BackgroundMilitary sexual trauma (MST) can have significant adverse effects on mental health and well-being, often leading to posttraumatic stress disorder (PTSD) symptoms and maladaptive beliefs. Although effective psychotherapies exist, stigma, confidentiality concerns, and systemic barriers often hinder help-seeking among service members and veterans. Mobile mental health apps offer an accessible and anonymous support alternative, potentially addressing such barriers. However, app effectiveness depends on user engagement and emerging evidence suggests that engagement may be shaped by symptom severity. ObjectiveThis retrospective observational study aimed to explore the relationship between posttraumatic symptom severity and user engagement with Beyond MST (US Department of Veterans Affairs [VA] National Center for PTSD), an app for individuals who experienced MST. Specific aims included (1) characterizing trauma-related symptom levels and app engagement among users who completed in-app assessments, and (2) evaluating how PTSD symptom severity, negative posttraumatic cognitions, and mental well-being relate to objective measures of engagement. MethodsAnonymous usage data from 27,517 users collected between March 11, 2021 and July 29, 2024, were analyzed. Three subsamples were identified: those who completed the in-app PTSD checklist for DSM-5 (Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition]; PCL-5, n=3689), the Posttraumatic Maladaptive Beliefs Scale (PMBS; n=2197), and the Warwick-Edinburgh Mental Well-Being Scale (WEMWBS; n=2160). Engagement metrics included duration of use (ie, days of use and minutes of use), frequency of feature access (ie, coping tool and psychoeducation access), and frequency of feature use (ie, total assessment completions). Regression analyses, including quadratic terms, were conducted to evaluate how symptom severity and well-being levels influenced engagement and identify possible curvilinear trends. ResultsMedian engagement levels ranged across subsamples as follows: 3-4 days of use (IQR 5-6), 22-30 minutes of use (IQR 33.7-42.9), 1-5 feature accesses (IQR 6-9), and 2-3 assessment completions (IQR 2). Subsamples were highly symptomatic. Analyses revealed that moderate PTSD symptom and negative posttraumatic cognition severity were associated with higher engagement relative to users with very low and very high symptom levels, particularly for days of use and frequency of coping tool access. Conversely, higher mental well-being scores were generally linked to increased app engagement with linear effects. Effect sizes were small, suggesting limited clinical impact. ConclusionsThis study highlights the possible challenges in engaging highly symptomatic individuals with digital mental health interventions. Although Beyond MST successfully reaches its targeted population, very low or high symptom levels and lower well-being may hinder sustained engagement. These findings suggest that symptom levels should be considered in app development (ie, personalization) and when integrating apps into professional care. Interpretation is limited by the anonymous nature of the data, which prevented characterization of users and their trauma histories. Further research is needed to clarify how symptom patterns influence engagement, especially in trauma contexts. International Registered Report Identifier (IRRID)RR2-10.31979/etd.882a-5fcx

This qualitative study provides a platform for women veterans to inform our perspective of their experienced impacts following military sexual trauma (MST). We engaged 23 women veterans in semistructured interviews and used a grounded theory-informed thematic analytic approach, to interpret women's experiences. Women described negative impacts of their MST experiences across psychological, behavioral, and occupational domains. Less frequently, women discussed experiences of posttraumatic growth. These results aid our understanding of the complexities of women's posttrauma experiences and suggest that holistic intervention frameworks focused on a range of potential intervention targets are warranted in helping women veterans recover from MST.

