The network structures of depressive and insomnia symptoms among cancer patients using propensity score matching: Findings from the Health and Retirement Study (HRS).

OBJECTIVE: Both depression and insomnia are found to be more prevalent in cancer patients compared to the general population. This study compared the network structures of depression and insomnia among cancer patients versus cancer-free participants (controls hereafter). METHOD: The 8-item Center for Epidemiological Studies Depression Scale (CESD-8) and the 4-item Jenkins Sleep Scale (JSS-4) were used to measure depressive and insomnia symptoms, respectively. Propensity score matching (PSM) was used to construct the control group using data from the Health and Retirement Study (HRS). In total, a sample consisting of 2216 cancer patients and 2216 controls was constructed. Central (influential) and bridge symptoms were estimated using the expected influence (EI) and bridge expected influence (bridge EI), respectively. Network stability was assessed using the case-dropping bootstrap method. RESULT: The prevalence of depression (CESD-8 total score ≥ 4) in cancer patients was significantly higher compared to the control group (28.56 % vs. 24.73 %; P = 0.004). Cancer patients also had more severe depressive symptoms relative to controls, but there was no significant group difference for insomnia symptoms. The network structures of depressive and insomnia symptoms were comparable between cancer patients and controls. "Felt sadness" (EI: 6.866 in cancer patients; EI: 5.861 in controls), "Felt unhappy" (EI: 6.371 in cancer patients; EI: 5.720 in controls) and "Felt depressed" (EI: 6.003 in cancer patients; EI: 5.880 in controls) emerged as the key central symptoms, and "Felt tired in morning" (bridge EI: 1.870 in cancer patients; EI: 1.266 in controls) and "Everything was an effort" (bridge EI: 1.046 in cancer patients; EI: 0.921 in controls) were the key bridge symptoms across both groups. CONCLUSION: Although cancer patients had more frequent and severe depressive symptoms compared to controls, no significant difference was observed in the network structure or strength of the depressive and insomnia symptoms. Consequently, psychosocial interventions for treating depression and insomnia in the general population could be equally applicable for cancer patients who experience depression and insomnia.

Inter-relationships of depression and insomnia symptoms with life satisfaction in stroke and stroke-free older adults: Findings from the Health and Retirement Study based on network analysis and propensity score matching.

BACKGROUND: Depression and insomnia are common co-occurring psychiatric problems among older adults who have had strokes. Nevertheless, symptom-level relationships between these disorders remain unclear. OBJECTIVES: In this study, we compared inter-relationships of depression and insomnia symptoms with life satisfaction among older stroke patients and stroke-free peers in the United States. METHODS: The study included 1026 older adults with a history of stroke and 3074 matched controls. Data were derived from the US Health and Retirement Study. Depression, insomnia and life satisfaction were assessed. Propensity score matching was employed to identify demographically-similar groups of stroke patients and controls. Central and bridge symptoms were assessed using Expected influence (EI) and bridge EI, respectively. RESULTS: The prevalence of depression in the stroke group (25.0 %) was higher than that of controls (14.3 %, P < 0.001). In stroke group, "Feeling depressed" (CESD1; EI: 5.80), "Feeling sad" (CESD7; EI: 4.67) and "Not enjoying life" (CESD6; EI: 4.51) were the most central symptoms, while "Feeling tired in the morning" (JSS4; BEI: 1.60), "Everything was an effort" (CESD2; BEI: 1.21) and "Waking up during the night" (JSS2; BEI: 0.98) were key bridge symptoms. In controls, the most central symptoms were "Lack of happiness" (CESD4; EI: 6.45), "Feeling depressed" (CESD1; EI: 6.17), and "Feeling sad" (CESD7; EI: 6.12). Furthermore, "Feeling tired in the morning" (JSS4; BEI: 1.93), "Everything was an effort" (CESD2; BEI: 1.30), and "Waking up too early" (JSS3; BEI: 1.12) were key bridge symptoms. Life satisfaction had the most direct associations with "Not enjoying life" (CESD6) and "Feeling lonely" (CESD5) in the two groups, respectively. CONCLUSION: Older adults with stroke exhibited more severe depression and insomnia symptoms. Interventions targeting central and bridge symptoms may help to mitigate the co-occurrence of these symptoms.

The impact of urbanization on youth mental health in Hong Kong.

