The Network Structure of Sleep Disorders, Depression, and Anxiety Among Healthcare Workers in High-Population Density City of China

The Network Structure of Sleep Disorders, Depression, and Anxiety Among Healthcare Workers in High-Population Density City of China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Network Structure of Sleep Disorders, Depression, and Anxiety Among Healthcare Workers in High-Population Density City of China Yin Lin, Xiaoya Sun, Andi Huang, Xuan-Zhen Wu, Yaohui Han, weiting Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6518812/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Objective To identify the network structure of depressive symptom and anxiety symptoms among healthcare workers with and without sleep disorders in a high population density city of China. Methods A cross-sectional study was conducted from March to December 2023, the psychological distress were assessed using the Symptom Checklist-90 (SCL-90) and the Pittsburgh Sleep Quality Index (PSQI) among 1,373 healthcare workers. Network analysis was employed to identify the network structure of depressive symptoms and anxiety among individuals with and without sleep disorders separately. Results A stronger correlation between anxiety and depression symptoms, a higher number of edges, more integration and overlap, and a tighter network structure was found among individuals with sleep disorders. Notably, the key bridging symptoms was "sudden, unexplained fear" (A3) and "fear" (A4). Conclusion The sudden, unexplained fear and fear are the core symptoms of comorbid depression and anxiety among hospital medical staff. Focusing on the two symptoms as the main intervention targets may be helpful to prevent and treat comorbidities of depression and anxiety among healthcare workers. Healthcare Workers Depression Anxiety Network Analysis Figures Figure 1 Figure 2 1. Introduction Sleep disorders, depression, and anxiety are severe and recurrent mental health issues that affect millions of people worldwide[ 1 ]. According to the "China National Mental Health Development Report (2021–2022)"reported that the prevalence of depressive symptom is is 10.6%, for anxiety is 15.8% among adults, while the prevalence of insomnia in the general population of China ranges from 6–50%[ 2 ]. A large scale epidemiological survey showed that the prevalence of insomnia was 24.8% in high-population density area of China[ 3 ]. Chronic insomnia not only impairs physical health but also leads to emotional disorders, with depression and anxiety being the most common manifestations[4]. Studies have shown that insomnia symptoms can increase the risk of developing depression and anxiety one year later[ 5 ]. The incidence of depression is notably higher among individuals with sleep disorders, with the prevalence being 31.1% in those with insomnia compared to only 2.7% in those without sleep disorders[ 6 ]. Along with the rapid urbanization in China, the state of mental health also receives growing attention. Empirical measures, however, have not been developed to assess the impact of urbanization on mental health and the dramatic spatial variations. A Population Census in 2010 showed that highly populated cities along the eastern coast show high Center of Epidemiological Studies Depression Scale scores[ 7 ]. Depressive disorders are 39% more prevalent in urban than in rural areas across Europe[ 8 ], Studies have shown that individuals living in cities with high population density have a higher risk of depression[ 9 ]. The living cost is higher in those cities than other areas, one study found that housing prices significantly mediated the associations between the level of urbanicity and depressive symptoms[ 10 ]. A cross-sectional population study showed that the living density was significantly associated with anxiety and stress of residents[ 11 ]. Healthcare workers often face significant job-related stress and challenges due to the nature of their work, which can lead to prolonged psychological distress[ 12 ]. Medical professionals, operating under high-intensity work conditions and confronted with patients' negative emotions, are at an elevated risk of experiencing negative emotional reactions such as anxiety, depression, and hypochondria. A research showed that 72.95% of the subjects experienced severe stress, 40.58% suffered from insomnia, and 65.7% of the respondents had anxiety symptoms of varying degrees of severity [ 13 ].Gawrych’s study showed that the pandemic exacerbates symptoms of depression, anxiety, psychological distress and affects sleep quality[ 14 ]. A meta-analysis confirmed high levels of psychological stress, anxiety, and depression among healthcare personnel, showed that the overall incidence of anxiety was 24.06%[ 15 ].The work-related stress and mental health issues among healthcare professionals have emerged as global public health concerns, significantly compromising their physical and mental well-being as well as their career trajectories [ 16 – 17 ]. The primary mental health problems faced by healthcare professionals manifest as depressive and anxious symptoms [ 18 ], including their comorbid occurrence [ 19 ]. These depressive and anxious symptoms among healthcare workers not only adversely affect their own physical and mental health but also exert a negative impact on the quality of medical services provided and, consequently, on patient safety and health outcomes [ 20 ]. Compared to isolated depressive or anxious symptoms, the comorbidity of these conditions may lead to more severe health consequences, such as an elevated risk of chronic diseases and more pronounced functional impairments[ 21 ]. The network analysis method visualizes the complex interactions between multiple factors by constructing a network graph, which can effectively identify core symptoms and their interrelationships. Unlike traditional research methods, network analysis can quantify the relationships between individual symptoms of depression and anxiety. Within the framework of network analysis theory, there exist interactions among symptoms within symptom clusters of mental disorders. Different nodes are connected by edges, which represent the relationships (partial correlations) between nodes [ 22 ]. The node centrality statistic known as expected influence (EI) is utilized to measure the characteristics of nodes and identify centrally influential symptoms within the network [ 23 ]. Central symptoms in the network model exhibit the strongest associations with other symptoms and may play a primary role in the onset or maintenance of a symptom syndrome [ 24 ]. Consequently, preventive and intervention measures targeting central symptoms may be more effective. Bridge symptoms within the network model may play a crucial role in the occurrence and persistence of comorbidities of the disorder, providing insights for clinicians in preventing and treating comorbid symptoms of mental illnesses.At present, this method has been widely used in multiple fields such as mental health and psychology[ 25 ].A network analysis showed the strongest edge in the network was between nervousness and uncontrollable worry in the anxiety community, the sad mood was the core and bridge symptom.[ 26 ] A network analysis among employees found depression, daytime dysfunction, and well-being were the "bridges" connecting the domains of sleep and mental health in the undirected network.[ 27 ] Two cross-sectional surveys were conducted depressed affect emerged as a robust central symptom and bridge symptom across Anxiety-Depression networks[ 28 ]. Research on the mental health status of healthcare workers, particularly in the context of sleep disorders following COVID-19, is limited. This study focuses on this unique group, analyzing the interactions between symptoms in populations with and without sleep disorders, and identifying bridging symptoms between depression and anxiety among healthcare workers in one of the most high-population density city of China. 