Background: To explore the dynamic network structure and centrality of depressive and anxiety symptoms before the peak of COVID-19 and during the COVID-19 outbreak in the post-COVID-19 era.

Methods: A web-based survey was conducted in two stages: the pre-peak stage in the post-COVID-19 era and the outbreak stage in the post-COVID-19 era. The Patient Health Questionnaire-9 (PHQ-9) and General Anxiety Disorder-7 (GAD-7) scales were used. Network models were created to explore the relationship between psychopathology symptoms both within and across anxiety and depression. Symptom network analysis was conducted to evaluate network and bridge centrality, and the network properties were compared between the pre-peak and the outbreak stage.

Results: A total of 1849 participants in the pre-peak stage and 1363 participants in the COVID-19 outbreak stage in the post-COVID-19 era were recruited. D4 “Fatigue” and A4 “Trouble relaxing” had the highest expected influence during the outbreak stage. D2 “Depressed or sad mood”, D5 “Appetite changes”, and D6 “Feeling of worthlessness” played an important role in the network during the peak of the postpandemic era.

Conclusions: This study provides novel insights into the changes in central features during different COVID-19 stages that could activate the connection between anxiety and depression. Implications for interventions and clinical preventions are discussed.

• Depression

• Anxiety

• Network structure

• The post-COVID-19 era

Wu S, Zhao R, Ren L, Wang Y, Ni S, et al. (2024) Changes in Network Structure and Centrality of Depressive and Anxiety Symptoms between the before Peak and COVID-19 Outbreak in the Post-COVID-19 Era. Ann Psychiatry Ment Health 12(1): 1191.

The COVID-19 pandemic has caused substantial threats to people’s physical health and lives and has triggered psychological distress, such as anxiety and depression [1]. Unlike previous infections, worldwide mass media reports have highlighted the unique threat of COVID-19, increasing people’s psychological distress and panic [2]. A number of recent studies have focused on mental health problems during COVID-19, with the most frequently reported symptoms being depression and anxiety [1,3,4]. In a meta-analysis, researchers found that the prevalence estimates of depression and anxiety display a substantial increase in the general population during the COVID-19 pandemic and are estimated to be 33.7% and 31.9%, respectively [5]. In the postepidemic stage, to stop the spread of the COVID-19 epidemic to campuses, the Ministry of Education requested that the start of school be postponed. College students’ vacation was extended and they stayed at home for a long time; they were forced to reduce going out and were unable to study and participate in social activities normally, which may affect their studies and aggravate their anxiety and depression [6,7]. With the weakening of the epidemic situation in China, the resumption of work and production across the country is being carried out in an orderly manner. However, the psychological crisis produced by the epidemic is still not over, and viral transmission, incubation times, and the care of severely affected patients rightly dominate the news [8]. In China, the epidemic began to resurface in some areas after a stable period and the government took city closure measures. The closure of cities may pose another threat to psychological health (e.g., depression and anxiety). Therefore, it is necessary to correctly assess collateral damage in this postepidemic period, also called the “post-COVID-19 era”, especially mental health problems [9].

Although only recently pioneered by Borsboom and his colleagues [10-12], network analysis has quickly become a hot topic in contemporary clinical psychology. Network analysis can be used to extract the structure of psychiatric disturbances from clinical data [11,13,14]. Additionally, a network model is useful in understanding the mechanism of comorbidities and provides information for clinicians to prevent and treat comorbidities [15,16]. Highly central nodes are thought to maintain the network structure since, once activated, they can quickly spread that activation to the entire system, playing a major role in the onset and/or maintenance of a syndrome or disorder [17-20]. Within the network approach to psychopathology, central nodes have accordingly been theoretically [11], and empirically [21,22], viewed with respect to the prognosis of a disorder.

To date, no published network analysis studies have explored network analysis changes in psychology before and after the closure of cities in the postepidemic era. This study may provide a more fine-grained analysis of the theoretical construct, the between/within-disorder edges, central symptoms and bridge symptoms and provide potential clinical intervention targets.

Participants

The participants were undergraduate students in Shaanxi Province, China. The data of wave 1 were collected from October 25 to November 29, 2021 and wave 2 from January 5 to 9, 2022 via a Chinese online survey platform (Wenjuanxing). All participants provided informed consent before participation. The final sample consisted of 1849 participants (59% female, mean age = 19.00, SD = 1.32, range = 17–23 years) of wave 1 and 1363 participants (52% female, mean age = 19.77, SD = 1.24, range = 17-23 years) of wave 2.

