Prevalence and network structure of depression, insomnia and suicidality among mental health professionals who recovered from COVID-19: a national survey in China.

Psychiatric syndromes are common following recovery from Coronavirus Disease 2019 (COVID-19) infection. This study investigated the prevalence and the network structure of depression, insomnia, and suicidality among mental health professionals (MHPs) who recovered from COVID-19. Depression and insomnia were assessed with the Patient Health Questionnaire (PHQ-9) and Insomnia Severity Index questionnaire (ISI7) respectively. Suicidality items comprising suicidal ideation, suicidal plan and suicidal attempt were evaluated with binary response (no/yes) items. Network analyses with Ising model were conducted to identify the central symptoms of the network and their links to suicidality. A total of 9858 COVID-19 survivors were enrolled in a survey of MHPs. The prevalence of depression and insomnia were 47.10% (95% confidence interval (CI) = 46.09-48.06%) and 36.2% (95%CI = 35.35-37.21%), respectively, while the overall prevalence of suicidality was 7.8% (95%CI = 7.31-8.37%). The key central nodes included "Distress caused by the sleep difficulties" (ISI7) (EI = 1.34), "Interference with daytime functioning" (ISI5) (EI = 1.08), and "Sleep dissatisfaction" (ISI4) (EI = 0.74). "Fatigue" (PHQ4) (Bridge EI = 1.98), "Distress caused by sleep difficulties" (ISI7) (Bridge EI = 1.71), and "Motor Disturbances" (PHQ8) (Bridge EI = 1.67) were important bridge symptoms. The flow network indicated that the edge between the nodes of "Suicidality" (SU) and "Guilt" (PHQ6) showed the strongest connection (Edge Weight= 1.17, followed by "Suicidality" (SU) - "Sad mood" (PHQ2) (Edge Weight = 0.68)). The network analysis results suggest that insomnia symptoms play a critical role in the activation of the insomnia-depression-suicidality network model of COVID-19 survivors, while suicidality is more susceptible to the influence of depressive symptoms. These findings may have implications for developing prevention and intervention strategies for mental health conditions following recovery from COVID-19.

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.

Depression, anxiety and suicidality among Chinese mental health professionals immediately after China's dynamic zero-COVID policy: A network perspective.

BACKGROUND: Using network analysis, the interactions between mental health problems at the symptom level can be explored in depth. This study examined the network structure of depressive and anxiety symptoms and suicidality among mental health professionals after the end of China's Dynamic Zero-COVID Policy. METHODS: A total of 10,647 mental health professionals were recruited nationwide from January to February 2023. Depression and anxiety were assessed using the 9-item Patient Health Questionnaire (PHQ-9) and 7-item Generalized Anxiety Disorder Scale (GAD-7), respectively, while suicidality was defined by a 'yes' response to any of the standard questions regarding suicidal ideation (SI), suicide plan (SP) and suicide attempt (SA). Expected Influence (EI) and Bridge Expected Influence (bEI) were used as centrality indices in the symptom network to characterize the structure of the symptoms. RESULTS: The prevalence of depression, anxiety, and suicidality were 45.99 %, 28.40 %, and 7.71 %, respectively. The network analysis identified GAD5 ("Restlessness") as the most central symptom, followed by PHQ4 ("Fatigue") and GAD7 ("Feeling afraid"). Additionally, PHQ6 ("Guilt"), GAD5 ("Restlessness"), and PHQ8 ("Motor disturbance") were bridge nodes linking depressive and anxiety symptoms with suicidality. The flow network indicated that the strongest connections of S ("Suicidality") was with PHQ6 ("Guilt"), GAD7 ("Feeling afraid"), and PHQ2 ("Sad mood"). CONCLUSIONS: Depression, anxiety, and suicidality among mental health professionals were highly prevalent after China's Dynamic Zero-COVID Policy ended. Effective measures should target central and bridge symptoms identified in this network model to address the mental health problems in those at-risk.

A comparison of psychiatric symptoms between mental health professionals with and without post-infection sequelae of COVID-19.

