Early intervention in psychosis (EIP) services improve outcomes for young people, but approximately 30% disengage.

To test whether a new motivational engagement intervention would prolong engagement and whether it was cost-effective.

We conducted a multicentre, single-blind, parallel-group, cluster randomised controlled trial involving 20 EIP teams at five UK National Health Service (NHS) sites. Teams were randomised using permuted blocks stratified by NHS trust. Participants were all young people (aged 14–35 years) presenting with a first episode of psychosis between May 2019 and July 2020 (N = 1027). We compared the novel Early Youth Engagement (EYE-2) intervention plus standardised EIP (sEIP) with sEIP alone. The primary outcome was time to disengagement over 12–26 months. Economic outcomes were mental health costs, societal costs and socio-occupational outcomes over 12 months. Assessors were masked to treatment allocation for primary disengagement and cost-effectiveness outcomes. Analysis followed intention-to-treat principles. The trial was registered at ISRCTN51629746.

Disengagement was low at 15.9% overall in standardised stand-alone services. The adjusted hazard ratio for EYE-2 + sEIP (n = 652) versus sEIP alone (n = 375) was 1.07 (95% CI 0.76–1.49; P = 0.713). The health economic evaluation indicated lower mental healthcare costs linked to reductions in unplanned mental healthcare with no compromise of clinical outcomes, as well as some evidence for lower societal costs and more days in education, training, employment and stable accommodation in the EYE-2 group.

We found no evidence that EYE-2 increased time to disengagement, but there was some evidence for its cost-effectiveness. This is the largest study to date reporting positive engagement, health and cost outcomes in a total EIP population sample. Limitations included high loss to follow-up for secondary outcomes and low completion of societal and socio-occupational data. COVID-19 affected fidelity and implementation. Future engagement research should target engagement to those in greatest need, including in-patients and those with socio-occupational goals.

Recent developments in computational psychiatry have led to the hypothesis that mood represents an expectation (prior belief) on the likely interoceptive consequences of action (i.e. emotion). This stems from ideas about how the brain navigates its external world by minimising an upper bound on surprisal (free energy) of sensory information and echoes developments in other perceptual domains.

In this paper we aim to present a simple partial observable Markov decision process that models mood updating in response to stressful or non-stressful environmental fluctuations while seeking to minimise surprisal in relation to prior beliefs about the likely interoceptive signals experienced with specific actions (attenuating or amplifying stress and pleasure signals).

We examine how, by altering these prior beliefs we can model mood updating in depression, mania and anxiety.

We discuss how these models provide a computational account of mood and its related psychopathology and relate it to previous research in reward processing.

Models such as this can provide hypotheses for experimental work and also open up the potential modelling of predicted disease trajectories in individual patients.

Diagnoses of personality disorder are prevalent among people using community secondary mental health services. Identifying cost-effective community-based interventions is important when working with finite resources.

To assess the cost-effectiveness of primary or secondary care community-based interventions for people with complex emotional needs who meet criteria for a diagnosis of personality disorder to inform healthcare policy-making.

Systematic review (PROSPERO: CRD42020134068) of databases. We included economic evaluations of interventions for adults with complex emotional needs associated with a diagnosis of personality disorder in community mental health settings published before 18 September 2019. Study quality was assessed using the CHEERS statement.

Eighteen studies were included. The studies mainly evaluated psychotherapeutic interventions. Studies were also identified that evaluated altering the setting in which care was delivered and joint crisis plans. No strong economic evidence to support a single intervention or model of community-based care was identified.

Robust economic evidence to support a single intervention or model of community-based care for people with complex emotional needs is lacking. The strongest evidence was for dialectical behaviour therapy, with all three identified studies indicating that it is likely to be cost-effective in community settings compared with treatment as usual. More robust evidence is required on the cost-effectiveness of community-based interventions on which decision makers can confidently base guidelines or allocate resources. The evidence should be based on consistent measures of costs and outcomes with sufficient sample sizes to demonstrate impacts on these.

Despite established clinical associations among major depression (MD), alcohol dependence (AD), and alcohol consumption (AC), the nature of the causal relationship between them is not completely understood. We leveraged genome-wide data from the Psychiatric Genomics Consortium (PGC) and UK Biobank to test for the presence of shared genetic mechanisms and causal relationships among MD, AD, and AC.

Linkage disequilibrium score regression and Mendelian randomization (MR) were performed using genome-wide data from the PGC (MD: 135 458 cases and 344 901 controls; AD: 10 206 cases and 28 480 controls) and UK Biobank (AC-frequency: 438 308 individuals; AC-quantity: 307 098 individuals).

Positive genetic correlation was observed between MD and AD (rgMD−AD = + 0.47, P = 6.6 × 10−10). AC-quantity showed positive genetic correlation with both AD (rgAD−AC quantity = + 0.75, P = 1.8 × 10−14) and MD (rgMD−AC quantity = + 0.14, P = 2.9 × 10−7), while there was negative correlation of AC-frequency with MD (rgMD−AC frequency = −0.17, P = 1.5 × 10−10) and a non-significant result with AD. MR analyses confirmed the presence of pleiotropy among these four traits. However, the MD-AD results reflect a mediated-pleiotropy mechanism (i.e. causal relationship) with an effect of MD on AD (beta = 0.28, P = 1.29 × 10−6). There was no evidence for reverse causation.

This study supports a causal role for genetic liability of MD on AD based on genetic datasets including thousands of individuals. Understanding mechanisms underlying MD-AD comorbidity addresses important public health concerns and has the potential to facilitate prevention and intervention efforts.