Background: Loss of belongingness may be particularly pronounced for women veterans, representing a threat to long-term well-being. Improvements in social support through engagement in a structured peer support program may mitigate the negative effects of loss of belongingness on well-being. Objective: We assessed the impact of participation in a peer-led, structured, social support group-based network on outcomes related to well-being [i.e., belongingness, social support, quality of life, posttraumatic stress disorder (PTSD), depression]. Subgroup analyses examined relative impact among those who completed the intervention and those reporting clinical levels of PTSD and depression symptoms. Methods: We analyzed survey data consisting of reliable and valid measures collected at baseline, postgroup and 3-month follow-up among 393 participants in the Woven Veterans Network’s (WoVeN) group program. Results: We observed improvements in posttraumatic stress disorder (PTSD) symptoms over time. We observed additional benefits among those who received an adequate dose of the intervention (significant improvements on PTSD, belongingness) and those with clinical levels of mental health symptoms (significant improvements on PTSD, depression, belongingness, quality of life). Conclusions: Impacts on social support may have been masked due to ceiling effects given wide dispersion baseline social support in this sample. This social support network had particularly profound impacts on well-being for those veterans who suffered from conditions for which isolation and loneliness are particularly salient.

BACKGROUND:Loss of belongingness may be particularly pronounced for women veterans, representing a threat to long-term well-being. Improvements in social support through engagement in a structured peer support program may mitigate the negative effects of loss of belongingness on well-being. OBJECTIVE:We assessed the impact of participation in a peer-led, structured, social support group-based network on outcomes related to well-being [i.e., belongingness, social support, quality of life, posttraumatic stress disorder (PTSD), depression]. Subgroup analyses examined relative impact among those who completed the intervention and those reporting clinical levels of PTSD and depression symptoms. METHODS:We analyzed survey data consisting of reliable and valid measures collected at baseline, postgroup and 3-month follow-up among 393 participants in the Woven Veterans Network's (WoVeN) group program. RESULTS:We observed improvements in posttraumatic stress disorder (PTSD) symptoms over time. We observed additional benefits among those who received an adequate dose of the intervention (significant improvements on PTSD, belongingness) and those with clinical levels of mental health symptoms (significant improvements on PTSD, depression, belongingness, quality of life). CONCLUSIONS:Impacts on social support may have been masked due to ceiling effects given wide dispersion baseline social support in this sample. This social support network had particularly profound impacts on well-being for those veterans who suffered from conditions for which isolation and loneliness are particularly salient.

BackgroundIdentifying predictors of suicidal ideation (SI) is important to inform suicide prevention efforts, particularly among high-risk populations like military veterans. Although many studies have examined the contribution of psychopathology to veterans' SI, fewer studies have examined whether experiencing good psychosocial well-being with regard to multiple aspects of life can protect veterans from SI or evaluated whether SI risk prediction can be enhanced by considering change in life circumstances along with static factors.MethodsThe study drew from a longitudinal population-based sample of 7141 U.S. veterans assessed throughout the first three years after leaving military service. Machine learning methods (cross-validated random forests) were applied to examine the predictive utility of static and change-based well-being indicators to veterans' SI, as compared to psychopathology predictors.ResultsAlthough psychopathology models performed better, the full set of well-being predictors demonstrated acceptable discrimination in predicting new-onset SI and accounted for approximately two-thirds of cases of SI in the top strata (quintile) of predicted risk. Greater engagement in health promoting behavior and social well-being were most important in predicting reduced SI risk, with several change-based predictors of SI identified but stronger associations observed for static as compared to change-based indicator sets as a whole.ConclusionsFindings support the value of considering veterans' broader well-being in identifying individuals at risk for suicidal ideation and suggest the possibility that well-being promotion efforts may be useful in reducing suicide risk. Findings also highlight the need for additional attention to change-based predictors to better understand their potential value in identifying individuals at risk for SI.

Women serving in the U.S. military are more likely to report mental health problems than men, including symptoms of depression and posttraumatic stress disorder (PTSD). Women also experience much higher rates of sexual harassment, gender discrimination, and sexual assault than men. This study examines how unwanted gender-based experiences among military service members relate to differences in health. The authors find that, once experiences of gender discrimination, sexual harassment, and sexual assault are accounted for, gender differences in health are largely attenuated. That is, the vulnerability to physical and mental health problems among female service members appears to be highly correlated with these unwanted gender-based experiences. The results highlight the possible health benefits of improved prevention of gender discrimination, sexual harassment, and sexual assault, and they indicate the need to address the mental and physical health of service members exposed to these types of experiences.

