Evidence Based Mental Health最新文献

Background: Behavioural and cognitive interventions remain credible approaches in addressing loneliness and depression. There was a need to rapidly generate and assimilate trial-based data during COVID-19.

Objectives: We undertook a parallel pilot RCT of behavioural activation (a brief behavioural intervention) for depression and loneliness (Behavioural Activation in Social Isolation, the BASIL-C19 trial ISRCTN94091479). We also assimilate these data in a living systematic review (PROSPERO CRD42021298788) of cognitive and/or behavioural interventions.

Methods: Participants (≥65 years) with long-term conditions were computer randomised to behavioural activation (n=47) versus care as usual (n=49). Primary outcome was PHQ-9. Secondary outcomes included loneliness (De Jong Scale). Data from the BASIL-C19 trial were included in a metanalysis of depression and loneliness.

Findings: The 12 months adjusted mean difference for PHQ-9 was -0.70 (95% CI -2.61 to 1.20) and for loneliness was -0.39 (95% CI -1.43 to 0.65).The BASIL-C19 living systematic review (12 trials) found short-term reductions in depression (standardised mean difference (SMD)=-0.31, 95% CI -0.51 to -0.11) and loneliness (SMD=-0.48, 95% CI -0.70 to -0.27). There were few long-term trials, but there was evidence of some benefit (loneliness SMD=-0.20, 95% CI -0.40 to -0.01; depression SMD=-0.20, 95% CI -0.47 to 0.07).

Discussion: We delivered a pilot trial of a behavioural intervention targeting loneliness and depression; achieving long-term follow-up. Living meta-analysis provides strong evidence of short-term benefit for loneliness and depression for cognitive and/or behavioural approaches. A fully powered BASIL trial is underway.

Clinical implications: Scalable behavioural and cognitive approaches should be considered as population-level strategies for depression and loneliness on the basis of a living systematic review.

Question: Digital interventions based on cognitive-behavioural therapy (iCBT) is associated with reductions in suicidal ideation. However, fine-grained analyses of effects and potential effect-moderating variables are missing. This study aimed to investigate the effectiveness of iCBT on suicidal ideation, effect moderators, effects on suicide attempts and predictors of adherence.

Study selection and analysis: We systematically searched CENTRAL, PsycINFO, Embase and PubMed for randomised controlled trials that investigated iCBT for suicidal ideation or behaviours. Participants reporting baseline suicidal ideation were eligible. We conducted a one-stage individual participant data (IPD) meta-analysis. Suicidal ideation was the primary outcome, analysed as three indices: severity of suicidal ideation, reliable changes and treatment response.

Findings: We included IPD from nine out of ten eligible trials (2037 participants). iCBT showed significant reductions of suicidal ideation compared with control conditions across all indices (severity: b=-0.247, 95% CI -0.322 to -0.173; reliable changes: b=0.633, 95% CI 0.408 to 0.859; treatment response: b=0.606, 95% CI 0.410 to 0.801). In iCBT, the rate of reliable improvement was 40.5% (controls: 27.3%); the deterioration rate was 2.8% (controls: 5.1%). No participant-level moderator effects were identified. The effects on treatment response were higher for trials with waitlist-controls compared with active controls. There were insufficient data on suicide attempts. Human support and female gender predicted treatment adherence. The main source of potential bias was missing outcome data.

Conclusions: The current evidence indicates that iCBT is effective in reducing suicidal ideation irrespective of age, gender and previous suicide attempts. Future studies should rigorously assess suicidal behaviour and drop-out reasons.

Background: Non-serious adverse events (NSAEs) should be captured and reported because they can have a significant negative impact on patients and treatment adherence. However, the reporting of NSAEs in randomised controlled trials (RCTs) is limited.

Objective: To identify the most important NSAEs of antidepressants for patients and clinicians, to be evaluated in RCTs and meta-analyses.

Methods: We conducted online international surveys in English, German and French, including (1) adults prescribed an antidepressant for a depressive episode and (2) healthcare professionals (HCPs) prescribing antidepressants. Participants ranked the 30 most frequent NSAEs reported in the scientific literature. We fitted logit models for sets of ranked items and calculated for each AE the probability to be ranked higher than the least important AE. We also identified additional patient-important AEs not included in the ranking task via open-ended questions.

