Employment and relationship are crucial for social integration. However, individuals with major psychiatric disorders often face challenges in these domains.

We investigated employment and relationship status changes among patients across the affective and psychotic spectrum – in comparison with healthy controls, examining whether diagnostic groups or functional levels influence these transitions.

The sample from the longitudinal multicentric PsyCourse Study comprised 1260 patients with affective and psychotic spectrum disorders and 441 controls (mean age ± s.d., 39.91 ± 12.65 years; 48.9% female). Multistate models (Markov) were used to analyse transitions in employment and relationship status, focusing on transition intensities. Analyses contained multiple multistate models adjusted for age, gender, job or partner, diagnostic group and Global Assessment of Functioning (GAF) in different combinations to analyse the impact of the covariates on the hazard ratio of changing employment or relationship status.

The clinical group had a higher hazard ratio of losing partner (hazard ratio 1.46, P < 0.001) and job (hazard ratio 4.18, P < 0.001) than the control group (corrected for age/gender). Compared with controls, clinical groups had a higher hazard of losing partner (affective group, hazard ratio 2.69, P = 0.003; psychotic group, hazard ratio 3.06, P = 0.001) and job (affective group, hazard ratio 3.43, P < 0.001; psychotic group, hazard ratio 4.11, P < 0.001). Adjusting for GAF, the hazard ratio of losing partner and job decreased in both clinical groups compared with controls.

Patients face an increased hazard of job loss and relationship dissolution compared with healthy controls, and this is partially conditioned by the diagnosis and functional level. These findings underscore a high demand for destigmatisation and support for individuals in managing their functional limitations.

The N100, an early auditory event-related potential, has been found to be altered in patients with psychosis. However, it is unclear if the N100 is a psychosis endophenotype that is also altered in the relatives of patients.

We conducted a family study using the auditory oddball paradigm to compare the N100 amplitude and latency across 243 patients with psychosis, 86 unaffected relatives, and 194 controls. We then conducted a systematic review and a random-effects meta-analysis pooling our results and 14 previously published family studies. We compared data from a total of 999 patients, 1192 relatives, and 1253 controls in order to investigate the evidence and degree of N100 differences.

In our family study, patients showed reduced N100 amplitudes and prolonged N100 latencies compared to controls, but no significant differences were found between unaffected relatives and controls. The meta-analysis revealed a significant reduction of the N100 amplitude and delay of the N100 latency in both patients with psychosis (standardized mean difference [s.m.d.] = −0.48 for N100 amplitude and s.m.d. = 0.43 for N100 latency) and their relatives (s.m.d. = − 0.19 for N100 amplitude and s.m.d. = 0.33 for N100 latency). However, only the N100 latency changes in relatives remained significant when excluding studies with affected relatives.

N100 changes, especially prolonged N100 latencies, are present in both patients with psychosis and their relatives, making the N100 a promising endophenotype for psychosis. Such changes in the N100 may reflect changes in early auditory processing underlying the etiology of psychosis.

We summarize what we assess as the past year's most important findings within climate change research: limits to adaptation, vulnerability hotspots, new threats coming from the climate–health nexus, climate (im)mobility and security, sustainable practices for land use and finance, losses and damages, inclusive societal climate decisions and ways to overcome structural barriers to accelerate mitigation and limit global warming to below 2°C.

We synthesize 10 topics within climate research where there have been significant advances or emerging scientific consensus since January 2021. The selection of these insights was based on input from an international open call with broad disciplinary scope. Findings concern: (1) new aspects of soft and hard limits to adaptation; (2) the emergence of regional vulnerability hotspots from climate impacts and human vulnerability; (3) new threats on the climate–health horizon – some involving plants and animals; (4) climate (im)mobility and the need for anticipatory action; (5) security and climate; (6) sustainable land management as a prerequisite to land-based solutions; (7) sustainable finance practices in the private sector and the need for political guidance; (8) the urgent planetary imperative for addressing losses and damages; (9) inclusive societal choices for climate-resilient development and (10) how to overcome barriers to accelerate mitigation and limit global warming to below 2°C.

Science has evidence on barriers to mitigation and how to overcome them to avoid limits to adaptation across multiple fields.

New insights into the pathophysiology of mental disorders and innovations in psychiatric care depend on the availability of representative, longitudinal and multidimensional datasets across diverse, transdiagnostic populations. Biobanks usually attempt to collect such data in parallel to clinical routine, which is resource-intensive, puts additional burden on health-care providers, and may reduce the generalizability of the results. Despite containing rich phenotypic and biological information, data generated in routine clinical care is seldomly used for research purposes, because it is usually unstructured and locked in data silos. To truly link clinical practice and research, solutions that optimize the generation and scientific utilization of real-world clinical data are needed.

Evaluation of a new digital infrastructure which warrants the efficient, automatized, and structured collection of real-world data in psychiatric care, and integrates the generated data into existing biobanking efforts.

We have developed a new documentation system which augments the existing IT-structures, enables the collection of routine clinical data in a structured format and involves patients in the data generation process. In an implementation science approach, to replicate and extend the findings of Blitz et al. (JMIR Ment Health 2021), we are investigating the acceptance, efficacy, and safety of the system in our outpatient clinic for affective disorders.

