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1.  The NEO Five-Factor Inventory: Latent Structure and Relationships With Dimensions of Anxiety and Depressive Disorders in a Large Clinical Sample 
Assessment  2010;18(1):27-38.
The present study evaluated the latent structure of the NEO Five-Factor Inventory (NEO FFI) and relations between the five-factor model (FFM) of personality and dimensions of DSM-IV anxiety and depressive disorders (panic disorder, generalized anxiety disorder [GAD], obsessive–compulsive disorder, social phobia [SOC], major depressive disorder [MDD]) in a large sample of outpatients (N = 1,980). Exploratory structural equation modeling (ESEM) was used to show that a five-factor solution provided acceptable model fit, albeit with some poorly functioning items. Neuroticism demonstrated significant positive associations with all but one of the disorder constructs whereas Extraversion was inversely related to SOC and MDD. Conscientiousness was inversely related to MDD but demonstrated a positive relationship with GAD. Results are discussed in regard to potential revisions to the NEO FFI, the evaluation of other NEO instruments using ESEM, and clinical implications of structural paths between FFM domains and specific emotional disorders.
doi:10.1177/1073191110382848
PMCID: PMC5639474  PMID: 20881102
NEO Five-Factor Inventory; five-factor model; latent structure; anxiety; depression; clinical sample; exploratory structural equation modeling
2.  Using administrative data to identify U.S. Army soldiers at high-risk of perpetrating minor violent crimes 
Growing concerns exist about violent crimes perpetrated by U.S. military personnel. Although interventions exist to reduce violent crimes in high-risk populations, optimal implementation requires evidence-based targeting. The goal of the current study was to use machine learning methods (stepwise and penalized regression; random forests) to develop models to predict minor violent crime perpetration among U.S. Army soldiers. Predictors were abstracted from administrative data available for all 975,057 soldiers in the U.S. Army 2004–2009, among whom 25,966 men and 2,728 women committed a first founded minor violent crime (simple assault, blackmail-extortion-intimidation, rioting, harassment). Temporally prior administrative records measuring socio-demographic, Army career, criminal justice, medical/pharmacy, and contextual variables were used to build separate male and female prediction models that were then tested in an independent 2011–2013 sample. Final model predictors included young age, low education, early career stage, prior crime involvement, and outpatient treatment for diverse emotional and substance use problems. Area under the receiver operating characteristic curve was 0.79 (for men and women) in the 2004–2009 training sample and 0.74–0.82 (men-women) in the 2011–2013 test sample. 30.5–28.9% (men-women) of all administratively-recorded crimes in 2004–2009 were committed by the 5% of soldiers having highest predicted risk, with similar proportions (28.5–29.0%) when the 2004–2009 coefficients were applied to the 2011–2013 test sample. These results suggest that it may be possible to target soldiers at high-risk of violence perpetration for preventive interventions, although final decisions about such interventions would require weighing predicted effectiveness against intervention costs and competing risks.
doi:10.1016/j.jpsychires.2016.09.028
PMCID: PMC5125854  PMID: 27741501
crime perpetration; military violence; prediction model; risk model; violence prediction
3.  Predicting Sexual Assault Perpetration in the US Army Using Administrative Data 
Introduction
The Department of Defense uses a universal prevention framework for sexual assault prevention, with each branch implementing their own branch-wide programs. Intensive interventions exist, but would be cost-effective only if targeted at high-risk personnel. This study developed actuarial models to identify male U.S. Army soldiers at high risk of administratively-recorded sexual assault perpetration.
Methods
This study investigated administratively-recorded sexual assault perpetration among the 821,807 male Army soldiers serving 2004–2009. Other temporally prior administrative data were used as predictors. Penalized discrete-time (person-month) survival analysis (conducted in 2016) was used to select the smallest possible number of stable predictors to maximize number of sexual assaults among the 5% of soldiers with highest predicted risk of perpetration (top-ventile concentration of risk [COR]). Separate models were developed for assaults against non-family and intra-family adults and minors.
Results
4,640 male soldiers were found to be perpetrators against non-family adults, 1,384 against non-family minors, 380 against intra-family adults, and 335 against intra-family minors. Top-ventile COR was 16.2–20.2% predicting perpetration against non-family adults and minors and 34.2–65.1% against intra-family adults and minors. Final predictors consisted largely of measures of prior crime involvement and the presence-treatment of mental disorders.
