How well can U.S. military veterans' suicidal ideation be predicted from static and change-based indicators of their psychosocial well-being as they adapt to civilian life?

BACKGROUND: Identifying 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. METHODS: The 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. RESULTS: Although 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. CONCLUSIONS: Findings 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.

Prediction of Suicide Attempts Among Persons with Depression: A Population-Based Case Cohort Study.

Studies have highlighted the potential importance of modeling interactions for suicide attempt prediction. This case-cohort study identified risk factors for suicide attempts among persons with depression in Denmark using statistical approaches that do (random forests) or do not model interactions (least absolute shrinkage and selection operator regression [LASSO]). Cases made a non-fatal suicide attempt (n = 6,032) between 1995 and 2015. The comparison subcohort was a 5% random sample of all persons in Denmark on January 1, 1995 (n = 11,963). We used random forests and LASSO for sex-stratified prediction of suicide attempts from demographic variables, psychiatric and somatic diagnoses, and treatments. Poisonings, psychiatric disorders, and medications were important predictors for both sexes. Area under the receiver operating characteristic curve (AUC) values were higher in LASSO models (0.85 [95% CI = 0.84, 0.86] in men; 0.89 [95% CI = 0.88, 0.90] in women) than random forests (0.76 [95% CI = 0.74, 0.78] in men; 0.79 [95% CI = 0.78, 0.81] in women). Automatic detection of interactions via random forests did not result in better model performance than LASSO models that did not model interactions. Due to the complex nature of psychiatric comorbidity and suicide, modeling interactions may not always be the optimal statistical approach to enhancing suicide attempt prediction in high-risk samples.

Stressful life events and incident depression among U.S. military personnel.

PURPOSE: Although stressful life events (i.e., stressors) and depression are often assumed to be linked, the relation between stressors and incident depression is rarely studied, particularly in the military. The National Guard is a part-time subset of the U.S. military for whom civilian life stressors may be particularly salient, due to the soldiers' dual roles and frequent transitions between military and civilian life. METHODS: We used a dynamic cohort study of National Guard members from 2010 to 2016 to investigate the relationship between recent stressful experiences (e.g., divorce) and incident depression, with an exploratory analysis of effect modification by income. RESULTS: Respondents endorsing at least one of nine past-year stressful events (a time-varying exposure, lagged by 1 year) had almost twice the adjusted rate of incident depression compared to those with no stressful events (HR = 1.8; 95% CI 1.4, 2.4). This association may be modified by income: among individuals making under $80,000 per year, those with past-year stressors had twice the rate of depression compared to those with no stressors, but among those making over $80,000, past-year stressors were associated with only 1.2 times the rate of depression. CONCLUSION: Stressful life events outside of deployment are important determinants of incident depression among National Guard servicemembers, but the effect of these events may be buffered by higher income.

Risk of psychopathology following traumatic events among immigrants and native-born persons in Denmark.

PURPOSE: Immigrants may have increased risk of mental disorders compared with native-born persons. We aimed to expand the limited research on immigrants' posttraumatic psychopathology related to traumatic experiences in their country of resettlement. METHODS: We obtained data from a cohort of Danish residents with ≥ 1 traumatic event recorded in health and administrative national registries during 1994-2016. We calculated risks of posttraumatic stress disorder (PTSD), depression, and substance use disorders (SUD) within 5 years post-trauma among native-born Danes and immigrants who had been in Denmark for ≥ 10 years at the time of their index trauma (including immigrants overall and immigrants from specific regions). Risks were compared via age- and sex-standardized risk ratios (SRR) with 95% confidence intervals (CI). We restricted to persons with no record of the disorder under investigation in the 10 years pre-trauma, and stratified by non-interpersonal (e.g., transport accidents) versus interpersonal trauma (e.g., assaults). RESULTS: Following non-interpersonal trauma, immigrants were more likely than native-born Danes to be diagnosed with PTSD (SRR = 5.2, 95% CI 4.6, 5.9), about as likely to be diagnosed with depression (SRR = 0.98, 95% CI 0.92, 1.1), and less likely to be diagnosed with SUD (SRR = 0.89, 95% CI 0.82, 0.95). Results were similar following interpersonal trauma, except the SRR for PTSD was reduced in magnitude (SRR = 3.0, 95% CI 1.7, 5.4). There were differences by region of birth. CONCLUSION: Immigrants to Denmark have higher risk of PTSD following traumatic experiences than do native-born Danes, possibly due to the combined influence of adverse pre-, peri-, and/or post-migration experiences.