Post-infection sequelae of COVID-19 (PISC) have raised public health concerns. However, it is not clear whether infected mental health professionals (MHPs) with PISC have experienced more psychiatric symptoms than MHPs without PISC do. This study examined differences in the prevalence of self-reported depression, anxiety, insomnia and suicidality as well as the network structures of these symptoms between these two groups. Participants completed questionnaire measures of psychiatric symptoms and demographics. Expected influence was used to measure centrality of symptoms and network comparison tests were adopted to compare differences in the two network models. The sample comprised 2,596 participants without PISC and 2,573 matched participants with PISC. MHPs with PISC had comparatively higher symptom levels related to depression (55.2% vs. 23.5 %), anxiety (32.0% vs. 14.9 %), insomnia (43.3% vs. 17.3 %), and suicidality (9.6% vs. 5.3 %). PHQ4 ("Fatigue"), PHQ6 ("Guilt"), and GAD2 ("Uncontrollable Worrying") were the most central symptoms in the "without PISC" network model. Conversely, GAD3 ("Worry too much"), GAD5 ("Restlessness"), and GAD4 ("Trouble relaxing") were more central in the "with PISC" network model. In sum, MHPs with PISC experienced comparatively more psychiatric symptoms and related disturbances. Network results provide foundations for the expectation that MHPs with PISC may benefit from interventions that address anxiety-related symptoms, while those without PISC may benefit from interventions targeting depression-related symptoms.

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

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

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

BACKGROUND: Studies on sleep problems among caregivers of psychiatric patients, especially during the COVID-19 pandemic, are limited. This study examined the prevalence and correlates of insomnia symptoms (insomnia hereafter) among caregivers of psychiatric inpatients during the COVID-19 pandemic as well as the association with quality of life (QoL) from a network analysis perspective. METHODS: A multi-center cross-sectional study was conducted on caregivers of inpatients across seven tertiary psychiatric hospitals and psychiatric units of general hospitals. Network analysis explored the structure of insomnia using the R program. The centrality index of "Expected influence" was used to identify central symptoms in the network, and the "flow" function was adopted to identify specific symptoms that were directly associated with QoL. RESULTS: A total of 1,101 caregivers were included. The overall prevalence of insomnia was 18.9% (n = 208; 95% CI = 16.7-21.3%). Severe depressive (OR = 1.185; P < 0.001) and anxiety symptoms (OR = 1.099; P = 0.003), and severe fatigue (OR = 1.320; P < 0.001) were associated with more severe insomnia. The most central nodes included ISI2 ("Sleep maintenance"), ISI7 ("Distress caused by the sleep difficulties") and ISI1 ("Severity of sleep onset"), while "Sleep dissatisfaction" (ISI4), "Distress caused by the sleep difficulties" (ISI7) and "Interference with daytime functioning" (ISI5) had the strongest negative associations with QoL. CONCLUSION: The insomnia prevalence was high among caregivers of psychiatric inpatients during the COVID-19 pandemic, particularly in those with depression, anxiety and fatigue. Considering the negative impact of insomnia on QoL, effective interventions that address insomnia and alteration of sleep dissatisfaction should be developed.

Prevalence of COVID-19 fear and its association with quality of life and network structure among Chinese mental health professionals after ending China's dynamic zero-COVID policy: a national survey.

Background: China recorded a massive COVID-19 pandemic wave after ending its Dynamic Zero-COVID Policy on January 8, 2023. As a result, mental health professionals (MHPs) experienced negative mental health consequences, including an increased level of fear related to COVID-19. This study aimed to explore the prevalence and correlates of COVID-19 fear among MHPs following the end of the Policy, and its association with quality of life (QoL) from a network analysis perspective. Methods: A cross-sectional national study was conducted across China. The correlates of COVID-19 fear were examined using both univariate and multivariate analyses. An analysis of covariance (ANCOVA) was conducted to determine the relationship between fear of COVID-19 and QoL. Central symptoms were identified using network analysis through the "Expected Influence" of the network model while specific symptoms directly correlated with QoL were identified through the "flow function." Results: A total of 10,647 Chinese MHPs were included. The overall prevalence of COVID-19 fear (FCV-19S total score ≥ 16) was 60.8% (95% CI = 59.9-61.8%). The binary logistic regression analysis found that MHPs with fear of COVID-19 were more likely to be married (OR = 1.198; p < 0.001) and having COVID-19 infection (OR = 1.235; p = 0.005) and quarantine experience (OR = 1.189; p < 0.001). Having better economic status (good vs. poor: OR = 0.479; p < 0.001; fair vs. poor: OR = 0.646; p < 0.001) and health status (good vs. poor: OR = 0.410; p < 0.001; fair vs. poor: OR = 0.617; p < 0.001) were significantly associated with a lower risk of COVID-19 fear. The ANCOVA showed that MHPs with fear of COVID-19 had lower QoL [F = 228.0, p < 0.001]. "Palpitation when thinking about COVID-19" was the most central symptom in the COVID-19 fear network model, while "Uncomfortable thinking about COVID-19" had the strongest negative association with QoL (average edge weight = -0.048). Conclusion: This study found a high prevalence of COVID-19 fear among Chinese MHPs following the end of China's Dynamic Zero-COVID Policy. Developing effective prevention and intervention measures that target the central symptoms as well as symptoms correlated with QoL in our network structure would be important to address COVID-19 fear and improve QoL.

