Skip to main content
NCBI home page
SAGE - PMC COVID-19 Collection logoLink to SAGE - PMC COVID-19 Collection
. 2022 Dec 13;69(4):916–927. doi: 10.1177/00207640221141784

Social anxiety and depression symptoms in Chinese left-behind children after the lifting of COVID-19 lockdown: A network analysis

Kuiliang Li 1,2,*, Lei Ren 3,*, Lei Zhang 1, Chang Liu 4, Mengxue Zhao 2, Xiaoqing Zhan 1, Ling Li 5,✉, Xi Luo 1,✉, Zhengzhi Feng 2,✉
PMCID: PMC9749067  PMID: 36511138

Abstract

Background:

Returning to social life after the lifting of COVID-19 lockdown may increase risk of social anxiety, which is highly co-morbid with depression. However, few studies have reported the association between them.

Aims:

To explore the complex relationship between social anxiety and depression symptoms in left-behind children after the lifting of the COVID-19 lockdown.

Methods:

A cross-sectional survey was conducted 6 months after the lockdown removal. A total of 3,107 left-behind children completed the survey with a mean age of 13.33 and a response rate of 87.77%. Depression and social anxiety severity were assessed by the DSM-5 Patient Health Questionnaire for Adolescents and the DSM-5 Social Anxiety Disorder Questionnaire, respectively. The symptom-level association between the two disorders was examined using network analysis.

Results:

After the lifting of COVID-19 lockdown, the prevalence of depression and social anxiety in left-behind children was 19.57% and 12.36%, respectively, with a co-morbidity rate of 8.98%. Network analysis showed that “Social tension” and “Social avoidance” had the greatest expected influence; “Humiliation” and “Motor” were bridge symptom nodes in the network. The directed acyclic graph indicated that “Social fright” was at the upstream of all symptoms.

Conclusion:

Attention should be paid to social anxiety symptoms in left-behind children after the lifting of COVID-19 lockdown. Prevention and intervention measures should be taken promptly to reduce the comorbidity of social anxiety and depression symptoms in the left-behind children after the lifting of lockdown.

Keywords: Depression, social anxiety, left-behind children, adolescents, network analysis, lockdown removal

Introduction

With the outbreak of COVID-19, many countries especially China have shared plenty of valuable information on disease treatment and control (Chen et al., 2020; Wang, Pan et al., 2020; Zhu et al., 2020). Scientists not only have fought against COVID-19 from the perspective of biomedicine but conducted a great deal of research on mental health. Researchers have attached importance to the mental health of different groups during the COVID-19 pandemic (Almeida et al., 2020; El Hayek et al., 2020; Walton et al., 2020), including medical staff (Abbasi, 2020) and college students (Wang, Hegde et al., 2020), but vulnerable groups, such as children and adolescents, are paid less attention (Holmes et al., 2020).

According to WHO reports, 20% of children and adolescents worldwide had mental health problems currently, yet effective treatment coverage was extremely low (WHO, 2022). Children and adolescents are more susceptible to depression and anxiety due to social isolation and fear of the SARS-Cov-2 infection during the COVID-19 pandemic (Loades et al., 2020; Singh et al., 2020). Moreover, they were at a greater risk of social anxiety after the lockdown is lifted (Lim et al., 2022). Among children and adolescents, left-behind children need special attention. Left-behind children are children under the age of 18 who have been separated from at least one parent for more than 6 months (Wang et al., 2019). There are currently hundreds of millions of left-behind children worldwide (Fellmeth et al., 2018), with approximately 61 million in China (Yuan & Wang, 2016). Previous studies have shown that compared with non-left-behind children, left-behind children show more mental health problems due to impaired parent-child relationships, reduced parental support, and deficient parental guidance (Hou et al., 2021; Wang et al., 2019). The current study therefore focuses on the mental health of left-behind children during the COVID-19 pandemic.

The evidence above suggests that left-behind children may be more vulnerable to mental illness during the COVID-19 pandemic. Individuals need to resume social activities after the lifting of the lockdown, which may cause an increased risk of social anxiety (Lim et al., 2022; Samantaray et al., 2022). Social anxiety and depression symptoms often co-occurred (Adams et al., 2018), implying that COVID-19 may raise the risk of social anxiety and depression co-morbidity in left-behind children after the lifting of lockdown. However, it remains unclear whether the prevalence of depression-social anxiety comorbidity will also increase. The co-occurrence of these two mental disorders can be explored via network modeling of psychopathology (Jones et al., 2021). Many studies have investigated the network structure of depression and anxiety symptoms during the COVID-19 pandemic (Bai et al., 2021; Liu et al., 2022; Ren et al., 2021). A study identified “depressed” and “nervousness” in adolescents as core symptoms in the network and “depressed” as a bridge symptom connecting the two disorders (Liu et al., 2022), which provides insights into reducing the impact on the mental health of adolescents during the COVID-19 pandemic. However, there is currently no network analysis of social anxiety and depression symptoms during the COVID-19 pandemic. Considering the unique characteristics of left-behind children and the high co-morbidity of social anxiety and depression, this study explored the network structure of social anxiety and depression symptoms in left-behind children after the COVID-19 lockdown was lifted. The following research questions were asked:

  • (1) What are the most important symptoms in the network of social anxiety and depression symptoms?

  • (2) Which symptoms are the bridges between social anxiety and depression symptoms network?

  • (3) Which symptom of social anxiety and depression network has the highest predictive (and potentially causal) priority?

Methods

Participants and study design

A cross-sectional survey was conducted in 32 primary and secondary schools from October to November 2020 in Chongqing, China, and all students over the age of seven were recruited to participate in the study. The survey was carried out by well-trained researchers responsible for giving necessary explanations and instructions. Students participated in the study by filling out a questionnaire in the classroom. Meanwhile, verbal consent has been obtained from their parents, who were notified by schools that the questionnaire was part of a mental health survey. This study has been reviewed and approved by the Medical Ethics Committee of the Department of Medical Psychology, Army Medical University (Project No. CWS20J007).

A total of 18,133 questionnaires were collected. First, we excluded 3,854 questionnaires of the participants who did not age between 11 and 17 based on the applicability of the questionnaires or did not indicate age. Among the remaining 14,279 questionnaires, 10,739 filled by the children who were not currently left behind (children who had lived separately from both parents for less than 6 months) were excluded. Among the remaining 3,540 questionnaires, 433 incomplete questionnaires were further excluded. Finally, a total of 3,107 valid questionnaires (87.77% respondents) from left-behind children were included for subsequent statistical analysis. These respondents completed the survey 6 to 7 months after the local lockdown policy was canceled and met the criteria of left-behind children (that is, all respondents were children whose parents had gone out to work for over 6 months after the lockdown was lifted). They received investigations of demographic features, and depression and social anxiety symptoms, which were analyzed using network analysis.