Conclusions
Administrative data can be used to develop actuarial models that identify a high proportion of sexual assault perpetrators. If a system is developed to routinely consolidate administrative predictors, predictions could be generated periodically to identify those in need of preventive intervention. Whether this would be cost-effective, though, would depend on intervention costs, effectiveness, and competing risks.
doi:10.1016/j.amepre.2017.06.022
PMCID: PMC5683072  PMID: 28818420
sexual assault; perpetration; military; prediction model; risk model; violence prediction
4.  Using self-report surveys at the beginning of service to develop multi-outcome risk models for new soldiers in the U.S. Army 
Psychological medicine  2017;47(13):2275-2287.
Background
The U.S. Army uses universal preventives interventions for several negative outcomes (e.g., suicide, violence, sexual assault) with especially high risks in the early years of service. More intensive interventions exist, but would be cost-effective only if targeted at high-risk soldiers. We report results of efforts to develop models for such targeting from self-report surveys administered at the beginning of Army service.
Methods
21,832 new soldiers completed a self-administered questionnaire (SAQ) in 2011–2012 and consented to link administrative data to SAQ responses. Penalized regression models were developed for 12 administratively-recorded outcomes occurring by December 2013: suicide attempt, mental hospitalization, positive drug test, traumatic brain injury (TBI), other severe injury, several types of violence perpetration and victimization, demotion, and attrition.
Results
The best-performing models were for TBI (AUC=0.80), major physical violence perpetration (AUC=0.78), sexual assault perpetration (AUC=0.78), and suicide attempt (AUC=0.74). Although predicted risk scores were significantly correlated across outcomes, prediction was not improved by including risk scores for other outcomes in models. Of particular note: 40.5% of suicide attempts occurred among the 10% of new soldiers with highest predicted risk, 57.2% of male sexual assault perpetrations among the 15% with highest predicted risk, and 35.5% of female sexual assault victimizations among the 10% with highest predicted risk.
Conclusions
Data collected at the beginning of service in self-report surveys could be used to develop risk models that define small proportions of new soldiers accounting for high proportions of negative outcomes over the first few years of service.
doi:10.1017/S003329171700071X
PMCID: PMC5679702  PMID: 28374665
Army; military; predictive modeling; risk assessment; mental health; violence; disciplinary problems
5.  GENERALIZED ANXIETY DISORDER WITHIN THE COURSE OF MAJOR DEPRESSIVE DISORDER: EXAMINING THE UTILITY OF THE DSM-IV HIERARCHY RULE 
Depression and anxiety  2009;26(10):909-916.
Background
The current Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) specifies that generalized anxiety disorder (GAD) should not be diagnosed if it occurs exclusively during an episode of a major depressive disorder (MDD) or another mood disorder. This hierarchy rule was intended to promote diagnostic parsimony, but may result in the loss of important clinical information. The goal of this study was to compare individuals with MDD, comorbid MDD and GAD, and GAD within the course of MDD at intake and 12-month follow-up on self-report measures, clinician ratings, and rates of comorbidity.
Methods
Participants were divided into three diagnostic groups: MDD without GAD (n = 124), comorbid MDD and GAD (n = 59), and GAD within the course of MDD (n = 166). All the participants completed a semi-structured clinical interview and self-report measures assessing psychopathology, temperament, and functional impairment. A subset of the total sample completed a follow-up assessment of 12 months postintake.
Results
Individuals with comorbid MDD and GAD and GAD within the course of MDD reported more psychopathology, negative affect, and functional impairment at intake than individuals with MDD only. The presence of GAD at intake, however, did not differentially predict symptom severity, functional impairment, or the presence of comorbidity at 12-month follow-up.
Conclusions
Cross-sectional findings indicate that individuals with GAD within the course of MDD experience levels of psychopathology, functional impairment, and comorbidity similar to those found in individuals with comorbid GAD and MDD. Preliminary longitudinal findings, however, suggest that the presence of GAD in patients with MDD does not have prognostic significance.
doi:10.1002/da.20607
PMCID: PMC5639477  PMID: 19798759
anxiety; classification; comorbidity; depression; diagnosis
6.  Initial Interpretation and Evaluation of a Profile-Based Classification System for the Anxiety and Mood Disorders: Incremental Validity Compared to DSM-IV Categories 
Psychological assessment  2014;26(4):1212-1224.
