COVID-19 physical distancing measures can potentially increase the likelihood of mental disorders. It is unknown whether these measures are associated with depression and anxiety.

To investigate meta-analytic global levels of depression and anxiety during the COVID-19 pandemic and how the implementation of mitigation strategies (i.e. public transportation closures, stay-at-home orders, etc.) impacted such disorders.

PubMed, MEDLINE, Web of Science, BIOSIS Citation Index, Current Content Connect, PsycINFO, CINAHL, medRxiv, and PsyArXiv databases for depression and anxiety prevalences; Oxford Covid-19 Government Response Tracker for the containment and closure policies indexes; Global Burden of Disease Study for previous levels of depression and anxiety.

Original studies conducted during COVID-19 pandemic, which assessed categorical depression and anxiety, using PHQ-9 and GAD-7 scales (cutoff ⩾10).

General population, healthcare providers, students, and patients. National physical distancing measures.

Meta-analysis and meta-regression.

In total, 226 638 individuals were assessed within the 60 included studies. Global prevalence of both depression and anxiety during the COVID-19 pandemic was 24.0% and 21.3%, respectively. There were differences in the prevalence of both anxiety and depression reported across regions and countries. Asia (17.6% and 17.9%), and China (16.2% and 15.5%) especially, had the lowest prevalence of both disorders. Regarding the impact of mitigation strategies on mental health, only public transportation closures increased the prevalence of anxiety, especially in Europe.

Country-level data on physical distancing measures and previous anxiety/depression may not necessarily reflect local (i.e. city-specific) contexts.

Mental health concerns should not be viewed only as a delayed consequence of the COVID-19 pandemic, but also as a concurrent epidemic. Our data provide support for policy-makers to consider real-time enhanced mental health services, and increase initiatives to foster positive mental health outcomes.

Poor mental health has consistently been associated with substance use (smoking, alcohol drinking, cannabis use, and consumption of caffeinated drinks). To properly inform public health policy it is crucial to understand the mechanisms underlying these associations, and most importantly, whether or not they are causal.

In this pre-registered systematic review, we assessed the evidence for causal relationships between mental health and substance use from Mendelian randomization (MR) studies, following PRISMA. We rated the quality of included studies using a scoring system that incorporates important indices of quality, such as the quality of phenotype measurement, instrument strength, and use of sensitivity methods.

Sixty-three studies were included for qualitative synthesis. The final quality rating was ‘−’ for 16 studies, ‘– +’ for 37 studies, and ‘+’for 10 studies. There was robust evidence that higher educational attainment decreases smoking and that there is a bi-directional, increasing relationship between smoking and (symptoms of) mental disorders. Another robust finding was that higher educational attainment increases alcohol use frequency, but decreases binge-drinking and alcohol use problems, and that mental disorders causally lead to more alcohol drinking without evidence for the reverse.

The current MR literature increases our understanding of the relationship between mental health and substance use. Bi-directional causal relationships are indicated, especially for smoking, providing further incentive to strengthen public health efforts to decrease substance use. Future MR studies should make use of large(r) samples in combination with detailed phenotypes, a wide range of sensitivity methods, and triangulate with other research methods.

Given the widespread nature and clinical consequences of self-harm and suicidal ideation among adolescents, establishing the efficacy of developmentally appropriate treatments that reduce both self-harm and suicidal ideation in the context of broader adolescent psychopathology is critical.

We conducted a systematic review and meta-analysis of the Dialectical Behaviour Therapy for Adolescents (DBT-A) literature on treating self-injury in adolescents (12–19 years). We searched for eligible trials and treatment evaluations published prior to July 2020 in MEDLINE/PubMed, Scopus, Google Scholar, EMBASE, and the Cochrane Library databases for clinical trials. Twenty-one studies were identified [five randomized-controlled trials (RCTs), three controlled clinical trials (CCTs), and 13 pre-post evaluations]. We extracted data for predefined primary (self-harm, suicidal ideation) and secondary outcomes (borderline personality symptoms; BPD) and calculated treatment effects for RCTs/CCTs and pre-post evaluations. This meta-analysis was pre-registered with OSF: osf.io/v83e7.

Overall, the studies comprised 1673 adolescents. Compared to control groups, DBT-A showed small to moderate effects for reducing self-harm (g = −0.44; 95% CI −0.81 to −0.07) and suicidal ideation (g = −0.31, 95% CI −0.52 to −0.09). Pre-post evaluations suggested large effects for all outcomes (self-harm: g = −0.98, 95% CI −1.15 to −0.81; suicidal ideation: g = −1.16, 95% CI −1.51 to −0.80; BPD symptoms: g = −0.97, 95% CI −1.31 to −0.63).

