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Background According to current classification systems, patients with major depressive disorder (MDD) may have very different combinations of symptoms. This symptomatic diversity hinders the progress of research into the causal mechanisms and treatment allocation. Theoretically founded subtypes of depression such as atypical, psychotic, and melancholic depression have limited clinical applicability. Data-driven analyses of symptom dimensions or subtypes of depression are scarce. In this systematic review, we examine the evidence for the existence of data-driven symptomatic subtypes of depression. Methods We undertook a systematic literature search of MEDLINE, PsycINFO and Embase in May 2012. We included studies analyzing the depression criteria of the Diagnostic and Statistical Manual of Mental Disorders , fourth edition (DSM-IV) of adults with MDD in latent variable analyses. Results In total, 1176 articles were retrieved, of which 20 satisfied the inclusion criteria. These reports described a total of 34 latent variable analyses: 6 confirmatory factor analyses, 6 exploratory factor analyses, 12 principal component analyses, and 10 latent class analyses. The latent class techniques distinguished 2 to 5 classes, which mainly reflected subgroups with different overall severity: 62 of 71 significant differences on symptom level were congruent with a latent class solution reflecting severity. The latent class techniques did not consistently identify specific symptom clusters. Latent factor techniques mostly found a factor explaining the variance in the symptoms depressed mood and interest loss (11 of 13 analyses), often complemented by psychomotor retardation or fatigue (8 of 11 analyses). However, differences in found factors and classes were substantial. Conclusions The studies performed to date do not provide conclusive evidence for the existence of depressive symptom dimensions or symptomatic subtypes. The wide diversity of identified factors and classes might result either from the absence of patterns to be found, or from the theoretical and modeling choices preceding analysis.

Nearly 9 million Americans live in extreme-poverty neighborhoods, places that also tend to be racially segregated and dangerous. Yet, the effects on the well-being of residents of moving out of such communities into less distressed areas remain uncertain. Using data from Moving to Opportunity, a unique randomized housing mobility experiment, we found that moving from a high-poverty to lower-poverty neighborhood leads to long-term (10- to 15-year) improvements in adult physical and mental health and subjective well-being, despite not affecting economic self-sufficiency. A 1—standard deviation decline in neighborhood poverty (13 percentage points) increases subjective well-being by an amount equal to the gap in subjective well-being between people whose annual incomes differ by $13,000—a large amount given that the average control group income is $20,000. Subjective well-being is more strongly affected by changes in neighborhood economic disadvantage than racial segregation, which is important because racial segregation has been declining since 1970, but income segregation has been increasing.

Subthreshold depression, defined as a depressive status falling short of the diagnostic threshold for major depression, is common, disabling and constitutes a risk factor for future depressive episodes. Cognitive behavioral therapies (CBT) have been shown to be effective but are usually provided as packages of various skills. Little research has been done to investigate whether all their components are beneficial and contributory to mental health promotion. We addressed this issue by developing a smartphone CBT app that implements five representative CBT skills (behavioral activation, cognitive restructuring, problem solving, assertion training and behavior therapy for insomnia), and conducting a master randomized study that included four 2 × 2 factorial trials to enable precise estimation of skill-specific efficacies. Between September 2022 and February 2024, we recruited 3,936 adult participants with subthreshold depression. Among those randomized, the follow-up rate was 97% at week 6 and adherence to the app was 84%. The study showed that all included CBT skills and their combinations differentially beat all three control conditions of delayed treatment, health information or self-check, with effect sizes ranging between −0.67 (95% confidence interval: −0.81 to −0.53) and −0.16 (−0.30 to −0.02) for changes in depressive symptom severity from baseline to week 6, as measured with the Patient Health Questionnaire-9 scores. Knowledge of the active ingredients of CBT can better inform the design of more effective and scalable psychotherapies in the future. (UMIN Clinical Trials Registry UMIN000047124 ). A trial that used a master randomized design found that the skills commonly found in smartphone-based cognitive behavioral therapy, when tested alone or in combination with behavioral activation, were differentially efficacious in improving mental health symptoms in adults with subthreshold depression.

We consider the problem of selecting the optimal subgroup to treat when data on covariates are available from a randomized trial or observational study. We distinguish between four different settings including: (1) treatment selection when resources are constrained; (2) treatment selection when resources are not constrained; (3) treatment selection in the presence of side effects and costs; and (4) treatment selection to maximize effect heterogeneity. We show that, in each of these cases, the optimal treatment selection rule involves treating those for whom the predicted mean difference in outcomes comparing those with versus without treatment, conditional on covariates, exceeds a certain threshold. The threshold varies across these four scenarios, but the form of the optimal treatment selection rule does not. The results suggest a move away from the traditional subgroup analysis for personalized medicine. New randomized trial designs are proposed so as to implement and make use of optimal treatment selection rules in healthcare practice.