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.

Prevalence of depression and its association with quality of life among guardians of hospitalized psychiatric patients during the COVID-19 pandemic: a network perspective.

Background: The COVID-19 pandemic has greatly affected treatment-seeking behaviors of psychiatric patients and their guardians. Barriers to access of mental health services may contribute to adverse mental health consequences, not only for psychiatric patients, but also for their guardians. This study explored the prevalence of depression and its association with quality of life among guardians of hospitalized psychiatric patients during the COVID-19 pandemic. Methods: This multi-center, cross-sectional study was conducted in China. Symptoms of depression and anxiety, fatigue level and quality of life (QOL) of guardians were measured with validated Chinese versions of the Patient Health Questionnaire - 9 (PHQ-9), Generalized Anxiety Disorder Scale - 7 (GAD-7), fatigue numeric rating scale (FNRS), and the first two items of the World Health Organization Quality of Life Questionnaire - brief version (WHOQOL-BREF), respectively. Independent correlates of depression were evaluated using multiple logistic regression analysis. Analysis of covariance (ANCOVA) was used to compare global QOL of depressed versus non-depressed guardians. The network structure of depressive symptoms among guardians was constructed using an extended Bayesian Information Criterion (EBIC) model. Results: The prevalence of depression among guardians of hospitalized psychiatric patients was 32.4% (95% CI: 29.7-35.2%). GAD-7 total scores (OR = 1.9, 95% CI: 1.8-2.1) and fatigue (OR = 1.2, 95% CI: 1.1-1.4) were positively correlated with depression among guardians. After controlling for significant correlates of depression, depressed guardians had lower QOL than non-depressed peers did [F(1, 1,101) = 29.24, p < 0.001]. "Loss of energy" (item 4 of the PHQ-9), "concentration difficulties" (item 7 of the PHQ-9) and "sad mood" (item 2 of the PHQ-9) were the most central symptoms in the network model of depression for guardians. Conclusion: About one third of guardians of hospitalized psychiatric patients reported depression during the COVID-19 pandemic. Poorer QOL was related to having depression in this sample. In light of their emergence as key central symptoms, "loss of energy," "concentration problems," and "sad mood" are potentially useful targets for mental health services designed to support caregivers of psychiatric patients.

Associations between post-traumatic stress symptoms and quality of life among psychiatric healthcare personnel in China during the COVID-19 pandemic: A network approach.

Background: Post-traumatic stress symptoms (PTSS) are commonly reported by psychiatric healthcare personnel during the coronavirus disease 2019 (COVID-19) pandemic and negatively affect quality of life (QOL). However, associations between PTSS and QOL at symptom level are not clear. This study examined the network structure of PTSS and its connection with QOL in psychiatric healthcare personnel during the COVID-19 pandemic. Methods: This cross-sectional study was carried out between March 15 and March 20, 2020 based on convenience sampling. Self-report measures including the 17-item Post-Traumatic Stress Disorder Checklist - Civilian version (PCL-C) and World Health Organization Quality of Life Questionnaire - Brief Version (WHOQOL-BREF) were used to measure PTSS and global QOL, respectively. Network analysis was used to investigate the central symptoms of PTSS and pattern of connections between PTSS and QOL. An undirected network was constructed using an extended Bayesian Information Criterion (EBIC) model, while a directed network was established based on the Triangulated Maximally Filtered Graph (TMFG) method. Results: Altogether, 10,516 psychiatric healthcare personnel completed the assessment. "Avoidance of thoughts" (PTSS-6), "Avoidance of reminders" (PTSS-7), and "emotionally numb" (PTSS-11) were the most central symptoms in the PTSS community, all of which were in the Avoidance and Numbing domain. Key bridge symptoms connecting PTSS and QOL were "Sleep disturbances" (PTSS-13), "Irritability" (PTSS-14) and "Difficulty concentrating" (PTSS-15), all of which were within the Hyperarousal domain. Conclusion: In this sample, the most prominent PTSS symptoms reflected avoidance while symptoms of hyper-arousal had the strongest links with QOL. As such, these symptom clusters are potentially useful targets for interventions to improve PTSS and QOL among healthcare personnel at work under pandemic conditions.

