Major depressive disorder (MDD) is highly prevalent and burdensome for individuals and society. While there are psychological interventions able to prevent and treat MDD, uptake remains low. To overcome structural and attitudinal barriers, an indirect approach of using online insomnia interventions seems promising because insomnia is less stigmatized, predicts MDD onset, is often comorbid and can outlast MDD treatment. This individual-participant-data meta-analysis evaluated the potential of the online insomnia intervention GET.ON Recovery as an indirect treatment to reduce depressive symptom severity (DSS) and potential MDD onset across a range of participant characteristics.

Efficacy on depressive symptom outcomes was evaluated using multilevel regression models controlling for baseline severity. To identify potential effect moderators, clinical, sociodemographic, and work-related variables were investigated using univariable moderation and random-forest methodology before developing a multivariable decision tree.

IPD were obtained from four of seven eligible studies (N = 561); concentrating on workers with high work-stress. DSS was significantly lower in the intervention group both at post-assessment (d = −0.71 [95% CI−0.92 to −0.51]) and at follow-up (d = −0.84 [95% CI −1.11 to −0.57]). In the subsample (n = 121) without potential MDD at baseline, there were no significant group differences in onset of potential MDD. Moderation analyses revealed that effects on DSS differed significantly across baseline severity groups with effect sizes between d = −0.48 and −0.87 (post) and d = − 0.66 to −0.99 (follow-up), while no other sociodemographic, clinical, or work-related characteristics were significant moderators.

An online insomnia intervention is a promising approach to effectively reduce DSS in a preventive and treatment setting.

This study investigates associations of several dimensions of childhood adversities (CAs) with lifetime mental disorders, 12-month disorder persistence, and impairment among incoming college students.

Data come from the World Mental Health International College Student Initiative (WMH-ICS). Web-based surveys conducted in nine countries (n = 20 427) assessed lifetime and 12-month mental disorders, 12-month role impairment, and seven types of CAs occurring before the age of 18: parental psychopathology, emotional, physical, and sexual abuse, neglect, bullying victimization, and dating violence. Poisson regressions estimated associations using three dimensions of CA exposure: type, number, and frequency.

Overall, 75.8% of students reported exposure to at least one CA. In multivariate regression models, lifetime onset and 12-month mood, anxiety, and substance use disorders were all associated with either the type, number, or frequency of CAs. In contrast, none of these associations was significant when predicting disorder persistence. Of the three CA dimensions examined, only frequency was associated with severe role impairment among students with 12-month disorders. Population-attributable risk simulations suggest that 18.7–57.5% of 12-month disorders and 16.3% of severe role impairment among those with disorders were associated with these CAs.

CAs are associated with an elevated risk of onset and impairment among 12-month cases of diverse mental disorders but are not involved in disorder persistence. Future research on the associations of CAs with psychopathology should include fine-grained assessments of CA exposure and attempt to trace out modifiable intervention targets linked to mechanisms of associations with lifetime psychopathology and burden of 12-month mental disorders.

Although non-suicidal self-injury (NSSI) is an issue of major concern to colleges worldwide, we lack detailed information about the epidemiology of NSSI among college students. The objectives of this study were to present the first cross-national data on the prevalence of NSSI and NSSI disorder among first-year college students and its association with mental disorders.

Data come from a survey of the entering class in 24 colleges across nine countries participating in the World Mental Health International College Student (WMH-ICS) initiative assessed in web-based self-report surveys (20 842 first-year students). Using retrospective age-of-onset reports, we investigated time-ordered associations between NSSI and Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-IV) mood (major depressive and bipolar disorder), anxiety (generalized anxiety and panic disorder), and substance use disorders (alcohol and drug use disorder).

NSSI lifetime and 12-month prevalence were 17.7% and 8.4%. A positive screen of 12-month DSM-5 NSSI disorder was 2.3%. Of those with lifetime NSSI, 59.6% met the criteria for at least one mental disorder. Temporally primary lifetime mental disorders predicted subsequent onset of NSSI [median odds ratio (OR) 2.4], but these primary lifetime disorders did not consistently predict 12-month NSSI among respondents with lifetime NSSI. Conversely, even after controlling for pre-existing mental disorders, NSSI consistently predicted later onset of mental disorders (median OR 1.8) as well as 12-month persistence of mental disorders among students with a generalized anxiety disorder (OR 1.6) and bipolar disorder (OR 4.6).

NSSI is common among first-year college students and is a behavioral marker of various common mental disorders.

Problem-solving therapy (PST) is one of the best examined types of psychotherapy for adult depression. No recent meta-analysis has examined the effects of PST compared to control groups or to other treatments. We wanted to verify whether PST is effective, whether effects are comparable to those of other treatments, and whether we could identify the possible sources of high heterogeneity that was found in earlier meta-analyses.

We conducted systematic searches in bibliographical databases, including PubMed, PsycInfo, Embase and the Cochrane database of randomized trials.

