First responders to disasters are at risk of developing post-traumatic stress disorder (PTSD). The trajectories of post-traumatic stress symptom severity differ among individuals, even if they are exposed to similar events. These trajectories have not yet been reported in non-Western first responders.

We aimed to explore post-traumatic stress symptom severity trajectories and their risk factors in first responders to the 2011 Great East Japan Earthquake (GEJE) – a historically large earthquake that resulted in a tsunami and a nuclear disaster.

A total of 55 632 Japan Ground Self-Defense Force (JGSDF) personnel dispatched to the GEJE were enrolled in this 7-year longitudinal cohort study. PTSD symptom severity was measured using the Impact of Event Scale-Revised. Trajectories were identified using latent growth mixture models (LGMM). Nine potential risk factors for the symptom severity trajectories were analysed using multinomial logistic regression.

Five symptom severity trajectories were identified: ‘resilient’ (54.8%), ‘recovery’ (24.6%), ‘incomplete recovery’ (10.7%), ‘late-onset’ (5.7%), and ‘chronic’ (4.3%). The main risk factors for the four non-resilient trajectories were older age, personal disaster experiences and working conditions. These working conditions included duties involving body recovery or radiation exposure risk, longer deployment length, later or no post-deployment leave and longer post-deployment overtime.

The majority of first responders to GEJE were resilient and developed few or no PTSD symptoms. A substantial minority experienced late-onset and chronic symptom severity trajectories. The identified risk factors can inform policies for prevention, early detection and intervention in individuals at risk of developing symptomatic trajectories.

There is increasing research examining excess mortality in people with bipolar disorder using life expectancy and related measures, which quantify the disease impact on survival. However, there has been no meta-analysis to date summarising existing data on life expectancy in those with bipolar disorder.

To systematically review and quantitatively synthesise estimates of life expectancy and years of potential life lost (YPLL) in people with bipolar disorder.

We searched Embase, Medline, PsycINFO and Web of Science databases up to 31 March 2021. We generated pooled life expectancy using random-effects models, and derived YPLL summary estimate by calculating averaged values weighted by sample size of individual studies. Subgroup analyses were conducted for gender, geographical region, study period, a given age (set-age) for lifespan estimation and causes of death. The study was registered with PROSPERO (CRD42021241705).

Eleven and 13 studies were included in the review for life expectancy (n = 96 601) and YPLL (n = 128 989), respectively. Pooled life expectancy was 66.88 years (95% CI 64.47–69.28; I2 = 99.9%, P < 0.001), was higher in women than men (70.51 (95% CI 68.61–72.41) v. 64.59 (95% CI 61.16–68.03); z = 2.00, P = 0.003) and was lowest in Africa. Weighted average YPLL was 12.89 years (95% CI 12.72–13.07), and was greatest in Africa. More YPLL was observed when lifespan was estimated at birth than at other set-age. YPLLs attributable to natural and unnatural deaths were 5.94 years (95% CI 5.81–6.07) and 5.69 years (95% CI 5.59–5.79), respectively.

Bipolar disorder is associated with substantially shortened life expectancy. Implementation of multilevel, targeted interventions is urgently needed to reduce this mortality gap.

About 30% of patients drop out of cognitive–behavioural therapy (CBT), which has implications for psychiatric and psychological treatment. Findings concerning drop out remain heterogeneous.

This paper aims to compare different machine-learning algorithms using nested cross-validation, evaluate their benefit in naturalistic settings, and identify the best model as well as the most important variables.

The data-set consisted of 2543 out-patients treated with CBT. Assessment took place before session one. Twenty-one algorithms and ensembles were compared. Two parameters (Brier score, area under the curve (AUC)) were used for evaluation.

