Prevalence and factors associated with symptoms of depression, anxiety and insomnia among traffic police officers in Beijing, China: a cross-sectional survey

Prevalence and factors associated with symptoms of depression, anxiety and insomnia among traffic police officers in Beijing, China: a cross-sectional survey | 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 Prevalence and factors associated with symptoms of depression, anxiety and insomnia among traffic police officers in Beijing, China: a cross-sectional survey Shufang Feng, Minghe Liu, Xiao Bai, Chen Mo, Tianyao Shi, Yongqing Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6656633/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Police officers represent a distinct subpopulation that is at an elevated risk for mental health issues in China especially during the COVID-19 pandemic period. This study examined the prevalence of depression, anxiety, and insomnia, along with associated risk factors, among police officers in Beijing during the post-pandemic period (five years following the COVID-19 outbreak). Aims The objective of this study was to identify the sociodemographic and clinical factors associated with symptoms of depression, anxiety, and sleep disturbances in the Haidian District of Beijing, China. Methods A cross-sectional study was conducted from March 2024 to April 2024 among 158 randomly selected participants in the Haidian District. The Patient Health Questionnaire-9 (PHQ-9) was utilized to screen for depression, while the Generalized Anxiety Disorder-7 (GAD-7) was employed to assess the severity of anxiety symptoms. The Pittsburgh Sleep Quality Index (PSQI) and Insomnia Severity Index (ISI) questionnaire were administered to evaluate sleep quality and disturbances. Descriptive statistics were used to present frequencies and percentages. univariate and multivariate analyses were performed to identify associations among predictors of depression, anxiety, and insomnia. Results The study included 158 participants, of whom 66.7% were men, with an average age of 41.32 years (± 8.43). Approximately 18.98% of the 137 police officers screened positive for depression, while 14.06% of the 128 participants screened positive for anxiety. PSQI indicated that 16.9% of the 124 participants exhibited clinical (moderate-severe) insomnia of insomnia, and the ISI revealed that 12.5% of the 120 participants were affected by insomnia. None of the examined sociodemographic factors demonstrated significant correlations with depression, anxiety, with the exception of age in relation to insomnia ( P = 0.027). Participants experiencing anxiety and insomnia were found to have an increased risk of reporting depression (anxiety odds ratio [OR]: 48.48, 95% confidence interval [CI]: 5.30 to 445.49, P < .001. ; insomnia odds ratio [OR]: 82.75, 95% [CI]: 3.75 to 1825.6, P = 0.005). Conclusion The prevalence of depression, anxiety, and insomnia among police officers in Haidian district, Beijing, China is remarkably high. The study's findings emphasize the necessity of regularly screening police officers for these mental health issues and implementing tailored mental health services for this population. traffic police officers depression anxiety insomnia China post-pandemic period Figures Figure 1 Strengths and limitations of this study ⇒ We used depression, anxiety and insomnia self-rating scale, which is a commonly used screening tool and has been validated for use in China. ⇒ Participants were randomly selected working under three different traffic units of Haidian district, Beijing city for at least 6 months, and the rigorous methods of data collection allow for generalizability to the entire patrol department in Beijing district ⇒ This study followed a cross-sectional design that limits our ability to infer causal relationships between independent variables and mental / sleep health outcomes. ⇒ The tool used in the study was a screening tool and the outcome measures should not be interpreted as diagnoses of mental health conditions. ⇒ The sample was demographically different from the general patrol officer population; therefore, it is unclear whether generalizable to the department and other law enforcement agencies. Introduction Occupational roles are significant to our lives, as we spend considerable time engaged in work-related activities. Promoting and maintaining the highest level of physical, mental, and social well-being for all working people are the comprehensive goal of World Health Organization Joint Committee on Occupational Health[ 1 ]. Police officers serve as the frontline force in maintaining social order, shouldering crucial responsibilities for safeguarding societal stability and ensuring citizen safety. However, the particularity and complexity of their work frequently exposes them to highly stressful environments and substantial psychological pressures. These pressures stem from various professional challenges, such as crime investigation, patrolling, emergency response, and community interaction, as well as occupational stress resulting from long and often rotating shifts[ 2 – 4 ]. All of these factors have adverse effects on both their psychological well-being and physical health [ 5 , 6 ]. Major depression disorder (MDD) is among the most prevalent mental health disorders worldwide especially during the COVID-19 pandemic[ 7 ]. In 2017, there were approximately 56.36 million patients with MDD in China, accounting for 21.3% of all global cases[ 8 ]. The disability-adjusted lifespan caused by MDD have reached 8.577 million, which means that MDD has gradually become an important public health problem affecting the Chinese population[ 9 ]. Furthermore, the prevalence of MDD among traffic police officers is particularly alarming, with studies indicating that prevalence rates are higher than in the general population in the United States and other countries [ 2 , 10 – 12 ].Police officers are at an elevated risk for developing MDD due to multifactorial causes such as work-related exposure and traumatic events. This mental health issue can significantly impair an officer's ability to perform their duties, affecting decision-making, concentration, and overall job performance. It has been estimated that 8.5% of police officers globally experience suicidal ideation, which is twice as high as the figures reported in the general population[ 13 – 15 ]. Anxiety, closely linked with MDD, is also prevalent among police officers due to constant exposure to hazardous situations, high expectations for public safety, and the psychological toll of dealing with non-compliant or aggressive individuals[ 16 ]. Additionally, sleep problems, including insomnia, disrupted sleep patterns, and non-restorative sleep, are common among traffic police officers. These issues are often a direct result of shift work and the need to remain vigilant, which disrupts the body's natural circadian rhythms. The resultant sleep deprivation not only compounds the risk of developing MDD and anxiety but also poses significant safety risks, both to the officers themselves and the public they serve. This study aimed to assess the prevalence of depression, anxiety, and insomnia, along with their associated factors, among traffic police officers in Beijing five years after the outbreak of the COVID-19 pandemic. Understanding the mental health status of police officers through survey research in the capital of China is essential. By investigating issues related to anxiety, depression, and sleep, we can gain a comprehensive understanding of the current mental health needs of police officers, enabling the provision of appropriate mental health support and intervention measures. Such research efforts facilitate the development of targeted interventions that enhance job satisfaction and the quality of life for police officers, thereby promoting social harmony and stability. Method Study design and setting: The objective of this study was to assess the prevalence of depression, anxiety, and insomnia, as well as identify the related risk factors among traffic police officers in Beijing China. We conducted a district based cross-sectional study in Beijing city, China. Data were collected over the period from March 27th, 2024 to April 11th, 2024. Study population and sampling: The study population was the traffic policer working under three different traffic units of Haidian district, Beijing city for at least 6 months. The list of names of these officers was provided by the Traffic Police commander. A sample of 170 police officers was calculated using the proportional formula for calculating sample size (Results from OpenEpi, Version 3, open-source calculator–SSPropor), An estimated 3.8% of the population experience depression, including 5% of adults (4% among men and 6% among women), and 5.7% of adults older than 60 years. 4% of the global population currently experience an anxiety disorder. Approximately 10% of the adult population suffers from an insomnia disorder[ 17 ]. 5% standard error (SE) were used for calculation. Measures: Data were collected from participants at their respective traffic units using self-administered questionnaires, which included sections on sociodemographic information (age, gender, education, BMI, and health habits), as well as surveys on depression, anxiety, and insomnia. The principal investigator, with the assistance of research assistants, oversaw the data collection process. Participants were provided with electronic versions of the questionnaires, along with instructions on how to complete them. Completing the questionnaires in a private setting was emphasized to ensure data accuracy and confidentiality. On average, each participant took 15 to 20 minutes to complete the questionnaires. The severity of depressive symptoms was evaluated using the validated Patient Health Questionnaire-9 (PHQ-9) [ 18 ]. This questionnaire comprises nine items assessing common symptoms of depression, including low mood, loss of interest, sleep disturbances, and self-evaluation. Participants select the response that best corresponds to their condition for each item, based on the past two weeks, with scores ranging from 0 (not at all) to 3 (nearly every day). The item scores are summed to obtain a total score between 0 and 27. Higher scores denote more severe depressive symptoms, with scores categorized as 10–14 for moderate, 15–19 for moderately severe, and 20–27 for severe depression. The scale demonstrates 78% sensitivity and 87% specificity. Validation was conducted by comparing with diagnoses from consultant psychiatrists using the Structured Clinical Interview for DSM Disorders. A threshold score of ≥ 10 is commonly used to diagnose. MDD in alignment with the initial validation study by Kroenke et al. which reported a sensitivity and specificity of 88% for detecting MDD[ 18 ]. Generalized Anxiety Disorder-7 (GAD-7) is a questionnaire designed to assess the severity of anxiety symptoms. It comprises seven items that encompass common symptoms such as feeling anxious, excessive worry, anxious mood, and physical discomfort, aligning with most of the DSM-IV criteria for generalized anxiety disorder[ 19 ]. Participants indicate the response that best reflects their condition for each item based on the past two weeks. Scores range from 0 (not at all) to 3 (nearly every day), and the item scores are summed to produce a total score between 0 and 21. A higher total score signifies more severe anxiety symptoms. A score of ≥ 10 is considered a reasonable cut-off for identifying cases of GAD. The Pittsburgh Sleep Quality Index (PSQI) is a widely utilized self-report questionnaire designed to assess sleep quality and disturbances over a one-month period[ 20 ]. It consists of 24 items, 19 of which are self-reported, while 5 are evaluated by a sleeping partner, and are not included in the score calculation. These 19 items are grouped into seven components, covering aspects such as sleep quality, sleep latency, sleep duration, and medication use, providing a comprehensive and multidimensional evaluation of various aspects of sleep. Participants select the response that best corresponds to their condition for each item based on the past month. Scores range from 0 (no problem) to 3 (severe problem), and the scoring for each item is summed to obtain a total score ranging from 0 to 21. A higher total score indicates poorer sleep quality. The PSQI demonstrates moderate value in screening for insomnia, with an optimal cut-off score of 