PURPOSE OF REVIEW: Urbanization is a multifaceted process that can have both positive and negative effects on mental health, especially in adolescents. This paper attempts to summarize the impact of urbanization on youth mental health in Hong Kong. RECENT FINDINGS: Several studies have shown that urbanization is associated with an increased risk of youth mental health problems in Hong Kong. Psychosocial factors like perceived stress, social isolation, and exposure to environmental pollutants may contribute to a negative association between urbanization and mental health issues. Academic pressure, poor transparency and accessibility to mental health services in Hong Kong further exacerbate youth's mental health wellbeing. Environmental factors like housing conditions, green spaces, and visible urban greenery have been found to influence mental health outcomes. Existing empirical studies have found a positive association between urbanization and mental health problems, while others have found no association or even a negative association. SUMMARY: Further research is warranted to investigate the complex relationship between urbanization and youth mental health in Hong Kong. Exploration of effective interventions is necessary to mitigate the negative impacts of urbanization on youth mental health. Understanding this relationship can inform health policy-making and formulate interventions to promote youth's mental health well being in the short-and long run.

Pain trajectories and their associations with cognition among older adults: a 10-year cohort study from network perspective.

BACKGROUND: Few studies have examined the associations between pain trajectories and cognitive function in older adults. This study explored the associations between pain trajectories and different cognitive domains in older adults from a network perspective. METHODS: Data on pain trajectories were derived from the Health and Retirement Study between 2010 and 2020 using latent class growth analyses. Measurements of key cognition domains, including memory, attention, calculation, orientation and language, were included. Linear regression and network analysis were performed to evaluate the associations between different pain trajectories and cognition. RESULTS: A total of 9,551 older adults were included in this study and three trajectories of pain were identified. After controlling for the covariates, persistent severe pain trajectory was associated with poorer overall cognition, memory and calculation ability when compared to mild or non-persistent pain trajectory. In the pain and cognition network model, memory (expected influence (EI) = 0.62), language (EI = 0.58) and calculation (EI = 0.41) were the most central domains. CONCLUSIONS: Pain trajectories appeared stable over time among older adults in this study. Severity of persistent pain was an important risk factor for poor cognition, especially in relation to memory and calculation domains. Interventions targeting memory, language and calculation domains might be useful in addressing cognitive decline in older adults with persistent pain.

The prevalence of poor sleep quality in the general population in China: a meta-analysis of epidemiological studies.

BACKGROUND: The high prevalence of poor sleep quality (PSQ) in the general population leads to negative health outcomes. Since estimates of PSQ prevalence in the Chinese general population vary widely, this meta-analysis aimed to refine these estimates and to identify moderating factors. METHODS: A comprehensive literature search was undertaken in both international (PubMed, PsycINFO, Web of Science, and EMBASE) and Chinese (Wanfang, and the China National Knowledge Infrastructure databases) databases from inception to 23 November 2023. Studies were required to have used standard scales such as the Chinese version of the Pittsburgh Sleep Quality Index (PSQI). The pooled prevalence of PSQ and 95% confidence intervals (CIs) were calculated using a random-effects model. Subgroup and meta-regression analyses were performed to identify sources of heterogeneity. RESULTS: In 32 studies with a combined 376,824 participants, the pooled prevalence of PSQ was 19.0% (95% CI 15.8-22.8%; range 6.6-43.6%). Across 22 studies that reported PSQI data, the pooled mean score was 4.32 (95%CI 3.82-4.81; SD = 0.502). The pooled mean sleep duration across 8 studies was 7.62 (95% CI 7.23-8.00; SD = 0.194) hours. Subgroup analyses showed that lower education (Q = 4.12, P = 0.042), living in less developed regions (Q = 60.28, P < 0.001), and lower PSQI cutoff values (Q = 9.80, P = 0.007) were significantly associated with PSQ. Meta-regression analyses showed that study quality was inversely associated with estimated PSQ prevalence (ß = - 0.442, P = 0.004). LIMITATIONS: Although measures such as subgroup and meta-regression analyses were performed, substantial heterogeneity remained. Information related to sleep quality, such as comorbid physical diseases or psychiatric disorders, substance use, occupational types, and employment status, were not reported in most studies. CONCLUSION: One in five people in the general population of China may have PSQ and people with lower education or living in western regions may be more susceptible.

Mapping prodromal symptoms in patients with bipolar disorder: A network perspective.