Measures

Depression symptoms

Depression symptoms in adolescents were assessed using the Patient Health Questionnaire for Adolescents (PHQ-A). The scale contains nine items, such as “Little interest or pleasure in doing things” or “Feeling down, depressed, or hopeless.” Participants were asked to choose the item based upon how often each event occurred over the past 2 weeks. Scores for each item range from 0 (“Not at all”) to 3 (“Nearly every day”), with a higher total indicating a higher level of depression. The Cronbach’ α for the current PHQ-A scale was set at 0.902.

Social anxiety symptoms

Social anxiety was assessed using the Chinese-modified Social Anxiety Scale from the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The scale consists of 10 items using a 5-point Likert scale, with each item ranging from 0 (“Never”) to 4 (“All of the time”). A higher total score means more severe social anxiety. The Cronbach’ α for this scale was set at 0.923. The Chinese version of assessment tools is from Meilihua Health Systems (Meilihua Health Systems, 2019). The original tools and scoring criteria are available in the Online Assessment Measures of the American Psychiatry Association (American Psychiatric Association, 2013b).

Data analysis

Graphical LASSO network

This network was estimated via Gaussian graphical models (GGMs; Epskamp, Borsboom et al., 2018). Within GGMs, the edges represent partial correlations between two nodes after controlling for all other nodes in the network. The GGMs were estimated based on nonparametric Spearman rho correlation matrices. The regularization of GGMs was carried out using the graphical LASSO (least absolute shrinkage and selection operator) algorithm (Friedman et al., 2008). This regularization process shrinks all edges, and the edges with small partial correlation shrink to zero, so a more interpretative and stable network can be obtained (Epskamp & Fried, 2018; Friedman et al., 2008). Meanwhile, the hyperparameter of the Extended Bayesian information criterion (EBIC) was set to be 0.5, which achieves a good balance between the sensitivity and specificity of extracting real edges (Epskamp & Fried, 2018; Foygel & Drton, 2010). The visualization of the network is derived from the fruchterman-reingold algorithm (Fruchterman & Reingold, 1991). This network was constructed and visualized using the R-package qgraph (Epskamp et al., 2012).

In the present network, the node expected influence was calculated via the R-package qgraph (Epskamp et al., 2012). The expected influence is defined as the sum of the value of all edges connecting to a given node. The higher the expected influence, the more important the node is in the network. Moreover, the node bridge expected influence was computed by the R-package networktools (Jones et al., 2021). Bridge expected influence is defined as the sum of the value of all edges connecting a given node with nodes in the other community. Higher bridge expected influence values mean a greater extent for increasing the risk of contagion to other communities (Jones et al., 2021). In the present network, we divided nodes into two communities in advance: one community was depression, which included nine symptoms of depression, and the other was social anxiety, consisting of 10 social anxiety symptoms.

We examined the robustness of the network using the R-package bootnet (Epskamp, Borsboom et al., 2018). The accuracy of edge weights was evaluated by calculating 95% confidence interval using a non-parametric bootstrap approach (2000 bootstrap samples) and computing bootstrapped difference test for edge weights. The stabilities of node expected influences and node bridge expected influences were evaluated by computing correlation stability (CS)-coefficient, using a case-dropping bootstrap approach (2,000 bootstrap samples) and computing bootstrapped difference test for them. The value of the CS-coefficient should not be below 0.25 and preferably should be above 0.5 (Epskamp, Borsboom et al., 2018).

Directed acyclic graph (DAG)

DAG can encode the conditional independence relationships between the nodes in cross-sectional data and identify admissible causal relationships among them (Briganti et al., 2022). Thus, DAG is a directed network, which can reflect the predicted direction of the probabilistic dependence between nodes (McNally, 2016; Moffa et al., 2017). R-package bnlearn and Bayesian hill-climbing algorithm were applied to calculate the DAG for symptoms of depression and social anxiety (Scutari, 2010). The algorithm continuously adds, deletes and reverses the edges’ direction until the best fitting is obtained according to the Bayesian information criterion (BIC) (McNally, Heeren et al., 2017). This includes an iterative process of randomly restarting this procedure with different possible edges connecting different node pairs, perturbing the structure, and applying 50 different random restarts to avoid local maxima. Following previous studies (Bernstein et al., 2017; Blanchard et al., 2021; Heeren et al., 2020; McNally, Mair et al., 2017), we performed 100 perturbations for each restart.

To ensure the stability of the DAG, we used the bootstrap approach (10,000 bootstrap samples with replacement) to obtain the final DAG structure, including a two-step process (e.g. Bernstein et al., 2017; Blanchard et al., 2021; Heeren et al., 2020; McNally, Mair et al., 2017). First, we determined how frequently a given edge appeared in the 10,000 bootstrapped DAGs. We then used the optimal cut-point approach of Scutari and Nagarajan (2013) for retaining edges, which obtained DAG with both high sensitivity and specificity. Second, when determining the direction of each edge, if there were edges in the same direction in 51% or more of the 10,000 bootstrapped DAGs, the directional edges would be represented in the final DAG.

To make DAG easier to interpret, two visualizations of the resulting outputs were generated, as recommended by previous studies (Bernstein et al., 2017; Blanchard et al., 2021; Heeren et al., 2020). In the first visualization, edge thickness represents the change in the BIC values when that edge is removed from the DAG. In the second one, edge thickness represents directional probabilities.

Results

Descriptive statistics

A total of 3,107 left-behind children were included in the final analysis after the lifting of the lockdown, including 1,615 boys, 1,477 girls, and 15 students not reporting gender in the questionnaires. The mean age was 13.33 years (SD = 1.84). According to the results of the survey, the prevalence of depression among the left-behind children was 19.57% (based on a cut-off score of 10), the prevalence of social anxiety was 12.36% (based on an APA manual score of two) and the comorbidity was 8.98%. Further, 72.66% of the left-behind children with social anxiety also had depression symptoms and 45.89% of them with depression had social anxiety, indicating a very high co-morbidity. More information about the participants can be found in Table 1. All symptom items and codes are shown in Table 2.

Table 1.

Participant demographics and descriptive statistics of symptoms (n = 3,107).