Findings: We included 1631 patients from 44 different countries (1290 (79.1%) women, mean age 39.4 (SD 13), 289 (37.1%) with severe depression (PHQ-9 score ≥20)) and 281 HCPs (224 (79.7%) psychiatrists). The most important NSAEs for patients were insomnia (95.9%, 95% CI 95.2% to 96.5%), anxiety (95.2%, 95% CI 94.3% to 95.9%) and fatigue (94.6%, 95% CI 93.6% to 95.4%). The most important NSAEs for HCPs were sexual dysfunction (99.2%, 95% CI 98.5% to 99.6%), weight gain (98.9%, 95% CI 97.7% to 99.4%) and erectile problems (98.8%, 95% CI 97.7% to 99.4%). Participants reported 66 additional NSAEs, including emotional numbing (8.6%), trouble with concentration (7.6%) and irritability (6%).

Conclusions: These most important NSAEs should be systematically reported in antidepressant trials.

Clinical implications: The most important NSAEs should contribute to the core outcome set for harms in depression.

Background: Most people with mental disorders in communities exposed to adversity in low-income and middle-income countries (LMICs) do not receive effective care. Digital mental health interventions are scalable when digital access is adequate, and can be safely delivered during the COVID-19 pandemic.

Objective: To examine the effects of a new WHO-guided digital mental health intervention, Step-by-Step, supported by a non-specialist helper in Lebanon, in the context of concurring economic, humanitarian and political crises, a large industrial disaster and the COVID-19 pandemic.

Methods: We conducted a single-blind, two-arm pragmatic randomised trial, comparing guided Step-by-Step with enhanced care as usual (ECAU) among people suffering from depression and impaired functioning. Primary outcomes were depression (Patient Health Questionnaire 9 (PHQ-9)) and impaired functioning (WHO Disability Assessment Schedule-12 (WHODAS)) at post-treatment.

Findings: 680 people with depression (PHQ-9>10) and impaired functioning (WHODAS>16) were randomised to Step-by-Step or ECAU. Intention-to-treat analyses showed effects on depression (standardised mean differences, SMD: 0.71; 95% CI: 0.45 to 0.97), impaired functioning (SMD: 0.43; 95% CI: 0.21 to 0.65), post-traumatic stress (SMD: 0.53; 95% CI: 0.27 to 0.79), anxiety (SMD: 0.74; 95% CI: 0.49 to 0.99), subjective well-being (SMD: 0.37; 95% CI: 0.12 to 0.62) and self-identified personal problems (SMD: 0.56; 95% CI 0.29 to 0.83). Significant effects on all outcomes were retained at 3-month follow-up.

Conclusions: Guided digital mental health interventions can be effective in the treatment of depression in communities exposed to adversities in LMICs, although some uncertainty remains because of high attrition.

Clinical implications: Guided digital mental health interventions should be considered for implementation in LMICs.

Trial registration number: ClinicalTrials.gov NCT03720769.

Background: Despite uncertain benefits, people with personality disorder are commonly treated with antipsychotic medication.

Objective: To investigate the association between antipsychotics and violent crimes and suicidal behaviour in individuals with personality disorder.

Methods: We used nationwide Danish registries to identify all individuals with diagnosed personality disorder aged 18-64 years during 2007 to 2016. Antipsychotics were recorded in dispensed prescriptions, and individuals were followed up for police-recorded suspicions for violent crimes and healthcare presentations of suicidal behaviour. We applied a within-individual design where outcome rates for individuals with personality disorder during medicated periods were compared with rates during non-medicated periods.

