Suicidal behaviors are prevalent among college students; however, students remain reluctant to seek support. We developed a predictive algorithm to identify students at risk of suicidal behavior and used telehealth to reduce subsequent risk.

Data come from several waves of a prospective cohort study (2016–2022) of college students (n = 5454). All first-year students were invited to participate as volunteers. (Response rates range: 16.00–19.93%). A stepped-care approach was implemented: (i) all students received a comprehensive list of services; (ii) those reporting past 12-month suicidal ideation were directed to a safety planning application; (iii) those identified as high risk of suicidal behavior by the algorithm or reporting 12-month suicide attempt were contacted via telephone within 24-h of survey completion. Intervention focused on support/safety-planning, and referral to services for this high-risk group.

5454 students ranging in age from 17–36 (s.d. = 5.346) participated; 65% female. The algorithm identified 77% of students reporting subsequent suicidal behavior in the top 15% of predicted probabilities (Sensitivity = 26.26 [95% CI 17.93–36.07]; Specificity = 97.46 [95% CI 96.21–98.38], PPV = 53.06 [95% CI 40.16–65.56]; AUC range: 0.895 [95% CIs 0.872–0.917] to 0.966 [95% CIs 0.939–0.994]). High-risk students in the Intervention Cohort showed a 41.7% reduction in probability of suicidal behavior at 12-month follow-up compared to high-risk students in the Control Cohort.

Predictive risk algorithms embedded into universal screening, coupled with telehealth intervention, offer significant potential as a suicide prevention approach for students.

Despite their documented efficacy, substantial proportions of patients discontinue antidepressant medication (ADM) without a doctor's recommendation. The current report integrates data on patient-reported reasons into an investigation of patterns and predictors of ADM discontinuation.

Face-to-face interviews with community samples from 13 countries (n = 30 697) in the World Mental Health (WMH) Surveys included n = 1890 respondents who used ADMs within the past 12 months.

10.9% of 12-month ADM users reported discontinuation-based on recommendation of the prescriber while 15.7% discontinued in the absence of prescriber recommendation. The main patient-reported reason for discontinuation was feeling better (46.6%), which was reported by a higher proportion of patients who discontinued within the first 2 weeks of treatment than later. Perceived ineffectiveness (18.5%), predisposing factors (e.g. fear of dependence) (20.0%), and enabling factors (e.g. inability to afford treatment cost) (5.0%) were much less commonly reported reasons. Discontinuation in the absence of prescriber recommendation was associated with low country income level, being employed, and having above average personal income. Age, prior history of psychotropic medication use, and being prescribed treatment from a psychiatrist rather than from a general medical practitioner, in comparison, were associated with a lower probability of this type of discontinuation. However, these predictors varied substantially depending on patient-reported reasons for discontinuation.

Dropping out early is not necessarily negative with almost half of individuals noting they felt better. The study underscores the diverse reasons given for dropping out and the need to evaluate how and whether dropping out influences short- or long-term functioning.

Insecure attachment styles are associated with retrospectively reported suicide attempts (SAs). It is not known if attachment styles are prospectively associated with medically documented SAs.

A representative sample of US Army soldiers entering service (n = 21 772) was surveyed and followed via administrative records for their first 48 months of service. Attachment style (secure, preoccupied, fearful, dismissing) was assessed at baseline. Administrative medical records identified SAs. Discrete-time survival analysis examined associations of attachment style with future SA during service, adjusting for time in service, socio-demographics, service-related variables, and mental health diagnosis (MH-Dx). We examined whether associations of attachment style with SA differed based on sex and MH-Dx.

In total, 253 respondents attempted suicide. Endorsed attachment styles included secure (46.8%), preoccupied (9.1%), fearful (15.7%), and dismissing (19.2%). Examined separately, insecure attachment styles were associated with increased odds of SA: preoccupied [OR 2.5 (95% CI 1.7–3.4)], fearful [OR 1.6 (95% CI 1.1–2.3)], dismissing [OR 1.8 (95% CI 1.3–2.6)]. Examining attachment styles simultaneously along with other covariates, preoccupied [OR 1.9 (95% CI 1.4–2.7)] and dismissing [OR 1.7 (95% CI 1.2–2.4)] remained significant. The dismissing attachment and MH-Dx interaction was significant. In stratified analyses, dismissing attachment was associated with SA only among soldiers without MH-Dx. Other interactions were non-significant. Soldiers endorsing any insecure attachment style had elevated SA risk across the first 48 months in service, particularly during the first 12 months.

