Epidemiology and Psychiatric Sciences最新文献

Aims: Studies conducted during the COVID-19 pandemic found high occurrence of suicidal thoughts and behaviours (STBs) among healthcare workers (HCWs). The current study aimed to (1) develop a machine learning-based prediction model for future STBs using data from a large prospective cohort of Spanish HCWs and (2) identify the most important variables in terms of contribution to the model's predictive accuracy.

Methods: This is a prospective, multicentre cohort study of Spanish HCWs active during the COVID-19 pandemic. A total of 8,996 HCWs participated in the web-based baseline survey (May-July 2020) and 4,809 in the 4-month follow-up survey. A total of 219 predictor variables were derived from the baseline survey. The outcome variable was any STB at the 4-month follow-up. Variable selection was done using an L1 regularized linear Support Vector Classifier (SVC). A random forest model with 5-fold cross-validation was developed, in which the Synthetic Minority Oversampling Technique (SMOTE) and undersampling of the majority class balancing techniques were tested. The model was evaluated by the area under the Receiver Operating Characteristic (AUROC) curve and the area under the precision-recall curve. Shapley's additive explanatory values (SHAP values) were used to evaluate the overall contribution of each variable to the prediction of future STBs. Results were obtained separately by gender.

Results: The prevalence of STBs in HCWs at the 4-month follow-up was 7.9% (women = 7.8%, men = 8.2%). Thirty-four variables were selected by the L1 regularized linear SVC. The best results were obtained without data balancing techniques: AUROC = 0.87 (0.86 for women and 0.87 for men) and area under the precision-recall curve = 0.50 (0.55 for women and 0.45 for men). Based on SHAP values, the most important baseline predictors for any STB at the 4-month follow-up were the presence of passive suicidal ideation, the number of days in the past 30 days with passive or active suicidal ideation, the number of days in the past 30 days with binge eating episodes, the number of panic attacks (women only) and the frequency of intrusive thoughts (men only).

Conclusions: Machine learning-based prediction models for STBs in HCWs during the COVID-19 pandemic trained on web-based survey data present high discrimination and classification capacity. Future clinical implementations of this model could enable the early detection of HCWs at the highest risk for developing adverse mental health outcomes.

Study registration: NCT04556565.

Aims: Multimorbidity, especially physical-mental multimorbidity, is an emerging global health challenge. However, the characteristics and patterns of physical-mental multimorbidity based on the diagnosis of mental disorders in Chinese adults remain unclear.

Methods: A cross-sectional study was conducted from November 2004 to April 2005 among 13,358 adults (ages 18-65years) residing in Liaoning Province, China, to evaluate the occurrence of physical-mental multimorbidity. Mental disorders were assessed using the Composite International Diagnostic Interview (version 1.0) with reference to the Diagnostic and Statistical Manual of Mental Disorders (3rd Edition Revised), while physical diseases were self-reported. Physical-mental multimorbidity was assessed based on a list of 16 physical and mental morbidities with prevalence ≥1% and was defined as the presence of one mental disorder and one physical disease. The chi-square test was used to calculate differences in the prevalence and comorbidity of different diseases between the sexes. A matrix heat map was generated of the absolute number of comorbidities for each disease. To identify complex associations and potential disease clustering patterns, a network analysis was performed, constructing a network to explore the relationships within and between various mental disorders and physical diseases.

Results: Physical-mental multimorbidity was confirmed in 3.7% (498) of the participants, with a higher prevalence among women (4.2%, 282) than men (3.3%, 216). The top three diseases with the highest comorbidity rate and average number of comorbidities were dysphoric mood (86.3%; 2.86), social anxiety disorder (77.8%; 2.78) and major depressive disorder (77.1%; 2.53). A physical-mental multimorbidity network was visually divided into mental and physical domains. Additionally, four distinct multimorbidity patterns were identified: 'Affective-addiction', 'Anxiety', 'Cardiometabolic' and 'Gastro-musculoskeletal-respiratory', with the digestive-respiratory-musculoskeletal pattern being the most common among the total sample. The affective-addiction pattern was more prevalent in men and rural populations. The cardiometabolic pattern was more common in urban populations.

Conclusions: The physical-mental multimorbidity network structure and the four patterns identified in this study align with previous research, though we observed notable differences in the proportion of these patterns. These variations highlight the importance of tailored interventions that address specific multimorbidity patterns while maintaining broader applicability to diverse populations.

Aim: HOPE (National Institute for Health and Care Research Global Health Research Group on Homelessness and Mental Health in Africa) aims to develop and evaluate interventions that address the unmet needs of people who are homeless and have severe mental illness (SMI) living in three African countries in ways that are rights-based, contextually grounded, scalable and sustainable.