M (SD)/n (%)
Age 13.33 (1.84)
Gender
 Male 1,615 (51.98)
 Female 1,477 (47.54)
 Missed 15 (0.48)
Social anxiety severity (Average total score)
 0 1,752 (56.39)
 1 971 (31.25)
 2 252 (8.11)
 3 106 (3.41)
 4 26 (0.84)
Depression severity (Total raw score)
 0–4 1,649 (53.07)
 5–9 850 (27.36)
 10–14 325 (10.46)
 15–19 170 (5.47)
 20–27 113 (3.64)
Comorbidity of social anxiety and depression
Cut-off is 2 for social anxiety and 10 for depression 279 (8.98)
Open in a new tab

Note. The SD is shown in italics to distinguish it from %s in the other brackets.

Table 2.

Abbreviation, Mean, Standard Deviation of depression, and social anxiety symptoms.

Symptoms Abbreviation Mean SD
Depression symptoms
Feeling down, depressed, irritable, or hopeless D1: Sadness 0.69 0.82
Little interest or pleasure in doing things D2: Anhedonia 0.79 0.87
Trouble falling asleep, staying asleep, or sleeping too much D3: Sleeping problems 0.64 0.91
Poor appetite, weight loss, or overeating D4: Appetite 0.49 0.81
Feeling tired, or having little energy D5: Fatigue 0.80 0.91
Feeling bad about yourself, or that you are a failure or have let yourself or your family down D6: Failure 0.83 0.97
Trouble concentrating on things like school work, reading, or watching TV D7: Concentration 0.73 0.92
Moving or speaking so slowly that other people could have noticed D8: Motor 0.34 0.70
Thoughts that you would be better off dead or of hurting yourself in some way D9: Suicidal ideation 0.37 0.75
Social Anxiety Symptoms
Felt moments of sudden terror, fear, or fright in social situations SA1: Social fright 0.68 1.02
Felt anxious, worried, or nervous about social situations SA2: Social tension 0.77 1.06
Had thoughts of being rejected, humiliated, embarrassed, ridiculed, or offending others SA3: Humiliation 0.61 0.95
Felt a racing heart, sweaty, trouble breathing, faint, or shaky in social situations SA4: Social bumping heart 0.48 0.89
Felt tense muscles, felt on edge or restless, or had trouble relaxing in social situations SA5: Social agitation 0.54 0.95
Avoided, or did not approach or enter, social situations SA6: Social avoidance 0.66 1.07
Left social situations early or participated only minimally (e.g. said little, avoided eye contact) SA7: Social involvement 0.75 1.13
Spent a lot of time preparing what to say or how to act in social situations SA8: Social repetition 0.63 1.03
Distracted myself to avoid thinking about social situations SA9: Distraction 0.66 1.05
Needed help to cope with social situations (e.g. alcohol or medications, superstitious objects) SA10: Seek social help 0.29 0.76
Open in a new tab

Note. SD: Standard Deviation. All symptom scores start at 0.

Graphical LASSO network

The structure of the network is shown in Figure 1. The node expected influence of the network is shown in Figure 2a. Social anxiety symptoms SA2 (Social tension) and SA6 (Social avoidance), and depression symptom D6 (Failure) demonstrated the highest expected influences, indicating that these three symptoms have the closest association in the network. The CS-coefficient of the node expected influence was 0.75, indicating that the estimation of the node expected influence is adequately stable (Figure S1 in the Supplemental Material). The bootstrapped difference test for node expected influences is shown in Figure S2 in the Supplemental Material.

Figure 1.

Figure 1.

Open in a new tab

Network structure of social anxiety and depression symptoms.

Blue edges represent positive correlations, and red edges represent negative correlations. The thickness of the edge reflects the magnitude of the correlation. The cut-off value is set to be .05. See Table 2 for label names.

Figure 2.

Figure 2.

Open in a new tab

Network centrality plot of social anxiety and depression symptoms: (a) depicts the expected influence of variables selected in the present network (z-score); (b) depicts the bridge expected influence of variables selected in the present network (z-score). The highest nodes expected influence and bridge expected influences are thickened and highlighted in red. See Table 2 for label names.

The node bridge expected influence of the network is shown in Figure 2b. Social anxiety symptom SA3 (Humiliation), and depression symptom D8 (Motor) demonstrated the highest bridge expected influences, indicating that these two symptoms are the bridge symptoms connecting the two symptom communities. Thus, these two symptoms may have the strongest ability to activate/deactivate the co-occurrence of depression and social anxiety symptoms. The CS-coefficient of the node bridge expected influence was 0.75, indicating that the estimation of the node bridge expected influence is adequately stable (Figure S3 in the Supplemental Material). The bootstrapped difference test for node bridge expected influences is shown in Figure S4 in the Supplemental Material.

Directed acyclic graph

Figure 3a shows the importance of each edge to the overall DAG structure (edge thickness represents the change in the BIC when that edge is removed from the DAG). A thicker edge means that it is more crucial to model fit. The most important edge of DAG structure was SA1-SA2 (change in the BIC value: −803.52) and SA1-SA4 (change in the BIC value: −678.48). The least important edge was D6-D3 (change in the BIC value: −7.58) and D8-SA10 (change in the BIC value: −8.80). The change in BIC values for each edge can be found in Table S1 in Supplemental Materials. Figure 3b shows the directional probability of each edge. A thicker edge means the current direction is in a greater proportion of the bootstrapped DAGs. The thickest edge connected D6 to D8 (0.971; i.e. this edge was pointing in that direction in 9,710 of the 10,000 bootstrapped DAGs). The exact directional probability for each edge in Figure 3b can be found in Table S1 in the Supplemental Material. Structurally, social anxiety symptom SA1 (Social fright) arises at the upstream of the whole DAG, directly influencing the other symptoms of social anxiety and depression (i.e. SA2 [Social tension], SA4 [Social bumping heart], and D1[Sadness]). In addition, depression symptom D1 (Sadness) arises at the upstream of the depression symptoms.

Figure 3.

Figure 3.

Open in a new tab

Directed acyclic graph (DAG) for symptoms of social anxiety and depression: (a) edge thickness represents the importance of that edge to the overall DAG structure; (b) edge thickness represents the directional probability. See Table 2 for label names.