Insecure attachment styles, particularly preoccupied and dismissing, are associated with increased future SA risk among soldiers. Elevated risk is most substantial during first year of service but persists through the first 48 months. Dismissing attachment may indicate risk specifically among soldiers not identified by the mental healthcare system.

To investigate the occurrence of traumatic stress symptoms (TSS) among healthcare workers active during the COVID-19 pandemic and to obtain insight as to which pandemic-related stressful experiences are associated with onset and persistence of traumatic stress.

This is a multicenter prospective cohort study. Spanish healthcare workers (N = 4,809) participated at an initial assessment (i.e., just after the first wave of the Spain COVID-19 pandemic) and at a 4-month follow-up assessment using web-based surveys. Logistic regression investigated associations of 19 pandemic-related stressful experiences across four domains (infection-related, work-related, health-related and financial) with TSS prevalence, incidence and persistence, including simulations of population attributable risk proportions (PARP).

Thirty-day TSS prevalence at T1 was 22.1%. Four-month incidence and persistence were 11.6% and 54.2%, respectively. Auxiliary nurses had highest rates of TSS prevalence (35.1%) and incidence (16.1%). All 19 pandemic-related stressful experiences under study were associated with TSS prevalence or incidence, especially experiences from the domains of health-related (PARP range 88.4–95.6%) and work-related stressful experiences (PARP range 76.8–86.5%). Nine stressful experiences were also associated with TSS persistence, of which having patient(s) in care who died from COVID-19 had the strongest association. This association remained significant after adjusting for co-occurring depression and anxiety.

TSSs among Spanish healthcare workers active during the COVID-19 pandemic are common and associated with various pandemic-related stressful experiences. Future research should investigate if these stressful experiences represent truly traumatic experiences and carry risk for the development of post-traumatic stress disorder.

Several hypotheses may explain the association between substance use, posttraumatic stress disorder (PTSD), and depression. However, few studies have utilized a large multisite dataset to understand this complex relationship. Our study assessed the relationship between alcohol and cannabis use trajectories and PTSD and depression symptoms across 3 months in recently trauma-exposed civilians.

In total, 1618 (1037 female) participants provided self-report data on past 30-day alcohol and cannabis use and PTSD and depression symptoms during their emergency department (baseline) visit. We reassessed participant's substance use and clinical symptoms 2, 8, and 12 weeks posttrauma. Latent class mixture modeling determined alcohol and cannabis use trajectories in the sample. Changes in PTSD and depression symptoms were assessed across alcohol and cannabis use trajectories via a mixed-model repeated-measures analysis of variance.

Three trajectory classes (low, high, increasing use) provided the best model fit for alcohol and cannabis use. The low alcohol use class exhibited lower PTSD symptoms at baseline than the high use class; the low cannabis use class exhibited lower PTSD and depression symptoms at baseline than the high and increasing use classes; these symptoms greatly increased at week 8 and declined at week 12. Participants who already use alcohol and cannabis exhibited greater PTSD and depression symptoms at baseline that increased at week 8 with a decrease in symptoms at week 12.

Our findings suggest that alcohol and cannabis use trajectories are associated with the intensity of posttrauma psychopathology. These findings could potentially inform the timing of therapeutic strategies.

Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions.

We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011–2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016–2018, LS2: 2018–2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample.

Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10–30% of respondents with the highest predicted risk included 44.9–92.5% of 12-month SAs.

An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

Personality traits (e.g. neuroticism) and the social environment predict risk for internalizing disorders and suicidal behavior. Studying these characteristics together and prospectively within a population confronted with high stressor exposure (e.g. U.S. Army soldiers) has not been done, yet could uncover unique and interactive predictive effects that may inform prevention and early intervention efforts.

Five broad personality traits and social network size were assessed via self-administered questionnaires among experienced soldiers preparing for deployment (N = 4645) and new soldiers reporting for basic training (N = 6216). Predictive models examined associations of baseline personality and social network variables with recent distress disorders or suicidal behaviors assessed 3- and 9-months post-deployment and approximately 5 years following enlistment.