DBT-A appears to be a valuable treatment in reducing both adolescent self-harm and suicidal ideation. However, evidence that DBT-A reduces BPD symptoms was only found in pre-post evaluations.

Fibromyalgia is a chronic condition characterised by widespread musculoskeletal pain. Although accumulating evidence suggests that exposure to stressful events increases the risk for this complex disorder, this is the first meta-analysis to compare the impact of a full range of lifetime stressors (e.g. physical trauma through to emotional neglect) on adult fibromyalgia.

This review was performed in accordance with PRISMA guidelines. Random-effects models examined associations between different stressor exposures and fibromyalgia status with meta-regression investigating the effects of publication year and study quality on effect sizes.

Nineteen studies were included in the meta-analysis. Significant associations with fibromyalgia status were observed for all six exposure types examined: odds ratios (OR) were highest for physical abuse (OR 3.23, 95% confidence interval 1.99–5.23) and total abuse (3.06, 1.71–5.46); intermediate for sexual abuse (2.65, 1.85–3.79) and smaller for medical trauma (1.80, 1.19–2.71), other lifetime stressors (1.70, 1.31–2.20), and emotional abuse (1.52, 1.27–1.81). Results were not significantly changed when childhood, as opposed to adult, exposures were used in studies that reported both. Meta-regression analyses demonstrated no effect of publication year or study quality on effect sizes.

This study confirmed a significant association between stressor exposure and adult fibromyalgia with the strongest associations observed for physical abuse. Limitations related to current available literature were identified; we provide several suggestions for how these can be addressed in future studies. Stressors are likely to be one of many risk factors for fibromyalgia which we argue is best approached from a biopsychosocial perspective.

Between 5% and 14% of women suffer from fear of childbirth (FOC) which is associated with difficulties during birth and in postnatal psychological adjustment. Therefore, effective interventions are needed to improve outcomes for women. A systematic review and meta-analysis was used to identify effective interventions for treating women with FOC.

Literature searches were undertaken on online databases. Hand searches of reference lists were also carried out. Studies were included in the review if they recruited women with FOC and aimed to reduce FOC and/or improve birth outcomes. Data were synthesised qualitatively and quantitatively using meta-analysis. The literature searches provided a total of 4474 citations.

After removing duplicates and screening through abstracts, titles and full texts, 66 papers from 48 studies were identified for inclusion in the review. Methodological quality was mixed with 30 out of 48 studies having a medium risk of bias. Interventions were categorised into six broad groups: cognitive behavioural therapy, other talking therapies, antenatal education, enhanced midwifery care, alternative interventions and interventions during labour. Results from the meta-analysis showed that most interventions reduced FOC, regardless of the approach (mean effect size = −1.27; z = −4.53, p < 0.0001) and that other talking therapies may reduce caesarean section rates (OR 0.48, 95% CI 0.48–0.90).

Poor methodological quality of studies limits conclusions that can be drawn; however, evidence suggests that most interventions investigated reduce FOC. Future high-quality randomised controlled trials are needed so that clear conclusions can be made.

This meta-analysis on peripheral blood compounds in drug-naïve first-episode patients with either schizophrenia or major depressive disorder (MDD) examined which compounds change following psychopharmacological treatment.

The Embase, PubMed and PsycINFO databases were systematically searched for longitudinal studies reporting measurements of blood compounds in drug-naïve first-episode schizophrenia or MDD.

For this random-effects meta-analysis, we retrieved a total of 31 studies comprising 1818 schizophrenia patients, and 14 studies comprising 469 MDD patients. Brain-derived neurotrophic factor (BDNF) increased following treatment in schizophrenia (Hedges' g (g): 0.55; 95% confidence interval (CI) 0.39–0.70; p < 0.001) and MDD (g: 0.51; CI 0.06–0.96; p = 0.027). Interleukin (IL)-6 levels decreased in schizophrenia (g: −0.48; CI −0.85 to −0.11; p = 0.011), and for MDD a trend of decreased IL-6 levels was observed (g: −0.39; CI −0.87 to 0.09; p = 0.115). Tumor necrosis factor alpha (TNFα) also decreased in schizophrenia (g: −0.34; CI −0.68 to −0.01; p = 0.047) and in MDD (g: −1.02; CI −1.79 to −0.25; p = 0.009). Fasting glucose levels increased only in schizophrenia (g: 0.26; CI 0.07–0.44; p = 0.007), but not in MDD. No changes were found for C-reactive protein, IL-1β, IL-2 and IL-4.

Psychopharmacological treatment has modulating effects on BDNF and TNFα in drug-naïve first-episode patients with either schizophrenia or MDD. These findings support efforts for further research into transdiagnostic preventive strategies and augmentation therapy for those with immune dysfunctions.

Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes.

Of 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment.

Eight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56–0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72–0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy.

The negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.