Limitations in anxiety and mood disorder diagnostic reliability and validity due to the categorical approach to classification used by the Diagnostic and Statistical Manual of Mental Disorders (DSM) have been long recognized. Although these limitations have led researchers to forward alternative classification schemes, few have been empirically evaluated. In a sample of 1,218 outpatients with anxiety and mood disorders, the present study examined the validity of Brown and Barlow's (2009) proposal to classify the anxiety and mood disorders using an integrated dimensional-categorical approach based on transdiagnostic emotional disorder vulnerabilities and phenotypes. Latent class analyses of seven transdiagnostic dimensional indicators suggested that a six-class (i.e., profile) solution provided the best model fit and was the most conceptually interpretable. Interpretation of the classes was further supported when compared with DSM-IV diagnoses (i.e., within-class prevalence of diagnoses, using diagnoses to predict class membership). In addition, hierarchical multiple regression models were used to demonstrate the incremental validity of the profiles; class probabilities consistently accounted for unique variance in anxiety and mood disorder outcomes above and beyond DSM diagnoses. These results provide support for the potential development and utility of a hybrid dimensional-categorical profile approach to anxiety and mood disorder classification. In particular, the availability of dimensional indicators and corresponding profiles may serve as a useful complement to DSM diagnoses for both researchers and clinicians.
doi:10.1037/pas0000023
PMCID: PMC4274231  PMID: 25265416
anxiety and mood disorders; dimensional-categorical classification; hybrid classification; latent class analysis
7.  Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) 
Molecular psychiatry  2016;10.1038/mp.2016.110.
The 2013 U.S. Veterans Administration/Department of Defense Clinical Practice Guidelines (VA/DoD CPG) require comprehensive suicide risk assessments for VA/DoD patients with mental disorders but provide minimal guidance on how to carry out these assessments. Given that clinician-based assessments are known not to be strong predictors of suicide, we investigated whether a precision medicine model using administrative data after outpatient mental health specialty visits could be developed to predict suicides among outpatients. We focused on male non-deployed Regular U.S. Army soldiers because they account for the vast majority of such suicides. Four machine learning classifiers (naïve Bayes, random forests, support vector regression, elastic net penalized regression) were explored. 41.5% of Army suicides in 2004-2009 occurred among the 12.0% of soldiers seen as outpatient by mental health specialists, with risk especially high within 26 weeks of visits. An elastic net classifier with 10-14 predictors optimized sensitivity (45.6% of suicide deaths occurring after the 15% of visits with highest predicted risk). Good model stability was found for a model using 2004-2007 data to predict 2008-2009 suicides, although stability decreased in a model using 2008-2009 data to predict 2010-2012 suicides. The 5% of visits with highest risk included only 0.1% of soldiers (1047.1 suicides/100,000 person-years in the 5 weeks after the visit). This is a high enough concentration of risk to have implications for targeting preventive interventions. An even better model might be developed in the future by including the enriched information on clinician-evaluated suicide risk mandated by the VA/DoD CPG to be recorded.
doi:10.1038/mp.2016.110
PMCID: PMC5247428  PMID: 27431294
Army; machine learning; military; predictive modeling; risk assessment; suicide
8.  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) 
Depression and anxiety  2014;32(1):3-12.
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.
doi:10.1002/da.22317
PMCID: PMC5113817  PMID: 25338964
military personnel; prevalence; suicide; suicide ideation; suicide attempt
9.  Predicting non-familial major physical violent crime perpetration in the U.S. Army from administrative data 
Psychological medicine  2015;46(2):303-316.
BACKGROUND
Although interventions exist to reduce violent crime, optimal implementation requires accurate targeting. We report the results of an attempt to develop an actuarial model using machine learning methods to predict future violent crimes among U.S. Army soldiers.
METHODS
A consolidated administrative database for all 975,057 soldiers in the U.S. Army in 2004-2009 was created in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). 5,771 of these soldiers committed a first founded major physical violent crime (murder-manslaughter, kidnapping, aggravated arson, aggravated assault, robbery) over that time period. Temporally prior administrative records measuring socio-demographic, Army career, criminal justice, medical/pharmacy, and contextual variables were used to build an actuarial model for these crimes separately among men and women using machine learning methods (cross-validated stepwise regression; random forests; penalized regressions). The model was then validated in an independent 2011-2013 sample.