Longitudinal associations of chronic health stress and COVID-19-related anxiety among outpatients with anxiety and mood disorders.

BACKGROUND: The 2019 coronavirus (COVID-19) pandemic led to elevated levels of psychological distress on a global scale. Given that individuals with pre-existing physical conditions are at risk for worse COVID-19 outcomes, those dealing with the stress of physical health problems (including knowing someone with health problems) may experience more severe distress during the pandemic. METHODS: Patients with emotional disorders who completed a diagnostic assessment in the 6 months prior to COVID-19 were surveyed in May-June 2020 on their emotional reactions to COVID-19 (N = 77). RESULTS: Multiple linear regression was used to test the hypothesis that chronic stress due to having and knowing others with physical health problems would predict COVID-related worries and behaviours, holding pre-COVID levels of depression, anxiety and worry about health constant. Chronic stress surrounding the health of others was significantly associated with experiencing more severe COVID-related worry and behaviours. In comparison, chronic stress due to one's own health problems had weak and non-significant associations with COVID-related worries and behaviours. CONCLUSIONS: Results indicate that outpatients who report stress about surrounding loved one's health are at risk for experiencing more severe distress during a health pandemic and thus, may benefit from targeted outreach, assessment and intervention.

The Joint Effects of Depression and Comorbid Psychiatric Disorders on Suicide Deaths: Competing Antagonism as an Explanation for Subadditivity.

BACKGROUND: Previous studies of the effect of interaction between psychiatric disorders on suicide have reported mixed results. We investigated the joint effect of depression and various comorbid psychiatric disorders on suicide. METHODS: We conducted a population-based case-cohort study with all suicide deaths occurring between 1 January 1995 and 31 December 2015 in Denmark (n = 14,103) and a comparison subcohort comprised of a 5% random sample of the source population at baseline (n = 265,183). We quantified the joint effect of pairwise combinations of depression and major psychiatric disorders (e.g., organic disorders, substance use disorders, schizophrenia, bipolar disorder, neurotic disorders, eating disorders, personality disorders, intellectual disabilities, developmental disorders, and behavioral disorders) on suicide using marginal structural models and calculated the relative excess risk due to interaction. We assessed for the presence of competing antagonism for negative relative excess risk due to interactions. RESULTS: All combinations of depression and comorbid psychiatric disorders were associated with increased suicide risk. For example, the rate of suicide among men with depression and neurotic disorders was 20 times (95% CI = 15, 26) the rate in men with neither disorder. Most disorder combinations were associated with subadditive suicide risk, and there was evidence of competing antagonism in most of these cases. CONCLUSIONS: Subadditivity may be explained by competing antagonism. When both depression and a comorbid psychiatric disorder are present, they may compete to cause the outcome such that having 2 disorders may be no worse than having a single disorder with respect to suicide risk.

Using Danish national registry data to understand psychopathology following potentially traumatic experiences.

Research on posttraumatic psychopathology has focused primarily on posttraumatic stress disorder (PTSD); other posttraumatic psychiatric diagnoses are less well documented. The present study aimed to (a) develop a methodology to derive a cohort of individuals who experienced potentially traumatic events (PTEs) from registry-based data and (b) examine the risk of psychopathology within 5 years of experiencing a PTE. Using data from Danish national registries, we created a cohort of individuals with no age restrictions (range: 0-108 years) who experienced at least one of eight possible PTEs between 1994 and 2016 (N = 1,406,637). We calculated the 5-year incidence of nine categories of ICD-10 psychiatric disorders among this cohort and examined standardized morbidity ratios (SMRs) comparing the incidence of psychopathology in this group to the incidence in a nontraumatic stressor cohort (i.e., nonsuicide death of a relative; n = 423,270). Stress disorders (2.5%), substance use disorders (4.1%), and depressive disorders (3.0%) were the most common diagnoses following PTEs. Overall, the SMRs for the associations between any PTE and psychopathology varied from 1.9, 95% CI [1.9, 2.0], for stress disorders to 5.2, 95% CI [5.1. 5.3], for personality disorders. All PTEs except pregnancy-related trauma were associated with all forms of psychopathology. Associations were consistent regardless of whether a stress disorder was present. Traumatic experiences have a broad impact on psychiatric health. The present findings demonstrate one approach to capturing trauma exposure in medical record registry data. Increased traumatic experience characterization across studies will help improve the field's understanding of posttraumatic psychopathology.