5, and a score of ≥ 6 confirms the presence of sleep disturbances[ 20 , 21 ]. The Insomnia Severity Index (ISI) is a questionnaire designed to evaluate the severity of insomnia. It comprises seven items covering various aspects such as insomnia severity, sleep onset difficulties, nighttime awakenings, and early morning awakenings. Participants select the response that best reflects their condition for each item based on the past two weeks. Scores range from 0 (no problem) to 4 (severe problem) for each item, and these are summed to produce a total score ranging from 0 to 28, with higher scores indicating greater insomnia severity. Data management and analysis: All data entry, cleaning, and subsequent analysis were conducted using SPSS Version 26 (IBM Corp., Armonk, NY) for Windows. In data cleaning, we performed a missing value analysis. A double tailed p-value of < 0.05 indicated statistical significance in all analyses. Descriptive analysis of continuous variables was expressed as means (M) and standard deviations (SD), while categorical variables were presented as proportions and percentages of the total. Depression, anxiety, and insomnia were analyzed as dichotomous variables. Participants were categorized into two groups based on their scores: PHQ-9 < 10 and ≥ 10 for no depression and depression groups; GAD-7 < 10 and ≥ 10 for no anxiety and anxiety groups; and PSQI 15 for clinic insomnia. The χ2 test was employed to compare categorical variables between groups. For continuous variables, one-way analysis of variance was used for normally distributed variables, and the Kruskal-Wallis H test was applied to compare skewed distributions between groups. A multivariate logistic regression analysis was conducted to identify potential factors associated with depression. In this analysis, depression status served as the outcome (dependent) variable, while socio-demographic factors, anxiety, and insomnia were considered predictor (independent) variables. Crude odds ratios, with 95% confidence intervals, were calculated as measures of effect. Ethical and administrative issues: Ethical clearance for the study was obtained from the Ethics Review Committee of the Third Medical Center, Chinese PLA General Hospital. Administrative clearance was granted by the Inspector General of Police, Haidian District. Informed written consent was secured from all participants before data collection, following a detailed explanation of the study's purpose and procedures. Results 1. Sociodemographic characteristics of the study participants: Of the total of 170 police officer enrolled, with a response rate of 92.94% (158 of 170), the remainder did not meet the inclusion criteria (see the flowchart Fig. 1). Twelve participants were excluded for failing to answer any questions in the first section of the questionnaire, which covered sociodemographic information. The majority of participants were male (66.7%). Twenty-two participants were aged 21–30 years (13.9%), 53 were 31–40 years (33.5%), 54 were 41–50 years (34.2%), and 29 were 51–60 years (18.4%). Approximately 56.3% of the officers had completed senior high school education. The mean body mass index (BMI) was 27.24 (± 8.24), with 38.0% classified as overweight (BMI 24-27.9) and obese (29.1%, BMI ≥ 28). Among the respondents, 52.5% were non-smokers, 64.6% did not consume alcohol, and 72.2% did not drink coffee, while 70.3% were tea consumers. About 57% of the participants reported regular exercise (see Table 1 ). Table 1 Sociodemographic characteristics of the study participants Characteristic N Percent (%) Sex (n = 158) Male 107 66.7 Female 51 33.3 Age groups(n = 158) 21–30 22 13.9 31–40 53 33.5 41–50 54 34.2 51–60 29 18.4 Degree of education(n = 158) Primary school or below 1 0.6 Junior high school 18 11.4 Senior high school 89 56.3 Bachelor 47 29.7 Master 1 0.6 Doctor 2 1.3 BMI for Chinese (n = 158) Underweight (< 18.5) 4 2.5 Normal (18.5–23.9) 48 30.4 Overweight (24–27.9) 60 38.0 Obese (≥ 28) 46 29.1 Smoking (n = 158) Yes 75 47.2 No 83 52.5 Alcohol Drinking (n = 158) Yes 56 35.4 No 102 64.6 Tea Drinking (n = 158) Yes 111 70.3 No 47 29.7 Coffee Drinking (n = 158) Yes 44 27.8 No 114 72.2 Regular exercise (n = 158) Yes 90 57.0 No 68 43.0 2. Prevalence of depression, anxiety and insomnia among traffic police officers In the depression analysis, 21 patients were excluded due to non-response to PHQ-9 questions, resulting in 137 police officers in the final analysis. For the anxiety analysis, 30 participants were excluded due to non-response to GAD-7 questions, and 128 police officers met the criteria for analysis. In the insomnia analysis, 34 and 38 participants were excluded for non-response to PSQI and ISI questions, respectively, leaving 124 and 121 police officers in the final analysis (Fig. 1). Table 2 presents individualized descriptive statistics for GAD-7, PHQ-9, PSQI, and ISI, respectively. The average PHQ-9 score for depression was 5.18 (M = 5.18; SD = 39.032), the average GAD-7 score for anxiety was 3.62 (M = 3.62; SD = 28.36), and the average PSQI and ISI scores for insomnia were 6.2 (M = 6.2; SD = 19.02) and 5.94 (M = 5.94; SD = 45.85) (Table 2 ). Using a cut-off score of 10 for the PHQ-9, we identified 26 (18.98%) traffic police officers with moderate or severe depression symptoms. For the GAD-7, using a cut-off of 10, 18 (14.06%) officers were identified with moderate or severe anxiety symptoms. With a PSQI cut-off score of 5, 21 (16.9%) officers had sleep disturbances and ISI score 15 (12.5%) have sleep problem. The component of PSQI score were also calculated with mean ( ± SD) (Table 2 ). Table 2 Prevalence of depression, anxiety and sleep quality among traffic police officers Outcomes Categories Number (%) PHQ-9 (0 ~ 27, n = 137) (Score of depression subscale) Normal (0 ~ 4) 83 (60.6) Mild (5 ~ 9) 28 (20.4) Moderate (10 ~ 14) 12 (8.8) Moderate to Severe (15 ~ 19) 8 (5.8) Severe (20 ~ 27) 6 (4.4) Total with depression 26 (18.98) Mean (± SD) 5.18 (39.028) GAD-7(0 ~ 21, n = 128) (Score of anxiety subscale) Normal (0 ~ 4) 98 (76.6) Mild (5 ~ 9) 12 (9.4) Moderate (10 ~ 14) 10 (7.8) Moderate to Severe (15 ~ 18) 4 (3.1) Severe (19 ~ 21) 4 (3.1) Total with anxiety 18 (14.06) Mean (± SD) 3.62 (28.364) PSQI quality (0 ~ 21, n = 124) (Score of Sleep quality) No problem 15 4 (3.2) Total with sleep problem 21(16.9) ISI (0 ~ 28, n = 120) (Score of insomnia) No clinically significant insomnia (0 ~ 7) 81 (67.5) Subthreshold insomnia (8 ~ 14) 24 (20.0) Clinical insomnia (mild severity) (15 ~ 21) 10 (8.3) Clinical insomnia (moderate severity) (22 ~ 28) 5 (4.2) Total with sleep problem 15 (12.5) Mean (± SD) 5.94 (45.854) Component Score Mean ( ± SD) PSQI component score (0 ~ 3, n = 124) Subjective sleep quality (C1) 0.93(0.085) Sleep latency (C2) 0.83 (0.939) Sleep duration (C3) 1.3 (0.910) Sleep efficiency (C4) 0.96 (1.112) Sleep disturbance (C5) 0.85 (0.515) Use of sleep medication (C6) 0.25 (0.498) Daytime dysfunction (C7) 1.08 (1.148) Global PSQI Score mean (± SD) 6.2 (19.024) 3. Sociodemographic factors associated with depression, anxiety and insomnia among traffic police officers Tables 3 , and Supplementary table1 and 2 present bivariate Chi-square (χ²) and correlation analyses concerning sociodemographic information and the depression, anxiety, insomnia, respectively. Bivariate analyses were conducted to assess the associations between symptoms of depression, anxiety, and insomnia and various sociodemographic characteristics, such as sex, age, education level, BMI, smoking, alcohol consumption, tea consumption, coffee consumption, and regular exercise. None of the sociodemographic factors, except for age, showed weakly statistic differences between insomnia and non-insomnia (P = 0.027) among traffic police officers. The associations among depression, anxiety, and insomnia were statistically significant (p < 0.001). Table 3 Factors associated with depression among traffic police officers (n = 137) Variables Total (n = 137) No depression (n = 111) Depression (n = 26) Statistic P Age, n (%) 0.254 21–30 21 (15.33) 15 (13.51) 6 (23.08) 31–40 48 (35.04) 38 (34.23) 10 (38.46) 41–50 44 (32.12) 38 (34.23) 6 (23.08) 51–60 24 (17.52) 20 (18.02) 4 (15.38) Sex, n (%) χ²=1.30 0.254 Male 92 (67.15) 77 (69.37) 15 (57.69) Female 45 (32.85) 34 (30.63) 11 (42.31) Degree of education, n(%) 0.452 Primary school or below 1 (0.73) 1 (0.90) 0 (0.00) Junior high school 15 (10.95) 12 (10.81) 3 (11.54) Senior high school 79 (57.66) 66 (59.46) 13 (50.00) Bachelor 40 (29.20) 31 (27.93) 9 (34.62) Master 1 (0.73) 0 (0.00) 1 (3.85) Doctor 1 (0.73) 1 (0.90) 0 (0.00) BMI, mean, n(%) 0.320 Underweight (< 18.5) 3 (2.19) 2 (1.80) 1 (3.85) Normal (18.5–23.9) 42 (30.66) 32 (28.83) 10 (38.46) Overweight (24–27.9) 50 (36.50) 44 (39.64) 6 (23.08) Obese (≥ 28) 42 (30.66) 33 (29.73) 9 (34.62) Smoking, n(%) χ²=1.67 0.196 No 74 (54.01) 57 (51.35) 17 (65.38) Yes 63 (45.99) 54 (48.65) 9 (34.62) Alcohol Drinking, n(%) χ²=0.06 0.802 No 92 (67.15) 74 (66.67) 18 (69.23) Yes 45 (32.85) 37 (33.33) 8 (30.77) Tea Drinking, n(%) χ²=1.30 0.254 No 45 (32.85) 34 (30.63) 11 (42.31) Yes 92 (67.15) 77 (69.37) 15 (57.69) Coffee Drinking, n(%) χ²=3.81 0.051 No 100 (72.99) 85 (76.58) 15 (57.69) Yes 37 (27.01) 26 (23.42) 11 (42.31) Regular exercise, n(%) χ²=1.32 0.251 No 60 (43.80) 46 (41.44) 14 (53.85) Yes 77 (56.20) 65 (58.56) 12 (46.15) Anxiety, n(%) χ²=34.58 < .001 No 96 (85.71) 87 (95.60) 9 (42.86) Yes 16 (14.29) 4 (4.40) 12 (57.14) Insomnia, n (%) χ²=15.03 < .001 No 54 (50.47) 51 (60.00) 3 (13.64) Yes 53 (49.53) 34 (40.00) 19 (86.36) GAD-7 Score, n (%) - < .001 Normal (0 ~ 4) 86 (76.79) 81 (89.01) 5 (23.81) Mild (5 ~ 9) 10 (8.93) 6 (6.59) 4 (19.05) Moderate (10 ~ 14) 9 (8.04) 3 (3.30) 6 (28.57) Moderate to Severe (15 ~ 18) 4 (3.57) 1 (1.10) 3 (14.29) Severe (19 ~ 21) 3 (2.68) 0 (0.00) 3 (14.29) PSQI Score, n(%) - < .001 No problem 15 3 (2.80) 0 (0.00) 3 (13.64) ISI Score, n(%) - < .001 No problem (0 ~ 7) 70 (65.42) 65 (77.38) 5 (21.74) Mild (8 ~ 14) 23 (21.50) 18 (21.43) 5 (21.74) Moderate (15 ~ 21) 10 (9.35) 1 (1.19) 9 (39.13) Severe (22 ~ 28) 4 (3.74) 0 (0.00) 4 (17.39) χ²: Chi-square test, -: Fisher exact, *Factors that were statistically signiffcant (p < 0.05). Table 4 Associations of depression with anxiety and insomnia (n = 124) Variables Univariate Multivariate β S.E Z P OR (95%CI) β S.E Z P OR (95%CI) Anxiety No 1.00 (Reference) 1.00 (Reference) Yes 3.37 0.68 4.99 < .001 29.00 (7.72 ~ 108.93) 3.88 1.13 3.43 < .001 48.58 (5.30 ~ 445.49) Insomnia No 1.00 (Reference) 1.00 (Reference) Yes 2.25 0.66 3.41 < .001 9.50 (2.61 ~ 34.60) 4.42 1.58 2.80 0.005 82.75 (3.75 ~ 1825.66) Sex Male 1.00 (Reference) 1.00 (Reference) Female 0.51 0.45 1.13 0.257 1.66 (0.69 ~ 3.99) 1.07 1.69 0.63 0.527 2.91 (0.11 ~ 79.62) Smoking No 1.00 (Reference) 1.00 (Reference) Yes -0.58 0.45 -1.28 0.200 0.56 (0.23 ~ 1.36) 0.06 1.09 0.06 0.953 1.07 (0.12 ~ 9.12) Alcohol Drinking No 1.00 (Reference) 1.00 (Reference) Yes -0.12 0.47 -0.25 0.802 0.89 (0.35 ~ 2.23) -0.66 1.21 -0.55 0.583 0.51 (0.05 ~ 5.52) Tea Drinking No 1.00 (Reference) 1.00 (Reference) Yes -0.51 0.45 -1.13 0.257 0.60 (0.25 ~ 1.45) -1.41 0.99 -1.42 0.154 0.24 (0.04 ~ 1.70) Coffee Drinking No 1.00 (Reference) 1.00 (Reference) Yes 0.87 0.46 1.92 0.055 2.40 (0.98 ~ 5.86) 2.69 1.12 2.40 0.016 14.77 (1.64 ~ 133.04) Regular exercise No 1.00 (Reference) 1.00 (Reference) Yes -0.50 0.44 -1.14 0.254 0.61 (0.26 ~ 1.43) -0.13 0.98 -0.13 0.895 0.88 (0.13 ~ 5.96) BMI, mean, n(%) Underweight (< 18.5) 1.00 (Reference) 1.00 (Reference) Normal (18.5–23.9) -0.47 1.28 -0.37 0.713 0.63 (0.05 ~ 7.64) -3.16 2.29 -1.38 0.169 0.04 (0.00 ~ 3.81) Overweight (24–27.9) -1.30 1.30 -1.00 0.317 0.27 (0.02 ~ 3.48) -5.39 2.51 -2.14 0.032 0.00 (0.00 ~ 0.63) Obese (≥ 28) -0.61 1.28 -0.47 0.636 0.55 (0.04 ~ 6.72) -4.05 2.53 -1.60 0.110 0.02 (0.00 ~ 2.49) Age, n (%) 21–30 1.00 (Reference) 1.00 (Reference) 31–40 -0.42 0.60 -0.70 0.485 0.66 (0.20 ~ 2.13) 0.29 1.42 0.21 0.837 1.34 (0.08 ~ 21.65) 41–50 -0.93 0.65 -1.42 0.155 0.39 (0.11 ~ 1.42) -0.03 1.33 -0.02 0.983 0.97 (0.07 ~ 13.30) 51–60 -0.69 0.73 -0.95 0.343 0.50 (0.12 ~ 2.09) 0.12 1.98 0.06 0.952 1.13 (0.02 ~ 55.12) OR: Odds Ratio, CI: Confidence Interval 4. Factors association with depression among police officers. Univariate and multivariate logistic regression revealed that the police officers who have anxiety and insomnia were 3.88 (95% Cl: 2.156 to 90.410) and 15.267 (95% Cl:2.052-113.585) times respectively more likely to have depressive symptoms compared with normal score (see Tables 6). Discussion This study investigates