Predictors of anxiety-depression comorbidity in Chinese medical staff: A Random Forest analysis based on the Health Ecology Model

Predictors of anxiety-depression comorbidity in Chinese medical staff: A Random Forest analysis based on the Health Ecology Model | 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 Predictors of anxiety-depression comorbidity in Chinese medical staff: A Random Forest analysis based on the Health Ecology Model Yichen YE, Fan YANG (co-first author), Xiaoyi WU, Weiying LIANG, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8806075/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Anxiety-depression comorbidity is a serious issue among medical staff, yet few studies have analyzed its influencing factors using a comprehensive approach. This study aims to explore the prevalence and key predictors of this comorbidity among Chinese medical staff based on the health ecology model. Methods A multi-center cross-sectional study was conducted with 1,468 medical staff from 6 hospitals in China. Self-measures including Generalized Anxiety Disorder 7-item (GAD-7), Patient Health Questionnaire-9 (PHQ-9), Self-rated Health Scale, Physical Exercise Questionnaire, Pittsburgh Sleeping Quality Index (PSQI), Night Shift frequency Questionnaire and the Chinese Version Perceived Stress Scale were used to evaluate participants' prevalence of anxiety-depression comorbidity and its influencing factors. The chi-squared test and binary logistic regression were used to analyze the influencing factors of anxiety-depression comorbidity, the random forest model (RFM) was used to determine the importance of the predictors. Results The prevalence of anxiety-depression comorbidity was 34.60%. The individual (self-rated health), behavioral (physical exercise, sleep disorder), and living and working (night shift frequency, perceived stress) factors were significantly associated with anxiety-depression comorbidity ( P < 0.05). Based on the RFM, the top five predictors were higher perceived stress, sleep disorder, poor self-rated health, high night shift frequency and less physical exercise in sequence. Conclusions Anxiety-depression comorbidity is highly prevalent among Chinese medical staff. Perceived stress acts as the strongest predictor. Effective interventions should prioritize stress management, sleep improvement, reasonable shift scheduling, and physical exercise to mitigate this mental health burden. Anxiety Depression Comorbidity Medical staff Health ecology model Figures Figure 1 Background Medical staff’s mental health has emerged as a public health issue globally, especially in the context of escalating healthcare demands [ 1 , 2 ]. Numerous studies have investigated anxiety and depression among medical professionals [ 3 – 5 ], consistently reporting prevalence rates higher than those of the general population in countries such as China and the United States [ 6 ]. Globally, the prevalence of anxiety among physicians ranges from 8% to 78.9%, while depression ranges from 4.8% to 66.5% [ 5 ]. Similarly, recent research indicates that the prevalence of anxiety and depression in clinical nurses can reach as high as 67.3% and 67.2%, respectively [ 7 ]. These statistics underscore the significant mental health challenges inherent in the demanding healthcare environment. Anxiety-depression comorbidity [ 8 ] refers to the co-occurrence of anxiety and depressive symptoms. This condition involves shared risk factors and often leads to the exacerbation of symptoms. Such comorbidity not only diminishes the well-being of medical staff [ 8 ] but also compromises healthcare service quality and patient outcomes [ 9 ]. Consequently, the anxiety-depression comorbidity of medical staff has received increasing attention from researchers. However, few studies have specifically focused on this comorbidity within the medical workforce. Previous studies have estimated the prevalence of anxiety-depression comorbidity among Chinese medical staff ranging from 14.2% [ 4 ] to 23% [ 3 ]. Furthermore, while many studies have explored the factors influencing anxiety or depression in isolation, which often focus on single variables such as self-rated health [ 10 ], physical activity [ 11 ], or sleep disorders [ 3 , 4 ], few have analyzed medical staff’s anxiety-depression