Mental disorders affect nearly one-third of adults in Germany, with a 12-month prevalence of approximately 28%. Following Germany's 2019 Digital Care Act, digital health applications (Digitale Gesundheitsanwendungen [DiGA]) became reimbursable interventions by the statutory health insurance for mental health conditions. However, adoption remains uneven. General practitioners (GPs) issue most mental health DiGA prescriptions, while psychotherapists or psychiatrists prescribe far fewer, even though most DiGA target mental health. Existing studies imply profession-specific barriers but lack quantitative evidence on preference drivers or remain descriptive. Whether and how these preferences differ across professional groups has not been systematically quantified.

This study aimed to quantify and compare GPs' and psychotherapists' or psychiatrists' preferences for factors associated with DiGA prescription decisions using best-worst scaling (BWS).

A cross-sectional BWS study was conducted among outpatient GPs and psychotherapists or psychiatrists. Eleven DiGA objects were evaluated using a balanced incomplete block design. Preferences were analyzed using conditional logit regression. Likelihood ratio tests assessed differences between professional groups, with stratified models estimating group-specific odds ratios (OR) and 95% CIs.

Of 484 respondents (244 GPs and 240 psychotherapists or psychiatrists), 408 completed the BWS experiment. Group-specific ORs ranged from 0.47 to 1.72 (likelihood ratio test: χ²₁₀=328.76; P<.001), suggesting that GPs and psychotherapists or psychiatrists operate within a broadly shared evaluative space but assign different relative emphasis to specific factors influencing DiGA prescription decisions. Relative to the reference object (intuitive usability for patients), GPs most strongly preferred scientific recommendations (OR 1.72, 95% CI 1.42-2.09) and patients' interest in DiGA (OR 1.70, 95% CI 1.40-2.06) while significantly showing lower preferences for cross-device availability (OR 0.57, 95% CI 0.47-0.69) and access to patient-entered data (OR 0.47, 95% CI 0.39-0.57). Psychotherapists or psychiatrists showed different preference patterns, most strongly preferring device availability (OR 1.47, 95% CI 1.22-1.76) and contact points for technical support (OR 1.36, 95% CI 1.13-1.64) while showing significantly lower preference for alignment with scientific recommendations (OR 0.75, 95% CI 0.62-0.90).

Professional role is a source of preference heterogeneity for factors associated with DiGA prescription decisions. GPs assigned comparatively greater weight to evidence-based recommendations and patient interest, while psychotherapists or psychiatrists emphasized technical integration feasibility and peer experience. These structured differences in priority gradients indicate that uniform implementation approaches may not adequately reflect the evaluative frameworks of both professional groups.

Although the COVID-19 pandemic accelerated adoption of telehealth for mental health services, it is unclear how many, and which types, of mental health treatment facilities (MHTFs) offer telehealth mental health care to children.

To quantify what percentage of MHTFs in the US offer telehealth services for children and what facility-level, state-level, and county-level factors are associated with offering any telehealth services and specifically medication management via telehealth.

This cross-sectional study involved a secret shopper study of all outpatient MHTFs contained within the Substance Abuse and Mental Health Services Administration's Behavioral Health Treatment Locator, conducted from September 24, 2024, to May 12, 2025.

The primary outcomes were whether MHTFs offered (1) telehealth-based behavioral services for children and (2) telehealth-based medication management for children. Multivariable logistic regressions estimated associations between facility-level and county-level characteristics and each outcome.

Among 5559 respondent facilities (response rate, 73.0%), 4314 (77.6%) offered treatment to children. Among children-treating facilities, 3345 (77.5%) offered telehealth services, and 2419 (56.1%) offered telehealth-based medication management. Hawaii (1 of 1 facility [100.0%]), Montana (32 of 33 facilities [97.0%]), and Colorado (79 of 85 facilities [92.9%]) had the highest rates of offering telehealth services to children, whereas Georgia (52 of 81 facilities [64.2%]), Vermont (9 of 15 facilities [60.0%]), and Alabama (25 of 46 facilities [54.3%]) had the lowest. Noncommunity mental health centers had lower odds (adjusted odds ratio [aOR], 0.36; 95% CI, 0.25-0.52) of offering telehealth for children compared with community mental health centers. Facilities that did not accept Medicaid or private insurance as a form of payment were less likely to provide medication management via telehealth (aOR, 0.46; 95% CI, 0.25-0.86) compared with facilities that accepted Medicaid as a form of payment. Public facilities had greater odds of providing telehealth-based medication management (aOR, 1.86; 95% CI, 1.20-2.88) than private for-profit facilities. Facilities in counties in the second (aOR, 0.67; 95% CI, 0.48-0.93), third (aOR, 0.66; 95% CI, 0.45-0.97), and highest (aOR, 0.67; 95% CI, 0.45-0.99) household income quartiles were less likely to offer telehealth medication management.

