Explore the vast range of titles available.

Considerable research has documented that exposure to traumatic events has negative effects on physical and mental health. Much less research has examined the predictors of traumatic event exposure. Increased understanding of risk factors for exposure to traumatic events could be of considerable value in targeting preventive interventions and anticipating service needs. General population surveys in 24 countries with a combined sample of 68 894 adult respondents across six continents assessed exposure to 29 traumatic event types. Differences in prevalence were examined with cross-tabulations. Exploratory factor analysis was conducted to determine whether traumatic event types clustered into interpretable factors. Survival analysis was carried out to examine associations of sociodemographic characteristics and prior traumatic events with subsequent exposure. Over 70% of respondents reported a traumatic event; 30.5% were exposed to four or more. Five types - witnessing death or serious injury, the unexpected death of a loved one, being mugged, being in a life-threatening automobile accident, and experiencing a life-threatening illness or injury - accounted for over half of all exposures. Exposure varied by country, sociodemographics and history of prior traumatic events. Being married was the most consistent protective factor. Exposure to interpersonal violence had the strongest associations with subsequent traumatic events. Given the near ubiquity of exposure, limited resources may best be dedicated to those that are more likely to be further exposed such as victims of interpersonal violence. Identifying mechanisms that account for the associations of prior interpersonal violence with subsequent trauma is critical to develop interventions to prevent revictimization.

Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although 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 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10–12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71–0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62–0.70) despite the latter models including more predictors. A total of 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.

Little is known about the prevalence or correlates of DSM-IV pathological gambling (PG). Data from the US National Comorbidity Survey Replication (NCS-R), a nationally representative US household survey, were used to assess lifetime gambling symptoms and PG along with other DSM-IV disorders. Age of onset (AOO) of each lifetime disorder was assessed retrospectively. AOO reports were used to study associations between temporally primary disorders and the subsequent risk of secondary disorders. Most respondents (78.4%) reported lifetime gambling. Lifetime problem gambling (at least one Criterion A symptom of PG) (2.3%) and PG (0.6%) were much less common. PG was significantly associated with being young, male, and Non-Hispanic Black. People with PG reported first gambling significantly earlier than non-problem gamblers (mean age 16.7 v. 23.9 years, z=12.7, p<0.001), with gambling problems typically beginning during the mid-20s and persisting for an average of 9.4 years. During this time the largest annual gambling losses averaged US$4800. Onset and persistence of PG were predicted by a variety of prior DSM-IV anxiety, mood, impulse-control and substance use disorders. PG also predicted the subsequent onset of generalized anxiety disorder, post-traumatic stress disorder (PTSD) and substance dependence. Although none of the NCS-R respondents with PG ever received treatment for gambling problems, 49.0% were treated at some time for other mental disorders. DSM-IV PG is a comparatively rare, seriously impairing, and undertreated disorder whose symptoms typically start during early adulthood and is frequently secondary to other mental or substance disorders that are associated with both PG onset and persistence.

Research on the structure of co-morbidity among common mental disorders has largely focused on current prevalence rather than on the development of co-morbidity. This report presents preliminary results of the latter type of analysis based on the US National Comorbidity Survey Replication Adolescent Supplement (NCS-A). A national survey was carried out of adolescent mental disorders. DSM-IV diagnoses were based on the Composite International Diagnostic Interview (CIDI) administered to adolescents and questionnaires self-administered to parents. Factor analysis examined co-morbidity among 15 lifetime DSM-IV disorders. Discrete-time survival analysis was used to predict first onset of each disorder from information about prior history of the other 14 disorders. Factor analysis found four factors representing fear, distress, behavior and substance disorders. Associations of temporally primary disorders with the subsequent onset of other disorders, dated using retrospective age-of-onset (AOO) reports, were almost entirely positive. Within-class associations (e.g. distress disorders predicting subsequent onset of other distress disorders) were more consistently significant (63.2%) than between-class associations (33.0%). Strength of associations decreased as co-morbidity among disorders increased. The percentage of lifetime disorders explained (in a predictive rather than a causal sense) by temporally prior disorders was in the range 3.7-6.9% for earliest-onset disorders [specific phobia and attention deficit hyperactivity disorder (ADHD)] and much higher (23.1-64.3%) for later-onset disorders. Fear disorders were the strongest predictors of most other subsequent disorders. Adolescent mental disorders are highly co-morbid. The strong associations of temporally primary fear disorders with many other later-onset disorders suggest that fear disorders might be promising targets for early interventions.

The 2013 US 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 not known 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 nondeployed Regular US Army soldiers because they account for the vast majority of such suicides. Four machine learning classifiers (naive Bayes, random forests, support vector regression and elastic net penalized regression) were explored. Of the Army suicides in 2004–2009, 41.5% occurred among 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.

Days out of role because of health problems are a major source of lost human capital. We examined the relative importance of commonly occurring physical and mental disorders in accounting for days out of role in 24 countries that participated in the World Health Organization (WHO) World Mental Health (WMH) surveys. Face-to-face interviews were carried out with 62 971 respondents (72.0% pooled response rate). Presence of ten chronic physical disorders and nine mental disorders was assessed for each respondent along with information about the number of days in the past month each respondent reported being totally unable to work or carry out their other normal daily activities because of problems with either physical or mental health. Multiple regression analysis was used to estimate associations of specific conditions and comorbidities with days out of role, controlling by basic socio-demographics (age, gender, employment status and country). Overall, 12.8% of respondents had some day totally out of role, with a median of 51.1 a year. The strongest individual-level effects (days out of role per year) were associated with neurological disorders (17.4), bipolar disorder (17.3) and post-traumatic stress disorder (15.2). The strongest population-level effect was associated with pain conditions, which accounted for 21.5% of all days out of role (population attributable risk proportion). The 19 conditions accounted for 62.2% of all days out of role. Common health conditions, including mental disorders, make up a large proportion of the number of days out of role across a wide range of countries and should be addressed to substantially increase overall productivity.

