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Network Analysis of Anxiety Symptoms in Front-Line Medical Staff during the COVID-19 Pandemic

Background: This research analyses the relations between anxiety symptoms from the network perspective to deepen the understanding of anxiety in front-line medical staff during the COVID-19 pandemic and can also provide a reference for determining potential goals of clinical interventions. Methods:...

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Autores principales: Wu, Lin, Ren, Lei, Li, Fengzhan, Shi, Kang, Fang, Peng, Wang, Xiuchao, Feng, Tingwei, Wu, Shengjun, Liu, Xufeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10452648/
https://www.ncbi.nlm.nih.gov/pubmed/37626510
http://dx.doi.org/10.3390/brainsci13081155
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author Wu, Lin
Ren, Lei
Li, Fengzhan
Shi, Kang
Fang, Peng
Wang, Xiuchao
Feng, Tingwei
Wu, Shengjun
Liu, Xufeng
author_facet Wu, Lin
Ren, Lei
Li, Fengzhan
Shi, Kang
Fang, Peng
Wang, Xiuchao
Feng, Tingwei
Wu, Shengjun
Liu, Xufeng
author_sort Wu, Lin
collection PubMed
description Background: This research analyses the relations between anxiety symptoms from the network perspective to deepen the understanding of anxiety in front-line medical staff during the COVID-19 pandemic and can also provide a reference for determining potential goals of clinical interventions. Methods: A convenience sampling was adopted, and the Generalized Anxiety Disorder 7-item scale (GAD-7) was administered to front-line medical staff through online platforms. A regularized partial correlation network of anxiety was constructed and then we evaluated its accuracy and stability. The expected influence and predictability were used to describe the relative importance and the controllability, using community detection to explore community structure. The gender-based differences and the directed acyclic graph were implemented. Results: The connections between A1 “Feeling nervous, anxious or on edge” and A2 “Not being able to stop or control worrying”, A6 “Becoming easily annoyed or irritable” and A7 “Feeling afraid as if something awful might happen”, etc., were relatively strong; A2 “Not being able to stop or control worrying” and A3 “Worrying too much about different things” had the highest expected influence, and A2 “Not being able to stop or control worrying” had the highest predictability. The community detection identified two communities. The results of the gender network comparison showed the overall intensity of the anxiety network in women was higher than that in men; DAG indicated that A2 “Not being able to stop or control worrying” had the highest probabilistic priority; the lines from A2 “Not being able to stop or control worrying” to A1 “Feeling nervous, anxious or on edge” and A2 “Not being able to stop or control worrying” to A7 “Feeling afraid as if something awful might happen” represented the most important arrows. Conclusion: There exist broad interconnections among anxiety symptoms of front-line medical staff on the GAD-7. A2 “Not being able to stop or control worrying” might be the core symptom and a potential effective intervention target. It was possible to bring an optimal result for the entire GAD symptom network by interfering with A2 “Not being able to stop or control worrying”. GAD may have two “subsystems”. The modes of interconnection among anxiety may be consistent between genders.
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spelling pubmed-104526482023-08-26 Network Analysis of Anxiety Symptoms in Front-Line Medical Staff during the COVID-19 Pandemic Wu, Lin Ren, Lei Li, Fengzhan Shi, Kang Fang, Peng Wang, Xiuchao Feng, Tingwei Wu, Shengjun Liu, Xufeng Brain Sci Article Background: This research analyses the relations between anxiety symptoms from the network perspective to deepen the understanding of anxiety in front-line medical staff during the COVID-19 pandemic and can also provide a reference for determining potential goals of clinical interventions. Methods: A convenience sampling was adopted, and the Generalized Anxiety Disorder 7-item scale (GAD-7) was administered to front-line medical staff through online platforms. A regularized partial correlation network of anxiety was constructed and then we evaluated its accuracy and stability. The expected influence and predictability were used to describe the relative importance and the controllability, using community detection to explore community structure. The gender-based differences and the directed acyclic graph were implemented. Results: The connections between A1 “Feeling nervous, anxious or on edge” and A2 “Not being able to stop or control worrying”, A6 “Becoming easily annoyed or irritable” and A7 “Feeling afraid as if something awful might happen”, etc., were relatively strong; A2 “Not being able to stop or control worrying” and A3 “Worrying too much about different things” had the highest expected influence, and A2 “Not being able to stop or control worrying” had the highest predictability. The community detection identified two communities. The results of the gender network comparison showed the overall intensity of the anxiety network in women was higher than that in men; DAG indicated that A2 “Not being able to stop or control worrying” had the highest probabilistic priority; the lines from A2 “Not being able to stop or control worrying” to A1 “Feeling nervous, anxious or on edge” and A2 “Not being able to stop or control worrying” to A7 “Feeling afraid as if something awful might happen” represented the most important arrows. Conclusion: There exist broad interconnections among anxiety symptoms of front-line medical staff on the GAD-7. A2 “Not being able to stop or control worrying” might be the core symptom and a potential effective intervention target. It was possible to bring an optimal result for the entire GAD symptom network by interfering with A2 “Not being able to stop or control worrying”. GAD may have two “subsystems”. The modes of interconnection among anxiety may be consistent between genders. MDPI 2023-08-01 /pmc/articles/PMC10452648/ /pubmed/37626510 http://dx.doi.org/10.3390/brainsci13081155 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wu, Lin
Ren, Lei
Li, Fengzhan
Shi, Kang
Fang, Peng
Wang, Xiuchao
Feng, Tingwei
Wu, Shengjun
Liu, Xufeng
Network Analysis of Anxiety Symptoms in Front-Line Medical Staff during the COVID-19 Pandemic
title Network Analysis of Anxiety Symptoms in Front-Line Medical Staff during the COVID-19 Pandemic
title_full Network Analysis of Anxiety Symptoms in Front-Line Medical Staff during the COVID-19 Pandemic
title_fullStr Network Analysis of Anxiety Symptoms in Front-Line Medical Staff during the COVID-19 Pandemic
title_full_unstemmed Network Analysis of Anxiety Symptoms in Front-Line Medical Staff during the COVID-19 Pandemic
title_short Network Analysis of Anxiety Symptoms in Front-Line Medical Staff during the COVID-19 Pandemic
title_sort network analysis of anxiety symptoms in front-line medical staff during the covid-19 pandemic
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10452648/
https://www.ncbi.nlm.nih.gov/pubmed/37626510
http://dx.doi.org/10.3390/brainsci13081155
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