In this cross-sectional study of a sample of US MHTFs, most facilities offered telehealth services for children, but the offering varied across facility types and states. Addressing potential gaps in telehealth offering will be essential to advancing equity and ensuring that all children can benefit from emerging modalities of remote mental health care.

This study aimed to evaluate the cost effectiveness of delivering treatment interventions for major depression among Ethiopian adults aged 18-64 years.

A multiple cohort Markov model was developed to simulate how population cohorts move between three health states over time: healthy, depression and dead. Three drug interventions (tricyclic antidepressants [TCAs], selective serotonin reuptake inhibitors [SSRIs] and serotonin norepinephrine reuptake inhibitors [SNRIs]), one non-pharmacological therapy (psychotherapy) and one combination therapy (drug plus psychotherapy) were compared to a common comparator, a partial null scenario. We modelled interventions for Ethiopians aged 18-64 years with major depression in 2021 (n = 2,431,898). The study employed a cost-utility analysis framework to estimate the incremental cost-effectiveness ratios (ICERs), expressed as a cost per quality-adjusted life year (QALY). The model was run over a 10-year period, adopted a health sector perspective to estimate population-level costs and benefits with a 3% annual discount rate. Uncertainty analysis was conducted using a Monte Carlo simulation with 3000 iterations.

Psychotherapy was associated with incremental cost of US$17million (M) (95% CI US$9M-US$26M) and QALY gains of 11,857 (95% CI 125-35,227). In comparison, antidepressants, such as SNRIs, had a higher cost of US$236M (95% confidence interval [CI] US$132M-US$353M) with QALY gains of 15,369 (95% CI - 304 to 50,059). Combination therapy was associated with the highest health benefit (40,755 QALYs [95% CI - 210 to 124,928]) and incurred an incremental cost of US$162M (95% CI US$89M-US$244M). The ICER for psychotherapy was US$1,419 per QALY gained (95% CI US$344-US$22,433/QALY), suggesting cost effectiveness when adopting a one-times GDP per capita per QALY threshold. In contrast, combination therapy had an ICER of US$3,973/QALY (95% CI dominated to $63,677/QALY) and may be an appropriate option for individuals requiring both pharmacotherapy and psychotherapy. Conversely, drug therapies did not appear to be cost effective.

Psychotherapy appears to be a cost-effective intervention in Ethiopia, while combination therapy may be an alternative cost-effective option. However, access to psychotherapy and combination therapy in Ethiopia is currently restricted to hospitals and the private sector, largely due to a shortage of trained professionals such as clinical psychologists. To address this gap, policymakers should explore cost-effective strategies to expand the availability of psychotherapy services.

Research on chronic suicidal thoughts and behaviors (STB) in Australia is limited. This study aimed to determine the prevalence and identify predictors of chronic STB among Australian males.

A nine-year retrospective cohort study was conducted, analysing data from the first four waves of the Ten to Men Study. The study included 3,070 males who reported lifetime STB at Wave 1. Chronic STB was defined as the presence suicidal thoughts, plans, or attempts over subsequent waves. Modified Poisson and mixed-effects Poisson regression models were used to identify predictors.

The prevalence of chronic suicidal thoughts and attempts were 34.5% and 13.6%, respectively, with an overall chronic STB prevalence of 37.2%. At Wave 2, depression (IRR = 1.59; 95% CI [1.28, 1.98]; P < 0.001), homosexuality (IRR = 1.50; 95% CI [1.06, 2.14]; P = 0.023), and alcohol use disorder (IRR = 1.21; 95% CI [1.01, 1.46]; P = 0.044) were significantly associated with chronic STB. Over nine years, depression (IRR = 2.57; 95% CI [2.05, 3.22]; P < 0.001), alcohol use disorder (IRR = 1.53; 95% CI [1.27, 1.85]; P < 0.001), disability (IRR = 1.43; 95% CI [1.12, 1.82]; P = 0.004), and marijuana use (IRR = 1.33; 95% CI [1.05, 1.67]; P = 0.016) were key predictors.

Among Australian men who report lifetime STB, more than one in three experience chronic STB over a nine-year period. Depression, alcohol use disorder, disability, illicit substance use, and socioeconomic factors emerged as primary predictors. Further research is needed to explore intervention strategies addressing these factors.