2. Methods 2.1Participants The data for this study were collected from psychological health assessments of healthcare workers in Futian District, Shenzhen, from March to December 2023. Inclusion Criteria: Healthcare workers in Futian District, Shenzhen, who were able to complete the questionnaire independently or with assistance. Exclusion Criteria: Questionnaires with excessively short completion times, incomplete information, or severe logical errors were considered invalid and excluded from the analysis. A total of 1,373 valid questionnaires were collected, with 440 male and 933 female participants, and an average age of 42.93 ± 7.18 years.After obtaining full informed consent, participants were directed to a mobile application to access the psychological health assessment platform and completed the electronic questionnaire by scanning a QR code. 2.2 Measures Social-demographical informations, which included age, sex, educational attainment, job position, and length of practices, were collected using a self-reported form. The Symptom Checklist-90 (SCL-90) was used to assess depression and anxiety [ 29 ]. The SCL-90 consists of 90 items and includes nine subscales: somatization, obsessive-compulsive symptoms, interpersonal sensitivity, depression, anxiety, hostility, phobia, paranoia, and psychoticism. The depression subscale scores range from 13 to 65 points. Higher scores reflect more severe depression.The depression subscale includes 13 items: loss of sexual interest, lack of energy, thoughts of ending life, easy crying, feeling trapped, self-blame, loneliness, melancholy, worry, disinterest, hopelessness, effort, and feeling useless. The anxiety subscale scores range from 10 to 50 points. Higher scores reflect more severe anxiety. The anxiety subscale includes 10 items: nervousness, trembling, sudden fear, fearfulness, palpitations, tension, terror, unease, strangeness, and feeling pushed. The Pittsburgh Sleep Quality Index (PSQI) was used to assess the sleep disorders[ 30 ]. Originally developed by the Buysse research team, it is easy to manage and highly correlated with sleep monitoring, making it widely used both domestically and internationally. The total score ranges from 0 to 21, with seven sub-dimensions evaluating sleep disorders, sleep latency, sleep duration, sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. Research in China indicates that a score above 7 is the critical value for identifying adult sleep disorders[ 31 ]. 2.3 Statistical Analysis Data analysis was performed using SPSS version 23.0. Descriptive statistics for continuous variables were presented as mean ± standard deviation (± s), and t-tests were conducted for comparisons. Categorical data were described using prevalence or proportions and analyzed with chi-square tests. Network analysis was performed using R software. According to current guidelines, the "qgraph" and "bootnet" packages in R were used to generate the anxiety and depression network using the "EBICglasso" function. The network model consists of two parameters: nodes and edges. Each node represents an item, and edges indicate the relationships between pairs of items. The network model includes 23 nodes. The width of the edges represents the strength of the correlation between items, with wider edges indicating stronger correlations. Blue edges indicate positive correlations between nodes, while red edges represent negative correlations. Node centrality was evaluated using the "qgraph" package. Strength, a centrality measure, was used to assess a node's importance in the overall network, calculated as the sum of the weights of all edges connected to the node. Betweenness centrality refers to the frequency with which a node lies on the shortest path between two other symptoms. Higher centrality values suggest that a node occupies a more central position in the network. 3. Results 3.1 The prevalence of depressive symptoms, anxiety, and sleep disorder The assessment results indicated prevalence of depressive symptoms, anxiety symptoms at 8.01%, 6.12%, respectively. The average scores were: depression (17.14 ± 7.12), anxiety (12.34 ± 4.59),. Among the 1,373 participants, 455 met the criteria for sleep disorders with a prevalence of 33.14%. The prevalence were 25.68% for males and 36.66% for females. There were statistically significant differences in gender and educational level between the sleep disorder group and the non-sleep disorder group ( P < 0.05; Table 1 ). In the sleep disorder group, the prevalence were 19.78% for depression symptoms and 15.16% for anxiety symptoms. In the non-sleep disorder group, the prevalence were 2.18% for depression symptoms and 1.63% for anxiety symptoms. Table 1 Clinical and demographic variables Total sample N = 9069 Without SD N = 5760 With SD N = 3309 χ2/t P Age(mean,SD) 40.71 9.65 40.40 9.80 41.26 9.35 −4.10 <0.01 Gender 73.18 <0.01 Male 3984 43.93 2725 47.31 1259 38.05 Female 5085 56.07 3035 52.69 2050 61.95 Marital Status 27.22 <0.01 Married 6786 74.83 4302 74.69 2484 75.07 Single 1796 19.80 1196 20.76 600 18.13 Others 487 5.37 262 4.55 225 6.80 Educational Level 25.63 <0.01 Master's and above 2382 26.27 1600 27.78 782 23.63 Bachelor's 6008 66.25 3769 65.43 2239 67.66 Associate's or below 679 7.49 391 6.79 288 8.70 Job Level 42.63 <0.01 Teaching Staff 3348 36.92 2046 35.52 1302 39.35 Comprehensive Management 1718 18.94 1179 20.47 539 16.29 Administrative Law Enforcement 1503 16.57 906 15.73 597 18.04 Medical Staff 1151 12.69 773 13.42 378 11.42 Professional and Technical 786 8.67 488 8.47 298 9.01 Others 540 5.95 353 6.13 187 5.65 Retired Personnel 23 0.25 15 0.26 8 0.24 Job Level 35.37 <0.01 Director-level and above 652 7.19 471 8.18 181 5.47 Section Chief 1850 20.40 1148 19.93 702 21.21 Senior Professional and Technical 1167 12.87 755 13.11 412 12.45 Intermediate Professional and Technical 2481 27.36 1507 26.16 974 29.43 Junior Professional and Technical 732 8.07 465 8.07 267 8.07 Clerk 1219 13.44 772 13.40 447 13.51 Others 968 10.67 642 11.15 326 9.85 Years of Service 28.71 25years 2759 30.42 1670 28.99 1089 32.91 15−24years 2625 28.94 1651 28.66 974 29.43 5−14years 2072 22.85 1335 23.18 737 22.27 <5years 1613 17.79 1104 19.17 509 15.38 3.2 Basic Descriptive Characteristics of Depression and Anxiety Severity Table 2 presents the mean scores, standard deviations (SDs), and predictability for each item of depression and anxiety measured by the SCL-90. Statistical differences in scale items were observed between the SD group and the group without SD ( P < 0.001). Table 2 Basic Descriptive Characteristics of Depression and Anxiety Severity Total Without SD With SD t P Nervousness 1.62 ± 0.90 1.32 ± 0.59 2.14 ± 1.08 -47.34 <0.001 Trembling 1.14 ± 0.47 1.04 ± 0.22 1.31 ± 0.68 -28.61 Suddenly scared 1.21 ± 0.60 1.06 ± 0.28 1.47 ± 0.86 -33.55 Fearful 1.26 ± 0.67 1.08 ± 0.32 1.57 ± 0.94 -36.53 Heart pounding 1.34 ± 0.72 1.13 ± 0.39 1.71 ± 0.97 -40.81 Tense 1.50 ± 0.87 1.22 ± 0.52 1.96 ± 1.11 -43.92 Terror 1.20 ± 0.60 1.05 ± 0.25 1.46 ± 0.88 -33.72 Restless 1.24 ± 0.64 1.06 ± 0.28 1.55 ± 0.92 -38.16 Strange 1.21 ± 0.61 1.06 ± 0.27 1.48 ± 0.89 -33.69 Pushed 1.67 ± 0.97 1.40 ± 0.72 2.14 ± 1.16 -37.82 Sexual interest loss 1.61 ± 0.98 1.36 ± 0.73 2.05 ± 1.17 -35.14 Low in energy 2.01 ± 1.07 1.64 ± 0.80 2.64 ± 1.17 -49.27 Ending life 1.13 ± 0.49 1.03 ± 0.20 1.31 ± 0.74 -27.18 Crying easily 1.29 ± 0.67 1.12 ± 0.40 1.59 ± 0.90 -35.36 Being trapped 1.14 ± 0.52 1.04 ± 0.23 1.33 ± 0.77 -26.87 Blaming 1.40 ± 0.79 1.17 ± 0.47 1.79 ± 1.03 -39.68 Lonely 1.39 ± 