The present study characterized heterogeneity in the cognitive profiles of monolingual and bilingual Latino older adults enrolled in the HABS-HD.

A total of 859 cognitively unimpaired older adults completed neuropsychological testing. Raw scores for cognitive tests were converted to z-scores adjusted for age, education, sex, and language of testing. A latent profile analysis (LPA) was conducted for monolingual and bilingual speaker groups. A series of 2–5 class solutions were examined, and the optimal model was selected based on fit indices, posterior probabilities, proportion of sample sizes, and pattern of scores. Identified classes were compared on sociodemographic, psychosocial, and health characteristics.

For the monolingual group (n = 365), a 3-class solution was optimal; this consisted of a Low Average Memory group with low average verbal memory performances on the SEVLT Total Learning and Delayed Recall trials, as well as an Average Cognition group and a High Average Cognition group. For the bilingual group (n = 494), a 3-class solution was observed to be optimal; this consisted of a Low Average Memory group, with low average verbal memory performances on the learning and delayed recall trials of Logical Memory; a Low Average Executive group, where performance on Trails A and B and Digit Substitution were the lowest; and a High Average Cognition group, where performance was generally in the high average range across most cognitive measures.

Cognitive class solutions differed across monolingual and bilingual groups and illustrate the need to better understand cognitive variability in linguistically diverse samples of Latino older adults.

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.

Cognitive therapies are developed on the principle that specific cognitive appraisals are key determinants in the development and maintenance of mental health disorders. It is likely that particular appraisals of the coronavirus pandemic will have explanatory power for subsequent mental health outcomes in the general public. To enable testing of this hypothesis we developed a questionnaire assessing coronavirus-related cognitions.

12 285 participants completed online a 46-item pool of cognitions about coronavirus and six measures of different mental health problems. The sample was randomly split into derivation and validation samples. Exploratory factor analyses determined the factor structure, selection of items, and model fit in the derivation sample. Confirmatory factor analysis (CFA) then tested this model in the validation sample. Associations of the questionnaire with mental health outcomes were examined.

The 26-item, seven-factor, Oxford Psychological Investigation of Coronavirus Questionnaire [TOPIC-Q] was developed. CFA demonstrated a good model fit (χ2 = 2108.43, df = 278, p < 0.001, comparative fit index (CFI) = 0.950, Tucker−Lewis index (TLI) = 0.942, root mean square error of approximation (RMSEA) = 0.033, standardized root mean square residual (SRMR) = 0.038). The factors were: cognitions about (1) safety and vulnerability, (2) negative long-term impact, (3) having the virus, (4) spreading the virus, (5) social judgment, (6) negative self, and (7) being targeted. The questionnaire explained significant variance in depression (45.8%), social anxiety (37.3%), agoraphobia (23.2%), paranoia (27.3%), post-traumatic stress disorder (57.1%), and panic disorder (31.4%). Cognitions about negative long-term impact had the greatest explanatory power across disorders.

TOPIC-Q provides a method to assess appraisals of the pandemic, which is likely to prove helpful both in longitudinal studies assessing mental health outcomes and in delivery of psychological therapy.

Relapse prevention strategies based on monitoring of early warning signs (EWS) are advocated for the management of psychosis. However, there has been a lack of research exploring how staff, carers and patients make sense of the utility of EWS, or how these are implemented in context.

To develop a multiperspective theory of how EWS are understood and used, which is grounded in the experiences of mental health staff, carers and patients.

Twenty-five focus groups were held across Glasgow and Melbourne (EMPOWER Trial, ISRCTN: 99559262). Participants comprised 88 mental health staff, 21 patients and 40 carers from UK and Australia (total n = 149). Data were analysed using constructivist grounded theory.

All participants appeared to recognise EWS and acknowledged the importance of responding to EWS to support relapse prevention. However, recognition of and acting on EWS were constructed in a context of uncertainty, which appeared linked to risk appraisals that were dependent on distinct stakeholder roles and experiences. Within current relapse management, a process of weighted decision-making (where one factor was seen as more important than others) described how stakeholders weighed up the risks and consequences of relapse alongside the risks and consequences of intervention and help-seeking.

Mental health staff, carers and patients speak about using EWS within a weighted decision-making process, which is acted out in the context of relationships that exist in current relapse management, rather than an objective response to specific signs and symptoms.

Obsessive–compulsive disorder (OCD) has similar prevalence rates across ethnic groups. However, ethnic minorities are underrepresented in clinical trials of OCD. It is unclear whether this is also the case in clinical services.

To explore whether ethnic minorities with OCD are underrepresented in secondary and tertiary mental health services in the South London and Maudsley (SLaM) NHS Foundation Trust.

The ethnic distribution of patients with OCD seen between 1999 and 2013 in SLaM (n = 1528) was compared with that of the general population in the catchment area using census data. A cohort of patients with depression (n = 22 716) was used for comparative purposes.

Ethnic minorities with OCD were severely underrepresented across services (–57%, 95% CI –62% to –52%). The magnitude of the observed inequalities was significantly more pronounced than in depression (–29%, 95% CI–31% to–27%).

There is a clear need to understand the reasons behind such ethnic inequalities and implement measures to reduce them.