We included 30 randomized controlled trials on PST (with 3530 patients), in which PST was compared to control conditions, with other therapies, and with pharmacotherapy. We could compare these 30 trials on PST also with 259 trials on other psychotherapies for adult depression. The effect size of PST versus control groups was g = 0.79 (0.57–1.01) with very high heterogeneity (I2 = 84; 95% CI: 77–88). The effect size from the 9 studies with low risk of bias was g = 0.34 (95% CI: 0.22–0.46) with low heterogeneity (I2 = 32; 95% CI: 0–68), which is comparable to the effects of other psychotherapies. PST was a little more effective than other therapies in direct comparisons, but that may be explained by the considerable number of studies with researcher allegiance towards PST. In meta-regression analyses of all controlled studies, no significant difference between PST and other therapies was found.

PST is probably an effective treatment for depression, with effect sizes that are small, but comparable to those found for other psychological treatments of depression.

In the 1950s, Eysenck suggested that psychotherapies may not be effective at all. Twenty-five years later, the first meta-analysis of randomised controlled trials showed that the effects of psychotherapies were considerable and that Eysenck was wrong. However, since that time methods have become available to assess biases in meta-analyses.

We examined the influence of these biases on the effects of psychotherapies for adult depression, including risk of bias, publication bias and the exclusion of waiting list control groups.

The unadjusted effect size of psychotherapies compared with control groups was g = 0.70 (limited to Western countries: g = 0.63), which corresponds to a number-needed-to-treat of 4.18. Only 23% of the studies could be considered as a low risk of bias. When adjusting for several sources of bias, the effect size across all types of therapies dropped to g = 0.31.

These results suggest that the effects of psychotherapy for depression are small, above the threshold that has been suggested as the minimal important difference in the treatment of depression, and Eysenck was probably wrong. However, this is still not certain because we could not adjust for all types of bias. Unadjusted meta-analyses of psychotherapies overestimate the effects considerably, and for several types of psychotherapy for adult depression, insufficient evidence is available that they are effective because too few low-risk studies were available, including problem-solving therapy, interpersonal psychotherapy and behavioural activation.

Almost nothing is known about the potential negative effects of Internet-based psychological treatments for depression. This study aims at investigating deterioration and its moderators within randomized trials on Internet-based guided self-help for adult depression, using an individual patient data meta-analyses (IPDMA) approach.

Studies were identified through systematic searches (PubMed, PsycINFO, EMBASE, Cochrane Library). Deterioration in participants was defined as a significant symptom increase according to the reliable change index (i.e. 7.68 points in the CES-D; 7.63 points in the BDI). Two-step IPDMA procedures, with a random-effects model were used to pool data.

A total of 18 studies (21 comparisons, 2079 participants) contributed data to the analysis. The risk for a reliable deterioration from baseline to post-treatment was significantly lower in the intervention v. control conditions (3.36 v. 7.60; relative risk 0.47, 95% confidence interval 0.29–0.75). Education moderated effects on deterioration, with patients with low education displaying a higher risk for deterioration than patients with higher education. Deterioration rates for patients with low education did not differ statistically significantly between intervention and control groups. The benefit–risk ratio for patients with low education indicated that 9.38 patients achieve a treatment response for each patient experiencing a symptom deterioration.

Internet-based guided self-help is associated with a mean reduced risk for a symptom deterioration compared to controls. Treatment and symptom progress of patients with low education should be closely monitored, as some patients might face an increased risk for symptom deterioration. Future studies should examine predictors of deterioration in patients with low education.

Although mental disorders are significant predictors of educational attainment throughout the entire educational career, most research on mental disorders among students has focused on the primary and secondary school years.

The World Health Organization World Mental Health Surveys were used to examine the associations of mental disorders with college entry and attrition by comparing college students (n = 1572) and non-students in the same age range (18–22 years; n = 4178), including non-students who recently left college without graduating (n = 702) based on surveys in 21 countries (four low/lower-middle income, five upper-middle-income, one lower-middle or upper-middle at the times of two different surveys, and 11 high income). Lifetime and 12-month prevalence and age-of-onset of DSM-IV anxiety, mood, behavioral and substance disorders were assessed with the Composite International Diagnostic Interview (CIDI).

One-fifth (20.3%) of college students had 12-month DSM-IV/CIDI disorders; 83.1% of these cases had pre-matriculation onsets. Disorders with pre-matriculation onsets were more important than those with post-matriculation onsets in predicting subsequent college attrition, with substance disorders and, among women, major depression the most important such disorders. Only 16.4% of students with 12-month disorders received any 12-month healthcare treatment for their mental disorders.

Mental disorders are common among college students, have onsets that mostly occur prior to college entry, in the case of pre-matriculation disorders are associated with college attrition, and are typically untreated. Detection and effective treatment of these disorders early in the college career might reduce attrition and improve educational and psychosocial functioning.

Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful.

We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments.

Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials.

Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.