the prevalence of depression, anxiety, and insomnia among 158 police officers in Haidian District Beijing, China, five years after the onset of the COVID-19 pandemic. The results revealed that 18.98% of participants screened positive for depression, while 14.06% screened positive for anxiety. The Pittsburgh Sleep Quality Index (PSQI) indicated that 16.9% of the 124 participants exhibited signs of clinical (moderate-severe) insomnia and Insomnia Severity Index (ISI) revealed that 12.5% of the 120 participants were affected by insomnia. Sociodemographic factors did not significantly correlate with psychological issues, except for age weakly statistic differ between insomnia and non-insomnia (P = 0.027) among traffic police officers. Participants experiencing moderate anxiety and insomnia were found to have an increased risk of reporting depression. Global epidemiological data from the World Health Organization (WHO) indicate a significant prevalence of mental health disorders across populations. Current estimates suggest that depression affects approximately 3.8% of the global population, with differential prevalence rates observed across demographic groups: 5% among adults (4% in males and 6% in females) and 5.7% in individuals aged over 60 years, representing approximately 280 million cases worldwide [ 22 ]. Sleep disorders, particularly insomnia, affect an estimated 10% of the adult population [ 17 ]. The COVID-19 pandemic has exacerbated mental health challenges, as evidenced by epidemiological studies conducted in China. Research findings indicate that 34.4% of respondents exhibited clinically significant depressive symptoms during the pandemic period [ 22 ].. Furthermore, empirical data demonstrate that more than one-third of the study population experienced moderate to severe mental health disturbances [ 23 ], representing a marked increase compared to pre-pandemic baseline levels. These findings underscore the substantial impact of global health crises on population mental health outcomes. Following the COVID-19 outbreak, China's economic growth rate fell from 6–2.2% in 2020. This unprecedented pandemic altered the domestic situation and posed significant economic challenges. To maintain social stability, police officers confronted substantial challenges, enduring immense pressure while ensuring social security and public order in a complex environment [ 24 ]. A related survey indicated that during the pandemic, frontline police officers were responsible for additional tasks, such as patient isolation and quarantine control, which extended their long and irregular working hours [ 25 ]. They worked tirelessly on the front lines during and after the pandemic. Consequently, they are considered a high-risk group for developing psychological illnesses due to stressful tasks, job burnout, and emotional exhaustion [ 26 , 27 ]. These responsibilities also placed them in stressful circumstances, potentially impacting their mental health and work performance [ 28 ]. Epidemiological studies have revealed significant mental health challenges among traffic police officers across diverse geographical contexts. A cross-sectional investigation conducted among Ethiopian police officers identified prevalence rates of 28.9% for depression and 30.2% for anxiety, with sleep disturbances affecting 13.8% (subthreshold insomnia) and 2.1% (clinical insomnia) of the cohort [ 29 ]. Similarly, research among traffic police officers in Kathmandu, Nepal, demonstrated elevated psychological distress, with 41.3% exhibiting depressive symptoms, 47% reporting anxiety symptoms, and 44% experiencing stress-related symptoms [ 30 ]. Comparative data from China indicate a positive mental health detection rate of 37.75% among police personnel [ 31 ]. The observed variability in prevalence rates across studies may be attributed to methodological differences in population sampling and assessment instruments. Parallel investigations among healthcare professionals, another high-risk occupational group, reveal comparable mental health challenges. Liang et al. [ 32 ] documented that 30.43%, 20.29%, and 14.49% of frontline medical practitioners in China experienced depression, anxiety, and insomnia, respectively, during the COVID-19 pandemic. Complementary findings from Bangladesh indicate slightly higher prevalence rates among frontline physicians, with 36.5% reporting anxiety, 38.4% depression, and 18.6% insomnia [ 33 ]. Notably, comparative analyses suggest that Wuhan police personnel exhibited superior mental health outcomes relative to healthcare staff, potentially attributable to differential levels of occupational stress and workload intensity [ 24 ].. The current investigation reveals that police officers demonstrate elevated levels of depression and anxiety compared to WHO normative data, though these levels remain below those reported during the peak pandemic period. Furthermore, officers presenting with moderate anxiety and insomnia symptoms showed significantly increased vulnerability to depressive disorders, consistent with previous epidemiological findings [ 24 , 34 – 36 ]. Proactive implementation of psychological support mechanisms plays a crucial role in monitoring police officers' mental health status and facilitating timely interventions. Systematic psychological evaluations, coupled with regular check-ins and professional counseling sessions, enable officers to effectively process occupational stressors, develop adaptive coping mechanisms, and enhance emotional resilience. The establishment of dedicated psychological clinics and confidential consultation hotlines provides secure environments for personnel to articulate their concerns, while routine psychological assessments should be institutionalized within police departments. The current deficiency in proactive psychological counseling services significantly impedes the mitigation of negative emotional states and psychological distress among officers. Furthermore, the development of comprehensive psychological profiles for police personnel is imperative to gain a holistic understanding of their post-pandemic mental and physical well-being. For officers exhibiting symptoms of severe psychological disorders, evidence-based interventions and immediate therapeutic support should be implemented through targeted assistance programs. Limitations This study has limitations. First, participants were included only from 3 police department and samples were limited thus, external validity is limited. However, the rigorous methods of data collection allow for generalizability to the entire patrol department in Beijing district. Second, given the time restraint of the survey, we included only the brief screeners of mental illness symptoms. However, the short versions have high sensitivity and specificity for mental illness in primary care. Third, the sample was significantly different demographically from the general patrol officer population; therefore, it is unclear whether our findings are generalizable to the department and other law enforcement agencies. Conclusions This study reveals the prevalence of depression, anxiety, and insomnia symptoms among front-line police officers in Haidian District Beijing, China, during the post-pandemic period (five years following the COVID-19 outbreak). The findings underscored the sociodemographic factors such as age, marital status, and education level on police mental health. It is crucial to prioritize the development and awareness of mental health screening among police officers. Policymakers and social psychological healthcare organizations should establish internal teams to address mental health issues and implement innovative stress-coping strategies to effectively enhance officers' mental well-being. Declarations Acknowledgements: The authors would like to acknowledge Dr Yongqing Zhang for her assistance in language editing Author contributions: Ruyi Zhang and Minghe Liu contributed equally to conception and data curation and was responsible for writing original draft preparation and revising it critically. Shufang Feng took responsibility for formal analysis and review and editing. Tianyao Shi contributed to project administration, interpretation of data, organization, and coordination. Chen Mo supported participant recruitment and data analysis and interpretation. All authors contributed to the article and approved the submitted version. Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not- for- profit sectors. Conflict of interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Ethics statement: The studies involving human participants were reviewed and approved by the Ethics Committee of The Third Medical Center, Chinese PLA General Hospital, Beijing, China (reference number: KY-2024-045). The participants provided their written informed consent to participate in this study. We ensured voluntary participation in the study, and the confidentiality and privacy of the participants were maintained. Data availability statement: Data set will be made available upon reasonable request. Provenance and peer review: Not commissioned; externally peer reviewed. Open access: This is an open access article distributed in accordance with the Creative Commons Attribution Non-Commercial (CC BY- NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non- commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. ORCID iD: Shufang Feng: https://orcid.org/0000-0002-1681-7096. References van den Berg TI et al (2008) The influence of psychosocial factors at work and life style on health and work ability among professional workers. Int Arch Occup Environ Health 81(8):1029–1036 Hartley TA et al (2011) Health disparities in police officers: comparisons to the U.S. general population. Int J Emerg Ment Health 13(4):211–220 McCraty R, Atkinson M (2012) Resilience Training Program Reduces Physiological and Psychological Stress in Police Officers. Glob Adv Health Med 1(5):44–66 Stogner J, Miller BL, McLean K (2020) Police Stress, Mental Health, and Resiliency during the COVID-19 Pandemic. Am J Crim Justice 45(4):718–730 Lucas T, Weidner N, Janisse J (2012) Where does work stress come from? A generalizability analysis of stress in police officers. Psychol Health 27(12):1426–1447 Queiros C et al (2020) Job Stress, Burnout and Coping in Police Officers: Relationships and Psychometric Properties of the Organizational Police Stress Questionnaire. Int J Environ Res Public Health, 17(18) Collaborators C-MD (2021) Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet 398(10312):1700–1712 Bareeqa SB et al (2021) Prevalence of depression, anxiety and stress in china during COVID-19 pandemic: A systematic review with meta-analysis. Int J Psychiatry Med 56(4):210–227 Bai R et al (2022) Trends in depression incidence in China, 1990–2019. J Affect Disord 296:291–297 Garbarino S et al (2002) Sleepiness and sleep disorders in shift workers: a study on a group of italian police officers. Sleep 25(6):648–653 Jetelina KK et al (2020) Prevalence of Mental Illness and Mental Health Care Use Among Police Officers. JAMA Netw Open 3(10):e2019658 Angehrn A et al (2022) Sex differences in mental disorder symptoms among Canadian police officers: the mediating role of social support, stress, and sleep quality. Cogn Behav Ther 51(1):3–20 Chae MH, Boyle DJ (2013) Police suicide: prevalence, risk, and protective factors. Policing: An International Journal of Police Strategies & Management, 36(1): pp. 91–118 Njiro BJ et al (2021) Depression, suicidality and associated risk factors among police officers in urban Tanzania: a cross-sectional study. Gen Psychiatr 34(3):e100448 Violanti JM et al (2009) Suicide in Police Work: Exploring Potential Contributing Influences. Am J Criminal Justice 34(1):41–53 van der Velden PG et al (2013) Police officers: a high-risk group for the development of mental health disturbances? A cohort study. BMJ Open 3(1):e001720 Morin CM, Jarrin DC (2022) Epidemiology of Insomnia: Prevalence, Course, Risk Factors, and Public Health Burden. Sleep Med Clin 17(2):173–191 Kroenke K, Spitzer R (2002) The PHQ-9: A New Depression Diagnostic and Severity Measure. Psychiatric Annals 32:509–521 Spitzer RL et al (2006) A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med 166(10):1092–1097 Buysse DJ et al (1989) The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res 28(2):193–213 Salahuddin M et al (2017) Validation of the Pittsburgh sleep quality index in community dwelling Ethiopian adults. Health Qual Life