Findings: The cohort included 166 328 people with diagnosed personality disorder, of whom 79 253 were prescribed antipsychotics, presented at least one outcome and were thus included in the within-individual analyses. Compared with periods when individuals were not on antipsychotic medication, violent crime suspicions were 40% lower (incident rate ratio (IRR) 0.60, 95% CI 0.55 to 0.63) in men and 10% lower (IRR 0.90, 95% CI 0.79 to 1.01) in women, while rates of suicidal behaviour were 32% lower both in men (IRR 0.68, 95% CI 0.66 to 0.71) and in women (IRR 0.68, 95% CI 0.65 to 0.70). In subgroup analyses, the magnitude of the association varied across specific personality disorders for criminal outcomes but less for suicidal behaviour, with largest association in dissocial personality disorder for violent criminality (IRR 0.53, 95% CI 0.47 to 0.59).

Conclusions: Treatment with antipsychotics was associated with reduced risks for violent crime suspicions and suicidal behaviour among individuals with personality disorder.

Clinical implications: Potential effects of antipsychotics on suicidal behaviour and violence should be taken into account when considering treatment options for people with personality disorders.

Objective: A network meta-analysis (NMA) usually assesses multiple outcomes across several treatment comparisons. The Vitruvian plot aims to facilitate communication of multiple outcomes from NMAs to patients and clinicians.

Methods: We developed this tool following the recommendations on the communication of benefit-risk information from the available literature. We collected and implemented feedback from researchers, statisticians, methodologists, clinicians and people with lived experience of physical and mental health issues.

Results: We present the Vitruvian plot, which graphically presents absolute estimates and relative performance of competing interventions against a common comparator for several outcomes of interest. We use two alternative colour schemes to highlight either the strength of statistical evidence or the confidence in the evidence. Confidence in the evidence is evaluated across six domains (within-study bias, reporting bias, indirectness, imprecision, heterogeneity and incoherence) using the Confidence in Network Meta-Analysis (CINeMA) system.

Conclusions: The Vitruvian plot allows reporting of multiple outcomes from NMAs, with colourings appropriate to inform credibility of the presented evidence.

Objectives: The WHO's Mental Health Gap Action Programme Intervention Guide (mhGAP-IG) has been widely used in low and middle-income countries. We reviewed literature describing interventions and training programmes beyond the mhGAP-IG, in primary healthcare (PHC) and community-based healthcare (CBH).

Design: We searched studies excluded from our updated mhGAP-IG systematic review, and included in other relevant systematic reviews, for evidence and experience of initiatives integrating mental health into PHC and CBH. Our 24 November 2020 mhGAP-IG search encompassed MEDLINE, Embase, PsycINFO, Web of Knowledge, Scopus, CINAHL, LILACS, ScieELO, Cochrane, PubMed databases, 3ie and Google Scholar. Although heterogeneity prevented meta-analysis, we descriptively summarised the evidence-base.

Results: Out of 1827 results, we identified 208 relevant records. They described randomised controlled trials of mental health interventions (98 studies, n=55 523 participants), non-randomised studies measuring clinical outcomes (22 studies, n=7405), training outcomes (36 studies, n=12 280) and implementation outcomes (21 studies, n=1090), plus descriptive accounts (18 studies, n=2526), baseline surveys and exploratory studies (6 studies, n=17 093) and commentaries (7 studies). Most (40%) were conducted in the African region, region of the Americas (16%), and South-East Asia (13%). Randomised and non-randomised studies reported improved symptoms, substance use, functioning, parenting and child outcomes. Non-randomised studies reported improved clinical knowledge, confidence and skills following training.

Conclusions: The literature beyond the mhGAP-IG is extensive and shares common findings. Future priorities are less-studied regions, interventions for severe mental illness, exploring ways that mhGAP-IG and alternative approaches complement each other in different contexts and scaling-up mental health integration.PROSPERO registration numberCRD42017068459.

Background: Internet-based cognitive-behavioural therapy (iCBT) is effective for subthreshold depression. However, which skills provided in iCBT packages are more effective than others is unclear. Such knowledge can inform construction of more effective and efficient iCBT programmes.

Objective: To examine the efficacy of five components of iCBT for subthreshold depression.

