Comparing PTSD symptom networks in type I vs. type II trauma survivors. Issue 2 (19th December 2022)

• Record Type: Journal Article
• Authors: Stefanovic, Mina; Ehring, Thomas; Wittekind, Charlotte E.; Kleim, Birgit; Rohde, Judith; Krüger-Gottschalk, Antje; Knaevelsrud, Christine; Rau, Heinrich; Schäfer, Ingo; Schellong, Julia; Dyer, Anne; Takano, Keisuke
• Is Part Of: European journal of psychotraumatology. Volume 13:Issue 2(2022)
• Publication Date: 2022-12-19
• Subjects: PTSD -- trauma type -- network analysis -- Bayesian graphical Gaussian models -- network comparison; TEPT -- tipo de trauma -- análisis de redes -- Modelos Gaussianos -- Gráficos Bayesianos; PTSD -- 创伤类型 -- 网络分析 -- 贝叶斯图形高斯模型; Post-traumatic stress disorder -- Periodicals; Stress Disorders, Post-Traumatic; Post-traumatic stress disorder; Electronic journals; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 616.8521
• DOI: 10.1080/20008066.2022.2114260
• Languages: English
• ISSNs: 2000-8198

Abstract

ABSTRACT: Background: Network analysis has gained increasing attention as a new framework to study complex associations between symptoms of post-traumatic stress disorder (PTSD). A number of studies have been published to investigate symptom networks on different sets of symptoms in different populations, and the findings have been inconsistent. Objective: We aimed to extend previous research by testing whether differences in PTSD symptom networks can be found in survivors of type I (single event; sudden and unexpected, high levels of acute threat) vs. type II (repeated and/or protracted; anticipated) trauma (with regard to their index trauma). Method: Participants were trauma-exposed individuals with elevated levels of PTSD symptomatology, most of whom (94%) were undergoing assessment in preparation for PTSD treatment in several treatment centres in Germany and Switzerland ( n = 286 with type I and n = 187 with type II trauma). We estimated Bayesian Gaussian graphical models for each trauma group and explored group differences in the symptom network. Results: First, for both trauma types, our analyses identified the edges that were repeatedly reported in previous network studies. Second, there was decisive evidence that the two networks were generated from different multivariate normal distributions, i.e. the networks differed on a global level. Third, explorative edge-wise comparisons showed moderate or strong evidence for specific 12 edges. Edges which emerged as especially important in distinguishing the networks were between intrusions and flashbacks, highlighting the stronger positive association in the group of type II trauma survivors compared to type I survivors. Flashbacks showed a similar pattern of results in the associations with detachment and sleep problems (type II > type I). Conclusion: Our findings suggest that trauma type contributes to the heterogeneity in the symptom network. Future research on PTSD symptom networks should include this variable in the analyses to reduce heterogeneity. HIGHLIGHTS: The current study aimed to investigate trauma type as a potential moderator of PTSD symptom networks, distinguishing between type I trauma (single event; sudden and unexpected, high level of acute threat) vs. type II trauma (repeated and/or protracted; anticipated) with regard to their index trauma. Findings suggest that the PTSD symptom network structure differs between type I and type II trauma survivors. Edges which emerged as especially important in distinguishing the networks were between intrusions and flashbacks, highlighting the stronger positive association in the group of type II trauma survivors compared to type I survivors. Flashbacks showed a similar pattern of results in the associations with detachment and sleep problems (type II > type I). Analysis revealed that trauma type contributes to the heterogeneity in the symptom network and it is important variable to consider in the future research.