The best model was an ensemble that used Random Forest and nearest-neighbour modelling. During the training process, it was significantly better than generalised linear modelling (GLM) (Brier score: d = –2.93, 95% CI (−3.95, −1.90)); AUC: d = 0.59, 95% CI (0.11 to 1.06)). In the holdout sample, the ensemble was able to correctly identify 63.4% of cases of patients, whereas the GLM only identified 46.2% correctly. The most important predictors were lower education, lower scores on the Personality Style and Disorder Inventory (PSSI) compulsive scale, younger age, higher scores on the PSSI negativistic and PSSI antisocial scale as well as on the Brief Symptom Inventory (BSI) additional scale (mean of the four additional items) and BSI overall scale.

Machine learning improves drop-out predictions. However, not all algorithms are suited to naturalistic data-sets and binary events. Tree-based and boosted algorithms including a variable selection process seem well-suited, whereas more advanced algorithms such as neural networks do not.

Autism and autistic traits are risk factors for suicidal behaviour.

To explore the prevalence of autism (diagnosed and undiagnosed) in those who died by suicide, and identify risk factors for suicide in this group.

Stage 1: 372 coroners’ inquest records, covering the period 1 January 2014 to 31 December 2017 from two regions of England, were analysed for evidence that the person who died had diagnosed autism or undiagnosed possible autism (elevated autistic traits), and identified risk markers. Stage 2: 29 follow-up interviews with the next of kin of those who died gathered further evidence of autism and autistic traits using validated autism screening and diagnostic tools.

Stage 1: evidence of autism (10.8%) was significantly higher in those who died by suicide than the 1.1% prevalence expected in the UK general alive population (odds ratio (OR) = 11.08, 95% CI 3.92–31.31). Stage 2: 5 (17.2%) of the follow-up sample had evidence of autism identified from the coroners’ records in stage 1. We identified evidence of undiagnosed possible autism in an additional 7 (24.1%) individuals, giving a total of 12 (41.4%); significantly higher than expected in the general alive population (1.1%) (OR = 19.76, 95% CI 2.36–165.84). Characteristics of those who died were largely similar regardless of evidence of autism, with groups experiencing a comparably high number of multiple risk markers before they died.

Elevated autistic traits are significantly over-represented in those who die by suicide.

Autism spectrum disorder (ASD) is a highly heterogeneous disorder that affects nearly 1 in 189 females and 1 in 42 males. However, the neurobiological basis of gender differences in ASD is poorly understood, as most studies have neglected females and used methods ill-suited to capture such differences.

To identify robust functional brain organisation markers that distinguish between females and males with ASD and predict symptom severity.

We leveraged multiple neuroimaging cohorts (ASD n = 773) and developed a novel spatiotemporal deep neural network (stDNN), which uses spatiotemporal convolution on functional magnetic resonance imaging data to distinguish between groups.

stDNN achieved consistently high classification accuracy in distinguishing between females and males with ASD. Notably, stDNN trained to distinguish between females and males with ASD could not distinguish between neurotypical females and males, suggesting that there are gender differences in the functional brain organisation in ASD that differ from normative gender differences. Brain features associated with motor, language and visuospatial attentional systems reliably distinguished between females and males with ASD. Crucially, these results were observed in a large multisite cohort and replicated in a fully independent cohort. Furthermore, brain features associated with the motor network's primary motor cortex node predicted the severity of restricted/repetitive behaviours in females but not in males with ASD.

Our replicable findings reveal that the brains of females and males with ASD are functionally organised differently, contributing to their clinical symptoms in distinct ways. They inform the development of gender-specific diagnoses and treatment strategies for ASD, and ultimately advance precision psychiatry.

Despite efforts to predict suicide risk in children, the ability to reliably identify who will engage in suicide thoughts or behaviours has remained unsuccessful.

We apply a novel machine-learning approach and examine whether children with suicide thoughts or behaviours could be differentiated from children without suicide thoughts or behaviours based on a combination of traditional (sociodemographic, physical health, social–environmental, clinical psychiatric) risk factors, but also more novel risk factors (cognitive, neuroimaging and genetic characteristics).

