A systematic review and meta-analysis was conducted to investigate the prevalence and antecedents/outcomes of loneliness and social isolation among individuals with severe mental disorders (SMD), such as schizophrenia, schizoaffective disorder, bipolar disorder or major depressive disorder.

Five well-known electronic databases (PubMed, PsycINFO, CINAHL, Web of Science and Scopus) were searched (plus a hand search). Observational studies that report the prevalence and, if available, antecedents and consequences of loneliness/isolation among individuals with SMD were included. Key characteristics were extracted, and a meta-analysis was performed. Our systematic review was preregistered on PROSPERO (ID: CRD42024559043). The PRISMA guidelines were followed. The Joanna Briggs Institute (JBI) standardized critical appraisal tool developed for prevalence studies was applied to assess the quality of the included studies.

The initial search yielded 4506 records, and after duplicate removal and screening, a total of 10 studies were finally included. The studies included used data from Europe, Asia, North America, and Oceania. Two studies employed a longitudinal design, while all other studies had a cross-sectional design. Most of the studies included between 100 and 500 individuals with SMD. All studies involved both male and female participants, with women typically comprising about 40% of the sample. The average age of participants often ranged from approximately 30 to 40 years. The estimated prevalence of loneliness was 59.1% (95% CI: 39.6% to 78.6%, I2 = 99.3, P < .001) among individuals with any diagnosis of SMD. Furthermore, the estimated prevalence of objective social isolation was 63.0% (95% CI: 58.6% to 67.4%) among individuals with schizophrenia or schizophrenia spectrum disorder. The quality of the studies was moderate to good. Subjective well-being and depressive symptoms in particular were found to contribute to loneliness in the included studies.

The present systematic review with meta-analysis identified high levels of loneliness and objective social isolation among those with SMD. These findings stress the importance of monitoring and addressing social needs in this vulnerable group, which may have a positive effect on the life quality of individuals with SMD. Future research in neglected regions (e.g. South America and Africa) is recommended. Different diagnoses within severe mental disorders should be distinguished in future studies. Furthermore, additional longitudinal studies are required to explore the antecedents and consequences of loneliness and social isolation among individuals with SMD.

Loneliness has become a major public health issue of the recent decades due to its severe impact on health and mortality. Little is known about the relation between loneliness and social anxiety. This study aimed (1) to explore levels of loneliness and social anxiety in the general population, and (2) to assess whether and how loneliness affects symptoms of social anxiety and vice versa over a period of five years.

The study combined data from the baseline assessment and the five-year follow-up of the population-based Gutenberg Health Study. Data of N = 15 010 participants at baseline (Mage = 55.01, s.d.age = 11.10) were analyzed. Multiple regression analyses with loneliness and symptoms of social anxiety at follow-up including sociodemographic, physical illnesses, and mental health indicators at baseline were used to test relevant covariates. Effects of loneliness on symptoms of social anxiety over five years and vice versa were analyzed by autoregressive cross-lagged structural equation models.

At baseline, 1076 participants (7.41%) showed symptoms of social anxiety and 1537 (10.48%) participants reported feelings of loneliness. Controlling for relevant covariates, symptoms of social anxiety had a small significant effect on loneliness five years later (standardized estimate of 0.164, p < 0.001). Vice versa, there was no significant effect of loneliness on symptoms of social anxiety taking relevant covariates into account.

Findings provided evidence that symptoms of social anxiety are predictive for loneliness. Thus, prevention and intervention efforts for loneliness need to address symptoms of social anxiety.

One of the major challenges in clinical psychiatry remains the absence of well established objective measures of symptoms’ severity. Clinical insights are mainly provided through keen behavioral observation and subjective questionnaires and scales.

The aim of this paper is to predict depression severity through speech using the features extracted from the speech as provided by participants during a semi-structured dialogue with a virtual avatar.

We use data from a subset of the DAICWOZ dataset consisting in 142 dialogues between participants and a virtual avatar during which the avatar uses several prompts to maintain a conversation with the participant. The avatar uses prompts involving the topics of travel, dream jobs, and memorable experiences. From the speech generated from the dialogue, we extract participant utterances separated by prompt and extract features from the three sets of transcripts. We extract content features from the transcript and acoustic features from the excerpt corresponding to the speech from the participant for the prompt in question.We perform regression experiments on the PHQ8 items using the features extracted from each set of transcripts. Furthermore, we combine the features extracted from each set of transcripts and compute partial spearman correlations between them and the PHQ8 items using gender as a covariate.