Despite substantial efforts to counter sexual assault and harassment in the military, both remain persistent in the Armed Services. In February 2021, President Biden directed the U.S. Department of Defense to establish a 90-day Independent Review Commission on Sexual Assault in the Military (IRC) to assess the department's efforts and make actionable recommendations. As servicemembers discharge from the military, effects of military sexual trauma (MST) are often seen in the Veterans Health Administration (VA). In response to an IRC inquiry about VA MST research, we organized an overview on prevalence, adverse consequences, and evidence-based treatments targeting the sequelae of MST. Women are significantly more likely to experience MST than their male counterparts. Other groups with low societal and institutional power (e.g., lower rank) are also at increased risk. Although not all MST survivors experience long-term adverse consequences, for many, they can be significant, chronic, and enduring and span mental and physical health outcomes, as well as cumulative impairments in functioning. Adverse consequences of MST come with commonalities shared with sexual trauma in other settings (e.g., interpersonal betrayal, victim-blaming) as well as unique aspects of the military context, where experiences of interpersonal betrayal may be compounded by perceptions of institutional betrayal (e.g., fear of reprisal or ostracism, having to work/live alongside a perpetrator). MST's most common mental health impact is posttraumatic stress disorder, which rarely occurs in isolation, and may coincide with major depression, anxiety, eating disorders, substance use disorders, and increased suicidality. Physical health impacts include greater chronic disease burden (e.g., hypertension), and impaired reproductive health and sexual functioning. Advances in treatment include evidence-based psychotherapies and novel approaches relying on mind-body interventions and peer support. Nonetheless, much work is needed to enhance detection, access, care, and support or even the best interventions will not be effective.

OBJECTIVE:Despite growing numbers of veteran women, it is unclear whether the impact of common postdeployment mental health concerns on key aspects of family functioning varies by gender. We examined whether associations between PTSD, depression, and problematic alcohol use and intimate relationship quality and parenting self-efficacy differed among men and women in a large, gender-balanced sample of post-9/11 veterans. METHOD:Participants included 2,348 veterans (51.49% women) of the wars in Iraq and Afghanistan who were part of a larger study of gender differences in effects of wartime deployment. Veterans who were married or in a relationship (n = 1,536, 49.09% women) reported overall relationship quality. Veterans with children under age 18 (n = 1,049; 51.57% women) self-reported on their sense of efficacy as parents. All participants reported symptoms of PTSD, depression, and problematic alcohol use. We used a series of hierarchical linear regressions to test gender as a moderator of each postdeployment mental health concern and the family functioning constructs of interest. RESULTS:Each postdeployment mental health concern was associated with reduced relationship quality and parenting self-efficacy, and these associations were largely consistent across gender. However, links between reduced parenting self-efficacy and increased PTSD and depressive symptoms were stronger in women compared with men. CONCLUSIONS:Postdeployment mental health concerns are associated with impairment in key family relationships for both veteran men and women. This impact may be particularly profound for parenting self-efficacy among female veterans, highlighting the potential importance of targeted interventions in this domain. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Introduction Little is known about predictors of military veterans' suicidal ideation as they transition from service to civilian life, a potentially high-risk period that represents a critical time for intervention. This study examined factors associated with veterans' suicidal ideation in the first year after military separation. Methods A national sample of U.S. veterans (N=7,383) from The Veterans Metrics Initiative Study reported on their mental health, psychosocial well-being, and demographic/military characteristics in an online survey at 3 and 9 months after separation. Cross-validated random forest models and mean decrease in accuracy values were used to identify key predictors of suicidal ideation. Bivariate ORs were calculated to examine the magnitude and direction of main effects associations between predictors and suicidal ideation. Data were collected in 2016/2017 and analyzed in 2021. Results In the first year after separation, 15.1% of veterans reported suicidal ideation. Endorsing depression symptoms and, to a lesser extent, identifying oneself as experiencing depression, were most predictive of suicidal ideation. Other psychopathology predictors included higher anxiety and posttraumatic stress disorder symptoms. Psychosocial well-being predictors included higher health satisfaction and functioning, community satisfaction and functioning, and psychological resilience. Logistic models performed similarly to random forest models, suggesting that relationships between predictors and suicidal ideation were better represented as main effects than interactions. Conclusions Results highlight the potential value of bolstering key aspects of military veterans' mental health and psychosocial well-being to reduce their risk for suicidal ideation in the first year after separation. Findings can inform interventions aimed at helping veterans acclimate to civilian life.