Measures

Depression symptoms: The Patient Health Questionnaire-9 (PHQ-9) is used to measure the frequency of depressive symptoms in clinical practice and research [23]. The PHQ-9 has 9 items, and the score of each item varies from 0 to 3. The higher the total score is, the higher the severity of depression. The internal consistency of the PHQ-9 in this study was excellent (α = 0.93).

Anxiety symptoms: The Generalized Anxiety Disorder 7-Item Questionnaire (GAD-7) is used to measure the frequency of symptoms of GAD [24]. The GAD-7 has 7 items rated from 0 to 3. The higher the total score is, the higher the level of GAD severity. The internal consistency of the GAD-7 in this study was excellent (α = 0.93).

Network analysis

Statistical analyses were conducted in R-statistical software. We fit Gaussian graphical models (GGMs) to the data (i.e., an undirected network) [25]. We used Spearman correlations as input when estimating the network structure [26,27]. We used unregularized model selection in estimating GGMs [27]. The current network estimation was based on the ggm Mod Select method in the R package qgraph [28]. The network was visualized using the R package qgraph [28].

We calculated the nodes’ expected influence using the R package qgraph [28]. The higher the expected influence, the more central the node is in the network. We also calculated the bridge expected influence via the R package network tools [15]. Higher values for bridge expected influence indicate a greater risk of increasing contagion to other communities [15].

The accuracy of edge weights was assessed by plotting the 95% confidence interval for each edge within the presented networks. The stability of the node expected influence and bridge expected influence was evaluated by calculating the correlation stability (CS) coefficient. The ideal CS coefficient is above .5 and should not be below .25 [29]. We further conducted bootstrapped difference tests for edge weights, the node and bridge expected influence. The aforementioned procedures were conducted via the R package bootnet [29].

Finally, we used the R package Network Comparison Test to compared global strength and the edge weight for the three networks, the node expected influence for the   depression and anxiety networks, and the bridge expected influence for the depression-anxiety network in wave 1 and wave 2. The differences in weight for each edge were compared between the two networks after controlling for multiple tests (Bonferroni- Holm correction of p values) [13].

Comparison of symptom scores in wave 1 and wave 2

For depression, D1, D3, D4 and D5 showed significantly higher mean values in wave 2 than wave 1, while D6 and D7 showed the opposite pattern. For anxiety, 6 symptoms (except A4) showed significantly higher mean values in wave 2 than in wave 1. The total scores of depression and anxiety both showed significantly higher mean values in wave 2 than in wave 1.

Description and differences of depression networks in wave 1 and wave 2

In the depression network in wave 1 (Figure 1a), 25 edges were not zero (69.44%) among 36 possible edges.

Figure 1: Depression networks in wave 1 and wave 2.

The strongest edges were D1-D2 (weight = 0.25), D6-D7 (weight = 0.24), and D1-D7 (weight = 0.23). In the depression network in wave 2 (Figure 1b), 23 edges were not zero (63.89%) among 36 possible edges. The strongest edges were D1-D4 (weight = 0.34), D3-D4 (weight = 0.30), and D7-D8 (weight = 0.28).

For wave 1, D4 and D6 had the highest expected influence. The correlation stability coefficient of the node expected influences in wave 1 was 0.75 (> 0.25). For wave 2, D4 and D7 had the highest expected influence. The correlation stability coefficient of node expected influences in wave 2 was 0.75 (> 0.25). (Figure 1c).

There was no significant difference in the global connectivity of depression networks in wave 1 and wave 2 (global strength [S] = 0.04, wave 1 = 3.85, wave 2 = 3.89, P = 0.35). Only one edge had significantly different weight in wave 1 and wave 2 (P < 0.05; (Figure 1a and Figure 1b): D7-D8 (stronger positive connection in wave 2 than in wave 1). D5, D6, D7 and D8 had significantly different expected influence in the depression networks in wave 1 and wave 2 (Figure 1c).

Depression networks in wave 1 (a) and wave 2 (b). Blue edges represent positive correlations, and red edges represent negative correlations. The thickness of the edge reflects the magnitude of the correlation. Centrality plot depicting the expected influence of depression networks in wave 1 and wave 2 (c) (Figure 1).

Description of and differences in anxiety networks in wave 1 and wave 2

In the anxiety network in wave 1 (Figure 2a), 18 edges were not zero (85.71%) among 21 possible edges. The strongest edges were A2-A3 (weight = 0.31), A1-A2 (weight = 0.26), and A3-A4 (weight = 0.25). In the anxiety network in wave 2 (Figure 1b), 16 edges were not zero (76.19%) among 21 possible edges. The strongest edges were A1-A2 (weight = 0.43), A5-A7 (weight = 0.31), and A4-A5 (weight = 0.31).