Methods: We will work in the capital city (Addis Ababa) in Ethiopia, a regional city (Tamale) in Ghana, and the capital city (Nairobi) and a rural county (Makueni) in Kenya to understand different approaches to intervention needed across varied settings.We will be guided by the MRC/NIHR framework on complex interventions and implementation frameworks and emphasise co-production. Formative work will include synthesis of global evidence (systematic review, including grey literature, and a Delphi consensus exercise) on interventions and approaches to homelessness and SMI. We will map contexts; conduct focused ethnography to understand lived experiences of homelessness and SMI; carry out a cross-sectional survey of people who are homeless (n = 750 Ghana/Ethiopia; n = 350 Kenya) to estimate prevalence of SMI and identify prioritised needs; and conduct in-depth interviews and focus group discussions with key stakeholders to understand experiences, challenges and opportunities for intervention. This global and local evidence will feed into Theory of Change (ToC) workshops with stakeholders to establish agreement about valued primary outcomes, map pathways to impact and inform selection and implementation of interventions. Intervention packages to address prioritised needs will be co-produced, piloted and optimised for feasibility and acceptability using participatory action research. We will use rights-based approaches and focus on community-based care to ensure sustainability. Realist approaches will be employed to analyse how contextual variation affects mechanisms and outcomes to inform methods for a subsequent evaluation of larger scale implementation. Extensive capacity-strengthening activities will focus on equipping early career researchers and peer researchers. People with lived experience of SMI and policymakers are an integral part of the research team. Community engagement is supported by working closely with multisectoral Community Advisory Groups.

Conclusions: HOPE will develop evidence to support action to respond to the needs and preferences of people experiencing homelessness and SMI in diverse settings in Africa. We are creating a new partnership of researchers, policymakers, community members and people with lived experience of SMI and homelessness to enable African-led solutions. Key outputs will include contextually relevant practice and policy guidance that supports achievement of inclusive development.

Aims: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Aims: Only little empirical evidence exists on mental health in LGBTIQ+ refugees. In the present study, trauma exposure, experiences of sexual violence and current treatment needs for physical and mental health were investigated in association with symptoms of anxiety, depression, post-traumatic stress disorder (PTSD) and somatic symptom burden in LGBTIQ+ asylum-seekers resettled in Germany and seeking psychosocial support.

Methods: Data was collected in cooperation with a counselling centre for LGBTIQ+ asylum-seekers between Mai 2018 and March 2024, with a total of 120 completed questionnaires of adult clients. The questionnaire (11 different languages) included sociodemographic and flight-related questions as well as standardized instruments for assessing PTSD (PCL-5), depression (PHQ-9), somatic symptom burden (SSS-8), and anxiety (HSCL-25). Prevalence rates were calculated according to the cut-off scores of each questionnaire. Four logistic regression analyses were conducted to test for potential associations between being screened positive for anxiety, depression, somatic symptom burden or PTSD and the number of traumatic events, experiences of sexual violence as well as current treatment needs for physical and mental health.

Results: The great majority, 74.2% (95% CI: 66-82) of the respondents, screened positive for at least one of the mental disorders investigated, with 45% (95% CI: 36-54) suffering from somatic symptom burden, 44.2% (95% CI: 35-53) from depression, 58.3% (95% CI: 50-67) from PTSD, and 62.5% (95% CI: 54-71) from anxiety; 69.5% participants reported having been exposed to sexual violence. Current treatment needs for physical health problems were reported by 47% and for mental health problems by 56.7%. Participants with experiences of sexual violence were more likely to be screened positive for depression (OR: 6.787, 95% CI: 1.45-31.65) and PTSD (OR: 6.121, 95% CI: 1.34-27.95).

Conclusions: The study provides initial insights on mental health and associated factors in a highly burdened and hard-to-reach population. The findings are important for healthcare systems and political authorities in terms of assuring better protection and healthcare for LGBTIQ+ refugees and asylum-seekers.

Aims: Mental health problems in adolescence are increasingly prevalent and have tremendous impacts on life-long health and mortality. Although household poverty is a known risk factor for adolescent mental health, evidence of the timing hypothesis is scarce. We aimed to examine the longitudinal associations of poverty across childhood with mental health in adolescence, focusing on the timing of exposure.

Methods: We used the data of 5,671 children from a Japanese population-based longitudinal cohort, which recruited the first graders (aged 6-7 years) and followed biannually until eighth grade (aged 13-14 years) in Adachi, Tokyo. Household poverty was defined as households having any of the following experiences: annual income less than Japanese yen 3 million, payment difficulties and material deprivations, measured in first, second, fourth, sixth and eighth grades. Adolescent mental health included parent-report internalizing and externalizing problems (the Strengths and Difficulties Questionnaire), self-report depression (the Patient Health Questionnaire-9) and self-esteem (the Japanese version Children's Perceived Competence Scale) in eighth grade. We applied g-estimation of structural nested mean modelling to account for time-varying confounders.