Outcomes 15(1):58 Kang L et al (2020) Impact on mental health and perceptions of psychological care among medical and nursing staff in Wuhan during the 2019 novel coronavirus disease outbreak: A cross-sectional study. Brain Behav Immun 87:11–17 Ahmed MZ et al (2020) Epidemic of COVID-19 in China and associated Psychological Problems. Asian J Psychiatr 51:102092 Yuan L et al (2020) A Survey of Psychological Responses During the Coronavirus Disease 2019 (COVID-19) Epidemic among Chinese Police Officers in Wuhu. Risk Manag Healthc Policy 13:2689–2697 Huang Q, Bodla AA, Chen C (2021) An Exploratory Study of Police Officers' Perceptions of Health Risk, Work Stress, and Psychological Distress During the COVID-19 Outbreak in China. Front Psychol 12:632970 Emsing M et al (2021) Trajectories of Mental Health Status Among Police Recruits in Sweden. Front Psychiatry 12:753800 van der Velden PG et al (2013) Police officers: a high-risk group for the development of mental health disturbances? A cohort study. BMJ Open, 3(1) Demou E, Hale H, Hunt K (2020) Understanding the mental health and wellbeing needs of police officers and staff in Scotland. Police Pract Res 21(6):702–716 Tsehay M et al (2021) Generalized Anxiety Disorder, Depressive Symptoms, and Sleep Problem During COVID-19 Outbreak in Ethiopia Among Police Officers: A Cross-Sectional Survey. Front Psychol 12:713954 Yadav B et al (2022) Prevalence and factors associated with symptoms of depression, anxiety and stress among traffic police officers in Kathmandu, Nepal: a cross-sectional survey. BMJ Open 12(6):e061534 Wu J et al (2023) A study on mental health and its influencing factors among police officers during the COVID-19 epidemic in China. Front Psychiatry 14:1192577 Liang Y et al (2020) Mental Health in Frontline Medical Workers during the 2019 Novel Coronavirus Disease Epidemic in China: A Comparison with the General Population. Int J Environ Res Public Health, 17(18) Barua L et al (2020) Psychological burden of the COVID-19 pandemic and its associated factors among frontline doctors of Bangladesh: a cross-sectional study. F1000Res:1304 Yan J et al (2021) Hospitality workers’ COVID-19 risk perception and depression: A contingent model based on transactional theory of stress model. Int J Hospitality Manage 95:102935 Jansson-Frojmark M, Lindblom K (2008) A bidirectional relationship between anxiety and depression, and insomnia? A prospective study in the general population. J Psychosom Res 64(4):443–449 Garbarino S, Magnavita N (2019) Sleep problems are a strong predictor of stress-related metabolic changes in police officers. A prospective study. PLoS ONE 14(10):e0224259 Supplementary Tables Supplementary Table 1 and 2 are not available with this version Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6656633","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456041410,"identity":"def71f42-89c6-455c-8cd0-4dffc7e81ff9","order_by":0,"name":"Shufang Feng","email":"","orcid":"","institution":"Department of Gerontology, The Third Medical Center, Chinese PLA General Hospital, Beijing, China.","correspondingAuthor":false,"prefix":"","firstName":"Shufang","middleName":"","lastName":"Feng","suffix":""},{"id":456041411,"identity":"a3227110-ae5b-44bc-bbbb-b43b2ba49ab6","order_by":1,"name":"Minghe Liu","email":"","orcid":"","institution":"Department of Gerontology, The Third Medical Center, Chinese PLA General Hospital, Beijing, China.","correspondingAuthor":false,"prefix":"","firstName":"Minghe","middleName":"","lastName":"Liu","suffix":""},{"id":456041412,"identity":"199012ff-1939-466e-855e-5b2827e93686","order_by":2,"name":"Xiao Bai","email":"","orcid":"","institution":"Department of Gerontology, The Third Medical Center, Chinese PLA General Hospital, Beijing, China.","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Bai","suffix":""},{"id":456041413,"identity":"f2745c03-2318-4766-9982-d5f443923509","order_by":3,"name":"Chen Mo","email":"","orcid":"","institution":"Department of Gerontology, The Third Medical Center, Chinese PLA General Hospital, Beijing, China.","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Mo","suffix":""},{"id":456041414,"identity":"2b10a075-1cc6-4d5b-9c21-83c6d1a692f8","order_by":4,"name":"Tianyao Shi","email":"","orcid":"","institution":"State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology and Toxicology, Beijing, China.","correspondingAuthor":false,"prefix":"","firstName":"Tianyao","middleName":"","lastName":"Shi","suffix":""},{"id":456041415,"identity":"0cf2554c-3777-4c2d-aab9-d202e51603bc","order_by":5,"name":"Yongqing Zhang","email":"","orcid":"","institution":"Department of Gerontology, The Third Medical Center, Chinese PLA General Hospital, Beijing, China.","correspondingAuthor":false,"prefix":"","firstName":"Yongqing","middleName":"","lastName":"Zhang","suffix":""},{"id":456041416,"identity":"d6e12c36-b6e5-4ec6-b989-b6f317fb014a","order_by":6,"name":"Ruyi Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYDACCTB5AIj5Hxz+USEhxw/lEqOFh/EwwxkLY8kGErQwH2Zsq0jc0EDAXfKzm589/PLnjrw5/9oDhwvnSTBuYGB+eOgGHi2Mc46ZG8vwPDPcOeNdwuGZ2ySYzRnYDA7n4NHCLJFgJi0hcZhxw40DBgd4t0mwWTbwMODVwiaR/k1awuCwPUTLHAkegwMEtPBI5JhJfkg4nLjhfI/BYd4GCQmCWiQkcsqkGQ4cTt5wgy3h4IxjEgaSzQT8Ij8jfZvkjz+HbTecP3z4w4eauvp+9ubHn/FpAQFmHrB9CTAuAeUgwPgDRPIfIELpKBgFo2AUjEgAABSmWAzykz/yAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Health Management, The Third Medical Center, Chinese PLA General Hospital, Beijing, China.","correspondingAuthor":true,"prefix":"","firstName":"Ruyi","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-05-13 14:42:24","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6656633/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6656633/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82891799,"identity":"6a13877a-24d8-41ce-96b9-21bed589db1a","added_by":"auto","created_at":"2025-05-16 12:15:25","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":286543,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow chart of police officer enrolled. PHQ-9, Patient Health Questionnaire-9; GAD-7, Generalized Anxiety Disorder-7; PSQI, Pittsburgh Sleep Quality Index.\u003c/p\u003e","description":"","filename":"Figure.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6656633/v1/68d1d953851879bc96292b33.jpg"},{"id":82893297,"identity":"6e7194e4-28bc-4a24-858c-985d881a56e8","added_by":"auto","created_at":"2025-05-16 12:23:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1706778,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6656633/v1/cd02376a-db5a-4e57-af89-32f9697bd6d4.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003ePrevalence and factors associated with symptoms of depression, anxiety and insomnia among traffic police officers in Beijing, China: a cross-sectional survey\u003c/p\u003e","fulltext":[{"header":"Strengths and limitations of this study","content":"\u003cp\u003e\u0026rArr; We used depression, anxiety and insomnia self-rating scale, which is a commonly used screening tool and has been validated for use in China.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026rArr; Participants were randomly selected working under three different traffic units of Haidian district, Beijing city for at least 6 months, and the rigorous methods of data collection allow for generalizability to the entire patrol department in Beijing district\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026rArr; This study followed a cross-sectional design that limits our ability to infer causal relationships between independent variables and mental / sleep health outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026rArr;\u0026nbsp;The tool used in the study was a screening tool and the outcome measures should not be interpreted as diagnoses of mental health conditions.\u003c/p\u003e\n\u003cp\u003e\u0026rArr; The sample was demographically different from the general patrol officer population; therefore, it is unclear whether generalizable to the department and other law enforcement agencies.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eOccupational roles are significant to our lives, as we spend considerable time engaged in work-related activities. Promoting and maintaining the highest level of physical, mental, and social well-being for all working people are the comprehensive goal of World Health Organization Joint Committee on Occupational Health[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Police officers serve as the frontline force in maintaining social order, shouldering crucial responsibilities for safeguarding societal stability and ensuring citizen safety. However, the particularity and complexity of their work frequently exposes them to highly stressful environments and substantial psychological pressures. These pressures stem from various professional challenges, such as crime investigation, patrolling, emergency response, and community interaction, as well as occupational stress resulting from long and often rotating shifts[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. All of these factors have adverse effects on both their psychological well-being and physical health [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMajor depression disorder (MDD) is among the most prevalent mental health disorders worldwide especially during the COVID-19 pandemic[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In 2017, there were approximately 56.36\u0026nbsp;million patients with MDD in China, accounting for 21.3% of all global cases[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The disability-adjusted lifespan caused by MDD have reached 8.577\u0026nbsp;million, which means that MDD has gradually become an important public health problem affecting the Chinese population[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, the prevalence of MDD among traffic police officers is particularly alarming, with studies indicating that prevalence rates are higher than in the general population in the United States and other countries [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].Police officers are at an elevated risk for developing MDD due to multifactorial causes such as work-related exposure and traumatic events. This mental health issue can significantly impair an officer's ability to perform their duties, affecting decision-making, concentration, and overall job performance. It has been estimated that 8.5% of police officers globally experience suicidal ideation, which is twice as high as the figures reported in the general population[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Anxiety, closely linked with MDD, is also prevalent among police officers due to constant exposure to hazardous situations, high expectations for public safety, and the psychological toll of dealing with non-compliant or aggressive individuals[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, sleep problems, including insomnia, disrupted sleep patterns, and non-restorative sleep, are common among traffic police officers. These issues are often a direct result of shift work and the need to remain vigilant, which disrupts the body's natural circadian rhythms. The resultant sleep deprivation not only compounds the risk of developing MDD and anxiety but also poses significant safety risks, both to the officers themselves and the public they serve.\u003c/p\u003e \u003cp\u003eThis study aimed to assess the prevalence of depression, anxiety, and insomnia, along with their associated factors, among traffic police officers in Beijing five years after the outbreak of the COVID-19 pandemic. Understanding the mental health status of police officers through survey research in the capital of China is essential. By investigating issues related to anxiety, depression, and sleep, we can gain a comprehensive understanding of the current mental health needs of police officers, enabling the provision of appropriate mental health support and intervention measures. Such research efforts facilitate the development of targeted interventions that enhance job satisfaction and the quality of life for police officers, thereby promoting social harmony and stability.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting:\u003c/h2\u003e \u003cp\u003eThe objective of this study was to assess the prevalence of depression, anxiety, and insomnia, as well as identify the related risk factors among traffic police officers in Beijing China. We conducted a district based cross-sectional study in Beijing city, China. Data were collected over the period from March 27th, 2024 to April 11th, 2024.