Examining Military Population And Trauma Type As Moderators Of Treatment Outcome For First-Line Psychotherapies For PTSD: A Meta-Analysis

There is conflicting evidence as to whether military populations (i.e., veteran and active-duty military service members) demonstrate a poorer response to psychotherapy for posttraumatic stress disorder (PTSD) compared to civilians. Existing research may be complicated by the fact that treatment outcomes differences could be due to the type of trauma exposure (e.g., combat) or population differences (e.g., military culture). This meta-analysis evaluated PTSD treatment outcomes as a function of trauma type (combat v. assault v. mixed) and population (military v. civilian). Unlike previous meta-analyses, we focused exclusively on manualized, first-line psychotherapies for PTSD as defined by expert treatment guidelines. Treatment outcomes were large across trauma types and population; yet differences were observed between trauma and population subgroups. Military populations demonstrated poorer treatment outcomes compared to civilians. The combat and assault trauma subgroups had worse treatment outcomes compared to the mixed trauma subgroup, but differences were not observed between assault and combat subgroups. Higher attrition rates predicted poorer treatment outcomes, but did not vary between military populations and civilians. Overall, manualized, first-line psychotherapies for PTSD should continue to be used for civilians and military populations with various trauma types. However, greater emphasis should be placed on enhancing PTSD psychotherapies for military populations and on treatment retention across populations based on findings from this meta-analysis

Patterns of Acute Stress Disorder in a Sample of Blast-Injured Military Service Members: A Latent Profile Analysis

Objective: The primary aims of this study were to identify latent profiles of acute stress disorder (ASD) symptoms and to evaluate postconcussive symptom differences across the identified profiles as measured by the Acute Stress Disorder Scale and the Military Acute Concussion Evaluation, respectively. Method: Participants (N = 315) in the current study were predominantly active-duty (75.0%), enlisted (97.8%) males (97.4%) serving in the U.S. Army (87.8%). Approximately, half of the sample reported being married or engaged (51.1%) and was on average 25.94 (SD = 6.31) years old. Participants were referred to the Air Force Theater Hospital, 332nd Air Expeditionary Wing, Joint Base Balad, Iraq, to be evaluated as part of routine clinical assessment for neurocognitive and psychological symptoms following exposure to a blast. Results: A 3-profile solution was identified as the most parsimonious and bestfitting model based on statistical model fit indices. Blast injured service members in Profile 3 had greater ASD total and subscale severity compared to the other 2 subgroups, with effect size estimates largely differing by hyperarousal and reexperiencing symptoms. Furthermore, Profiles 2 and 3 were more likely to demonstrate postconcussive symptoms compared to Profile 1. Conclusions: Findings provide novel information on heterogenous ASD symptom profiles during the acute phase following a blast injury and highlight the relationship between psychological and physical symptoms. Classification of blast-injured service members may help identify at-risk individuals who would benefit from further clinical care and mitigate long-term psychological and neurocognitive issues

Evidence-based posttraumatic stress disorder treatment in a community sample : military-affiliated versus civilian patient outcomes

Posttraumatic stress disorder (PTSD) is a significant mental health issue among military service members and veterans. Although the U.S. Department of Veterans Affairs (VA) provides crucial resources for behavioral health care, many veterans seek mental health services through community clinics. Previous research illustrates that military and veteran patients benefit less from evidence-based treatments (EBTs) for PTSD than civilians. However, most PTSD treatment outcome research on military and veteran populations is conducted in VA or military settings. Little is known about outcomes among military-affiliated patients in community settings. The primary aim of this study was to directly compare civilian versus military-affiliated patient outcomes on PTSD and depression symptoms using the PTSD Checklist for DSM-5 (PCL-5) and the nine-item Patient Health Questionnaire (PHQ-9) in a community setting. Participants (N = 502) included military-affiliated (veteran, Guard/Reservist, active duty) and civilian patients who engaged in cognitive processing therapy (CPT) or prolonged exposure (PE) for PTSD in community clinics. Both groups demonstrated significant reductions on the PCL-5, military-affiliated: d = −0.91, civilian: d = -1.18; and PHQ-9, military-affiliated: d = -0.65, civilian: d = -0.88, following treatment. However, military-affiliated patients demonstrated smaller posttreatment reductions on the PCL-5, Mdiff = 5.75, p =.003, and PHQ-9, Mdiff = 1.71, p =.011, compared to civilians. Results demonstrate that military-affiliated patients benefit from EBTs for PTSD, albeit to a lesser degree than civilians, even in community settings. These findings also highlight the importance of future research on improving EBTs for military personnel with PTSD

A composite likelihood inference in latent variable models for ordinal longitudinal responses

The paper proposes a composite likelihood estimation approach that uses bivariate instead of multivariate marginal probabilities for ordinal longitudinal responses using a latent variable model. The model considers time-dependent latent variables and item-specific random effects to be accountable for the interdependencies of the multivariate ordinal items. Time-dependent latent variables are linked with an autoregressive model. Simulation results have shown composite likelihood estimators to have a small amount of bias and mean square error and as such they are feasible alternatives to full maximum likelihood. Model selection criteria developed for composite likelihood estimation are used in the applications. Furthermore, lower-order residuals are used as measures-of-fit for the selected models