RESULTS
Key predictors were indicators of disadvantaged social/socio-economic status, early career stage, prior crime, and mental disorder treatment. Area under the receiver operating characteristic curve was .80-.82 in 2004-2009 and .77 in a 2011-2013 validation sample. 36.2-33.1% (male-female) of all administratively-recorded crimes were committed by the 5% of soldiers having highest predicted risk in 2004-2009 and an even higher proportion (50.5%) in the 2011-2013 validation sample.
CONCLUSIONS
Although these results suggest that the models could be used to target soldiers at high risk of violent crime perpetration for preventive interventions, final implementation decisions would require further validation and weighing of predicted effectiveness against intervention costs and competing risks.
doi:10.1017/S0033291715001774
PMCID: PMC5111361  PMID: 26436603
crime perpetration; physical violence; military violence; risk model; actuarial model; machine learning
10.  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) 
Depression and anxiety  2014;32(1):13-24.
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 pre-enlistment 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 multi-morbidity 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 pre-enlistment mental disorders. This suggests that prior findings of higher prevalence of current disorders with pre-enlistment 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.
doi:10.1002/da.22316
PMCID: PMC5111824  PMID: 25338841
military personnel; mental disorders; prevalence; epidemiology; demographics
11.  The Relevance of Age of Onset to the Psychopathology of Social Phobia 
The present study aimed to examine the relevance of age of onset to the psychopathology of social phobia using a large clinical sample of 210 patients with social phobia. The two most common periods of onset were during adolescence (ages 14–17) and early childhood (prior to age 10). Structural regression modeling was used to test predictions that early onset social phobia would be associated with greater severity of the disorder, stronger current symptoms of depression and anxiety, greater functional impairment, and more pronounced levels of emotional disorder vulnerabilities (e.g., neuroticism/behavioral inhibition, extraversion, perceptions of control). Logistic regression was used to evaluate relationships between age of onset and the presence of acute and chronic stress at the time of onset. Results showed that earlier age of social phobia onset was associated with stronger current psychopathology, functional impairment, and emotional disorder vulnerabilities, and that later age of onset predicted the presence of an acutely stressful event around the time of disorder emergence. These results are discussed in regard to their clinical implications and congruence with prominent etiological models of the emotional disorders.
doi:10.1007/s10862-013-9338-5
PMCID: PMC3736863  PMID: 23935239
Social phobia; age of onset; vulnerability-stress; impairment; structural equation modeling
12.  Mental Disorders, Comorbidity and Pre-Enlistment Suicidal Behavior among New Soldiers in the US Army: Results from the Army Study to Assess Risk and Resilience in Service members (Army STARRS) 
Suicide & life-threatening behavior  2015;10.1111/sltb.12153.
We examined the associations between mental disorders and suicidal behavior (ideation, plans, and attempts) among new soldiers using data from the New Soldier Study (NSS) component of the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS; n=38,507). Most new soldiers with a pre-enlistment history of suicide attempt reported a prior mental disorder (59.0%). Each disorder examined was associated with increased odds of suicidal behavior (ORs=2.6–8.6). Only PTSD and disorders characterized by irritability and impulsive/aggressive behavior (i.e., bipolar disorder, conduct disorder, oppositional defiant disorder, and attention-deficit/hyperactivity disorder) predicted unplanned attempts among ideators. Mental disorders are important predictors of pre-enlistment suicidal behavior among new soldiers and should figure prominently in suicide screening and prevention efforts.
doi:10.1111/sltb.12153
PMCID: PMC4515394  PMID: 25622860
13.  Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports 
Molecular psychiatry  2016;21(10):1366-1371.
Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. While efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity, and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1,056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared to observed scores assessed 10–12 years after baseline. ML model prediction accuracy was also compared to that of conventional logistic regression models. Area under the receiver operating characteristic curve (AUC) based on ML (.63 for high chronicity and .71–.76 for the other prospective outcomes) was consistently higher than for the logistic models (.62–.70) despite the latter models including more predictors. 34.6–38.1% of respondents with subsequent high persistence-chronicity and 40.8–55.8% with the severity indicators were in the top 20% of the baseline ML predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML predicted risk distribution. These results confirm that clinically useful MDD risk stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.
doi:10.1038/mp.2015.198
PMCID: PMC4935654  PMID: 26728563
14.  Approximating a DSM-5 Diagnosis of PTSD Using DSM-IV Criteria 
Depression and anxiety  2015;32(7):493-501.
