Using network analysis to explore the key bridge symptoms between posttraumatic stress symptoms and posttraumatic growth among survivors 10 years after the Wenchuan earthquake in China

Interaction between posttraumatic stress symptoms and posttraumatic growth among adolescents who experience an earthquake: A repeated longitudinal study

For adolescents who experience an earthquake, posttraumatic stress symptoms (PTSSs) and posttraumatic growth (PTG) often co-occur. However, no study has yet examined how the interaction between them changes from the short term to the long term after an earthquake. This study conducted six surveys among local adolescents across three waves after the Wenchuan earthquake, and a directed network of PTSS and PTG co-occurrence was constructed for each wave. It was found that the bridge nodes between PTSSs and PTG were different for each wave. The connection between PTSSs and PTG became loose over time. The incubation effect of PTSSs on PTG was sustained until the middle term but was not observed in the long term. The suppression effect of PTSSs on PTG was only observed in the short term. PTG not only alleviated PTSSs but also exacerbated PTSSs. Finally, the effect of PTSSs on PTG was much stronger than that of PTG on PTSSs. This study suggests that efforts should be made to alleviate specific PTSSs or facilitate specific PTG elements among adolescents for different terms after an earthquake, and PTG is more likely to be an outcome of trauma rather than a strategy for coping with trauma.

Network analysis of posttraumatic stress and posttraumatic growth symptoms among women in subsequent pregnancies following pregnancy loss

BACKGROUND

Pregnant women who have undergone pregnancy loss often display both posttraumatic stress (PTS) and posttraumatic growth (PTG). However, the precise relationship and structure of symptomatic levels of PTS and PTG have not been well understood. This study aimed to assess the associations between PTS and PTG symptoms in women during subsequent pregnancies following a previous pregnancy loss.

METHODS

A total of 406 pregnant women with a history of pregnancy loss were included in this study. The Impact of Events Scale-6 (IES-6) and the Posttraumatic Growth Inventory Short Form (PTGI-SF) were used to assess symptoms of PTS and PTG, respectively. The Graphical Gaussian Model was employed to estimate the network model. Central symptoms and bridge symptoms were identified based on "expected influence" and "bridge expected influence" indices, respectively. The stability and accuracy of the network were examined using the case-dropping procedure and nonparametric bootstrapped procedure.

RESULTS

The network analysis identified PTG3 ("Ability to do better things") as the most central symptom, followed by PTS3 ("Avoidance of thoughts") and PTG6 ("New path for life") in the sample. Additionally, PTS3 ("Avoidance of thoughts") and PTG9 ("Perception of greater personal strength") were bridge symptoms linking PTS and PTG clusters. The network structure was robust in stability and accuracy tests.

CONCLUSIONS

Interventions targeting the central symptoms identified, along with key bridge symptoms, have the potential to alleviate the severity of PTS experienced by women with a history of pregnancy loss and promote their personal growth.

Dynamic structure of posttraumatic growth among victims of the 2021 Henan floods: A 6-month, three-wave longitudinal study

Posttraumatic growth (PTG) following traumatic events is a dynamic and transformational process. However, its dynamic structure is currently unknown. The study aimed to estimate the dynamic structure of PTG at the nuance level based on PTG measurement items using network analysis. A three-wave longitudinal study was conducted from July 20, 2021, to January 30, 2022, among the victims experiencing the 2021 Henan floods. The final sample (n = 297) completed reports of PTG after 0, 3, and 6 months of the disaster. We employed the graphical vector autoregressive model approach to estimate extended network models. Contemporaneous network results revealed strong positive associations between domains of PTG in the same measurement window, especially between new possibilities and personal strength. Moreover, temporal network results-the internal interplays among PTG items across measurement windows-revealed that the domain of relating to others plays a central role in the dynamics of PTG. Although other domains predicted an increase in relating to others, relating to others inhibited the development of other domains, especially new possibilities and personal strength. Our study identifies the culture-specific process of PTG and provides empirical evidence on the explanatory models of PTG and the Janus-Face model of PTG.

