A network analysis of anxiety and depression symptoms among Chinese nurses in the late stage of the COVID-19 pandemic

A simulation-based network analysis of intervention targets for comorbid symptoms of depression and anxiety in Chinese healthcare workers in the post-dynamic zero-COVID policy era

BACKGROUND

After the official end of the dynamic zero-COVID policy in China, healthcare workers continued to heavy workloads and psychological stress. In this new phase, concerns related to work and family, rather than infection, may have become new sources of psychological issues such as depression and anxiety among healthcare workers, leading to new patterns of comorbidity. However, few studies have addressed these issues. To fill this gap, this study used network analysis to examine new features and mechanisms of comorbidity between depression and anxiety symptoms, and simulated symptom-specific interventions to identify effective targets for intervention.

METHODS

A total of 708 Chinese healthcare workers (71.2% females; Age: M = 37.55, SD = 9.37) were recruited and completed the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7). This study first calculated the incidence rates of anxiety, depression, and their comorbidity, and then constructed the comorbid Ising network. Central and bridge symptoms were identified with expected influence (EI) and bridge EI, respectively. The NodeIdentifyR algorithm (NIRA) was then used to simulate interventions within the network, examining the effects of alleviating or aggravating specific symptoms on the network's severity.

RESULTS

48.2% of Chinese healthcare workers reported experiencing depression (19.8%), anxiety (11.7%), or both (16.2%). In the anxiety-depression network, "guilt" and "appetite changes" were identified as the central symptoms, and "guilt" and "excessive worry" were identified as the bridge symptoms. Simulated interventions suggested that alleviating "Anhedonia" can the most reduce the overall severity of the network, while aggravating "guilt" can the most increase the overall severity. These two symptoms were considered the key target for treatment and prevention, respectively.

CONCLUSIONS

Chinese healthcare workers still face high risk of depression, anxiety, and comorbidity in the post-dynamic zero-COVID policy era. Our findings highlight the key roles of guilt, appetite changes, and excessive worry in the network of depression and anxiety symptoms. Future research should apply the results of the simulated interventions, develop intervention strategies targeting anhedonia, and focus on preventing guilt to improve the healthcare workers' mental health.

TRIAL REGISTRATION

Not applicable.

Inter-relationships of depression and anxiety symptoms among widowed and non-widowed older adults: findings from the Chinese Longitudinal Healthy Longevity Survey based on network analysis and propensity score matching

Background

Depression and anxiety are prevalent mental health issues among older adult widowed adults. However, the symptom-level relationships between these conditions remain unclear. Due to the high correlations and complex relationships among various symptoms, this study employs network analysis to explore differences in the network structures of depression and anxiety symptoms between widowed and non-widowed older adults.

Methods

Propensity score matching was used to identify widowed older adults with similar demographic characteristics. Data from 1,736 widowed and 1,736 matched controls were analyzed using the Chinese Longitudinal Healthy Longevity Survey (2017-2018). Depression and anxiety were measured by the Center for Epidemiologic Studies Depression Scale-10 (CESD-10) and the seven-item Generalized Anxiety Disorder Scale (GAD-7), respectively. Central and bridge symptoms were evaluated using expected influence (EI) and bridge expected influence (BEI), respectively.

Results

Network analysis revealed similarities in central symptoms between widowed and non-widowed older adults, with both groups exhibiting "Feeling depressed or down" (CESD3), "Feeling tense and having difficulty relaxing" (GAD4), and "Being unable to stop or control worrying" (GAD2) as core symptoms. However, differences emerged in bridge symptoms. In the widowed group, "Feeling anxious, worried, or distressed" (GAD1) was most strongly connected to "Felt lonely" (CESD8); "Worrying too much about various things" (GAD3) was strongly linked to "Feeling increasingly exhausted and useless with age" (CESD4); and "Feeling depressed or down" (CESD3) had a strong association with "Becoming easily annoyed or irritable" (GAD6). In the non-widowed group, "Feeling anxious, worried, or distressed" (GAD1) exhibited the strongest association with "Having good sleep quality" (CESD10); "Getting upset over small matters" (CESD1) was closely connected to "Feeling anxious, worried, or distressed" (GAD1); and "Worrying too much about various things" (GAD3) was most strongly connected to "Feeling depressed or down" (CESD3).