The study included 5885 unrelated children (50% female, 67% White, 9–11 years of age) from the Adolescent Brain Cognitive Development (ABCD) study. We performed penalised logistic regression analysis to distinguish between: (a) children with current or past suicide thoughts or behaviours; (b) children with a mental illness but no suicide thoughts or behaviours (clinical controls); and (c) healthy control children (no suicide thoughts or behaviours and no history of mental illness). The model was subsequently validated with data from seven independent sites involved in the ABCD study (n = 1712).

Our results showed that we were able to distinguish the suicide thoughts or behaviours group from healthy controls (area under the receiver operating characteristics curve: 0.80 child-report, 0.81 for parent-report) and clinical controls (0.71 child-report and 0.76–0.77 parent-report). However, we could not distinguish children with suicidal ideation from those who attempted suicide (AUROC: 0.55–0.58 child-report; 0.49–0.53 parent-report). The factors that differentiated the suicide thoughts or behaviours group from the clinical control group included family conflict, prodromal psychosis symptoms, impulsivity, depression severity and history of mental health treatment.

This work highlights that mostly clinical psychiatric factors were able to distinguish children with suicide thoughts or behaviours from children without suicide thoughts or behaviours. Future research is needed to determine if these variables prospectively predict subsequent suicidal behaviour.

The potential for early interventions to reduce the later prevalence of common mental disorders (CMD) first experienced in adolescence is unclear.

To examine the course of CMD and evaluate the extent to which the prevalence of CMD could be reduced by preventing adolescent CMD, or by intervening to change four young adult processes, between the ages of 20 and 29 years, that could be mediating the link between adolescent and adult disorder.

This was a prospective cohort study of 1923 Australian participants assessed repeatedly from adolescence (wave 1, mean age 14 years) to adulthood (wave 10, mean age 35 years). Causal mediation analysis was undertaken to evaluate the extent to which the prevalence of CMD at age 35 years in those with adolescent CMD could be reduced by either preventing adolescent CMD, or by intervening on four young adult mediating processes: the occurrence of young adult CMD, frequent cannabis use, parenting a child by age 24 years, and engagement in higher education and employment.

At age 35, 19.2% of participants reported CMD; a quarter of these participants experienced CMD during both adolescence and young adulthood. In total, 49% of those with CMD during both adolescence and young adulthood went on to report CMD at age 35 years. Preventing adolescent CMD reduced the population prevalence at age 35 years by 3.9%. Intervening on all four young adult processes among those with adolescent CMD, reduced this prevalence by 1.6%.

In this Australian cohort, a large proportion of adolescent CMD resolved by adulthood, and by age 35 years, the largest proportion of CMD emerged among individuals without prior CMD. Time-limited, early intervention in those with earlier adolescent disorder is unlikely to substantially reduce the prevalence of CMD in midlife.

Retrospective self-reports of childhood trauma are associated with a greater risk of psychopathology in adulthood than prospective measures of trauma. Heritable reporter characteristics are anticipated to account for part of this association, whereby genetic predisposition to certain traits influences both the likelihood of self-reporting trauma and of developing psychopathology. However, previous research has not considered how gene–environment correlation influences these associations.

To investigate reporter characteristics associated with retrospective self-reports of childhood trauma and whether these associations are accounted for by gene–environment correlation.

In 3963 unrelated individuals from the Twins Early Development Study, we tested whether polygenic scores for 21 psychiatric, cognitive, anthropometric and personality traits were associated with retrospectively self-reported childhood emotional and physical abuse. To assess the presence of gene–environment correlation, we investigated whether these associations remained after controlling for composite scores of environmental adversity across development.

Retrospectively self-reported childhood trauma was associated with polygenic scores for autism spectrum disorder (ASD), body mass index (BMI), post-traumatic stress disorder (PTSD) and risky behaviours. When composite scores of environmental adversity were controlled for, only associations with the polygenic scores for ASD and PTSD remained significant.

Genetic predisposition to ASD and PTSD may increase liability to experiencing or interpreting events as traumatic. Associations between genetic predisposition for risky behaviour and BMI with self-reported childhood trauma may reflect gene–environment correlation. Studies of the association between retrospectively self-reported childhood trauma and later-life outcomes should consider that genetically influenced reporter characteristics may confound associations, both directly and through gene–environment correlation.

