Internet addiction and residual depressive symptoms among clinically stable adolescents with major psychiatric disorders during the COVID-19 pandemic: a network analysis perspective.

To assess the inter-relationships between residual depressive symptoms (RDS) and Internet addiction (IA) using network analysis among clinically stable adolescents with major psychiatric disorders during the COVID-19 pandemic. RDS and IA were assessed using the Patient Health Questionnaire-9 (PHQ-9) and the Internet Addiction Test (IAT), respectively. Central symptoms and bridge symptoms in the network model were examined. A total of 1,454 adolescents met the study criteria and were included in the analyses. The prevalence of IA was 31.2% (95% CI: 28.8%-33.6%). In the network analysis, the nodes IAT15 ("Preoccupation with the Internet"), PHQ2 ("Sad mood"), and PHQ1 ("Anhedonia") were the most central symptoms in the IA-RDS network model. Bridge symptoms included IAT10 ("Sooth disturbing about your Internet use"), PHQ9 ("Suicide ideation"), and IAT3 ("Prefer the excitement online to the time with others"). Additionally, PHQ2 ("Sad mood") was the main node linking "Anhedonia" to other IA clusters. Internet addiction was common among clinically stable adolescents with major psychiatric disorders during the COVID-19 pandemic. Core and bridge symptoms identified in this study could be prioritized as targets for the prevention and treatment of IA in this population.

Prevalence, correlates, and network analysis of depression and its association with quality of life in survivors with myocardial infarction during the COVID-19 pandemic.

BACKGROUND: Depression is common among myocardial infarction (MI) survivors and is strongly associated with poor quality of life (QOL). The aim of this study was to examine the prevalence, correlates and the network structure of depression, and its association with QOL in MI survivors during the COVID-19 pandemic. METHODS: This cross-sectional study evaluated depression and QOL in MI survivors with the Chinese version of the nine-item Patient Health Questionnaire (PHQ-9) and the World Health Organization Quality of Life-BREF (WHOQOL-BREF), respectively. Univariable analyses, multivariable analyses, and network analyses were performed. RESULTS: The prevalence of depression (PHQ-9 total score ≥ 5) among 565 MI survivors during the COVID-19 pandemic was 38.1 % (95 % CI: 34.1-42.1 %), which was significantly associated with poor QOL. Patients with depression were less likely to consult a doctor regularly after discharge, and more likely to experience more severe anxiety symptoms and fatigue. Item PHQ4 "Fatigue" was the most central symptom in the network, followed by PHQ6 "Guilt" and PHQ2 "Sad mood". The flow network showed that PHQ4 "Fatigue" had the highest negative association with QOL. CONCLUSION: Depression was prevalent among MI survivors during the COVID-19 pandemic and was significantly associated with poor QOL. Those who failed to consult a doctor regularly after discharge or reported severe anxiety symptoms and fatigue should be screened for depression. Effective interventions for MI survivors targeting central symptoms, especially fatigue, are needed to reduce the negative impact of depression and improve QOL.

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.

Antidepressants for COVID-19: A systematic review.

OBJECTIVE: To systematically examine the efficacy and safety of antidepressants for the treatment of coronavirus disease 2019 (COVID-19). METHODS: A systematic search was performed independently by two researchers based on Chinese Journal Net, WanFang, PsycINFO, Cochrane Library, PubMed, and EMBASE. RESULTS: Seven studies (n = 92,947) including three retrospective studies (n = 91,083), two randomized clinical trials (RCTs, n = 1649), two prospective cohort study (n = 215) involving (n = 92,947) patients with COVID-19 were examined. For RCTs, fluvoxamine outperformed placebo in reducing clinical deterioration and hospitalisation for COVID-19 patients. For retrospective studies, antidepressants (2 studies) and fluoxetine (1 study) possibly reduced the risk of mortality in patients with COVID-19. Results from two remaining studies supported the superiority of fluvoxamine in reducing risk of mortality in COVID-19 patients. The two RCTs that examined the safety of fluvoxamine for COVID-19 patients found inconsistent results but no significant group differences in the dropout rate. CONCLUSION: This systematic review found emerging evidence for fluvoxamine in reducing the risk of mortality and hospitalisation in COVID-19 patients, but inconsistent evidence for the safety of fluvoxamine in COVID-19 patients. More studies are needed to determine the efficacy and safety of antidepressants for the treatment of COVID-19.