Predicting Sex-Specific Nonfatal Suicide Attempt Risk Using Machine Learning and Data From Danish National Registries.

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.

Using machine learning to predict suicide in the 30 days after discharge from psychiatric hospital in Denmark.

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.

Developing and Validating Clinical Questionnaires.

Coinciding with the development and revision of conceptual models of psychopathology, there has been a proliferation in the number of self-report clinical questionnaires and studies evaluating their psychometric properties. Unfortunately, many clinical measures are constructed and evaluated using suboptimal methods. This review provides current guidelines for the conceptualization, development, and psychometric validation of clinical questionnaires using latent variable methods. A two-stage exploratory-confirmatory framework is provided. The exploratory stage includes item selection and revision, initial structural evaluation, and preliminary tests of concurrent validity (e.g., convergent and discriminant). The confirmatory stage involves replicating factor structure using a more restrictive model, identifying areas of model strain, conducting additional tests of concurrent and predictive validity, and evaluating measurement invariance. Recommendations are provided for (a) item generation, (b) how to use different types of exploratory and confirmatory factor models to determine structure, and (c) evaluating reliability and validity using a latent variable measurement model approach.

Stress Disorders and the Risk of Nonfatal Suicide Attempts in the Danish 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.

Predeployment predictors of psychiatric disorder-symptoms and interpersonal violence during combat deployment.

BACKGROUND: Preventing suicides, mental disorders, and noncombat-related interpersonal violence during deployment are priorities of the US Army. We used predeployment survey and administrative data to develop actuarial models to identify soldiers at high risk of these outcomes during combat deployment. METHODS: The models were developed in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) Pre-Post Deployment Study, a panel study of soldiers deployed to Afghanistan in 2012-2013. Soldiers completed self-administered questionnaires before deployment and one (T1), three (T2), and nine months (T3) after deployment, and consented to administrative data linkage. Seven during-deployment outcomes were operationalized using the postdeployment surveys. Two overlapping samples were used because some outcomes were assessed at T1 (n = 7,048) and others at T2-T3 (n = 7,081). Ensemble machine learning was used to develop a model for each outcome from 273 predeployment predictors, which were compared to simple logistic regression models. RESULTS: The relative improvement in area under the receiver operating characteristic curve (AUC) obtained by machine learning compared to the logistic models ranged from 1.11 (major depression) to 1.83 (suicidality).The best-performing machine learning models were for major depression (AUC = 0.88), suicidality (0.86), and generalized anxiety disorder (0.85). Roughly 40% of these outcomes occurred among the 5% of soldiers with highest predicted risk. CONCLUSIONS: Actuarial models could be used to identify high risk soldiers either for exclusion from deployment or preventive interventions. However, the ultimate value of this approach depends on the associated costs, competing risks (e.g. stigma), and the effectiveness to-be-determined interventions.

Improving risk prediction accuracy for new soldiers in the U.S. Army by adding self-report survey data to administrative data.

BACKGROUND: High rates of mental disorders, suicidality, and interpersonal violence early in the military career have raised interest in implementing preventive interventions with high-risk new enlistees. The Army Study to Assess Risk and Resilience in Servicemembers (STARRS) developed risk-targeting systems for these outcomes based on machine learning methods using administrative data predictors. However, administrative data omit many risk factors, raising the question whether risk targeting could be improved by adding self-report survey data to prediction models. If so, the Army may gain from routinely administering surveys that assess additional risk factors. METHODS: The STARRS New Soldier Survey was administered to 21,790 Regular Army soldiers who agreed to have survey data linked to administrative records. As reported previously, machine learning models using administrative data as predictors found that small proportions of high-risk soldiers accounted for high proportions of negative outcomes. Other machine learning models using self-report survey data as predictors were developed previously for three of these outcomes: major physical violence and sexual violence perpetration among men and sexual violence victimization among women. Here we examined the extent to which this survey information increases prediction accuracy, over models based solely on administrative data, for those three outcomes. We used discrete-time survival analysis to estimate a series of models predicting first occurrence, assessing how model fit improved and concentration of risk increased when adding the predicted risk score based on survey data to the predicted risk score based on administrative data. RESULTS: The addition of survey data improved prediction significantly for all outcomes. In the most extreme case, the percentage of reported sexual violence victimization among the 5% of female soldiers with highest predicted risk increased from 17.5% using only administrative predictors to 29.4% adding survey predictors, a 67.9% proportional increase in prediction accuracy. Other proportional increases in concentration of risk ranged from 4.8% to 49.5% (median = 26.0%). CONCLUSIONS: Data from an ongoing New Soldier Survey could substantially improve accuracy of risk models compared to models based exclusively on administrative predictors. Depending upon the characteristics of interventions used, the increase in targeting accuracy from survey data might offset survey administration costs.