Bipolar disorder (BD) is a major mental disorder that significantly impairs behavior and social functioning. This study assessed the network structure of prodromal symptoms in patients with BD prior to their index mood episode. Semi-structured interviews were conducted with the Bipolar Prodrome Symptom Scale-Retrospective (BPSS-R) to examine patients' prodromal symptoms. Network analysis was conducted to elucidate inter-relations between prodromal symptoms. A total of 120 eligible patients participated in this study. Network analysis indicated that the observed model was stable. The edge Mania3-Depression9 ('Racing thoughts' - 'Thinking about suicide', edge weight = 14.919) showed the strongest positive connection in the model, followed by the edge Mania1-depression1 ('Extremely energetic/active' - 'Depressed mood', edge weight = 14.643). The only negative correlation in the model was for Mania7-depression2 ('Overly self-confident' - 'Tiredness or lack of energy', edge weight = -1.068). Nodes Mania3 ('Racing thoughts'), Depression9 ('Thinking about suicide'), Mania1 ('Extremely energetic/active'), and Depression1 ('Depressed mood') were the most central symptoms. Both depressive and manic or hypomanic symptoms appeared in the prodromal phase. Symptoms reflecting 'Racing thoughts', 'Thinking about suicide', 'Extremely energetic/active', and 'Depressed mood' should be thoroughly assessed and targeted as crucial prodromal symptoms in interventions to reduce the risk of BD episodes.

Prevalence and network analysis of internet addiction, depression and their associations with sleep quality among commercial airline pilots: A national survey in China.

OBJECTIVE: Airline pilots are members of a unique occupational group that is often confronted with sleep routine disruptions, yet relatively few studies have examined their mental health status. This study assessed the prevalence and network structure of internet addiction, depression and sleep quality problems in commercial airline pilots. METHOD: A total of 7055 airline pilots were included in analyses. Internet addiction and depression were measured with the Internet Addiction Test (IAT) and 9-item Patient Health Questionnaire (PHQ-9), respectively. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI). The network model was constructed based on an Ising model and its association with sleep quality was evaluated using a flow procedure. RESULTS: Internet addiction, depression and sleep quality were common among airline pilots. The prevalence of internet addiction was 8.0 % (95 % CI: 7.3-8.6 %), while the rates of depression and poor sleep quality were 23.3 % (95 % CI: 22.3-24.2 %) and 33.0 % (95 % CI: 31.9-34.1 %), respectively. In the depression and internet addiction network model, "Fatigue" (PHQ4; Expected Influence (EI): 2.04) and "Depressed/moody/nervous only while being offline" (IAT20; EI: 1.76) were most central symptoms while "Fatigue" (PHQ4; Bridge EI: 1.30) was also the most important bridge symptom. The flow network model of sleep quality with internet addiction and depression showed that "Appetite" (PHQ5) had the strongest positive association with poor sleep quality. CONCLUSION: Internet addiction, depression and sleep quality were common among airline pilots and warrant regular screening and timely treatment. Strategies to improve sleep hygiene may be useful in preventing onsets or exacerbations in depression and internet addiction among airline pilots.

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.

Mental health status among children and adolescents in one-child and multichild families: a meta-analysis of comparative studies.

PURPOSE OF REVIEW: Controversy remains about the difference in mental health status among children and adolescents between one-child and multichild families in China. Thus, we conducted a meta-analysis of studies comparing mental health status between both groups and explored their potential moderating factors. RECENT FINDINGS: Totally, 113 eligible studies encompassing 237 899 participants (one-child families: 83 125; multichild families: 154 774) were included. The pooled SMD of SCL-90 total score was -0.115 [95% confidence interval (95% CI): -0.152; -0.078; I2  = 86.9%]. Specifically, children and adolescents from one-child families exhibited lower scores in terms of somatization (SMD = -0.056; 95% CI: -0.087; -0.026), obsessive-compulsive symptoms (SMD = -0.116; 95% CI: -0.154; -0.079), interpersonal sensitivity (SMD = -0.140; 95% CI: -0.171; -0.109), depression (SMD = -0.123; 95% CI: -0.159; -0.088); anxiety (SMD = -0.121; 95% CI: -0.151; -0.092); phobic anxiety (SMD = -0.124; 95% CI: -0.166; -0.081); paranoid ideation (SMD = -0.040; 95% CI: -0.070; -0.009); and psychoticism (SMD = -0.119; 95% CI: -0.148; -0.089). Study publication year was significantly associated with differences in mental health status between both groups ( P  = 0.015). SUMMARY: Children and adolescents from one-child families had better mental health status compared to those from multichild families in China. Future studies should investigate the underlying factors contributing to such mental health differences, and the potential interventions that could address these mental health problems.

