Cognitive impairment is a core feature of schizophrenia, associated with poor functional outcomes. The course of cognitive function in the years following illness onset has remained a subject of debate, with a previous analysis finding no worsening, providing support for the neurodevelopmental model of schizophrenia. Since then, many more studies have reported on longitudinal cognitive performance in early psychosis, with some indicating deterioration, which does not align with this view.

This study aims to quantitatively review the literature on the longitudinal trajectory of cognitive deficits in the years following psychosis onset, in comparison with healthy controls. It is the first to also synthesise longitudinal data on social cognition.

Electronic databases (‘PubMed’, ‘PsycInfo’ and ‘Scopus’) were searched (to end September 2021). Meta-analyses of 25 longitudinal studies of cognition in early psychosis were conducted (1480 patients, 789 health controls). Unlike previous analyses, randomised controlled trials and those with multiple cognitive testing periods within the first year were excluded to minimise bias (PROSPERO, ID: CRD42021241525).

Small improvements were observed for global cognition (g = 0.25, 95% CI 0.17–0.33) and individual cognitive domains, but these were comparable with healthy controls and likely an artefact of practice effects.

There is no evidence of continued cognitive decline or improvement in the early years following psychosis onset, with a need for more studies over longer follow-up periods. Practice effects highlight the importance of including control samples in longitudinal and intervention studies. Further data are needed to evaluate the course of social cognition subdomains.

Mood disorders are characterised by pronounced symptom heterogeneity, which presents a substantial challenge both to clinical practice and research. Identification of subgroups of individuals with homogeneous symptom profiles that cut across current diagnostic categories could provide insights in to the transdiagnostic relevance of individual symptoms, which current categorical diagnostic systems cannot impart.

To identify groups of people with homogeneous clinical characteristics, using symptoms of manic and/or irritable mood, and explore differences between groups in diagnoses, functional outcomes and genetic liability.

We used latent class analysis on eight binary self-reported symptoms of manic and irritable mood in the UK Biobank and PROTECT studies, to investigate how individuals formed latent subgroups. We tested associations between the latent classes and diagnoses of psychiatric disorders, sociodemographic characteristics and polygenic risk scores.

Five latent classes were derived in UK Biobank (N = 42 183) and were replicated in the independent PROTECT cohort (N = 4445), including ‘minimally affected’, ‘inactive restless’, active restless’, ‘focused creative’ and ‘extensively affected’ individuals. These classes differed in disorder risk, polygenic risk score and functional outcomes. One class that experienced disruptive episodes of mostly irritable mood largely comprised cases of depression/anxiety, and a class of individuals with increased confidence/creativity reported comparatively lower disruptiveness and functional impairment.

Findings suggest that data-driven investigations of psychopathological symptoms that include sub-diagnostic threshold conditions can complement research of clinical diagnoses. Improved classification systems of psychopathology could investigate a weighted approach to symptoms, toward a more dimensional classification of mood disorders.

Previous analyses of grey and white matter volumes have reported that schizophrenia is associated with structural changes. Deep learning is a data-driven approach that can capture highly compact hierarchical non-linear relationships among high-dimensional features, and therefore can facilitate the development of clinical tools for making a more accurate and earlier diagnosis of schizophrenia.

To identify consistent grey matter abnormalities in patients with schizophrenia, 662 people with schizophrenia and 613 healthy controls were recruited from eight centres across China, and the data from these independent sites were used to validate deep-learning classifiers.

We used a prospective image-based meta-analysis of whole-brain voxel-based morphometry. We also automatically differentiated patients with schizophrenia from healthy controls using combined grey matter, white matter and cerebrospinal fluid volumetric features, incorporated a deep neural network approach on an individual basis, and tested the generalisability of the classification models using independent validation sites.

We found that statistically reliable schizophrenia-related grey matter abnormalities primarily occurred in regions that included the superior temporal gyrus extending to the temporal pole, insular cortex, orbital and middle frontal cortices, middle cingulum and thalamus. Evaluated using leave-one-site-out cross-validation, the performance of the classification of schizophrenia achieved by our findings from eight independent research sites were: accuracy, 77.19–85.74%; sensitivity, 75.31–89.29% and area under the receiver operating characteristic curve, 0.797–0.909.

These results suggest that, by using deep-learning techniques, multidimensional neuroanatomical changes in schizophrenia are capable of robustly discriminating patients with schizophrenia from healthy controls, findings which could facilitate clinical diagnosis and treatment in schizophrenia.

Clozapine is the only drug licensed for treatment-resistant schizophrenia (TRS) but the real-world clinical and cost-effectiveness of community initiation of clozapine is unclear.

The aim was to assess the feasibility and cost-effectiveness of community initiation of clozapine.

This was a naturalistic study of community patients recommended for clozapine treatment.