0.79 1.18 ± 0.48 1.75 ± 1.04 -36.29 Blue 1.49 ± 0.87 1.21 ± 0.53 1.97 ± 1.11 -44.36 Worrying 1.59 ± 0.94 1.29 ± 0.60 2.12 ± 1.15 -45.97 No interest 1.47 ± 0.86 1.22 ± 0.53 1.91 ± 1.10 -41.27 Hopeless 1.42 ± 0.87 1.18 ± 0.52 1.84 ± 1.15 -38.36 Ending life 1.30 ± 0.71 1.10 ± 0.36 1.65 ± 0.98 -39.13 Worthlessness 1.31 ± 0.75 1.11 ± 0.39 1.66 ± 1.05 -36.20 Depression 18.56 ± 8.28 15.64 ± 4.29 23.61 ± 10.72 -50.73 Anxiety 13.39 ± 5.73 11.43 ± 2.61 16.78 ± 7.73 -48.85 3.2 Network Structure The study divided participants into two groups: those with sleep disorders and those without, and analyzed the network structures of depression and anxiety for each group. The results indicated: As shown in Fig. 1 : This group exhibited fewer edges overall, but the correlations between edges were stronger. Notable strong correlations between items were observed, such as between "Suddenly feeling afraid without reason" (A3) and "Feeling afraid" (A4); "Feeling hopeless about the future" (D11) and "Feeling worthless" (D13); and "Suddenly feeling afraid without reason" (A3) and "Feeling deceived or trapped" (D5). As shown in Fig. 2: This group showed a greater number of edges. Except for the correlation between "Suddenly feeling afraid without reason" (A3) and "Feeling afraid" (A4), other correlations were comparatively weaker. Strong correlations were frequently found between "Suddenly feeling afraid without reason" (A3) and "Feeling afraid" (A4); "Suddenly feeling afraid without reason" (A3) and "Feeling deceived or trapped" (D5); and "Decreased interest in others" (D1) and "Feeling a decrease in energy and slowed activity" (D2). Compared to Fig. 1 and Fig. 2, the network symptoms in the sleep disorder group exhibited more clustering and overlap, resulting in a denser network structure with stronger connections between nodes. 4. Discussion This study primarily employed network analysis to investigate the associations between depressive and anxiety symptoms among healthcare workers in Futian District, Shenzhen, with a specific focus on differentiating between those with and without sleep disorders. The findings revealed a notable comorbid link between sleep disorders and symptoms of depression and anxiety, suggesting that the presence of sleep disorders may intensify the manifestation of these symptoms. Furthermore, the study identified sudden and inexplicable fear (the repetition of "fear" here is likely unintentional and has been adjusted for clarity) as a central symptom characterizing the comorbidity of depression and anxiety among hospital medical staff. Previous research has reported common pathogenic factors between depression, anxiety, and insomnia[ 32 ]. Healthcare workers, due to irregular night shifts or the inherent stress of their profession, often experience disruptions in their circadian rhythms or high levels of psychological stress, which can increase anxiety levels[ 33 ]. Studies have shown that individuals with anxiety often present insomnia as a clinical symptom, and insufficient sleep can further exacerbate anxiety[ 34 , 35 ]. An epidemiological study demonstrated that sleep disorders, particularly insomnia, affect 50% of individuals with anxiety disorders[ 36 ]. Furthermore, research indicates that individuals with anxiety are 2.6 times more likely to experience insomnia compared to those without anxiety. Excessive worry about sleep and sleep deprivation can lead to heightened awareness of sleep difficulties and exacerbate insomnia[ 37 ]. Disrupted activity in the medial prefrontal cortex can increase activity in the limbic system and decrease hippocampal activity, which may be mechanisms through which insomnia leads to anxiety symptoms[ 38 ]. Consistent with our findings, prior studies have shown that 90% of individuals with depression experience sleep disorders, and the incidence of depression among individuals with insomnia is 31.1%, compared to just 2.7% among those without insomnia[ 39 ]. A study conducted a one-year follow-up study of 2,787 middle school students in Guangdong Province, finding that baseline insomnia symptoms increased the risk of developing anxiety and depression after one year[ 40 ]. Dombrovski found that sleep disturbances are an independent risk factor for early relapse in elderly depression patients[ 41 ]. Both insomnia and depression can enhance cortisol awakening response, which is considered an indicator of excessive arousal[ 42 ]. Insomnia can activate the hypothalamic-pituitary-adrenal (HPA) axis, inhibiting the serotonin system in the prefrontal cortex—an area associated with depression. Additionally, HPA axis hyperactivity increases cortisol secretion, which, in turn, can suppress the HPA axis and damage hippocampal cells, further strengthening the relationship between insomnia and depression. The development and maintenance of mental disorders involve interactions among various symptoms, with psychological symptoms forming a dynamic network of interactions. A network analysis of the relationship between depressive and anxiety symptoms in psychiatric patients indicated that sadness and worry are among the most prominent symptoms in the network[ 43 ]. The relationships between symptoms within each disorder are closer than those between symptoms of the two disorders. Another network analysis of depression and anxiety in Chinese female nursing students identified "psychomotor agitation/retardation" and "sense of worthlessness" as key bridging symptoms, given their strong association with suicidal ideation[ 44 ]. The clinical implications for prevention and intervention based on these symptoms are discussed. This study has several limitations. Firstly, the cross-sectional nature of the data does not allow for causal inferences, and future research should include large-scale longitudinal studies. Secondly, the findings are specific to healthcare workers in Futian District and may not be generalizable to other populations. Finally, differences in screening tools may lead to variations in network structures. 5. Conclusion The study underscores a critical concern within the healthcare sector, revealing that sleep disorders significantly exacerbate depressive and anxiety symptoms among healthcare workers.Furthermore, recognizing that sudden, unexplained fear serves as a core symptom in comorbid depression and anxiety among hospital medical staff, a targeted approach focusing on alleviating this fear—alongside other related symptoms—as primary intervention targets may prove instrumental in preventing and treating the comorbidities of depression and anxiety among this vital workforce. Abbreviations Abbreviations Full English Name SCL-90 The Symptom Checklist-90 PSQI The Pittsburgh Sleep Quality Index COVID-19 Corona Virus Disease 2019 EI expected influence HPA hypothalamic-pituitary-adrenal Declarations Ethics approval and consent to participate This study was adhered to the Declaration of Helsinki, and conducted in strict compliance with the ethical guidelines and principles established by the Ethics Committee of Shenzhen Futian District Chronic Disease Prevention and Treatment Hospital. Prior to the initiation of the study, the Ethics Committee carried out a comprehensive evaluation of the study design, methodology, potential risks and benefits to participants, as well as the measures implemented to safeguard participants' rights, privacy, and confidentiality. Additionally, informed consent was obtained from all participants. After careful deliberation, the committee granted its approval for this study. Consent for publication Not Applicable. Availability of data and materials All datasets generated for this study are included in the manuscript.The data that support the fundings of this study are available from the corresponding author upon