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

Network analysis of anxiety and depressive symptoms during the COVID-19 pandemic in older adults in the United Kingdom

The health crisis caused by COVID-19 in the United Kingdom and the confinement measures that were subsequently implemented had unprecedented effects on the mental health of older adults, leading to the emergence and exacerbation of different comorbid symptoms including depression and anxiety. This study examined and compared depression and anxiety symptom networks in two specific quarantine periods (June-July and November-December) in the older adult population in the United Kingdom. We used the database of the English Longitudinal Study of Aging COVID-19 Substudy, consisting of 5797 participants in the first stage (54% women) and 6512 participants in the second stage (56% women), all over 50 years of age. The symptoms with the highest centrality in both times were: "Nervousness (A1)" and "Inability to relax (A4)" in expected influence and predictability, and "depressed mood (D1"; bridging expected influence). The latter measure along with "Irritability (A6)" overlapped in both depression and anxiety clusters in both networks. In addition, a the cross-lagged panel network model was examined in which a more significant influence on the direction of the symptom "Nervousness (A1)" by the depressive symptoms of "Anhedonia (D6)", "Hopelessness (D7)", and "Sleep problems (D3)" was observed; the latter measure has the highest predictive capability of the network. The results report which symptoms had a higher degree of centrality and transdiagnostic overlap in the cross-sectional networks (invariants) and the cross-lagged panel network model of anxious and depressive symptomatology.

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.

Machine Learning and Bayesian Network Analyses Identifies Psychiatric Disorders and Symptom Associations with Insomnia in a national sample of 31,285 Treatment-Seeking College Students

Background

A better understanding of the structure of relations among insomnia and anxiety, mood, eating, and alcohol-use disorders is needed, given its prevalence among young adults. Supervised machine learning provides the ability to evaluate the discriminative accuracy of psychiatric disorders associated with insomnia. Combined with Bayesian network analysis, the directionality between symptoms and their associations may be illuminated.

Methods

The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. Firstly, an elastic net regularization model was trained to predict, via repeated 10-fold cross-validation, which psychiatric disorders were associated with insomnia severity. Seven disorders were included: major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder. Secondly, using a Bayesian network approach, completed partially directed acyclic graphs (CPDAG) built on training and holdout samples were computed via a Bayesian hill-climbing algorithm to determine symptom-level interactions of disorders most associated with insomnia [based on SHAP (SHapley Additive exPlanations) values)] and were evaluated for stability across networks.

Results

Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .449 (.016); RMSE = 5.00 [.081]), with comparable performance in accounting for variance explained in the holdout sample [R2 = .33; RMSE = 5.47). SHAP indicated the presence of any psychiatric disorder was associated with higher insomnia severity, with major depressive disorder demonstrated to be the most associated disorder. CPDAGs showed excellent fit in the holdout sample and suggested that depressed mood, fatigue, and self-esteem were the most important depression symptoms that presupposed insomnia.

Conclusion

These findings offer insights into associations between psychiatric disorders and insomnia among college students and encourage future investigation into the potential direction of causality between insomnia and major depressive disorder.

Trial registration

Trial may be found on the National Institute of Health RePORTER website: Project Number: R01MH115128-05.

Inter-relationships of insomnia and psychiatric symptoms with suicidal ideation among patients with chronic schizophrenia: A network perspective

BACKGROUND

Insomnia is common in patients with schizophrenia, which contributes to worsening psychiatric symptoms and suicidality. We aimed to assess the inter-relationships of insomnia and psychopathology with suicidal ideation (SI) among 1407 Chinese patients with chronic schizophrenia via the network approach.

METHOD

We used Positive and Negative Syndrome Scale, Insomnia Severity Index, and Beck Scale for Suicidal Ideation to assess psychiatric symptoms, insomnia, and SI, respectively. Lifetime suicidal attempts (SA) were collected.

RESULTS

(1) The incidence of insomnia, lifetime SI, lifetime SA, and current SI was 13.5% (n = 190), 22.8% (n = 321), 13.5% (n = 190), and 9.7% (n = 136), respectively. (2) Patients with insomnia had worse clinical symptoms and higher suicidal risk. (3) Daytime dysfunction, sleep-related distress, conceptual disorganization, delusions, anxiety, and poor rapport were the core symptoms, while late sleep onset and sleep dissatisfaction emerged as bridge symptoms connecting insomnia and psychopathology. (4) Depressive mood, hallucinations, poor impulse control, guilty feelings, insomnia-related impaired quality of life, and sleep dissatisfaction were directly associated with SI.