Of 158 patients recommended for clozapine treatment, 88 (56%) patients agreed to clozapine initiation and, of these, 58 (66%) were successfully established on clozapine. The success rate for community initiation was 65.4%; which was not significantly different from that for in-patient initiation (58.82%, χ2(1,88) = 0.47, P = 0.49). Following clozapine initiation, there was a significant reduction in median out-patient visits over 1 year (from 24.00 (interquartile range (IQR) = 14.00–41.00) to 13.00 visits (IQR = 5.00–24.00), P < 0.001), and 2 years (from 47.50 visits (IQR = 24.75–71.00) to 22.00 (IQR = 11.00–42.00), P < 0.001), and a 74.71% decrease in psychiatric hospital bed days (z = −2.50, P = 0.01). Service-use costs decreased (1 year: –£963/patient (P < 0.001); 2 years: –£1598.10/patient (P < 0.001). Subanalyses for community-only initiation also showed significant cost reductions (1 year: –£827.40/patient (P < 0.001); 2 year: –£1668.50/patient (P < 0.001) relative to costs prior to starting clozapine. Relative to before initiation, symptom severity was improved in patients taking clozapine at discharge (median Positive and Negative Syndrome Scale total score: initial visit: 80 (IQR = 71.00–104.00); discharge visit 50.5 (IQR = 44.75–75.00), P < 0.001) and at 2 year follow-up (Health of Nation Outcome Scales total score median initial visit: 13.00 (IQR = 9.00–15.00); 2 year follow-up: 8.00 (IQR = 3.00–13.00), P = 0.023).

These findings indicate that community initiation of clozapine is feasible and is associated with significant reductions in costs, service use and symptom severity.

The COVID-19 pandemic and associated lockdowns were predicted to have a major impact on mental health, however current studies have produced contradictory findings with limited longitudinal data.

Nine years of linked, individual-level administrative data were used to examine changes in psychotropic medication uptake before and during the pandemic.

Medication data from a population-wide prescribing database were linked to demographic and socioeconomic indicators from healthcare registration records (n = 1 801 860). Monthly prescription uptake was split (pre-restrictions: January 2012 to February 2020 and during restrictions: March to October 2020). Auto regressive integrated moving average (ARIMA) models were trained in R taking into consideration trends and seasonal effects. Forecast (‘expected’) monthly values were compared with ‘actual’ values, stratified by demographic factors.

Over the study period 38.5% of the study population were in receipt of ≥1 psychotropic medication. Uptake of these medications have been following a strong upward trend since January 2012. In March 2020 uptake of all medications increased beyond expected values, returning to expected trends from May 2020 for antidepressants, anxiolytics and antipsychotics. In the 8 months during restrictions uptake of hypnotic medication was 12% higher than expected among those <18 years, and anxiolytic medication higher than expected in those >65 years.

Results suggest an initial ‘stockpiling’ of medications in March 2020 before trends mostly returned to expected levels. The anticipated tsunami of mental ill health is not yet manifest in psychotropic medication uptake. There are indications of increased anxiety and sleep difficulties in some subgroups, although these conditions may resolve as we emerge from the pandemic without need for psychiatric intervention.

Research on the effectiveness of pharmacotherapies for schizophrenia and comorbid substance use disorder (SUD) is very sparse, and non-existent on the prevention of the development of SUDs in patients with schizophrenia.

To compare the real-world effectiveness of antipsychotics in schizophrenia in decreasing risk of developing an initial SUD, and psychiatric hospital admission and SUD-related hospital admission among patients with an SUD.

Two independent national cohorts including all persons diagnosed with schizophrenia (N = 45 476) were followed up for 22 (Finland: 1996–2017) and 11 (Sweden: 2006–2016) years. Risk of developing an SUD was calculated with between-individual models, and risks of psychiatric and SUD-related hospital admission were calculated with within-individual models, using Cox regression and adjusted hazard ratios (aHRs) for using versus not using certain antipsychotics.

For patients with schizophrenia without an SUD, clozapine use (Finland: aHR 0.20, 95% CI 0.16–0.24, P < 0.001; Sweden: aHR 0.35, 95% CI 0.24–0.50, P < 0.001) was associated with lowest risk of developing an initial SUD in both countries. Antipsychotic polytherapy was associated with second lowest risk (aHR 0.54, 95% CI 0.44–0.66) in Sweden, and third lowest risk (aHR 0.47, 95% CI 0.42–0.53) in Finland. Risk of relapse (psychiatric hospital admission and SUD-related hospital admission) were lowest for clozapine, antipsychotic polytherapy and long-acting injectables in both countries. Results were consistent across both countries.

Clozapine and antipsychotic polytherapy are most strongly associated with reduced risk of developing SUDs among patients with schizophrenia, and with lower relapse rates among patients with both diagnoses.