For wave 1, A2 and A6 had the highest expected influence. The correlation stability coefficient of the node expected influences in wave 1 was 0.75 (> 0.25). For wave 2, A1 and A4 had the highest expected influence. The correlation stability coefficient of the node expected influences in wave 2 was 0.75 (> 0.25) (Figure 2c).

There was a significant difference in the global connectivity of anxiety networks in wave 1 and wave 2 (Global strength [S] = 0.08, wave 1 = 3.13, wave 2 = 3.21, p < 0.001). Only one edge had significantly different weights in wave 1 and wave 2 (P < 0.05; (Figure 2a and Figure 2b): A1 - A2 (stronger positive connection in wave 2 than in wave 1). A1, A4 and A6 had significantly different expected influences in the anxiety networks in wave 1 and wave 2 (Figure 2c).

Figure 2: Anxiety networks in wave 1 and wave 2.

Anxiety networks in wave 1 (a) and wave 2 (b). Blue edges represent positive correlations, and red edges represent negative correlations. The thickness of the edge reflects the magnitude of the correlation. Centrality plot depicting the expected influence of anxiety networks in wave 1 and wave 2 (c) (Figure 2).

Description of and differences in depression-anxiety networks in wave 1 and wave 2

In the network in wave 1 (Figure 3a), 18 between-community edges were not zero (28.57%) among 63 possible edges. The strongest between-community edges were D8-A5 (weight = 0.23), D6-A1 (weight = 0.15), and D5-A4 (weight = 0.13). In the network in wave 2 (Figure 1b), 15 between-community edges were not zero (23.81%) among 63 possible edges. The strongest between-community edges were D2-A1 (weight = 0.17), D2-A6 (weight = 0.16), and D8-A5 (weight = 0.15).

For wave 1, A1, D8 and D6 had the highest bridge expected influence. The correlation stability coefficient of the expected influences of the node bridge in wave 1 was 0.52 (> 0.25). For wave 2, D2, A7 and A6 had the highest bridge expected influence. The correlation stability coefficient of the expected influences of the node bridge in wave 2 was 0.59 (> 0.25) (Figure 3c).

There was no significant difference in the global connectivity of depression-anxiety networks in wave 1 and wave 2 (global strength [S] = 0.02, wave 1 = 7.35, wave 2 = 7.37, p = 0.79). There was no between-community edge had significantly different weights in wave 1 and wave 2. D2 and D6 had significantly different bridge expected influence in the depression-anxiety networks in wave 1 and wave 2 (Figure 3c).

Depression-anxiety networks in wave 1 (a) and wave 2 (b). Blue edges represent positive correlations, and red edges represent negative correlations. The thickness of the edge reflects the magnitude of the correlation. Centrality plot depicting the bridge expected influence of depression-anxiety networks in wave 1 and wave 2 (c) (Figure 3).

Figure 3: Depression-anxiety networks in wave 1 and wave 2.

The novelty of the current study is its evaluation of the psychopathological symptom changes before the peak of and during the COVID-19 outbreak in the postepidemic period in China. The current study identified centrality and bridge symptoms and aimed to identify the risks of co-occurrence between anxiety and depressive symptoms during different phrases of the post- COVID-19 era to prevent increasing psychological depression and anxiety.

Comparison of symptom scores in wave 1 and wave 2

The total scores of depression and anxiety both showed significantly higher mean values in wave 2 than in wave 1. The scores of 4 depression symptoms and 6 anxiety symptoms showed significantly higher in wave 2 than in wave 1. The closure of the city in the later stage of the epidemic further worsened the mental health of college students, which can be seen from the total score of anxiety and depression and the score of each symptom reflected in the results of this study. Researchers have suggested that the mental health consequences of COVID-19 could last over time and that mental health problems could peak later than the actual pandemic [30]. Our results are consistent with the prediction and show that after the COVID-19 peak, the prevalence of depression and anxiety increased. This could be due to the far-reaching influences of COVID-19, such as the induced economic uncertainty, the fear of economic crisis and recession, and increased unemployment [31].

Description and differences of depression networks in wave 1 and wave 2 

Although there was no significant difference in global connectivity, there were actual differences in depression networks between wave 1 and wave 2 (i.e., higher connectivity in wave 2 than wave 1). D7 - D8 had stronger positive connection in wave 2 than wave 1. D4 in the later stage of the epidemic remains the most core symptom in the network, with the centrality of D7 and D8 increasing. In the later stage of the epidemic, fatigue is an important aspect of the mental health of college students, which is consistent with the results of previous study [32]. Fatigue has been shown to have a strong correlation with anhedonia in depression and other psychiatric disorders [33,34]. Interestingly, in the postepidemic stage, after entering the outbreak period, the centrality of mental sports was enhanced, indicating that problems related to mental sports make the mental health problems of college students in closed isolation more prominent. After the peak of the epidemic, normal social activities began to recover and symptoms related to mental movement were relieved accordingly [35]. When people return to a closed isolation state from a gradually recovered life, problems due to psychomotor- related factors continue to appear and increase accordingly. Thus, the importance of psychomotor agitation/delay in the depression network structure may be due to the isolation and lockdown strategies implemented in some countries that cause individuals to feel socially isolated. The results of this study differ from a survey study conducted by Wang et al.[36].Given several studies of psychomotor/delay on the spread of other symptoms, these symptoms have been widely identified as a sign of depression [37-40].

