Prospective Association of Unmet Mental Health Treatment Needs With Suicidal Behavior Among Combat-Deployed Soldiers.

OBJECTIVE: Military personnel frequently report discontinuing or not pursuing psychiatric treatment despite perceiving a need for services. This study aimed to examine how unmet need for treatment or support among U.S. Army soldiers relates to future suicidal ideation (SI) or suicide attempt (SA). METHODS: Mental health treatment need and help seeking in the past 12 months were evaluated for soldiers (N=4,645) who subsequently deployed to Afghanistan. Weighted logistic regression models were used to examine the prospective association of predeployment treatment needs with SI and SA during and after deployment, with adjustment for potential confounders. RESULTS: Compared with soldiers without predeployment treatment needs, those who reported not seeking help despite needing it had increased risk for SI during deployment (adjusted OR [AOR]=1.73), past-30-day SI at 2-3 months postdeployment (AOR=2.08), past-30-day SI at 8-9 months postdeployment (AOR=2.01), and SA through 8-9 months postdeployment (AOR=3.65). Soldiers who sought help and stopped treatment without improvement had elevated SI risk at 2-3 months postdeployment (AOR=2.35). Those who sought help and stopped after improving did not have increased SI risk during or 2-3 months after deployment but had elevated risks for SI (AOR=1.71) and SA (AOR=3.43) by 8-9 months postdeployment. Risks for all suicidality outcomes were also elevated among soldiers who reported receiving ongoing treatment before deployment. CONCLUSIONS: Unmet or ongoing needs for mental health treatment or support before deployment are associated with increased risk for suicidal behavior during and after deployment. Detecting and addressing treatment needs among soldiers before deployment may help prevent suicidality during deployment and reintegration periods.

Associations of polygenic risk scores with posttraumatic stress symptom trajectories following combat deployment.

BACKGROUND: Identification of genetic risk factors may inform the prevention and treatment of posttraumatic stress disorder (PTSD). This study evaluates the associations of polygenic risk scores (PRS) with patterns of posttraumatic stress symptoms following combat deployment. METHOD: US Army soldiers of European ancestry (n = 4900) provided genomic data and ratings of posttraumatic stress symptoms before and after deployment to Afghanistan in 2012. Latent growth mixture modeling was used to model posttraumatic stress symptom trajectories among participants who provided post-deployment data (n = 4353). Multinomial logistic regression models tested independent associations between trajectory membership and PRS for PTSD, major depressive disorder (MDD), schizophrenia, neuroticism, alcohol use disorder, and suicide attempt, controlling for age, sex, ancestry, and exposure to potentially traumatic events, and weighted to account for uncertainty in trajectory classification and missing data. RESULTS: Participants were classified into low-severity (77.2%), increasing-severity (10.5%), decreasing-severity (8.0%), and high-severity (4.3%) posttraumatic stress symptom trajectories. Standardized PTSD-PRS and MDD-PRS were associated with greater odds of membership in the high-severity v. low-severity trajectory [adjusted odds ratios and 95% confidence intervals, 1.23 (1.06-1.43) and 1.18 (1.02-1.37), respectively] and the increasing-severity v. low-severity trajectory [1.12 (1.01-1.25) and 1.16 (1.04-1.28), respectively]. Additionally, MDD-PRS was associated with greater odds of membership in the decreasing-severity v. low-severity trajectory [1.16 (1.03-1.31)]. No other associations were statistically significant. CONCLUSIONS: Higher polygenic risk for PTSD or MDD is associated with more severe posttraumatic stress symptom trajectories following combat deployment. PRS may help stratify at-risk individuals, enabling more precise targeting of treatment and prevention programs.

Recent Stressful Experiences and Suicide Risk: Implications for Suicide Prevention and Intervention in U.S. Army Soldiers.

Objectives: To identify the extent to which the presence of recent stressful events are risk factors for suicide among active-duty soldiers as reported by informants. Methods: Next-of-kin (NOK) and supervisors (SUP) of active duty soldiers (n = 135) who died by suicide and two groups of living controls: propensity-matched (n = 128) and soldiers who reported suicidal ideation in the past year, but did not die (SI) (n = 108) provided data via structured interviews from the Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Multivariate logistic regression analyses were used to create a risk score for suicide. Results: The odds of suicide increased significantly for soldiers experiencing relationship problems, military punishment, and perceived failure or humiliation in the month prior to death. Suicide risk models with these risk factors predicted suicide death among those who reported SI in the past year (OR = 5.9, [95% CI = 1.5, 24.0] χ 2 = 6.24, p = 0.0125, AUC, 0.73 (0.7, 0.8) NOK) and (OR = 8.6, [95% CI = 1.4, 51.5] χ 2 = 5.49, p = 0.0191, AUC, 0.78 (0.7, 0.8); SUP) suggesting the combination of these recent stressors may contribute to the transition from ideation to action. Conclusions: Our findings suggest for the first time recent stressors distinguished suicide ideating controls from suicide decedents in the month prior to death as reported by informants. Implications for preventive intervention efforts for clinicians, supervisors and family members in identifying the transition from ideation to action are discussed.