reasonable request. Competing Interests The authors declare no potential conflicts of interest Funding The study was supported Futian Healthcare Research Project(No.FTWS088) and Subject of Shenzhen health economics society (2025211). Authors' contributions LY played a pivotal role in overseeing the overall design and planning, ensuring a coherent and methodical approach.SX contributed by drafting the initial manuscript, meticulously organizing and cleansing the data to ensure their accuracy and suitability for analysis. HA and YW elaborated on the methodology section, providing a detailed account of the specific implementation steps and data analysis techniques employed, thereby enriching the study's technical rigor.WX and HY collaboratively reviewed and revised the entire manuscript, exercising meticulous attention to both logical coherence and linguistic precision. Acknowledgements This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Written informed consent for publication was obtained from all participants. Clinical trial number: not applicable References Solmi M, Radua J, Olivola M, et al. Age at Onset of Mental Disorders Worldwide: Large-Scale Meta-Analysis of 192 Epidemiological Studies. Mol Psychiatry. 2022;27(1):281–95. Cao XL, Wang SB, Zhong BL, et al. The Prevalence of Insomnia in the General Population in China: A Meta-Analysis. PLoS ONE. 2017;12(2):e170772. 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Beard C, Millner AJ, Forgeard MJ, et al. Network Analysis of Depression and Anxiety Symptom Relationships in a Psychiatric Sample. Psychol Med. 2016;46(16):3359–69. Ren L, Wang Y, Wu L, et al. Network structure of depression and anxiety symptoms in Chinese female nursing students. BMC Psychiatry. 2021;21(1):279. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 Jun, 2025 Reviews received at journal 24 May, 2025 Reviewers agreed at journal 19 May, 2025 Reviewers invited by journal 09 May, 2025 Editor assigned by journal 09 May, 2025 Editor invited by journal 08 May, 2025 Submission checks completed at journal 07 May, 2025 First submitted to journal 07 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6518812","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":454183217,"identity":"b898700c-f85d-4de6-863b-d1ce4e648d6b","order_by":0,"name":"Yin Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBACPmYwZZMAphIKiNDCBtGSlsDABtJiQIwWCHUYooWBKC3svMekbu44n8cv35344YEBgzy/2AFCDuNLk849c7tYso13swTQYYYzZycQ0sJjJp3bdjtxwzHeDSAtCQa3idNyDqRl8w9StBwAadlGtC3G1rltyYkz23K3WSQYSBD2Cz//GcPbuW12if3MZzff/FFhI88vTUALELBIIHEkcCpDBswfiFI2CkbBKBgFIxcAAIxcOpW9kUlJAAAAAElFTkSuQmCC","orcid":"","institution":"Chronic Disease Prevention and Control Institute of Futian District","correspondingAuthor":true,"prefix":"","firstName":"Yin","middleName":"","lastName":"Lin","suffix":""},{"id":454183218,"identity":"34b0884c-f039-4fed-96d8-57d209c33eb7","order_by":1,"name":"Xiaoya Sun","email":"","orcid":"","institution":"Chronic Disease Prevention and Control Institute of Futian District","correspondingAuthor":false,"prefix":"","firstName":"Xiaoya","middleName":"","lastName":"Sun","suffix":""},{"id":454183220,"identity":"c0edb953-7f8d-42f6-bdc5-dc4e98c7f2db","order_by":2,"name":"Andi Huang","email":"","orcid":"","institution":"Chronic Disease Prevention and Control Institute of Futian District","correspondingAuthor":false,"prefix":"","firstName":"Andi","middleName":"","lastName":"Huang","suffix":""},{"id":454183221,"identity":"930a6716-2bc7-4438-b768-3058ee746102","order_by":3,"name":"Xuan-Zhen Wu","email":"","orcid":"","institution":"Chronic Disease Prevention and Control Institute of Futian District","correspondingAuthor":false,"prefix":"","firstName":"Xuan-Zhen","middleName":"","lastName":"Wu","suffix":""},{"id":454183222,"identity":"7e60c17d-0d08-4b55-b9f5-bef451883326","order_by":4,"name":"Yaohui Han","email":"","orcid":"","institution":"Chronic Disease Prevention and Control Institute of Futian District","correspondingAuthor":false,"prefix":"","firstName":"Yaohui","middleName":"","lastName":"Han","suffix":""},{"id":454183223,"identity":"91287f8d-0ccb-4726-88ef-10a94c7dfb28","order_by":5,"name":"weiting Yang","email":"","orcid":"","institution":"Chronic Disease Prevention and Control Institute of Futian District","correspondingAuthor":false,"prefix":"","firstName":"weiting","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-04-24 08:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6518812/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6518812/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82747108,"identity":"a8058452-b3a1-4c8a-984b-318e884df348","added_by":"auto","created_at":"2025-05-14 19:00:43","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":632762,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork of Depression and Anxiety Symptoms. Note: Wider edges representing stronger correlations. Blue edges represent positive correlations between two nodes, whereas red edges represent negative correlations. \"ISI=0\" means no insomnia symptoms; \"ISI=1\" means insomnia symptoms are observed. A1: Crying easily, A2: Trembling, A3: Suddenly scared, A4: Fearful, A5: Heart pounding, A6: Tense, A7: Terror, A8: Restless, A9: Strange, A10: Pushed, D1: Sexual interest loss, D2: Low in energy, D3: Ending life, D4: Crying easily, D5: Being trapped, D6: Blaming, D7: Lonely, D8: Blue, D9: Worrying, D10: No interest, D11: Hopeless, D12: Ending life, D13: Worthlessness.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6518812/v1/a9ef3820da65dabdac506a9c.jpeg"},{"id":82747419,"identity":"4cf0b08e-825c-4fad-9f1e-da38b1637148","added_by":"auto","created_at":"2025-05-14 19:08:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48442,"visible":true,"origin":"","legend":"\u003cp\u003eCentrality Indices of the Depression and Anxiety Network. \u0026nbsp;Note: \"ISI=0\" means no insomnia symptoms; \"ISI=1\" means insomnia symptoms are observed. A1: Crying easily, A2: Trembling, A3: Suddenly scared, A4: Fearful, A5: Heart pounding, A6: Tense, A7: Terror, A8: Restless, A9: Strange, A10: Pushed, D1: Sexual interest loss, D2: Low in energy, D3: Ending life, D4: Crying easily, D5: Being trapped, D6: Blaming, D7: Lonely, D8: Blue, D9: Worrying, D10: No interest, D11: Hopeless, D12: Ending life, D13: Worthlessness.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6518812/v1/505c3f1f2b2191ecb4654228.png"},{"id":82747902,"identity":"9550d5a3-f94f-45b7-8a8d-e2c827b35d09","added_by":"auto","created_at":"2025-05-14 19:24:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1522622,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6518812/v1/07b99df5-68ec-44a8-869d-7012461a4855.