Discussion

The left-behind children are at a high risk of developing psychological problems after the COVID-19 lockdown is lifted (Racine et al., 2022). We found that the prevalence of depression in left-behind children was 19.57%, which was much higher than that of a previous study in non-left-behind children (12.33%) (Liu et al., 2021). Moreover, the prevalence of social anxiety among left-behind children after the lifting of the lockdown was 12.36%, yet a previous study suggested that its prevalence was approximately 9% (Burstein et al., 2011). The comorbidity rate of the two diseases in the left-behind children was 8.98%, higher than that in non-left-behind children (4.5%) from a previous study (Klemanski et al., 2017). Previous evidence suggests that individuals with social anxiety disorder in clinical samples had a 30 to 70% probability of developing depression, while individuals with depression had a 15% to 27% probability of developing social anxiety in community and clinical samples (Adams et al., 2016). Our results showed higher comorbidity rates of 72.66% and 45.89%, respectively. These results suggest that the mental health status of left-behind children may be affected after lockdown removal. However, few related studies have been conducted, hence it is of great importance to further study the relationship between depression and social anxiety.

It is also important to study the network structure of social anxiety and depression symptoms in left-behind children during the pandemic. However, many network analysis studies only explored the relation between depression and anxiety symptoms in adolescents during the COVID-19 pandemic (Bai et al., 2021; Liu et al., 2022), and the relation between depression and social anxiety remains unclear in left-behind children after the lifting of the lockdown. The only two network analysis studies of social anxiety and depression both used clinical samples (Heeren et al., 2018; Langer et al., 2019). To our knowledge, this is the first study on the network structure of social anxiety and depression symptoms in left-behind children during the pandemic. Our findings would contribute to the prevention and intervention of social anxiety and depression in left-behind children after the COVID-19 lockdown is lifted.

SA2 (Social tension) and SA6 (Social avoidance) symptoms were the most prominent symptoms in the social anxiety and depression network of left-behind children after the lifting of the lockdown, consistent with previous studies (Heeren & McNally, 2018; Heeren et al., 2018; Langer et al., 2019) which reported that avoidance and fear of social scenarios are core nodes in the network. These results were in line with our expectations that social isolation may make people socially disconnected (Santini et al., 2020). They need to face more social interaction scenarios after the lockdown is lifted (Lim et al., 2022), which may lead to maladaptation. According to previous theories, problematic beliefs about the social world (e.g. “If people know I am anxious, they will think I am weak”) affect the appraisal of the social situation (e.g. appraising the social situations as more threatening than they actually are), creating anxiety and thus inducing avoidance of threatening situations and causing more generalized social anxiety (Heeren et al., 2020). Furthermore, social situations almost always evoke anxiousness or fear, and individuals typically respond in an avoidant manner (e.g. diverting attention, refusing to participate in activities, or refusing to go to school) according to the DSM-5 diagnostic criteria for social anxiety (American Psychiatric Association, 2013a). These symptoms hence play an important role in maintaining SAD.

Besides, we found that SA3 (Humiliation) and D8 (Motor) were bridge symptom nodes in the network. Fear or anxiousness in peer situations is a necessary prerequisite for social anxiety according to DSM-5, as individuals in this situation fear being judged negatively by others (Heimberg et al., 2014), causing impaired self-esteem. Previous studies have shown that left-behind children have lower self-esteem compared with non-left-behind children of the same age (Dai & Chu, 2018), and lower self-esteem is associated with a greater impact of family functioning on their prosocial behavior (Gao et al., 2019). In other words, left-behind children may show lower levels of self-esteem due to impaired family function, resulting in low prosocial behaviors, which in turn cause more pronounced social anxiety symptoms. Experiencing SA3 (Humiliation) may affect motor levels of the individual, and motor often acts as a bridge symptom in comorbidity networks, consistent with the depression and anxiety network in clinical samples (Kaiser et al., 2021). These results suggest that intervention against SA3 (Humiliation) may prevent the progression of social anxiety to depression, and similarly, intervention against D8 (Motor) may prevent the progression of depression symptoms to social anxiety.

Interestingly, social anxiety, depression and loneliness are positively correlated with each other (Danneel et al., 2020). Studies during the pandemic have shown that social isolation increases loneliness, stress, and fear, thereby increasing the risk of depression and anxiety (Courtney et al., 2020; Guessoum et al., 2020; Liu et al., 2022). But these findings may be more applicable to the general population, because left-behind children are more adaptable to the loneliness caused by social isolation for they have been separated from their parents for a long time. Previous research also found that left-behind children during the COVID-19 pandemic experienced lower levels of loneliness than non-left-behind children, yet higher levels of depression and anxiety (Wang et al., 2021). Therefore, we believe that loneliness during COVID-19 may not be the most important factor causing depression and anxiety in left-behind children. In contrast, left-behind children need to face more social situations after the lifting of the lockdown, mostly in the school environment (Morrissette, 2021), which may result in maladaptation and increase the risk of social anxiety.

To further investigate the relations between the symptoms of the two disorders, we used the DAG to explore the predictive (and potentially causal) priority of these symptoms. Although the current study is a cross-sectional survey that ignores the effect of time, the DAG is able to provide some hypotheses on admissible causal relations (Briganti et al., 2022; Tennant et al., 2021). We found that SA1 (social fright) was at the upstream of all symptoms (social anxiety and depression symptoms), implying that downstream symptoms were dependent on SA1 symptom (McNally, Mair et al., 2017). This means that if left-behind children experience SA1 (social fright), they are more likely to experience symptoms such as SA2 (Social tension), SA4 (Social bumping heart) and D1 (Sadness) than vice versa (McNally, Mair et al., 2017). These results suggest that left-behind children are more likely to develop depression symptoms while they experience social anxiety symptoms of different levels (i.e., social fright or social tension) (Nordahl et al., 2018). This is consistent with the previous findings that when social anxiety co-occurs with depression, social anxiety always precedes depression (Stein et al., 2001).

In depression symptoms, D1 (Sadness) was at the upstream of all depression symptoms. Studies have shown that 70% of individuals conceptualize unprovoked grief as a mental illness, especially depression, so we need to pay attention to unprovoked grief experienced by left-behind children, which may be a prerequisite for depression. Interestingly, we found that depression symptoms are not at the lowest level of the DAG (e.g. D1, D6, and D9 have higher levels than SA3, SA9, and SA8), and are ultimately connected to SA10 (Seek social help; the lowest level of the DAG), indicating that depression has high comorbidity with social anxiety. This implies that depressed individuals may seek external means (such as alcohol or drugs) to alleviate symptoms, which may explain why alcohol addiction is present in both social anxiety and depressed adolescents (H. Blumenthal et al.,2015; H. Blumenthal et al., 2010; Johannessen et al., 2017 ). In short, narrowing the range of symptoms caused by social anxiety may block the risk of developing other comorbidities, and special attention should be paid to these upstream symptoms, which may play a key role in preventing social anxiety and depression symptoms in left-behind children.

