Comorbidity Network Analysis of Depression, Anxiety and Stress&amp;nbsp;During the COVID-19 Pandemic

Liang, Li Kui; Ren, Lei; Luo, Xi; Zhi, Feng Zheng

doi:10.21203/rs.3.rs-40285/v1

Cited by 3 publications

(3 citation statements)

References 40 publications

(58 reference statements)

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“…For example, Bai et al (2021) also found "tense" a central symptom. Liang et al (2020) also identified "tense" or "I found it difficult to relax" in a Chinese population, although they used DASS-21 to assess symptoms. Nevertheless, while symptoms like "sadness", "unstoppable worry", and "irritability" were identified in other studies (e.g., Bai et al, 2021;Kaiser et al, 2020;Ren et al, 2021), our study did not.…”

Section: Discussionmentioning

confidence: 99%

Psychological Adjustment Among Chinese International Students During COVID-19: A Network Analysis

Bi¹,

Jiang²,

Chen³

2021

Preprint

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A particularly vulnerable yet often-ignored subpopulation is Chinese international students (CIS). Literature suggests CIS suffer from high prevalence of common mental health disorders. Recent advances popularize the use of network analysis on psychopathology data. Our study (N = 421) is the first to investigate depression and anxiety simultaneously from a network perspective in CIS. Results of the Gaussian graphical model (GGM) suggested that: (1) central symptoms identified via the centrality index of strength included depressive symptoms of “anhedonia” and anxiety symptoms of “restlessness” and “tense”; (2) bridge symptoms identified via the bridge expected influence index included depressive symptom of “psychomotor agitation/retardation” and anxiety symptoms of “afraid” and “restlessness”. Results of the Bayesian directed acyclic graph (DAG) demonstrated the predictive priority of depressive symptoms of “anhedonia” and “sadness” in driving comorbidity. The network analyses highlight the node of “anhedonia” (a central node in GGM and the top node in DAG) and several other mostly physical symptoms including “restlessness”, “tense”, “psychomotor agitation/retardation”, and “afraid” as candidates for interventions and show great value in generating clinical insights beyond western sample. Implications and limitations are discussed.

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Section: Discussionmentioning

confidence: 99%

Psychological Adjustment Among Chinese International Students During COVID-19: A Network Analysis

Bi¹,

Jiang²,

Chen³

2021

Preprint

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“…Furthermore, the network analysis revealed nervousness and stress as bridge symptoms linking perceived stress with anxiety and depression disorders. 50 …”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, the network analysis revealed nervousness and stress as bridge symptoms linking perceived stress with anxiety and depression disorders. 50 Depression, anxiety and stress are common MHD. Symptoms of one of these mental conditions increase the chances of getting other mental disorders, leading to diagnostic comorbidity.…”

Section: Discussionmentioning

confidence: 99%

Network analysis of mental health problems among adults in Addis Ababa, Ethiopia: a community-based study during the COVID-19 pandemic

Workneh,

Worku,

Assefa

et al. 2024

BMJ Open

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ObjectiveCOVID-19 has negatively impacted mental health of adults globally with increased rates of psychiatric comorbidities. However, network analysis studies to examine comorbidities and correlations between symptoms of different mental disorders are uncommon in low-income countries. This study aimed to investigate the network structure of depression, anxiety and perceived stress among adults in Addis Ababa and identify the most central and bridge symptoms within the depressive-anxiety-perceived symptoms network model.DesignCommunity-based cross-sectional study.SettingThis study was carried out on a sample of the general population in Addis Ababa during the first year of the COVID-19 pandemic. A total of 1127 participants were included in this study, of which 747 (66.3%) were females, and the mean age was 36 years.Primary and secondary outcome measuresSymptoms of depression, anxiety and stress were measured using the Patient Health Questionnaire, Generalized Anxiety Disorder Scale and the Perceived Stress Scale, respectively.Network analysis was conducted to investigate the network structure. The centrality index expected influence (EI) and bridge EI (1-step) were applied to determine the central and bridge symptoms. Case-dropping procedure was used to examine the network stability.ResultThe sad mood (EI=1.52) was the most central and bridge symptom in the depression, anxiety and perceived stress network model. Irritability (bridge EI=1.12) and nervousness and stressed (bridge EI=1.33) also served as bridge symptoms. The strongest edge in the network was between nervousness and uncontrollable worry (weight=0.36) in the anxiety community. The network had good stability and accuracy. The network structure was invariant by gender and age based on the network structure invariance test.ConclusionsIn this study, the sad mood was the core and bridge symptom. This and the other central and bridge symptoms identified in the study should be targeted to prevent mental health disorders and comorbidities among adults.

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Internal Linkages among Depressive, Anxiety, and Acute Stress Symptoms in 2021 Henan Floods Victims during the Disaster: A Network Approach

Wang,

2023

J Psychopathol Behav Assess

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Comorbidity Network Analysis of Depression, Anxiety and Stress During the COVID-19 Pandemic

Cited by 3 publications

References 40 publications

Psychological Adjustment Among Chinese International Students During COVID-19: A Network Analysis

Psychological Adjustment Among Chinese International Students During COVID-19: A Network Analysis

Network analysis of mental health problems among adults in Addis Ababa, Ethiopia: a community-based study during the COVID-19 pandemic

Internal Linkages among Depressive, Anxiety, and Acute Stress Symptoms in 2021 Henan Floods Victims during the Disaster: A Network Approach

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