Purpose The study sought to examine the association of mental disorders with educational attainment in a community sample. Methods Data were from 5001 respondents aged 15–54 in the 1990–1992 National Comorbidity Survey (NCS), re-interviewed in the 2001–2003 NCS follow-up (NCS-2). Discrete-time survival analysis was used to examine the association of disorders present at baseline (NCS) or having first onset after the baseline (assessed in NCS-2) with educational outcomes among 3954 eligible respondents. Mental disorders were categorized into internalizing fear disorders (simple phobia, social phobia, panic disorder with/without agoraphobia and agoraphobia without panic disorder), internalizing anxiety-misery disorders (major depressive disorder, generalized anxiety disorder and post-traumatic stress disorder), externalizing disorders (alcohol and drug use disorders, conduct disorder) and bipolar disorder. Analyses were conducted separately in students and non-students at baseline. Results Among students, baseline bipolar and externalizing disorders, as well as fear, anxiety-misery and externalizing disorders with onset after baseline were associated with lower odds of high school graduation; baseline anxiety-misery disorders with lower odds of going to college; and baseline externalizing disorders and bipolar disorder with onset after baseline with lower odds of college graduation. Among non-students, baseline fear disorders were associated with lower odds of high school graduation and bipolar disorder with lower odds of going to college. Assuming that the regression coefficients represent causal effects, mental disorders accounted for 5.8–11.0 % of high school and 3.2–11.4 % of college non-completion. Conclusions Expanding access to mental health services for youth might have a net positive societal value by helping to prevent some of these adverse educational outcomes.

Objectives. Although previous research has shown that low socioeconomic status (SES) is associated with mental illness, it is unclear which aspects of SES are most important. We investigated this issue by examining associations between 5 aspects of SES and adolescent mental disorders. Methods. Data came from a national survey of US adolescents (n = 6483). Associations among absolute SES (parental income and education), relative SES (relative deprivation, subjective social status), and community level income variation (Gini coefficient) with past-year mental disorders were examined. Results. Subjective social status (mean 0, variance 1) was most consistently associated with mental disorder. Odds ratios with mood, anxiety, substance, and behavior disorders after controlling for other SES indicators were all statistically significant and in the range of 0.7 to 0.8. Associations were strongest for White adolescents. Parent education was associated with low risk for anxiety disorder, relative deprivation with high risk for mood disorder, and the other 2 indicators were associated with none of the disorders considered. Conclusions. Associations between SES and adolescent mental disorders are most directly the result of perceived social status, an aspect of SES that might be more amenable to interventions than objective aspects of SES.

We examine long-term neighborhood effects on low-income families using data from the Moving to Opportunity (MTO) randomized housing-mobility experiment. This experiment offered to some public-housing families but not to others the chance to move to less-disadvantaged neighborhoods. We show that ten to 15 years after baseline, MTO: (i) improves adult physical and mental health; (ii) has no detectable effect on economic outcomes or youth schooling or physical health; and (iii) has mixed results by gender on other youth outcomes, with girls doing better on some measures and boys doing worse. Despite the somewhat mixed pattern of impacts on traditional behavioral outcomes, MTO moves substantially improve adult subjective well-being.

Suicide is a leading cause of death worldwide. Mental disorders are among the strongest predictors of suicide; however, little is known about which disorders are uniquely predictive of suicidal behavior, the extent to which disorders predict suicide attempts beyond their association with suicidal thoughts, and whether these associations are similar across developed and developing countries. This study was designed to test each of these questions with a focus on nonfatal suicide attempts. Data on the lifetime presence and age-of-onset of Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) mental disorders and nonfatal suicidal behaviors were collected via structured face-to-face interviews with 108,664 respondents from 21 countries participating in the WHO World Mental Health Surveys. The results show that each lifetime disorder examined significantly predicts the subsequent first onset of suicide attempt (odds ratios [ORs] = 2.9-8.9). After controlling for comorbidity, these associations decreased substantially (ORs = 1.5-5.6) but remained significant in most cases. Overall, mental disorders were equally predictive in developed and developing countries, with a key difference being that the strongest predictors of suicide attempts in developed countries were mood disorders, whereas in developing countries impulse-control, substance use, and post-traumatic stress disorders were most predictive. Disaggregation of the associations between mental disorders and nonfatal suicide attempts showed that these associations are largely due to disorders predicting the onset of suicidal thoughts rather than predicting progression from thoughts to attempts. In the few instances where mental disorders predicted the transition from suicidal thoughts to attempts, the significant disorders are characterized by anxiety and poor impulse-control. The limitations of this study include the use of retrospective self-reports of lifetime occurrence and age-of-onset of mental disorders and suicidal behaviors, as well as the narrow focus on mental disorders as predictors of nonfatal suicidal behaviors, each of which must be addressed in future studies. This study found that a wide range of mental disorders increased the odds of experiencing suicide ideation. However, after controlling for psychiatric comorbidity, only disorders characterized by anxiety and poor impulse-control predict which people with suicide ideation act on such thoughts. These findings provide a more fine-grained understanding of the associations between mental disorders and subsequent suicidal behavior than previously available and indicate that mental disorders predict suicidal behaviors similarly in both developed and developing countries. Future research is needed to delineate the mechanisms through which people come to think about suicide and subsequently progress from ideation to attempts.