Network Analysis of Health-related Behaviors, Insomnia, and Depression Among Urban Left-behind Adolescents in China

Mental health of urban left-behind adolescents (LBA) is a public issue of growing concern. This study aims to examine the symptom level associations among multiple health-related behaviors, insomnia, and depression in urban LBA. Data on a sample of urban LBA aged 11-19 (N = 3,601) from the Adolescent Mental Health Survey in Shenzhen, China, were used. Health-related behaviors (i.e., Internet use, physical inactivity, social jetlag, smoking, and alcohol consumption), insomnia, and depressive symptoms were assessed using a self-administered questionnaire. Graphical Gaussian Model (GGM) was used to describe key bridging nodes in an undirected network. Directed Acyclic Graph (DAG) was used to construct a directed network and estimate the most likely causal associations among behaviors/symptoms. In the undirected network, Internet use was identified as the key bridging node most strongly associated with insomnia and depression. Two other key bridging nodes include difficulty initiating sleep and appetite change. In the directed network, anhedonia emerged as the most pivotal symptom, which could cause insomnia symptoms and behavioral changes, either directly, or through triggering other depressive symptoms, such as low energy and appetite change. These findings have implications for understanding the occurrence and maintenance process of health-related behaviors, insomnia, and depression in urban LBA. In practice, Internet use should be considered a priority in targeting multiple health behavior interventions. Meanwhile, early screening and treatment for anhedonia are of great significance as well.

A network analysis of the long-term quality of life and mental distress of COVID-19 survivors 1 year after hospital discharge

Objectives

COVID-19 survivors suffer from persistent mental distress and impaired quality of life (QOL) after recovery from the infection. However, the symptom-symptom interaction between these psychological variables remained unexplored. The present study aimed to determine the symptom network of mental distress (depression, anxiety, sleep disturbance, fatigue, and post-traumatic stress disorder) and their association with QOL among 535 COVID-19 survivors 1 year after hospital discharge.

Methods

9-item Patient Health Questionnaire, 7-item Generalized Anxiety Disorder Scale, Chalder fatigue scale, Impact of Event Scale-Revised, Pittsburgh Sleep Quality Index, and 36-Item Short-Form Health Survey were applied to measure depression, anxiety, fatigue, PTSD, sleep disturbances, and QOL, respectively. Two networks were estimated using Gaussian graphical model. Network 1 consisted of mental symptoms to determine the central and bridge symptoms. Network 2 additionally included QOL to determine which mental symptoms were mostly related to QOL.

Results

60% of the COVID-19 survivors experienced mental distress 1 year after hospital discharge. Uncontrollable and excessive worry, psychomotor symptoms, intrusion, and daytime dysfunction were the most central symptoms. Daytime dysfunction and fatigue (especially mental fatigue and loss of energy) served as the bridge symptoms across the mental distress network and exhibited the most substantial association with QOL.

Conclusion

Our study demonstrated several key symptoms that played a vital role in mental distress and QOL among COVID-19 survivors. Prompt screening and targeted interventions for these symptoms might hold great promise in preventing mental distress and improving QOL in COVID-19 survivors.

Network structure of insomnia and depressive symptoms among shift workers in China

A bidirectional relationship between insomnia and depression has been observed. However, few studies have used network analysis to explore the interaction patterns in that association at the symptom level. This study aimed to estimate network structures of insomnia and depressive symptoms among shift workers, as well as to compare the differences in network properties between individuals without and with insomnia symptoms and/or at risk of depression. A total of 1883 shift workers were included in our study. Insomnia symptoms were evaluated by three items based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, and depressive symptoms were assessed by the Center for Epidemiologic Studies Depression Scale. Network analyses were used for the statistical analysis. "Difficulty initiating sleep", "Hard to get started", and "Depressed mood" with higher expected influence (EI) values were identified as the most central symptoms within the insomnia-depressive networks among shift workers. The significant differences between individuals without and with insomnia symptoms and/or at risk of depression were observed in symptoms of "Difficulty initiating sleep" and "Hard to get started". "Depressed mood", "Difficulty initiating sleep", or "Hard to get started" were the most key symptoms that trigger and sustain the structure of insomnia and depressive symptom among shift workers. Hence, timely intervention for the above three symptoms in future research or clinical practice (e.g., cognitive behavioral therapy for insomnia) may be crucial in alleviating insomnia and depressive symptoms among shift workers.