Description of and differences in anxiety networks in wave 1 and wave 2

For wave 1, A2 and A6 had the highest expected influence. For wave 2, A4 and A1 had the highest expected influence. Note that Wang et al. also identified uncontrollable worry and restlessness as highly central nodes in the network structure in their Chinese sample [36] and that Hoffar et al.[41], likewise identified uncontrollable worry (but not restlessness) as the central node in their Norwegian sample. These results are similar to the result of uncontrollable worry as the highest central node in our study. This observation is particularly striking given the varying countries, cultural backgrounds, and political responses vis-à-vis the pandemic between these studies and ours. For decades, research on anxiety has emphasized the determining role of worries in triggering anxiety when one’s concerns are broadly diffuse, future-oriented and related to threats that are not immediately present and may or may not occur, as in the present pandemic context (e.g., worry about the uncertain postpandemic economic recession, worry about possible job loss, worry about uncertain risks of future COVID-19 resurgence) [42-44]. Early results have suggested that worrying during the lockdown, even when adjusting for many other psychological variables, was significantly associated with a higher level of psychological distress and a lower level of life satisfaction during the later phase of the COVID-19 pandemic [45-47]. This view could provide suggestions for the transition of the central node in this study from wave 1 to wave 2.

Description of and differences in depression-anxiety networks in wave 1 and wave 2

In the wave 1, A1 was the most central symptom in the entire network, followed by D8 and D6. As such, these symptoms were the most likely candidates for triggering or maintaining remaining anxiety and depressive symptoms. In the second outbreak stage, the bridge expected influence of these symptoms decreased, but except D6, the bridge expected influence of A1 and D8 was still relatively high compared with other symptoms. The symptoms above could be plausible candidates for hallmark bridge symptoms that contribute to the comorbidity of anxiety and depression. Specific measures related to COVID-19, such as self- isolation, isolation and social distancing, may increase people’s loneliness and emotional problems [48]. Depression and anxiety are frequently co-occurring mental disorders, and previous research has indicated the likelihood of a causal relationship between these two mood disorders [49]. A cognitive neuroscience study using the default model network indicated that cortical areas of the DMN showed functional connectivity associated with anxiety and depression [50]. A previous study suggested that interventions should focus on depression and anxiety symptoms, which are most closely related to other symptoms, since those symptoms should theoretically decrease the associated risk [51]. Moreover, symptoms with high centrality may also have crucial roles in the network [52]. These core symptoms could play important roles in maintaining the psychopathology network, and treating these symptoms could help to cure the psychopathology.

There are also several limitations. First, the results may not be directly generalizable to clinical settings. Secondly, we used the self-report questionnaire in this study, which are subjective to some extent. Third, the current sample was in different time stages and the global application of the results should be fully considered.

Thisis the first network analysis to focuson psychopathological symptoms describing changes in the centrality of depressive and anxiety symptoms before the peak and during the COVID-19 outbreak in the post-COVID-19 era. It provides valuable insights to understand the interactions between depression and anxiety. Intervention focused on central symptoms can minimize the overall situation of depression and anxiety.

Author Contributions

Conceptualization, S.W.,R.Z.,L.R.; methodology, Y.W.; software, S.N.; validation, J.L.; formal analysis, L.W.; investigation, Y.H.; resources, Y.W.; data curation, S.N.; writing—original draft preparation, R.Z., L.R.; writing—review and editing, S.W.,Y.H.; visualization, T.X.; supervision, X.L.; project administration, X.L.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Key Project of the “14th Five-Year Plan” for Logistics Research (BKJ21J013) and National Natural Science Foundation of China (72374208).

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Review Committee of Xijing Hospital (KY20224106-1).

Informed Consent Statement

Informed consent was obtained from all participants involved in the study.

Supplementary materials

Supplementary material associated with this article can be found in the supplementary file.

Data Availability Statement

The datasets presented in this article are not readily available because the datasets involve unfinished research projects.

Acknowledgments

We would like to thank all the individuals who participated in the study. We also thank all the administrative staff and teachers in the university who help us with the recruitment.

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