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Network Structure of Sleep Disorders, Depression, and Anxiety Among Healthcare Workers in High-Population Density City of China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSleep disorders, depression, and anxiety are severe and recurrent mental health issues that affect millions of people worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to the \"China National Mental Health Development Report (2021\u0026ndash;2022)\"reported that the prevalence of depressive symptom is is 10.6%, for anxiety is 15.8% among adults, while the prevalence of insomnia in the general population of China ranges from 6\u0026ndash;50%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. A large scale epidemiological survey showed that the prevalence of insomnia was 24.8% in high-population density area of China[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Chronic insomnia not only impairs physical health but also leads to emotional disorders, with depression and anxiety being the most common manifestations[4]. Studies have shown that insomnia symptoms can increase the risk of developing depression and anxiety one year later[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The incidence of depression is notably higher among individuals with sleep disorders, with the prevalence being 31.1% in those with insomnia compared to only 2.7% in those without sleep disorders[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlong with the rapid urbanization in China, the state of mental health also receives growing attention. Empirical measures, however, have not been developed to assess the impact of urbanization on mental health and the dramatic spatial variations. A Population Census in 2010 showed that highly populated cities along the eastern coast show high Center of Epidemiological Studies Depression Scale scores[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Depressive disorders are 39% more prevalent in urban than in rural areas across Europe[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e8\u003c/span\u003e], Studies have shown that individuals living in cities with high population density have a higher risk of depression[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The living cost is higher in those cities than other areas, one study found that housing prices significantly mediated the associations between the level of urbanicity and depressive symptoms[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A cross-sectional population study showed that the living density was significantly associated with anxiety and stress of residents[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHealthcare workers often face significant job-related stress and challenges due to the nature of their work, which can lead to prolonged psychological distress[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Medical professionals, operating under high-intensity work conditions and confronted with patients' negative emotions, are at an elevated risk of experiencing negative emotional reactions such as anxiety, depression, and hypochondria. A research showed that 72.95% of the subjects experienced severe stress, 40.58% suffered from insomnia, and 65.7% of the respondents had anxiety symptoms of varying degrees of severity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e13\u003c/span\u003e].Gawrych\u0026rsquo;s study showed that the pandemic exacerbates symptoms of depression, anxiety, psychological distress and affects sleep quality[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A meta-analysis confirmed high levels of psychological stress, anxiety, and depression among healthcare personnel, showed that the overall incidence of anxiety was 24.06%[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e15\u003c/span\u003e].The work-related stress and mental health issues among healthcare professionals have emerged as global public health concerns, significantly compromising their physical and mental well-being as well as their career trajectories [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The primary mental health problems faced by healthcare professionals manifest as depressive and anxious symptoms [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e18\u003c/span\u003e], including their comorbid occurrence [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These depressive and anxious symptoms among healthcare workers not only adversely affect their own physical and mental health but also exert a negative impact on the quality of medical services provided and, consequently, on patient safety and health outcomes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Compared to isolated depressive or anxious symptoms, the comorbidity of these conditions may lead to more severe health consequences, such as an elevated risk of chronic diseases and more pronounced functional impairments[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe network analysis method visualizes the complex interactions between multiple factors by constructing a network graph, which can effectively identify core symptoms and their interrelationships. Unlike traditional research methods, network analysis can quantify the relationships between individual symptoms of depression and anxiety. Within the framework of network analysis theory, there exist interactions among symptoms within symptom clusters of mental disorders. Different nodes are connected by edges, which represent the relationships (partial correlations) between nodes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The node centrality statistic known as expected influence (EI) is utilized to measure the characteristics of nodes and identify centrally influential symptoms within the network [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Central symptoms in the network model exhibit the strongest associations with other symptoms and may play a primary role in the onset or maintenance of a symptom syndrome [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Consequently, preventive and intervention measures targeting central symptoms may be more effective. Bridge symptoms within the network model may play a crucial role in the occurrence and persistence of comorbidities of the disorder, providing insights for clinicians in preventing and treating comorbid symptoms of mental illnesses.At present, this method has been widely used in multiple fields such as mental health and psychology[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e25\u003c/span\u003e].A network analysis showed the strongest edge in the network was between nervousness and uncontrollable worry in the anxiety community, the sad mood was the core and bridge symptom.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e26\u003c/span\u003e] A network analysis among employees found depression, daytime dysfunction, and well-being were the \"bridges\" connecting the domains of sleep and mental health in the undirected network.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e27\u003c/span\u003e] Two cross-sectional surveys were conducted depressed affect emerged as a robust central symptom and bridge symptom across Anxiety-Depression networks[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResearch on the mental health status of healthcare workers, particularly in the context of sleep disorders following COVID-19, is limited. This study focuses on this unique group, analyzing the interactions between symptoms in populations with and without sleep disorders, and identifying bridging symptoms between depression and anxiety among healthcare workers in one of the most high-population density city of China.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1Participants\u003c/h2\u003e \u003cp\u003eThe data for this study were collected from psychological health assessments of healthcare workers in Futian District, Shenzhen, from March to December 2023. Inclusion Criteria: Healthcare workers in Futian District, Shenzhen, who were able to complete the questionnaire independently or with assistance.\u003c/p\u003e \u003cp\u003eExclusion Criteria: Questionnaires with excessively short completion times, incomplete information, or severe logical errors were considered invalid and excluded from the analysis. A total of 1,373 valid questionnaires were collected, with 440 male and 933 female participants, and an average age of 42.93\u0026thinsp;\u0026plusmn;\u0026thinsp;7.18 years.After obtaining full informed consent, participants were directed to a mobile application to access the psychological health assessment platform and completed the electronic questionnaire by scanning a QR code.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measures\u003c/h2\u003e \u003cp\u003eSocial-demographical informations, which included age, sex, educational attainment, job position, and length of practices, were collected using a self-reported form.