Network analysis of comorbid posttraumatic stress disorder and depression in adolescents across COVID-19 epidemic and Typhoon Lekima

Depression and Anxiety among Migrant Older Adults during the COVID-19 Pandemic in China: Network Analysis of Continuous Cross-Sectional Data

Many Chinese migrant older adults are more prone to mental health problems due to their "migrant" status. During the COVID-19 pandemic, restrictions on their mobility exacerbated these conditions. Mental health is a crucial dimension of healthy aging. Network analysis offers a novel method for exploring interactions between mental health problems at the symptom level. This study employs network analysis to examine the interactions between comorbid depressive and anxiety symptoms across different stages of the COVID-19 pandemic. Surveys were conducted from September 2019 to January 2020 (T1), September 2020 to January 2021 (T2), and September 2021 onwards (T3). Depression and anxiety symptoms were measured by the Patient Health Questionnaire-9 (PHQ-9) and the Hospital Anxiety and Depression Scale-Anxiety (HADS-A). Expected Influence (EI) and Bridge Expected Influence (Bridge EI) were used to identify central and bridge symptoms in the network. Network stability and accuracy tests were performed. Among the Chinese migrant older adults, the anxiety prevalence was 18.50% at T1, 21.11% at T2, and 9.38% at T3. The prevalence of depression was 26.95% at T1, 55.44% at T2, and 60.24% at T3. The primary central symptoms included 'Afraid something will happen' (A2), 'Irritability' (A6), 'Panic' (A7), 'Feeling of worthlessness' (D6), 'Anhedonia' (D1), and 'Feeling of fear' (A5). The major bridge symptoms included 'Feeling of fear' (A5), 'Panic' (A7), 'Irritability' (A6), 'Fatigue' (D4), 'Anhedonia' (D1), and 'Depressed or sad mood' (D2). Differences in network structure were observed across the periods. The network analysis further revealed the evolving relationships between central and bridge symptoms over time, highlighting the importance of targeted intervention strategies for central and bridge symptoms of comorbid depression and anxiety at different periods.

A network analysis of the interrelationships between depression, anxiety, insomnia and quality of life among fire service recruits

Background

Research on the mental health and quality of life (hereafter QOL) among fire service recruits after the end of the COVID-19 restrictions is lacking. This study explored the network structure of depression, anxiety and insomnia, and their interconnections with QOL among fire service recruits in the post-COVID-19 era.

Methods

This cross-sectional study used a consecutive sampling of fire service recruits across China. We measured the severity of depression, anxiety and insomnia symptoms, and overall QOL using the nine-item Patient Health Questionnaire (PHQ-9), seven-item Generalized Anxiety Disorder scale (GAD-7), Insomnia Severity Index (ISI) questionnaire, and World Health Organization Quality of Life-brief version (WHOQOL-BREF), respectively. We estimated the most central symptoms using the centrality index of expected influence (EI), and the symptoms connecting depression, anxiety and insomnia symptoms using bridge EI.

Results

In total, 1,560 fire service recruits participated in the study. The prevalence of depression (PHQ-9 ≥ 5) was 15.2% (95% CI: 13.5-17.1%), while the prevalence of anxiety (GAD-7 ≥ 5) was 11.2% (95% CI: 9.6-12.8%). GAD4 ("Trouble relaxing") had the highest EI in the whole network model, followed by ISI5 ("Interference with daytime functioning") and GAD6 ("Irritability"). In contrast, PHQ4 ("Fatigue") had the highest bridge EI values in the network, followed by GAD4 ("Trouble relaxing") and ISI5 ("Interference with daytime functioning"). Additionally, ISI4 "Sleep dissatisfaction" (average edge weight = -1.335), which was the central symptom with the highest intensity value, had the strongest negative correlation with QOL.

Conclusion

Depression and anxiety were important mental health issues to address among fire service recruits in the post-COVID-19 era in China. Targeting central and bridge symptoms identified in network analysis could help address depression and anxiety among fire service recruits in the post-COVID-19 era.

Joint developmental trajectories of depression and post-traumatic stress disorder symptoms among Chinese children during COVID-19

BACKGROUND

In early 2020, Chinese children started to demonstrate severe depression and post-traumatic stress disorder symptoms (PTSS) caused by lockdown and self-isolation (measures taken at the beginning of the COVID-19 pandemic).

OBJECTIVES

Concerning the significant impact of the pandemic on children's physical and mental development, the study aimed to explore children's depression and PTSS during the COVID-19 pandemic and the protective effects of family resilience on the trajectories.

METHODS

883 children participated and completed three waves of online follow-up questionnaires. The latent growth mixture modeling (LGMM) analysis was used to explore the trajectories of children's depression and PTSS based on the individual approach.