Conclusion

Common central and bridge symptoms highlight universal intervention targets. Addressing "Feeling depressed or down" in widowed and "Getting upset over small matters" in non-widowed older adults may help prevent depression-anxiety comorbidity. These findings support targeted interventions to improve mental health outcomes. Future research should evaluate tailored intervention effectiveness.

Network analysis of correlations between suicide exposure, depression, and anxiety symptoms in adolescents

BACKGROUND

Adolescents with a history of suicide exposure, defined as experiencing the suicide death of a family member, friend, or other acquaintances, are more likely to experience mental health issues such as depression or anxiety.

AIMS

This study aimed to explore prevalence rates and the network of adolescents' suicide exposure, depression, and anxiety symptoms, and to clarify the correlations between suicide exposure and symptoms of depression and anxiety.

METHOD

A total of 8,957 adolescents were included in this cross-sectional study. Data regarding general information, symptoms of depression and anxiety, and suicide exposure were collected from mid-September to early October 2021. Network analysis was employed to assess relationships between suicide exposure and individual symptoms of both depression and anxiety. Central symptoms were identified by strength; the flow network was visualized to identify symptoms directly related to suicide exposure.

RESULTS

The prevalence rates of suicide exposure, depression, and anxiety were 5.28%, 12.87%, and 10.48%. Results indicated that suicide exposure was associated with both depression and anxiety, and had the strongest positive association with suicidal ideation. Central symptoms of the network were sad mood, nervousness, fatigue, irritability, and uncontrollable worry. Bridge symptoms were suicidal ideation and irritability. Appetite changes, suicidal ideation, uncontrollable worry, sleep difficulties, and irritability were symptoms directly related to suicide exposure.

CONCLUSIONS

There were significant inter-symptom associations between suicide exposure, depression, and anxiety in adolescents. It is recommended that future studies explore whether targeted interventions and long-term monitoring concerning these inter-symptom associations can protect adolescents with suicide exposure.

Network of job demands-resources and depressive symptoms in critical care nurses: a nationwide cross-sectional study

BACKGROUND

Critical care nurses are vulnerable to depression, which not only lead to poor well-being and increased turnover intention, but also affect their working performances and organizational productivity as well. Work related factors are important drivers of depressive symptoms. However, the non-liner and multi-directional relationships between job demands-resources and depressive symptoms in critical care nurses has not been adequately analyzed. Understanding these relationships would be helpful for reducing depression, increasing nurses' well-being and retain healthcare forces.

METHODS

This was a cross-sectional study using baseline data from the Nurses' mental health study (NMHS), a prospective cohort study on nurses from 67 tertiary hospitals in 31 province-level administrative regions in China. Only clinical nurses working in the ICU were included (n = 13,745). Data were collected using online questionnaires, and analyzed using network analysis and structural equation model. Job demands (average working hours per week, average number of night shifts per month, paperwork burden and work-life balance), job resources (supervisor support, co-worker support, leader justice, organizational climate satisfaction, work meaning, and career prospect), personal resource (resilience) and depressive symptoms were main variables in the networks, while demographic data and social health (social-emotional support and loneliness) were covariates.

RESULTS

The prevalence of severe, moderately severe, moderate, mild, and none or minimum depressive symptoms in critical care nurses of this study were 1.21, 3.42, 9.76, 42.88, and 42.07% respectively. In the final network, 132 of 210 possible edges (62.8%) were not zero. "Fatigue" had the highest expected influence, followed by "Motor", and "Appetite". Meanwhile, in terms of job demands-resources and personal resources, the node with the highest expected influence was "Supervisor support", followed by "Work meaning" and "Co-worker support". Three bridge variables were identified: "Resilience-adaptation", "Average working hours per week", and "Co-worker support". The final structural equation model basically supported the results of network analysis with an acceptable model-fit (GFI = 0.918, AGFI = 0.896, PCFI = 0.789, PNFI = 0.788, NFI = 0.909, IFI = 0.911, CFI = 0.911, SRMR = 0.040, and RMSEA = 0.064).