Internal capsule microstructure mediates the relationship between childhood maltreatment and PTSD following adulthood trauma exposure.

Childhood trauma is a known risk factor for trauma and stress-related disorders in adulthood. However, limited research has investigated the impact of childhood trauma on brain structure linked to later posttraumatic dysfunction. We investigated the effect of childhood trauma on white matter microstructure after recent trauma and its relationship with future posttraumatic dysfunction among trauma-exposed adult participants (n = 202) recruited from emergency departments as part of the AURORA Study. Participants completed self-report scales assessing prior childhood maltreatment within 2-weeks in addition to assessments of PTSD, depression, anxiety, and dissociation symptoms within 6-months of their traumatic event. Fractional anisotropy (FA) obtained from diffusion tensor imaging (DTI) collected at 2-weeks and 6-months was used to index white matter microstructure. Childhood maltreatment load predicted 6-month PTSD symptoms (b = 1.75, SE = 0.78, 95% CI = [0.20, 3.29]) and inversely varied with FA in the bilateral internal capsule (IC) at 2-weeks (p = 0.0294, FDR corrected) and 6-months (p = 0.0238, FDR corrected). We observed a significant indirect effect of childhood maltreatment load on 6-month PTSD symptoms through 2-week IC microstructure (b = 0.37, Boot SE = 0.18, 95% CI = [0.05, 0.76]) that fully mediated the effect of childhood maltreatment load on PCL-5 scores (b = 1.37, SE = 0.79, 95% CI = [-0.18, 2.93]). IC microstructure did not mediate relationships between childhood maltreatment and depressive, anxiety, or dissociative symptomatology. Our findings suggest a unique role for IC microstructure as a stable neural pathway between childhood trauma and future PTSD symptoms following recent trauma. Notably, our work did not support roles of white matter tracts previously found to vary with PTSD symptoms and childhood trauma exposure, including the cingulum bundle, uncinate fasciculus, and corpus callosum. Given the IC contains sensory fibers linked to perception and motor control, childhood maltreatment might impact the neural circuits that relay and process threat-related inputs and responses to trauma.

Stressful Life Events and Risk of Homelessness After Active Duty: An Assessment of Risk and Resilience Among Servicemembers.

OBJECTIVES: The transition from military to civilian life may present increased exposure to various stressful life events (SLEs) that can increase the risk of homelessness (eg, loss of employment, dissolution of romantic relationships). We assessed the extent to which exposure to SLEs occurring proximal to US Army soldier transitions out of active duty was associated with risk of homelessness. METHODS: A total of 16 589 respondents who were no longer on active duty but participated while on active duty during 2011-2014 baseline surveys completed follow-up surveys during 2016-2018 and 2018-2019. The follow-up surveys assessed SLEs and homelessness occurring in the past 12 months. We used modified Poisson regression models to evaluate how much differential SLE exposure and effects explained the aggregate association of a risk index with homelessness among a sample of 6837 respondents, weighted to represent the full sample. RESULTS: More than half (n = 3510, 52.8%) of respondents reported experiencing any SLEs in the past 12 months. Most (60.5%) of the difference in prevalence of homelessness among respondents defined as being at high risk of homelessness (vs lower risk) was explained by differential exposure to, and/or effects of, these SLEs. Personal betrayal by a loved one and economic problems played the largest roles in adjusted risk differences (0.045 and 0.074, respectively). CONCLUSIONS: Homelessness might be reduced by gearing interventions toward soldiers at high risk of homelessness who are transitioning out of active duty to reduce exposure to and effects of modifiable SLEs on experiencing homelessness.

Four 2×2 factorial trials of smartphone CBT to reduce subthreshold depression and to prevent new depressive episodes among adults in the community-RESiLIENT trial (Resilience Enhancement with Smartphone in LIving ENvironmenTs): a master protocol.