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 three 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.
doi:10.1002/da.22364
PMCID: PMC4490033  PMID: 25845710
PTSD/posttraumatic stress disorder; Assessment/Diagnosis; Anxiety/Anxiety disorders; measurement/psychometrics; trauma
15.  The Effects of Extraverted Temperament on Agoraphobia in Panic Disorder 
Journal of abnormal psychology  2010;119(2):420-426.
Although situational avoidance is viewed as the most disabling aspect of panic disorder (PD), few studies have evaluated how dimensions of neurotic (i.e., NT; neuroticism, behavioral inhibition) and extraverted (i.e. ET; extraversion, behavioral activation) temperament may influence the presence and severity of agoraphobia (AG). Using logistic regression and structural equation modeling, the present study examined the unique effects of ET on situational avoidance in a sample of 274 outpatients diagnosed with PD with and without AG. Results showed low ET (i.e., introversion) to be associated with both the presence and severity of situational avoidance. Findings are discussed in regard to conceptualizations of conditioned avoidance, activity levels, sociability, and positive emotions within the context of PD with AG.
doi:10.1037/a0018614
PMCID: PMC3487397  PMID: 20455614
16.  Classifying U.S. Army Military Occupational Specialties Using the Occupational Information Network 
Military medicine  2014;179(7):752-761.
Objectives
To derive job condition scales for future studies of the effects of job conditions on soldier health and job functioning across Army Military Occupation Specialties (MOSs) and Areas of Concentration (AOCs) using Department of Labor (DoL) Occupational Information Network (O*NET) ratings.
Methods
A consolidated administrative dataset was created for the “Army Study to Assess Risk and Resilience in Servicemembers” (Army STARRS) containing all soldiers on active duty between 2004 and 2009. A crosswalk between civilian occupations and MOS/AOCs (created by DoL and the Defense Manpower Data Center) was augmented to assign scores on all 246 O*NET dimensions to each soldier in the dataset. Principal components analysis was used to summarize these dimensions.
Results
Three correlated components explained the majority of O*NET dimension variance: “physical demands” (20.9% of variance), “interpersonal complexity” (17.5%), and “substantive complexity” (15.0%). Although broadly consistent with civilian studies, several discrepancies were found with civilian results reflecting potentially important differences in the structure of job conditions in the Army versus the civilian labor force.
Conclusions
Principal components scores for these scales provide a parsimonious characterization of key job conditions that can be used in future studies of the effects of MOS/AOC job conditions on diverse outcomes.
doi:10.7205/MILMED-D-13-00446
PMCID: PMC4764059  PMID: 25003860
17.  The Temporal Course of Anxiety Sensitivity in Outpatients with Anxiety and Mood Disorders: Relationships with Behavioral Inhibition and Depression 
Journal of anxiety disorders  2011;25(4):615-621.
The present study evaluated the temporal course of three dimensions of anxiety sensitivity (AS; concerns over physical symptoms, mental incapacitation, and social embarrassment) and their relationships with behavioral inhibition (BI) and depression (DEP) in 606 outpatients with anxiety and mood disorders. A semi-structured interview and self-report questionnaires were administered on three occasions over a two-year period. All three constructs decreased over the study period and AS temporally functioned more similar to DEP than BI. Cross-sectional and temporal correlations supported the discriminant validity of AS from BI. As expected, initial levels of BI predicted less improvement in all AS dimensions. In contrast, higher initial levels of mental incapacitation AS were associated with greater improvement in DEP. Our results are discussed in regard to the measurement of AS in clinical samples, conceptualizations of AS as a lower-order vulnerability, and prognostic implications of directional paths between BI and AS and AS and DEP.
doi:10.1016/j.janxdis.2011.02.001
PMCID: PMC3074026  PMID: 21377316
anxiety sensitivity; personality; depression; longitudinal study; outpatients
18.  Temperament, Hopelessness, and Attempted Suicide: Direct and Indirect Effects 
The present study evaluated if hopelessness mediated the relations between temperament and recent suicide attempter status in a psychiatric sample. Negative and positive temperament (particularly the positive temperament-positive emotionality subscale) uniquely predicted levels of hopelessness. Although these temperament constructs also demonstrated significant indirect effects on recent suicide attempter status, the effects were partially (for the broad temperament scales) or fully (for the positive emotionality subscale) mediated by levels of hopelessness. These findings indicate that a tendency to experience excessive negative emotions as well as a paucity of positive emotions may lead individuals to experience hopelessness. Although temperament may indirectly influence suicide attempter status, hopelessness mediates these relations.