Assess the utility of access to healthcare, clinical conditions, and social determinants of health (SDoH) variables in population-level suicide prediction models.

Negative binomial regression models were constructed using data from population-level surveys, state death certificates, federal records of behavioral health services, and U.S. Census data. Outcomes of interest were suicidal ideation and suicide attempt (SISA), inpatient psychiatric hospitalization (IPH), and suicide death. The relative changes in pseudo R² were used to assess the impact of variable categories (i.e., clinical conditions, access to healthcare, and geo-derived SDoH) when added to a demographic-only baseline suicide prediction model.

Clinical data showed a significant impact, with the largest percent increase in pseudo R² compared to the demographic-only baseline model (321.9% for SISA; 736.9% for IPH; 18.9% for suicide death). Access to healthcare and geo-derived SDoH also improved model performances for all outcomes, but considerably lower than clinical variables. Models with all variable categories had the highest pseudo R², with .68, .58, and .46 for SISA, IPH, and suicide, respectively. Availability of emergency mental health services was found to be protective against IPH (IRR .90; 95% CI .84-.96) and suicide death (IRR .91; 95% CI .84-.97).

Clinical data proved to have the most effective variables in predicting a continuum of suicidal behaviors. While the impacts of access to healthcare and SDoH factors were comparatively limited, these variables also contributed to additional model improvements. These findings show the utility of population-level healthcare services and SDoH for ecological suicide behavior risk prediction.

Maternal attention-deficit/hyperactivity disorder (ADHD) is increasingly recognised during the reproductive years, often with delayed diagnosis. Although ADHD has been linked to perinatal complications, it remains unclear whether risks reflect ADHD itself, comorbidity, vulnerability, behaviours, and medication use. We examined associations between maternal ADHD, stratified by diagnosis before and after childbirth, and ADHD medication exposure during pregnancy with a range of pregnancy, childbirth, and neonatal outcomes.

We conducted a nationwide register-based cohort study, including 741,905 singleton live births in Denmark (2010-2022). Childbirths were classified by maternal ADHD diagnosis before childbirth (n = 12,859), after childbirth (n = 15,683), and no ADHD (n = 713,363). Among women diagnosed before childbirth, ADHD medication exposure was classified; no exposure (n = 10,118), first-trimester (n = 1,129) and continued (n = 1,612) based on prescription-timing. Analyses were preformed using Poisson GEE, accounting for repeated births and adjusting for sociodemographics, psychiatric history, and somatic comorbidity.

Maternal ADHD diagnosed before childbirth was associated with preterm childbirth (aRR 1.13, 95% CI 1.04-1.22; aRR 1.29, 95% CI 1.07-1.56) and low birthweight (aRR 1.19, 95% CI 1.09-1.30). Early pregnancy haemorrhage was modestly elevated for ADHD diagnosed before and after childbirth (aRR 1.15, 95% CI 1.08-1.25; aRR 1.14, 95% CI 1.07-1.22). ADHD diagnosed after childbirth was associated with infection (aRR 1.19, 95% CI 1.11-1.27), hyperemesis (aRR 1.23, 95% CI 1.13-1.34), and Apgar score < 7) (moderate aRR 1.48, 95% CI 1.09-2.01; severe aRR 1.32, 95% CI 1.09-1.59). ADHD medication in pregnancy (vs. unmedicated) was associated with gestational hypertension (first-trimester aRR 1.57, 95% CI 1.21-2.02; continued aRR 1.39, 95% CI 1.01-1.91).

Maternal ADHD, whether diagnosed before childbirth or postpartum, was associated with small increases in selected obstetric and neonatal risks after adjustment for relevant sociodemographic and clinical factors. Continued use of ADHD medication was significantly associated with gestational hypertension only. However, these findings should be interpreted with caution due to the potential for residual confounding, including confounding by indication.

Obesity disproportionately affects Hispanic women in the United States, particularly in underserved communities within the southeast. This project aimed to build infrastructure for patient-centered outcomes research (PCOR) to address obesity among Hispanic women in the southeastern United States. A three-phase approach, led by a core team of five, was implemented to engage 35 stakeholders, including Hispanic women, family members, community leaders, and healthcare providers. Phase 1 involved bilingual, culturally tailored workshops focused on Hispanic health, obesity, and research methods to train stakeholders to engage in PCOR. In Phase 2, stakeholders collaboratively developed and prioritized 18 PCOR questions across 12 topical areas, with key concerns focused on language barriers, alternative therapies, and obesity. Additional priorities included mental health, chronic disease management, nutrition, and women's health. Phase 3 focused on establishing a sustainable research community to support ongoing engagement and address the PCOR questions. This initiative laid the foundation for continued PCOR efforts, strengthened partnerships, and empowered stakeholders to guide future research. The project offers a replicable model for developing community-driven research infrastructure to improve health outcomes and advance equity among Hispanic women in similar settings.