BACKGROUND:Risk for nonfatal suicide attempts is heightened in the month after psychiatric hospitalization discharge. Investigations of factors associated with such attempts are limited. METHODS:We conducted a case-subcohort study using data from Danish medical, administrative, and social registries to develop sex-specific risk models using two machine learning methods: classification trees and random forests. Cases included individuals who received a diagnostic code for a nonfatal suicide attempt within 30 days of discharge following a psychiatric hospitalization between January 1, 1995 and December 31, 2015 (n = 3166, 56.5% female). The comparison subcohort consisted of a 5% random sample of individuals living in Denmark (n = 24,559, 51.3% female) on January 1, 1995 who had a psychiatric hospitalization during the study period. RESULTS:Histories of self-poisoning, substance-related disorders, and eating disorders were important predictors of nonfatal suicide attempt among women, with notable interactions observed between age, self-poisoning history, and other characteristics (e.g., medication use). Self-poisoning, substance-related disorders, and severe stress reactions were among the most important variables for men, with key interactions noted between self-poisoning history, age, major depressive disorder diagnosis, and prescription classes. LIMITATIONS:Findings are based on Danish administrative data, which may be subject to inaccuracies, missingness, etc. It is unclear whether results would generalize to other populations. CONCLUSIONS:Markers of behavioral dysregulation were important predictors of nonfatal suicide attempts in the 30 days after psychiatric hospitalization discharge for both sexes. Examining risk markers for nonfatal suicide attempt following discharge is important to enhance support for this vulnerable population.

Accurate documentation of the associations between stress disorders and suicide attempts provides important information about a high-risk population and target group for preventative interventions. In this case-cohort study, cases were all individuals born or residing in Denmark who made a nonfatal suicide attempt during 1995-2015 (n = 22,974). The comparison subcohort included a 5% random sample of the Danish population on January 1, 1995 (n = 265,183). Stress disorder diagnoses and suicide attempts were identified using ICD-10 codes from national medical registries. The presence of any stress disorder substantially increased the rate of suicide attempts versus the comparison subcohort, rate per 100,000 person-years (PYs) = 604 vs. 13. We observed associations between each type of stress disorder and suicide attempts, hazard ratios (HRs) = 10.1-37.6, even after adjustment for potential confounders, adjusted HRs = 1.8-8.3, with the strongest associations for adjustment disorder relative to other diagnoses. After adjusting for demographic and health variables, the rate of suicide attempts among individuals with any stress disorder diagnosis was nearly 13 times the suicide attempt rate in the comparison cohort. A bias analysis demonstrated that associations remained robust despite potential differential misclassification of suicide attempts. Study strengths included the use of individual-level data linked across administrative and medical registries in the setting of universal health care and the use of longitudinal analyses capturing data over 20 years. The study demonstrated associations between the full range of stress disorders and suicide attempts, extending research specific to posttraumatic stress disorder.

