Hierarchical Network Structure: A Novel Approach to Conceptualising ICD-11 Complex PTSD using a general population sample from Africa

Robinson, Martin; Levin, Yafit; Hyland, Philip; Karatzias, Thanos; Ben-Ezra, Menachem

Abstract

Background
Investigations have sought to model the structure of ICD-11 Complex PTSD (CPTSD) using factor analytic models, finding support for higher-order domains representing PTSD and Disturbances in Self Organisation (DSO). Network analysis has alternatively modelled CPTSD through dimensional symptom associations.
Methodology
This study investigated the structure of CPTSD leveraging a novel approach, Hierarchical Exploratory Graph Analysis, using African general population samples (N=2,524).
Results
The hierarchical graph model was estimated identifying a structure comprising six lower-order communities, indicative of ICD-11 CPTSD symptom domains, and two higher-order communities, indicative of PTSD and DSO. Results indicate the superiority of the hierarchical model, confirming the conceptualization of ICD-11 PTSD and CPTSD symptom groupings.
Limitations
The cross-sectional nature of these data, and novelty of the methods used prompt calls for additional investigation to support these findings.
Conclusions
Hierarchical Exploratory Graph Analysis may offer a valuable means to better understanding the complexity of CPTSD symptomology through a novel network modelling approach. Relationships between Sense of Threat and Affect Dysregulation may serve as bridging symptoms between PTSD and DSO difficulties. These may be prioritised as the therapeutic targets for CPTSD. This pioneering approach using EGA, offers new insights into the intricate structure of CPTSD, potentially informing the use of assessments and interventions across diverse populations.

Citation

Robinson, M., Levin, Y., Hyland, P., Karatzias, T., & Ben-Ezra, M. (2025). Hierarchical Network Structure: A Novel Approach to Conceptualising ICD-11 Complex PTSD using a general population sample from Africa. Journal of Affective Disorders, 369, 813-818. https://doi.org/10.1016/j.jad.2024.10.015

• Journal Article Type: Article
• Acceptance Date: Oct 7, 2024
• Online Publication Date: Oct 12, 2024
• Publication Date: 2025-01
• Deposit Date: Oct 8, 2024
• Publicly Available Date: Oct 12, 2024
• Print ISSN: 0165-0327
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 369
• Pages: 813-818
• DOI: https://doi.org/10.1016/j.jad.2024.10.015