Methamphetamine has been consistently associated with positive psychotic symptoms, but little is known about whether the reverse also occurs.

This study determined whether the relationship between methamphetamine use and positive psychotic symptoms is bidirectional over 12 months. The impact of lifetime psychotic disorders and methamphetamine dependence on these relationships was also examined.

A total of 201 regular (at least monthly) primary methamphetamine users were recruited from free needle and syringe programmes in three Australian cities. Data on the frequency of methamphetamine and other drug use (from Timeline Followback inteviews) and the severity of positive psychotic symptoms (using the Brief Psychiatric Rating Scale) in the past 2 weeks were collected in 12 contiguous monthly face-to-face interviews (mean of 9.14/11 (s.d. = 3.16) follow-ups completed). Diagnoses were derived using the Psychiatric Research Interview for DSM-IV Substance and Mental Disorders.

The mean age of participants was 31.71 years (s.d. = 8.19) and 39% (n = 77) were women. At baseline 55% (n = 110) were dependent on methamphetamine and 51% (n = 102) had a lifetime psychotic disorder. Cross-lagged dynamic panel models found a significant bidirectional relationship between psychotic symptoms and methamphetamine use (Comparative Fit Index (CFI) = 0.94, standardised root mean square residual (SRMR) = 0.05, root mean square error of approximation (RMSEA) = 0.05, 95% CI 0.04–0.06). The magnitude of the relationship in each direction was similar, and the presence of methamphetamine dependence or a lifetime psychotic disorder did not have an impact on results.

A dynamic, bidirectional relationship between methamphetamine and psychotic symptoms of similar magnitude in each direction was found over 1 year. This suggests integrated treatments that target methamphetamine, psychotic symptoms and their interrelationship may be of most benefit.

Clozapine, an antipsychotic with unique efficacy in treatment-resistant psychosis, is associated with increased susceptibility to infection, including pneumonia.

To investigate associations between clozapine treatment and increased risk of COVID-19 infection in patients with schizophrenia-spectrum disorders who are receiving antipsychotic medications in a geographically defined population in London, UK.

Using information from South London and Maudsley NHS Foundation Trust (SLAM) clinical records, via the Clinical Record Interactive Search system, we identified 6309 individuals who had an ICD-10 diagnosis of schizophrenia-spectrum disorders and were taking antipsychotics at the time of the COVID-19 pandemic onset in the UK. People who were on clozapine treatment were compared with those on any other antipsychotic treatment for risk of contracting COVID-19 between 1 March and 18 May 2020. We tested associations between clozapine treatment and COVID-19 infection, adjusting for gender, age, ethnicity, body mass index (BMI), smoking status and SLAM service use.

Of 6309 participants, 102 tested positive for COVID-19. Individuals who were on clozapine had increased risk of COVID-19 infection compared with those who were on other antipsychotic medication (unadjusted hazard ratio HR = 2.62, 95% CI 1.73–3.96), which was attenuated after adjusting for potential confounders, including clinical contact (adjusted HR = 1.76, 95% CI 1.14–2.72).

These findings provide support for the hypothesis that clozapine treatment is associated with an increased risk of COVID-19 infection. Further research will be needed in other samples to confirm this association. Potential clinical implications are discussed.

Schizophrenia patients have markedly elevated prevalence of diabetes compared with the general population. However, risk of mortality and diabetes-related complications among schizophrenia patients with co-occurring diabetes is understudied.

We investigated whether schizophrenia increased the risk of overall mortality, complications and post-complication mortality in people with diabetes.

This population-based, propensity-score matched (1:10) cohort study identified 6991 patients with incident diabetes and pre-existing schizophrenia and 68 682 patients with incident diabetes only between 2001 and 2016 in Hong Kong using a medical record database of public healthcare services. Association between schizophrenia and all-cause mortality was examined with a Cox proportional hazards model. Effect of schizophrenia on first-year complication occurrence following diabetes diagnosis and post-complication mortality rates were evaluated.

Schizophrenia was associated with increased all-cause mortality (adjusted hazards ratio [aHR] 1.11, 95% CI 1.05–1.18), particularly among men and older age groups. Schizophrenia patients with diabetes had higher metabolic complication rate (aHR 1.99, 95% CI 1.63–2.42), lower microvascular complication rate (aHR 0.75, 95% CI 0.65–0.86) and comparable macrovascular complication rate (aHR 0.93, 95% CI 0.85–1.03), relative to patients with diabetes only. Among patients with diabetes complications, schizophrenia was associated with elevated all-cause mortality after macrovascular (aHR 1.19, 95% CI 1.04–1.37) and microvascular (aHR 1.33, 95% CI 1.08–1.64) complications. Gender-stratified analyses revealed that a significant effect of schizophrenia on heightened post-complication mortality was observed in men only.