INTRODUCTION: The health burden due to depression is ever increasing in the world. Prevention is a key to reducing this burden. Guided internet cognitive-behavioural therapies (iCBT) appear promising but there is room for improvement because we do not yet know which of various iCBT skills are more efficacious than others, and for whom. In addition, there has been no platform for iCBT that can accommodate ongoing evolution of internet technologies. METHODS AND ANALYSIS: Based on our decade-long experiences in developing smartphone CBT apps and examining them in randomised controlled trials, we have developed the Resilience Training App Version 2. This app now covers five CBT skills: cognitive restructuring, behavioural activation, problem-solving, assertion training and behaviour therapy for insomnia. The current study is designed as a master protocol including four 2×2 factorial trials using this app (1) to elucidate specific efficacies of each CBT skill, (2) to identify participants' characteristics that enable matching between skills and individuals, and (3) to allow future inclusion of new skills. We will recruit 3520 participants with subthreshold depression and ca 1700 participants without subthreshold depression, to examine the short-term efficacies of CBT skills to reduce depressive symptoms in the former and to explore the long-term efficacies in preventing depression in the total sample. The primary outcome for the short-term efficacies is the change in depressive symptoms as measured with the Patient Health Questionnaire-9 at week 6, and that for the long-term efficacies is the incidence of major depressive episodes as assessed by the computerised Composite International Diagnostic Interview by week 50. ETHICS AND DISSEMINATION: The trial has been approved by the Ethics Committee of Kyoto University Graduate School of Medicine (C1556). TRIAL REGISTRATION NUMBER: UMIN000047124.

Use of serial smartphone-based assessments to characterize diverse neuropsychiatric symptom trajectories in a large trauma survivor cohort.

The authors sought to characterize adverse posttraumatic neuropsychiatric sequelae (APNS) symptom trajectories across ten symptom domains (pain, depression, sleep, nightmares, avoidance, re-experiencing, anxiety, hyperarousal, somatic, and mental/fatigue symptoms) in a large, diverse, understudied sample of motor vehicle collision (MVC) survivors. More than two thousand MVC survivors were enrolled in the emergency department (ED) and completed a rotating battery of brief smartphone-based surveys over a 2-month period. Measurement models developed from survey item responses were used in latent growth curve/mixture modeling to characterize homogeneous symptom trajectories. Associations between individual trajectories and pre-trauma and peritraumatic characteristics and traditional outcomes were compared, along with associations within and between trajectories. APNS across all ten symptom domains were common in the first two months after trauma. Many risk factors and associations with high symptom burden trajectories were shared across domains. Both across and within traditional diagnostic boundaries, APNS trajectory intercepts, and slopes were substantially correlated. Across all domains, symptom severity in the immediate aftermath of trauma (trajectory intercepts) had the greatest influence on the outcome. An interactive data visualization tool was developed to allow readers to explore relationships of interest between individual characteristics, symptom trajectories, and traditional outcomes ( http://itr.med.unc.edu/aurora/parcoord/ ). Individuals presenting to the ED after MVC commonly experience a broad constellation of adverse posttraumatic symptoms. Many risk factors for diverse APNS are shared. Individuals diagnosed with a single traditional outcome should be screened for others. The utility of multidimensional categorizations that characterize individuals across traditional diagnostic domains should be explored.

Design and field procedures for the clinical reappraisal of the Composite International Diagnostic Interview version 3.3 in Qatar's national mental health study.

BACKGROUND: The Composite International Diagnostic Interview (CIDI) has been clinically reappraised in several studies conducted mainly in the US and Europe. This report describes the methodology used to conduct one of the Middle East's largest clinical reappraisal studies. The study was carried out in conjunction with the World Mental Health Qatar-the first national psychiatric epidemiological study of common mental disorders in the country. This study aimed to evaluate the diagnostic consistency of core modules of the newly translated and adapted Arabic version of the CIDI 5.0 against the independent clinical diagnoses based on the Structured Clinical Interview for DSM-5 (SCID-5). METHODS: Telephone follow-up interviews were administered by trained clinicians using the latest research edition of the SCID for DSM-5. Telephone administered interviews were key in the data collection, as the study took place during the COVID-19 pandemic. RESULTS: Overall, within 12 months, 485 interviews were completed. The response rate was 52%. Quality control monitoring documented excellent adherence of clinical interviews to the rating protocol. CONCLUSIONS: The overall methods used in this study proved to be efficient and effective. For future research, instrument cultural adaptation within the cultural context is highly recommended.

Development and evaluation of a risk algorithm predicting alcohol dependence after early onset of regular alcohol use.