Global prevalence of poor sleep quality in cancer patients: A systematic review and meta-analysis.

OBJECTIVE: Poor sleep quality is common in patients with cancer, but the prevalence rates varied widely across studies. This systematic review and meta-analysis examined the pooled prevalence of poor sleep quality among patients with cancer. METHODS: Systematic literature searches were independently conducted in the major databases (Web of Science, PubMed, EMBASE and PsycINFO). Studies that reported the prevalence of poor sleep quality in patients with cancer were analyzed using a random effects model. Funnel plots and Egger's tests were used to assess publication bias. Statistical analyses were performed using R software. RESULTS: A total of 59 epidemiological studies involving 16,223 patients were included. The pooled prevalence of poor sleep quality in patients with cancer was 57.4% [95% confidence interval (CI): 53.3% - 61.6%]. Additionally, three comparative studies with 372 patients and 412 healthy controls were included. Compared to healthy controls, patients with cancer had a significantly higher risk for poor sleep quality [odd ratio (OR) = 3.0; 95%CI: 1.2-7.2; P < 0.05]. Subgroup analyses of the studies revealed that studies from Middle East & North Africa region and low income countries, and on gynecological cancer as well as those with a lower cut-off value of sleep quality (all P < 0.01) reported a higher prevalence of poor sleep quality. Meta-regression analyses showed that higher prevalence of poor sleep quality was associated with higher prevalence of comorbid depression (P < 0.05) and anxiety (P < 0.01), but was associated with a lower education level (P < 0.05) and alcohol use ratio (P < 0.05). CONCLUSION: Poor sleep quality is common among patients with cancer. Considering the overall high prevalence rate and negative impact of poor sleep quality, appropriate measures to identify and improve poor sleep quality are needed to enhance the clinical outcomes in this group.

Interactive changes in depression and loneliness symptoms prior to and during the COVID-19 pandemic: A longitudinal network analysis.

OBJECTIVES: Depression and loneliness co-occur frequently. This study examined interactive changes between depression and loneliness among older adults prior to and during the COVID-19 pandemic from a longitudinal network perspective. METHODS: This network study was based on data from three waves (2016-2017, 2018-2019, and 2020) of the English Longitudinal Study of Ageing (ELSA). Depression and loneliness were measured with the eight-item version of the Center for Epidemiologic Studies Depression Scale (CESD-8) and three item version of the University of California Los Angeles (UCLA) Loneliness Scale, respectively. A network model was constructed using an Ising Model while network differences were assessed using a Network Comparison Test. Central symptoms were identified via Expected Influence (EI). RESULTS: A total of 4,293 older adults were included in this study. The prevalence and network of depression and loneliness did not change significantly between the baseline and pre-pandemic assessments but increased significantly from the pre-pandemic assessment to during COVID-19 assessment. The central symptom with the strongest increase from pre-pandemic to pandemic assessments was "Inability to get going" (CESD8) and the edge with the highest increase across depression-loneliness symptom communities was "Lack companionship" (UCLA1) - "Inability to get going" (CESD8). Finally, "Feeling depressed" (CESD1) and "Everything was an effort" (CESD2) were the most central symptoms over the three assessment periods. CONCLUSIONS: The COVID-19 pandemic was associated with significant changes in the depression-loneliness network model. The most changed symptoms and edges could be treatment targets for reducing the risk of depression and loneliness in older adults.

Prevalence of sleep disturbances in children and adolescents during COVID-19 pandemic: a meta-analysis and systematic review of epidemiological surveys.

The COVID-19 pandemic and the ensuing widespread lockdown measures have had a negative impact on the mental health of children and adolescents. We thus conducted a meta-analysis of the worldwide prevalence of sleep disturbances in children and adolescents during the COVID-19 pandemic. We performed a systematic literature search of the major international (PubMed, PsycINFO, Web of Science) and Chinese (Chinese Nation Knowledge Infrastructure (CNKI) and WANFANG) databases from their commencement dates to 27 December 2022. Altogether, 57 articles covering 206,601 participants were included in the meta-analysis. The overall prevalence of sleep disturbances was 34.0% (95% confidence interval (CI): 28-41%). The prevalence of parent-reported sleep disturbances during the COVID-19 pandemic was significantly higher than that of self-reported (p = 0.005) sleep disturbances. Epidemiological studies jointly conducted across Asia and Europe had a higher prevalence of sleep disturbances compared to those conducted in Asia, Europe, America, Oceania, or South America alone (p < 0.001). Children had a significantly higher prevalence of sleep disturbances compared to adolescents alone or a mixed cohort of children and adolescents (p = 0.022). Meta-regression analyses revealed that mean age (p < 0.001), quality evaluation score (p < 0.001), and percentage of men (p < 0.001) showed negative associations, while time of survey (B = 1.82, z = 34.02, p < 0.001) showed a positive association with the prevalence of sleep disturbances. Sleep disturbances were common in children and adolescents during the COVID-19 pandemic.