Post COVID-19 mental health symptoms and quality of life among COVID-19 frontline clinicians: a comparative study using propensity score matching approach.

BACKGROUND: The extent and severity of post-COVID-19 mental health symptoms among frontline clinicians are not clear. This study compared mental health symptoms (i.e., depression, anxiety, and insomnia symptoms) and global quality of life (QOL) after the first COVID-19 outbreak between the COVID-19 treating and non-COVID-19 treating frontline clinicians. METHODS: This cross-sectional, comparative, convenient-sampling study was conducted between October 13 and 22, 2020, which was five months after the first COVID-19 outbreak in China was brought under control. The severity of depression, anxiety, insomnia symptoms, and global QOL of the clinicians were assessed using the Patient Health Questionnaire-9 items (PHQ-9), Generalized Anxiety Disorder Scale-7 items (GAD-7), Insomnia Severity Index (ISI), and the World Health Organization Quality of Life Questionnaire-brief version (WHOQOL-BREF), respectively. The propensity score matching (PSM) method was used to identify comparable COVID-19 treating and non-COVID-19 treating frontline clinicians. A generalized linear model (GLM) was used to assess the differences in PHQ-9, GAD-7, ISI, and QOL scores between the COVID-19 treating and non-COVID-19 treating frontline clinicians. RESULTS: In total, 260 COVID-19 treating frontline clinicians and 260 matched non- COVID-19 treating frontline clinicians were included. Non-COVID-19 treating frontline clinicians experienced more frequent workplace violence (WPV) than the COVID-19 treating frontline clinicians (χ2 = 7.6, p = 0.006). COVID-19 treating frontline clinicians reported higher QOL compared to their non-COVID-19 treating frontline counterparts (b = 0.3, p = 0.042), after adjusting for WPV experience. COVID-19 treating and non- COVID-19 treating frontline clinicians reported similar PHQ-9, GAD-7, and ISI total scores (all p values > 0.05). CONCLUSION: This study did not reveal more severe post-COVID-19 mental health symptoms in COVID-19 treating frontline clinicians compared to non-COVID-19 treating frontline clinicians. It is possible that the implementation of timely and appropriate mental health, social and financial supports could have prevented the worsening of mental health symptoms among the COVID-19 treating frontline clinicians after the first COVID-19 outbreak in China.

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 analysis of comorbid depression and anxiety and their associations with quality of life among clinicians in public hospitals during the late stage of the COVID-19 pandemic in China.

BACKGROUND: Mental health problems are common among clinicians working in public hospitals even in the late stage of the COVID-19 pandemic. Network analysis is a novel approach to explore interactions between mental health problems at the symptom level. This study examined the network structure of comorbid depression and anxiety and their associations with quality of life (QOL) among hospital clinicians in China during the late stage of the COVID-19 pandemic. METHODS: A total of 4931 participants were recruited from October 13 to 22, 2020. The nine-item Patient Health Questionnaire (PHQ-9), seven-item Generalized Anxiety Disorder Scale (GAD-7), and the World Health Organization Quality of Life Questionnaire-Brief Version (WHOQOL-BREF) were used to measure depressive and anxiety symptoms, and QOL, respectively. Central and bridge symptoms were identified with centrality and bridge centrality indices, respectively. Network stability was examined using the case-dropping procedure. RESULTS: The prevalence of depression (defined as PHQ-9 total score ≥ 5) was 35.1 % [95 % confidence interval (CI) = 33.73-36.41 %)], the prevalence of anxiety (GAD-7 total score ≥ 5) was 32.5 % (95 % CI = 31.20-33.84 %), while the prevalence of comorbid depression and anxiety was 26.9 % (95 % CI = 25.7-28.2 %). "Impaired motor skills", "Trouble relaxing" and "Uncontrollable worry" were the central symptoms in the whole depression-anxiety network. "Irritability", "Feeling afraid" and "Sad mood" were the most key bridge symptoms linking depression and anxiety. Three symptoms ("Fatigue", "Trouble relaxing" and "Nervousness") were the most strongly and negatively associated with QOL. Neither gender nor the experiences of caring for COVID-19 patients was associated with network global strength, distribution of edge weights or individual edge weights. LIMITATIONS: The causality between variables could not be established. Depressive and anxiety symptoms were assessed by self-report measures, which may result in recall bias and limitations in capturing clinical phenomena. CONCLUSIONS: Both the central (i.e., "Impaired motor skills", "Trouble relaxing" and "Uncontrollable worry") and bridge symptoms (i.e., "Irritability", "Feeling afraid" and "Sad mood") identified in this network analysis should be targeted in specific treatment and preventive measures for comorbid depressive and anxiety symptoms among clinicians in the late stage of the pandemic. Furthermore, "Fatigue", "Trouble relaxing" and "Nervousness" are key symptoms to address to improve clinicians' QOL.