AIMS: Likelihood of alcohol dependence (AD) is increased among people who transition to greater levels of alcohol involvement at a younger age. Indicated interventions delivered early may be effective in reducing risk, but could be costly. One way to increase cost-effectiveness would be to develop a prediction model that targeted interventions to the subset of youth with early alcohol use who are at highest risk of subsequent AD. DESIGN: A prediction model was developed for DSM-IV AD onset by age 25 years using an ensemble machine-learning algorithm known as 'Super Learner'. Shapley additive explanations (SHAP) assessed variable importance. SETTING AND PARTICIPANTS: Respondents reporting early onset of regular alcohol use (i.e. by 17 years of age) who were aged 25 years or older at interview from 14 representative community surveys conducted in 13 countries as part of WHO's World Mental Health Surveys. MEASUREMENTS: The primary outcome to be predicted was onset of life-time DSM-IV AD by age 25 as measured using the Composite International Diagnostic Interview, a fully structured diagnostic interview. FINDINGS: AD prevalence by age 25 was 5.1% among the 10 687 individuals who reported drinking alcohol regularly by age 17. The prediction model achieved an external area under the curve [0.78; 95% confidence interval (CI) = 0.74-0.81] higher than any individual candidate risk model (0.73-0.77) and an area under the precision-recall curve of 0.22. Overall calibration was good [integrated calibration index (ICI) = 1.05%]; however, miscalibration was observed at the extreme ends of the distribution of predicted probabilities. Interventions provided to the 20% of people with highest risk would identify 49% of AD cases and require treating four people without AD to reach one with AD. Important predictors of increased risk included younger onset of alcohol use, males, higher cohort alcohol use and more mental disorders. CONCLUSIONS: A risk algorithm can be created using data collected at the onset of regular alcohol use to target youth at highest risk of alcohol dependence by early adulthood. Important considerations remain for advancing the development and practical implementation of such models.

Development of a model to predict combined antidepressant medication and psychotherapy treatment response for depression among veterans.

BACKGROUND: Although research shows that more depressed patients respond to combined antidepressants (ADM) and psychotherapy than either alone, many patients do not respond even to combined treatment. A reliable prediction model for this could help treatment decision-making. We attempted to create such a model using machine learning methods among patients in the US Veterans Health Administration (VHA). METHODS: A 2018-2020 national sample of VHA patients beginning combined depression treatment completed self-report assessments at baseline and 3 months (n = 658). A learning model was developed using baseline self-report, administrative, and geospatial data to predict 3-month treatment response defined by reductions in the Quick Inventory of Depression Symptomatology Self-Report and/or in the Sheehan Disability Scale. The model was developed in a 70 % training sample and tested in the remaining 30 % test sample. RESULTS: 30.0 % of patients responded to treatment. The prediction model had a test sample AUC-ROC of 0.657. A strong gradient was found in probability of treatment response from 52.7 % in the highest predicted quintile to 14.4 % in the lowest predicted quintile. The most important predictors were episode characteristics (symptoms, comorbidities, history), personality/psychological resilience, recent stressors, and treatment characteristics. LIMITATIONS: Restrictions in sample definition, a low recruitment rate, and reliance on patient self-report rather than clinician assessments to determine treatment response limited the generalizability of results. CONCLUSIONS: A machine learning model could help depressed patients and providers predict likely response to combined ADM-psychotherapy. Parallel information about potential harms and costs of alternative treatments would be needed, though, to inform optimal treatment selection.

The impact of early remission on disease trajectory and patient outcomes in major depression disorder (MDD): A targeted literature review and microsimulation modeling approach based on the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study.

BACKGROUND: While literature has suggested that the duration of a major depressive episode (MDE) may affect both symptomatic and functional outcomes in major depressive disorder (MDD), study designs are limited in their ability to isolate a causal relationship. METHODS: A targeted literature review was conducted using the MEDLINE database to assess whether there was an association between (1) shorter duration of an MDE, or (2) increased rapidity of symptom improvement, and MDD outcomes in adult patients. Given findings from the literature, we hypothesized that rapid symptom improvement could be associated with other longer-term clinical outcomes and used a previously-developed microsimulation model to test this hypothesis. The base case of the model replicated step-therapy treatment patterns, for 10,000 simulated patients, based on lines of therapy related to standard of care, observed remission rates, and observed time to relapse from the STAR*D study. In alternative scenario analyses, the step 1 remission rate was varied by +25 % and +50 % from the base case value to simulate the potential impact of improved earlier remission on disease trajectory and patient-level clinical outcomes. RESULTS: The literature review (N = 35 studies) suggests a statistically significant relationship between the duration of MDE or early symptom improvement and MDD outcomes. The microsimulation model corroborated these findings and demonstrated that increasing the rate of remission in step 1 results in patients experiencing decreased number of treatment steps, faster time to remission, decreased rate of reaching treatment-resistant depression, and delayed time to relapse. LIMITATIONS: Rates of relapse in STAR*D were deemed unreliable due to the high-loss of follow-up; rates of relapse for the MDD DTM were instead derived using parametric extrapolation methods (i.e., exponential, Weibull, log-logistic, Gaussian, log-normal, logistic). Adherence to treatment was assumed to be 100 %; however, non-adherence is expected to result in lower cumulative remission rates. CONCLUSION: Findings from the literature, coupled with quantification through a novel microsimulation model, demonstrate the potential impact of increased remission on disease trajectory and patient outcomes in MDD. While additional analyses with the model may be warranted to explore the impact of novel interventions on population health, including long-term outcomes (i.e., 5-year follow-up, lifetime follow-up), efforts by clinicians to increase remission early in the disease trajectory may improve long-term outcomes.