The Campi Flegrei area, one of the most active volcanic regions in Italy, has faced a prolonged crisis, characterized by recurring seismic events. By adopting a psychosocial and community-based perspective, this study investigates how seismic and volcanic risk perception, perceived severity, trust in institutions and media, and coping strategies influence the psychological impact of the emergency. Additionally, residents' psychosocial well-being was assessed using Keyes' Mental Health Continuum Model, classifying participants as flourishing, moderate, or languishing. Data were collected through a questionnaire administered to 532 residents living in the Campi Flegrei red zone. Regression analyses showed that risk perception, perceived severity, and maladaptive coping strategies positively predicted psychological impact, while trust in sources negatively predicted it. ANOVA showed that languishing individuals reported worse psychological outcomes. These results highlight the urgent need for targeted community-based psychosocial interventions to mitigate the adverse effects of prolonged exposure to seismic and volcanic hazards.

As community mental health services expand in scope and responsibility, mental health nurses are playing a central role in delivering integrated, recovery-oriented care. However, they face substantial challenges in adapting to community practice and sustaining professional development. Clinical supervision can mitigate these demands through its formative, normative, restorative functions. This study explored the supervision experiences of community mental health nurse specialists in South Korea to provide evidence for specifying the effective functions and operational systems of supervision using an exploratory qualitative design. Twelve community mental health nurse specialists participated in three focus group interviews conducted between June 2024 and July 2025. Data were analysed using reflexive thematic analysis. Four themes and 10 subthemes were generated: translating community mental health ideals into professional practice; serving as an anchor in unfamiliar terrain; supervision undermined by superficiality and disrespect; and competency development constrained by structural barriers. Supervision was perceived as a critical space for clarifying clinical direction, regulating emotional involvement and supporting professional adaptation. However, formalistic delivery and structural constraints limited its impact. These findings highlight the need for integrated and systematic supervision approaches aligned with the community mental health paradigm, supported by organisational and policy commitments to protected time, staffing, education and financial resources. Strengthening supervision under such conditions may enhance practitioner development and service user outcomes.

BackgroundDepression is a widespread mental health disorder that disproportionately affects women of reproductive age due to a combination of biological, social, and environmental factors. It significantly impacts productivity, increases morbidity and disability, and poses challenges to the global economy. In Bangladesh, there have been few studies addressing this issue using modern analytical methods, despite its importance for public health.ObjectivesThe study aims to develop the best predictive model for depression risk factor analysis and to assess the PHQ-9 scale.DesignThis study extracted data from the cross-sectional survey.MethodsWe utilized data from the BDHS 2022, which gathered information on depression using the Patient Health Questionnaire (PHQ-9). The study included 13,113 ever-married women aged 15-49 years. To develop the predictive model, several machine learning algorithms were used. The performance of each model was assessed using metrics such as accuracy, precision, recall, and specificity. SHapley Additive exPlanations (SHAP) analysis was conducted to interpret and rank each feature's contribution to the model's output.ResultsApproximately 4.54% of women experienced moderate to severe depression. The Boruta algorithm identified 21 significant risk factors from a total of 25 variables, spanning demographic, socioeconomic, household, and reproductive domains, for predicting depressive symptoms. The Random Forest (RF) and Decision Tree models showed good performance across different performance metrics, achieving sensitivity of (0.068, 95% CI:0.064-0.072) and (0.409, 95% CI:0.395-0.423), specificity of (0.946, 95% CI:0.945-0.948) and (0.640, 95% CI: 0.629-0.651), and accuracy of (0.906, 95% CI:0.905-0.907), and (0.630, 95% CI:0.620-0.641). Whereas, boosting models also showed comparable performance. SHAP analysis revealed that household size, number of children under 5 in the household, and number of women in the household were the most influential predictors.ConclusionThe study demonstrated the effectiveness of the RF and decision tree model in detecting depression among Bangladeshi women, proving to be a valuable tool for identifying and predicting risk factors related to women's mental health. The findings indicate that combining machine learning with the PHQ-9 would help screen for depressive symptoms in large-scale public health settings while accounting for different covariate effects.