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.

Mapping network connectivity between internet addiction and residual depressive symptoms in patients with depression.

Background and aims: Depression often triggers addictive behaviors such as Internet addiction. In this network analysis study, we assessed the association between Internet addiction and residual depressive symptoms in patients suffering from clinically stable recurrent depressive disorder (depression hereafter). Materials and methods: In total, 1,267 depressed patients were included. Internet addiction and residual depressive symptoms were measured using the Internet Addiction Test (IAT) and the two-item Patient Health Questionnaire (PHQ-2), respectively. Central symptoms and bridge symptoms were identified via centrality indices. Network stability was examined using the case-dropping procedure. Results: The prevalence of IA within this sample was 27.2% (95% CI: 24.7-29.6%) based on the IAT cutoff of 50. IAT15 ("Preoccupation with the Internet"), IAT13 ("Snap or act annoyed if bothered without being online") and IAT2 ("Neglect chores to spend more time online") were the most central nodes in the network model. Additionally, bridge symptoms included the node PHQ1 ("Anhedonia"), followed by PHQ2 ("Sad mood") and IAT3 ("Prefer the excitement online to the time with others"). There was no gender difference in the network structure. Conclusion: Both key central and bridge symptoms found in the network analysis could be potentially targeted in prevention and treatment for depressed patients with comorbid Internet addiction and residual depressive symptoms.

COVID-19 vaccine acceptance and perceived stigma in patients with depression: a network perspective.

The association between coronavirus disease (COVID-19) vaccine acceptance and perceived stigma of having a mental illness is not clear. This study examined the association between COVID-19 vaccine acceptance and perceived stigma among patients with recurrent depressive disorder (depression hereafter) using network analysis. Participants were 1149 depressed patients (842 men, 307 women) who completed survey measures of perceived stigma and COVID-19 vaccine attitudes. T-tests, chi-square tests, and Kruskal-Wallis tests were used to compare differences in demographic and clinical characteristics between depressed patients who indented to accepted vaccines and those who were hesitant. Hierarchical multiple regression analyses assessed the unique association between COVID-19 vaccine acceptance and perceived stigma, independent of depression severity. Network analysis examined item-level relations between COVID-19 vaccine acceptance and perceived stigma after controlling for depressive symptoms. Altogether, 617 depressed patients (53.7%, 95 confidence intervals (CI) %: 50.82-56.58%) reported they would accept future COVID-19 vaccination. Hierarchical multiple regression analyses indicated higher perceived stigma scores predicted lower levels of COVID-19 vaccination acceptance (ß = -0.125, P < 0.001), even after controlling for depression severity. In the network model of COVID-19 vaccination acceptance and perceived stigma nodes, "Feel others avoid me because of my illness", "Feel useless", and "Feel less competent than I did before" were the most influential symptoms. Furthermore, "COVID-19 vaccination acceptance" had the strongest connections with illness stigma items reflecting social rejection or social isolation concerns ("Employers/co-workers have discriminated", "Treated with less respect than usual", "Sense of being unequal in my relationships with others"). Given that a substantial proportion of depressed patients reported hesitancy with accepting COVID-19 vaccines and experiences of mental illness stigma related to social rejection and social isolation, providers working with this group should provide interventions to reduce stigma concerns toward addressing reluctance in receiving COVID-19 vaccines.

Suicidality in clinically stable bipolar disorder and schizophrenia patients during the COVID-19 pandemic.