Strengths and limitations

For the first time to our knowledge, our study explored the network structure of social anxiety and depression symptoms network in left-behind children after the lifting of the COVID-19 lockdown. We analyzed core and bridge symptoms of social anxiety and depression via the Graphical LASSO network and the predictive (and potentially causal) priority of these symptoms via DAG. The large sample size ensures the relative reliability of the results, and in our study, the stability indicators of the final symptoms network are all excellent. The findings may provide guidance for mental illness prevention and intervention of 900 million left-behind children worldwide during the pandemic (BBC, 2015). Special attention should be paid to left-behind children in low- and medium-income countries, who are more likely to be affected by the pandemic (Barros et al., 2020; Sharpe et al., 2021).

Our study has several limitations. First, the data we used were all derived from a cross-sectional survey, therefore dynamic interpretation of symptoms was not possible and the results may be applicable to specific groups. However, the results found by the network analysis may play an important role in preventing the social anxiety and depression symptoms of left-behind children after the lifting of the COVID-19 lockdown. Nonetheless, further follow-up studies are still needed in the future to determine how the symptoms faced by left-behind children in the context of COVID-19 evolve as the pandemic develops. Second, although we used the DAG network to explore the predictive (and potentially causal) priority of these symptoms, we could not confirm the causal relationship due to the limitation of a cross-sectional design. Third, we cannot know whether the left-behind children also had a higher social anxiety level during the lockdown period because we did not obtain data during the lockdown period. Whether the level of social anxiety has changed due to isolation is worthy of further follow-up research. Fourth, it remains unclear whether our findings are applicable to groups with different cultural backgrounds, especially people from Western countries, leaving room for further research. Finally, it should be noted that our survey lasted from October 2020 to November 2020, which is the early stage of the COVID-19 pandemic. Therefore, the interpretation and application of our study results need to take the environmental changes into consideration. For example, under the current dynamic zero-COVID policy, a small-scale short-duration lockdown may be implemented. It is unclear if our results are applicable to this circumstance. We suggest that future studies pay attention to the development of the pandemic and the changes in policies and their dynamic effect on the mental health of left-behind children.

Conclusion

This study explored the network structure of social anxiety and depression symptoms for the first time in left-behind children after the lifting of the COVID-19 lockdown. In addition to the high prevalence of depression and social anxiety among left-behind children, the comorbidity rates of the two disorders are also quite high. The results of network analysis found that symptoms SA2 (Social tension) and SA6 (Social avoidance) had the highest expected influence; SA3 (Humiliation) and D8 (Motor) were bridge symptoms in the network; and SA1 (Social fright) was also identified as a key priority symptom across the DAG because it was at the upstream of all symptoms. The findings show that special attention should be paid to the prevention and intervention of social anxiety and depression symptoms in left-behind children after the lifting of the lockdown, which may help to reduce social anxiety, depression and their comorbidity.

Supplemental Material

sj-docx-1-isp-10.1177_00207640221141784 – Supplemental material for Social anxiety and depression symptoms in Chinese left-behind children after the lifting of COVID-19 lockdown: A network analysis
Click here for additional data file. (1.1MB, docx)

Supplemental material, sj-docx-1-isp-10.1177_00207640221141784 for Social anxiety and depression symptoms in Chinese left-behind children after the lifting of COVID-19 lockdown: A network analysis by Kuiliang Li, Lei Ren, Lei Zhang, Chang Liu, Mengxue Zhao, Xiaoqing Zhan, Ling Li, Xi Luo and Zhengzhi Feng in International Journal of Social Psychiatry

Acknowledgments

We thank the boys and girls for participating in the study and Nan Liu and Ting Chen for collecting data.

Footnotes

Author contribution: Data acquisition: Kuiliang Li, Mengxue Zhao, Ling Li Formal analysis: Kuiliang Li, Lei Ren Writing: Kuiliang Li, Lei Ren, Lei Zhang, Chang Liu, Xi Luo, Zhengzhi Feng Review and editing: Kuiliang Li, Lei Ren, Lei Zhang, Xiaoqing Zhan, Xi Luo, Zhengzhi Feng.

Each author signed a form for disclosure of potential conflicts of interest. None of the authors reported any financial or other conflicts of interest concerning the work described.

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the National Natural Science Foundation of China (NSFC: 81971278). The fund was used for software development in the research to provide survey results reports for survey participants and also played a role in data analysis.

Ethics statement: This study has been reviewed and approved by the Medical Ethics Committee of the Department of Medical Psychology, Army Medical University (Project No. CWS20J007). Participants were aware of the informed consent before participation in this study, were informed that the survey was anonymous, and were assured that personal information would not be disclosed.

Supplemental material: Supplemental material for this article is available online.