With our best regression model we obtain an R2 of 0.1, explaining 10% of the variance in the PHQ total score. Additionally, we obtain a mean absolute error of 1.25, suggesting that the regressor can detect with more or less precision clinically meaningful differences in depression severity between participants. Partial correlations between the total score and the features show significant correlations between features dependent on the amount of speech generated by each participant, along with the complexity of syntactic structures used.

Automatic analysis of spontaneous speech could help with the detection and monitoring of signs of depression. By combining the use of this technology with timely intervention strategies for instance provided by a virtual agent it could contribute to timely prevention.

A. König: None Declared, M. Mina Employee of: ki:elements GmbH, S. Schäfer Employee of: ki:elements GmbH, N. Linz Shareolder of: ki:elements GmbH, Employee of: ki:elements GmbH, J. Tröger Shareolder of: ki:elements GmbH, Employee of: ki:elements GmbH

Health services research (HSR) is affected by a widespread problem related to service terminology including non-commensurability (using different units of analysis for comparisons) and terminological unclarity due to ambiguity and vagueness of terms. The aim of this study was to identify the magnitude of the terminological bias in health and social services research and health economics by applying an international classification system.

This study, that was part of the PECUNIA project, followed an ontoterminology approach (disambiguation of technical and scientific terms using a taxonomy and a glossary of terms). A listing of 56 types of health and social services relevant for mental health was compiled from a systematic review of the literature and feedback provided by 29 experts in six European countries. The disambiguation of terms was performed using an ontology-based classification of services (Description and Evaluation of Services and DirectoriEs – DESDE), and its glossary of terms. The analysis focused on the commensurability and the clarity of definitions according to the reference classification system. Interrater reliability was analysed using κ.

The disambiguation revealed that only 13 terms (23%) of the 56 services selected were accurate. Six terms (11%) were confusing as they did not correspond to services as defined in the reference classification system (non-commensurability bias), 27 (48%) did not include a clear definition of the target population for which the service was intended, and the definition of types of services was unclear in 59% of the terms: 15 were ambiguous and 11 vague. The κ analyses were significant for agreements in unit of analysis and assignment of DESDE codes and very high in definition of target population.

Service terminology is a source of systematic bias in health service research, and certainly in mental healthcare. The magnitude of the problem is substantial. This finding has major implications for the international comparability of resource use in health economics, quality and equality research. The approach presented in this paper contributes to minimise differentiation between services by taking into account key features such as target population, care setting, main activities and type and number of professionals among others. This approach also contributes to support financial incentives for effective health promotion and disease prevention. A detailed analysis of services in terms of cost measurement for economic evaluations reveals the necessity and usefulness of defining services using a coding system and taxonomical criteria rather than by ‘text-based descriptions’.

Depression, the most frequent and harmful mental disorder, has been associated with specific somatic diseases as the leading cause of death. The purposes of this prospective study were to predict incident chronic diseases based on baseline depressive symptoms and to test sex-dependent effects.

In a representative German community sample of over 12 000 participants, baseline depressive symptoms (assessed using the Patient Health Questionnaire-9) were tested as a predictor of new onset of cardiovascular disease (CVD), chronic obstructive lung disease, diabetes, cancer, and migraine at 5-year follow-up. To study disease incidence, we created subsamples for each chronic disease by excluding participants who already had the respective disease at baseline. Potential confounders were included in logistic regression models and sex-specific analyses were performed.

Controlling for demographic characteristics and loneliness, in men and women, baseline depressive symptoms were predictive of CVD, chronic obstructive lung disease, diabetes, and migraine, but not of cancer. When we additionally adjusted for metabolic and lifestyle risk factors, there was an 8% increase of chronic obstructive lung disease and migraine per point of depressive symptoms. There was a trend for CVD (4%; p = 0.053). Sex-sensitive analyses revealed trends for the relevance of depressive symptoms for CVD in men (p = 0.065), and for diabetes in women (p = 0.077).

These findings underscore the need to implement screening for depression in the treatment of major somatic illnesses. At the same time, depressed patients should be screened for metabolic and lifestyle risk factors and for somatic diseases and offered lifestyle interventions.

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.

Accumulating evidence suggests beneficial effects of media stories featuring individuals mastering their suicidal crises, but effects have not been assessed for psychiatric patients.

We randomized n = 172 adult psychiatric patients (n = 172, 97.1% inpatients) to read an educative article featuring a person mastering a suicidal crisis (n = 92) or an unrelated article (n = 80) in a single-blind randomized controlled trial. Questionnaire data were collected before (T1) and after exposure (T2) as well as 1 week later (study end-point, T3). The primary outcome was suicidal ideation as assessed with the Reasons for Living Inventory; secondary outcomes were help-seeking intentions, mood, hopelessness, and stigmatization. Differences between patients with affective versus other diagnoses were explored based on interaction tests.

