This study aimed to identify risk and protective factors for mental health across student cohorts to guide mental health provision.

Cross-sectional data from the My World Survey 2-Post Second Level (MWS2-PSL) were used. The sample consisted of N = 9935 students (18–65 years) from 12 third-level institutions (7 out of 7 universities and 5 out of 14 Institute of Technologies (IoTs)) across Ireland. Key outcomes of interest were depression, anxiety and suicidality. Risk factors included drug/alcohol use, risky sexual behaviours and exposure to stressors. Protective factors included coping strategies, help-seeking, resilience, self-esteem, life satisfaction, optimism and social support. These factors were profiled by degree type (undergraduate, postgraduate taught, postgraduate research), access route, and institution type (IoT, university). Chi-square tests of Independence and one-way ANOVAs compared groups on key risk and protective factors.

A total 71% of respondents were female, 85% were aged 23 or under and there was a 2.2% response rate in IoTs versus 10.6% in university students. Undergraduates demonstrated higher levels of depression, anxiety, self-harm and suicidal ideation than postgraduates. Undergraduates showed higher risk and lower protective factors than postgraduates. Students attending Institutes of Technology reported higher levels of depression and anxiety, lower protective and higher risk factors than university students.

In this sample of students, undergraduates, especially those attending Institutes of Technology, were at increased risk of mental health difficulties. Findings suggest the need to tailor interventions to meet cohort needs, and consider the differing vulnerabilities and strengths across student cohorts. Due to limitations of this study, such as selection bias, further research is warranted.

Despite a wide range of proposed risk factors and theoretical models, prediction of eating disorder (ED) onset remains poor. This study undertook the first comparison of two machine learning (ML) approaches [penalised logistic regression (LASSO), and prediction rule ensembles (PREs)] to conventional logistic regression (LR) models to enhance prediction of ED onset and differential ED diagnoses from a range of putative risk factors.

Data were part of a European Project and comprised 1402 participants, 642 ED patients [52% with anorexia nervosa (AN) and 40% with bulimia nervosa (BN)] and 760 controls. The Cross-Cultural Risk Factor Questionnaire, which assesses retrospectively a range of sociocultural and psychological ED risk factors occurring before the age of 12 years (46 predictors in total), was used.

All three statistical approaches had satisfactory model accuracy, with an average area under the curve (AUC) of 86% for predicting ED onset and 70% for predicting AN v. BN. Predictive performance was greatest for the two regression methods (LR and LASSO), although the PRE technique relied on fewer predictors with comparable accuracy. The individual risk factors differed depending on the outcome classification (EDs v. non-EDs and AN v. BN).

Even though the conventional LR performed comparably to the ML approaches in terms of predictive accuracy, the ML methods produced more parsimonious predictive models. ML approaches offer a viable way to modify screening practices for ED risk that balance accuracy against participant burden.

Varied longitudinal courses of suicidal ideation (SI) may be linked to unique sets of risk and protective factors.

A national probability sample of 2291 U.S. veterans was followed over four assessments spanning 7 years to examine how a broad range of baseline risk and protective factors predict varying courses of SI.

Most veterans (82.6%) denied SI at baseline and all follow-ups, while 8.7% had new onset SI, 5.4% chronic SI, and 3.3% remitted SI. Compared to the no-SI group, chronic SI was associated with childhood trauma, baseline major depressive and/or posttraumatic stress disorder (MDD/PTSD), physical health difficulties, and recent traumatic stressors. Remitted veterans had the highest risk of a prior suicide attempt (SA) compared to no-SI [relative risk ratio (RRR) = 3.31] and chronic SI groups (RRR = 4.65); and high rates of MDD/PTSD (RRR = 7.62). New onset SI was associated with recent stressors and physical health difficulties. All symptomatic SI groups reported decrements in protective factors, specifically, social connectedness, trait curiosity/exploration, and purpose in life.

Nearly one-in-five veterans reported SI over a 7-year period, most of whom evidenced new onset or remitted SI courses. Chronic and remitted SI may represent particularly high-risk SI courses; the former was associated with higher rates of prospective SA, and psychiatric and physical distress, and the latter with increased likelihood of prior SA, and isolation from social and mental health supports. Physical disability, MDD/PTSD, and recent stressors may be important precipitating or maintaining factors of SI, while social connectedness may be a key target for suicide prevention efforts.

With longitudinal executive function (EF) data from the Victoria Longitudinal Study, we investigated three research goals pertaining to key characteristics of EF in non-demented aging: (a) examining variability in EF longitudinal trajectories, (b) establishing trajectory classes, and (c) identifying biomarker predictors discriminating these classes.

We used a trajectory analyses sample (n = 781; M age = 71.42) for the first and second goals and a prediction analyses sample (n = 570; M age = 70.10) for the third goal. Eight neuropsychological EF measures were used as indicators of three EF dimensions: inhibition, updating, and shifting. Data-driven classification analyses were applied to the full trajectory distribution. Machine learning prediction analyses tested 15 predictors from genetic, functional, lifestyle, mobility, and demographic risk domains.

First, we observed: (a) significant variability in EF trajectories over a 40-year band of aging and (b) significantly variable patterns of EF decline. Second, a four-class EF trajectory model was observed, characterized with classes differentiated by an algorithm of level and slope information. Third, the highest group class was discriminated from lowest by several prediction factors: more education, more novel cognitive activity, lower pulse pressure, younger age, faster gait, lower body mass index, and better balance.

First, with longitudinal variability in EF aging, the data-driven approach showed that long-term trajectories can be differentiated into separable classes. Second, prediction analyses discriminated class membership by a combination of multiple biomarkers from demographic, lifestyle, functional, and mobility domains of risk for brain and cognitive aging decline.

There is a paucity of long-term prospective disaster studies of the psychological sequelae among survivors.

At 1½ and 25 years after the Spitak earthquake, 142 early adolescents from two cities were assessed: Gumri (moderate–severe exposure) and Spitak (very severe exposure). The Gumri group included treated and not-treated subjects, while the Spitak group included not-treated subjects. Instruments included: DSM-III-R PTSD-Reaction Index (PTSD-RI); DSM-5 PTSD-Checklist (PCL); Depression Self-Rating Scale (DSRS); and Center for Epidemiological Studies-Depression Scale (CES-D).

(1) Between 1½ and 25 years, PTSD rates and mean scores decreased significantly in the three groups (over 50%). However, at 25 years 9.1–22.4% met DSM-5 PTSD criteria. (2) At 1½ years, the Spitak group had higher PTSD-RI (p < 0.001) and DSRS scores (p < 0.001) compared to the Gumri-not-treated group. At 25 years, the Spitak group that had experienced fewer post-earthquake adversities (p < 0.03), had a greater decrease in PTSD-RI scores (p < 0.02), and lower CES-D scores (p < 0.01). (3) Before treatment, PTSD-RI and DSRS scores did not differ between the Gumri-treated and not-treated groups. At 25-years, the Gumri-treated group showed a greater decrease in PTSD-RI scores (p < 0.03), and lower mean PTSD-RI (p < 0.02), PCL (p < 0.02), and CES-D (p < 0.01) scores. (4) Predictors of PTSD symptom severity at 25-years included: home destruction, treatment, social support, post-earthquake adversities, and chronic medical illnesses.

Post-disaster PTSD and depressive symptoms can persist for decades. Trauma-focused treatment, alleviation of post-disaster adversities, improving the social ecology, and monitoring for chronic medical illnesses are essential components of recovery programs.