comorbidity using a comprehensive, multi-dimensional analytical approach. The Social Ecological Model of Health [ 12 ], often referred to as the Health Ecology Model, emphasizes both individual and social environmental factors. In this framework, health outcomes are viewed as the product of dynamic interactions across five strata: individual characteristics, behavioral characteristics, interpersonal networks, living and working conditions, and the policy environment [ 13 ]. While this model has been widely utilized in chronic disease management, it provides a novel theoretical framework for addressing the gaps in the current study. By facilitating multi-level and multi-factor analysis, this model allows for a more accurate identification of high-risk medical staff. Nevertheless, its application in research on anxiety-depression comorbidity remains limited. Based on this model [ 12 ] and previously identified risk factors [ 14 , 15 ], this study aimed to explore individual (e.g., sex, age, self-rated health), behavioral (e.g., smoking, excessive alcohol consumption, physical exercise, sleep disorder), interpersonal (e.g., marital status, number of children, close friends), living and working conditions (e.g., job position, working years, education level, night shift frequency, perceived stress), and policy environment (e.g., employment contract type) factors that influence anxiety-depression comorbidity in Chinese medical staff (Fig. 1 ). Additionally, determining the relative importance of these predictors is crucial for prioritizing interventions. To address this, a Random Forest Model (RFM) was employed to rank the influencing factors. This approach is significant for constructing tailored and effective intervention regimens. The findings are expected to provide valuable insights and a theoretical basis for hospital administrators to create supportive environments and targeted psychological interventions, thereby improving the mental health of medical staff in China and enhancing the overall quality of medical services. The findings are expected to provide valuable insights and theoretical basis for hospital administrators to create supportive environments and targeted psychological interventions, so as to improve the mental health of medical staff in China and further improve the quality of medical services. Methods Study design, participants and data collection In this multi-center cross-sectional study, a convenience sampling method was adopted to recruit medical staff from 6 university affiliated hospitals in Guangzhou, China in May 2022. The inclusion criteria were: (1) possessing a Physician or Nursing License; (2) having been working in the hospital for over one year. The exclusion criteria were: (1) reported a history of malignant tumor or mental illness; (2) on a formal leave during the data collection period. According to Kendall’s sample size determination for multi-factor analysis[ 16 ], the sample size should be 10–20 times the number of variables. As 16 independent variables were used in this study, the required sample size was calculated to be 160 cases. Considering a potential 20% invalid response rate, the required sample size was adjusted accordingly, yielding a minimum target of 200 completed questionnaires. All data were collected via Wenjuanxing platform ( www.wjx.cn ) and distributed through WeChat by trained investigators at the research hospitals. Participants were instructed to complete the questionnaire anonymously and independently. A total of 1,590 questionnaires were collected. After excluding invalid responses, 1,468 valid questionnaires were included in the final analysis, yielding an effective response rate of 92.33%. Dependent variable and measures The dependent variable was anxiety-depression comorbidity. (1) Anxiety: anxiety symptoms were assessed using the questionnaire of Generalized Anxiety Disorder-7 (GAD-7) [ 17 ].The frequency of anxiety symptoms in the past 2 weeks were measured in 7 items using a 4-level Likert scoring method, ranging from 0 to 3 for “not at all” to “almost every day”. A higher score indicates a higher level of anxiety symptoms. A total score ≥ 5 points was suggested for identifying the presence of anxiety symptoms. The Cronbach’s α reported in previous studies was 0.920 [ 17 ], and