CONCLUSION

Our findings called for formal assessment of insomnia in patients with schizophrenia, which should cover both nocturnal and daytime insomnia symptoms. Targeted interventions for key symptoms may help reduce insomnia, psychiatric symptoms, and SI in patients with schizophrenia.

Network analysis of mental health problems among adults in Addis Ababa, Ethiopia: a community-based study during the COVID-19 pandemic

OBJECTIVE

COVID-19 has negatively impacted mental health of adults globally with increased rates of psychiatric comorbidities. However, network analysis studies to examine comorbidities and correlations between symptoms of different mental disorders are uncommon in low-income countries. This study aimed to investigate the network structure of depression, anxiety and perceived stress among adults in Addis Ababa and identify the most central and bridge symptoms within the depressive-anxiety-perceived symptoms network model.

DESIGN

Community-based cross-sectional study.

SETTING

This study was carried out on a sample of the general population in Addis Ababa during the first year of the COVID-19 pandemic. A total of 1127 participants were included in this study, of which 747 (66.3%) were females, and the mean age was 36 years.

PRIMARY AND SECONDARY OUTCOME MEASURES

Symptoms of depression, anxiety and stress were measured using the Patient Health Questionnaire, Generalized Anxiety Disorder Scale and the Perceived Stress Scale, respectively.Network analysis was conducted to investigate the network structure. The centrality index expected influence (EI) and bridge EI (1-step) were applied to determine the central and bridge symptoms. Case-dropping procedure was used to examine the network stability.

RESULT

The sad mood (EI=1.52) was the most central and bridge symptom in the depression, anxiety and perceived stress network model. Irritability (bridge EI=1.12) and nervousness and stressed (bridge EI=1.33) also served as bridge symptoms. The strongest edge in the network was between nervousness and uncontrollable worry (weight=0.36) in the anxiety community. The network had good stability and accuracy. The network structure was invariant by gender and age based on the network structure invariance test.

CONCLUSIONS

In this study, the sad mood was the core and bridge symptom. This and the other central and bridge symptoms identified in the study should be targeted to prevent mental health disorders and comorbidities among adults.

Investigating sleep quality and sleep hygiene awareness among Chinese adults: an association and network analysis study

PURPOSE

The relationships between sleep quality and sleep hygiene awareness in the Chinese population were unclear. We aimed to investigate the associations and related factors between sleep quality and sleep hygiene awareness in adults and to identify the most central domain for sleep quality using network analysis.

METHODS

A cross-sectional survey was conducted from April 22 to May 5, 2020. Adults (18 years old or above) who had access to smartphones were invited to participate in this survey. The Pittsburg Sleep Quality Index (PSQI) and the Sleep Hygiene Awareness and Practice Scale (SHAPS) were used to evaluate the sleep quality and sleep hygiene awareness of the participants. Propensity score matching (PSM) was used as sensitivity analysis to reduce the confounding effects. Multiple logistic regression was performed to evaluate the associations. The R packages "bootnet" and "qgraph" were used to estimate the connection and calculate the network centrality indices between good and poor sleepers.

RESULTS

In total, 939 respondents were included in the analysis. Of them, 48.8% (95% CI: 45.6-52.0%) were identified as poor sleepers. Participants with nervous system diseases, psychiatric diseases, and psychological problems were more likely to have poor sleep quality. The notion that using sleep medication regularly was beneficial to sleep was associated with poor sleep quality. Similarly, the notion that waking up at the same time each day disrupted sleep was also associated with poor sleep quality. The findings were consistent before and after PSM. Subjective sleep quality was the most central domain for sleep quality in good and poor sleepers.

CONCLUSION

Poor sleep quality was positively associated with certain sleep hygiene notions in Chinese adults. Effective measures such as self-relief, sleep hygiene education, and cognitive behavioral treatment may have been needed to improve sleep quality, especially during the COVID-19 outbreak.