CONCLUSIONS

There was a rather strong interconnectedness between depressive symptoms and job demands-resources. Fatigue, motor, and appetite were core depressive symptoms of critical care nurses. Close attention to those symptoms could help recognize depression in critical care nurses. Supervisor support, work meaning, and co-worker support played vital roles as job resources in reducing depression, while negative impact of long average working hours per week were more contagious. Resilience, as personal resources, could help mediate the associations between job demands-resources and depression. In clinical practice, it's recommended for nursing managers to (1) encourage critical care nurses to find their "meaning in work", (2) implement resilience enhancing programs for nurse, (3) build and maintain meaningful relationships with nurses and support them in daily work, and (4) create a harmonious and dedicated working environment where co-workers are willing to help and support each other. Improvements in those modifiable aspects could help reduce risk and prevent exacerbations of depressive symptoms in critical care nurses.

Factors of suicide-related behaviors based on stress-vulnerability model and prevention strategies among nurses: a scoping review

Objective

The objective of this scoping review was to explore, appraise and synthesize the current literature regarding the incidence, factors influencing, and prevention strategies related to suicide risk among nurses.

Methods

An extensive literature search was conducted using databases such as PubMed, Web of Science, Medline, and Embase from its formation to June 20, 2024, specifically focusing on the suicide-related behaviors of nurses written in Chinese or English. Two researchers independently screened the literature, and disagreements were debated until a consensus was reached. Data extraction was conducted for the studies that were included. The process of data synthesis was carried out using narrative analysis.

Results

The study encompassed 40 papers from 15 different countries. This study found that nurses' suicide ideation ranged from 4.3 to 44.58%, while suicide attempts ranged from 2.9 to 12.6%. Based on the stress-vulnerability model, factors influencing nurses' suicide-related behaviors include vulnerability (personality traits, coping styles), stressors (mental disorders, workplace bullying, etc.) and protective factors (social support, resilience, etc.). The strategies for preventing nurse suicide encompass primary prevention (for all nurses), secondary prevention (for nurses at risk of suicide), and tertiary prevention (for nurses who have attempted suicide).

Conclusion

The suicide rate among nurses exceeds that of the general population. Mental disorders and workplace bullying are significant stressors that contribute to nurse suicide. Suicide-related behaviors among nurses can be effectively prevented and managed through the implementation of the tertiary prevention strategies. Primary prevention is essential in reducing suicide. Cognitive exercises and schedule shifts reasonably are primary preventive measures tailored for nurses. This study addresses the gaps in influencing factors about suicide-related behaviors among nurses and the strategies for preventing suicide, and provides a complete review of the current situation of nurses' suicide-related behaviors, providing references for the safe management of nurses' suicide.

Interrelationships of stress, burnout, anxiety, depression, quality of life and suicidality among Chinese residents under Standardized Residency Training: a network analysis

BACKGROUND

Chinese resident physicians confront challenges such as staff shortages and heavy workloads, leading to a heightened prevalence of mental distress. This study aims to investigate the symptom network of stress, burnout, anxiety, depression (SBAD) and adverse personal outcomes in this cohort.

METHODS

From October 2020 to April 2022, 994 physicians were recruited across China through snowball sampling. Stress, burnout, anxiety and depression were assessed using the 10-item Perceived Stress Scale, a two-item burnout questionnaire, Generalized Anxiety Disorder Scale-7 and the Patient Health Questionnaire-9, respectively. Data on adverse personal outcomes (low quality of life [QOL] and suicidal ideation) were collected. We constructed and visualized two networks, calculating expected influence (EI) and bridge EI indices to identify central and bridge symptoms.

RESULTS

In the SBAD network, perceived helplessness was the most central and critical bridge symptom connecting stress and mental distress, with emotional exhaustion identified as the secondary bridge symptom. Perceived helplessness, perceived self-efficacy and emotional exhaustion exhibited the highest negative correlations with QOL. Worthless (PHQ6) and motor (PHQ8) symptoms were strongly correlated with suicidal ideation. The estimated SBAD network showed excellent stability and accuracy.