A recently updated Cochrane review supports the efficacy of psychotherapy for borderline personality disorder (BPD).

To evaluate the effects of standalone and add-on psychotherapeutic treatments more concisely.

We applied the same methods as the 2020 Cochrane review, but focused on adult samples and comparisons of active treatments and unspecific control conditions. Standalone treatments (i.e. necessarily including individual psychotherapy as either the sole or one of several treatment components) and add-on interventions (i.e. complementing any ongoing individual BPD treatment) were analysed separately. Primary outcomes were BPD severity, self-harm, suicide-related outcomes and psychosocial functioning. Secondary outcomes were remaining BPD diagnostic criteria, depression and attrition.

Thirty-one randomised controlled trials totalling 1870 participants were identified. Among standalone treatments, statistically significant effects of low overall certainty were observed for dialectical behaviour therapy (self-harm: standardised mean difference (SMD) −0.54, P = 0.006; psychosocial functioning: SMD −0.51, P = 0.01) and mentalisation-based treatment (self-harm: risk ratio 0.51, P < 0.0007; suicide-related outcomes: risk ratio 0.10, P < 0.0001). For adjunctive interventions, moderate-quality evidence of beneficial effects was observed for DBT skills training (BPD severity: SMD −0.66, P = 0.002; psychosocial functioning: SMD −0.45, P = 0.002), and statistically significant low-certainty evidence was observed for the emotion regulation group (BPD severity: mean difference −8.49, P < 0.00001), manual-assisted cognitive therapy (self-harm: mean difference −3.03, P = 0.03; suicide-related outcomes: SMD −0.96, P = 0.005) and the systems training for emotional predictability and problem-solving (BPD severity: SMD −0.48, P = 0.002).

There is reasonable evidence to conclude that psychotherapeutic interventions are helpful for individuals with BPD. Replication studies are needed to enhance the certainty of findings.

Relapses in major depression are frequent and are associated with a high burden of disease. Although short-term studies suggest a normalisation of depression-associated brain functional alterations directly after treatment, long-term investigations are sparse.

To examine brain function during negative emotion processing in association with course of illness over a 2-year span.

In this prospective case–control study, 72 in-patients with current depression and 42 healthy controls were investigated during a negative emotional face processing paradigm, at baseline and after 2 years. According to their course of illness during the study interval, patients were divided into subgroups (n = 25 no-relapse, n = 47 relapse). The differential changes in brain activity were investigated by a group × time analysis of covariance for the amygdala, hippocampus, insula and at whole-brain level.

A significant relapse × time interaction emerged within the amygdala (PTFCE-FWE = 0.011), insula (PTFCE-FWE = 0.001) and at the whole-brain level mainly in the temporal and prefrontal cortex (PTFCE-FWE = 0.027), resulting from activity increases within the no-relapse group, whereas in the relapse group, activity decreased during the study interval. At baseline, the no-relapse group showed amygdala, hippocampus and insula hypoactivity compared with healthy controls and the relapse group.

This study reveals course of illness-associated activity changes in emotion processing areas. Patients in full remission show a normalisation of their baseline hypo-responsiveness to the activation level of healthy controls after 2 years. Brain function during emotion processing could further serve as a potential predictive marker for future relapse.

Although recognised as the most effective antipsychotic for treatment-resistant schizophrenia, clozapine remains underused. One reason is the widespread concern about non-adherence to clozapine because of poor adherence before initiating clozapine.

To determine if prior poor out-patient adherence to treatmentbefore initiating clozapine predisposes to poor out-patient adherence to clozapine or to any antipsychotics (including clozapine) after its initiation.

This cohort study included 3228 patients with schizophrenia living in Quebec (Canada) initiating (with a 2-year clearance period) oral clozapine (index date) between 2009 and 2016. Using pharmacy data, out-patient adherence to treatment was measured by the medication possession ratio (MPR), over a 1-year period preceding and following the index date. Five groups of patients were formed based on their prior MPR level (independent variable). Two dependent variables were defined after clozapine initiation (good out-patient adherence to any antipsychotics and to clozapine only). Along with multiple logistic regressions, state sequence analysis was used as a visual representation of antipsychotic-use trajectories over time, before and after clozapine initiation.