it was 0.959 in the current study. (2) Depression: depression was assessed using the questionnaire of Patient Health Questionnaire-9 (PHQ-9) [ 18 ]. The frequency of depressive symptoms over the past two weeks was measured across 9 items using a 4-point Likert scale, ranging from 0 to 3 for “not at all” to “almost every day”. A higher score indicates a higher level of depression symptoms. A total score ≥ 5 points was suggested for diagnosis of depression disorder. The Cronbach’s α was 0.890 in previous studies [ 18 ] and 0.930 in the current study. (3) Anxiety-depression comorbidity: The simultaneous presence of a GAD-7 score 5 and a PHQ-9 score ≥ 5 indicated the presence of anxiety-depression comorbidity [ 19 ]. The prevalence of anxiety-depression comorbidity is the proportion of the number of participants with anxiety-depression comorbidity in the total number of participants. Independent variables and measures The independent variables were selected based on the health ecology model [ 12 ] and a review of relevant literature [ 14 , 15 ] including the following: (1) Self-rated health: the Self-rated Health Scale [ 20 ] was used to assess medical staff’s self-rated health through a single question. participants were asked " How do you think your health is at present?". 5-level Likert scoring method was adopted with 1–5 points assigned to "very bad to excellent". (2) Smoking: smoking is defined as smoking an average of more than one cigarette per day in the last one month[ 21 ]; (3) Excessive alcohol consumption: the daily intake of ≥ 50 mL of distilled spirits or ≥ 100 mL of wine [ 21 ] in the last one month; (4) Physical exercise: based on the physical activity guideline for the Chinese population [ 22 ], the questionnaire was designed to investigate the physical activity items that individuals carried out for at least 10 minutes during their leisure time in the latest week, such as running, swimming, cycling, playing ball, climbing, etc. (5) Sleep disorder: the Chinese version of sleep disorder dimension in Pittsburgh Sleep Quality Index (PSQI)[ 23 , 24 ] was used. It consists of 9 items. 4-level Likert scoring method was adopted with 0–3 points assigned to 4 choices: no, <once a week, once to twice a week, ≥3times per week. A cumulative score of "0" indicates no sleep disorders, “1ཞ9” indicates mild sleep disorder, "10–18" indicates moderate sleep disorder, and "19–27" indicates severe sleep disorder. The Cronbach's α of this scale was 0.83 [ 24 ], and the Cronbach's α measured in this study was 0.857. (6) Night shift frequency: participants were asked "On average, how many night shifts do you typically work per week?”. (7) Perceived stress: the Chinese Version Perceived Stress Scale (CPSS) [ 25 , 26 ] was used to assess the degree to which one’s life situations were appraised as stressful in the past month. The scale comprises 14 items using a 5-point Likert scoring method, with response options ranging from 0 (never) to 4 (very often) points. A higher score indicates a higher level of perceived stress. A total score ≥ 26 points was classified as perceived health risk stress (HRS). The CPSS has demonstrated good reliability and validity, with reported Cronbach’s α 0.780 [ 26 ]. In the present study, the Cronbach's α was 0.816. Statistical analysis Data were analyzed using SPSS (version 27.0) and R software (version 4.4.3). Results with P < 0.05 indicated statistically significant differences. Count data were reported as the frequency and percentage. Measurement data were expressed as the mean and standard deviation ( SD ). The χ 2 test was used to compare the difference in the prevalence of anxiety-depression comorbidity among medical staff with different characteristics. Binary logistic regression analysis was used to explore the influencing factors of anxiety-depression comorbidity. The odds ratio ( OR ) and 95% confidence interval ( CI ) were calculated. Finally, a random forest model (RFM) was conducted to estimate the importance rank of each influencing factor. The variable importance was quantified by the mean decrease in accuracy when that variable was permuted, with higher values indicating a greater influence on predicting the outcome. Ethical considerations This study was approved by the Institutional Review Board of the university