CONCLUSIONS

Our study emphasizes that perceived helplessness may be a high-priority target for preventing and intervening in mental distress and improving QOL among residents. Burnout transcends workplace problems and is widely connected to depression, anxiety and QOL. Implementing early detection and intervention measures at three levels-the individual physician, health system and professional colleges, and external regulators-is crucial for preventing and alleviating stress and mental distress among residents.

The Symptom Structure and Causal Relationships of Comorbid Anxiety and Depression Among Chinese Primary and Middle School Teachers: A Network Analysis

Background

In China, as educational reforms progress, the characteristics of teachers' work have undergone significant changes, resulting in extremely high levels of stress that can trigger anxiety and depression. Anxiety and depression often co-occur, with two mainstream theories explaining this co-existence: the tripartite model and the diathesis-stress model. However, systematic research focusing on this population is relatively scarce, and the applicability of these models has not been thoroughly tested. This study aims to use network analysis methods to examine the interactions between symptoms and analyze the co-existence of anxiety and depression, thereby expanding the research on teachers.

Methods

Data were provided by the Science Database of People Mental Health, which includes 1670 teachers with a mean age of 30.01. The Self-Rating Anxiety Scale and Self-Rating Depression Scale were used to estimate the network structures of anxiety and depression, respectively. Shared symptoms between depression and anxiety were identified using network analysis and clique percolation methods. Bayesian Networks was used to estimate causal relationships between symptoms. Data were analyzed using R packages. Network structure was constructed with the qgraph package, node centrality and bridge symptoms were evaluated using the networktools package, and network stability was measured via the bootnet package. The Clique Percolation method was implemented with the CliqurPercolation package, and Bayesian network modeling was performed via the Bnlearn package.

Results

Dizziness and Easy Fatigability & Weakness were central symptoms in the network. Bridging strength results showed that, the important bridging symptoms included Tachycardia, Depressed Affect, Fatigue, Crying Spell, Easy Fatigability & Weakness, Nightmares, Face Flushing, and Sweating were the strong bridging symptoms. Additionally, Sleep Disturbance played a key mediating role. Depressed Affect and Dissatisfaction were activation symptoms for anxiety-depression co-existence.

Conclusion

Using network analysis, this study elucidated core, bridging, and shared symptoms, as well as potential causal pathways between anxiety and depression. Specifically, somatic symptoms are crucial in maintaining and developing the anxiety-depression network among teachers. Sleep disturbance serves as the sole gateway for mild symptoms to develop into other communities. The Bayesian network identified two key activating symptoms within the teacher anxiety-depression network, validating the applicability of the tripartite model among teachers.

Network analysis of resilience, anxiety and depression in clinical nurses

BACKGROUND

Resilience is a protective feature against anxiety and depression disorders. However, the precise relationship and structure of resilience and anxiety and depression remain poorly understood. This study sought to investigate the link among resilience' components and anxiety as well as depression.

METHODS

1,279 clinical nurses were recruited. 10-item Connor-Davidson Resilience Scale, Generalized Anxiety Disorder 7, and Patient Health Questionnaire 9 were employed to evaluate resilience, anxiety, and depression, respectively. The regularized partial-correlation network was generated utilizing data from cross-sectional survey and the bridge expected influence index was utilized to quantify bridge components.

RESULTS

The rates of anxiety and depression within clinical nurses were 67.3% and 67.2%, accordingly. Four strongest bridge edges appeared in the resilience-anxiety network, like "Adapt to change"- "Fear that something might happen", and "Stay focused under pressure"- "Uncontrollable worry". Two strongest bridge edges appeared in the resilience-depression network, like "Adapt to change"- "Concentration difficulties" and "Stay focused under pressure"- "Fatigue". "Adapt to change" was recognized as bridging nodes in both the resilience-anxiety network and the resilience-depression network.

CONCLUSIONS

Interventions targeting the bridge component "Adapt to change" within resilience, may mitigate the intensity of anxiety and depression symptoms among clinical nurses.