RESULTS

Two types of depression trajectories were identified and defined as the resilient group (83.01 %) and the recovery group (16.99 %); Two types of PTSS trajectories were identified and defined as the resilient group (71.12 %) and the recovery group (28.88 %); Two types of the joint trajectories of depression and PTSS were identified and defined as the resilient group (83.47 %) and the chronic group (16.53 %). The results indicated that maintaining a positive outlook (a dimension of family resilience) was the potential predictor of PTSS trajectories.

CONCLUSION

The trajectories of depression and PTSS among Chinese children during the COVID-19 pandemic were heterogeneous, and there were similar evolving subtypes. Family resilience could be a critical protective factor for children and families.

How loneliness linked to anxiety and depression: a network analysis based on Chinese university students

BACKGROUND

There is conclusive evidence of a multifaceted and bidirectional relationship between loneliness and depression and anxiety. Nonetheless, more extensive research is needed to examine their relationships at a more granular level. This study employed a network analysis approach to identify the pathological mechanisms underpinning those relationships and to identify important bridge nodes as potential targets for intervention.

METHODS

941 University students were included in this study. The ULS-6 (the short-form UCLA Loneliness Scale) was used to assess loneliness, the PHQ-9 (Patient Health questionnaire-9) and GAD-7 (Generalized anxiety disorder 7-item) scales were used to assess the symptoms of depression and anxiety. We constructed two network structures of loneliness-anxiety and loneliness-depression and computed bridge expected influence for each symptom. In addition, we showed a flow network of "Suicide" containing symptoms of depression and loneliness.

RESULTS

All edges were positive in both networks constructed and the strongest edges were present within disorder communities. The overall connection between loneliness and depression was stronger compared to anxiety. The results demonstrated that the loneliness item "People are around me but not with me" was identified as bridge symptom in both networks. Furthermore, "Suicide" was directly connected to five symptoms of depression and four items of loneliness, with the strongest connections being between it and "Feeling of worthlessness" and "Psychomotor agitation/retardation".

CONCLUSIONS

Our findings provide a more nuanced explanation of the link between loneliness and depression and anxiety. The results identified the bridge symptom "People are around me but not with me", which had the strongest effect on enhancing symptoms of depression and anxiety. Clinical improvements based on the findings of this study and the impact of the intervention are discussed.

Comorbidity of posttraumatic stress disorder and depression among adolescents following an earthquake: A longitudinal study based on network analysis

BACKGROUND

High comorbidity between posttraumatic stress disorder (PTSD) and depression among adolescents often follows severe traumatic events. Models on the pathway to comorbidity dispute greatly and how PTSD and depression get comorbidity, remain unclear.

METHODS

A follow-up investigation was conducted of 424 adolescent survivors of the Jiuzhaigou earthquake at 12 months (T1) and 27 months (T2).

RESULTS

Contemporaneous network analysis and cross-lagged panel network analysis showed that PTSD and depression are two separate disorders with strong associations via links between dysphoric symptoms of PTSD and somatic or non-somatic symptoms of depression. However, the association weakened from T1 to T2, and internal connections between symptoms within each disorder became stronger.

LIMITATION

We only measured the comorbidity of PTSD and depression at two time points following the earthquake, which may limit the long-term applicability of our findings following trauma.

CONCLUSIONS

The findings also showed that the centrality in contemporaneous networks may indicate node connectivity rather than the influence or potential causality among nodes. These results help to elucidate the relationship between PTSD and depression and could contribute to the development of appropriate therapies.

Gender differences in the prevalence and impact factors of adolescent dissociative symptoms during the coronavirus disease 2019 pandemic

The purpose of this study was to explore the differences between the prevalence and impact factors of adolescent dissociative symptoms (ADSs) by using sex-stratification during the coronavirus disease 2019 (COVID-19) pandemic. A school-based, two-center cross-sectional study was conducted in Hangzhou City, China, between January 1, 2021 and April 30, 2022. The sample included 1,916 adolescents aged 13-18 years that were randomly selected using a multiphase, stratified, cluster sampling technique. A two-stage assessment procedure was used to find out the ADSs. We used a multivariate logistic regression analysis to assess the impact factors of ADSs during the COVID-19 pandemic. The adolescent dissociative scores (t = 4.88, P < 0.001) and positive ADSs rate (Chi-square = 15.76, P < 0.001) in males were higher than in females. Gender-stratified, stepwise multiple logistic regression analysis revealed that the conflict relationship of teacher-student [adjusted odds ratio (AOR) 1.06, 95% confidence interval (CI) 1.01-1.10], family expressiveness (AOR 0.87, 95% CI 0.78-0.98), family conflict (AOR 1.15, 95% CI 1.05-1.27), family organization (AOR 0.88, 95% CI 0.78-0.99), and family cohesion (AOR 0.87, 95% CI 0.77-0.99) were linked to ADSs only in males, while individual psychological states of somatic complaint (AOR 1.04, 95% CI 1.00-1.08) and paranoid ideation (AOR 1.09, 95% CI 1.01-1.19) were associated with female ADSs only. The ADSs seemed to be prevalent in Hangzhou City, studied during the COVID-19 pandemic. Gender differences in the prevalence and impact factors of dissociative symptoms seem to be significant among adolescents. Thus, gender-specific intervention programs against ADSs should be considered as reducing this risk.