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population and sampling:\u003c/h3\u003e\n\u003cp\u003eThe study population was the traffic policer working under three different traffic units of Haidian district, Beijing city for at least 6 months. The list of names of these officers was provided by the Traffic Police commander. A sample of 170 police officers was calculated using the proportional formula for calculating sample size (Results from OpenEpi, Version 3, open-source calculator\u0026ndash;SSPropor), An estimated 3.8% of the population experience depression, including 5% of adults (4% among men and 6% among women), and 5.7% of adults older than 60 years. 4% of the global population currently experience an anxiety disorder. Approximately 10% of the adult population suffers from an insomnia disorder[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. 5% standard error (SE) were used for calculation.\u003c/p\u003e\n\u003ch3\u003eMeasures:\u003c/h3\u003e\n\u003cp\u003eData were collected from participants at their respective traffic units using self-administered questionnaires, which included sections on sociodemographic information (age, gender, education, BMI, and health habits), as well as surveys on depression, anxiety, and insomnia. The principal investigator, with the assistance of research assistants, oversaw the data collection process. Participants were provided with electronic versions of the questionnaires, along with instructions on how to complete them. Completing the questionnaires in a private setting was emphasized to ensure data accuracy and confidentiality. On average, each participant took 15 to 20 minutes to complete the questionnaires.\u003c/p\u003e \u003cp\u003eThe severity of depressive symptoms was evaluated using the validated Patient Health Questionnaire-9 (PHQ-9) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This questionnaire comprises nine items assessing common symptoms of depression, including low mood, loss of interest, sleep disturbances, and self-evaluation. Participants select the response that best corresponds to their condition for each item, based on the past two weeks, with scores ranging from 0 (not at all) to 3 (nearly every day). The item scores are summed to obtain a total score between 0 and 27. Higher scores denote more severe depressive symptoms, with scores categorized as 10\u0026ndash;14 for moderate, 15\u0026ndash;19 for moderately severe, and 20\u0026ndash;27 for severe depression. The scale demonstrates 78% sensitivity and 87% specificity. Validation was conducted by comparing with diagnoses from consultant psychiatrists using the Structured Clinical Interview for DSM Disorders. A threshold score of \u0026ge;\u0026thinsp;10 is commonly used to diagnose. MDD in alignment with the initial validation study by Kroenke et al. which reported a sensitivity and specificity of 88% for detecting MDD[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGeneralized Anxiety Disorder-7 (GAD-7) is a questionnaire designed to assess the severity of anxiety symptoms. It comprises seven items that encompass common symptoms such as feeling anxious, excessive worry, anxious mood, and physical discomfort, aligning with most of the DSM-IV criteria for generalized anxiety disorder[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Participants indicate the response that best reflects their condition for each item based on the past two weeks. Scores range from 0 (not at all) to 3 (nearly every day), and the item scores are summed to produce a total score between 0 and 21. A higher total score signifies more severe anxiety symptoms. A score of \u0026ge;\u0026thinsp;10 is considered a reasonable cut-off for identifying cases of GAD.\u003c/p\u003e \u003cp\u003eThe Pittsburgh Sleep Quality Index (PSQI) is a widely utilized self-report questionnaire designed to assess sleep quality and disturbances over a one-month period[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It consists of 24 items, 19 of which are self-reported, while 5 are evaluated by a sleeping partner, and are not included in the score calculation. These 19 items are grouped into seven components, covering aspects such as sleep quality, sleep latency, sleep duration, and medication use, providing a comprehensive and multidimensional evaluation of various aspects of sleep. Participants select the response that best corresponds to their condition for each item based on the past month. Scores range from 0 (no problem) to 3 (severe problem), and the scoring for each item is summed to obtain a total score ranging from 0 to 21. A higher total score indicates poorer sleep quality. The PSQI demonstrates moderate value in screening for insomnia, with an optimal cut-off score of 5, and a score of \u0026ge;\u0026thinsp;6 confirms the presence of sleep disturbances[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Insomnia Severity Index (ISI) is a questionnaire designed to evaluate the severity of insomnia. It comprises seven items covering various aspects such as insomnia severity, sleep onset difficulties, nighttime awakenings, and early morning awakenings. Participants select the response that best reflects their condition for each item based on the past two weeks. Scores range from 0 (no problem) to 4 (severe problem) for each item, and these are summed to produce a total score ranging from 0 to 28, with higher scores indicating greater insomnia severity.\u003c/p\u003e\n\u003ch3\u003eData management and analysis:\u003c/h3\u003e\n\u003cp\u003eAll data entry, cleaning, and subsequent analysis were conducted using SPSS Version 26 (IBM Corp., Armonk, NY) for Windows. In data cleaning, we performed a missing value analysis. A double tailed p-value of \u0026lt;\u0026thinsp;0.05 indicated statistical significance in all analyses. Descriptive analysis of continuous variables was expressed as means (M) and standard deviations (SD), while categorical variables were presented as proportions and percentages of the total. Depression, anxiety, and insomnia were analyzed as dichotomous variables. Participants were categorized into two groups based on their scores: PHQ-9\u0026thinsp;\u0026lt;\u0026thinsp;10 and \u0026ge;\u0026thinsp;10 for no depression and depression groups; GAD-7\u0026thinsp;\u0026lt;\u0026thinsp;10 and \u0026ge;\u0026thinsp;10 for no anxiety and anxiety groups; and PSQI\u0026thinsp;\u0026lt;\u0026thinsp;6 and \u0026ge;\u0026thinsp;6 for no sleep problem and sleep problem; ISI\u0026thinsp;\u0026gt;\u0026thinsp;15 for clinic insomnia. The χ2 test was employed to compare categorical variables between groups. For continuous variables, one-way analysis of variance was used for normally distributed variables, and the Kruskal-Wallis H test was applied to compare skewed distributions between groups.\u003c/p\u003e \u003cp\u003eA multivariate logistic regression analysis was conducted to identify potential factors associated with depression. In this analysis, depression status served as the outcome (dependent) variable, while socio-demographic factors, anxiety, and insomnia were considered predictor (independent) variables. Crude odds ratios, with 95% confidence intervals, were calculated as measures of effect.\u003c/p\u003e\n\u003ch3\u003eEthical and administrative issues:\u003c/h3\u003e\n\u003cp\u003e Ethical clearance for the study was obtained from the Ethics Review Committee of the Third Medical Center, Chinese PLA General Hospital. Administrative clearance was granted by the Inspector General of Police, Haidian District. Informed written consent was secured from all participants before data collection, following a detailed explanation of the study's purpose and procedures.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e1. Sociodemographic characteristics of the study participants:\u003c/h2\u003e \u003cp\u003eOf the total of 170 police officer enrolled, with a response rate of 92.94% (158 of 170), the remainder did not meet the inclusion criteria (see the flowchart Fig.\u0026nbsp;1). Twelve participants were excluded for failing to answer any questions in the first section of the questionnaire, which covered sociodemographic information. The majority of participants were male (66.7%). Twenty-two participants were aged 21\u0026ndash;30 years (13.9%), 53 were 31\u0026ndash;40 years (33.5%), 54 were 41\u0026ndash;50 years (34.2%), and 29 were 51\u0026ndash;60 years (18.4%). Approximately 56.3% of the officers had completed senior high school education. The mean body mass index (BMI) was 27.24 (\u0026plusmn;\u0026thinsp;8.24), with 38.0% classified as overweight (BMI 24-27.9) and obese (29.1%, BMI\u0026thinsp;\u0026ge;\u0026thinsp;28). Among the respondents, 52.5% were non-smokers, 64.6% did not consume alcohol, and 72.2% did not drink coffee, while 70.3% were tea consumers. About 57% of the participants reported regular exercise (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eSociodemographic characteristics of the study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (n\u0026thinsp;=\u0026thinsp;158)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.7\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge groups(n\u0026thinsp;=\u0026thinsp;158)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDegree of education(n\u0026thinsp;=\u0026thinsp;158)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI for Chinese (n\u0026thinsp;=\u0026thinsp;158)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight (\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal (18.5\u0026ndash;23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight (24\u0026ndash;27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese (\u0026ge;\u0026thinsp;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (n\u0026thinsp;=\u0026thinsp;158)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol Drinking (n\u0026thinsp;=\u0026thinsp;158)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTea Drinking (n\u0026thinsp;=\u0026thinsp;158)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoffee Drinking (n\u0026thinsp;=\u0026thinsp;158)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular exercise (n\u0026thinsp;=\u0026thinsp;158)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.0\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\n\u003ch3\u003e2. Prevalence of depression, anxiety and insomnia among traffic police officers\u003c/h3\u003e\n\u003cp\u003eIn the depression analysis, 21 patients were excluded due to non-response to PHQ-9 questions, resulting in 137 police officers in the final analysis. For the anxiety analysis, 30 participants were excluded due to non-response to GAD-7 questions, and 128 police officers met the criteria for analysis. In the insomnia analysis, 34 and 38 participants were excluded for non-response to PSQI and ISI questions, respectively, leaving 124 and 121 police officers in the final analysis (Fig.\u0026nbsp;1). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents individualized descriptive statistics for GAD-7, PHQ-9, PSQI, and ISI, respectively. The average PHQ-9 score for depression was 5.18 (M\u0026thinsp;=\u0026thinsp;5.18; SD\u0026thinsp;=\u0026thinsp;39.032), the average GAD-7 score for anxiety was 3.62 (M\u0026thinsp;=\u0026thinsp;3.62; SD\u0026thinsp;=\u0026thinsp;28.36), and the average PSQI and ISI scores for insomnia were 6.2 (M\u0026thinsp;=\u0026thinsp;6.2; SD\u0026thinsp;=\u0026thinsp;19.02) and 5.94 (M\u0026thinsp;=\u0026thinsp;5.94; SD\u0026thinsp;=\u0026thinsp;45.85) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Using a cut-off score of 10 for the PHQ-9, we identified 26 (18.98%) traffic police officers with moderate or severe depression symptoms. For the GAD-7, using a cut-off of 10, 18 (14.06%) officers were identified with moderate or severe anxiety symptoms. With a PSQI cut-off score of 5, 21 (16.9%) officers had sleep disturbances and ISI score 15 (12.5%) have sleep problem. The component of PSQI score were also calculated with mean \u003cb\u003e(\u003c/b\u003e\u0026plusmn;\u0026thinsp;\u003cb\u003eSD)\u003c/b\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003ePrevalence of depression, anxiety and sleep quality among traffic police officers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003ePHQ-9 (0\u0026thinsp;~\u0026thinsp;27, n\u0026thinsp;=\u0026thinsp;137) (Score of depression subscale)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal (0\u0026thinsp;~\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83 (60.