Anxiety, depression, and post-traumatic stress disorder in nurses exposed to horizontal violence: a network analysis

BACKGROUND

Horizontal violence can cause serious mental health problems for nurses, particularly anxiety, depression, and post-traumatic stress disorder. However, the intrinsic linkage mechanism between mental symptoms of anxiety, depression, and post-traumatic stress disorder in nurses exposed to horizontal violence is unclear. This study aims to elucidate the characteristics of anxiety, depression, and post-traumatic stress disorder networks among nurses with horizontal violence exposure.

METHODS

Data for this cross-sectional study were obtained from the baseline portion of a short longitudinal survey conducted at four tertiary hospitals in Shandong Province, China. A total of 510 nurses with horizontal violence exposure completed the General Information Scale, the Negative Acts Questionnaire, the Seven-item Generalized Anxiety Disorder Scale, the Nine-item Patient Health Questionnaire, and the Four-item SPAN. The network model was constructed using network analysis. The expected influence and the bridge expected influence of nodes were calculated. The stability and accuracy of the network were estimated.

RESULTS

The results show that A4 (Trouble relaxing) and P1 (Startle) had the highest expected influence in the network. D9 (Suicidality ideation) and A5 (Restlessness) were the key bridge symptoms.

CONCLUSIONS

"Trouble relaxing", "Startle", "Suicidality ideation", and "Restlessness" are all mental symptoms that need to be urgently improved the most in nurses exposed to horizontal violence. Nursing administrators and policymakers should implement mental health intervention programs for these symptoms as early as possible to maximize nurses' mental health.

Psychopathology symptoms of frontline nurses under sudden public health crisis: A network analysis

Public health crises can significantly impact the emotional well-being of healthcare workers. Network analysis is a novel approach to exploring interactions between mental disorders at the symptom level. This study aimed to elucidate the characteristics of post-traumatic stress disorder (PTSD), anxiety, depression, and insomnia symptoms network among frontline nurses under sudden public health crisis. A cross-sectional survey was conducted online among 556 frontline nurses through convenience sampling in Hubei Province, China, from 21 February 2020, to 10 March 2020. Symptoms of PTSD, anxiety, depression, and insomnia were assessed by the Post-Traumatic Stress Disorder Checklist (PCL-5), Generalized Anxiety Disorder scale (GAD-7), Patient Health Questionnaire (PHQ-9) and Insomnia Severity Index, respectively. Central symptoms (the most important symptoms, activation has the strongest influence the other nodes) and bridge symptoms (nodes where deactivation can prevent activation from spreading from one disorder to another) were identified via centrality and bridge centrality indices, respectively. Network stability was examined using the case-dropping procedure. We found that the correlation between PHQ-9 item 9 'suicidal thoughts' and PCL-5 item 16 'reckless or self-destructive behaviour' was the strongest. Moreover, 'reckless or self-destructive behaviour' was the strongest central symptom, and PHQ-9 item 3 'sleep problems' was the most important bridge symptom. Other major symptoms included GAD-7 item 6 'uncontrollable anxiety' and PHQ-9 item 2 'depressed or sad mood'. Timely, systemic targeting interventions on central symptoms and bridge symptoms may effectively alleviate co-occurring experiences of psychopathological symptoms among frontline nurses.

Examining Anxiety and Insomnia in Internship Students and Their Association with Internet Gaming Disorder

Background: Internships are a mandatory graduation requirement to help medical students transition to the work environment. Some individuals are prone to anxiety in an unfamiliar environment, which is a public concern among young adults. Here, we investigated the mechanism between internet gaming disorder and anxiety and insomnia among internship students. Methods: A convenient sample of 267 internship students was collected in a cross-sectional study module between 17 July and 27 December 2022. The survey contained a 7-item Generalized Anxiety Disorder (GAD-7), Athens Insomnia Scale (AIS), and Internet Gaming Disorder Scale-Short-Form (IGDS9-SF). The association was estimated using Pearson's correlations, and network analysis was performed to characterize these associations. Results: Our results indicate that about 60% of participants exhibited mild to severe anxiety and insomnia, while 2.28% showed symptoms of internet gaming disorder. Also, we found a moderate association between anxiety and insomnia. An item-level analysis indicated that GAD_1 "feeling anxious" and GAD_5 "unable to sit still" are essential for gaming, and that GAD_2 "uncontrollable worrying" is crucial for insomnia. This indicated an interplay between these items, supported by our centrality analysis, where we found that GAD_1 and GAD_2 depicted high centrality. Conclusions: We found high rates of anxiety and insomnia in internship students and the association between selected symptoms of anxiety and insomnia. At the same time, low rates of internet gaming disorder could be attributed to a lack of time for entertainment and an increased awareness of its risks. Given these findings, an awareness of anxiety and insomnia risk should be emphasized.

