Mapping post-traumatic stress disorder symptoms and quality of life among residents of Wuhan, China after the COVID-19 outbreak: A network perspective

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.

Intrarelationships between suboptimal health status and anxiety symptoms: A network analysis

BACKGROUND

Suboptimal health status is a global public health concern of worldwide academic interest, which is an intermediate health status between health and illness. The purpose of the survey is to investigate the relationship between anxiety statuses and suboptimal health status and to identify the central symptoms and bridge symptoms.

METHODS

This study recruited 26,010 participants aged <60 from a cross-sectional study in China in 2022. General Anxiety Disorder-7 (GAD-7) and suboptimal health status short form (SHSQ-9) were used to quantify the levels of anxiety and suboptimal health symptoms, respectively. The network analysis method by the R program was used to judge the central and bridge symptoms. The Network Comparison Test (NCT) was used to investigate the network differences by gender, place of residence, and age in the population.

RESULTS

In this survey, the prevalence of anxiety symptoms, SHS, and comorbidities was 50.7 %, 54.8 %, and 38.5 %, respectively. "Decreased responsiveness", "Shortness of breath", "Uncontrollable worry" were the nodes with the highest expected influence. "Irritable", "Exhausted" were the two symptom nodes with the highest expected bridge influence in the network. There were significant differences in network structure among different subgroup networks.

LIMITATIONS

Unable to study the causal relationship and dynamic changes among variables. Anxiety and sub-health were self-rated and may be limited by memory bias.

CONCLUSIONS

Interventions targeting central symptoms and bridge nodes may be expected to improve suboptimal health status and anxiety in Chinese residents. Researchers can build symptom networks for different populations to capture symptom relationships.

Unveiling Coronasomnia: Pandemic Stress and Sleep Problems During the COVID-19 Outbreak

The COVID-19 pandemic posed an unprecedented challenge to public well-being, necessitating an examination of its health impact. This review discusses the relationship between pandemic-induced stressors and individual sleep patterns and quality. The pandemic stressors include lockdown or physical distancing measures, direct virus exposure, and the dissemination of misinformation and disinformation. The pandemic led to delayed sleep-wake cycles, except for healthcare professionals, and worsened sleep quality. The prevalence of insomnia was higher for women due to pre-existing conditions and susceptibility stressors such as lockdown stress and family responsibilities. Healthcare professionals, who experienced worsened work conditions during the pandemic, reported higher rates of insomnia and sleep difficulties due to infection anxiety and post-traumatic stress from direct virus exposure. For the general population, stress stemmed from social isolation under lockdown and overwhelming false information available online, resulting in sleep problems. Taken together, the findings highlight the importance of promoting social interactions, providing psychological support services, and caution in navigating health information. In summary, this review underscores the need for individual- and group-centered approaches in ongoing research and interventions to address pandemic-related stress and sleep issues during COVID-19.

Comparison of subjective quality of life domains in schizophrenia, mood, and anxiety disorders; an individual patient data meta-analysis

This study sought to compare satisfaction with different subjective quality of life domains across individuals with three different mental health disorders. Samples from four separate studies were combined to conduct a one-step individual patient data meta-analysis. 5329 individuals with either a schizophrenia (n = 1839), mood (n = 1650), or anxiety disorder (n = 1840) were included. Mean satisfaction ratings for each life domain were compared across disorders. Associations between satisfaction ratings and personal characteristics were investigated using multivariable mixed effect models. Results showed that individuals with an anxiety disorder had the widest range of scores and reported lower satisfaction in most domains compared to those with a schizophrenia or mood disorder. Individuals with a schizophrenia disorder rated domains to do with 'others', such as relationships with family and sex life, as lower than individuals with a mood or anxiety disorder. Satisfaction ratings were often more impacted by personal characteristics, such as employment status, than by diagnostic category. These results demonstrate that specific life areas are impacted differently in the three mental health disorders studied. However, further research needs to consider the impact of personal characteristics across disorders, and the subjective quality of life in individuals with anxiety disorders in particular warrants further investigation.

Prevalence and network structure of post-traumatic stress symptoms and their association with suicidality among Chinese mental health professionals immediately following the end of China's Dynamic Zero-COVID Policy: a national survey