Results: If adolescents were exposed to household poverty at any grade across childhood, on average, they would report more severe depressive symptoms (ψ = 0.32 [95% CI 0.13; 0.51]) and lower self-esteem (ψ = -0.41 [-0.62; -0.21]) in eighth grade. There were also average associations of household poverty at any grade with more internalizing (ψ = 0.19 [0.10; 0.29]) and externalizing problems (ψ = 0.10 [0.002; 0.19]). Although the associations between household poverty and mental health were stronger in younger ages (e.g., poverty in the second grade → depression: ψ = 0.54 [-0.12; 1.19] vs. poverty in the eighth grade → depression: ψ = -0.01 [-0.66; 0.64]), overlapping 95% CIs indicated no statistically significantly different associations by the timing of exposure.

Conclusion: We found the average effect of exposure to household poverty at any grade on mental health outcomes in eighth grade, failing to support the timing hypothesis. The findings indicate that the effects of household poverty accumulate over time in childhood and impact adolescent mental health (cumulative hypothesis) rather than the effects differ by the timing of exposure. While cumulative effects suggest a persistent intervention in poor households across childhood, we highlight intervention at any timing in childhood may be effective in alleviating adolescent mental health problems.

Aims: Developing integrated mental health services focused on the needs of children and young people is a key policy goal in England. The THRIVE Framework and its implementation programme, i-THRIVE, are widely used in England. This study examines experiences of staff using i-THRIVE, estimates its effectiveness, and assesses how local system working relationships influence programme success.

Methods: This evaluation uses a quasi-experimental design (10 implementation and 10 comparison sites.) Measurements included staff surveys and assessment of 'THRIVE-like' features of each site. Additional site-level characteristics were collected from health system reports. The effect of i-THRIVE was evaluated using a four-group propensity-score-weighted difference-in-differences model; the moderating effect of system working relationships was evaluated with a difference-in-difference-in-differences model.

Results: Implementation site staff were more likely to report using THRIVE and more knowledgeable of THRIVE principles than comparison site staff. The mean improvement of fidelity scores among i-THRIVE sites was 16.7, and 8.8 among comparison sites; the weighted model did not find a statistically significant difference. However, results show that strong working relationships in the local system significantly enhance the effectiveness of i-THRIVE. Sites with highly effective working relationships showed a notable improvement in 'THRIVE-like' features, with an average increase of 16.41 points (95% confidence interval: 1.69-31.13, P-value: 0.031) over comparison sites. Sites with ineffective working relationships did not benefit from i-THRIVE (-2.76, 95% confidence interval: - 18.25-12.73, P-value: 0.708).

Conclusions: The findings underscore the importance of working relationship effectiveness in the successful adoption and implementation of multi-agency health policies like i-THRIVE.

Aims: The study assessed the interactions and the impact of specialist mobile community care teams (assertive outreach teams or AOTs) implemented in the mental health (MH) system of Bizkaia (Spain) using a methodology derived from an ecosystem perspective.

Methods: First, the experts assessed the system's services and codified them according to an international classification system. Second, following an iterative methodology for expert-knowledge elicitation, a clients' flow diagram showing the inter-dependencies of the system's components was developed. It included variables and their relationships represented in a causal model. Third, the system elements where the AOTs had a major impact (stress nodes) were identified. Fourth, three scenarios (variable combinations representing the 'stress points' of the system) were modelled to assess its relative technical efficiency (technical performance indicator).

Results: The classification system identified the lack of fidelity of the AOTs to the original assertive community treatment model, categorizing them as non-acute low-intensity mobile care. The causal model identified the following elements of the system as 'stress nodes' in relation to AOT: users' families; social services (outside of the healthcare system); acute hospitals; non-acute residential facilities and, to a lesser extent, acute hospital day care services. When the stress nodes inside the healthcare system were modelled separately, acute and non-acute hospital care services resulted in a large deterioration in the system performance, while acute day hospital care had only a small impact.

Conclusions: The development of the expert-knowledge-based causal model from an ecosystem perspective was helpful in combining information from different levels, from nano to macro, to identify the components in the system likely to be most affected by a potential policy intervention, such as the closure of AOTs. It was also able to illustrate the interaction between the MH system components over time and the impact of the potential changes on the technical performance of the system. Such approaches have potential future application in assisting with service planning and decision-making in other health systems and socio-economic contexts.