster University; 2003.
53 Shaw JM, Young JM, Butow PN, . Improving psychosocial outcomes for caregivers of people with poor prognosis gastrointestinal cancers: a randomized controlled trial (Family Connect). Support Care Cancer. 2016;24 (2 ):585-595. doi:10.1007/s00520-015-2817-3 26111955
54 Cheng HY. The Effect of a Psychoeducational Intervention on Stroke Family Caregivers’ Outcomes and Stroke Survivors’ Utilisation of Health and Social Services. Dissertation. The Chinese University of Hong Kong; 2014.
55 Chien WT, Chan SW. The effectiveness of mutual support group intervention for Chinese families of people with schizophrenia: a randomised controlled trial with 24-month follow-up. Int J Nurs Stud. 2013;50 (10 ):1326-1340. doi:10.1016/j.ijnurstu.2013.01.004 23433723
56 Hengartner MP, Passalacqua S, Andreae A, Rössler W, von Wyl A. The role of perceived social support after psychiatric hospitalisation: post hoc analysis of a randomised controlled trial testing the effectiveness of a transitional intervention. Int J Soc Psychiatry. 2017;63 (4 ):297-306. doi:10.1177/0020764017700664 28347183
57 Jones DL, Zulu I, Vamos S, Cook R, Chitalu N, Weiss SM. Determinants of engagement in HIV treatment and care among Zambians new to antiretroviral therapy. J Assoc Nurses AIDS Care. 2013;24 (5 ):e1-e12. doi:10.1016/j.jana.2012.06.009 23009738
58 Dush DM, Ayres SY, Curtis C, Worthington GJ, Gabriel RM, Chinnery T. Reducing psychiatric hospital use of the rural poor through intensive transitional acute care. Psychiatr Rehabil J. 2001;25 (1 ):28-34. doi:10.1037/h0095053 11529449
59 Yang FSZY, Guo ZH. Effect of psychosocial intervention on quality of life and other factors in patients with recurrent depression. Article in Chinese. Chin J Clin Rehab. 2005;9 (4 ):16-18.
60 Chien WT, Thompson DR. Effects of a mindfulness-based psychoeducation programme for Chinese patients with schizophrenia: 2-year follow-up. Br J Psychiatry. 2014;205 (1 ):52-59. doi:10.1192/bjp.bp.113.134635 24809397
61 Grant C, Goodenough T, Harvey I, Hine C. A randomised controlled trial and economic evaluation of a referrals facilitator between primary care and the voluntary sector. BMJ. 2000;320 (7232 ):419-423. doi:10.1136/bmj.320.7232.419 10669447
62 Jiang Y, Koh KWL, Ramachandran HJ, . The effectiveness of a nurse-led home-based heart failure self-management programme (the HOM-HEMP) for patients with chronic heart failure: A three-arm stratified randomized controlled trial. Int J Nurs Stud. 2021;122 :104026. doi:10.1016/j.ijnurstu.2021.104026 34271265
63 Valencia JG. Brief group psychoeducation for schizophrenia. Accessed May 26, 2023. https://clinicaltrials.gov/ct2/show/NCT02911519
64 Van Lieshout RJ, Layton H, Savoy CD, . Public health nurse-delivered group cognitive behavioural therapy for postpartum depression: a randomized controlled trial. Can J Psychiatry. 2022;67 (6 ):432-440. doi:10.1177/07067437221074426 35060398
65 van Lieshout MRJ, Bleijenberg N, Schuurmans MJ, de Wit NJ. The effectiveness of a proactive multicomponent intervention program on disability in independently living older people: a randomized controlled trial. J Nutr Health Aging. 2018;22 (9 ):1051-1059. doi:10.1007/s12603-018-1101-x 30379302
66 Boter H; HESTIA Study Group. Multicenter randomized controlled trial of an outreach nursing support program for recently discharged stroke patients. Stroke. 2004;35 (12 ):2867-2872. doi:10.1161/01.STR.0000147717.57531.e5 15514186
67 Wiggins M, Oakley A, Roberts I, . The Social Support and Family Health Study: a randomised controlled trial and economic evaluation of two alternative forms of postnatal support for mothers living in disadvantaged inner-city areas. Health Technol Assess. 2004;8 (32 ):e2321019. doi:10.3310/hta8320 15298823