Studies on post-traumatic stress symptoms (PTSS) among mental health professionals (MHPs) are limited, particularly since restrictions due to coronavirus disease (COVID-19) have been lifted such as the recent termination of China's Dynamic Zero-COVID Policy. The current study filled this gap by exploring the prevalence, correlates, and network structure of PTSS as well as its association with suicidality from a network analysis perspective. A cross-sectional, national survey was conducted using a convenience sampling method on MHPs between January 22 and February 10, 2023. PTSS were assessed using the Post-Traumatic Stress Disorder Checklist-Civilian version, while suicidality was assessed using standardized questions related to ideation, plans, and attempts. Univariate and multivariate analyses examined correlates of PTSS. Network analysis explored the structure of PTSS and suicidality. The centrality index of "Expected influence" was used to identify the most central symptoms in the network, reflecting the relative importance of each node in the network. The "flow" function was adopted to identify specific symptoms that were directly associated with suicidality. A total of 10,647 MHPs were included. The overall rates of PTSS and suicidality were 6.7% (n = 715; 95% CI = 6.2-7.2%) and 7.7% (n = 821; 95% CI = 7.2-8.2%), respectively. Being married (OR = 1.523; P < 0.001), quarantine experience (OR = 1.288; P < 0.001), suicidality (OR = 3.750; P < 0.001) and more severe depressive symptoms (OR = 1.229; P < 0.001) were correlates of more PTSS. Additionally, higher economic status (e.g., good vs. poor: OR = 0.324; P = 0.001) and health status (e.g., good vs. poor: OR = 0.456; P < 0.001) were correlates of reduced PTSS. PCL6 ("Avoiding thoughts"; EI = 1.189), PCL7 ("Avoiding reminders"; EI = 1.157), and PCL11 ("Feeling emotionally numb"; EI = 1.074) had the highest centrality, while PCL12 ("Negative belief"), PCL 16 ("Hypervigilance") and PCL 14 ("Irritability") had the strongest direct, positive associations with suicidality. A high prevalence of lingering PTSS was found among MHPs immediately after China's "Dynamic Zero-COVID Policy" was terminated. Avoidance and hyper-arousal symptoms should be monitored among at-risk MHPs after the COVID-19 pandemic and serve as potential targets for the prevention and treatment of PTSS in this population.

Prevalence of insomnia and its association with quality of life in caregivers of psychiatric inpatients during the COVID-19 pandemic: a network analysis

BACKGROUND

Studies on sleep problems among caregivers of psychiatric patients, especially during the COVID-19 pandemic, are limited. This study examined the prevalence and correlates of insomnia symptoms (insomnia hereafter) among caregivers of psychiatric inpatients during the COVID-19 pandemic as well as the association with quality of life (QoL) from a network analysis perspective.

METHODS

A multi-center cross-sectional study was conducted on caregivers of inpatients across seven tertiary psychiatric hospitals and psychiatric units of general hospitals. Network analysis explored the structure of insomnia using the R program. The centrality index of "Expected influence" was used to identify central symptoms in the network, and the "flow" function was adopted to identify specific symptoms that were directly associated with QoL.

RESULTS

A total of 1,101 caregivers were included. The overall prevalence of insomnia was 18.9% (n = 208; 95% CI = 16.7-21.3%). Severe depressive (OR = 1.185; P < 0.001) and anxiety symptoms (OR = 1.099; P = 0.003), and severe fatigue (OR = 1.320; P < 0.001) were associated with more severe insomnia. The most central nodes included ISI2 ("Sleep maintenance"), ISI7 ("Distress caused by the sleep difficulties") and ISI1 ("Severity of sleep onset"), while "Sleep dissatisfaction" (ISI4), "Distress caused by the sleep difficulties" (ISI7) and "Interference with daytime functioning" (ISI5) had the strongest negative associations with QoL.

CONCLUSION

The insomnia prevalence was high among caregivers of psychiatric inpatients during the COVID-19 pandemic, particularly in those with depression, anxiety and fatigue. Considering the negative impact of insomnia on QoL, effective interventions that address insomnia and alteration of sleep dissatisfaction should be developed.

Predicting depression and anxiety of Chinese population during COVID-19 in psychological evaluation data by XGBoost

BACKGROUND

Due to the onset of sudden stress, COVID-19 has greatly impacted the incidence of depression and anxiety. However, challenges still exist in identifying high-risk groups for depression and anxiety during COVID-19. Studies have identified how resilience and social support can be employed as effective predictors of depression and anxiety. This study aims to select the best combination of variables from measures of resilience, social support, and alexithymia for predicting depression and anxiety.

METHODS

The eXtreme Gradient Boosting (XGBoost¹) model was applied to a dataset including data on 29,841 participants that was collected during the COVID-19 pandemic. Discriminant analyses on groups of participants with depression (DE²), anxiety (AN³), comorbid depression and anxiety (DA⁴), and healthy controls (HC⁵), were performed. All variables were selected according to their importance for classification. Further, analyses were performed with selected features to determine the best variable combination.

RESULTS

The mean accuracies achieved by three classification tasks, DE vs HC, AN vs HC, and DA vs HC, were 0.78, 0.77, and 0.89. Further, the combination of 19 selected features almost exhibited the same performance as all 56 variables (accuracies = 0.75, 0.75, and 0.86).

CONCLUSIONS

Resilience, social support, and some demographic data can accurately distinguish DE, AN, and DA from HC. The results can be used to inform screening practices for depression and anxiety. Additionally, the model performance of a limited scale including only 19 features indicates that using a simplified scale is feasible.