Depressive symptoms and gender differences in older adults in Hong Kong during the COVID-19 pandemic: a network analysis approach.

Background: The 2019 novel coronavirus disease (COVID-19) outbreak had a detrimental impact on the mental health of older adults. This study evaluated the central symptoms and their associations in the network of depressive symptoms and compared the network structure differences between male and female older adults in Hong Kong. Methods: Altogether, 3,946 older adults participated in this study. We evaluated the centrality indicators for network robustness using stability and accuracy tests, and examined the potential differences between the structure and connectivity of depression networks in male and female older adults. Results: The overall prevalence of depressive symptoms was 43.7% (95% CI=40.6-46.7%) in males, and 54.8% (95% CI=53.1-56.5%) in females (P<0.05). Sad Mood, Guilt, Motor problems and Lack of Energy were influential symptoms in the network model. Gender differences were found in the network global strength, especially in the following edges: Sad Mood--Guilt, Concentration--Guilt, Anhedonia--Motor, Lack of Energy--Suicide, Appetite--Suicide and Concentration--Suicide. Conclusions: Central symptoms in the depressive symptom network among male and female older adults may be prioritized in the treatment and prevention of depression during the pandemic.

Mapping Network Connectivity Among Symptoms of Depression and Pain in Wuhan Residents During the Late-Stage of the COVID-19 Pandemic.

Background: Symptoms of depression and pain often overlap, and they negatively influence the prognosis and treatment outcome of both conditions. However, the comorbidity of depression and pain has not been examined using network analysis, especially in the context of a pandemic. Thus, we mapped out the network connectivity among the symptoms of depression and pain in Wuhan residents in China during the late stage of the COVID-19 pandemic. Methods: This cross-sectional study was conducted from May 25, 2020 to June 18, 2020 in Wuhan, China. Participants' depressive and pain symptoms were assessed using the 9-item Patient Health Questionnaire (PHQ9) and a pain numeric rating scale (NRS), respectively. Network analyses were performed. Results: In total, 2,598 participants completed all assessments. PHQ4 (fatigue) in the depression community showed the highest strength value, followed by PHQ6 (worthlessness) and PHQ2 (depressed or sad mood). PHQ4 (fatigue) was also the most key bridge symptom liking depression and pain, followed by PHQ3 (sleep difficulties). There were no significant differences in network global strength (females: 4.36 vs. males: 4.29; S = 0.075, P = 0.427), network structure-distribution of edge weights (M = 0.12, P = 0.541), and individual edge weights between male and female participants. Conclusion: Depressive and pain symptoms showed strong cross-association with each other. "Fatigue" was the strongest central and bridge symptom in the network model, while "sleep difficulties" was the second strongest bridge symptom. Targeting treatment of both fatigue and sleep problems may help improve depressive and pain symptoms in those affected.

Network Analysis of Depression, Anxiety, Posttraumatic Stress Symptoms, Insomnia, Pain, and Fatigue in Clinically Stable Older Patients With Psychiatric Disorders During the COVID-19 Outbreak.

