Network Analysis of Depressive Symptoms Among Residents of Wuhan in the Later Stage of the COVID-19 Pandemic

Exposure to blue space surroundings and depressive symptoms in young Chinese adults: The mediating role of sleep

OBJECTIVE

Existing evidence suggests that the natural environment can influence mental health. However, limited research has focused on the relationship between blue space and depressive symptoms in young adults. To investigate the association between blue space surroundings and depressive symptoms in young adults in China and explore the underlying mechanisms.

METHODS

The study was conducted between September and November 2019, including 2,743 young adults from China. We assessed the exposure to blue space around participants' living environments during June, July, and August 2019 using the Modified Normalized Difference Water Index (MNDWI). Blue indexes were calculated for 300 m, 1000 m, and 3000 m circular buffer zones near residential environments. Logistic regression models were employed to explore the associations between blue space exposures (quartiles) and depressive symptoms, exploring potential mechanisms through structural equation modeling (SEM), while accounting for potential confounders. Stratification analysis was used to identify sensitive populations.

RESULTS

Depressive symptoms were found in 148 (5.3%) of the 2,743 young adults in the study. We observed a negative correlation between depressive symptoms and average MNDWIs at participants' addresses (OR: 0.84; 95%CI: 0.72-0.98), within 300m (OR: 0.81; 95%CI: 0.70-0.95), 1000m (OR: 0.80; 95%CI: 0.69-0.93), and 3000m (OR:0.77; 95%CI: 0.66-0.89) buffer zones. Within the 1000m buffer zone, sleep was found to mediate 21% of the relationship between the presence of blue space and depressive symptoms. The stratified analysis revealed a stronger association between low MNDWI levels within the 1000m buffer zone and depressive symptoms in females (P < 0.05). Additionally, average MNDWI levels within the 3000m buffer zone were associated with depressive symptoms in both females and males.

CONCLUSIONS

Blue space could improve depressive symptoms, particularly in females, with sleep playing a mediating role. Incorporating blue spaces into environmental planning is important for improving mental health.

The Inter-Relationships Between Depressive Symptoms and Suicidality Among Macau Residents After the "Relatively Static Management" COVID-19 Strategy: A Perspective of Network Analysis

Background

Suicidality is a global public health problem which has increased considerably during the coronavirus disease 2019 (COVID-19) pandemic. This study examined the inter-relationships between depressive symptoms and suicidality using network analysis among Macau residents after the "relatively static management" COVID-19 strategy.

Methods

An assessment of suicidal ideation (SI), suicide plan (SP), suicide attempt (SA) and depressive symptoms was conducted with the use of individual binary response items (yes/no) and Patient Health Questionnaire (PHQ-9). In the network analysis, central and bridge symptoms were identified in the network through "Expected Influence" and "Bridge Expected Influence", and specific symptoms that were directly associated with suicidality were identified via the flow function. Network Comparison Tests (NCT) were conducted to examine the gender differences in network characteristics.

Results

The study sample included a total of 1008 Macau residents. The prevalence of depressive symptoms and suicidality were 62.50% (95% CI = 59.4-65.5%) and 8.9% (95% CI = 7.2-10.9%), respectively. A network analysis of the sample identified SI ("Suicidal ideation") as the most central symptom, followed by SP ("Suicide plan") and PHQ4 ("Fatigue"). SI ("Suicidal ideation") and PHQ6 ("Guilt") were bridge nodes connecting depressive symptoms and suicidality. A flow network revealed that the strongest connection was between S ("Suicidality") and PHQ6 ("Guilt"), followed by S ("Suicidality") and PHQ 7 ("Concentration"), and S ("Suicidality") and PHQ3 ("Sleep").

Conclusion

The findings indicated that reduction of specific depressive symptoms and suicidal thoughts may be relevant in decreasing suicidality among adults. Further, suicide assessment and prevention measures should address the central and bridge symptoms identified in this study.

