Examining the latent structure mechanisms for comorbid posttraumatic stress disorder and major depressive disorder

Villarosa‐Hurlocker, Margo C.; Vidaurri, Desirae N.; Cuccurullo, Lisa-Ann J.; Maieritsch, Kelly P.; Franklin, C. Laurel

doi:10.1016/j.jad.2017.12.076

Cited by 37 publications

(30 citation statements)

References 31 publications

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“…First, TRD and DMQ-Cope were examined as separate common factor models. Second, although a number of different factor structures have been reported for the PCL-5 (Ayer et al, 2011), our CFA modeling followed previous literature (Blevins et al, 2015;Hurlocker, Vidaurri, Cuccurullo, Maieritsch, & Franklin, 2018), which found evidence supporting a 4-factor solution (intrusion, avoidance, alterations in cognition and mood, arousal). The PCL-5 4-factor structure was compared to a single common factor model.…”

Section: F I G U R Esupporting

confidence: 63%

Examination of a novel measure of trauma‐related drinking to cope

et al. 2020

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Objectives The present study sought to fill a gap in the current literature by developing a concise self‐report questionnaire assessing drinking motives specific to coping with symptoms of posttraumatic stress disorder (PTSD). This new four‐item questionnaire is called the Trauma Related Drinking questionnaire (TRD). Method Using structural equation modeling, the latent structure of the TRD items and how they relate to other variables of interest were explored among a sample of 1,896 college undergraduates from a large public university. Results Broadly, we found evidence to suggest that TRD is a more specific measure of drinking to cope motives compared to the commonly used Drinking Motives Questionnaire coping subscale. Additionally, findings demonstrate support for the external validation of TRD, both with regard to PTSD and alcohol consumption and related problems. Conclusions Results support the use of TRD in future self‐medication research and as a clinically useful screening tool.

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Section: F I G U R Esupporting

confidence: 63%

Examination of a novel measure of trauma‐related drinking to cope

et al. 2020

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“…Including negative belief symptoms within the NACM cluster in DSM-5 may explain the significant node between it and depressive attribution that was found in our sample. Other studies have also found associations between the NACM cluster and depression, suicide ideation, and suicide risk (Brown, Contractor, & Benhamou, 2018;Hurlocker, Vidaurri, Cuccurullo, Maieritsch, & Franklin, 2018;Spitzer, Zuromski, Davis, Witte, & Weathers, 2018). Thus, whereas depression was related to three of the four PTSD clusters, the facilitation of PTSD, mainly through the NACM cluster, may have occurred because they both reflect the negative changes in the veterans' mood after their experiencing PMIEs in combat.…”

Section: Discussionmentioning

confidence: 91%

Associations between moral injury, PTSD clusters, and depression among Israeli veterans: a network approach

Levi‐Belz

Greene

Zerach

2020

European Journal of Psychotraumatology

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Background: Moral Injury (MI) is one of the adverse consequences of combat. Following exposure to potentially morally injurious events (PMIEs)--events perceived as violations of deep moral beliefs by oneself or trusted individuals--a significant minority of veterans could develop posttraumatic stress disorder (PTSD) and depression. Objective: The current study represents the first attempt to apply a network analysis model to examine an exploratory empirical conceptualization of a network of PMIEs during military service, post-traumatic stress disorder (PTSD) symptom clusters, depression, and combat exposure among Israel Defence Forces veterans. Method: A volunteer sample of 191 Israeli combat veterans were recruited during 2017, and completed validated self-report questionnaires tapping PMIEs, PTSD, and depression in a cross-sectional design study. A regularized Gaussian graphical model was estimated. Results: Network analysis revealed strong bridge associations between the PTSD nodes and most of the PMIEs nodes. The nodes of PMIE-betrayal and PTSD negative alterations in cognitions and mood (NACM) symptom cluster were found to have a bridging function between other PMIEs and PTSD. Depression was found to be connected to most of the PMIEs and PTSD nodes. Conclusions: The study's findings offer an overview of the complex relationships between PMIEs and PTSD clusters among Israeli veterans. PMIEs--notably, betrayal-based experiences--are related to PTSD clusters directly and through depressive symptoms. Some possible mechanisms for the links between PMIEs and PTSD and the clinical implications related to specific interventions are discussed. ARTICLE HISTORY

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“…These two disorders therefore contributed almost 60% of the relative influence on a new diagnosis of PTSD in individuals who did not report such a diagnosis 3 years earlier. This finding is not entirely surprising, given the number of studies that have found high levels of comorbidity among PSTD, Borderline Personality Disorder and Major Depressive Disorder [55][56][57][58]. What was surprising was the comparatively small effects attributed to community-based predictors.…”

Section: Discussionmentioning

confidence: 94%

Prospective prediction of PTSD diagnosis in a nationally representative sample using machine learning

2020

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Background Recent research has identified a number of pre-traumatic, peri-traumatic and post-traumatic psychological and ecological factors that put an individual at increased risk for developing PTSD following a life-threatening event. While these factors have been found to be associated with PTSD in univariate analyses, the complex interactions of these risk factors and how they contribute to individual trajectories of the illness are not yet well understood. In this study, we examine the impact of prior trauma, psychopathology, sociodemographic characteristics, community and environmental information, on PTSD onset in a nationally representative sample of adults in the United States, using machine learning methods to establish the relative contributions of each variable. Methods Individual risk factors identified in Waves 1 of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) were combined with community-level data for the years concurrent to the NESARC Wave 1 (n = 43,093) and 2 (n = 34,653) surveys. Machine learning feature selection and classification analyses were used at the national level to create models using individual- and community-level variables that would best predict the new onset of PTSD at Wave 2. Results Our classification algorithms yielded 89.7 to 95.6% accuracy for predicting new onset of PTSD at Wave 2. A prior diagnosis of DSM-IV-TR Borderline Personality Disorder, Major Depressive Disorder or Anxiety Disorder conferred the greatest relative influence in new diagnosis of PTSD. Distal risk factors such as prior psychiatric diagnosis accounted for significantly greater relative risk than proximal factors (such as adverse event exposure). Conclusions Our findings show that a machine learning classification approach can successfully integrate large numbers of known risk factors for PTSD into stronger models that account for high-dimensional interactions and collinearity between variables. We discuss the implications of these findings as pertaining to the targeted mobilization emergency mental health resources. These findings also inform the creation of a more comprehensive risk assessment profile to the likelihood of developing PTSD following an extremely adverse event.

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Examining the latent structure mechanisms for comorbid posttraumatic stress disorder and major depressive disorder

Cited by 37 publications

References 31 publications

Examination of a novel measure of trauma‐related drinking to cope

Examination of a novel measure of trauma‐related drinking to cope

Associations between moral injury, PTSD clusters, and depression among Israeli veterans: a network approach

Prospective prediction of PTSD diagnosis in a nationally representative sample using machine learning

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