References

  1. Abbasi J. (2020). Prioritizing physician mental health as COVID-19 marches on. JAMA – Journal of the American Medical Association, 323, 2235–2236. 10.1001/jama.2020.5205 [DOI] [PubMed] [Google Scholar]
  2. Adams G. C., Balbuena L., Meng X., Asmundson G. J. (2016). When social anxiety and depression go together: A population study of comorbidity and associated consequences. Journal of Affective Disorders, 206, 48–54. 10.1016/j.jad.2016.07.031 [DOI] [PubMed] [Google Scholar]
  3. Adams G. C., Wrath A. J., Mondal P., Asmundson G. J. G. (2018). Depression with or without comorbid social anxiety: Is attachment the culprit? Psychiatry Research, 269, 86–92. 10.1016/j.psychres.2018.08.037 [DOI] [PubMed] [Google Scholar]
  4. Almeida M., Shrestha A. D., Stojanac D., Miller L. J. (2020). The impact of the COVID-19 pandemic on women’s mental health. Archives of Women’s Mental Health, 23, 741–748. 10.1007/s00737-020-01092-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. American Psychiatric Association. (2013. a). Diagnostic and Statistical Manual of Mental Disorders (5th ed.). Author. 10.1176/appi.books.9780890425596. [DOI] [Google Scholar]
  6. American Psychiatric Association. (2013. b). DSM-5-TR Online assessment measures. APA. Retrieved January10, 2022, from https://www.psychiatry.org/psychiatrists/practice/dsm/educational-resources/assessment-measures [Google Scholar]
  7. Bai W., Xi H. T., Zhu Q., Ji M., Zhang H., Yang B. X., Cai H., Liu R., Zhao Y. J., Chen L., Ge Z. M., Wang Z., Han L., Chen P., Liu S., Cheung T., Tang Y. L., Jackson T., An F., Xiang Y. T. (2021). Network analysis of anxiety and depressive symptoms among nursing students during the COVID-19 pandemic. Journal of Affective Disorders, 294, 753–760. 10.1016/j.jad.2021.07.072 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Barros A. J. D., Wehrmeister F. C., Ferreira L. Z., Vidaletti L. P., Hosseinpoor A. R., Victora C. G. (2020). Are the poorest poor being left behind? Estimating global inequalities in reproductive, maternal, newborn and child health. BMJ Global Health, 5(1), e002229. 10.1136/bmjgh-2019-002229 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. BBC. (2015). Who are the “left behind children” around the world? Retrieved January9, 2022, from https://www.bbc.com/news/av/world-33996631
  10. Bernstein E. E., Heeren A., McNally R. J. (2017). Unpacking rumination and executive control: A network perspective. Clinical Psychological Science, 5(5), 816–826. 10.1177/2167702617702717 [DOI] [Google Scholar]
  11. Blanchard M. A., Roskam I., Mikolajczak M., Heeren A. (2021). A network approach to parental burnout. Child Abuse & Neglect, 111, 1–12. 10.1016/j.chiabu.2020.104826 [DOI] [PubMed] [Google Scholar]
  12. Blumenthal H., Ham L. S., Cloutier R. M., Bacon A. K., Douglas M. E. (2015). Social anxiety, disengagement coping, and alcohol-use behaviors among adolescents. Anxiety, Stress and Coping, 29(4), 1–15. 10.1080/10615806.2015.1058366 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Blumenthal H., Leen-Feldner E. W., Frala J. L., Badour C. L., Ham L. S. (2010). Social Anxiety and Motives for Alcohol Use Among Adolescents. Psychology of Addictive Behaviors, 24(3), 529–534. 10.1037/a0019794 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Briganti G., Scutari M., McNally R. J. (2022). A tutorial on Bayesian networks for psychopathology researchers. Psychological Methods. Advance online publication. 10.1037/met0000479 [DOI] [PubMed]
  15. Burstein M., He J. P., Kattan G., Albano A. M., Avenevoli S., Merikangas K. R. (2011). Social phobia and subtypes in the National Comorbidity Survey-adolescent supplement: Prevalence, correlates, and comorbidity. Journal of the American Academy of Child and Adolescent Psychiatry, 50(9), 870–880. 10.1016/j.jaac.2011.06.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Chen T., Wu D., Chen H., Yan W., Yang D., Chen G., Ma K., Xu D., Yu H., Wang H., Wang T., Guo W., Chen J., Ding C., Zhang X., Huang J., Han M., Li S., Luo X. . . ., Ning Q. (2020). Clinical characteristics of 113 deceased patients with coronavirus disease 2019: Retrospective study. BMJ, 368, 1–12. 10.1136/bmj.m1091 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Courtney D., Watson P., Battaglia M., Mulsant B. H., Szatmari P. (2020). COVID-19 impacts on child and youth anxiety and depression: Challenges and opportunities. The Canadian Journal of Psychiatry, 65(10), 688–691. 10.1177/0706743720935646 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Dai Q., Chu R. X. (2018). Anxiety, happiness and self-esteem of western Chinese left-behind children. Child Abuse & Neglect, 86, 403–413. 10.1016/j.chiabu.2016.08.002 [DOI] [PubMed] [Google Scholar]
  19. Danneel S., Geukens F., Maes M., Bastin M., Bijttebier P., Colpin H., Verschueren K., Goossens L. (2020). Loneliness, social anxiety symptoms, and depressive symptoms in adolescence: Longitudinal distinctiveness and correlated change. Journal of Youth and Adolescence, 49(11), 2246–2264. 10.1007/s10964-020-01315-w [DOI] [PubMed] [Google Scholar]
  20. El Hayek S., Cheaito M. A., Nofal M., Abdelrahman D., Adra A., Al Shamli S., AlHarthi M., AlNuaimi N., Aroui C., Bensid L., Emberish A. M., Larnaout A., Radwan A., Slaih M., Al Sinawi H. (2020). Geriatric Mental Health and COVID-19: An Eye-opener to the situation of the Arab countries in the Middle East and North Africa Region. American Journal of Geriatric Psychiatry, 28, 1058–1069. 10.1016/j.jagp.2020.05.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Epskamp S., Borsboom D., Fried E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195–212. 10.3758/s13428-017-0862-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Epskamp S., Cramer A. O. J., Waldorp L. J., Schmittmann V. D., Borsboom D. (2012). Qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48(4), 1–18. 10.18637/jss.v048.i04 [DOI] [Google Scholar]
  23. Epskamp S., Fried E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23(4), 617–634. 10.1037/met0000167 [DOI] [PubMed] [Google Scholar]
  24. Epskamp S., Waldorp L. J., Mõttus R., Borsboom D. (2018). The Gaussian graphical model in cross-sectional and time-series data. Multivariate Behavioral Research, 53(4), 453–480. 10.1080/00273171.2018.1454823 [DOI] [PubMed] [Google Scholar]
  25. Fellmeth G., Rose-Clarke K., Zhao C., Busert L. K., Zheng Y., Massazza A., Sonmez H., Eder B., Blewitt A., Lertgrai W., Orcutt M., Ricci K., Mohamed-Ahmed O., Burns R., Knipe D., Hargreaves S., Hesketh T., Opondo C., Devakumar D. (2018). Health impacts of parental migration on left-behind children and adolescents: A systematic review and meta-analysis. Lancet, 392(10164), 2567–2582. 10.1016/S0140-6736(18)32558-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Foygel R., Drton M. (2010). Extended Bayesian Information Criteria for Gaussian Graphical Models. Advances in Neural Information Processing Systems, 23, 604–612. [Google Scholar]