Prevalence, correlates, and network analysis of depression and its associated quality of life among ophthalmology nurses during the COVID-19 pandemic

Background

Nurses in Ophthalmology Department (OD) had a high risk of infection during the novel coronavirus disease 2019 (COVID-19) pandemic. This study examined the prevalence, correlates, and network structure of depression, and explored its association with quality of life (QOL) in Chinese OD nurses.

Methods

Based on a cross-sectional survey, demographic and clinical data were collected. Depression was measured with the 9-item Self-reported Patient Health Questionnaire (PHQ-9), and QOL was measured using the World Health Organization Quality of Life Questionnaire-brief version (WHOQOL-BREF). Univariate analyses, multivariate logistic regression analyses, and network analyses were performed.

Results

Altogether, 2,155 OD nurses were included. The overall prevalence of depression among OD nurses was 32.71% (95%CI: 30.73-34.70%). Multiple logistic regression analysis revealed that having family or friends or colleagues who were infected (OR = 1.760, p = 0.003) was significantly associated with higher risk of depression. After controlling for covariates, nurses with depression reported lower QOL (F(1, 2,155) = 596.784, p < 0.001) than those without depression. Network analyses revealed that 'Sad Mood', 'Energy Loss' and 'Worthlessness' were the key central symptoms.

Conclusion

Depression was common among OD nurses during the COVID-19 pandemic. Considering the negative impact of depression on QOL and daily life, regular screening for depression, timely counselling service, and psychiatric treatment should be provided for OD nurses, especially those who had infected family/friends or colleagues. Central symptoms identified in network analysis should be targeted in the treatment of depression.

Network structure of mobile phone addiction and anxiety symptoms among rural Chinese adolescents

BACKGROUND

The incidence of mobile phone addiction among adolescents in rural areas of China is increasing year by year, and has already exceeded that of some cities. And phone addiction increases the risk of anxiety and poor sleep. Therefore, this study used network analysis to investigate the relationship between mobile phone addiction and anxiety symptoms, and the predictability to sleep quality.

METHODS

From September 2021 to March 2022, a total of 1920 rural adolescents in Xuzhou, China were included. The survey included information on phone addiction, anxiety symptoms, and sleep quality. Network analysis was used to estimate the network structure of adolescents' mobile phone addiction and anxiety symptoms. LOWESS curve and linear regression were used to test the predictive ability of node-centrality on sleep quality.

RESULTS

In the network of mobile phone addiction-anxiety symptoms, the most influential symptoms were Failure to cut down the time, Anxiety if not used for some time, and Alleviate loneliness. Irritability was the most prominent bridging symptom. Gender difference had no effect on network structure. Nodes in the network are not predictive of sleep quality.

CONCLUSION

Failure to cut down the time is the most important symptom, suggesting that measures should be taken to reduce the amount of time spent on mobile phones. For example, increase outdoor exercise, increase the real company of friends and family, in order to reduce the occurrence of mobile phone addiction and anxiety.

Network analysis of comorbid insomnia and depressive symptoms among psychiatric practitioners during the COVID-19 pandemic

STUDY OBJECTIVES

Insomnia and depression are common mental health problems reported by mental health professionals during the COVID-19 pandemic. Network analysis is a fine-grained approach used to examine associations between psychiatric syndromes at a symptom level. This study was designed to elucidate central symptoms and bridge symptoms of a depression-insomnia network among psychiatric practitioners in China. The identification of particularly important symptoms via network analysis provides an empirical foundation for targeting specific symptoms when developing treatments for comorbid insomnia and depression within this population.

METHODS

A total of 10,516 psychiatric practitioners were included in this study. The Insomnia Severity Index (ISI) and 9-item Patient Health Questionnaire (PHQ-9) were used to estimate prevalence rates of insomnia and depressive symptoms, respectively. Analyses also generated a network model of insomnia and depression symptoms in the sample.