Among the personality traits, elevated neuroticism was consistently associated with increased mental health risk following deployment. Small social networks were also associated with increased mental health risk following deployment, beyond the variance accounted for by personality. Limited support was found for social network size moderating the association between personality and mental health outcomes. Small social networks also predicted distress disorders and suicidal behavior 5 years following enlistment, whereas unique effects of personality traits on these more distal outcomes were rare.

Heightened neuroticism and small social networks predict a greater risk for negative mental health sequelae, especially following deployment. Social ties may mitigate adverse impacts of personality traits on psychopathology in some contexts. Early identification and targeted intervention for these distinct, modifiable factors may decrease the risk of distress disorders and suicidal behavior.

Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).

A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.

In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.

Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

The transition from military service to civilian life is a high-risk period for suicide attempts (SAs). Although stressful life events (SLEs) faced by transitioning soldiers are thought to be implicated, systematic prospective evidence is lacking.

Participants in the Army Study to Assess Risk and Resilience in Servicemembers (STARRS) completed baseline self-report surveys while on active duty in 2011–2014. Two self-report follow-up Longitudinal Surveys (LS1: 2016–2018; LS2: 2018–2019) were subsequently administered to probability subsamples of these baseline respondents. As detailed in a previous report, a SA risk index based on survey, administrative, and geospatial data collected before separation/deactivation identified 15% of the LS respondents who had separated/deactivated as being high-risk for self-reported post-separation/deactivation SAs. The current report presents an investigation of the extent to which self-reported SLEs occurring in the 12 months before each LS survey might have mediated/modified the association between this SA risk index and post-separation/deactivation SAs.

The 15% of respondents identified as high-risk had a significantly elevated prevalence of some post-separation/deactivation SLEs. In addition, the associations of some SLEs with SAs were significantly stronger among predicted high-risk than lower-risk respondents. Demographic rate decomposition showed that 59.5% (s.e. = 10.2) of the overall association between the predicted high-risk index and subsequent SAs was linked to these SLEs.

It might be possible to prevent a substantial proportion of post-separation/deactivation SAs by providing high-risk soldiers with targeted preventive interventions for exposure/vulnerability to commonly occurring SLEs.

Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.

This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.

32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.

Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

This study investigates associations of several dimensions of childhood adversities (CAs) with lifetime mental disorders, 12-month disorder persistence, and impairment among incoming college students.

Data come from the World Mental Health International College Student Initiative (WMH-ICS). Web-based surveys conducted in nine countries (n = 20 427) assessed lifetime and 12-month mental disorders, 12-month role impairment, and seven types of CAs occurring before the age of 18: parental psychopathology, emotional, physical, and sexual abuse, neglect, bullying victimization, and dating violence. Poisson regressions estimated associations using three dimensions of CA exposure: type, number, and frequency.

Overall, 75.8% of students reported exposure to at least one CA. In multivariate regression models, lifetime onset and 12-month mood, anxiety, and substance use disorders were all associated with either the type, number, or frequency of CAs. In contrast, none of these associations was significant when predicting disorder persistence. Of the three CA dimensions examined, only frequency was associated with severe role impairment among students with 12-month disorders. Population-attributable risk simulations suggest that 18.7–57.5% of 12-month disorders and 16.3% of severe role impairment among those with disorders were associated with these CAs.

CAs are associated with an elevated risk of onset and impairment among 12-month cases of diverse mental disorders but are not involved in disorder persistence. Future research on the associations of CAs with psychopathology should include fine-grained assessments of CA exposure and attempt to trace out modifiable intervention targets linked to mechanisms of associations with lifetime psychopathology and burden of 12-month mental disorders.

Problematic anger is frequently reported by soldiers who have deployed to combat zones. However, evidence is lacking with respect to how anger changes over a deployment cycle, and which factors prospectively influence change in anger among combat-deployed soldiers.

Reports of problematic anger were obtained from 7298 US Army soldiers who deployed to Afghanistan in 2012. A series of mixed-effects growth models estimated linear trajectories of anger over a period of 1–2 months before deployment to 9 months post-deployment, and evaluated the effects of pre-deployment factors (prior deployments and perceived resilience) on average levels and growth of problematic anger.