68 Muralidharan A, Brown CH, E Peer J, . Living Well: an intervention to improve medical illness self-management among individuals with serious mental illness. Psychiatr Serv. 2019;70 (1 ):19-25. doi:10.1176/appi.ps.201800162 30353790
69 Gillard S, Bremner S, Patel A, ; ENRICH trial study group. Peer support for discharge from inpatient mental health care versus care as usual in England (ENRICH): a parallel, two-group, individually randomised controlled trial. Lancet Psychiatry. 2022;9 (2 ):125-136. doi:10.1016/S2215-0366(21)00398-9 35065722
70 Franse CB, van Grieken A, Alhambra-Borrás T, . The effectiveness of a coordinated preventive care approach for healthy ageing (UHCE) among older persons in five European cities: a pre-post controlled trial. Int J Nurs Stud. 2018;88 :153-162. doi:10.1016/j.ijnurstu.2018.09.006 30296635
71 Reid M, Glazener C, Murray GD, Taylor GS. A two-centred pragmatic randomised controlled trial of two interventions of postnatal support. BJOG. 2002;109 (10 ):1164-1170. doi:10.1111/j.1471-0528.2002.01306.x 12387471
72 Simpson A, Flood C, Rowe J, . Results of a pilot randomised controlled trial to measure the clinical and cost effectiveness of peer support in increasing hope and quality of life in mental health patients discharged from hospital in the UK. BMC Psychiatry. 2014;14 :30. doi:10.1186/1471-244X-14-30 24495599
73 McFarlane WR. Family interventions for schizophrenia and the psychoses: a review. Fam Process. 2016;55 (3 ):460-482. doi:10.1111/famp.12235 27411376
74 Cohen S, McKay G. Social support, stress and the buffering hypothesis: a theoretical analysis. In: Taylor SE, Singer JE, Baum A, eds. Handbook of Psychology and Health, Volume IV. Routledge; 1984:253-267. doi:10.4324/9781003044307
75 Bernstein MT, Walker JR, Chhibba T, . Health care services in IBD: factors associated with service utilization and preferences for service options for routine and urgent care. Inflamm Bowel Dis. 2017;23 (9 ):1461-1469. doi:10.1097/MIB.0000000000001215 28816754
76 Morriss R, Patel S, Malins S, . Clinical and economic outcomes of remotely delivered cognitive behaviour therapy versus treatment as usual for repeat unscheduled care users with severe health anxiety: a multicentre randomised controlled trial. BMC Med. 2019;17 (1 ):16. doi:10.1186/s12916-019-1253-5 30670044
77 Hunter RF, de la Haye K, Murray JM, . Social network interventions for health behaviours and outcomes: a systematic review and meta-analysis. PLoS Med. 2019;16 (9 ):e1002890. doi:10.1371/journal.pmed.1002890 31479454
78 Steffens NK, LaRue CJ, Haslam C, . Social identification-building interventions to improve health: a systematic review and meta-analysis. Health Psychol Rev. 2021;15 (1 ):85-112. doi:10.1080/17437199.2019.1669481 31530154
79 Lawn S, Smith A, Hunter K. Mental health peer support for hospital avoidance and early discharge: an Australian example of consumer driven and operated service. J Ment Health. 2008;17 (5 ):498-508. doi:10.1080/09638230701530242
80 Dieng M, Cust AE, Kasparian NA, Mann GJ, Morton RL. Economic evaluations of psychosocial interventions in cancer: a systematic review. Psychooncology. 2016;25 (12 ):1380-1392. doi:10.1002/pon.4075 26810383
81 Jhanjee S. Evidence based psychosocial interventions in substance use. Indian J Psychol Med. 2014;36 (2 ):112-118. doi:10.4103/0253-7176.130960 24860208