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.

The comorbid network characteristics of anxiety and depressive symptoms among Chinese college freshmen

BACKGROUND

This study aimed to investigate the interplay between anxiety and depressive symptoms in Chinese college freshmen using the causal system perspective (CSP), which differs from the traditional common cause perspective (CCP) by providing an alternative explanation by attributing comorbidity to direct interactions among symptoms.

METHODS

A convenience sample of 2,082 Chinese college freshmen (39.51% male, Mage = 18.61) from a normal university completed the Generalized Anxiety Disorder 7-Item Scale (GAD-7) and the Patient Health Questionnaire (PHQ-9). Network analysis was conducted and evaluated as to centrality, stability, node predictability, and bridging features. Moreover, the moderated network model (MNM) was utilized to detect the moderation effects of gender in the comorbidity network.

RESULTS

The network of anxiety and depressive symptoms exhibited stability, characterized by the core symptoms of "restlessness", "lack of energy", and "excessive worry about control", as well as the bridging symptoms of "fearfulness", "sad mood", and "irritability". Notably, the nodes representing "uncontrollable worry" and "difficulty in relaxation" demonstrated the highest predictive power. Gender did not exert any moderating effects on the anxiety and depressive symptom network.

CONCLUSION

These results reinforce that certain anxiety or depressive symptoms are more central than others, and thus play a more vital role in the comorbid network. These findings highlight underlying potential targeting symptoms to consider in future interventions.

Relationship between depression and burnout among nurses in Intensive Care units at the late stage of COVID-19: a network analysis

BACKGROUND

Mental health problems are critical and common in medical staff working in Intensive Care Units (ICU) even at the late stage of COVID-19, particularly for nurses. There is little research to explore the inner relationships between common syndromes, such as depression and burnout. Network analysis (NA) was a novel approach to quantified the correlations between mental variables from the perspective of mathematics. This study was to investigate the interactions between burnout and depression symptoms through NA among ICU nurses.

METHOD

A cross-sectional study with a total of 616 Chinese nurses in ICU were carried out by convenience sampling from December 19, 2022 to January19, 2023 via online survey. Burnout symptoms were measured by Maslach Burnout Inventory-General Survey (MBI-GS) (Chinese version), and depressive symptoms were assessed by the 9-item Patient Health Questionnaire (PHQ-9). NA was applied to build interactions between burnout and depression symptoms. We identified central and bridge symptoms by R package qgraph in the network model. R package bootnet was used to examined the stability of network structure.

RESULTS

The prevalence of burnout and depressive symptoms were 48.2% and 64.1%, respectively. Within depression-burnout network, PHQ4(Fatigue)-MBI2(Used up) and PHQ4(Fatigue)-MBI5(Breakdown) showed stronger associations. MBI2(Used up) had the strongest expected influence central symptoms, followed by MBI4(Stressed) and MBI7 (Less enthusiastic). For bridge symptoms. PHQ4(Fatigue), MBI5(Breakdown) and MBI2(Used up) weighed highest. Both correlation stability coefficients of central and bridge symptoms in the network structure were 0.68, showing a high excellent level of stability.

CONCLUSION

The symptom of PHQ4(Fatigue) was the bridge to connect the emotion exhaustion and depression. Targeting this symptom will be effective to detect mental disorders and relieve mental syndromes of ICU nurses at the late stage of COVID-19 pandemic.