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild (5\u0026thinsp;~\u0026thinsp;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (20.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate (10\u0026thinsp;~\u0026thinsp;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (8.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate to Severe (15\u0026thinsp;~\u0026thinsp;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (5.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere (20\u0026thinsp;~\u0026thinsp;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (4.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal with depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (18.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.18 (39.028)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eGAD-7(0\u0026thinsp;~\u0026thinsp;21, n\u0026thinsp;=\u0026thinsp;128)\u003c/p\u003e \u003cp\u003e(Score of anxiety subscale)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal (0\u0026thinsp;~\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (76.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild (5\u0026thinsp;~\u0026thinsp;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (9.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate (10\u0026thinsp;~\u0026thinsp;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (7.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate to Severe (15\u0026thinsp;~\u0026thinsp;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (3.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere (19\u0026thinsp;~\u0026thinsp;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (3.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal with anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (14.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.62 (28.364)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ePSQI quality (0\u0026thinsp;~\u0026thinsp;21, n\u0026thinsp;=\u0026thinsp;124)\u003c/p\u003e \u003cp\u003e(Score of Sleep quality)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo problem\u0026thinsp;\u0026lt;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (49.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild 6\u0026thinsp;~\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (33.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate 11\u0026thinsp;~\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (13.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere\u0026thinsp;\u0026gt;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal with sleep problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(16.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eISI (0\u0026thinsp;~\u0026thinsp;28, n\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e \u003cp\u003e(Score of insomnia)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo clinically significant insomnia (0\u0026thinsp;~\u0026thinsp;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (67.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubthreshold insomnia (8\u0026thinsp;~\u0026thinsp;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (20.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical insomnia (mild severity) (15\u0026thinsp;~\u0026thinsp;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (8.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical insomnia (moderate severity) (22\u0026thinsp;~\u0026thinsp;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (4.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal with sleep problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (12.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.94 (45.854)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eComponent\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eScore Mean (\u003c/b\u003e\u0026plusmn;\u0026thinsp;\u003cb\u003eSD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003ePSQI component score (0\u0026thinsp;~\u0026thinsp;3, n\u0026thinsp;=\u0026thinsp;124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubjective sleep quality (C1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93(0.085)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep latency (C2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83 (0.939)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep duration (C3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3 (0.910)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep efficiency (C4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96 (1.112)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep disturbance (C5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85 (0.515)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUse of sleep medication (C6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25 (0.498)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaytime dysfunction (C7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08 (1.148)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlobal PSQI Score mean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2 (19.024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3. Sociodemographic factors associated with depression, anxiety and insomnia among traffic police officers\u003c/h2\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and Supplementary table1 and 2 present bivariate Chi-square (χ\u0026sup2;) and correlation analyses concerning sociodemographic information and the depression, anxiety, insomnia, respectively. Bivariate analyses were conducted to assess the associations between symptoms of depression, anxiety, and insomnia and various sociodemographic characteristics, such as sex, age, education level, BMI, smoking, alcohol consumption, tea consumption, coffee consumption, and regular exercise. None of the sociodemographic factors, except for age, showed weakly statistic differences between insomnia and non-insomnia (P\u0026thinsp;=\u0026thinsp;0.027) among traffic police officers. The associations among depression, anxiety, and insomnia were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors associated with depression among traffic police officers (n\u0026thinsp;=\u0026thinsp;137)\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" 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 \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;137)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo depression (n\u0026thinsp;=\u0026thinsp;111)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistic\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\u003eAge, n (%)\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 \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (15.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (13.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (23.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (35.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (34.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (38.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (32.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (34.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (23.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (17.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (18.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (15.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\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 \u003cp\u003eχ\u0026sup2;=1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.254\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\u003e92 (67.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (69.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (57.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003e45 (32.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (30.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (42.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDegree of education, n(%)\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 \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (10.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (10.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (11.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (57.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (59.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (29.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (27.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (34.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (3.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, mean, n(%)\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 \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight (\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (3.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal (18.5\u0026ndash;23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (30.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (28.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (38.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight (24\u0026ndash;27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (36.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (39.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (23.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese (\u0026ge;\u0026thinsp;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (30.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (29.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (34.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n(%)\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 \u003cp\u003eχ\u0026sup2;=1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (54.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (51.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (65.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (45.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (48.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (34.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol Drinking, n(%)\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 \u003cp\u003eχ\u0026sup2;=0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 (67.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (66.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (69.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (32.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (33.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (30.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTea Drinking, n(%)\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 \u003cp\u003eχ\u0026sup2;=1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (32.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (30.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (42.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 (67.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (69.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (57.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoffee Drinking, n(%)\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 \u003cp\u003eχ\u0026sup2;=3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (72.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (76.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (57.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (27.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (23.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (42.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular exercise, n(%)\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 \u003cp\u003eχ\u0026sup2;=1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (43.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (41.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (53.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (56.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (58.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (46.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety, n(%)\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 \u003cp\u003eχ\u0026sup2;=34.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96 (85.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (95.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (42.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (14.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (4.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (57.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsomnia, n (%)\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 \u003cp\u003eχ\u0026sup2;=15.