Cognitive–behavioural therapy (CBT) is frequently used as an umbrella term to include a variety of psychological interventions. It remains unclear whether more complex CBT contributes to greater depression reduction.

To (a) compare the effectiveness of core, complex and ultra-complex CBT against other psychological intervention, medication, treatment-as-usual and no treatment in reducing depression at post-treatment and in the long term and (b) explore important factors that could moderate the effectiveness of these interventions.

MEDLINE, PsycInfo, Embase, Web of Science and the Cochrane Register of Controlled Trials were searched to November 2021. Only randomised controlled trials were eligible for the subsequent network meta-analysis.

We included 107 studies based on 15 248 participants. Core (s.m.d. = −1.14, 95% credible interval (CrI) −1.72 to −0.55 [m.d. = −8.44]), complex (s.m.d. = −1.24, 95% CrI −1.85 to −0.64 [m.d. = −9.18]) and ultra-complex CBT (s.m.d. = −1.45, 95% CrI −1.88 to −1.02 [m.d. = −10.73]) were all significant in reducing depression up to 6 months from treatment onset. The significant benefits of the ultra-complex (s.m.d. = −1.09, 95% CrI −1.61 to −0.56 [m.d. = −8.07]) and complex CBT (s.m.d. = −0.73, 95% CrI −1.36 to −0.11 [m.d. = −5.40]) extended beyond 6 months. Ultra-complex CBT was most effective in individuals presenting comorbid mental health problems and when delivered by non-mental health specialists. Ultra-complex and complex CBT were more effective for people younger than 59 years.

For people without comorbid conditions healthcare and policy organisations should invest in core CBT. For people <59 years of age with comorbid conditions investments should focus on ultra-complex and complex CBT delivered without the help of mental health professionals.

Coronavirus disease 2019 (COVID-19) has disproportionately affected people with mental health conditions.

We investigated the association between receiving psychotropic drugs, as an indicator of mental health conditions, and COVID-19 vaccine uptake.

We conducted a cross-sectional analysis of a prospective cohort of the Northern Ireland adult population using national linked primary care registration, vaccination, secondary care and pharmacy dispensing data. Univariable and multivariable logistic regression analyses investigated the association between anxiolytic, antidepressant, antipsychotic, and hypnotic use and COVID-19 vaccination status, accounting for age, gender, deprivation and comorbidities. Receiving any COVID-19 vaccine was the primary outcome.

There were 1 433 814 individuals, of whom 1 166 917 received a COVID-19 vaccination. Psychotropic medications were dispensed to 267 049 people. In univariable analysis, people who received any psychotropic medication had greater odds of receiving COVID-19 vaccination: odds ratio (OR) = 1.42 (95% CI 1.41–1.44). However, after adjustment, psychotropic medication use was associated with reduced odds of vaccination (ORadj = 0.90, 95% CI 0.89–0.91). People who received anxiolytics (ORadj = 0.63, 95% CI 0.61–0.65), antipsychotics (ORadj = 0.75, 95% CI 0.73–0.78) and hypnotics (ORadj = 0.90, 95% CI 0.87–0.93) had reduced odds of being vaccinated. Antidepressant use was not associated with vaccination (ORadj = 1.02, 95% CI 1.00–1.03).

We found significantly lower odds of vaccination in people who were receiving treatment with anxiolytic and antipsychotic medications. There is an urgent need for evidence-based, tailored vaccine support for people with mental health conditions.

Perinatal self-harm is of concern but poorly understood.

To determine if women's risk of self-harm changes in pregnancy and the first postpartum year, and if risk varies by mental illness, age and birth outcome.

This was a retrospective cohort study of 2 666 088 women aged 15–45 years from the 1 January 1990 to 31 December 2017 linked to 1 102 040 pregnancies and their outcomes, utilising the Clinical Practice Research Datalink and Pregnancy Register. We identified self-harm events and mental illness (depression/anxiety/addiction/affective/non-affective psychosis/eating/personality disorders) from clinical records and grouped women's age into 5-year bands. They calculated the rate of self-harm during discrete non-perinatal, pregnant and postpartum periods. We used a gap-time, stratified Cox model to manage multiple self-harm events, and calculated the unadjusted and adjusted hazard ratios (adjHR) of self-harm associated with pregnancy and the postpartum compared with non-perinatal periods. Pre-planned interactions tested if risk varied by mental illness, age and birth outcome.

The analysis included 57 791 self-harm events and 14 712 319 person-years of follow-up. The risk of self-harm shrank in pregnancy (2.07 v. 4.01 events/1000 person-years, adjHR = 0.53, 95% CI 0.49–0.58) for all women except for 15- to 19-year-olds (adjHR = 0.95, 95% CI 0.84–1.07) and the risk reduced most for women with mental illness (adjHR = 0.40, 95% CI 0.36–0.44). Postpartum, self-harm risk peaked at 6–12 months (adjHR = 1.08, 95% CI 1.02–1.15), at-risk groups included young women and women with a pregnancy loss or termination.

Maternity and perinatal mental health services are valuable. Family planning services might have psychological benefit, particularly for young women.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

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.