Network analysis of depression and anxiety symptoms and their associations with life satisfaction among Chinese hypertensive older adults: a cross-sectional study

Background

Hypertension is one of the most prevalent chronic diseases among the older adult population in China and older adults with hypertension are more susceptible to mental health problems. This study aimed to explore the network structure of depression and anxiety, and their association with life satisfaction (LS) in older adults with hypertension.

Methods

A total of 4,993 hypertensive individuals aged 60 and above were selected from the Chinese Longitudinal Healthy Longevity Survey (CLHLS 2017-2018). The design of the CLHLS study was approved by the Campus Institutional Review Board of Duke University (Pro00062871) and the Biomedical Ethics Committee of Peking University (IRB00001052-13,074). The Center for Epidemiologic Studies Depression Scale-10 (CESD-10) and the Generalized Anxiety Disorder Scale-7 (GAD-7) were used to assess depressive and anxiety symptoms. Central and bridge symptoms were identified via "Expected Influence" and "Bridge Expected Influence", respectively. Network stability was assessed using the case-dropping bootstrap technique.

Results

Network analysis identified CESD3 (Feeling blue/depressed), GAD4 (Trouble relaxing), and GAD2 (Uncontrollable worry) as the most influential central symptoms in the network of depression and anxiety. Concurrently, GAD1 (Nervousness or anxiety), CESD10 (Sleep disturbances), and CESD1 (Feeling bothered) stand as critical bridge symptoms between depression and anxiety disorders. Moreover, CESD7 (Lack of happiness) exhibited the strongest negative correlation with LS in Chinese hypertensive older adults.

Conclusion

This exploratory study represents the first investigation to examine the mutual relationship between depressive and anxiety symptoms among Chinese hypertensive older adults. Interventions addressing targeting bridge symptoms have the potential to alleviate depressive and anxiety symptoms. Furthermore, improving happiness, hope, and sleep quality in this population may mitigate the adverse effects of depression and anxiety on LS.

Network analysis of depression and anxiety symptoms and their associations with mobile phone addiction among Chinese medical students during the late stage of the COVID-19 pandemic

Network analysis provides a novel approach to discovering associations between mental disorders at the symptom level. This study aimed to examine the characteristics of the network of depression and anxiety symptoms and their associations with mobile phone addiction (MPA) among Chinese medical students during the late stage of the COVID-19 pandemic. A total of 553 medical students were included. Depression and anxiety symptoms and MPA were measured by the nine-item Patient Health Questionnaire (PHQ-9), the seven-item Generalized Anxiety Disorder Scale (GAD-7), and the Mobile Phone Addiction Index (MPAI), respectively. Central and bridge symptoms were identified with centrality indices and bridge centrality indices. Network stability was examined using the case-dropping procedure. "Uncontrollable worry", "restlessness" and "nervousness" were the central symptoms in the depression and anxiety network. "Restlessness" and "motor" were the most central bridge symptoms linking depression and anxiety. "Concentration", "anhedonia" and "sleep" were most strongly associated with MPA. "Uncontrollable worry", "restlessness", "nervousness," and "motor" may be the symptoms for interventions to target in medical students with comorbid depression and anxiety. From a network perspective, depressive symptoms may be more important than anxiety symptoms in medical students with MPA.

Anxiety prevalence and associated factors among frontline nurses following the COVID-19 pandemic: a large-scale cross-sectional study

INTRODUCTION

Nurses are more likely to experience anxiety following the coronavirus 2019 epidemic. Anxiety could compromise nurses' work efficiency and diminish their professional commitment. This study aims to investigate nurses' anxiety prevalence and related factors following the pandemic in multiple hospitals across China.

METHODS

An online survey was conducted from April 16 to July 3, 2023, targeting frontline nurses who had actively participated in China. Anxiety and depression symptoms were assessed using the Self-rating Anxiety Scale and the Self-rating Depression Scale (SDS), respectively. Multivariable logistic regression analysis was employed to identify factors linked with anxiety.

RESULTS

A total of 2,210 frontline nurses participated in the study. Overall, 65.07% of participants displayed clinically significant anxiety symptoms. Multivariable logistic regression revealed that nurses living with their families [2.52(95% CI: 1.68-3.77)] and those with higher SDS scores [1.26(95% CI: 1.24-1.29)] faced an elevated risk of anxiety. Conversely, female nurses [0.02(95% CI: 0.00-0.90)] and those who had recovered from infection [0.05(95%CI: 0.07-0.18)] demonstrated lower rates of anxiety.