Although prior poor adherence to antipsychotics was associated with poor adherence after clozapine initiation, the absolute risk of subsequent poor adherence remained low, regardless of previous adherence level. Most patients adhered to their treatment after initiating clozapine (>68% to clozapine and >84% to any antipsychotics).

Despite the fact that poor adherence prior to initiating clozapine is widely recognised by clinicians as a barrier for the prescription of clozapine, the current study supports the initiation of clozapine in all eligible patients.

Evidence on risk factors for postpartum depression (PPD) are fragmented and inconsistent.

To assess the strength and credibility of evidence on risk factors of PPD, ranking them based on the umbrella review methodology.

Databases were searched until 1 December 2020, for systematic reviews and meta-analyses of observational studies. Two reviewers assessed quality, credibility of associations according to umbrella review criteria (URC) and evidence certainty according to Grading of Recommendations-Assessment-Development-Evaluations criteria.

Including 185 observational studies (n = 3 272 093) from 11 systematic reviews, the association between premenstrual syndrome and PPD was the strongest (highly suggestive: odds ratio 2.20, 95%CI 1.81–2.68), followed by violent experiences (highly suggestive: odds ratio (OR) = 2.07, 95%CI 1.70–2.50) and unintended pregnancy (highly suggestive: OR=1.53, 95%CI 1.35–1.75). Following URC, the association was suggestive for Caesarean section (OR = 1.29, 95%CI 1.17–1.43), gestational diabetes (OR = 1.60, 95%CI 1.25–2.06) and 5-HTTPRL polymorphism (OR = 0.70, 95%CI 0.57–0.86); and weak for preterm delivery (OR = 2.12, 95%CI 1.43–3.14), anaemia during pregnancy (OR = 1.47, 95%CI 1.17–1.84), vitamin D deficiency (OR = 3.67, 95%CI 1.72–7.85) and postpartum anaemia (OR = 1.75, 95%CI 1.18–2.60). No significant associations were found for medically assisted conception and intra-labour epidural analgesia. No association was rated as ‘convincing evidence’. According to GRADE, the certainty of the evidence was low for Caesarean section, preterm delivery, 5-HTTLPR polymorphism and anaemia during pregnancy, and ‘very low’ for remaining factors.

The most robust risk factors of PDD were premenstrual syndrome, violent experiences and unintended pregnancy. These results should be integrated in clinical algorithms to assess the risk of PPD.

Young people with social disability and severe and complex mental health problems have poor outcomes, frequently struggling with treatment access and engagement. Outcomes may be improved by enhancing care and providing targeted psychological or psychosocial intervention.

We aimed to test the hypothesis that adding social recovery therapy (SRT) to enhanced standard care (ESC) would improve social recovery compared with ESC alone.

A pragmatic, assessor-masked, randomised controlled trial (PRODIGY: ISRCTN47998710) was conducted in three UK centres. Participants (n = 270) were aged 16–25 years, with persistent social disability, defined as under 30 hours of structured activity per week, social impairment for at least 6 months and severe and complex mental health problems. Participants were randomised to ESC alone or SRT plus ESC. SRT was an individual psychosocial therapy delivered over 9 months. The primary outcome was time spent in structured activity 15 months post-randomisation.

We randomised 132 participants to SRT plus ESC and 138 to ESC alone. Mean weekly hours in structured activity at 15 months increased by 11.1 h for SRT plus ESC (mean 22.4, s.d. = 21.4) and 16.6 h for ESC alone (mean 27.7, s.d. = 26.5). There was no significant difference between arms; treatment effect was −4.44 (95% CI −10.19 to 1.31, P = 0.13). Missingness was consistently greater in the ESC alone arm.