\u003c/p\u003e \u003cp\u003eThe Symptom Checklist-90 (SCL-90) was used to assess depression and anxiety [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The SCL-90 consists of 90 items and includes nine subscales: somatization, obsessive-compulsive symptoms, interpersonal sensitivity, depression, anxiety, hostility, phobia, paranoia, and psychoticism.\u003c/p\u003e \u003cp\u003eThe depression subscale scores range from 13 to 65 points. Higher scores reflect more severe depression.The depression subscale includes 13 items: loss of sexual interest, lack of energy, thoughts of ending life, easy crying, feeling trapped, self-blame, loneliness, melancholy, worry, disinterest, hopelessness, effort, and feeling useless.\u003c/p\u003e \u003cp\u003eThe anxiety subscale scores range from 10 to 50 points. Higher scores reflect more severe anxiety. The anxiety subscale includes 10 items: nervousness, trembling, sudden fear, fearfulness, palpitations, tension, terror, unease, strangeness, and feeling pushed.\u003c/p\u003e \u003cp\u003eThe Pittsburgh Sleep Quality Index (PSQI) was used to assess the sleep disorders[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Originally developed by the Buysse research team, it is easy to manage and highly correlated with sleep monitoring, making it widely used both domestically and internationally. The total score ranges from 0 to 21, with seven sub-dimensions evaluating sleep disorders, sleep latency, sleep duration, sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. Research in China indicates that a score above 7 is the critical value for identifying adult sleep disorders[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e \u003cp\u003eData analysis was performed using SPSS version 23.0. Descriptive statistics for continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (\u0026plusmn;\u0026thinsp;s), and t-tests were conducted for comparisons. Categorical data were described using prevalence or proportions and analyzed with chi-square tests. Network analysis was performed using R software.\u003c/p\u003e \u003cp\u003e According to current guidelines, the \"qgraph\" and \"bootnet\" packages in R were used to generate the anxiety and depression network using the \"EBICglasso\" function. The network model consists of two parameters: nodes and edges. Each node represents an item, and edges indicate the relationships between pairs of items. The network model includes 23 nodes. The width of the edges represents the strength of the correlation between items, with wider edges indicating stronger correlations. Blue edges indicate positive correlations between nodes, while red edges represent negative correlations.\u003c/p\u003e \u003cp\u003eNode centrality was evaluated using the \"qgraph\" package. Strength, a centrality measure, was used to assess a node's importance in the overall network, calculated as the sum of the weights of all edges connected to the node. Betweenness centrality refers to the frequency with which a node lies on the shortest path between two other symptoms. Higher centrality values suggest that a node occupies a more central position in the network.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The prevalence of depressive symptoms, anxiety, and sleep disorder\u003c/h2\u003e \u003cp\u003eThe assessment results indicated prevalence of depressive symptoms, anxiety symptoms at 8.01%, 6.12%, respectively. The average scores were: depression (17.14\u0026thinsp;\u0026plusmn;\u0026thinsp;7.12), anxiety (12.34\u0026thinsp;\u0026plusmn;\u0026thinsp;4.59),. Among the 1,373 participants, 455 met the criteria for sleep disorders with a prevalence of 33.14%. The prevalence were 25.68% for males and 36.66% for females. There were statistically significant differences in gender and educational level between the sleep disorder group and the non-sleep disorder group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the sleep disorder group, the prevalence were 19.78% for depression symptoms and 15.16% for anxiety symptoms. In the non-sleep disorder group, the prevalence were 2.18% for depression symptoms and 1.63% for anxiety symptoms.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Clinical and demographic variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal sample N\u0026thinsp;=\u0026thinsp;9069\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eWithout SD N\u0026thinsp;=\u0026thinsp;5760\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eWith SD N\u0026thinsp;=\u0026thinsp;3309\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eχ2/t\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(mean,SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e73.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e61.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e27.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e75.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e25.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster's and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor's\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e67.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssociate's or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e42.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTeaching Staff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComprehensive Management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministrative Law Enforcement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical Staff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional and Technical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetired Personnel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e35.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirector-level and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSection Chief\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior Professional and Technical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate Professional and Technical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior Professional and Technical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClerk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of Service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e28.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;25years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026minus;24years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026minus;14years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;5years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Basic Descriptive Characteristics of Depression and Anxiety Severity\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the mean scores, standard deviations (SDs), and predictability for each item of depression and anxiety measured by the SCL-90. Statistical differences in scale items were observed between the SD group and the group without SD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Basic Descriptive Characteristics of Depression and Anxiety Severity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithout SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWith SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNervousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-47.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"24\" rowspan=\"25\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrembling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-28.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuddenly scared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-33.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFearful\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-36.