Schizophrenia patients with co-occurring diabetes are at increased risk of excess mortality, including post-complication mortality. Further research identifying effective interventions is warranted to optimise diabetes-related outcomes in this vulnerable population.

Mental health policy makers require evidence-based information to optimise effective care provision based on local need, but tools are unavailable.

To develop and validate a population-level prediction model for need for early intervention in psychosis (EIP) care for first-episode psychosis (FEP) in England up to 2025, based on epidemiological evidence and demographic projections.

We used Bayesian Poisson regression to model small-area-level variation in FEP incidence for people aged 16–64 years. We compared six candidate models, validated against observed National Health Service FEP data in 2017. Our best-fitting model predicted annual incidence case-loads for EIP services in England up to 2025, for probable FEP, treatment in EIP services, initial assessment by EIP services and referral to EIP services for ‘suspected psychosis’. Forecasts were stratified by gender, age and ethnicity, at national and Clinical Commissioning Group levels.

A model with age, gender, ethnicity, small-area-level deprivation, social fragmentation and regional cannabis use provided best fit to observed new FEP cases at national and Clinical Commissioning Group levels in 2017 (predicted 8112, 95% CI 7623–8597; observed 8038, difference of 74 [0.92%]). By 2025, the model forecasted 11 067 new treated cases per annum (95% CI 10 383–11 740). For every 10 new treated cases, 21 and 23 people would be assessed by and referred to EIP services for suspected psychosis, respectively.

Our evidence-based methodology provides an accurate, validated tool to inform clinical provision of EIP services about future population need for care, based on local variation of major social determinants of psychosis.

Schizophrenia is considered a polygenic disorder. People with schizophrenia and those with genetic high risk of schizophrenia (GHR) have presented with similar neurodevelopmental deficits in hemispheric asymmetry. The potential associations between neurodevelopmental abnormalities and schizophrenia-related risk genes in both schizophrenia and those with GHR remains unclear.

To investigate the shared and specific alternations to the structural network in people with schizophrenia and those with GHR. And to identify an association between vulnerable structural network alternation and schizophrenia-related risk genes.

A total of 97 participants with schizophrenia, 79 participants with GHR and 192 healthy controls, underwent diffusion tensor imaging (DTI) scans at a single site. We used graph theory to characterise hemispheric and whole-brain structural network topological metrics. For 26 people in the schizophrenia group and 48 in the GHR group with DTI scans we also calculated their schizophrenia-related polygenic risk scores (SZ-PRSs). The correlations between alterations to the structural network and SZ-PRSs were calculated. Based on the identified genetic–neural association, bioinformatics enrichment was explored.

There were significant hemispheric asymmetric deficits of nodal efficiency, global and local efficiency in the schizophrenia and GHR groups. Hemispheric asymmetric deficit of local efficiency was significantly positively correlated with SZ-PRSs in the schizophrenia and GHR groups. Bioinformatics enrichment analysis showed that these risk genes may be linked to signal transduction, neural development and neuron structure. The schizophrenia group showed a significant decrease in the whole-brain structural network.

The shared asymmetric deficits in people with schizophrenia and those with GHR, and the association between anomalous asymmetry and SZ-PRSs suggested a vulnerability imaging marker regulated by schizophrenia-related risk genes. Our findings provide new insights into asymmetry regulated by risk genes and provides a better understanding of the genetic–neural pathological underpinnings of schizophrenia.

Recovery in schizophrenia is a complex process, involving clinical, societal and personal recovery. Until now, studies analysed these domains separately, without examining their mutual relations and changes over time.

This study aimed to examine different states of recovery and transition rates between states.

The Pharmacotherapy Monitoring and Outcome Survey (2006–2017) yearly assesses patients with schizophrenia in the Northern Netherlands. Data from 2327 patients with one up to 11 yearly measurements on clinical, societal and personal recovery were jointly analysed with a mixture latent Markov model (MLMM).

The selected MLMM had four states that differed in degree and pattern of recovery outcomes. Patients in state 1 were least recovered on any domain (16% of measurements), and partly recovered in states 2 (25%; featured by negative symptoms) and 3 (21%; featured by positive symptoms). Patients in state 4 (38%) were most recovered, except for work, study and housekeeping. At the subsequent measurement, the probability of remaining in the same state was 77–89%, transitioning to a better state was 4–12% and transitioning to a worse state was 4–6%; no transitions occurred between states 1 and 4. Female gender, shorter illness duration and less schizophrenia were more prevalent in better states.

Quite a high recovery rate was present among a substantial part of the measurements (38%, state 4), with a high probability (89%) of remaining in this state. Transition rates in the other states might increase to a more favourable state by focusing on adequate treatment of negative and positive symptoms and societal problems.