Faculty Bibliography

Background/UNASSIGNED:United Nations (UN) personnel address a diverse range of political, social, and cultural crises throughout the world. Compared with other occupations routinely exposed to traumatic stress, there remains a paucity of research on mental health disorders and access to mental healthcare in this population. To fill this gap, personnel from UN agencies were surveyed for mental health disorders and mental healthcare utilization. Methods/UNASSIGNED:= 17 363) from 11 UN entities completed online measures of generalized anxiety disorder (GAD), major depressive disorder (MDD), posttraumatic stress disorder (PTSD), trauma exposure, mental healthcare usage, and socio-demographic information. Results/UNASSIGNED:Exposure to one or more traumatic events was reported by 36.2% of survey responders. Additionally, 17.9% screened positive for GAD, 22.8% for MDD, and 19.9% for PTSD. Employing multivariable logistic regressions, low job satisfaction, younger age (<35 years of age), greater length of employment, and trauma exposure on or off-duty was significantly associated with all the three disorders. Among individuals screening positive for a mental health disorder, 2.05% sought mental health treatment within and 10.01% outside the UN in the past year. Conclusions/UNASSIGNED:UN personnel appear to be at high risk for trauma exposure and screening positive for a mental health disorder, yet a small percentage screening positive for mental health disorders sought treatment. Despite the mental health gaps observed in this study, additional research is needed, as these data reflect a large sample of convenience and it cannot be determined if the findings are representative of the UN.

While emotion dysregulation is associated with many psychological disorders, including posttraumatic stress disorder (PTSD), it remains uncertain whether pre-existing emotion dysregulation increases individual risk for prospectively developing PTSD in the aftermath of trauma exposure. Thus, the objective of the current study was to determine whether emotion dysregulation could prospectively predict the development of chronic PTSD symptoms following a traumatic event above and beyond other known associated factors, including depressive symptoms, baseline PTSD symptoms, total traumas experienced, and exposure to interpersonal trauma. Participants (N = 135) were recruited from the emergency department (ED) at Grady Memorial Hospital in Atlanta and follow-up assessments were conducted at 1-, 3-, 6-, and 12-months following trauma exposure. Latent Growth Mixture Modeling was used to identify PTSD symptom trajectories based on symptoms assessed at 1, 3, 6, and 12 months; three trajectories emerged: "chronic", "recovery", and "resilient". For the present study, probability of chronic PTSD symptoms was used as the outcome variable of interest. Linear regression modeling showed that emotion dysregulation was significantly associated with probability of developing chronic PTSD symptoms (p = 0.001) and accounted for an additional 7% of unique predictive variance when controlling for trauma exposure, baseline PTSD, and depressive symptoms. Our findings suggest that emotion dysregulation can be used as both a predictor of chronic PTSD and as a treatment target. Thus, identifying individuals with high levels of emotion dysregulation at the time of trauma and implementing treatments designed to improve emotion regulation could aid in decreasing the development of chronic PTSD among these at-risk individuals.

Some people develop symptoms of posttraumatic stress disorder (PTSD) after having experienced a traumatic event, whereas others do not. Intrusive memories are a cardinal symptom of PTSD and a better understanding of encoding and consolidation of intrusive memory may yield important insights on differences in the response to trauma. The primary aim of this study is to investigate whether psychosocial stress induction (Trier Social Stress Test) versus active control (placebo version) leading to respective biological stress responses during the encoding and consolidation of a film-elicited analogue trauma influences the development of intrusive memories over the course of 7 consecutive days. We hypothesized that the activation of the biological stress system increases the number of intrusive memories over the course of 7 days. This single-blind randomized placebo-controlled study examined 122 young healthy women. Biological stress response was measured by salivary cortisol, salivary α-amylase activity, and heart rate variability. Generalized linear mixed models were used to analyze longitudinal effects of activation of biological stress response on self-reported number of intrusive memories. Cross-validated regularized regression (least absolute shrinkage and selection operator) was applied for data-driven feature selection including known biological and psychological predictors. Corroborating our hypothesis, biological stress-responders to the Trier Social Stress Test reported significantly more intrusive memories after trauma film. A priori designed post hoc tests point at significantly more intrusions on Day 1 and 2 in biological stress responders. Least absolute shrinkage and selection operator regression revealed salivary cortisol, salivary α-amylase activity, heart rate variability, subjectively rated distress, fear, and (on trend level) dissociation during the trauma film as relevant predictors of intrusive memories. A heightened biological stress response in young women is associated with more intrusive memories the first days after experiencing a trauma analogue. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

OBJECTIVE/UNASSIGNED:Although several reports have documented heightened systemic inflammation in posttraumatic stress disorder (PTSD), few studies have assessed whether inflammatory markers serve as prospective biomarkers for PTSD risk. The present study aimed to characterize whether peripheral immune factors measured in blood samples collected in an emergency department immediately after trauma exposure would predict later chronic development of PTSD. METHODS/UNASSIGNED:Participants (N=505) were recruited from a hospital emergency department and underwent a 1.5-hour assessment. Blood samples were drawn, on average, about 3 hours after trauma exposure. Follow-up assessments were conducted 1, 3, 6, and 12 months after trauma exposure. Latent growth mixture modeling was used to identify classes of PTSD symptom trajectories. RESULTS/UNASSIGNED:Three distinct classes of PTSD symptom trajectories were identified: chronic (N=28), resilient (N=160), and recovery (N=85). Multivariate analyses of covariance revealed a significant multivariate main effect of PTSD symptom trajectory class membership on proinflammatory cytokines. Univariate analyses showed a significant main effect of trajectory class membership on plasma concentrations of proinflammatory tumor necrosis factor α (TNFα) and interferon-γ (IFNγ). Concentrations of proinflammatory TNFα and IFNγ were significantly lower in individuals in the chronic PTSD class compared with those in the recovery and resilient classes. There were no significant differences in interleukin (IL) 1β and IL-6 concentrations by PTSD symptom trajectory class. Anti-inflammatory and other cytokines, as well as chemokines and growth factor concentrations, were not associated with development of chronic PTSD. CONCLUSIONS/UNASSIGNED:Overall, the study findings suggest that assessing the proinflammatory immune response to trauma exposure immediately after trauma exposure, in the emergency department, may help identify individuals most at risk for developing chronic PTSD in the aftermath of trauma.

Posttraumatic stress responses are characterized by a heterogeneity in clinical appearance and etiology. This heterogeneity impacts the field's ability to characterize, predict, and remediate maladaptive responses to trauma. Machine learning (ML) approaches are increasingly utilized to overcome this foundational problem in characterization, prediction, and treatment selection across branches of medicine that have struggled with similar clinical realities of heterogeneity in etiology and outcome, such as oncology. In this article, we review and evaluate ML approaches and applications utilized in the areas of posttraumatic stress, stress pathology, and resilience research, and present didactic information and examples to aid researchers interested in the relevance of ML to their own research. The examined studies exemplify the high potential of ML approaches to build accurate predictive and diagnostic models of posttraumatic stress and stress pathology risk based on diverse sources of available information. The use of ML approaches to integrate high-dimensional data demonstrates substantial gains in risk prediction even when the sources of data are the same as those used in traditional predictive models. This area of research will greatly benefit from collaboration and data sharing among researchers of posttraumatic stress disorder, stress pathology, and resilience.