We found no evidence for the superiority of SRT as an adjunct to ESC. Participants in both arms made large, clinically significant improvements on all outcomes. When providing comprehensive evidence-based standard care, there are no additional gains by providing specialised SRT. Optimising standard care to ensure targeted delivery of existing interventions may further improve outcomes.

Victims of violence, accidents and threats are at risk for mental health problems. Lower coping self-efficacy and social support levels increase this risk. Although highly relevant, it is unknown if the coronavirus disease 2019 (COVID-19) pandemic amplifies these risks.

To examine if the prevalence, incidence and/or mean scores for post-traumatic stress disorder (PTSD), anxiety and depression symptoms, general mental health problems, coping self-efficacy, lack of emotional support and social acknowledgement are higher among adults victimised in the year after the COVID-19 outbreak compared with adults victimised in a similar period before the outbreak. Also, to compare symptoms, problems and support within non-victims during the same period.

Data was extracted from four surveys of the VICTIMS study (March 2018, 2019, 2020, 2021), based on a random sample of the Dutch population. Multivariate logistic regression analyses and mixed-effects models were used to examine differences between the two victim groups (2019: n = 421, 2021: n = 319) and non-victims (n = 3245).

Adults victimised after the outbreak more often had PTSD, anxiety and depression symptoms, general mental health problems and lower coping self-efficacy than those victimised before. They did not differ in lack of support and acknowledgement. Both victim groups differed from non-victims, where mental health problems and lack of support levels were much lower and almost stable.

The COVID-19 pandemic had a negative impact on the mental health and coping self-efficacy levels of victims, whereas mental health problems among non-victims remained virtually stable. Mental healthcare workers, general practitioners and victim services should take this impact into account.

People presenting with first-episode psychosis (FEP) have heterogenous outcomes. More than 40% fail to achieve symptomatic remission. Accurate prediction of individual outcome in FEP could facilitate early intervention to change the clinical trajectory and improve prognosis.

We aim to systematically review evidence for prediction models developed for predicting poor outcome in FEP.

A protocol for this study was published on the International Prospective Register of Systematic Reviews, registration number CRD42019156897. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidance, we systematically searched six databases from inception to 28 January 2021. We used the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and the Prediction Model Risk of Bias Assessment Tool to extract and appraise the outcome prediction models. We considered study characteristics, methodology and model performance.

Thirteen studies reporting 31 prediction models across a range of clinical outcomes met criteria for inclusion. Eleven studies used logistic regression with clinical and sociodemographic predictor variables. Just two studies were found to be at low risk of bias. Methodological limitations identified included a lack of appropriate validation, small sample sizes, poor handling of missing data and inadequate reporting of calibration and discrimination measures. To date, no model has been applied to clinical practice.

Future prediction studies in psychosis should prioritise methodological rigour and external validation in larger samples. The potential for prediction modelling in FEP is yet to be realised.

There are concerns that keyworkers may be at a greater risk for psychological distress than non-keyworkers during the COVID-19 pandemic. However, little research has included keyworkers outside of the healthcare sector or has disaggregated keyworkers into different subgroups.

To examine longitudinal changes in mental health over 12 months during the COVID-19 pandemic comparing four different groups of keyworkers with non-keyworkers.

Longitudinal data were from 21 874 adults living in England (21 March 2020 to 22 February 2021). Latent growth modelling was utilised to compare growth trajectories of depressive and anxiety symptoms in non-keyworkers and four types of keyworkers: (a) health and social care workers, (b) teachers and child care workers, (c) public service workers, and (d) essential services keyworkers (such as food chain or utility workers).

When accounting for both time-invariant and time-varying covariates, keyworkers in the essential services category had consistently higher levels of depressive and anxiety symptoms than non-keyworkers across the whole of the study period. There was little difference in the mental health trajectories between health/social care, teachers/child care and public service worker categories and non-keyworkers.

Our findings suggest that the risk for poorer mental health during the COVID-19 pandemic varies within the broad category of keyworkers generally, and that those working in utility, food chain and transport roles are especially at risk. Future research should focus on identifying which aspects of working conditions may be contributing to occupational stress in these groups.