Sexual assault is a global concern with post-traumatic stress disorder (PTSD), one of the common sequelae. Early intervention can help prevent PTSD, making identification of those at high risk for the disorder a priority. Lack of representative sampling of both sexual assault survivors and sexual assaults in prior studies might have reduced the ability to develop accurate prediction models for early identification of high-risk sexual assault survivors. Data come from 12 face-to-face, cross-sectional surveys of community-dwelling adults conducted in 11 countries. Analysis was based on the data from the 411 women from these surveys for whom sexual assault was the randomly selected lifetime traumatic event (TE). Seven classes of predictors were assessed: socio-demographics, characteristics of the assault, the respondent's retrospective perception that she could have prevented the assault, other prior lifetime TEs, exposure to childhood family adversities and prior mental disorders. Prevalence of Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) PTSD associated with randomly selected sexual assaults was 20.2%. PTSD was more common for repeated than single-occurrence victimization and positively associated with prior TEs and childhood adversities. Respondent's perception that she could have prevented the assault interacted with history of mental disorder such that it reduced odds of PTSD, but only among women without prior disorders (odds ratio 0.2, 95% confidence interval 0.1-0.9). The final model estimated that 40.3% of women with PTSD would be found among the 10% with the highest predicted risk. Whether counterfactual preventability cognitions are adaptive may depend on mental health history. Predictive modelling may be useful in targeting high-risk women for preventive interventions.

Research on post-traumatic stress disorder (PTSD) course finds a substantial proportion of cases remit within 6 months, a majority within 2 years, and a substantial minority persists for many years. Results are inconsistent about pre-trauma predictors. The WHO World Mental Health surveys assessed lifetime DSM-IV PTSD presence-course after one randomly-selected trauma, allowing retrospective estimates of PTSD duration. Prior traumas, childhood adversities (CAs), and other lifetime DSM-IV mental disorders were examined as predictors using discrete-time person-month survival analysis among the 1575 respondents with lifetime PTSD. 20%, 27%, and 50% of cases recovered within 3, 6, and 24 months and 77% within 10 years (the longest duration allowing stable estimates). Time-related recall bias was found largely for recoveries after 24 months. Recovery was weakly related to most trauma types other than very low [odds-ratio (OR) 0.2-0.3] early-recovery (within 24 months) associated with purposefully injuring/torturing/killing and witnessing atrocities and very low later-recovery (25+ months) associated with being kidnapped. The significant ORs for prior traumas, CAs, and mental disorders were generally inconsistent between early- and later-recovery models. Cross-validated versions of final models nonetheless discriminated significantly between the 50% of respondents with highest and lowest predicted probabilities of both early-recovery (66-55% v. 43%) and later-recovery (75-68% v. 39%). We found PTSD recovery trajectories similar to those in previous studies. The weak associations of pre-trauma factors with recovery, also consistent with previous studies, presumably are due to stronger influences of post-trauma factors.

This study presents national data on the comparative role impairments of common mental and chronic medical disorders in the general population. These data come from the National Comorbidity Survey Replication, a nationally representative household survey. Disorder-specific role impairment was assessed with the Sheehan Disability Scales, a multidimensional instrument that asked respondents to attribute impairment to particular conditions. Overall impairment was significantly higher for mental than chronic medical disorders in 74% of pair-wise comparisons between the two groups of conditions, and severe impairment was reported by a significantly higher portion of persons with mental disorders (42.0%) than chronic medical disorders (24.4%). However, treatment was provided for a significantly lower proportion of mental (21.4%) than chronic medical (58.2%) disorders. Although mental disorders were associated with comparable or higher impairment than chronic medical conditions in all domains of function, they showed different patterns of deficits; whereas chronic medical disorders were most likely to be associated with impairment in domains of work and home functioning, mental disorders were most commonly associated with problems in social and close-relation domains. Comorbidity between chronic medical and mental disorders significantly increased the reported impairment associated with each type of disorder. The results indicate a serious mismatch between a high degree of impairment and a low rate of treatment for mental disorders in the United States. Efforts to reduce disability will need to address the disproportionate burden and distinct patterns of deficits of mental disorders and the potentially synergistic impact of comorbid mental and chronic medical disorders.

The US Army suicide rate has increased sharply in recent years. Identifying significant predictors of Army suicides in Army and Department of Defense (DoD) administrative records might help focus prevention efforts and guide intervention content. Previous studies of administrative data, although documenting significant predictors, were based on limited samples and models. A career history perspective is used here to develop more textured models. The analysis was carried out as part of the Historical Administrative Data Study (HADS) of the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). De-identified data were combined across numerous Army and DoD administrative data systems for all Regular Army soldiers on active duty in 2004-2009. Multivariate associations of sociodemographics and Army career variables with suicide were examined in subgroups defined by time in service, rank and deployment history. Several novel results were found that could have intervention implications. The most notable of these were significantly elevated suicide rates (69.6-80.0 suicides per 100 000 person-years compared with 18.5 suicides per 100 000 person-years in the total Army) among enlisted soldiers deployed either during their first year of service or with less than expected (based on time in service) junior enlisted rank; a substantially greater rise in suicide among women than men during deployment; and a protective effect of marriage against suicide only during deployment. A career history approach produces several actionable insights missed in less textured analyses of administrative data predictors. Expansion of analyses to a richer set of predictors might help refine understanding of intervention implications.