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Post-traumatic stress disorder and depressive symptoms among firefighters: a network analysis

BACKGROUND: Firefighters, as first responders with a high risk of occupational exposure to traumatic events and heavy working stress, have a high prevalence of PTSD symptoms and depressive symptoms. But no previous studies analyzed the relationships and hierarchies of PTSD and depressive symptoms am...

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Autores principales: Cheng, Peng, Wang, Lirong, Zhou, Ying, Ma, Wenjing, Zhao, Guangju, Zhang, Li, Li, Weihui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10193951/
https://www.ncbi.nlm.nih.gov/pubmed/37213609
http://dx.doi.org/10.3389/fpubh.2023.1096771
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author Cheng, Peng
Wang, Lirong
Zhou, Ying
Ma, Wenjing
Zhao, Guangju
Zhang, Li
Li, Weihui
author_facet Cheng, Peng
Wang, Lirong
Zhou, Ying
Ma, Wenjing
Zhao, Guangju
Zhang, Li
Li, Weihui
author_sort Cheng, Peng
collection PubMed
description BACKGROUND: Firefighters, as first responders with a high risk of occupational exposure to traumatic events and heavy working stress, have a high prevalence of PTSD symptoms and depressive symptoms. But no previous studies analyzed the relationships and hierarchies of PTSD and depressive symptoms among firefighters. Network analysis is a novel and effective method for investigating the complex interactions of mental disorders at the symptom level and providing a new understanding of psychopathology. The current study was designed to characterize the PTSD and depressive symptoms network structure in the Chinese firefighters. METHOD: The Primary Care PTSD Screen for DSM-5 (PC-PTSD-5) and the Self-Rating Depression Scale (SDS) were applied to assess PTSD and depressive symptoms, respectively. The network structure of PTSD and depressive symptoms was characterized using “expected influence (EI)” and “bridge EI” as centrality indices. The Walktrap algorithm was conducted to identify communities in the PTSD and depressive symptoms network. Finally, Network accuracy and stability were examined using the Bootstrapped test and the case-dropping procedure. RESULTS: A total of 1,768 firefighters were enrolled in our research. Network analysis revealed that the relationship between PTSD symptoms, “Flashback” and “Avoidance,” was the strongest. “Life emptiness” was the most central symptom with the highest EI in the PTSD and depression network model. Followed by “Fatigue” and “Interest loss.” Bridge symptoms connecting PTSD and depressive symptoms in our study were “Numb,” “High alertness,” “Sad mood,” and “Compunction and blame,” successively. The data-driven community detection suggested the differences in PTSD symptoms in the clustering process. The reliability of the network was approved by both stability and accuracy tests. CONCLUSION: To the best of our knowledge, the current study first demonstrated the network structure of PTSD and depressive symptoms among Chinese firefighters, identifying the central and bridge symptoms. Targeting interventions to the symptoms mentioned above may effectively treat firefighters suffering from PTSD and depressive symptoms.
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spelling pubmed-101939512023-05-19 Post-traumatic stress disorder and depressive symptoms among firefighters: a network analysis Cheng, Peng Wang, Lirong Zhou, Ying Ma, Wenjing Zhao, Guangju Zhang, Li Li, Weihui Front Public Health Public Health BACKGROUND: Firefighters, as first responders with a high risk of occupational exposure to traumatic events and heavy working stress, have a high prevalence of PTSD symptoms and depressive symptoms. But no previous studies analyzed the relationships and hierarchies of PTSD and depressive symptoms among firefighters. Network analysis is a novel and effective method for investigating the complex interactions of mental disorders at the symptom level and providing a new understanding of psychopathology. The current study was designed to characterize the PTSD and depressive symptoms network structure in the Chinese firefighters. METHOD: The Primary Care PTSD Screen for DSM-5 (PC-PTSD-5) and the Self-Rating Depression Scale (SDS) were applied to assess PTSD and depressive symptoms, respectively. The network structure of PTSD and depressive symptoms was characterized using “expected influence (EI)” and “bridge EI” as centrality indices. The Walktrap algorithm was conducted to identify communities in the PTSD and depressive symptoms network. Finally, Network accuracy and stability were examined using the Bootstrapped test and the case-dropping procedure. RESULTS: A total of 1,768 firefighters were enrolled in our research. Network analysis revealed that the relationship between PTSD symptoms, “Flashback” and “Avoidance,” was the strongest. “Life emptiness” was the most central symptom with the highest EI in the PTSD and depression network model. Followed by “Fatigue” and “Interest loss.” Bridge symptoms connecting PTSD and depressive symptoms in our study were “Numb,” “High alertness,” “Sad mood,” and “Compunction and blame,” successively. The data-driven community detection suggested the differences in PTSD symptoms in the clustering process. The reliability of the network was approved by both stability and accuracy tests. CONCLUSION: To the best of our knowledge, the current study first demonstrated the network structure of PTSD and depressive symptoms among Chinese firefighters, identifying the central and bridge symptoms. Targeting interventions to the symptoms mentioned above may effectively treat firefighters suffering from PTSD and depressive symptoms. Frontiers Media S.A. 2023-05-04 /pmc/articles/PMC10193951/ /pubmed/37213609 http://dx.doi.org/10.3389/fpubh.2023.1096771 Text en Copyright © 2023 Cheng, Wang, Zhou, Ma, Zhao, Zhang and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Cheng, Peng
Wang, Lirong
Zhou, Ying
Ma, Wenjing
Zhao, Guangju
Zhang, Li
Li, Weihui
Post-traumatic stress disorder and depressive symptoms among firefighters: a network analysis
title Post-traumatic stress disorder and depressive symptoms among firefighters: a network analysis
title_full Post-traumatic stress disorder and depressive symptoms among firefighters: a network analysis
title_fullStr Post-traumatic stress disorder and depressive symptoms among firefighters: a network analysis
title_full_unstemmed Post-traumatic stress disorder and depressive symptoms among firefighters: a network analysis
title_short Post-traumatic stress disorder and depressive symptoms among firefighters: a network analysis
title_sort post-traumatic stress disorder and depressive symptoms among firefighters: a network analysis
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10193951/
https://www.ncbi.nlm.nih.gov/pubmed/37213609
http://dx.doi.org/10.3389/fpubh.2023.1096771
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