Suchergebnisse

Suicide rates among recent veterans have led to interest in risk identification. Evidence of gender-and trauma-specific predictors of suicidal ideation necessitates the use of advanced computational methods capable of elucidating these important and complex associations. In this study, we used machine learning to examine gender-specific associations between predeployment and military factors, traumatic deployment experiences, and psychopathology and suicidal ideation (SI) in a national sample of veterans deployed during the Iraq and Afghanistan conflicts (n = 2,244). Classification, regression tree analyses, and random forests were used to identify associations with SI and determine their classification accuracy. Findings converged on several associations for men that included depression, posttraumatic stress disorder (PTSD), and somatic complaints. Sexual harassment during deployment emerged as a key factor that interacted with PTSD and depression and demonstrated a stronger association with SI among women. Classification accuracy for SI presence or absence was good based on the receiver operating characteristic area under the curve, men = .91, women = .92. The risk for SI was classifiable with good accuracy, with associations that varied by gender. The use of machine learning analyses allowed for the discovery of rich, nuanced results that should be replicated in other samples and may eventually be a basis for the development of gender-specific actuarial tools to assess SI risk among veterans.

OBJECTIVE: Given the number of veterans who have experienced military sexual trauma (MST) and the impact of these experiences on veteran health, Veterans Health Administration (VHA) providers frequently communicate with patients about these experiences, either as part of VHA's universal MST screening program or more comprehensive clinical care. The purpose of this study was to understand veterans' perceptions of communication related to MST disclosures with VHA providers. METHOD: We conducted qualitative interviews, including a numeric rating question, with 55 veterans whose medical records indicated recent MST-related interactions with VHA providers. The sample included men and women, with and without histories of MST. We analyzed interview transcripts using matrices and identified themes related to satisfaction with communication. RESULTS: Veterans from all groups reported generally high satisfaction with MST-related communication, although men, as a group, reported a much larger range of satisfaction ratings than women. Key provider-related indicators of satisfaction included providers' communication mechanics and non-specific patient-centered skills and characteristics. One patient-related indicator of lower satisfaction—inherent discomfort discussing MST experiences—was particularly represented among men with MST histories. CONCLUSIONS: These data from veteran patients will be reassuring to providers whose concerns about patient reactions to these conversations may inhibit them from raising this important issue. Understanding variables that promote patient satisfaction with MST disclosure communication is critical for promoting sensitive patient/provider interactions about MST. This ultimately can have important downstream effects on veteran health, allowing veterans to forge satisfying relationships with providers and ultimately facilitate recovery from traumatic experiences.

We examined military-related sexual trauma among deployed Operation Enduring Freedom and Operation Iraqi Freedom veterans. Of 125 729 veterans who received Veterans Health Administration primary care or mental health services, 15.1% of the women and 0.7% of the men reported military sexual trauma when screened. Military sexual trauma was associated with increased odds of a mental disorder diagnosis, including posttraumatic stress disorder, other anxiety disorders, depression, and substance use disorders. Sexual trauma is an important postdeployment mental health issue in this population.

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.

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.