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart pounding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-40.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-43.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerror\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-33.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRestless\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-38.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-33.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePushed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-37.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSexual interest loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-35.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow in energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-49.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnding life\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-27.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrying easily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-35.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeing trapped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-26.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlaming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-39.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLonely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-36.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-44.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorrying\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-45.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo interest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-41.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHopeless\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-38.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnding life\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-39.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorthlessness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-36.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e18.56\u0026thinsp;\u0026plusmn;\u0026thinsp;8.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e15.64\u0026thinsp;\u0026plusmn;\u0026thinsp;4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e23.61\u0026thinsp;\u0026plusmn;\u0026thinsp;10.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-50.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e13.39\u0026thinsp;\u0026plusmn;\u0026thinsp;5.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e11.43\u0026thinsp;\u0026plusmn;\u0026thinsp;2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e16.78\u0026thinsp;\u0026plusmn;\u0026thinsp;7.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-48.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Network Structure\u003c/h2\u003e \u003cp\u003eThe study divided participants into two groups: those with sleep disorders and those without, and analyzed the network structures of depression and anxiety for each group. The results indicated:\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: This group exhibited fewer edges overall, but the correlations between edges were stronger. Notable strong correlations between items were observed, such as between \"Suddenly feeling afraid without reason\" (A3) and \"Feeling afraid\" (A4); \"Feeling hopeless about the future\" (D11) and \"Feeling worthless\" (D13); and \"Suddenly feeling afraid without reason\" (A3) and \"Feeling deceived or trapped\" (D5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;2: This group showed a greater number of edges. Except for the correlation between \"Suddenly feeling afraid without reason\" (A3) and \"Feeling afraid\" (A4), other correlations were comparatively weaker. Strong correlations were frequently found between \"Suddenly feeling afraid without reason\" (A3) and \"Feeling afraid\" (A4); \"Suddenly feeling afraid without reason\" (A3) and \"Feeling deceived or trapped\" (D5); and \"Decreased interest in others\" (D1) and \"Feeling a decrease in energy and slowed activity\" (D2).\u003c/p\u003e \u003cp\u003eCompared to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;2, the network symptoms in the sleep disorder group exhibited more clustering and overlap, resulting in a denser network structure with stronger connections between nodes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study primarily employed network analysis to investigate the associations between depressive and anxiety symptoms among healthcare workers in Futian District, Shenzhen, with a specific focus on differentiating between those with and without sleep disorders. The findings revealed a notable comorbid link between sleep disorders and symptoms of depression and anxiety, suggesting that the presence of sleep disorders may intensify the manifestation of these symptoms. Furthermore, the study identified sudden and inexplicable fear (the repetition of \"fear\" here is likely unintentional and has been adjusted for clarity) as a central symptom characterizing the comorbidity of depression and anxiety among hospital medical staff.\u003c/p\u003e \u003cp\u003ePrevious research has reported common pathogenic factors between depression, anxiety, and insomnia[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Healthcare workers, due to irregular night shifts or the inherent stress of their profession, often experience disruptions in their circadian rhythms or high levels of psychological stress, which can increase anxiety levels[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Studies have shown that individuals with anxiety often present insomnia as a clinical symptom, and insufficient sleep can further exacerbate anxiety[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. An epidemiological study demonstrated that sleep disorders, particularly insomnia, affect 50% of individuals with anxiety disorders[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Furthermore, research indicates that individuals with anxiety are 2.6 times more likely to experience insomnia compared to those without anxiety. Excessive worry about sleep and sleep deprivation can lead to heightened awareness of sleep difficulties and exacerbate insomnia[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Disrupted activity in the medial prefrontal cortex can increase activity in the limbic system and decrease hippocampal activity, which may be mechanisms through which insomnia leads to anxiety symptoms[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Consistent with our findings, prior studies have shown that 90% of individuals with depression experience sleep disorders, and the incidence of depression among individuals with insomnia is 31.1%, compared to just 2.7% among those without insomnia[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. A study conducted a one-year follow-up study of 2,787 middle school students in Guangdong Province, finding that baseline insomnia symptoms increased the risk of developing anxiety and depression after one year[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Dombrovski found that sleep disturbances are an independent risk factor for early relapse in elderly depression patients[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Both insomnia and depression can enhance cortisol awakening response, which is considered an indicator of excessive arousal[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Insomnia can activate the hypothalamic-pituitary-adrenal (HPA) axis, inhibiting the serotonin system in the prefrontal cortex\u0026mdash;an area associated with depression. Additionally, HPA axis hyperactivity increases cortisol secretion, which, in turn, can suppress the HPA axis and damage hippocampal cells, further strengthening the relationship between insomnia and depression.