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Importance: The period from childhood to early adulthood involves increased susceptibility to the onset of mental disorders, with implications for policy making that may be better appreciated by disaggregated analyses of narrow age groups., Objective: To estimate the global prevalence and years lived with disability (YLDs) associated with mental disorders and substance use disorders (SUDs) across 4 age groups using data from the 2019 Global Burden of Disease (GBD) study., Design, Setting, and Participants: Data from the 2019 GBD study were used for analysis of mental disorders and SUDs. Results were stratified by age group (age 5 to 9, 10 to 14, 15 to 19, and 20 to 24 years) and sex. Data for the 2019 GBD study were collected up to 2018, and data were analyzed for this article from April 2022 to September 2023., Exposure: Age 5 to 9 years, 10 to 14 years, 15 to 19 years, and 20 to 24 years., Main Outcomes and Measures: Prevalence rates with 95% uncertainty intervals (95% UIs) and number of YLDs., Results: Globally in 2019, 293 million of 2516 million individuals aged 5 to 24 years had at least 1 mental disorder, and 31 million had an SUD. The mean prevalence was 11.63% for mental disorders and 1.22% for SUDs. For the narrower age groups, the prevalence of mental disorders was 6.80% (95% UI, 5.58-8.03) for those aged 5 to 9 years, 12.40% (95% UI, 10.62-14.59) for those aged 10 to 14 years, 13.96% (95% UI, 12.36-15.78) for those aged 15 to 19 years, and 13.63% (95% UI, 11.90-15.53) for those aged 20 to 24 years. The prevalence of each individual disorder also varied by age groups; sex-specific patterns varied to some extent by age. Mental disorders accounted for 31.14 million of 153.59 million YLDs (20.27% of YLDs from all causes). SUDs accounted for 4.30 million YLDs (2.80% of YLDs from all causes). Over the entire life course, 24.85% of all YLDs attributable to mental disorders were recorded before age 25 years., Conclusions and Relevance: An analytical framework that relies on stratified age groups should be adopted for examination of mental disorders and SUDs from childhood to early adulthood. Given the implications of the early onset and lifetime burden of mental disorders and SUDs, age-disaggregated data are essential for the understanding of vulnerability and effective prevention and intervention initiatives.

Importance: Schizophrenia spectrum disorders (SSDs) and autism spectrum disorder (ASD) both feature social cognitive deficits; however, these disorders historically have been examined separately using a range of tests and subdomain focus and at different time points in the life span. Moving beyond diagnostic categories and characterizing social cognitive deficits can enhance understanding of shared pathways across these disorders., Objective: To investigate how deficits in social cognitive domains diverge or overlap between SSDs and ASD based on the extant literature., Data Sources: Literature searches were conducted in MEDLINE, PsycInfo, Embase, and Web of Science from database inception until July 26, 2020., Study Selection: Original research articles were selected that reported performance-based measures of social cognition in both SSDs and ASD samples. Selected articles also had to be published in English and use International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, DSM-IV, or more recent diagnostic criteria., Data Extraction and Synthesis: This systematic review and meta-analysis was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-analyses and Meta-analysis of Observational Studies in Epidemiology reporting guidelines, including data extraction and quality assessment using a modified version of the Newcastle-Ottawa Scale. Data were pooled using a random-effects model., Main Outcomes and Measures: Effect sizes were calculated as Hedges g (SSDs vs ASD). The primary outcomes were performance on emotion processing tasks, theory of mind (ToM) tasks, and the Reading the Mind in the Eyes Test (RMET) in SSDs compared with ASD. Meta-regressions were performed for age difference, publication year, quality assessment scores, and antipsychotic medication use., Results: Of the 4175 screened articles, 36 studies directly comparing social cognitive performance in individuals with SSDs vs ASD were included in the qualitative analysis (n = 1212 for SSDs groups and n = 1109 for ASD groups), and 33 studies were included in the quantitative analyses (n = 1113 for SSDs groups and n = 1015 for ASD groups). Most study participants were male (number of studies [k] = 36, 72% [878 of 1212] in SSDs groups and 82% [907 of 1109] in ASD groups), and age (k = 35) was older in SSDs groups (mean [SD], 28.4 [9.5] years) than in ASD groups (mean [SD], 23.3 [7.6] years). Included studies highlighted the prevalence of small, male-predominant samples and a paucity of cross-disorder clinical measures. The meta-analyses revealed no statistically significant differences between SSDs and ASD on emotion processing measures (k = 15; g = 0.12 [95% CI, -0.07 to 0.30]; P = .21; I2 = 51.0%; 1 outlier excluded), ToM measures (k = 17; g = -0.01 [95% CI, -0.21 to 0.19]; P = .92; I2 = 56.5%; 1 outlier excluded), or the RMET (k = 13; g = 0.25 [95% CI, -0.04 to 0.53]; P = .10; I2 = 75.3%). However, SSDs vs ASD performance differences between studies were statistically significantly heterogeneous, which was only minimally explained by potential moderators., Conclusions and Relevance: In this analysis, similar levels of social cognitive impairment were present, on average, in individuals with SSDs and ASD. Cross-disorder studies of social cognition, including larger samples, consensus batteries, and consistent reporting of measures, as well as data across multiple levels of analysis, are needed to help identify subgroups within and across disorders that may be more homogeneous in etiology and treatment response.

Importance: Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood., Objective: To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia., Design, Setting, and Participants: Profiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Analysis for gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The numbers of cases and controls in each of the 6 disorders were as follows: ADHD: 1814 and 1602; ASD: 1748 and 1770; BD: 1547 and 3405; MDD: 2658 and 3572; OCD: 2266 and 2007; and schizophrenia: 2688 and 3244., Main Outcomes and Measures: Interregional profiles of group difference in cortical thickness between cases and controls., Results: A total of 12 721 cases and 15 600 controls, ranging from ages 2 to 89 years, were included in this study. Interregional profiles of group differences in cortical thickness for each of the 6 psychiatric disorders were associated with profiles of gene expression specific to pyramidal (CA1) cells, astrocytes (except for BD), and microglia (except for OCD); collectively, gene-expression profiles of the 3 cell types explain between 25% and 54% of variance in interregional profiles of group differences in cortical thickness. Principal component analysis revealed a shared profile of difference in cortical thickness across the 6 disorders (48% variance explained); interregional profile of this principal component 1 was associated with that of the pyramidal-cell gene expression (explaining 56% of interregional variation). Coexpression analyses of these genes revealed 2 clusters: (1) a prenatal cluster enriched with genes involved in neurodevelopmental (axon guidance) processes and (2) a postnatal cluster enriched with genes involved in synaptic activity and plasticity-related processes. These clusters were enriched with genes associated with all 6 psychiatric disorders., Conclusions and Relevance: In this study, shared neurobiologic processes were associated with differences in cortical thickness across multiple psychiatric disorders. These processes implicate a common role of prenatal development and postnatal functioning of the cerebral cortex in these disorders.