RESULTS

Prevalence rates of insomnia (ISI total score ≥8), depression (PHQ-9 total score ≥5) and comorbid insomnia and depression were 22.2% (95% confidence interval: 21.4-22.9%), 28.5% (95% confidence interval: 27.6-29.4%), and 16.0% (95% confidence interval: 15.3-16.7%), respectively. Network analysis revealed that "Distress caused by sleep difficulties" (ISI7) and "Sleep maintenance" (ISI2) had the highest strength centrality, followed by "Motor dysfunction" (PHQ8) and "Sad mood" (PHQ2). Furthermore, the nodes "Sleep dissatisfaction" (ISI4), "Fatigue" (PHQ4), and "Motor dysfunction" (PHQ8) had the highest bridge strengths in linking depression and insomnia communities.

CONCLUSIONS

Both central and bridge symptoms (ie, Distress caused by sleep difficulties, Sleep maintenance, Motor dysfunction, Sad mood, Sleep dissatisfaction, and Fatigue) should be prioritized when testing preventive measures and specific treatments to address comorbid insomnia and depression among psychiatric practitioners during the COVID-19 pandemic.

CITATION

Zhao N, Zhao Y-J, An F, et al. Network analysis of comorbid insomnia and depressive symptoms among psychiatric practitioners during the COVID-19 pandemic. J Clin Sleep Med. 2023;19(7):1271-1279.

Network connectivity between anxiety, depressive symptoms and psychological capital in Chinese university students during the COVID-19 campus closure

BACKGROUND

In the context of the outbreak of COVID-19 within mainland China, to understand the mental health status of university students during campus closure, this study analyzes the relationship between anxiety, depressive symptoms, and psychological capital and to reveals their central symptoms.

METHODS

A total of 12,945 university students were included in this study from April 10 to 19, 2022. Anxiety and depressive symptoms were measured by the seven-item Generalized Anxiety Disorder Scale (GAD-7) and two-item Patient Health Questionnaires (PHQ-2). Psychological capital was measured using the Psychological Capital Questionnaire (PCQ-24). The centrality and bridge centrality indexes were used to identify central and bridge symptoms, respectively. Network Comparison Test (NCT) was also administered to check whether network traits differed by gender and place of residence.

RESULTS

The most influential node in this study was Trouble relaxing (GAD4), followed by Uncontrollable worry (GAD2) and Excessive worry (GAD3). The main bridging symptoms were Depressed mood (PHQ2), Psychological capital. There are no differences in the network structure of students by place of residence, while there are more significant differences in the network structure of students by gender.

CONCLUSION

Central and bridging symptoms may be the core symptoms that trigger or maintain the development of anxiety and depression among university students during the COVID-19 campus closure. Timely and reasonable interventions targeting these symptoms may help reduce depression and anxiety in this population. In addition, improving university students' psychological capital may likewise contribute to the development of their good mental health.

Depression and quality of life among Macau residents in the 2022 COVID-19 pandemic wave from the perspective of network analysis

Background

In the summer of 2022, Macau experienced a surge of COVID-19 infections (the 618 COVID-19 wave), which had serious effects on mental health and quality of life (QoL). However, there is scant research on mental health problems and QoL among Macau residents during the 618 COVID-19 wave. This study examined the network structure of depressive symptoms (hereafter depression), and the interconnection between different depressive symptoms and QoL among Macau residents during this period.

Method

A cross-sectional study was conducted between 26th July and 9th September 2022. Depressive symptoms were measured with the 9-item Patient Health Questionnaire (PHQ-9), while the global QoL was measured with the two items of the World Health Organization Quality of Life-brief version (WHOQOL-BREF). Correlates of depression were explored using univariate and multivariate analyses. The association between depression and QoL was investigated using analysis of covariance (ANCOVA). Network analysis was used to evaluate the structure of depression. The centrality index "Expected Influence" (EI) was used to identify the most central symptoms and the flow function was used to identify depressive symptoms that had a direct bearing on QoL.