DISCUSSION

This study highlights the association between SDS score, gender, virus infection, living arrangements and anxiety. Frontline nurses need to be provided with emotional support to prevent anxiety. These insights can guide interventions to protect the mental well-being of frontline nurses in the post-pandemic period.

Network analysis of depression and anxiety symptoms in Chinese rheumatoid arthritis patients

Background

Rheumatoid arthritis (RA) patients are susceptible to comorbid anxiety and depression. From the network model perspective, comorbidity is due to direct interactions between depression and anxiety symptoms. The objective of this study was to assess the network structure of depression and anxiety symptoms in Chinese RA patients and identify the central and bridge symptoms as well as how depression and anxiety symptoms are related to quality of life (QoL) in the network.

Methods

A total of 402 Chinese RA patients were included in this study. Depression and anxiety symptoms were measured by the Hospital Anxiety and Depression Scale (HADS). R software was used to estimate the network. Specifically, we computed the predictability, expected influence (EI) and bridge expected influence (BEI) for each symptom and showed a flow network of "QoL".

Results

Our network revealed that the strongest edge was D2 "See the bad side of things" and D3 "Not feeling cheerful" across the whole network. For centrality indices, D3 "Not feeling cheerful" and D6 "Feeling down" had the highest EI values in the network, while A4 "Trouble relaxing" and D6 "Feeling down" had the highest BEI values of their respective community. As to "QoL", the strongest direct edge related to it was A1 "Nervousness".

Conclusions

"Feeling down" and "Not feeling cheerful" emerged as the strongest central symptoms, while "Trouble relaxing" and "Feeling down" were bridge symptoms in the anxiety-depression network of RA patients. Intervention on depression and anxiety symptoms in nurses should prioritize these symptoms.

Network of burnout, depression, anxiety, and dropout intention in medical undergraduates

BACKGROUND

Burnout, depression, and anxiety are highly prevalent among medical students, which often leads to their attrition. We aim to assess the inter-relationships of depression, burnout, and anxiety symptoms with dropout intention among Chinese medical undergraduates using the network analysis.

METHOD

A total of 3,648 Chinese medical undergraduates were recruited through snowball sampling. Learning burnout scale, 9-item Patient Health Questionnaire (PHQ-9), and General Anxiety Disorder Scale (GAD-7) was used to assess burnout, depression, and anxiety symptoms, respectively. We used the EBICglasso model to estimate the network. We compared the network based on gender, study phase, and clinical experience.

RESULTS

After removing repeated submissions and incorrect responses to the trap question, 3,536 participants were included in the final analysis. The prevalence of burnout, depression, anxiety, and dropout intention was 38, 62.7, 38.4, and 39% respectively, which is consistent with previous findings. Network analysis suggested that anxiety and depression items clustered together and displayed several strong bridge connections, while burnout items formed another cluster. All the strongest edges were within the respective distress. Cynicism symptoms 'I am fed up with study' and 'I want to study but I feel that studying is boring' were the most central symptoms, while 'fatigue' and 'worthless' were the bridge symptoms within the burnout-depression-anxiety network. Other central symptoms included 'worthless', 'I can handle my courses', 'nervous', and 'uncontrollable worry'. Cynicism symptoms 'I am interested in my major' and 'I feel that the knowledge I have learned is useless' were mostly related to dropout intention. Gender, study phase, and clinical experience didn't affect the global strength of the burnout-depression-anxiety network.

CONCLUSION

Our results indicated the predominance of cynicism symptoms within the burnout-depression-anxiety network and its substantial impact on dropout intention, suggesting that early detection and intervention for cynicism symptoms in Chinese medical students are in urgent need. Other central and bridge symptoms might also serve as potential targets for the prevention and treatment of burnout, depression, and anxiety among medical students. For example, studies suggest cognitive-behavioral therapy could quickly improve 'worthless', which might be beneficial in treating burnout, depression, and anxiety in medical students.

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.