The coronavirus disease 2019 (COVID-19) pandemic has a disproportionate impact on vulnerable subpopulations, including those with severe mental illness (SMI). This study examined the one-year prevalence of suicidal ideation (SI), suicide plans (SP), and suicide attempts (SA) in bipolar disorder (BD) and schizophrenia (SCZ) patients during the pandemic. Prevalence rates were compared between the two disorders and associated factors were examined. A survey was conducted in six tertiary psychiatric hospitals and psychiatric units. People with a diagnosis of BD or SCZ were invited to participate. SI, SP, and SA (suicidality for short) were assessed and associated factors were examined using binary logistical regression. The 1-year prevalence of SI, SP and SA in BD patients were 58.3%, (95% CI: 54.1-62.6%), 38.4% (95% CI: 34.3-42.6%) and 38.6% (95% CI: 34.5-42.8%), respectively, which were higher than the corresponding figures in SCZ patients (SI: 33.2%, 95% CI: 28.6-37.8%; SP: 16.8%, 95% CI: 13.2-20.5%; SA: 19.4%, 95% CI: 15.5-23.3%). Patients with younger age, experience of cyberbullying, a history of SA among family or friends, a higher fatigue and physical pain score, inpatient status, and severe depressive symptoms were more likely to have suicidality. The COVID-19 pandemic was associated with increased risk of suicidality, particularly in BD patients. It is of importance to regularly screen suicidality in BD and SCZ patients during the pandemic even if they are clinically stable.

The prevalence of internet addiction and its association with quality of life among clinically stable patients with major depressive disorder.

BACKGROUND: Internet addiction (IA) is associated with mental health problems but its impact on quality of life (QOL) is understudied. We examined the prevalence of IA and its association with QOL in clinically stable patients with major depressive disorder (MDD). METHODS: In a cross-sectional survey between September 2020 and July 2021, the Young's Internet Addiction Test (IAT), the Patient Health Questionnaire-2 (PHQ-2) and the World Health Organization Quality of Life Brief version scale (WHOQOL-BREF) were administered to 1267 patients with MDD. Logistic regression was used to examine the correlates of IA, while analysis of covariance (ANCOVA) was used to examine the association between IA and QOL." RESULTS: The prevalence of IA (IAT total scores ≥50) was 27.2 % (95 % CI: 24.7 %-29.6 %) in MDD patients. Compared to patients without IA, those with IA had lower QOL (F(1, 1267) = 19.1, P < 0.001). Logistic regression revealed that higher education (senior high school and above; OR = 1.85, 95 % CI: 1.13-3.03), family history of psychiatric disorders (OR = 1.72, 95 % CI: 1.08-2.73), and higher PHQ-2 total score (OR = 1.23, 95 % CI: 1.14-1.32) were positively associated with IA while older age (OR = 0.93, 95 % CI: 0.91-0.96) was inversely related to IA. CONCLUSION: IA is much more common in clinically stable patients with MDD compared to the reported figures in the general population. It would be prudent to screen and monitor internet use in MDD patients and treat those with IA.

Internet addiction and its association with quality of life in patients with major depressive disorder: a network perspective.

Depressive disorders and internet addiction (IA) are often comorbid. The aims of this study were to examine the network structure of IA in patients with major depressive disorders (MDD) and explore the association between IA and quality of life (QoL) in this population. This was a multicenter, cross-sectional survey. IA and QoL were assessed with the Internet Addiction Test (IAT) and the World Health Organization Quality of Life-brief version, respectively. Node expected influence (EI) was used to identify central symptoms in the network model, while the flow network of QoL was generated to examine its association with IA. A total of 1,657 patients with MDD was included. "Preoccupation with the Internet," "Job performance or productivity suffer because of the Internet," and "Neglect chores to spend more time online" were central symptoms. The symptom "Form new relationships with online users" had the strongest direct positive relation with QoL, while "Spend more time online over going out with others" and "Job performance or productivity suffer because of the Internet" had the strongest direct negative relations with QoL. Neglecting work caused by IA correlated with QoL, while making friends online appropriately was related to better QoL among MDD patients. Appropriate interventions targeting the central symptoms may potentially prevent or reduce the risk of IA in MDD patients.