Results

A total 1,008 participants were included in this study. The overall prevalence of depression was 62.5% (n = 630; 95% CI = 60.00-65.00%). Having depression was significantly associated with younger age (OR = 0.970; p < 0.001), anxiety (OR = 1.515; p < 0.001), fatigue (OR = 1.338; p < 0.001), and economic loss (OR = 1.933; p = 0.026). Participants with depression had lower QoL F (1, 1,008) =5.538, p = 0.019). The most central symptoms included PHQ2 ("Sad Mood") (EI: 1.044), PHQ4 ("Fatigue") (EI: 1.016), and PHQ6 ("Guilt") (EI: 0.975) in the depression network model, while PHQ4 ("Fatigue"), PHQ9 ("Suicide"), and PHQ6 ("Guilt") had strong negative associations with QoL.

Conclusion

Depression was common among Macao residents during the 618 COVID-19 wave. Given the negative impact of depression on QoL, interventions targeting central symptoms identified in the network model (e.g., cognitive behavioral therapy) should be developed and implemented for Macau residents with depression.

Differences in central symptoms of anxiety and depression between college students with different academic performance: A network analysis

Objective

The prevalence of anxiety and depressive symptoms for Chinese college students are high. Academic pressure is one of the prominent risk factors of psychological well-beings for Chinese college students. The application of network analysis provides researchers a more comprehensive understanding of symptom-symptom interaction in mental disorders. This study aims to find out whether there is a difference in central symptoms between students with different academic performance.

Method

A total sample of 1,291 college students was included in our study. Anxiety and depressive symptoms were measured by PHQ-9 and GAD-7. Central symptoms were identified through centrality indices. Network stability was examined using the case-dropping method.

Results

For the poor academic group, the most central symptom is PHQ-2 (feeling depressed). The most central symptom of the good academic group is GAD-2 (uncontrolled worry). The least central symptom for both groups is PHQ-9 (suicidal thought). Network structure is statistically different between two groups, global strength is not statistically different between two groups.

Conclusion

The pertinent symptom is feeling depressed, followed by uncontrolled worry and poor appetite, and for the good academic group, the pertinent symptom is an uncontrolled worry, theoretical explanation and clinical implications is discussed.

Night shifts, insomnia, anxiety, and depression among Chinese nurses during the COVID-19 pandemic remission period: A network approach

Background

The outbreak of the COVID-19 pandemic imposed a heavy workload on nurses with more frequent night shifts, which led to higher levels of insomnia, depression, and anxiety among nurses. The study aimed to describe the symptom-symptom interaction of depression, anxiety, and insomnia among nurses and to evaluate the impact of night shifts on mental distress via a network model.

Methods

We recruited 4,188 nurses from six hospitals in December 2020. We used the Insomnia Severity Index, Patient Health Questionnaire-9, and Generalized Anxiety Disorder Scale-7 to assess insomnia, depression, and anxiety, respectively. We used the gaussian graphical model to estimate the network. Index expected influence and bridge expected influence was adapted to identify the central and bridge symptoms within the network. We assessed the impact of night shifts on mental distress and compared the network structure based on COVID-19 frontline experience.

Results

The prevalence of depression, anxiety, and insomnia was 59, 46, and 55%, respectively. Nurses with night shifts were at a higher risk for the three mental disorders. "Sleep maintenance" was the central symptom. "Fatigue," "Motor," "Restlessness," and "Feeling afraid" were bridge symptoms. Night shifts were strongly associated with sleep onset trouble. COVID-19 frontline experience did not affect the network structure.

Conclusion

"Sleep maintenance," "Fatigue," "Motor," and "Restlessness" were important in maintaining the symptom network of anxiety, depression, and insomnia in nurses. Further interventions should prioritize these symptoms.

Impact of the COVID-19 Pandemic on the Mental Health and Quality of Life of Patients with Psoriasis in Tertiary Care; A One-year Follow-up

Abstract is missing (Short communication)

A network analysis and support vector regression approaches for visualising and predicting the COVID-19 outbreak in Malaysia

This study aims to (1) correlate and visualise the Coronavirus disease 19 (COVID-19) pandemic spread via Spearman rank coefficients of network analysis (NA) and (2) predict the cumulative number of COVID-19 confirmed and death cases via support vector regression (SVR) based on COVID-19 dataset in Malaysia between July 2020 to June 2021. The NA indicated increasing connectivity between different states throughout the time frame, revealing the most complex network of COVID-19 transmission in the second quarter of 2021. The SVR model predicted future COVID-19 cases and deaths in Malaysia in the second half of 2021. The study demonstrated that the NA and SVR could provide relatively simple yet valuable artificial intelligence techniques for visualising the degree of connectivity and predicting pandemic risk based on confirmed COVID-19 cases and deaths. The Malaysian health authorities used the NA and SVR model results for preventive measures in highly populated states.