COVID-19-Related News Consumption Linked with Stress and Worry, but Not Sleep Quality, Early in the Pandemic

Beginning in early 2020, the novel coronavirus was the subject of frequent and sustained news coverage. Building on prior literature on the stress-inducing effects of consuming news during a large-scale crisis, we used network analysis to investigate the association between coronavirus disease 2019 (COVID-19) news consumption, COVID-19-related psychological stress, worries about oneself and one's loved ones getting COVID-19, and sleep quality. Data were collected in March 2020 from 586 adults (45.2% female; 72.9% White) recruited via Amazon Mechanical Turk in the U.S. Participants completed online surveys assessing attitudes and behaviors related to COVID-19 and a questionnaire assessing seven domains of sleep quality. Networks were constructed using partial regularized correlation matrices. As hypothesized, COVID-19 news consumption was positively associated with COVID-19-related psychological stress and concerns about one's loved ones getting COVID-19. However, there were very few associations between COVID-19 news consumption and sleep quality indices, and gender did not moderate any of the observed relationships. This study replicates and extends previous findings that COVID-19-news consumption is linked with psychological stress related to the pandemic, but even under such conditions, sleep quality can be spared due to the pandemic allowing for flexibility in morning work/school schedules.

Psychological impact of COVID-19: A cross-lagged network analysis from the English Longitudinal Study of Aging COVID-19 database

Background

The COVID-19 pandemic and its subsequent health restrictions had an unprecedented impact on mental health, contributing to the emergence and reinforcement of various psychopathological symptoms. This complex interaction needs to be examined especially in a vulnerable population such as older adults.

Objective

In the present study we analyzed network structures of depressive symptoms, anxiety, and loneliness from the English Longitudinal Study of Aging COVID-19 Substudy over two waves (Months of June-July and November-December 2020).

Methods

For this purpose, we use measures of centrality (expected and bridge-expected influence) in addition to the Clique Percolation method to identify overlapping symptoms between communities. We also use directed networks to identify direct effects between variables at the longitudinal level.

Results

UK adults aged >50 participated, Wave 1: 5,797 (54% female) and Wave 2: 6,512 (56% female). Cross-sectional findings indicated that difficulty relaxing, anxious mood, and excessive worry symptoms were the strongest and similar measures of centrality (Expected Influence) in both waves, while depressive mood was the one that allowed interconnection between all networks (bridge expected influence). On the other hand, sadness and difficulty sleeping were symptoms that reflected the highest comorbidity among all variables during the first and second waves, respectively. Finally, at the longitudinal level, we found a clear predictive effect in the direction of the nervousness symptom, which was reinforced by depressive symptoms (difficulties in enjoying life) and loneliness (feeling of being excluded or cut off from others).

Conclusion

Our findings suggest that depressive, anxious, and loneliness symptoms were dynamically reinforced as a function of pandemic context in older adults in the UK.

Hope Buffers the Effect of Fear of COVID-19 on Depression among College Students: Insomnia as a Mediator

BACKGROUND

In the period of the global pandemic, psychophysical problems induced by the fear of COVID-19 among college students deserve attention since the dormitory environment in college greatly increases the possibility of COVID-19 infection.

METHODS

A hypothesized mediated moderation model was to be verified using a cross-sectional study among 2453 college students. Fear of COVID-19, insomnia, hope, and depression were assessed by using the relevant scales.

RESULTS

(1) The fear of COVID-19 was positively correlated to depression (β = 0.365, t = 5.553, 95% CI = [0.236, 0.494]); (2) hope moderated the influence of the fear of COVID-19 on depression (β = -0.093, t = -4.066, 95% CI = [-0.137, -0.048]), as well as on insomnia (β = -0.095, t = -4.841, 95% CI = [-0.133, -0.056]); and (3) the mediated moderation model with hope as the moderator and insomnia as the full mediating variable between fear of COVID-19 and depression was verified (β = -0.060, 95% CI = [-0.093, -0.028]).

CONCLUSIONS

The findings suggest that hope is a vital mechanism to explain the relationship between the fear of COVID-19 and depression in early adulthood. In practical application, mental health practitioners should focus on boosting hope and alleviating insomnia when addressing COVID-19-related depression issues among college students.

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.