doi:10.1111/sltb.12078
PMCID: PMC4717475  PMID: 24494785
temperament; personality; hopelessness; suicide attempt; mediation
19.  Predicting U.S. Army suicides after hospitalizations with psychiatric diagnoses in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) 
JAMA psychiatry  2015;72(1):49-57.
IMPORTANCE
The U.S. Army experienced a sharp rise in suicides beginning in 2004. Administrative data show that among those at highest risk are soldiers in the 12 months after inpatient treatment of a psychiatric disorder.
OBJECTIVE
To develop an actuarial risk algorithm predicting suicide in the 12 months after US Army soldier inpatient treatment of a psychiatric disorder to target expanded post-hospital care.
DESIGN, SETTING, AND PARTICIPANTS
There were 53,769 hospitalizations of active duty soldiers in 2004–2009 with ICD-9-CM psychiatric admission diagnoses. Administrative data available prior to hospital discharge abstracted from a wide range of data systems (socio81 demographic, Army career, criminal justice, medical/pharmacy) were used to predict suicides in the subsequent 12 months using machine learning methods (regression trees, penalized regressions) designed to evaluate cross-validated linear, nonlinear, and interactive predictive associations.
MAIN OUTCOME
Suicides of soldiers hospitalized with psychiatric disorders in the 12 months after hospital discharge.
RESULTS
68 soldiers died by suicide within 12 months of hospital discharge (12.0% of all Army suicides), equivalent to 263.9 suicides/100,000 person-years compared to 18.5 suicides/100,000 person-years in the total Army. Strongest predictors included socio-demographics (male, late age of enlistment), criminal offenses (verbal violence, weapons possession), prior suicidality, aspects of prior psychiatric inpatient and outpatient treatment, and disorders diagnosed during the focal hospitalizations. 52.9% of post-hospital suicides occurred after the 5% of hospitalizations with highest predicted suicide risk (3,824.1 suicides/100,000 person years). These highest-risk hospitalizations also accounted for significantly elevated proportions of several other adverse post-hospital outcomes (unintentional injury deaths, suicide attempts, re-hospitalizations).
CONCLUSIONS AND RELEVANCE
The high concentration of risk of suicides and other adverse outcomes might justify targeting expanded post-hospital interventions to soldiers classified as having highest post-hospital suicide risk, although final determination requires careful consideration of intervention costs, comparative effectiveness, and possible adverse effects.
doi:10.1001/jamapsychiatry.2014.1754
PMCID: PMC4286426  PMID: 25390793
Army; machine learning; elastic net regression; military; penalized regression; predictive modeling; risk assessment; suicide
20.  Relations Among Behavioral and Questionnaire Measures of Impulsivity in a Sample of Suicide Attempters 
Despite the focus on impulsivity within suicide research, it remains unclear the extent to which impulsivity assessments, that purportedly tap similar constructs, show significant overlap in samples of individuals with suicidal behaviors. In a sample of 69 suicide attempters, we took a multitrait, multimethod approach to examine the relation among various questionnaire and behavioral assessments of impulsivity facets. With the exception of urgency and go-stop performance there was little evidence of concordance between questionnaire and behavioral measures. These findings suggest researchers cannot presume that measures of “impulsivity” assess similar psychological processes and that more nuanced terminology is needed.