Utility of Wrist-Wearable Data for Assessing Pain, Sleep, and Anxiety Outcomes After Traumatic Stress Exposure.

Importance: Adverse posttraumatic neuropsychiatric sequelae after traumatic stress exposure are common and have higher incidence among socioeconomically disadvantaged populations. Pain, depression, avoidance of trauma reminders, reexperiencing trauma, anxiety, hyperarousal, sleep disruption, and nightmares have been reported. Wrist-wearable devices with accelerometers capable of assessing 24-hour rest-activity characteristics are prevalent and may have utility in measuring these outcomes. Objective: To evaluate whether wrist-wearable devices can provide useful biomarkers for recovery after traumatic stress exposure. Design, Setting, and Participants: Data were analyzed from a diverse cohort of individuals seen in the emergency department after experiencing a traumatic stress exposure, as part of the Advancing Understanding of Recovery After Trauma (AURORA) study. Participants recruited from 27 emergency departments wore wrist-wearable devices for 8 weeks, beginning in the emergency department, and completed serial assessments of neuropsychiatric symptoms. A total of 19 019 patients were screened. Of these, 3040 patients met study criteria, provided informed consent, and completed baseline assessments. A total of 2021 provided data from wrist-wearable devices, completed the 8-week assessment, and were included in this analysis. The data were randomly divided into 2 equal parts (n = 1010) for biomarker identification and validation. Data were collected from September 2017 to January 2020, and data were analyzed from May 2020 to November 2022. Exposures: Participants were recruited for the study after experiencing a traumatic stress exposure (most commonly motor vehicle collision). Main Outcomes and Measures: Rest-activity characteristics were derived and validated from wrist-wearable devices associated with specific self-reported symptom domains at a point in time and changes in symptom severity over time. Results: Of 2021 included patients, 1257 (62.2%) were female, and the mean (SD) age was 35.8 (13.0) years. Eight wrist-wearable device biomarkers for symptoms of adverse posttraumatic neuropsychiatric sequelae exceeded significance thresholds in the derivation cohort. One of these, reduced 24-hour activity variance, was associated with greater pain severity (r = -0.14; 95% CI, -0.20 to -0.07). Changes in 6 rest-activity measures were associated with changes in pain over time, and changes in the number of transitions between sleep and wake over time were associated with changes in pain, sleep, and anxiety. Simple cutoffs for these biomarkers identified individuals with good recovery for pain (positive predictive value [PPV], 0.85; 95% CI, 0.82-0.88), sleep (PPV, 0.63; 95% CI, 0.59-0.67, and anxiety (PPV, 0.76; 95% CI, 0.72-0.80) with high predictive value. Conclusions and Relevance: These findings suggest that wrist-wearable device biomarkers may have utility as screening tools for pain, sleep, and anxiety symptom outcomes after trauma exposure in high-risk populations.

Exposure to Bullying or Hazing During Deployment and Mental Health Outcomes Among US Army Soldiers.

Importance: Workplace bullying is associated with mental disorders and suicidality in civilians, but few studies have examined associations of bullying with these outcomes among military personnel. Objective: To evaluate associations of being bullied or hazed during deployment with major depressive disorder (MDD), intermittent explosive disorder, posttraumatic stress disorder (PTSD), suicidal ideation, and substance use disorder (SUD). Design, Setting, and Participants: This cohort study used data from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) New Soldier Study (NSS; April 1, 2011, to November 30, 2012) and wave 1 of the STARRS Longitudinal Study (STARRS-LS1; September 1, 2016, to April 30, 2018). A computerized survey administered at 3 US Army installations (NSS) and a web/telephone survey (STARRS-LS1) were used to collect data. Data were analyzed from October 11, 2021, to October 28, 2022. The STARRS-LS1 recruited a probability sample of active-duty soldiers and veterans who had participated in Army STARRS baseline surveys while on active duty (weighted response rate, 35.6%). Respondents whose baseline was the NSS and who had deployed to a combat theater at least once were eligible for this study. Exposures: Being bullied or hazed during a combat deployment. Main Outcomes and Measures: The primary outcomes were MDD, intermittent explosive disorder, PTSD, and suicidal ideation in the 12 months before STARRS-LS1 and SUD in the 30 days before STARRS-LS1, assessed with items from the Composite International Diagnostic Interview Screening Scales, PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and Columbia-Suicide Severity Rating Scale. Logistic regression was used to estimate associations of bullying or hazing exposure with the outcomes. Results: The 1463 participants were predominantly male (weighted percentage [SE], 90.4% [0.9%]) and had a mean (SE) age of 21.1 (0.1) years at baseline. At STARRS-LS1, 188 respondents (weighted percentage [SE], 12.2% [1.1%]) reported bullying or hazing during deployment. Weighted outcome prevalences were 18.7% (1.3%) for MDD, 5.2% (0.9%) for intermittent explosive disorder, 21.8% (1.5%) for PTSD, 14.2% (1.2%) for suicidal ideation, and 8.7% (1.0%) for SUD. In models that adjusted for baseline sociodemographic and clinical characteristics and other potential traumas, exposure to bullying or hazing was significantly associated with MDD (adjusted odds ratio [aOR], 2.92; 95% CI, 1.74-4.88), intermittent explosive disorder (aOR, 2.59; 95% CI, 1.20-5.59), PTSD (aOR, 1.86; 95% CI, 1.23-2.83), suicidal ideation (aOR, 1.91; 95% CI, 1.17-3.13), and SUD (aOR, 2.06; 95% CI, 1.15-3.70). Conclusions and Relevance: In this cohort study of combat-deployed soldiers, reports of being bullied or hazed during deployment were associated with mental disorders and suicidal thoughts. Recognition of these associations may inform efforts to prevent and address mental health problems among service members.