A model with random intercepts and slopes provided the best fit, indicating heterogeneity in soldiers' levels and trajectories of anger. First-time deployers reported the lowest anger overall, but the most growth in anger over time. Soldiers with multiple prior deployments displayed the highest anger overall, which remained relatively stable over time. Higher pre-deployment resilience was associated with lower reports of anger, but its protective effect diminished over time. First- and second-time deployers reporting low resilience displayed different anger trajectories (stable v. decreasing, respectively).

Change in anger from pre- to post-deployment varies based on pre-deployment factors. The observed differences in anger trajectories suggest that efforts to detect and reduce problematic anger should be tailored for first-time v. repeat deployers. Ongoing screening is needed even for soldiers reporting high resilience before deployment, as the protective effect of pre-deployment resilience on anger erodes over time.

Major depressive disorder (MDD) is characterised by a recurrent course and high comorbidity rates. A lifespan perspective may therefore provide important information regarding health outcomes. The aim of the present study is to examine mental disorders that preceded 12-month MDD diagnosis and the impact of these disorders on depression outcomes.

Data came from 29 cross-sectional community epidemiological surveys of adults in 27 countries (n = 80 190). The Composite International Diagnostic Interview (CIDI) was used to assess 12-month MDD and lifetime DSM-IV disorders with onset prior to the respondent's age at interview. Disorders were grouped into depressive distress disorders, non-depressive distress disorders, fear disorders and externalising disorders. Depression outcomes included 12-month suicidality, days out of role and impairment in role functioning.

Among respondents with 12-month MDD, 94.9% (s.e. = 0.4) had at least one prior disorder (including previous MDD), and 64.6% (s.e. = 0.9) had at least one prior, non-MDD disorder. Previous non-depressive distress, fear and externalising disorders, but not depressive distress disorders, predicted higher impairment (OR = 1.4–1.6) and suicidality (OR = 1.5–2.5), after adjustment for sociodemographic variables. Further adjustment for MDD characteristics weakened, but did not eliminate, these associations. Associations were largely driven by current comorbidities, but both remitted and current externalising disorders predicted suicidality among respondents with 12-month MDD.

These results illustrate the importance of careful psychiatric history taking regarding current anxiety disorders and lifetime externalising disorders in individuals with MDD.

The most common treatment for major depressive disorder (MDD) is antidepressant medication (ADM). Results are reported on frequency of ADM use, reasons for use, and perceived effectiveness of use in general population surveys across 20 countries.

Face-to-face interviews with community samples totaling n = 49 919 respondents in the World Health Organization (WHO) World Mental Health (WMH) Surveys asked about ADM use anytime in the prior 12 months in conjunction with validated fully structured diagnostic interviews. Treatment questions were administered independently of diagnoses and asked of all respondents.

3.1% of respondents reported ADM use within the past 12 months. In high-income countries (HICs), depression (49.2%) and anxiety (36.4%) were the most common reasons for use. In low- and middle-income countries (LMICs), depression (38.4%) and sleep problems (31.9%) were the most common reasons for use. Prevalence of use was 2–4 times as high in HICs as LMICs across all examined diagnoses. Newer ADMs were proportionally used more often in HICs than LMICs. Across all conditions, ADMs were reported as very effective by 58.8% of users and somewhat effective by an additional 28.3% of users, with both proportions higher in LMICs than HICs. Neither ADM class nor reason for use was a significant predictor of perceived effectiveness.

ADMs are in widespread use and for a variety of conditions including but going beyond depression and anxiety. In a general population sample from multiple LMICs and HICs, ADMs were widely perceived to be either very or somewhat effective by the people who use them.

Although non-suicidal self-injury (NSSI) is an issue of major concern to colleges worldwide, we lack detailed information about the epidemiology of NSSI among college students. The objectives of this study were to present the first cross-national data on the prevalence of NSSI and NSSI disorder among first-year college students and its association with mental disorders.