BACKGROUND:Suicide risk is high in the 30 days after discharge from psychiatric hospital, but knowledge of the profiles of high-risk patients remains limited. AIMS:To examine sex-specific risk profiles for suicide in the 30 days after discharge from psychiatric hospital, using machine learning and Danish registry data. METHOD:We conducted a case-cohort study capturing all suicide cases occurring in the 30 days after psychiatric hospital discharge in Denmark from 1 January 1995 to 31 December 2015 (n = 1205). The comparison subcohort was a 5% random sample of all persons born or residing in Denmark on 1 January 1995, and who had a first psychiatric hospital admission between 1995 and 2015 (n = 24 559). Predictors included diagnoses, surgeries, prescribed medications and demographic information. The outcome was suicide death recorded in the Danish Cause of Death Registry. RESULTS:For men, prescriptions for anxiolytics and drugs used in addictive disorders interacted with other characteristics in the risk profiles (e.g. alcohol-related disorders, hypnotics and sedatives) that led to higher risk of postdischarge suicide. In women, there was interaction between recurrent major depression and other characteristics (e.g. poisoning, low income) that led to increased risk of suicide. Random forests identified important suicide predictors: alcohol-related disorders and nicotine dependence in men and poisoning in women. CONCLUSIONS:Our findings suggest that accurate prediction of suicide during the high-risk period immediately after psychiatric hospital discharge may require a complex evaluation of multiple factors for men and women.

Persons with substance use disorders (SUDs) are at elevated risk of suicide death. We identified novel risk factors and interactions that predict suicide among men and women with SUD using machine learning. Case–cohort study. Denmark. The sample was restricted to persons with their first SUD diagnosis during 1995 to 2015. Cases were persons who died by suicide in Denmark during 1995 to 2015 ( n = 2774) and the comparison subcohort was a 5% random sample of individuals in Denmark on 1 January 1995 ( n = 13 179). Suicide death was recorded in the Danish Cause of Death Registry. Predictors included social and demographic information, mental and physical health diagnoses, surgeries, medications, and poisonings. Persons among the highest risk for suicide, as identified by the classification trees, were men prescribed antidepressants in the 4 years before suicide and had a poisoning diagnosis in the 4 years before suicide; and women who were 30+ years old and had a poisoning diagnosis 4 years before and 12 months before suicide. Among men with SUD, the random forest identified five variables that were most important in predicting suicide; reaction to severe stress and adjustment disorders, drugs used to treat addictive disorders, age 30+ years, antidepressant use, and poisoning in the 4 prior years. Among women with SUD, the random forest found that the most important predictors of suicide were prior poisonings and reaction to severe stress and adjustment disorders. Individuals in the top 5% of predicted risk accounted for 15% of all suicide deaths among men and 24% of all suicides among women. In Denmark, prior poisoning and comorbid psychiatric disorders may be among the most important indicators of suicide risk among persons with substance use disorders, particularly among women.

BACKGROUND AND AIMS:Persons with substance use disorders (SUDs) are at elevated risk of suicide death. We identified novel risk factors and interactions that predict suicide among men and women with SUD using machine learning. DESIGN:Case-cohort study. SETTING:Denmark. PARTICIPANTS:The sample was restricted to persons with their first SUD diagnosis during 1995 to 2015. Cases were persons who died by suicide in Denmark during 1995 to 2015 (n = 2774) and the comparison subcohort was a 5% random sample of individuals in Denmark on 1 January 1995 (n = 13 179). MEASUREMENTS:Suicide death was recorded in the Danish Cause of Death Registry. Predictors included social and demographic information, mental and physical health diagnoses, surgeries, medications, and poisonings. FINDINGS:Persons among the highest risk for suicide, as identified by the classification trees, were men prescribed antidepressants in the 4 years before suicide and had a poisoning diagnosis in the 4 years before suicide; and women who were 30+ years old and had a poisoning diagnosis 4 years before and 12 months before suicide. Among men with SUD, the random forest identified five variables that were most important in predicting suicide; reaction to severe stress and adjustment disorders, drugs used to treat addictive disorders, age 30+ years, antidepressant use, and poisoning in the 4 prior years. Among women with SUD, the random forest found that the most important predictors of suicide were prior poisonings and reaction to severe stress and adjustment disorders. Individuals in the top 5% of predicted risk accounted for 15% of all suicide deaths among men and 24% of all suicides among women. CONCLUSIONS:In Denmark, prior poisoning and comorbid psychiatric disorders may be among the most important indicators of suicide risk among persons with substance use disorders, particularly among women.

