This study aimed to compared Patient-Reported Outcomes Measurement Information System (PROMIS) anxiety, depression, and anger item bank among Korean, US and Dutch general population. Between December 2021 and January 2022, we surveyed representative Korean participants (N=2699). Then we compared the mean T-scores of PROMIS anxiety, depression, and anger full items bank among Korean, US (N=1696) and the Dutch (N=1002) populations. Differential item-functioning (DIF) analyses were also performed. We also compared each score by age group, sex, presence of comorbidities, and general health status. In Korean, the mean T-scores for anxiety, depression, and anger were 45.3 (standard deviation [SD]=11.6), 48.4 (SD=11.2), and 44.9 (SD=12.6), respectively. Among the general population in Korea, patients aged 35-44years and those with comorbidities had higher anxiety, depression, and anger scores. In the DIF analyses between the US and Korean populations, 28%, 32%, and 45% were flagged for uniform or non-uniform DIF in anxiety, depression and anger, respectively. Considering the cultural differences, we recommend using a harmonized approach that includes country-specific reference values while retaining a standardized core set of items to enable cross-country comparability.

The period after psychiatric hospital discharge is one of elevated risk for suicide-related behaviors (SRBs). Post-discharge clinical outreach, although potentially effective in preventing SRBs, would be more cost-effective if targeted at high-risk patients. To this end, a machine learning model was developed to predict post-discharge suicides among Veterans Health Administration (VHA) psychiatric inpatients and target a high-risk preventive intervention. The Veterans Coordinated Community Care (3C) Study is a multicenter randomized controlled trial using this model to identify high-risk VHA psychiatric inpatients (n=850) randomized with equal allocation to either the Coping Long Term with Active Suicide Program (CLASP) post-discharge clinical outreach intervention or treatment-as-usual (TAU). The primary outcome is SRBs over a 6-month follow-up. We will estimate average treatment effects adjusted for loss to follow-up and investigate the possibility of heterogeneity of treatment effects. Recruitment is underway and will end September 2024. Six-month follow-up will end and analysis will begin in Summer 2025. Results will provide information about the effectiveness of CLASP versus TAU in reducing post-discharge SRBs and provide guidance to VHA clinicians and policymakers about the implications of targeted use of CLASP among high-risk psychiatric inpatients in the months after hospital discharge. ClinicalTrials.Gov identifier: NCT05272176 (https://www. gov/ct2/show/NCT05272176).

There is a lack of research examining the association between obesity and depressive symptoms in relation to mental health. This study aimed to examine the correlation between Body Roundness Index (BRI) and depressive symptoms in elderly Chinese individuals. The study sample consisted of 11,842 individuals aged 65years or older from the 2018 Chinese Longitudinal Health Longevity Survey (CLHLS) database. A multivariate logistic regression analysis was used to investigate how BRI affects the likelihood of experiencing depressive symptoms, with restricted cubic spline (RCS) curves illustrating this impact. BRI values were calculated using a predefined formula for each participant, and depressive status was assessed using the Center for Epidemiologic Studies Depression Scale (CES-D-10). The mean age of the participants was 83.1±10.9years. A non-linear relationship was identified between the BRI score and the risk of depressive symptoms. The analysis showed that for BRI scores below 5.17, there was a significant 9% increase in the risk of depressive symptoms for every 1-point decrease in BRI score. Conversely, when the BRI was 5.17 or higher, a decrease in the BRI score did not lead to a significant increase in the risk of depressive symptoms. The study demonstrated a significant association between BRI and depressive symptoms in elderly Chinese individuals. Furthermore, it was noted that older adults classified as overweight and mildly obese had a lower likelihood of experiencing depressive symptoms and demonstrated improved mental health.

Although common, sleep disorders often remain undiagnosed in psychiatric patients. A screening instrument, like the Holland Sleep Disorders Questionnaire (HSDQ) could improve this. Previous work indicated a 6-factor structure for the HSDQ, but this hasn't been investigated in psychiatric patients. HSDQ data was collected in a psychiatric-outpatient sample (n=1082) and general-population sample (n=2089). Internal reliability of the HSDQ was investigated and Confirmatory Factor Analyses (CFA) were used to compare 1-, 6-, and second-order 6-factor models in both samples. Next, multigroup-CFA was used to investigate measurement invariance. Except for one subscale, internal reliability was acceptable in both samples. The 6-factor structure model fitted best in both samples and investigation of measurement invariance showed evidence for equality of the overall factor structure (configural invariance). Addition of equality constraints on factor loadings (metric invariance) and item thresholds (scalar invariance) showed good fit for all fit statistics, except for one. Exploratory analyses identified three items for metric and three different items for scalar invariance explaining this non-invariance. The HSDQ has a 6-factor structure in psychiatric patients, which is comparable to the general population. However, due to the observed non-invariance, users should be cautious with comparing HSDQ scores between psychiatric and general populations.

