The aim of the study was to investigate longitudinal trajectories of change in anxiety and depression symptoms in Polish adolescents during the second year of the COVID-19 pandemic and after the outbreak of the war in Ukraine. Additionally, we aimed to identify risk/protective factors and outcomes associated with these trajectories.

We collected data in three waves between November 2021 and May 2022. Adolescents (N = 281 in the first wave) completed the Patient Health Questionnaire-9, the Generalized Anxiety Disorder-7, the Filial Responsibility Scale for Youth, and questions related to the COVID-19 pandemic and war in Ukraine.

We identified three trajectories of depressive symptoms: resilient with low, stable symptoms (71% of participants), chronically elevated symptoms (11%), and acute symptoms followed by recovery (18%). We distinguished two trajectories of anxiety symptoms: resilient (75%) and chronic (25%). Non-resilient trajectories were predicted by higher levels of familial unfairness (perceived lack of equality and reciprocity in the family), relationship difficulties at school and at home, older age, and poor socioeconomic status. Chronic depressive and anxiety symptoms were associated with higher war-related concerns.

These findings can inform preventive and therapeutic interventions for at-risk adolescents to reduce negative long-term outcomes of social crises.

Visual and auditory signs of patient functioning have long been used for clinical diagnosis, treatment selection, and prognosis. Direct measurement and quantification of these signals can aim to improve the consistency, sensitivity, and scalability of clinical assessment. Currently, we investigate if machine learning-based computer vision (CV), semantic, and acoustic analysis can capture clinical features from free speech responses to a brief interview 1 month post-trauma that accurately classify major depressive disorder (MDD) and posttraumatic stress disorder (PTSD).

N = 81 patients admitted to an emergency department (ED) of a Level-1 Trauma Unit following a life-threatening traumatic event participated in an open-ended qualitative interview with a para-professional about their experience 1 month following admission. A deep neural network was utilized to extract facial features of emotion and their intensity, movement parameters, speech prosody, and natural language content. These features were utilized as inputs to classify PTSD and MDD cross-sectionally.

Both video- and audio-based markers contributed to good discriminatory classification accuracy. The algorithm discriminates PTSD status at 1 month after ED admission with an AUC of 0.90 (weighted average precision = 0.83, recall = 0.84, and f1-score = 0.83) as well as depression status at 1 month after ED admission with an AUC of 0.86 (weighted average precision = 0.83, recall = 0.82, and f1-score = 0.82).

Direct clinical observation during post-trauma free speech using deep learning identifies digital markers that can be utilized to classify MDD and PTSD status.

Complicated and persistent grief reactions afflict approximately 10% of bereaved individuals and are associated with severe disruptions of functioning. These maladaptive patterns were defined in Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) as persistent complex bereavement disorder (PCBD), but its criteria remain debated. The condition has been studied using network analysis, showing potential for an improved understanding of PCBD. However, previous studies were limited to self-report and primarily originated from a single archival dataset. To overcome these limitations, we collected structured clinical interview data from a community sample of newly conjugally bereaved individuals (N = 305).

Gaussian graphical models (GGM) were estimated from PCBD symptoms diagnosed at 3, 14, and 25 months after the loss. A directed acyclic graph (DAG) was generated from initial PCBD symptoms, and comorbidity networks with DSM-5 symptoms of major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) were analyzed 1 year post-loss.

In the GGM, symptoms from the social/identity PCBD symptoms cluster (i.e. role confusion, meaninglessness, and loneliness) tended to be central in the network at all assessments. In the DAG, yearning activated a cascade of PCBD symptoms, suggesting how symptoms lead into psychopathological configurations. In the comorbidity networks, PCBD and depressive symptoms formed separate communities, while PTSD symptoms divided in heterogeneous clusters.

The network approach offered insights regarding the core symptoms of PCBD and the role of persistent yearnings. Findings are discussed regarding both clinical and theoretical implications that will serve as a step toward a more integrated understanding of PCBD.

To determine whether there were incremental mental health impacts, specifically on depression trajectories, as a result of the 2008 economic crisis (the Great Recession) and subsequent Hurricane Sandy.

Using latent growth mixture modeling and the ORANJ BOWL dataset, we examined prospective trajectories of depression among older adults (mean age, 60.67; SD, 6.86) who were exposed to the 2 events. We also collected community economic and criminal justice data to examine their impact upon depression trajectories. Participants (N=1172) were assessed at 3 times for affect, successful aging, and symptoms of depression. We additionally assessed posttraumatic stress disorder (PTSD) symptomology after Hurricane Sandy.

We identified 3 prospective trajectories of depression. The majority (83.6%) had no significant change in depression from before to after these events (resilience), while 7.2% of the sample increased in depression incrementally after each event (incremental depression). A third group (9.2%) went from high to low depression symptomology following the 2 events (depressive-improving). Only those in the incremental depression group had significant PTSD symptoms following Hurricane Sandy.

We identified a small group of individuals for whom the experience of multiple stressful events had an incremental negative effect on mental health outcomes. These results highlight the importance of understanding the perseveration of depression symptomology from one event to another. (Disaster Med Public Health Preparedness. 2019;13:173–182)

Most previous attempts to determine the psychological cost of military deployment have been limited by reliance on convenience samples, lack of pre-deployment data or confidentiality and cross-sectional designs.

This study addressed these limitations using a population-based, prospective cohort of US military personnel deployed in support of the operations in Iraq and Afghanistan.

The sample consisted of US military service members in all branches including active duty, reserve and national guard who deployed once (n = 3393) or multiple times (n = 4394). Self-reported symptoms of post-traumatic stress were obtained prior to deployment and at two follow-ups spaced 3 years apart. Data were examined for longitudinal trajectories using latent growth mixture modelling.

Each analysis revealed remarkably similar post-traumatic stress trajectories across time. The most common pattern was low–stable post-traumatic stress or resilience (83.1% single deployers, 84.9% multiple deployers), moderate–improving (8.0%, 8.5%), then worsening–chronic posttraumatic stress (6.7%, 4.5%), high–stable (2.2% single deployers only) and high–improving (2.2% multiple deployers only). Covariates associated with each trajectory were identified.

The final models exhibited similar types of trajectories for single and multiple deployers; most notably, the stable trajectory of low post-traumatic stress pre- to post-deployment, or resilience, was exceptionally high. Several factors predicting trajectories were identified, which we hope will assist in future research aimed at decreasing the risk of post-traumatic stress disorder among deployers.