Covadonga M. Díaz‐Caneja

BACKGROUND: Cannabis use is associated with increased risk of later psychotic disorder but whether it affects incidence of the disorder remains unclear. We aimed to identify patterns of cannabis use with the strongest effect on odds of psychotic disorder across Europe and explore whether differences in such patterns contribute to variations in the incidence rates of psychotic disorder. METHODS: -tetrahydrocannabinol (THC) in the different types of cannabis available across the sites, we divided the types of cannabis used by participants into two categories: low potency (THC <10%) and high potency (THC ≥10%). Assuming causality, we calculated the population attributable fractions (PAFs) for the patterns of cannabis use associated with the highest odds of psychosis and the correlation between such patterns and the incidence rates for psychotic disorder across the study sites. FINDINGS: Between May 1, 2010, and April 1, 2015, we obtained data from 901 patients with first-episode psychosis across 11 sites and 1237 population controls from those same sites. Daily cannabis use was associated with increased odds of psychotic disorder compared with never users (adjusted odds ratio [OR] 3·2, 95% CI 2·2-4·1), increasing to nearly five-times increased odds for daily use of high-potency types of cannabis (4·8, 2·5-6·3). The PAFs calculated indicated that if high-potency cannabis were no longer available, 12·2% (95% CI 3·0-16·1) of cases of first-episode psychosis could be prevented across the 11 sites, rising to 30·3% (15·2-40·0) in London and 50·3% (27·4-66·0) in Amsterdam. The adjusted incident rates for psychotic disorder were positively correlated with the prevalence in controls across the 11 sites of use of high-potency cannabis (r = 0·7; p=0·0286) and daily use (r = 0·8; p=0·0109). INTERPRETATION: Differences in frequency of daily cannabis use and in use of high-potency cannabis contributed to the striking variation in the incidence of psychotic disorder across the 11 studied sites. Given the increasing availability of high-potency cannabis, this has important implications for public health. FUNDING SOURCE: Medical Research Council, the European Community's Seventh Framework Program grant, São Paulo Research Foundation, National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust and King's College London and the NIHR BRC at University College London, Wellcome Trust.

Suicidality in childhood and adolescence is of increasing concern. The aim of this paper was to review the published literature identifying key psychosocial risk factors for suicidality in the paediatric population. A systematic two-step search was carried out following the PRISMA statement guidelines, using the terms 'suicidality, suicide, and self-harm' combined with terms 'infant, child, adolescent' according to the US National Library of Medicine and the National Institutes of Health classification of ages. Forty-four studies were included in the qualitative synthesis. The review identified three main factors that appear to increase the risk of suicidality: psychological factors (depression, anxiety, previous suicide attempt, drug and alcohol use, and other comorbid psychiatric disorders); stressful life events (family problems and peer conflicts); and personality traits (such as neuroticism and impulsivity). The evidence highlights the complexity of suicidality and points towards an interaction of factors contributing to suicidal behaviour. More information is needed to understand the complex relationship between risk factors for suicidality. Prospective studies with adequate sample sizes are needed to investigate these multiple variables of risk concurrently and over time.

A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).