Sexual Assault Victimization and Mental Health Treatment, Suicide Attempts, and Career Outcomes Among Women in the US Army.

OBJECTIVES: To examine associations of administratively recorded sexual assault victimization during military service with subsequent mental health and negative career outcomes among US Army women controlling for nonrandom victimization exposure. METHODS: We used data from the Army Study to Assess Risk and Resilience in Servicemembers to apply propensity score methods to match all 4238 female Regular Army soldiers with administratively recorded sexual assault victimization during 2004 to 2009 to 5 controls per case with similar composite victimization risk. We examined associations of this victimization measure with administratively recorded mental health treatment, suicide attempt, and Army career outcomes over the subsequent 12 months by using survival analysis for dichotomous outcomes and conditional generalized linear models for continuous outcomes. RESULTS: Women with administratively recorded sexual assault had significantly elevated odds ratios (ORs) of subsequent mental health treatment (any, OR = 2.5; 95% confidence interval [CI] = 2.4, 2.6; specialty, OR = 3.1; 95% CI = 2.9, 3.3; inpatient, OR = 2.8; 95% CI = 2.5, 3.1), posttraumatic stress disorder treatment (any, OR = 6.3; 95% CI = 5.7, 6.9; specialty, OR = 7.7; 95% CI = 6.8, 8.6; inpatient, OR = 6.8; 95% CI = 5.4, 8.6), suicide attempt (OR = 3.0; 95% CI = 2.5, 3.6), demotion (OR = 2.1; 95% CI = 1.9, 2.3), and attrition (OR = 1.2; 95% CI = 1.1, 1.2). CONCLUSIONS: Sexual assault victimization is associated with considerable suffering and likely decreased force readiness.

Associations of childhood bullying victimization with lifetime suicidal behaviors among new U.S. Army soldiers.

BACKGROUND: Prior studies have documented associations of childhood bullying victimization with suicidal behaviors. However, many failed to adjust for concomitant risk factors and none investigated this relationship in military personnel. This study aimed to estimate independent associations of childhood bullying victimization with suicidal behaviors among U.S. Army soldiers. METHODS: Soldiers reporting for basic training completed a cross-sectional survey assessing mental disorders, suicidal behaviors, and childhood adversities including two types of bullying victimization: (1) Physical Assault/Theft and (2) Bullying Comments/Behaviors. Associations of childhood bullying experiences with suicidal behaviors were estimated using discrete-time survival analysis of person-year data from 30,436 soldiers. Models adjusted for sociodemographic factors, childhood maltreatment by adults, and mental disorders. RESULTS: After comprehensive adjustment for other risk factors, more frequent Physical Assault/Theft by peers during childhood was associated with increased odds of lifetime suicidal ideation (adjusted odds ratio [AOR] = 1.18, 95% CI: 1.11-1.26, P < .001) and attempt (AOR = 1.30, 95% CI: 1.13-1.50, P < .001). More frequent Bullying Comments/Behaviors were associated with increased risk of ideation (AOR = 1.30, 95% CI: 1.26-1.35, P < .001), plan (AOR = 1.44, 95% CI: 1.35-1.54, P < .001), attempt (AOR = 1.24, 95% CI: 1.15-1.33, P < .001), and onset of plan among ideators (AOR = 1.09, 95% CI: 1.03-1.15, P = .002). Relative to no bullying victimization, exposure to the most persistent bullying was associated with two- to fourfold increase in risk for suicidal behaviors. CONCLUSIONS: Childhood bullying victimization is associated with lifetime suicidal behaviors among new soldiers. Exposure to Bullying Comments/Behaviors during childhood is associated with progression from suicidal ideation to plan. Improved recognition of these relationships may inform risk mitigation interventions for soldiers.

APPROXIMATING A DSM-5 DIAGNOSIS OF PTSD USING DSM-IV CRITERIA.