\u003c/p\u003e \u003cp\u003eThe development and maintenance of mental disorders involve interactions among various symptoms, with psychological symptoms forming a dynamic network of interactions. A network analysis of the relationship between depressive and anxiety symptoms in psychiatric patients indicated that sadness and worry are among the most prominent symptoms in the network[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The relationships between symptoms within each disorder are closer than those between symptoms of the two disorders. Another network analysis of depression and anxiety in Chinese female nursing students identified \"psychomotor agitation/retardation\" and \"sense of worthlessness\" as key bridging symptoms, given their strong association with suicidal ideation[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The clinical implications for prevention and intervention based on these symptoms are discussed.\u003c/p\u003e \u003cp\u003eThis study has several limitations. Firstly, the cross-sectional nature of the data does not allow for causal inferences, and future research should include large-scale longitudinal studies. Secondly, the findings are specific to healthcare workers in Futian District and may not be generalizable to other populations. Finally, differences in screening tools may lead to variations in network structures.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe study underscores a critical concern within the healthcare sector, revealing that sleep disorders significantly exacerbate depressive and anxiety symptoms among healthcare workers.Furthermore, recognizing that sudden, unexplained fear serves as a core symptom in comorbid depression and anxiety among hospital medical staff, a targeted approach focusing on alleviating this fear\u0026mdash;alongside other related symptoms\u0026mdash;as primary intervention targets may prove instrumental in preventing and treating the comorbidities of depression and anxiety among this vital workforce.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull English Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eSCL-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eThe Symptom Checklist-90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003ePSQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eThe Pittsburgh Sleep Quality Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eCOVID-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eCorona Virus Disease 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eEI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eexpected influence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eHPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003ehypothalamic-pituitary-adrenal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was adhered to the Declaration of Helsinki, and conducted in strict compliance with the ethical guidelines and principles established by the Ethics Committee of Shenzhen Futian District Chronic Disease Prevention and Treatment Hospital. Prior to the initiation of the study, the Ethics Committee carried out a comprehensive evaluation of the study design, methodology, potential risks and benefits to participants, as well as the measures implemented to safeguard participants' rights, privacy, and confidentiality. Additionally, informed consent was obtained from all participants. After careful deliberation, the committee granted its approval for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets generated for this study are included in the manuscript.The data that support the fundings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported \u0026nbsp;Futian Healthcare Research Project(No.FTWS088) and \u0026nbsp;Subject of \u0026nbsp;Shenzhen health economics society (2025211).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLY played a pivotal role in overseeing the overall design and planning, ensuring a coherent and methodical approach.SX contributed by drafting the initial manuscript, meticulously organizing and cleansing the data to ensure their accuracy and suitability for analysis. HA and YW elaborated on the methodology section, providing a detailed account of the specific implementation steps and data analysis techniques employed, thereby enriching the study's technical rigor.WX and HY collaboratively reviewed and revised the entire manuscript, exercising meticulous attention to both logical coherence and linguistic precision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Written informed consent for publication was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: not applicable\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSolmi M, Radua J, Olivola M, et al. 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BMC Psychiatry. 2021;21(1):279.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Healthcare Workers, Depression, Anxiety, Network Analysis","lastPublishedDoi":"10.21203/rs.3.rs-6518812/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6518812/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo identify the network structure of depressive symptom and anxiety symptoms among healthcare workers with and without sleep disorders in a high population density city of China.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted from March to December 2023, the psychological distress were assessed using the Symptom Checklist-90 (SCL-90) and the Pittsburgh Sleep Quality Index (PSQI) among 1,373 healthcare workers. Network analysis was employed to identify the network structure of depressive symptoms and anxiety among individuals with and without sleep disorders separately.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA stronger correlation between anxiety and depression symptoms, a higher number of edges, more integration and overlap, and a tighter network structure was found among individuals with sleep disorders. Notably, the key bridging symptoms was \"sudden, unexplained fear\" (A3) and \"fear\" (A4).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe sudden, unexplained fear and fear are the core symptoms of comorbid depression and anxiety among hospital medical staff. Focusing on the two symptoms as the main intervention targets may be helpful to prevent and treat comorbidities of depression and anxiety among healthcare workers.\u003c/p\u003e","manuscriptTitle":"The Network Structure of Sleep Disorders, Depression, and Anxiety Among Healthcare Workers in High-Population Density City of China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-14 19:00:38","doi":"10.21203/rs.3.rs-6518812/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"228158907520533491014747687023538447358","date":"2025-06-17T09:08:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-24T12:10:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196777779554894571234887722113962956257","date":"2025-05-19T09:29:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-09T06:31:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-09T06:25:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-08T07:42:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-07T11:21:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2025-05-07T11:20:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ac196954-3fe0-473d-bb95-b4ddfc9e35d5","owner":[],"postedDate":"May 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-14T19:00:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-14 19:00:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6518812","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6518812","identity":"rs-6518812","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}