Evaluation of a Model to Target High-risk Psychiatric Inpatients for an Intensive Postdischarge Suicide Prevention Intervention.

Importance: The months after psychiatric hospital discharge are a time of high risk for suicide. Intensive postdischarge case management, although potentially effective in suicide prevention, is likely to be cost-effective only if targeted at high-risk patients. A previously developed machine learning (ML) model showed that postdischarge suicides can be predicted from electronic health records and geospatial data, but it is unknown if prediction could be improved by adding additional information. Objective: To determine whether model prediction could be improved by adding information extracted from clinical notes and public records. Design, Setting, and Participants: Models were trained to predict suicides in the 12 months after Veterans Health Administration (VHA) short-term (less than 365 days) psychiatric hospitalizations between the beginning of 2010 and September 1, 2012 (299 050 hospitalizations, with 916 hospitalizations followed within 12 months by suicides) and tested in the hospitalizations from September 2, 2012, to December 31, 2013 (149 738 hospitalizations, with 393 hospitalizations followed within 12 months by suicides). Validation focused on net benefit across a range of plausible decision thresholds. Predictor importance was assessed with Shapley additive explanations (SHAP) values. Data were analyzed from January to August 2022. Main Outcomes and Measures: Suicides were defined by the National Death Index. Base model predictors included VHA electronic health records and patient residential data. The expanded predictors came from natural language processing (NLP) of clinical notes and a social determinants of health (SDOH) public records database. Results: The model included 448 788 unique hospitalizations. Net benefit over risk horizons between 3 and 12 months was generally highest for the model that included both NLP and SDOH predictors (area under the receiver operating characteristic curve range, 0.747-0.780; area under the precision recall curve relative to the suicide rate range, 3.87-5.75). NLP and SDOH predictors also had the highest predictor class-level SHAP values (proportional SHAP = 64.0% and 49.3%, respectively), although the single highest positive variable-level SHAP value was for a count of medications classified by the US Food and Drug Administration as increasing suicide risk prescribed the year before hospitalization (proportional SHAP = 15.0%). Conclusions and Relevance: In this study, clinical notes and public records were found to improve ML model prediction of suicide after psychiatric hospitalization. The model had positive net benefit over 3-month to 12-month risk horizons for plausible decision thresholds. Although caution is needed in inferring causality based on predictor importance, several key predictors have potential intervention implications that should be investigated in future studies.

Mood disorders in higher education in Flanders during the 2nd and 3rd COVID-19 wave: Prevalence and help-seeking: Findings from the Flemish College Surveys (FLeCS).

To examine the prevalence of 12-month mood disorders and receipt of mental health treatment among a volunteer sample of higher education students during the 2nd and 3rd COVID-19 wave in the Flanders region. Web-based self-report surveys were obtained from 9101 students in higher education in the Flemish College Surveys (FLeCS) in Flanders, Belgium. As part of the World Health Organization's World Mental Health-International College Student Initiative, we screened for 12-month mood disorders (major depressive episode (MDE), mania/hypomania), and service use. We used poststratification weights to generate population-representative data on key socio-demographic characteristics. 50.6% of the respondents screened positive for 12-month mood disorders (46.8% MDE, of which 22.9% with very severe impact). Use of services was very low, with estimates of 35.4% for MDE, 31.7% for mania, and 25.5% for hypomania. Even among students with very severe disorders, treatment rates were never higher than 48.3%. Most common barriers for not using services were: the preference to handle the problem alone (83.4%) and not knowing where to seek professional help (79.8%). We found a high unmet need for mood problems among college students; though caution is needed in interpreting these findings given the volunteer nature of the sample. A reallocation of treatment resources for higher education students should be considered, particulary services that focus on innovative, low-threshold, and scalable interventions.