BACKGROUND: Diagnostic criteria for DSM-5 posttraumatic stress disorder (PTSD) are in many ways similar to DSM-IV criteria, raising the possibility that it might be possible to closely approximate DSM-5 diagnoses using DSM-IV symptoms. If so, the resulting transformation rules could be used to pool research data based on the two criteria sets. METHODS: The pre-post deployment study (PPDS) of the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) administered a blended 30-day DSM-IV and DSM-5 PTSD symptom assessment based on the civilian PTSD Checklist for DSM-IV (PCL-C) and the PTSD Checklist for DSM-5 (PCL-5). This assessment was completed by 9,193 soldiers from three US Army Brigade Combat Teams approximately 3 months after returning from Afghanistan. PCL-C items were used to operationalize conservative and broad approximations of DSM-5 PTSD diagnoses. The operating characteristics of these approximations were examined compared to diagnoses based on actual DSM-5 criteria. RESULTS: The estimated 30-day prevalence of DSM-5 PTSD based on conservative (4.3%) and broad (4.7%) approximations of DSM-5 criteria using DSM-IV symptom assessments were similar to estimates based on actual DSM-5 criteria (4.6%). Both approximations had excellent sensitivity (92.6-95.5%), specificity (99.6-99.9%), total classification accuracy (99.4-99.6%), and area under the receiver operating characteristic curve (0.96-0.98). CONCLUSIONS: DSM-IV symptoms can be used to approximate DSM-5 diagnoses of PTSD among recently deployed soldiers, making it possible to recode symptom-level data from earlier DSM-IV studies to draw inferences about DSM-5 PTSD. However, replication is needed in broader trauma-exposed samples to evaluate the external validity of this finding.

Lifetime prevalence of DSM-IV mental disorders among new soldiers in the U.S. Army: results from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).

BACKGROUND: The prevalence of 30-day mental disorders with retrospectively reported early onsets is significantly higher in the U.S. Army than among socio-demographically matched civilians. This difference could reflect high prevalence of preenlistment disorders and/or high persistence of these disorders in the context of the stresses associated with military service. These alternatives can to some extent be distinguished by estimating lifetime disorder prevalence among new Army recruits. METHODS: The New Soldier Study (NSS) in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) used fully structured measures to estimate lifetime prevalence of 10 DSM-IV disorders in new soldiers reporting for Basic Combat Training in 2011-2012 (n = 38,507). Prevalence was compared to estimates from a matched civilian sample. Multivariate regression models examined socio-demographic correlates of disorder prevalence and persistence among new soldiers. RESULTS: Lifetime prevalence of having at least one internalizing, externalizing, or either type of disorder did not differ significantly between new soldiers and civilians, although three specific disorders (generalized anxiety, posttraumatic stress, and conduct disorders) and multimorbidity were significantly more common among new soldiers than civilians. Although several socio-demographic characteristics were significantly associated with disorder prevalence and persistence, these associations were uniformly weak. CONCLUSIONS: New soldiers differ somewhat, but not consistently, from civilians in lifetime preenlistment mental disorders. This suggests that prior findings of higher prevalence of current disorders with preenlistment onsets among soldiers than civilians are likely due primarily to a more persistent course of early-onset disorders in the context of the special stresses experienced by Army personnel.

Prevalence and correlates of suicidal behavior among new soldiers in the U.S. Army: results from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).

BACKGROUND: The prevalence of suicide among U.S. Army soldiers has risen dramatically in recent years. Prior studies suggest that most soldiers with suicidal behaviors (i.e., ideation, plans, and attempts) had first onsets prior to enlistment. However, those data are based on retrospective self-reports of soldiers later in their Army careers. Unbiased examination of this issue requires investigation of suicidality among new soldiers. METHOD: The New Soldier Study (NSS) of the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) used fully structured self-administered measures to estimate preenlistment histories of suicide ideation, plans, and attempts among new soldiers reporting for Basic Combat Training in 2011-2012. Survival models examined sociodemographic correlates of each suicidal outcome. RESULTS: Lifetime prevalence estimates of preenlistment suicide ideation, plans, and attempts were 14.1, 2.3, and 1.9%, respectively. Most reported onsets of suicide plans and attempts (73.3-81.5%) occurred within the first year after onset of ideation. Odds of these lifetime suicidal behaviors among new soldiers were positively, but weakly associated with being female, unmarried, religion other than Protestant or Catholic, and a race/ethnicity other than non-Hispanic White, non-Hispanic Black, or Hispanic. CONCLUSIONS: Lifetime prevalence estimates of suicidal behaviors among new soldiers are consistent with retrospective reports of preenlistment prevalence obtained from soldiers later in their Army careers. Given that prior suicidal behaviors are among the strongest predictors of later suicides, consideration should be given to developing methods of obtaining valid reports of preenlistment suicidality from new soldiers to facilitate targeting of preventive interventions.