Poor sleep quality in schizophrenia patients: A systematic review and meta-analyses of epidemiological and case-control studies.

OBJECTIVE: Poor sleep quality is common in patients with schizophrenia but estimated prevalence rates in this population have been mixed. This systematic review and meta-analysis examined the prevalence of poor sleep quality in schizophrenia samples and moderators of prevalence from epidemiological studies as well as the risk of poor sleep quality in schizophrenia patients based on case-control studies. METHODS: Both international (PubMed, Web of Science, PsycINFO, EMBASE) and Chinese databases [Chinese Nation knowledge Infrastructure (CNKI) and WANFANG] were systematically searched. Studies that estimated the prevalence of poor sleep quality in schizophrenia were analyzed using a random effects model. Funnel plots and Egger's tests were used to assess publication bias. Statistical analyses were performed using R software. RESULTS: In total, 23 epidemiological studies and nine case-control studies were included. Based on the epidemiological studies, the pooled overall prevalence of poor sleep quality was 63.4 % [95 % confidence interval (CI): 57.0 %-69.9 %]. Additionally, based on the nine case-control studies, schizophrenia patients had a significantly higher risk for poor sleep quality compared to healthy controls [odd ratio (OR) = 4.5; 95%CI: 2.4-8.3; P < 0.0001]. CONCLUSION: Poor sleep quality is common among schizophrenia patients. Considering negative outcomes caused by poor sleep quality, regular screening on poor sleep quality should be conducted and effective interventions should be provided to those in need.

A network model of depressive and anxiety symptoms: a statistical evaluation.

BACKGROUND: Although network analysis studies of psychiatric syndromes have increased in recent years, most have emphasized centrality symptoms and robust edges. Broadening the focus to include bridge symptoms within a systematic review could help to elucidate symptoms having the strongest links in network models of psychiatric syndromes. We conducted this systematic review and statistical evaluation of network analyses on depressive and anxiety symptoms to identify the most central symptoms and bridge symptoms, as well as the most robust edge indices of networks. METHODS: A systematic literature search was performed in PubMed, PsycINFO, Web of Science, and EMBASE databases from their inception to May 25, 2022. To determine the most influential symptoms and connections, we analyzed centrality and bridge centrality rankings and aggregated the most robust symptom connections into a summary network. After determining the most central symptoms and bridge symptoms across network models, heterogeneity across studies was examined using linear logistic regression. RESULTS: Thirty-three studies with 78,721 participants were included in this systematic review. Seventeen studies with 23 cross-sectional networks based on the Patient Health Questionnaire (PHQ) and Generalized Anxiety Disorder (GAD-7) assessments of clinical and community samples were examined using centrality scores. Twelve cross-sectional networks based on the PHQ and GAD-7 assessments were examined using bridge centrality scores. We found substantial variability between study samples and network features. 'Sad mood', 'Uncontrollable worry', and 'Worrying too much' were the most central symptoms, while 'Sad mood', 'Restlessness', and 'Motor disturbance' were the most frequent bridge centrality symptoms. In addition, the connection between 'Sleep' and 'Fatigue' was the most frequent edge for the depressive and anxiety symptoms network model. CONCLUSION: Central symptoms, bridge symptoms and robust edges identified in this systematic review can be viewed as potential intervention targets. We also identified gaps in the literature and future directions for network analysis of comorbid depression and anxiety.

COVID-19 and Sleep Problems: A Perspective from Bibliometric Analysis.