Prevalence of suicidality in clinically stable patients with major depressive disorder during the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic is associated with an increased risk of mental health problems including suicide in many subpopulations, but its influence on stable patients with major depressive disorder (MDD) has been studied fleetingly. This study examined the one-year prevalence of suicidality including suicidal ideation (SI), suicide plans (SP), and suicide attempts (SA) as well as their correlates in clinically stable MDD patients during the COVID-19 pandemic. METHODS: A cross-sectional, observational study was conducted between October 1, 2020, and October 15, 2021, in six tertiary psychiatric hospitals. Socio-demographic information, clinical data and one-year prevalence of suicidality were recorded. RESULTS: Altogether, 1718 participants who met the eligibility criteria were included. The overall one-year prevalence of suicidality during the COVID-19 pandemic was 68.04% (95% confidence intervals (CI) =65.84-70.25%), with one-year SI prevalence of 66.4% (95%CI = 64.18-68.65%), SP prevalence of 36.26% (95%CI = 33.99-38.54%), and SA prevalence of 39.35% (95%CI = 37.04-41.66%). Binary logistic regression analyses revealed male gender, married marital status, college education level and above and age were negatively associated with risk of suicidality. Urban residence, unemployed work status, experiences of cyberbullying, a history of suicide among family members or friends, and more severe fatigue, physical pain, and residual depressive symptoms were positively associated with risk of suicidality. CONCLUSIONS: Suicidality is common among clinically stable MDD patients during the COVID-19 pandemic. Regular suicide screening and preventive measures should be provided to clinically stable MDD patients during the pandemic.

Workplace violence against frontline clinicians in emergency departments during the COVID-19 pandemic.

BACKGROUND: Frontline clinicians working in emergency departments (ED) were at disportionate risk of workplace violence (WPV). We investigated the prevalence of WPV and its relationship with quality of life (QOL) in this group of health professionals in China during the COVID-19 pandemic. METHODS: A cross-sectional, online study was conducted. The nine-item Workplace Violence Scale measured WPV. RESULTS: A total of 1,103 ED clinicians participated in this study. The overall prevalence of WPV against ED clinicians was 29.2% (95% CI [26.5%-31.9%]). Having family/friends/colleagues infected with COVID-19 (Odds Ratio (OR) = 1.82, P = 0.01), current smoking (OR = 2.98, P < 0.01) and severity of anxiety symptoms (OR = 1.08, P < 0.01) were independently and positively associated with WPV, while working in emergency intensive care units (OR = 0.45, P < 0.01) was negatively associated with WPV. After controlling for covariates, clinicians experiencing WPV had a lower global QOL compared to those without (F(1, 1103) = 10.9,P < 0.01). CONCLUSIONS: Prevalence of workplace violence against ED clinicians was common in China during the COVID-19 pandemic. Due to the negative impact of WPV on QOL and quality of care, timely preventive measures should be undertaken for ED clinicians.

Network Analysis of Insomnia in Chinese Mental Health Professionals During the COVID-19 Pandemic: A Cross-Sectional Study.

PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic is associated with increased risk of insomnia symptoms (insomnia hereafter) in health-care professionals. Network analysis is a novel approach in linking mechanisms at the symptom level. The aim of this study was to characterize the insomnia network structure in mental health professionals during the COVID-19 pandemic. PATIENTS AND METHODS: A total of 10,516 mental health professionals were recruited from psychiatric hospitals or psychiatric units of general hospitals nationwide between March 15 and March 20, 2020. Insomnia was assessed with the insomnia severity index (ISI). Centrality index (ie, strength) was used to identify symptoms central to the network. The stability of network was examined using a case-dropping bootstrap procedure. The network structures between different genders were also compared. RESULTS: The overall network model showed that the item ISI7 (interference with daytime functioning) was the most central symptom in mental health professionals with the highest strength. The network was robust in stability and accuracy tests. The item ISI4 (sleep dissatisfaction) was connected to the two main clusters of insomnia symptoms (ie, the cluster of nocturnal and daytime symptoms). No significant gender network difference was found. CONCLUSION: Interference with daytime functioning was the most central symptom, suggesting that it may be an important treatment outcome measure for insomnia. Appropriate treatments, such as stimulus control techniques, cognitive behavioral therapy and relaxation training, could be developed. Moreover, addressing sleep satisfaction in treatment could simultaneously ameliorate daytime and nocturnal symptoms.