The mental health of university students in South Africa: Results of the national student survey.

BACKGROUND: We estimate 30-day prevalence of 11 common mental disorders among a representative sample of university students in South Africa and explore disparities in student mental health across historically segregated institutions and marginalised groups. METHODS: Cross-sectional data collected in self-report surveys of students (n = 28,268) from 17 universities were weighted to adjust for differences in survey responses. Poisson regression was used to estimate risk ratios (RRs). RESULTS: Prevalence estimates were highest (21.0-24.5 %) for two anxiety disorders (social anxiety disorder, PTSD) and two disruptive behavior disorders (eating disorder, ADHD). Prevalence estimates were higher for any anxiety disorder (37.1 %) and any disruptive behavior disorder (38.7 %) than for any mood disorder (16.3 %) or any substance use disorder (6.6 %). Prevalence estimates varied significantly by historical segregation status of institutions (F3 = 221.6, p < .001), with prevalence consistently highest in Historically White Institutions (HWIs). Across all institutions, risk of any disorder was lower among oldest than younger students (RR = 0.7, 95%CI = 0.7-0.8), and elevated among gender non-conforming (RR = 1.3, 95%CI = 1.1-1.4), female (RR = 1.2, 95%CI = 1.1-1.2), and sexual minority (RR = 1.2, 95%CI = 1.2-1.3) students. Black students attending HWIs had elevated risk of any disorder relative to White students. LIMITATIONS: Reliance on self-report measures together with relatively low and variable response rates across institutions limit generalizability of results. CONCLUSIONS: Modest risks associated with sociodemographic factors suggest a need to focus on mental health of female, gender nonconforming and sexual minority students at all universities along with Black students attending HWIs.

Prospective associations of emotion reactivity and risk behaviors with suicide attempts in US Army soldiers.

BACKGROUND: Emotion reactivity and risk behaviors (ERRB) are transdiagnostic dimensions associated with suicide attempt (SA). ERRB patterns may identify individuals at increased risk of future SAs. METHODS: A representative sample of US Army soldiers entering basic combat training (n = 21 772) was surveyed and followed via administrative records for their first 48 months of service. Latent profile analysis of baseline survey items assessing ERRB dimensions, including emotion reactivity, impulsivity, and risk-taking behaviors, identified distinct response patterns (classes). SAs were identified using administrative medical records. A discrete-time survival framework was used to examine associations of ERRB classes with subsequent SA during the first 48 months of service, adjusting for time in service, socio-demographic and service-related variables, and mental health diagnosis (MH-Dx). We examined whether associations of ERRB classes with SA differed by year of service and for soldiers with and without a MH-Dx. RESULTS: Of 21 772 respondents (86.2% male, 61.8% White non-Hispanic), 253 made a SA. Four ERRB classes were identified: 'Indirect Harming' (8.9% of soldiers), 'Impulsive' (19.3%), 'Risk-Taking' (16.3%), and 'Low ERRB' (55.6%). Compared to Low ERRB, Impulsive [OR 1.8 (95% CI 1.3-2.4)] and Risk-Taking [OR 1.6 (95% CI 1.1-2.2)] had higher odds of SA after adjusting for covariates. The ERRB class and MH-Dx interaction was non-significant. Within each class, SA risk varied across service time. CONCLUSIONS: SA risk within the four identified ERRB classes varied across service time. Impulsive and Risk-Taking soldiers had increased risk of future SA. MH-Dx did not modify these associations, which may therefore help identify risk in those not yet receiving mental healthcare.

A longitudinal investigation of non-suicidal self-injury persistence patterns, risk factors, and clinical outcomes during the college period.

BACKGROUND: Although non-suicidal self-injury (NSSI) is known typically to begin in adolescence, longitudinal information is lacking about patterns, predictors, and clinical outcomes of NSSI persistence among emerging adults. The present study was designed to (1) estimate NSSI persistence during the college period, (2) identify risk factors and high-risk students for NSSI persistence patterns, and (3) evaluate the association with future mental disorders and suicidal thoughts and behaviors (STB). METHODS: Using prospective cohorts from the Leuven College Surveys (n = 5915), part of the World Mental Health International College Student Initiative, web-based surveys assessed mental health and psychosocial problems at college entrance and three annual follow-up assessments. RESULTS: Approximately one in five (20.4%) students reported lifetime NSSI at college entrance. NSSI persistence was estimated at 56.4%, with 15.6% reporting a high-frequency repetitive pattern (≥five times yearly). Many hypothesized risk factors were associated with repetitive NSSI persistence, with the most potent effects observed for pre-college NSSI characteristics. Multivariate models suggest that an intervention focusing on the 10-20% at the highest predicted risk could effectively reach 34.9-56.7% of students with high-frequency repetitive NSSI persistence (PPV = 81.8-93.4, AUC = 0.88-0.91). Repetitive NSSI persistence during the first two college years predicted 12-month mental disorders, role impairment, and STB during the third college year, including suicide attempts. CONCLUSIONS: Most emerging adults with a history of NSSI report persistent self-injury during their college years. Web-based screening may be a promising approach for detecting students at risk for a highly persistent NSSI pattern characterized by subsequent adverse outcomes.