Data come from a survey of the entering class in 24 colleges across nine countries participating in the World Mental Health International College Student (WMH-ICS) initiative assessed in web-based self-report surveys (20 842 first-year students). Using retrospective age-of-onset reports, we investigated time-ordered associations between NSSI and Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-IV) mood (major depressive and bipolar disorder), anxiety (generalized anxiety and panic disorder), and substance use disorders (alcohol and drug use disorder).

NSSI lifetime and 12-month prevalence were 17.7% and 8.4%. A positive screen of 12-month DSM-5 NSSI disorder was 2.3%. Of those with lifetime NSSI, 59.6% met the criteria for at least one mental disorder. Temporally primary lifetime mental disorders predicted subsequent onset of NSSI [median odds ratio (OR) 2.4], but these primary lifetime disorders did not consistently predict 12-month NSSI among respondents with lifetime NSSI. Conversely, even after controlling for pre-existing mental disorders, NSSI consistently predicted later onset of mental disorders (median OR 1.8) as well as 12-month persistence of mental disorders among students with a generalized anxiety disorder (OR 1.6) and bipolar disorder (OR 4.6).

NSSI is common among first-year college students and is a behavioral marker of various common mental disorders.

Definition of disorder subtypes may facilitate precision treatment for posttraumatic stress disorder (PTSD). We aimed to identify PTSD subtypes and evaluate their associations with genetic risk factors, types of stress exposures, comorbidity, and course of PTSD.

Data came from a prospective study of three U.S. Army Brigade Combat Teams that deployed to Afghanistan in 2012. Soldiers with probable PTSD (PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition ≥31) at three months postdeployment comprised the sample (N = 423) for latent profile analysis using Gaussian mixture modeling and PTSD symptom ratings as indicators. PTSD profiles were compared on polygenic risk scores (derived from external genomewide association study summary statistics), experiences during deployment, comorbidity at three months postdeployment, and persistence of PTSD at nine months postdeployment.

Latent profile analysis revealed profiles characterized by prominent intrusions, avoidance, and hyperarousal (threat-reactivity profile; n = 129), anhedonia and negative affect (dysphoric profile; n = 195), and high levels of all PTSD symptoms (high-symptom profile; n = 99). The threat-reactivity profile had the most combat exposure and the least comorbidity. The dysphoric profile had the highest polygenic risk for major depression, and more personal life stress and co-occurring major depression than the threat-reactivity profile. The high-symptom profile had the highest rates of concurrent mental disorders and persistence of PTSD.

Genetic and trauma-related factors likely contribute to PTSD heterogeneity, which can be parsed into subtypes that differ in symptom expression, comorbidity, and course. Future studies should evaluate whether PTSD typology modifies treatment response and should clarify distinctions between the dysphoric profile and depressive disorders.

There is growing interest in using composite individualized treatment rules (ITRs) to guide depression treatment selection, but best approaches for doing this are not widely known. We develop an ITR for depression remission based on secondary analysis of a recently published trial for second-line antidepression medication selection using a cutting-edge ensemble machine learning method.

Data come from the SUN(^_^)D trial, an open-label, assessor blinded pragmatic trial of previously-untreated patients with major depressive disorder from 48 clinics in Japan. Initial clinic-level randomization assigned patients to 50 or 100 mg/day sertraline. We focus on the 1549 patients who failed to remit within 3 weeks and were then rerandomized at the individual-level to continuation with sertraline, switching to mirtazapine, or combining mirtazapine with sertraline. The outcome was remission 9 weeks post-baseline. Predictors included socio-demographics, clinical characteristics, baseline symptoms, changes in symptoms between baseline and week 3, and week 3 side effects.

Optimized treatment was associated with significantly increased cross-validated week 9 remission rates in both samples [5.3% (2.4%), p = 0.016 50 mg/day sample; 5.1% (2.7%), p = 0.031 100 mg/day sample] compared to randomization (30.1–30.8%). Optimization was also associated with significantly increased remission in both samples compared to continuation [24.7% in both: 11.2% (3.8%), p = 0.002 50 mg/day sample; 11.7% (3.9%), p = 0.001 100 mg/day sample]. Non-significant gains were found for optimization compared to switching or combining.

An ITR can be developed to improve second-line antidepressant selection, but replication in a larger study with more comprehensive baseline predictors might produce stronger and more stable results.