OBJECTIVE:Although some studies document that posttraumatic stress disorder (PTSD) increases suicide risk, other studies have produced the paradoxical finding that PTSD decreases suicide risk. We sought to understand methodologic biases that may explain these paradoxical findings through the use of directed acyclic graphs (DAGs).METHOD:DAGs are causal diagrams that visually encode a researcher's assumptions about data generating mechanisms and assumed causal relations among variables. DAGs can connect theories to data and guide statistical choices made in study design and analysis. In this article, we describe DAGs and explain how they can be used to identify biases that may arise from inappropriate analytic decisions and data limitations.RESULTS:We define a particular form of bias, collider bias, that is a likely explanation for why studies have found a supposedly protective association of PTSD with suicide. This protective association is interpreted by some researchers as evidence that PTSD reduces the risk of suicide. Collider bias may occur through inappropriate adjustment for a psychiatric comorbidity, such as adjustment for variables that are affected by PTSD and share common causes with suicide.CONCLUSIONS:We recommend that researchers collect longitudinal measurements of psychiatric comorbidities, which would help establish the temporal ordering of variables and avoid the biases discussed in this article. Furthermore, researchers could use DAGs to explore how results may be impacted by design and analytic decisions prior to execution. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Suicide attempts are a leading cause of injury globally. Accurate prediction of suicide attempts might offer opportunities for prevention. This case-cohort study used machine learning to examine sex- specific risk profiles for suicide attempts in Danish nationwide registry data. Cases were all persons who made a nonfatal suicide attempt between 1995 and 2015 (n = 22,974); the subcohort was a 5% random sample of the population at risk on January 1, 1995 (n = 265,183). We developed sex-stratified classification trees and random forests using 1,458 predictors, including demographic factors, family histories, psychiatric and physical health diagnoses, surgery, and prescribed medications. We found that substance use disorders/treatment, prescribed psychiatric medications, previous poisoning diagnoses, and stress disorders were important factors for predicting suicide attempts among men and women. Individuals in the top 5% of predicted risk accounted for 44.7% of all suicide attempts among men and 43.2% of all attempts among women. Our findings illuminate novel risk factors and interactions that are most predictive of nonfatal suicide attempts, while consistency between our findings and previous work in this area adds to the call to move machine learning suicide research toward the examination of high-risk subpopulations.

Purpose Intimate partner violence (IPV) is a pervasive public health problem. Existing research has focused on reports from victims and few studies have considered pre-marital factors. The main objective of this study was to identify pre-marital predictors of IPV in the current marriage using information obtained from husbands and wives. Methods Data from were obtained from married heterosexual couples in six countries. Potential predictors included demographic and relationship characteristics, adverse childhood experiences, dating violence, and psychiatric disorders. Reports of IPV and other characteristics from husbands and wives were considered independently and in relation to spousal reports. Results Overall, 14.4% of women were victims of IPV in the current marriage. Analyses identified ten significant variables including age at first marriage (husband), education, relative number of previous marriages (wife), history of one or more categories of childhood adversity (husband or wife), history of dating violence (husband or wife), early initiation of sexual intercourse (husband or wife), and four combinations of internalizing and externalizing disorders. The final model was moderately predictive of marital violence, with the 5% of women accounting for 18.6% of all cases of marital IPV. Conclusions Results from this study advance understanding of pre-marital predictors of IPV within current marriages, including the importance of considering differences in the experiences of partners prior to marriage and may provide a foundation for more targeted primary prevention efforts.