Depression and PTSD in the aftermath of strict COVID-19 lockdowns: a cross-sectional and longitudinal network analysis

Background: Post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) are two highly comorbid psychological outcomes commonly studied in the context of stress and potential trauma. In Hubei, China, of which Wuhan is the capital, residents experienced unprecedented stringent lockdowns in the early months of 2020 when COVID-19 was first reported. The comorbidity between PTSD and MDD has been previously studied using network models, but often limited to cross-sectional data and analysis. Objectives: This study aims to examine the cross-sectional and longitudinal network structures of MDD and PTSD symptoms using both undirected and directed methods. Methods: Using three types of network analysis - cross-sectional undirected network, longitudinal undirected network, and directed acyclic graph (DAG) - we examined the interrelationships between MDD and PTSD symptoms in a sample of Hubei residents assessed in April, June, August, and October 2020. We identified the most central symptoms, the most influential bridge symptoms, and causal links among symptoms. Results: In both cross-sessional and longitudinal networks, the most central depressive symptoms included sadness and depressed mood, whereas the most central PTSD symptoms changed from irritability and hypervigilance at the first wave to difficulty concentrating and avoidance of potential reminders at later waves. Bridge symptoms showed similarities and differences between cross-sessional and longitudinal networks with irritability/anger as the most influential bridge longitudinally. The DAG found feeling blue and intrusive thoughts the gateways to the emergence of other symptoms. Conclusions: Combining cross-sectional and longitudinal analysis, this study elucidated central and bridge symptoms and potential causal pathways among PTSD and depression symptoms. Clinical implications and limitations are discussed.

How do physical and emotional abuse affect depression and problematic behaviors in adolescents? The roles of emotional regulation and anger

BACKGROUND

Rates of physical and emotional abuse are high among Chinese adolescents and elicit distinct psychopathologies. However, it remains unclear whether physical and emotional abuse relate to depression and behavior problems similarly or differently. In addition, few studies have examined if they share underlying mechanisms in adolescents.

OBJECTIVE

This study used longitudinal data to examine the mechanisms underlying the effect of physical and emotional abuse on depression and problematic behaviors through emotional regulation and anger in Chinese adolescents.

PARTICIPANTS AND SETTINGS

Participants were 1689 adolescents (with age ranging from 12 to 17 years) from junior and senior high schools in Zhejiang Province, China.

METHODS

Participants completed a childhood trauma questionnaire and an emotion regulation strategies questionnaire at time 1 (T1), and they completed an anger scale, a depression scale, and a problematic behaviors questionnaire one year later (T2). Structural equation modeling was used to examine the research hypotheses.

RESULTS

Physical abuse had direct positive effects on problematic behaviors but not on depression. However, emotional abuse had direct effects on depression and problematic behaviors, and indirect effects on both psychopathologies through expressive suppression and anger.

CONCLUSIONS

Physical and emotional abuse had distinct effects and influencing mechanisms on adolescents' externalizing and internalizing problems. Compared with physical abuse, emotional abuse elicited more harms and subsequent psychopathologies.

Research Into Mental Health Prediction of Community Workers Involved in the Prevention of COVID-19 Epidemic Based on Cloud Model

To get to know the mental status of community workers involved in the prevention of COVID-19 epidemic, provide them with mental counseling and guidance, and predict their mental health status, a cloud model for the mental health prediction of community workers involved in the prevention of COVID-19 epidemic was constructed in this paper. First, the method to collect data about mental health was determined; second, the basic definition of cloud was discussed, the digital features of cloud were analyzed, and then, the cloud theory model was constructed; third, a model to predict the mental health of community workers involved in the prevention of COVID-19 epidemic was constructed based on the cloud theory, and corresponding algorithm was designed. Finally, a community was chosen as the research object to analyze and predict its mental health status. The research results suggest that the model can effectively predict the mental health status of community workers involved in the prevention of COVID-19 epidemic.