Methods: We conducted an factorial trial using a smartphone app, randomly allocating presence or absence of five iCBT skills including self-monitoring, behavioural activation (BA), cognitive restructuring (CR), assertiveness training (AT) and problem-solving. Participants were university students with subthreshold depression. The primary outcome was the change on the Patient Health Questionnaire-9 (PHQ-9) from baseline to week 8. Secondary outcomes included changes in CBT skills.

Findings: We randomised a total of 1093 participants. In all groups, participants had a significant PHQ-9 reduction from baseline to week 8. Depression reduction was not significantly different between presence or absence of any component, with corresponding standardised mean differences (negative values indicate specific efficacy in favour of the component) ranging between -0.04 (95% CI -0.16 to 0.08) for BA and 0.06 (95% CI -0.06 to 0.18) for AT. Specific CBT skill improvements were noted for CR and AT but not for the others.

Conclusions: There was significant reduction in depression for all participants regardless of the presence and absence of the examined iCBT components.

Clinical implication: We cannot yet make evidence-based recommendations for specific iCBT components. We suggest that future iCBT optimisation research should scrutinise the amount and structure of components to examine.

Trial registration number: UMINCTR-000031307.

Objective: There is little evidence for finding optimal antipsychotic treatment for schizophrenia, especially in paediatrics. To evaluate the performance and clinical benefit of several prediction methods for 1-year treatment continuation of antipsychotics.

Design and settings: Population-based prognostic study conducting using the nationwide claims database in Korea.

Participants: 5109 patients aged 2-18 years who initiated antipsychotic treatment with risperidone/aripiprazole for schizophrenia between 2010 and 2017 were identified.

Main outcome measures: We used the conventional logistic regression (LR) and common six machine-learning methods (least absolute shrinkage and selection operator, ridge, elstic net, randomforest, gradient boosting machine, and superlearner) to derive predictive models for treatment continuation of antipsychotics. The performance of models was assessed using the Brier score (BS), area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). The clinical benefit of applying these models was also evaluated by comparing the treatment continuation rate between patients who received the recommended medication by models and patients who did not.

Results: The gradient boosting machine showed the best performance in predicting treatment continuation for risperidone (BS, 0.121; AUROC, 0.686; AUPRC, 0.269). Among aripiprazole models, GBM for BS (0.114), SuperLearner for AUROC (0.688) and random forest for AUPRC (0.317) showed the best performance. Although LR showed lower performance than machine learnings, the difference was negligible. Patients who received recommended medication by these models showed a 1.2-1.5 times higher treatment continuation rate than those who did not.

Conclusions: All prediction models showed similar performance in predicting the treatment continuation of antipsychotics. Application of prediction models might be helpful for evidence-based decision-making in antipsychotic treatment.

Background: The COVID-19 pandemic has caused an increase in mental ill health compared with prepandemic levels. Longer-term trajectories of depression in adults during the pandemic remain unclear.

Objective: We used latent growth curve modelling to examine individual trajectories of depression symptoms, and their predictors, beyond the early stage of the pandemic.

Methods: Data were collected in three waves in May 2020, September/October 2020 and February/March 2021 in four UK cohorts (Millennium Cohort Study, Next Steps cohort, British Cohort and National Child Development Study). We included n=16 978 participants (mean age at baseline: 20, 30, 50 and 62, respectively). Self-reported depressive symptoms were the study outcome.

Findings: Symptoms of depression were higher in younger compared with older age groups (d=0.7) across all waves. While depressive symptoms remained stable from May 2020 to Autumn 2020 overall (standardized mean difference (SMD)=0.03, 95% CI 0.02 to 0.04), they increased in all age groups from May 2020 to Spring 2021 (SMD=0.12, 95% CI 0.11 to 0.13). Feelings of loneliness were the strongest predictor and concurrent correlate of increasing depressive symptoms across all cohorts, prepandemic mental health problems and having a long-term illness were also significantly associated with an increase in depression symptoms across all ages. By contrast, compliance with social distancing measures did not predict an increase in depression symptoms.

Conclusions: Feeling lonely and isolated had a large effect on depression trajectories across all generations, while social distancing measures did not.

Clinical implications: These findings highlight the importance of fostering the feeling of connectedness during COVID-19-related distancing measures.