The dangers of having only one pandemic exit strategy.

BACKGROUND: When it comes to pandemic response, preparation can be the key. Between 2020 and 2024, the fast-paced development of COVID-19-often compounded by pubic policies' failures to reflect the latest reality and the public's divergent reactions to the pandemic and the policies-means that society should prepare for exit strategies that can reflect the reality of the pandemic and the interests of the people. Yet oftentimes societies only have one exit strategy with limited scope. This paper investigates the dangers of having only one pandemic exit strategy for pandemics like COVID-19. METHODS: Analyses were based on a review of the literature on COVID-19 exit strategies and our own research. The PubMed literature search focused on two concepts-"COVID-19″ and "exit strategy"-and was limited to peer-reviewed papers published between 2020 and 2024 in English. RESULTS: A total of 31 articles were included in the final review. Analyses showed that existing studies on COVID-19 exit strategies often focused on using the modelling method to gauge one exit strategy. Exit strategies were often discussed in the context of implementing, easing, or lifting specific pharmaceutical or non-pharmaceutical interventions. Staged and country-wide coordinated exit strategies were also discussed in the literature, both of which were often deemed as comparatively rigorous options compared to single or stand-alone exit strategies. Drawing on the overall development of COVID-19 and our own research, we presented and discussed the importance of having multiple exit strategies that are considerate of all possible pandemic trajectories, diverse interests of the public, and the communication challenges officials might face in introducing or implementing pandemic policies. CONCLUSION: This paper underscored the importance of having multiple exit strategies for societies to prepare for pandemics. The insights of this study can help inform health policies so that they can more comprehensively and compassionately protect the needs and wants of the "public" in public health, particularly in grave times like COVID-19.

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.

Where should "Humans" be in "One Health"? Lessons from COVID-19 for One Health.

The culling of animals that are infected, or suspected to be infected, with COVID-19 has fuelled outcry. What might have contributed to the ongoing debates and discussions about animal rights protection amid global health crises is the lack of a unified understanding and internationally agreed-upon definition of "One Health". The term One Health is often utilised to describe the imperative to protect the health of humans, animals, and plants, along with the overarching ecosystem in an increasingly connected and globalized world. However, to date, there is a dearth of research on how to balance public health decisions that could impact all key stakeholders under the umbrella of One Health, particularly in contexts where human suffering has been immense. To shed light on the issue, this paper discusses whether One Health means "human-centred connected health" in a largely human-dominated planet, particularly amid crises like COVID-19. The insights of this study could help policymakers make more informed decisions that could effectively and efficiently protect human health while balancing the health and well-being of the rest of the inhabitants of our shared planet Earth.

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.

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.

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.

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.

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.

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.

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.

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.