OBJECTIVES: The Coronavirus Disease 2019 (COVID-19) pandemic and the containment measures for COVID-19 have affected sleep quality in the population. This study explored sleep-related research from a bibliometric perspective to provide an overview of the research outputs in this field. METHODS: Original and review articles were retrieved from the Web of Science Core Collection (WOSCC) database from December 2019 to 7 Aug 2023. R package "bibliometrix" was used to summarize the number of articles of authors, institutions, and countries; count the citations of the articles, and generate a Three-Fields Plot. VOSviewer software was applied to visualize the collaboration network among authors and institutions, and to conduct a co-occurrence analysis of keywords. RESULTS: A total of 4,499 articles on COVID-19 and sleep, and 25,883 articles on non-COVID-19 and sleep were included. Sleep related articles were mainly published by authors from China, the USA, and Italy. For COVID-19 and sleep research, Huazhong University of Science was the most productive institution. The Psychiatry Research was the most influential journal across the different subject categories of this field. "Mental health", "anxiety", and "depression" were the most common keywords, while "sleep quality" and "quality of life" were the likely topic areas in terms of future research directions. CONCLUSIONS: Our findings provide a comprehensive perspective for researchers to understand the wider landscape of both COVID-19 and non-COVID-19 sleep-related research area.

Inter-relationships between suicidality and depressive symptoms among children and adolescents experiencing crisis: A network perspective.

OBJECTIVES: Suicidality and depressive symptoms have emerged as major mental health issues reported among children and adolescents experiencing crises. In light of these concerns, this study was conducted to elucidate interrelationships between depressive symptoms and suicidality at an item level within this population during the COVID-19 pandemic, a worldwide public health crisis affecting children and adolescents. METHOD: A cross-sectional study design was used. Primary and secondary school students completed the Children's Depression Inventory - Short Version (CDI-S) and two standard suicidality questions tapping suicidal ideation and suicide plans, respectively. A network analysis was performed to examine inter-connections between depressive symptoms and suicidality. RESULTS: A total of 5380 students participated in the study. Prevalence of suicidal ideation and suicide plans were 12.8 % (95 % CI = 11.9 %-13.7 %) and 9.9 % (95 % CI = 9.2 %-10.8 %), respectively; the prevalence of depressive symptoms was 41.2 % (95%CI = 39.8 %-42.5 %). The network analysis identified CDI4 (self-hatred) as the most influential node with the highest centrality, followed by CDI8 (loneliness), CDI5 (crying), and CDI1 (sadness). Additionally, CDI5 (crying), CDI1 (sadness), CDI4 (self-hatred), and CDI10 (feeling unloved) were the most meaningful nodes linking depressive symptoms with suicidality. CONCLUSIONS: Critical depressive symptoms linked with suicidality among children and adolescents living through the COVID-19 pandemic included self-hatred, loneliness, crying, and sadness. Interventions that target these depressive symptoms may have increased utility in reducing the risk of suicidality within this population.

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.

The effects of psychosocial and behavioral interventions on depressive and anxiety symptoms during the COVID-19 pandemic: a systematic review and meta-analysis.

Psychosocial and behavioral interventions have been shown to significantly reduce depressive and anxiety symptoms in different populations. Recent evidence suggests that the mental health of the general population has deteriorated significantly since the start of Coronavirus Disease 2019 (COVID-19) pandemic. We conducted a systematic review and meta-analysis of studies on the therapeutic effects of psychosocial and behavioral interventions on depression and anxiety during the COVID-19 pandemic. We systematically searched six electronic databases between December 2019 and February 2022 including PubMed, PsycINFO, Scopus, Web of Science, CNKI, and Wanfang Data. We included randomized clinical trials of psychosocial and behavioral interventions in individuals with depressive or anxiety symptoms during the COVID-19 outbreak compared to various control conditions. A total of 35 eligible studies with 5457 participants were included. The meta-analysis results showed that psychosocial and behavioral interventions had statistically significant moderate effects on depression [SMD = - 0.73, 95% CI (- 1.01, - 0.45), I2 = 90%] and large effects on anxiety [SMD = - 0.90, 95% CI (- 1.19, - 0.60), I2 = 92%], especially in the general population and COVID-19 survivors. Exercise and cognitive behavioral therapy were found to be the most effective treatments with moderate-to-large effect size for depression and anxiety during the outbreak of COVID-19. We also found the internet-based approach could also achieve almost equally significant effects on depression and anxiety compared with face-to-face traditional approach. Our findings suggest that cognitive behavioral therapy and physical exercise intervention are significantly effective for depression and anxiety related to the COVID-19 pandemic regardless of the delivery modes, and gender differences should be taken into consideration for better implementation of interventions in clinical and community practice.

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.