We found that patients with affective disorders (n = 99) experienced a small-sized reduction of suicidal ideation at 1-week follow up (mean difference to control group [MD] at T3 = −0.17 [95% CI −0.33, −0.03], d = −0.15), whereas patients with nonaffective diagnoses (n = 73) experienced a small-sized increase (T2: MD = 0.24 [95% CI 0.06, 0.42], d = 0.19). Intervention group participants further experienced a nonsustained increase of help-seeking intentions (T2: MD = 0.53 [95% CI 0.11, 0.95], d = 0.19) and a nonsustained deterioration of mood (T2: MD = −0.14 [95% CI −0.27, −0.02], d = −0.17).

This study suggests that patients with affective disorders appear to benefit from media materials featuring mastery of suicidal crises. More research is needed to better understand which patient groups are at possible risk of unintended effects.

Mental health disorders and their treatments produce significant costs and benefits in both healthcare and non-healthcare sectors. The latter are often referred to as intersectoral costs and benefits (ICBs). Little is known about healthcare-related ICBs in the criminal justice sector and how to include these in health economics research.

The triple aim of this study is (i) to identify healthcare-related ICBs in the criminal justice sector, (ii) to validate the list of healthcare-related ICBs in the criminal justice sector on a European level by sector-specific experts, and (iii) to classify the identified ICBs.

A scientific literature search in PubMed and an additional grey literature search, carried out in six European countries, were used to retrieve ICBs. In order to validate the international applicability of the ICBs, a survey was conducted with an international group of experts from the criminal justice sector. The list of criminal justice ICBs was categorized according to the PECUNIA conceptual framework.

The full-text analysis of forty-five peer-reviewed journal articles and eleven grey literature sources resulted in a draft list of items. Input from the expert survey resulted in a final list of fourteen unique criminal justice ICBs, categorized according to the care atom.

This study laid further foundations for the inclusion of important societal costs of mental health-related interventions within the criminal justice sector. More research is needed to facilitate the further and increased inclusion of ICBs in health economics research.

Seasonal patterns in hospitalizations have been observed in various psychiatric disorders, however, it is unclear whether they also exist in schizophrenia. Previous studies found mixed results and those reporting the presence of seasonality differ regarding the characteristics of these patterns. Further, they are inconclusive whether sex is an influencing factor. The aim of this study was therefore to examine if seasonal patterns in hospitalizations can be found in schizophrenia, with special regard to a possible influence of sex, by using a large national dataset.

Data on all hospital admissions within Austria due to schizophrenia (F20.0–F20.6) for the time period of 2003–2016 were included. Age standardized monthly variation of hospitalization for women and men was analyzed and the level of significance adjusted for multiple testing.

The database comprised of 110,735 admissions (59.6% men). Significant seasonal variations were found in the total sample with hospitalization peaks in January and June and a trough in December (p < 0.0001). No significant difference in these patterns was found between women and men with schizophrenia (p < 0.0001).

Our study shows that schizophrenia-related hospitalizations follow a seasonal pattern in both men and women. The distribution of peaks might be influenced by photoperiod changes which trigger worsening of symptoms and lead to exacerbations in schizophrenia. Further research is necessary to identify underlying factors influencing seasonal patterns and to assess whether a subgroup of patients with schizophrenia is especially vulnerable to the impact of seasonal variations.

Two common approaches to identify subgroups of patients with bipolar disorder are clustering methodology (mixture analysis) based on the age of onset, and a birth cohort analysis. This study investigates if a birth cohort effect will influence the results of clustering on the age of onset, using a large, international database.

The database includes 4037 patients with a diagnosis of bipolar I disorder, previously collected at 36 collection sites in 23 countries. Generalized estimating equations (GEE) were used to adjust the data for country median age, and in some models, birth cohort. Model-based clustering (mixture analysis) was then performed on the age of onset data using the residuals. Clinical variables in subgroups were compared.

There was a strong birth cohort effect. Without adjusting for the birth cohort, three subgroups were found by clustering. After adjusting for the birth cohort or when considering only those born after 1959, two subgroups were found. With results of either two or three subgroups, the youngest subgroup was more likely to have a family history of mood disorders and a first episode with depressed polarity. However, without adjusting for birth cohort (three subgroups), family history and polarity of the first episode could not be distinguished between the middle and oldest subgroups.

These results using international data confirm prior findings using single country data, that there are subgroups of bipolar I disorder based on the age of onset, and that there is a birth cohort effect. Including the birth cohort adjustment altered the number and characteristics of subgroups detected when clustering by age of onset. Further investigation is needed to determine if combining both approaches will identify subgroups that are more useful for research.