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (50.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (60.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (13.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (49.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (86.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD-7 Score, n (%)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal (0\u0026thinsp;~\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (76.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (89.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (23.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild (5\u0026thinsp;~\u0026thinsp;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (8.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (6.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (19.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate (10\u0026thinsp;~\u0026thinsp;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (8.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (28.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate to Severe (15\u0026thinsp;~\u0026thinsp;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (3.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (14.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere (19\u0026thinsp;~\u0026thinsp;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (14.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI Score, n(%)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo problem\u0026thinsp;\u0026lt;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (50.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (60.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (13.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild 6\u0026thinsp;~\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (32.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (34.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (27.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate 11\u0026thinsp;~\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (14.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (5.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (45.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u0026thinsp;\u0026gt;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (13.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISI Score, n(%)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo problem (0\u0026thinsp;~\u0026thinsp;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (65.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (77.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (21.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild (8\u0026thinsp;~\u0026thinsp;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (21.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (21.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (21.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate (15\u0026thinsp;~\u0026thinsp;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (9.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (39.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere (22\u0026thinsp;~\u0026thinsp;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (3.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (17.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eχ\u0026sup2;: Chi-square test, -: Fisher exact, *Factors that were statistically signiffcant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of depression with anxiety and insomnia (n\u0026thinsp;=\u0026thinsp;124)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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=\"left\" 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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\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 \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.00 (7.72\u0026thinsp;~\u0026thinsp;108.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e48.58 (5.30\u0026thinsp;~\u0026thinsp;445.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsomnia\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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\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 \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.50 (2.61\u0026thinsp;~\u0026thinsp;34.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e82.75 (3.75\u0026thinsp;~\u0026thinsp;1825.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\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\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 \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00 (Reference)\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\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.66 (0.69\u0026thinsp;~\u0026thinsp;3.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.91 (0.11\u0026thinsp;~\u0026thinsp;79.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\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 \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56 (0.23\u0026thinsp;~\u0026thinsp;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.07 (0.12\u0026thinsp;~\u0026thinsp;9.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol Drinking\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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\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 \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89 (0.35\u0026thinsp;~\u0026thinsp;2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.51 (0.05\u0026thinsp;~\u0026thinsp;5.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTea Drinking\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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\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 \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60 (0.25\u0026thinsp;~\u0026thinsp;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.24 (0.04\u0026thinsp;~\u0026thinsp;1.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoffee Drinking\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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\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 \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.40 (0.98\u0026thinsp;~\u0026thinsp;5.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e14.77 (1.64\u0026thinsp;~\u0026thinsp;133.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular exercise\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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\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 \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.61 (0.26\u0026thinsp;~\u0026thinsp;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.88 (0.13\u0026thinsp;~\u0026thinsp;5.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, mean, n(%)\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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight (\u0026lt;\u0026thinsp;18.5)\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 \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal (18.5\u0026ndash;23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63 (0.05\u0026thinsp;~\u0026thinsp;7.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.04 (0.00\u0026thinsp;~\u0026thinsp;3.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight (24\u0026ndash;27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27 (0.02\u0026thinsp;~\u0026thinsp;3.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-5.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.00 (0.00\u0026thinsp;~\u0026thinsp;0.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese (\u0026ge;\u0026thinsp;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55 (0.04\u0026thinsp;~\u0026thinsp;6.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.02 (0.00\u0026thinsp;~\u0026thinsp;2.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, n (%)\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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u0026ndash;30\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 \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66 (0.20\u0026thinsp;~\u0026thinsp;2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.34 (0.08\u0026thinsp;~\u0026thinsp;21.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39 (0.11\u0026thinsp;~\u0026thinsp;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.97 (0.07\u0026thinsp;~\u0026thinsp;13.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50 (0.12\u0026thinsp;~\u0026thinsp;2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.13 (0.02\u0026thinsp;~\u0026thinsp;55.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eOR: Odds Ratio, CI: Confidence Interval\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cp\u003e \u003cb\u003e4. Factors association with depression among police officers.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eUnivariate and multivariate logistic regression revealed that the police officers who have anxiety and insomnia were 3.88 (95% Cl: 2.156 to 90.410) and 15.267 (95% Cl:2.052-113.585) times respectively more likely to have depressive symptoms compared with normal score (see Tables\u0026nbsp;6).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigates the prevalence of depression, anxiety, and insomnia among 158 police officers in Haidian District Beijing, China, five years after the onset of the COVID-19 pandemic. The results revealed that 18.98% of participants screened positive for depression, while 14.06% screened positive for anxiety. The Pittsburgh Sleep Quality Index (PSQI) indicated that 16.9% of the 124 participants exhibited signs of clinical (moderate-severe) insomnia and Insomnia Severity Index (ISI) revealed that 12.5% of the 120 participants were affected by insomnia. Sociodemographic factors did not significantly correlate with psychological issues, except for age weakly statistic differ between insomnia and non-insomnia (P\u0026thinsp;=\u0026thinsp;0.027) among traffic police officers. Participants experiencing moderate anxiety and insomnia were found to have an increased risk of reporting depression.\u003c/p\u003e \u003cp\u003eGlobal epidemiological data from the World Health Organization (WHO) indicate a significant prevalence of mental health disorders across populations. Current estimates suggest that depression affects approximately 3.8% of the global population, with differential prevalence rates observed across demographic groups: 5% among adults (4% in males and 6% in females) and 5.7% in individuals aged over 60 years, representing approximately 280\u0026nbsp;million cases worldwide [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Sleep disorders, particularly insomnia, affect an estimated 10% of the adult population [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The COVID-19 pandemic has exacerbated mental health challenges, as evidenced by epidemiological studies conducted in China. Research findings indicate that 34.4% of respondents exhibited clinically significant depressive symptoms during the pandemic period [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].. Furthermore, empirical data demonstrate that more than one-third of the study population experienced moderate to severe mental health disturbances [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], representing a marked increase compared to pre-pandemic baseline levels. These findings underscore the substantial impact of global health crises on population mental health outcomes.