OBJECTIVES: The Coronavirus Disease 2019 (COVID-19) pandemic has profound negative effects on the mental health of clinically stable older patients with psychiatric disorders. This study examined the influential nodes of psychiatric problems and their associations in this population using network analysis. METHODS: Clinically stable older patients with psychiatric disorders were consecutively recruited from four major psychiatric hospitals in China from May 22 to July 15, 2020. Depressive and anxiety syndromes (depression and anxiety hereafter), insomnia, posttraumatic stress symptoms (PTSS), pain, and fatigue were measured using the Patient Health Questionnaire, General Anxiety Disorder, Insomnia Severity Index, Posttraumatic Stress Disorder Checklist - Civilian Version, and Numeric Rating Scales for pain and fatigue, respectively. RESULTS: A total of 1063 participants were included. The network analysis revealed that depression was the most influential node followed by anxiety as indicated by the centrality index of strength. In contrast, the edge connecting depression and anxiety was the strongest edge, followed by the edge connecting depression and insomnia, and the edge connecting depression and fatigue as indicated by edge-weights. The network structure was invariant by gender based on the network structure invariance test (M = .14, P = .20) and global strength invariance tests (S = .08, P = .30). CONCLUSIONS: Attention should be paid to depression and its associations with anxiety, insomnia, and fatigue in the screening and treatment of mental health problems in clinically stable older psychiatric patients affected by the COVID-19 pandemic.

Prevalence of Suicidality and its Association with Quality of Life in Older Patients with Clinically Stable Psychiatric Disorders in China During the COVID-19 Pandemic.

AIMS: The pattern of suicidality in older patients with psychiatric disorders during the COVID-19 pandemic is not clear. This study examined the prevalence of suicidality and its association with quality of life (QOL) among older clinically stable patients with psychiatric disorders during the COVID-19 pandemic. METHODS: A multicenter, cross-sectional study was conducted from May 22 to July 15, 2020, among four major tertiary psychiatric hospitals in China. Suicidality was assessed by asking 3 standardized questions. Depressive symptoms, pain, and QOL were assessed with the 9-item Patient Health Questionnaire (PHQ-9), the numeric pain rating scale (NPRS), and the World Health Organization Quality of Life Questionnaire-brief version, respectively. RESULTS: A total of 1063 clinically stable patients participated and completed the assessment. The prevalence of suicidality was 11.8% (95% CI: 9.9%-13.7%) during the COVID-19 pandemic. Multiple logistic regression analysis revealed that poor treatment adherence (P = .009, OR = 1.86, 95% CI: 1.17-2.96) and perceived illness worsening during the COVID-19 outbreak (P = .02, OR = 2.07, 95% CI: 1.15-3.73), being diagnosed with major depressive disorder (P < .001, OR = 2.79, 95% CI: 1.68-4.64), PHQ-9 total score (P < .001, OR = 1.20, 95% CI: 1.15-1.24) and NPRS total score (P = .002, OR = 1.17, 95% CI: 1.06-1.29) were associated with higher risk of suicidality. After controlling for covariates, older psychiatric patients with suicidality had lower QOL compared to those without (F(1, 1063) =16.5, P<.001). CONCLUSIONS: Suicidality was common in older patients with clinically stable psychiatric disorders during the COVID-19 pandemic. Considering its negative impact on QOL and personal suffering, routine screening and preventive suicide measures should be implemented for older psychiatric patients.

Anxiety and depressive symptoms in college students during the late stage of the COVID-19 outbreak: a network approach.

Mental health problems are common in college students even in the late stage of the coronavirus disease 2019 (COVID-19) outbreak. Network analysis is a novel approach to explore interactions of mental disorders at the symptom level. The aim of this study was to elucidate characteristics of depressive and anxiety symptoms network in college students in the late stage of the COVID-19 outbreak. A total of 3062 college students were included. The seven-item Generalized Anxiety Disorder Scale (GAD-7) and nine-item Patient Health Questionnaire (PHQ-9) were used to measure anxiety and depressive symptoms, respectively. Central symptoms and bridge symptoms were identified based on centrality and bridge centrality indices, respectively. Network stability was examined using the case-dropping procedure. The strongest direct relation was between anxiety symptoms "Nervousness" and "Uncontrollable worry". "Fatigue" has the highest node strength in the anxiety and depression network, followed by "Excessive worry", "Trouble relaxing", and "Uncontrollable worry". "Motor" showed the highest bridge strength, followed by "Feeling afraid" and "Restlessness". The whole network was robust in both stability and accuracy tests. Central symptoms "Fatigue", "Excessive worry", "Trouble relaxing" and "Uncontrollable worry", and critical bridge symptoms "Motor", "Feeling afraid" and "Restlessness" were highlighted in this study. Targeting interventions to these symptoms may be important to effectively alleviate the overall level of anxiety and depressive symptoms in college students.