The Impact of the COVID-19 Pandemic and Associated Control Measures on the Mental Health of the General Population : A Systematic Review and Dose-Response Meta-analysis.

BACKGROUND: To what extent the COVID-19 pandemic and its containment measures influenced mental health in the general population is still unclear. PURPOSE: To assess the trajectory of mental health symptoms during the first year of the pandemic and examine dose-response relations with characteristics of the pandemic and its containment. DATA SOURCES: Relevant articles were identified from the living evidence database of the COVID-19 Open Access Project, which indexes COVID-19-related publications from MEDLINE via PubMed, Embase via Ovid, and PsycInfo. Preprint publications were not considered. STUDY SELECTION: Longitudinal studies that reported data on the general population's mental health using validated scales and that were published before 31 March 2021 were eligible. DATA EXTRACTION: An international crowd of 109 trained reviewers screened references and extracted study characteristics, participant characteristics, and symptom scores at each timepoint. Data were also included for the following country-specific variables: days since the first case of SARS-CoV-2 infection, the stringency of governmental containment measures, and the cumulative numbers of cases and deaths. DATA SYNTHESIS: In a total of 43 studies (331 628 participants), changes in symptoms of psychological distress, sleep disturbances, and mental well-being varied substantially across studies. On average, depression and anxiety symptoms worsened in the first 2 months of the pandemic (standardized mean difference at 60 days, -0.39 [95% credible interval, -0.76 to -0.03]); thereafter, the trajectories were heterogeneous. There was a linear association of worsening depression and anxiety with increasing numbers of reported cases of SARS-CoV-2 infection and increasing stringency in governmental measures. Gender, age, country, deprivation, inequalities, risk of bias, and study design did not modify these associations. LIMITATIONS: The certainty of the evidence was low because of the high risk of bias in included studies and the large amount of heterogeneity. Stringency measures and surges in cases were strongly correlated and changed over time. The observed associations should not be interpreted as causal relationships. CONCLUSION: Although an initial increase in average symptoms of depression and anxiety and an association between higher numbers of reported cases and more stringent measures were found, changes in mental health symptoms varied substantially across studies after the first 2 months of the pandemic. This suggests that different populations responded differently to the psychological stress generated by the pandemic and its containment measures. PRIMARY FUNDING SOURCE: Swiss National Science Foundation. (PROSPERO: CRD42020180049).

Anxiety sensitivity as a transdiagnostic risk factor for trajectories of adverse posttraumatic neuropsychiatric sequelae in the AURORA study.

Anxiety sensitivity, or fear of anxious arousal, is cross-sectionally associated with a wide array of adverse posttraumatic neuropsychiatric sequelae, including symptoms of posttraumatic stress disorder, depression, anxiety, sleep disturbance, pain, and somatization. The current study utilizes a large-scale, multi-site, prospective study of trauma survivors presenting to emergency departments. Hypotheses tested whether elevated anxiety sensitivity in the immediate posttrauma period is associated with more severe and persistent trajectories of common adverse posttraumatic neuropsychiatric sequelae in the eight weeks posttrauma. Participants from the AURORA study (n = 2,269 recruited from 23 emergency departments) completed self-report assessments over eight weeks posttrauma. Associations between heightened anxiety sensitivity and more severe and/or persistent trajectories of trauma-related symptoms identified by growth mixture modeling were analyzed. Anxiety sensitivity assessed two weeks posttrauma was associated with severe and/or persistent posttraumatic stress, depression, anxiety, sleep disturbance, pain, and somatic symptoms in the eight weeks posttrauma. Effect sizes were in the small to medium range in multivariate models accounting for various demographic, trauma-related, pre-trauma mental health-related, and personality-related factors. Anxiety sensitivity may be a useful transdiagnostic risk factor in the immediate posttraumatic period identifying individuals at risk for the development of adverse posttraumatic neuropsychiatric sequelae. Further, considering anxiety sensitivity is malleable via brief intervention, it could be a useful secondary prevention target. Future research should continue to evaluate associations between anxiety sensitivity and trauma-related pathology.