doi:10.1111/sltb.12030
PMCID: PMC4618602  PMID: 23601164
21.  The role of common mental and physical disorders in days out of role in the Iraqi general population: Results from the WHO World Mental Health Surveys 
In an effort to support mental health policy planning efforts in conjunction with the reconstruction of Iraq, a nationally representative face-to-face household survey was carried out that assessed the prevalence and correlates of common mental disorders in the Iraqi population. A total of 4,332 adult (ages 18+) respondents were interviewed (95.2% response rate). The current report presents data on the role impairments (number of days out-of-role in the past 30 days) associated with the nine mental disorders assessed in the survey in comparison to the impairments associated with ten chronic physical disorders also assessed in the survey. These disorders were all assessed with the WHO Composite International Diagnostic Interview. Days out-of-role was assessed with the WHO Disability Assessment Schedule. Both individual-level and societal-level effects of the disorders were estimated. Strongest individual-level predictors were bipolar and drug abuse disorders (176-95 days per year), with mental disorders making up five of the seven strongest predictors. The strongest population-level predictors were headache/migraine and arthritis (22-12% population proportions). Overall population proportions were 57% of days out-of-role due to the chronic physical disorders considered here and 18% for the mental disorders. Despite commonly-occurring mental disorders accounting for more individual-level days out-of-role than the physical disorders, mental disorders are much less likely to receive treatment in Iraq (e.g., due to stigma). These results highlight the need for culturally tailored mental health prevention and treatment programs in Iraq.
doi:10.1016/j.jpsychires.2014.02.006
PMCID: PMC3992882  PMID: 24581572
burden of disease; days out of role; human capital loss; mental disorders; prevalence
22.  A Person-Centered Analysis of Posttraumatic Stress Disorder Symptoms Following a Natural Disaster: Predictors of Latent Class Membership 
Journal of anxiety disorders  2013;28(1):16-24.
The present study applied latent class analysis to a sample of 810 participants residing in southern Mississippi at the time of Hurricane Katrina to determine if people would report distinct, meaningful PTSD symptom classes following a natural disaster. We found a four-class solution that distinguished persons on the basis of PTSD symptom severity/pervasiveness (Severe, Moderate, Mild, and Negligible Classes). Multinomial logistic regression models demonstrated that membership in the Severe and Moderate Classes was associated with potentially traumatic hurricane-specific experiences (e.g., being physically injured, seeing dead bodies), pre-hurricane traumatic events, co-occurring depression symptom severity and suicidal ideation, certain religious beliefs, and post-hurricane stressors (e.g., social support). Collectively, the findings suggest that more severe/pervasive typologies of natural disaster PTSD may be predicted by the frequency and severity of exposure to stressful/traumatic experiences (before, during, and after the disaster), co-occurring psychopathology, and specific internal beliefs.
doi:10.1016/j.janxdis.2013.11.002
PMCID: PMC3951614  PMID: 24334161
23.  The Direct and Interactive Effects of Neuroticism and Life Stress on the Severity and Longitudinal Course of Depressive Symptoms 
Journal of abnormal psychology  2011;120(4):844-856.
The direct and interactive effects of neuroticism and stressful life events (chronic and episodic stressors) on the severity and temporal course of depression symptoms were examined in 826 outpatients with mood and anxiety disorders, assessed on three occasions over a one-year period (intake, 6- and 12-month follow-ups). Neuroticism, chronic stress, and episodic stress were uniquely associated with intake depression symptom severity. A significant interaction effect indicated that the strength of the effect of neuroticism on initial depression severity increased as chronic stress increased. Although neuroticism did not have a significant direct effect on the temporal course of depression symptoms, chronic stress significantly moderated this relationship such that neuroticism had an increasingly deleterious effect on depression symptom improvement as the level of chronic stress over follow-up increased. In addition, chronic stress over follow-up (but not episodic stress) was uniquely predictive of less depression symptom improvement. Consistent with a stress generation framework, however, initial depression symptom severity was positively associated with chronic stress during follow-up. The results are discussed in regard to diathesis-stress conceptual models of emotional disorders and the various roles of stressful life events in the onset, severity, and maintenance of depressive psychopathology.
doi:10.1037/a0023035
PMCID: PMC3118941  PMID: 21381799
24.  Single Case Evaluation of an Intensive Cognitive Behavioral Treatment for Generalized Social Anxiety Disorder 
The present study investigated the efficacy of an 8-day, 6-session, intensive individual cognitive behavioral therapy protocol for social anxiety disorder using a multiple baseline across subjects design with 1, 2, and 3 months follow-up assessments. Participants were 5 outpatients with generalized social anxiety disorder. The intervention had variable effects on clinician-rated and self-report measures of anxiety and depression. The results question the efficacy of intensive psychotherapy as a general therapeutic strategy for social anxiety disorder. Directions for future research are discussed.
doi:10.1680/ijct.2008.1.2.114
PMCID: PMC2629593  PMID: 19169365
social anxiety disorder; cognitive-behavioral therapy; intensive treatment

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