82 Bremer D, Inhestern L, von dem Knesebeck O. Social relationships and physician utilization among older adults—a systematic review. PLoS One. 2017;12 (9 ):e0185672. doi:10.1371/journal.pone.0185672 28957429
83 Tian WH, Chen CS, Liu TC. The demand for preventive care services and its relationship with inpatient services. Health Policy. 2010;94 (2 ):164-174. doi:10.1016/j.healthpol.2009.09.012 19853320
84 Holt-Lunstad J, Smith TB. Loneliness and social isolation as risk factors for CVD: implications for evidence-based patient care and scientific inquiry. Heart. 2016;102 (13 ):987-989. doi:10.1136/heartjnl-2015-309242 27091845
85 Doshmangir L, Khabiri R, Jabbari H, Arab-Zozani M, Kakemam E, Gordeev VS. Strategies for utilisation management of hospital services: a systematic review of interventions. Global Health. 2022;18 (1 ):53. doi:10.1186/s12992-022-00835-3 35606776
86 Chronister J, Chou CC, Liao HY. The role of stigma coping and social support in mediating the effect of societal stigma on internalized stigma, mental health recovery, and quality of life among people with serious mental illness. J Community Psychol. 2013;41 (5 ):582-600. doi:10.1002/jcop.21558
87 Ma R, Mann F, Wang J, . The effectiveness of interventions for reducing subjective and objective social isolation among people with mental health problems: a systematic review. Soc Psychiatry Psychiatr Epidemiol. 2020;55 (7 ):839-876. doi:10.1007/s00127-019-01800-z 31741017
88 Mojtabai R, Stuart EA, Hwang I, Eaton WW, Sampson N, Kessler RC. Long-term effects of mental disorders on marital outcomes in the National Comorbidity Survey ten-year follow-up. Soc Psychiatry Psychiatr Epidemiol. 2017;52 (10 ):1217-1226. doi:10.1007/s00127-017-1373-1 28378065
89 Yusuf S, Wittes J. Interpreting geographic variations in results of randomized, controlled trials. N Engl J Med. 2016;375 (23 ):2263-2271. doi:10.1056/NEJMra1510065 27959693
90 Garcés IC, Scarinci IC, Harrison L. An examination of sociocultural factors associated with health and health care seeking among Latina immigrants. J Immigr Minor Health. 2006;8 (4 ):377-385. doi:10.1007/s10903-006-9008-8 16636902
91 Srivastava A, Avan BI, Rajbangshi P, Bhattacharyya S. Determinants of women’s satisfaction with maternal health care: a review of literature from developing countries. BMC Pregnancy Childbirth. 2015;15 :97. doi:10.1186/s12884-015-0525-0 25928085
92 Pocock S, Calvo G, Marrugat J, . International differences in treatment effect: do they really exist and why? Eur Heart J. 2013;34 (24 ):1846-1852. doi:10.1093/eurheartj/eht071 23475529
93 Dineen-Griffin S, Garcia-Cardenas V, Williams K, Benrimoj SI. Helping patients help themselves: a systematic review of self-management support strategies in primary health care practice. PLoS One. 2019;14 (8 ):e0220116. doi:10.1371/journal.pone.0220116 31369582
94 Betrán AP, Temmerman M, Kingdon C, . Interventions to reduce unnecessary caesarean sections in healthy women and babies. Lancet. 2018;392 (10155 ):1358-1368. doi:10.1016/S0140-6736(18)31927-5 30322586
95 Grover CA, Close RJ, Villarreal K, Goldman LM. Emergency department frequent user: pilot study of intensive case management to reduce visits and computed tomography. West J Emerg Med. 2010;11 (4 ):336-343.21079705
96 Newall N, McArthur J, Menec VH. A longitudinal examination of social participation, loneliness, and use of physician and hospital services. J Aging Health. 2015;27 (3 ):500-518. doi:10.1177/0898264314552420 25288587
97 Molloy GJ, McGee HM, O’Neill D, Conroy RM. Loneliness and emergency and planned hospitalizations in a community sample of older adults. J Am Geriatr Soc. 2010;58 (8 ):1538-1541. doi:10.1111/j.1532-5415.2010.02960.x 20646104