First results describing the technical safety, usage metrics, and acceptance of the system, and the quality of the collected data will be presented.

Challenges of collecting real-world data for biobanking and research purposes and perspectives on future digital solutions will be discussed.

No significant relationships.

Disruptive behavior disorders (DBD) are heterogeneous at the clinical and the biological level. Therefore, the aims were to dissect the heterogeneous neurodevelopmental deviations of the affective brain circuitry and provide an integration of these differences across modalities.

We combined two novel approaches. First, normative modeling to map deviations from the typical age-related pattern at the level of the individual of (i) activity during emotion matching and (ii) of anatomical images derived from DBD cases (n = 77) and controls (n = 52) aged 8–18 years from the EU-funded Aggressotype and MATRICS consortia. Second, linked independent component analysis to integrate subject-specific deviations from both modalities.

While cases exhibited on average a higher activity than would be expected for their age during face processing in regions such as the amygdala when compared to controls these positive deviations were widespread at the individual level. A multimodal integration of all functional and anatomical deviations explained 23% of the variance in the clinical DBD phenotype. Most notably, the top marker, encompassing the default mode network (DMN) and subcortical regions such as the amygdala and the striatum, was related to aggression across the whole sample.

Overall increased age-related deviations in the amygdala in DBD suggest a maturational delay, which has to be further validated in future studies. Further, the integration of individual deviation patterns from multiple imaging modalities allowed to dissect some of the heterogeneity of DBD and identified the DMN, the striatum and the amygdala as neural signatures that were associated with aggression.

The coronavirus disease 2019 (COVID-19) pandemic has required healthcare systems and hospitals to rapidly modify standard practice, including antimicrobial stewardship services. Our study examines the impact of COVID-19 on the antimicrobial stewardship pharmacist.

A survey was distributed nationally to all healthcare improvement company members.

Pharmacist participants were mostly leaders of antimicrobial stewardship programs distributed evenly across the United States and representing urban, suburban, and rural health-system practice sites.

Participants reported relative increases in time spent completing tasks related to medication access and preauthorization (300%; P = .018) and administrative meeting time (34%; P = .067) during the COVID-19 pandemic compared to before the pandemic. Time spent rounding, making interventions, performing pharmacokinetic services, and medication reconciliation decreased.

A shift away from clinical activities may negatively affect the utilization of antimicrobials.

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.

Poor transition planning contributes to discontinuity of care at the child–adult mental health service boundary (SB), adversely affecting mental health outcomes in young people (YP). The aim of the study was to determine whether managed transition (MT) improves mental health outcomes of YP reaching the child/adolescent mental health service (CAMHS) boundary compared with usual care (UC).

A two-arm cluster-randomised trial (ISRCTN83240263 and NCT03013595) with clusters allocated 1:2 between MT and UC. Recruitment took place in 40 CAMHS (eight European countries) between October 2015 and December 2016. Eligible participants were CAMHS service users who were receiving treatment or had a diagnosed mental disorder, had an IQ ⩾ 70 and were within 1 year of reaching the SB. MT was a multi-component intervention that included CAMHS training, systematic identification of YP approaching SB, a structured assessment (Transition Readiness and Appropriateness Measure) and sharing of information between CAMHS and adult mental health services. The primary outcome was HoNOSCA (Health of the Nation Outcome Scale for Children and Adolescents) score 15-months post-entry to the trial.

The mean difference in HoNOSCA scores between the MT and UC arms at 15 months was −1.11 points (95% confidence interval −2.07 to −0.14, p = 0.03). The cost of delivering the intervention was relatively modest (€17–€65 per service user).

MT led to improved mental health of YP after the SB but the magnitude of the effect was small. The intervention can be implemented at low cost and form part of planned and purposeful transitional care.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, with its impact on our way of life, is affecting our experiences and mental health. Notably, individuals with mental disorders have been reported to have a higher risk of contracting SARS-CoV-2. Personality traits could represent an important determinant of preventative health behaviour and, therefore, the risk of contracting the virus.

We examined overlapping genetic underpinnings between major psychiatric disorders, personality traits and susceptibility to SARS-CoV-2 infection.

Linkage disequilibrium score regression was used to explore the genetic correlations of coronavirus disease 2019 (COVID-19) susceptibility with psychiatric disorders and personality traits based on data from the largest available respective genome-wide association studies (GWAS). In two cohorts (the PsyCourse (n = 1346) and the HeiDE (n = 3266) study), polygenic risk scores were used to analyse if a genetic association between, psychiatric disorders, personality traits and COVID-19 susceptibility exists in individual-level data.

We observed no significant genetic correlations of COVID-19 susceptibility with psychiatric disorders. For personality traits, there was a significant genetic correlation for COVID-19 susceptibility with extraversion (P = 1.47 × 10−5; genetic correlation 0.284). Yet, this was not reflected in individual-level data from the PsyCourse and HeiDE studies.

We identified no significant correlation between genetic risk factors for severe psychiatric disorders and genetic risk for COVID-19 susceptibility. Among the personality traits, extraversion showed evidence for a positive genetic association with COVID-19 susceptibility, in one but not in another setting. Overall, these findings highlight a complex contribution of genetic and non-genetic components in the interaction between COVID-19 susceptibility and personality traits or mental disorders.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.