  27. Friedman J., Hastie T., Tibshirani R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3), 432–441. 10.1093/biostatistics/kxm045 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Fruchterman T. M. J., Reingold E. M. (1991). Graph drawing by force-directed placement. Software Practice and Experience, 21(11), 1129–1164. 10.1002/spe.4380211102 [DOI] [Google Scholar]
  29. Gao F., Yao Y., Yao C., Xiong Y., Ma H., Liu H. (2019). The status of pro-social tendency of left-behind adolescents in China: How family function and self-esteem affect pro-social tendencies. Frontiers in Psychology, 10, 1–13. 10.3389/fpsyg.2019.01202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Guessoum S. B., Lachal J., Radjack R., Carretier E., Minassian S., Benoit L., Moro M. R. (2020). Adolescent psychiatric disorders during the COVID-19 pandemic and lockdown. Psychiatry Research, 291, 113264. 10.1016/j.psychres.2020.113264 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Heeren A., Bernstein E. E., McNally R. J. (2020). Bridging maladaptive social self-beliefs and social anxiety: A network perspective. Journal of Anxiety Disorders, 74, 1–9. 10.1016/j.janxdis.2020.102267 [DOI] [PubMed] [Google Scholar]
  32. Heeren A., Jones P. J., McNally R. J. (2018). Mapping network connectivity among symptoms of social anxiety and comorbid depression in people with social anxiety disorder. Journal of Affective Disorders, 228, 75–82. 10.1016/j.jad.2017.12.003 [DOI] [PubMed] [Google Scholar]
  33. Heeren A., McNally R. J. (2018). Social Anxiety Disorder as a densely interconnected network of fear and avoidance for social situations. Cognitive Therapy and Research, 42(1), 103–113. 10.1007/s10608-017-9876-3 [DOI] [Google Scholar]
  34. Heimberg R. G., Hofmann S. G., Liebowitz M. R., Schneier F. R., Smits J. A., Stein M. B., Hinton D. E., Craske M. G. (2014). Social anxiety disorder in DSM-5. Depression and Anxiety, 31, 472–479. 10.1002/da.22231 [DOI] [PubMed] [Google Scholar]
  35. Holmes E. A., O’Connor R. C., Perry V. H., Tracey I., Wessely S., Arseneault L., Ballard C., Christensen H., Cohen Silver R., Everall I., Ford T., John A., Kabir T., King K., Madan I., Michie S., Przybylski A. K., Shafran R., Sweeney A. . . ., Bullmore E. (2020). Multidisciplinary research priorities for the COVID-19 pandemic: A call for action for mental health science. The Lancet Psychiatry, 7, 547–560. 10.1016/s2215-0366(20)30168-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Hou T., Mao X., Shao X., Liu F., Dong W., Cai W. (2021). Suicidality and its associated factors among students in rural China during COVID-19 pandemic: A Comparative Study of left-behind and Non-Left-Behind Children. Frontiers in Psychiatry, 12, 708305. 10.3389/fpsyt.2021.708305 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Johannessen E. L., Andersson H. W., Bjørngaard J. H., Pape K. (2017). Anxiety and depression symptoms and alcohol use among adolescents–across sectional study of Norwegian secondary school students. BMC Public Health, 17(1), 1–9. 10.1186/s12889-017-4389-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Jones P. J., Ma R., McNally R. J. (2021). Bridge Centrality: A network approach to understanding comorbidity. Multivariate Behavioral Research, 56(2), 353–367. 10.1080/00273171.2019.1614898 [DOI] [PubMed] [Google Scholar]
  39. Kaiser T., Herzog P., Voderholzer U., Brakemeier E. (2021). Unraveling the comorbidity of depression and anxiety in a large inpatient sample: Network analysis to examine bridge symptoms. Depression and Anxiety, 38(3), 307–317. 10.1002/da.23136 [DOI] [PubMed] [Google Scholar]
  40. Klemanski D. H., Curtiss J., McLaughlin K. A., Nolen-Hoeksema S. (2017). Emotion Regulation and the transdiagnostic role of repetitive negative thinking in adolescents with social anxiety and depression. Cognitive Therapy and Research, 41(2), 206–219. 10.1007/s10608-016-9817-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Langer J. K., Tonge N. A., Piccirillo M., Rodebaugh T. L., Thompson R. J., Gotlib I. H. (2019). Symptoms of social anxiety disorder and major depressive disorder: A network perspective. Journal of Affective Disorders, 243, 531–538. 10.1016/j.jad.2018.09.078 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Lim M. H., Qualter P., Thurston L., Eres R., Hennessey A., Holt-Lunstad J., Lambert G. W. (2022). A Global Longitudinal Study examining social restrictions severity on loneliness, social anxiety, and depression. Frontiers in Psychiatry, 13, 818030. 10.3389/fpsyt.2022.818030 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Liu R., Chen X., Qi H., Feng Y., Su Z., Cheung T., Jackson T., Lei H., Zhang L., Xiang Y. T. (2022). Network analysis of depressive and anxiety symptoms in adolescents during and after the COVID-19 outbreak peak. Journal of Affective Disorders, 301, 463–471. 10.1016/j.jad.2021.12.137 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Liu Y., Yue S., Hu X., Zhu J., Wu Z., Wang J., Wu Y. (2021). Associations between feelings/behaviors during COVID-19 pandemic lockdown and depression/anxiety after lockdown in a sample of Chinese children and adolescents. Journal of Affective Disorders, 284, 98–103. 10.1016/j.jad.2021.02.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Loades M. E., Chatburn E., Higson-Sweeney N., Reynolds S., Shafran R., Brigden A., Linney C., McManus M. N., Borwick C., Crawley E. (2020). Rapid systematic review: The impact of social isolation and loneliness on the mental health of children and adolescents in the context of COVID-19. Journal of the American Academy of Child and Adolescent Psychiatry, 59(11), 1218–1239.e3. 10.1016/j.jaac.2020.05.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. McNally R. J. (2016). Can network analysis transform psychopathology? Behaviour Research and Therapy, 86, 95–104. 10.1016/j.brat.2016.06.006 [DOI] [PubMed] [Google Scholar]
  47. McNally R. J., Heeren A., Robinaugh D. J. (2017). A Bayesian network analysis of posttraumatic stress disorder symptoms in adults reporting childhood sexual abuse. European Journal of Psychotraumatology, 8, 1341276. 10.1080/20008198.2017.1341276 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. McNally R. J., Mair P., Mugno B. L., Riemann B. C. (2017). Co-morbid obsessive-compulsive disorder and depression: A Bayesian network approach. Psychological Medicine, 47(7), 1204–1214. 10.1017/S0033291716003287 [DOI] [PubMed] [Google Scholar]
  49. Meilihua Health Systems. (2019). DSM-5 Assessment Scale (Chinese version). Retrieved August30, 2020, from https://www.mhealthu.com/index.php/list_liangbiao/120/267?page=2