To examine the impact of potential measurement bias (i.e., differential item functioning [DIF]) across sex, age, employment, location, and substance use disorders on the screening properties of epidemiological surveys that utilise screening questions when estimating prevalence of mood and anxiety disorders. Data comprised of 15,893 respondents who completed the 2020-2022 Australian National Survey of Mental Health and Wellbeing. Questions from the screening module of the Composite International Diagnostic Interview 3.0 were analysed using confirmatory factor analysis and DIF across subgroups of interest. Sensitivity, specificity, and classification rate were derived and compared across models that did and did not adjust for significant levels of DIF. Sources of DIF were identified across the items was due to age and sex at birth with relatively fewer items displaying DIF across employment, location, and substance use disorders. In terms of screening, the absolute differences in sensitivity and specificity between the DIF-free and DIF models ranged from 0.001 to 0.091. The current study found some evidence of DIF in the screening questions used to evaluate mental health disorder prevalence. However, the overall influence of DIF on screening into at least one mood and anxiety disorder module was found to be minimal.

Network approaches to psychopathology have become increasingly common in mental health research, with many theoretical and methodological developments quickly gaining traction. This article illustrates contemporary practices in applying network analytical tools, bridging the gap between network concepts and their empirical applications. We explain how we can use graphs to construct networks representing complex associations among observable psychological variables. We then discuss key network models, including dynamic networks, time-varying networks, network models derived from panel data, network intervention analysis, latent networks, and moderated models. In addition, we discuss Bayesian networks and their role in causal inference with a focus on cross-sectional data. After presenting the different methods, we discuss how network models and psychopathology theories can meaningfully inform each other. We conclude with a discussion that summarizes the insights each technique can provide in mental health research.

To develop a composite score for differential resilience to effects of combat-related stressors (CRS) on persistent DSM-IV post-traumatic stress disorder (PTSD) among US Army combat arms soldiers using survey data collected before deployment. A sample of n=2542 US Army combat arms soldiers completed a survey shortly before deployment to Afghanistan and then again two to three and 8-9months after redeployment. Retrospective self-reports were obtained about CRS. Precision treatment methods were used to determine whether differential resilience to persistent PTSD in the follow-up surveys could be developed from pre-deployment survey data in a 60% training sample and validated in a 40% test sample. 40.8% of respondents experienced high CRS and 5.4% developed persistent PTSD. Significant test sample heterogeneity was found in resilience (t=2.1, p=0.032), with average treatment effect (ATE) of high CRS in the 20% least resilient soldiers of 17.1% (SE=5.5%) compared to ATE=3.8% (SE=1.2%) in the remaining 80%. The most important predictors involved recent and lifetime pre-deployment distress disorders. A reliable pre-deployment resilience score can be constructed to predict variation in the effects of high CRS on persistent PTSD among combat arms soldiers. Such a score could be used to target preventive interventions to reduce PTSD or other resilience-related outcomes.

Ecological momentary interventions (EMI) are digital mobile health interventions administered in an individual's daily life to improve mental health by tailoring intervention components to person and context. Experience sampling via ecological momentary assessments (EMA) furthermore provides dynamic contextual information on an individual's mental health state. We propose a personalized data-driven generic framework to select and evaluate EMI based on EMA. We analyze EMA/EMI time-series from 10 individuals, published in a previous study. The EMA consist of multivariate psychological Likert scales. The EMI are mental health trainings presented on a smartphone. We model EMA as linear dynamical systems (DS) and EMI as perturbations. Using concepts from network control theory, we propose and evaluate three personalized data-driven intervention delivery strategies. Moreover, we study putative change mechanisms in response to interventions. We identify promising intervention delivery strategies that outperform empirical strategies in simulation. We pinpoint interventions with a high positive impact on the network, at low energetic costs. Although mechanisms differ between individuals - demanding personalized solutions - the proposed strategies are generic and applicable to various real-world settings. Combined with knowledge from mental health experts, DS and control algorithms may provide powerful data-driven and personalized intervention delivery and evaluation strategies.