\u003c/p\u003e \u003cp\u003eFollowing the COVID-19 outbreak, China's economic growth rate fell from 6\u0026ndash;2.2% in 2020. This unprecedented pandemic altered the domestic situation and posed significant economic challenges. To maintain social stability, police officers confronted substantial challenges, enduring immense pressure while ensuring social security and public order in a complex environment [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A related survey indicated that during the pandemic, frontline police officers were responsible for additional tasks, such as patient isolation and quarantine control, which extended their long and irregular working hours [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. They worked tirelessly on the front lines during and after the pandemic. Consequently, they are considered a high-risk group for developing psychological illnesses due to stressful tasks, job burnout, and emotional exhaustion [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These responsibilities also placed them in stressful circumstances, potentially impacting their mental health and work performance [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEpidemiological studies have revealed significant mental health challenges among traffic police officers across diverse geographical contexts. A cross-sectional investigation conducted among Ethiopian police officers identified prevalence rates of 28.9% for depression and 30.2% for anxiety, with sleep disturbances affecting 13.8% (subthreshold insomnia) and 2.1% (clinical insomnia) of the cohort [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Similarly, research among traffic police officers in Kathmandu, Nepal, demonstrated elevated psychological distress, with 41.3% exhibiting depressive symptoms, 47% reporting anxiety symptoms, and 44% experiencing stress-related symptoms [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Comparative data from China indicate a positive mental health detection rate of 37.75% among police personnel [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The observed variability in prevalence rates across studies may be attributed to methodological differences in population sampling and assessment instruments. Parallel investigations among healthcare professionals, another high-risk occupational group, reveal comparable mental health challenges. Liang et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] documented that 30.43%, 20.29%, and 14.49% of frontline medical practitioners in China experienced depression, anxiety, and insomnia, respectively, during the COVID-19 pandemic. Complementary findings from Bangladesh indicate slightly higher prevalence rates among frontline physicians, with 36.5% reporting anxiety, 38.4% depression, and 18.6% insomnia [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Notably, comparative analyses suggest that Wuhan police personnel exhibited superior mental health outcomes relative to healthcare staff, potentially attributable to differential levels of occupational stress and workload intensity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].. The current investigation reveals that police officers demonstrate elevated levels of depression and anxiety compared to WHO normative data, though these levels remain below those reported during the peak pandemic period. Furthermore, officers presenting with moderate anxiety and insomnia symptoms showed significantly increased vulnerability to depressive disorders, consistent with previous epidemiological findings [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProactive implementation of psychological support mechanisms plays a crucial role in monitoring police officers' mental health status and facilitating timely interventions. Systematic psychological evaluations, coupled with regular check-ins and professional counseling sessions, enable officers to effectively process occupational stressors, develop adaptive coping mechanisms, and enhance emotional resilience. The establishment of dedicated psychological clinics and confidential consultation hotlines provides secure environments for personnel to articulate their concerns, while routine psychological assessments should be institutionalized within police departments. The current deficiency in proactive psychological counseling services significantly impedes the mitigation of negative emotional states and psychological distress among officers. Furthermore, the development of comprehensive psychological profiles for police personnel is imperative to gain a holistic understanding of their post-pandemic mental and physical well-being. For officers exhibiting symptoms of severe psychological disorders, evidence-based interventions and immediate therapeutic support should be implemented through targeted assistance programs.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has limitations. First, participants were included only from 3 police department and samples were limited thus, external validity is limited. However, the rigorous methods of data collection allow for generalizability to the entire patrol department in Beijing district. Second, given the time restraint of the survey, we included only the brief screeners of mental illness symptoms. However, the short versions have high sensitivity and specificity for mental illness in primary care. Third, the sample was significantly different demographically from the general patrol officer population; therefore, it is unclear whether our findings are generalizable to the department and other law enforcement agencies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study reveals the prevalence of depression, anxiety, and insomnia symptoms among front-line police officers in Haidian District Beijing, China, during the post-pandemic period (five years following the COVID-19 outbreak). The findings underscored the sociodemographic factors such as age, marital status, and education level on police mental health. It is crucial to prioritize the development and awareness of mental health screening among police officers. Policymakers and social psychological healthcare organizations should establish internal teams to address mental health issues and implement innovative stress-coping strategies to effectively enhance officers' mental well-being.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors would like to acknowledge Dr Yongqing Zhang for her assistance in language editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions: Ruyi\u0026nbsp;\u003c/strong\u003eZhang\u003csup\u003e\u0026nbsp;\u003c/sup\u003eand Minghe Liu contributed equally to conception and data curation and was responsible for writing original draft preparation and revising it critically. Shufang Feng took responsibility for formal analysis and review and editing. Tianyao Shi contributed to project administration, interpretation of data, organization, and coordination. Chen Mo supported participant recruitment and data analysis and interpretation. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe authors have not declared a specific grant for this research from any funding agency in the public, commercial or not- for- profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement:\u0026nbsp;\u003c/strong\u003eThe studies involving human participants were reviewed and approved by the Ethics Committee of The Third Medical Center, Chinese PLA General Hospital, Beijing, China (reference number: KY-2024-045). The participants provided their written informed consent to participate in this study. We ensured voluntary participation in the study, and the confidentiality and privacy of the participants were maintained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003eData set will be made available upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProvenance and peer review:\u0026nbsp;\u003c/strong\u003eNot commissioned; externally peer reviewed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOpen access:\u0026nbsp;\u003c/strong\u003eThis is an open access article distributed in accordance with the Creative Commons Attribution Non-Commercial (CC BY- NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non- commercial. See:\u0026nbsp;http://creativecommons.org/licenses/by-nc/4.0/.\u003c/p\u003e\n\u003cp\u003eORCID iD: Shufang Feng: https://orcid.org/0000-0002-1681-7096.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003evan den Berg TI et al (2008) The influence of psychosocial factors at work and life style on health and work ability among professional workers. Int Arch Occup Environ Health 81(8):1029\u0026ndash;1036\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHartley TA et al (2011) Health disparities in police officers: comparisons to the U.S. general population. Int J Emerg Ment Health 13(4):211\u0026ndash;220\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCraty R, Atkinson M (2012) Resilience Training Program Reduces Physiological and Psychological Stress in Police Officers. 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A prospective study in the general population. J Psychosom Res 64(4):443\u0026ndash;449\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarbarino S, Magnavita N (2019) Sleep problems are a strong predictor of stress-related metabolic changes in police officers. A prospective study. PLoS ONE 14(10):e0224259\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Supplementary Tables","content":"\u003cp\u003eSupplementary Table 1 and 2 are not available with this version\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"The Third Medical Center, Chinese PLA General Hospital, Beijing, China.","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"traffic police officers, depression, anxiety, insomnia, China, post-pandemic period","lastPublishedDoi":"10.21203/rs.3.rs-6656633/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6656633/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePolice officers represent a distinct subpopulation that is at an elevated risk for mental health issues in China especially during the COVID-19 pandemic period. This study examined the prevalence of depression, anxiety, and insomnia, along with associated risk factors, among police officers in Beijing during the post-pandemic period (five years following the COVID-19 outbreak).\u003c/p\u003e\u003ch2\u003eAims\u003c/h2\u003e \u003cp\u003eThe objective of this study was to identify the sociodemographic and clinical factors associated with symptoms of depression, anxiety, and sleep disturbances in the Haidian District of Beijing, China.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted from March 2024 to April 2024 among 158 randomly selected participants in the Haidian District. The Patient Health Questionnaire-9 (PHQ-9) was utilized to screen for depression, while the Generalized Anxiety Disorder-7 (GAD-7) was employed to assess the severity of anxiety symptoms. The Pittsburgh Sleep Quality Index (PSQI) and Insomnia Severity Index (ISI) questionnaire were administered to evaluate sleep quality and disturbances. Descriptive statistics were used to present frequencies and percentages. univariate and multivariate analyses were performed to identify associations among predictors of depression, anxiety, and insomnia.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study included 158 participants, of whom 66.7% were men, with an average age of 41.32 years (\u0026plusmn;\u0026thinsp;8.43). Approximately 18.98% of the 137 police officers screened positive for depression, while 14.06% of the 128 participants screened positive for anxiety. PSQI indicated that 16.9% of the 124 participants exhibited clinical (moderate-severe) insomnia of insomnia, and the ISI revealed that 12.5% of the 120 participants were affected by insomnia. None of the examined sociodemographic factors demonstrated significant correlations with depression, anxiety, with the exception of age in relation to insomnia (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027). Participants experiencing anxiety and insomnia were found to have an increased risk of reporting depression (anxiety odds ratio [OR]: 48.48, 95% confidence interval [CI]: 5.30 to 445.49, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;.001.\u003c/b\u003e; insomnia odds ratio [OR]: 82.75, 95% [CI]: 3.75 to 1825.6, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe prevalence of depression, anxiety, and insomnia among police officers in Haidian district, Beijing, China is remarkably high. The study's findings emphasize the necessity of regularly screening police officers for these mental health issues and implementing tailored mental health services for this population.\u003c/p\u003e","manuscriptTitle":"Prevalence and factors associated with symptoms of depression, anxiety and insomnia among traffic police officers in Beijing, China: a cross-sectional survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 12:07:21","doi":"10.21203/rs.3.rs-6656633/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bd575af2-b01e-40df-bdb7-6c5181660b05","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-16T12:07:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-16 12:07:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6656633","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6656633","identity":"rs-6656633","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}