  50. Moffa G., Catone G., Kuipers J., Kuipers E., Freeman D., Marwaha S., Lennox B. R., Broome M. R., Bebbington P. (2017). Using directed acyclic graphs in epidemiological research in psychosis: An Analysis of the role of bullying in psychosis. Schizophrenia Bulletin, 43(6), 1273–1279. 10.1093/schbul/sbx013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Morrissette M. (2021). School closures and social anxiety during the COVID-19 Pandemic. Journal of the American Academy of Child and Adolescent Psychiatry, 60, 6–7. 10.1016/j.jaac.2020.08.436 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Nordahl H., Nordahl H. M., Vogel P. A., Wells A. (2018). Explaining depression symptoms in patients with social anxiety disorder: Do maladaptive metacognitive beliefs play a role? Clinical Psychology & Psychotherapy, 25(3), 457–464. 10.1002/cpp.2181 [DOI] [PubMed] [Google Scholar]
  53. Racine N., Korczak D. J., Madigan S. (2022). Evidence suggests children are being left behind in COVID-19 mental health research. European Child & Adolescent Psychiatry, 31(9), 1479–1480. 10.1007/s00787-020-01672-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Ren L., Wang Y., Wu L., Wei Z., Cui L. B., Wei X., Hu X., Peng J., Jin Y., Li F., Yang Q., Liu X. (2021). Network structure of depression and anxiety symptoms in Chinese female nursing students. BMC Psychiatry, 21(1), 279–312. 10.1186/s12888-021-03276-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Samantaray N. N., Kar N., Mishra S. R. (2022). A follow-up study on treatment effects of cognitive-behavioral therapy on social anxiety disorder: Impact of COVID-19 fear during post-lockdown period. Psychiatry Research, 310, 114439. 10.1016/j.psychres.2022.114439 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Santini Z. I., Jose P. E., York Cornwell E., Koyanagi A., Nielsen L., Hinrichsen C., Meilstrup C., Madsen K. R., Koushede V. (2020). Social disconnectedness, perceived isolation, and symptoms of depression and anxiety among older Americans (NSHAP): A longitudinal mediation analysis. The Lancet Public Health, 5(1), e62–e70. 10.1016/S2468-2667(19)30230-0 [DOI] [PubMed] [Google Scholar]
  57. Scutari M. (2010). Learning Bayesian networks with the bnlearn RPackage. Journal of Statistical Software, 35(3), 1–22. 10.18637/jss.v035.i03 [DOI] [Google Scholar]
  58. Scutari M., Nagarajan R. (2013). Identifying significant edges in graphical models of molecular networks. Artificial Intelligence in Medicine, 57(3), 207–217. 10.1016/j.artmed.2012.12.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Sharpe D., Rajabi M., Chileshe C., Joseph S. M., Sesay I., Williams J., Sait S. (2021). Mental health and wellbeing implications of the COVID-19 quarantine for disabled and disadvantaged children and young people: Evidence from a cross-cultural study in Zambia and Sierra Leone. BMC Psychology, 9(1), 1–15. 10.1186/s40359-021-00583-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Singh S., Roy D., Sinha K., Parveen S., Sharma G., Joshi G. (2020). Impact of COVID-19 and lockdown on mental health of children and adolescents: A narrative review with recommendations. Psychiatry Research, 293, 113429. 10.1016/j.psychres.2020.113429 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Stein M. B., Fuetsch M., Müller N., Höfler M., Lieb R., Wittchen H. U. (2001). Social anxiety disorder and the risk of depression: A prospective community study of adolescents and young adults. Archives of General Psychiatry, 58(3), 251. 10.1001/archpsyc.58.3.251 [DOI] [PubMed] [Google Scholar]
  62. Tennant P. W. G., Murray E. J., Arnold K. F., Berrie L., Fox M. P., Gadd S. C., Harrison W. J., Keeble C., Ranker L. R., Textor J., Tomova G. D., Gilthorpe M. S., Ellison G. T. H. (2021). Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: Review and recommendations. International Journal of Epidemiology, 50(2), 620–632. 10.1093/ije/dyaa213 [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Walton M., Murray E., Christian M. D. (2020). Mental health care for medical staff and affiliated healthcare workers during the COVID-19 pandemic. European Heart Journal Acute Cardiovascular Care, 9(3), 241–247. 10.1177/2048872620922795 [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Wang C., Pan R., Wan X., Tan Y., Xu L., Ho C. S., Ho R. C. (2020). Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China. International Journal of Environmental Research and Public Health, 17(5), 1–25. 10.3390/ijerph17051729 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Wang F., Lin L., Xu M., Li L., Lu J., Zhou X. (2019). Mental health among left-behind children in rural China in relation to parent-child communication. International Journal of Environmental Research and Public Health, 16(10), 1–10. 10.3390/ijerph16101855 [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Wang X., Hegde S., Son C., Keller B., Smith A., Sasangohar F. (2020). Investigating mental health of US college students during the COVID-19 pandemic: Cross-sectional survey study. Journal of Medical Internet Research, 22(9), e22817. 10.2196/22817 [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Wang Y., Liu W., Wang W., Lin S., Lin D., Wang H. (2021). Left-behind children’s social adjustment and relationship with parental coping with children’s negative emotions during the COVID-19 pandemic in China. International Journal of Psychology, 56(4), 512–521. 10.1002/ijop.12754 [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. WHO. (2022, November5). Mental health. Retrieved March5, 2022, from Word Health Organization; website: https://www.who.int/health-topics/mental-health#tab=tab_2 [Google Scholar]
  69. Yuan P., Wang L. (2016). Migrant workers: China boom leaves children behind. Nature, 529(7584), 25. 10.1038/529025a [DOI] [PubMed] [Google Scholar]
  70. Zhu F. C., Li Y. H., Guan X. H., Hou L. H., Wang W. J., Li J. X., Wu S. P., Wang B. S., Wang Z., Wang L., Jia S. Y., Jiang H. D., Wang L., Jiang T., Hu Y., Gou J. B., Xu S. B., Xu J. J., Wang X. W., . . ., Chen W. (2020). Safety, tolerability, and immunogenicity of a recombinant adenovirus type-5 vectored COVID-19 vaccine: A dose-escalation, open-label, non-randomised, first-in-human trial. Lancet, 395(10240), 1845–1854. 10.1016/s0140-6736(20)31208-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

sj-docx-1-isp-10.1177_00207640221141784 – Supplemental material for Social anxiety and depression symptoms in Chinese left-behind children after the lifting of COVID-19 lockdown: A network analysis
Click here for additional data file. (1.1MB, docx)

Supplemental material, sj-docx-1-isp-10.1177_00207640221141784 for Social anxiety and depression symptoms in Chinese left-behind children after the lifting of COVID-19 lockdown: A network analysis by Kuiliang Li, Lei Ren, Lei Zhang, Chang Liu, Mengxue Zhao, Xiaoqing Zhan, Ling Li, Xi Luo and Zhengzhi Feng in International Journal of Social Psychiatry

Articles from The International Journal of Social Psychiatry are provided here courtesy of SAGE Publications

RESOURCES