Prevalence and associated factors of depressive symptoms among the young adults during the post-epidemic period - Evidence from the first wave of COVID-19 in Hubei Province, China

Introduction

China emerged from the first wave of COVID-19 in a short period of time and returned to normal economic and living order nationwide, making China's entry into the post-COVID-19 epidemic period since April 2020. However, the COVID-19 epidemic had a great impact on young adults' psychological status and may continue into the post-epidemic period. The enormous economic, employment and entrepreneurship pressures of this period may exacerbate this negative impact. This study investigated the depression status of the young adults and put forward the suggestions on how to strengthen the psychological crisis intervention and social security to cultivate the resilience of the young adults after major public health emergencies.

Methods

This study conducted a questionnaire survey to identify the prevalence of depressive symptoms and explore the associated factors of depressive symptoms among 1069 young adults in X City, Hubei province in September 2020. And the multistage stratified random sampling method was used for sampling. Depressive symptoms were measured using the 10-item version of the Center for Epidemiological Studies Depression Scale (CES-D-10). Descriptive statistics and logistic regression analysis were adopted for statistical analysis.

Results

1069 respondents (67.68% male; mean age = 28.87 ± 4.18 years; age range = 18–35 years) were included in final analyses. About 25.9% of the respondents reported depressive symptoms (CES-D-10 score = 7.28 ± 3.85). Age, marital status, employment status, monthly disposable income, the cognition, experience and social relationship of the COVID-19 epidemic, and regional discrimination were significantly associated with depressive symptoms. Being male (P = 0.025), age of 25–29 years (P = 0.011), having a household size with 4–5 (P = 0.01) and more than 8 (P = 0.012) family members, a little pessimism about the prospect of COVID-19 epidemic prevention and control (P = 0.044), often (P = 0.018) or always (P = 0.009) participation in anti-epidemic volunteer work were likely to lead to depressive symptoms.

Conclusions

In the post-COVID-19 epidemic period, the psychological status of young people is generally stable, but some of them are depressed. Life, work and mental stress affect the generation of depressive symptoms among the young adults.

Multifarious Linkages Between Personality Traits and Psychological Distress During and After COVID-19 Campus Lockdown: A Psychological Network Analysis

BACKGROUND

The novel coronavirus disease pandemic is still proliferating and is not expected to end any time soon. Several lockdowns and social distancing measures might be implemented in the future. A growing body of research has explored the effect of personality on individuals' psychological wellbeing during the pandemic. However, most prior studies have not discussed the dynamic and reciprocal transactions between personality and psychological distress in various situations. Therefore, this study aims to explore the internal mechanisms of the ways in which certain personality traits triggered specific symptoms during and after college lockdown, by using network analysis.

METHODS

Based on survey data from 525 university students in China, the study detected the connection between individual personality and psychological distress through network analysis. Of the participants, 70.1% were female, and 20.9% were male. The mean age of the participants was 19.701 (SD = 1.319) years. We estimated networks via two steps: First, two networks that only contain the Big Five personality traits and the six symptoms of psychological distress during and after the lockdown measure were estimated. Second, we add control variables and re-estimated the networks to check whether the linkages among the Big Five personality traits and the six symptoms of psychological distress observed in the first step were stable. Moreover, we employed strength centrality as the key indicator to present the potential significance of diverse variables within a network.

RESULTS

The findings demonstrate that, first, "depress" was the central symptom in the network during the college lockdown, while "efforts" was the central symptom after the lockdown. Second, the symptoms of "restless" and "worthless" significantly declined after the lockdown. Third, we found that there is an internal mechanism through which personality affected certain psychological symptoms during and after lockdowns. Specifically, neuroticism triggered certain symptoms during and after the lockdown, while extraversion and conscientiousness suppressed certain symptoms. Substantial evidence on internal linkages is imperative to develop effective interventions.

CONCLUSION

This study explores the internal mechanisms of the ways in which certain personality traits trigger specific symptoms. Overall, our results provide empirical evidence that personality traits play a key role in how individuals with certain traits respond to college lockdown during a pandemic. The study makes a significant contribution to the literature because it is among the first few studies which explores the effects of personality traits on individual psychological distress using network analysis during the pandemic.

Network Analysis of Comorbid Anxiety and Insomnia Among Clinicians with Depressive Symptoms During the Late Stage of the COVID-19 Pandemic: A Cross-Sectional Study

BACKGROUND

A high proportion of clinicians experienced common anxiety, insomnia and depression during the COVID-19 pandemic. This study examined the item-level association of comorbid anxiety and insomnia symptoms among clinicians who suffered from depressive symptoms during the late stage of the COVID-19 pandemic using network analysis (NA).

METHODS

Clinicians with depressive symptoms (with a Patients Health Questionnaire (PHQ-9) total score of 5 and above) were included in this study. Anxiety and insomnia symptoms were measured using the Generalized Anxiety Disorder Scale - 7-item (GAD-7) and Insomnia Severity Index (ISI), respectively. Network analysis was conducted to investigate the network structure, central symptoms, bridge symptoms, and network stability of these disturbances. Expected influence (EI) was used to measure the centrality of index.

RESULTS

Altogether, 1729 clinicians were included in this study. The mean age was 37.1 [standard deviation (SD)=8.04 years], while the mean PHQ-9 total score was 8.42 (SD=3.33), mean GAD-7 total score was 6.45 (SD=3.13) and mean ISI total score was 8.23 (SD=5.26). Of these clinicians, the prevalence of comorbid anxiety symptoms (GAD-7≥5) was 76.8% (95% CI 74.82-78.80%), while the prevalence of comorbid insomnia symptoms (ISI≥8) was 43.8% (95% CI: 41.50-46.18%). NA revealed that nodes ISI7 ("Interference with daytime functioning") (EI=1.18), ISI4 ("Sleep dissatisfaction") (EI=1.08) and ISI5 ("Noticeability of sleep problem by others") (EI=1.07) were the most central (influential) symptoms in the network model of comorbid anxiety and insomnia symptoms in clinicians. Bridge symptoms included nodes PHQ3 ("Sleep") (bridge EI=0.55) and PHQ4 ("Fatigue") (bridge EI=0.49). Gender did not significantly influence the network structure, but "having the experience of caring for COVID-19 patients" significantly influenced the network structure.

CONCLUSION

Central symptoms and key bridge symptoms identified in this NA should be targeted in the treatment and preventive measures for clinicians suffering from comorbid anxiety, insomnia and depressive symptoms during the late stage of the COVID-19 pandemic.

Network connectivity between benevolent childhood experiences and uncertainty stress among Chinese university students

BACKGROUND

The purpose of this study was to explore the association between benevolent childhood experiences (BCEs) and uncertainty stress among Chinese university students by network analysis.

METHODS

A total of 1,830 university students from three Chinese cities were recruited. Respondents' BCEs and uncertainty stress were self-reported using online questionnaire. The structure of the BCEs-uncertainty stress and related centrality indicators were examined for this sample.

RESULTS

The overall network model showed that "no ways to suit the important changes in life" was the most influential, followed by "all things are not going well," "feel that there is nothing to do," and "worry about the future." And in this network, the most influential bridge symptom was "having a positive self-concept."

CONCLUSION

The central symptoms of the BCEs-uncertainty stress network should be prioritized as targets in interventions and prevention efforts to reduce uncertainty stress among Chinese university students. Improving university students' positive self-concept is important to alleviate the level of uncertainty stress among Chinese university students.