affiliated hospital (NO.2020-hs-57). All participants provided informed consent. Results Participants’ characteristics A total of 1,468 Chinese medical staff were included in this study. The sample comprised 466 physicians (31.74%) and 1,002 nurses (68.26%). There were 305 males (20.78%) and 1,163 females (79.22%). Regarding marital status, 1,056 (71.93%) married, while 412 (28.07%) were unmarried or widowed. Participants’ ages ranged from 20 to 59 years, with a mean age of 34.71 ± 8.61 years. Working experience ranged from 1 to 39 years, with a mean of 12.08 ± 9.00 years. In terms of education, 297 (20.23%) held an associate degree, 951 (64.78%) held a bachelor’s degree, and 220 (14.99%) held a master’s degree or above. Prevalence of anxiety-depression comorbidity in medical staff with different characteristics Of the 1,468 participants, 508 (34.60%) exhibited anxiety-depression comorbidity. Additionally, 253 (17.24%) had either anxiety or depression alone, while 707 (48.16%) were free of both disorders. Among them, the prevalence of anxiety disorder was 561 (38.22%) and depression disorder was 708 (48.23%). Table 1 compared the prevalence of anxiety-depression comorbidity in medical staff with different characteristics in five strata of health ecology model. The groups differed significantly for the following variables: individual characteristics (age, self-rated health), behavioral characteristics (physical exercise, sleep disorder), interpersonal network (close friends), living and working conditions (job position, working years, night shift frequency, perceived stress), P < 0.05. Table 1 Prevalence of anxiety-depression comorbidity among medical staff with different characteristics ( n = 1468) Factors N No anxiety or depression disorder n (%) Anxiety or depression disorder n (%) Anxiety-depression comorbidity n (%) χ 2 ༰ Individual characteristics Sex Male 305 151(49.51) 57(18.69) 97(31.80) 1.491 0.475 Female 1163 556(47.81) 196(16.85) 411(35.34) Age (years) 20ཞ29 486 240(49.38) 88(18.11) 158(32.51) 15.902 0.014 30ཞ39 605 264(43.64) 113(18.68) 228(37.68) 40ཞ49 257 131(50.97) 34(13.23) 92(35.80) 50ཞ59 120 72(60.00) 18(15.00) 30(25.00) Self-rated health Very bad 11 1(9.09) 0(0) 10(90.91) 274.661 <0.001 Bad 96 11(11.46) 14(14.58) 71(73.96) Neither good nor bad 594 194(32.66) 125(21.04) 275(46.30) Good 496 287(57.86) 92(18.55) 117(23.59) Excellent 271 214(78.97) 22(8.11) 35(12.92) Behavioral characteristics Smoking Yes 35 20(57.14) 7(20.00) 8(22.86) 2.190 0.335 No 1433 687(47.94) 246(17.17) 500(34.89) Excessive alcohol consumption Yes 77 31(40.26) 19(24.68) 27(35.06) 3.669 0.160 No 1391 676(48.60) 234(16.82) 481(34.58) Physical exercise Yes 942 415(44.06) 157(16.67) 370(39.27) 26.284 <0.001 No 526 292(55.51) 96(18.25) 138(26.24) Sleep disorder No 312 244(78.21) 33(10.58) 35(11.21) 313.691 <0.001 Mild 841 424(50.42) 158(18.79) 259(30.79) Moderate 274 34(12.41) 59(21.53) 181(66.06) Severe 41 5(12.20) 3(7.32) 33(80.48) Interpersonal network Marital status Married 1056 509(48.20) 181(17.14) 366(34.66) 0.024 0.988 Unmarried or widowed 412 198(48.06) 72(17.48) 142(34.46) Number of children 0 497 243(48.89) 92(18.51) 162(32.60) 2.880 0.824 1 537 263(48.98) 87(16.20) 187(34.82) 2 428 199(46.50) 73(17.06) 156(36.44) 3 6 2(33.33) 1(16.67) 3(50.00) Having close friends Yes 1256 638(50.80) 208(16.56) 410(32.64) 24.505 <0.001 No 212 69(32.55) 45(21.22) 98(46.23) Living and working conditions Job position Physician 466 241(51.72) 86(18.45) 139(29.83) 6.884 0.032 Nurse 1002 466(46.51) 167(16.67) 369(36.82) Working years 1ཞ4 300 162(54.00) 49(16.33) 89(29.67) 34.291 0.001 5ཞ9 426 188(44.13) 90(21.13) 148(34.74) 10ཞ14 312 138(44.23) 49(15.71) 125(40.06) 15ཞ19 122 52(42.62) 27(22.13) 43(35.25) 20ཞ24 92 46(50.00) 9(9.78) 37(40.22) 25ཞ29 119 57(47.90) 16(13.45) 46(38.65) 30ཞ41 97 64(65.98) 13(13.40) 20(20.62) Education level Associate degree 297 145(48.82) 53(17.85) 99(33.33) 6.271 0.180 Bachelor degree 951 442(46.48) 162(17.03) 347(36.49) Master degree or above 220 120(54.55) 38(17.27) 62(28.18) Night shift frequency No 380 215(56.58) 68(17.89) 97(25.53) 27.782 <0.001 Less than once a week 110 52(47.27) 16(14.55) 42(38.18) About once a week 405 198(48.89) 74(18.27) 133(32.84) Once to twice a week or more 573 242(42.23) 95(16.58) 236(41.19) Perceived health risk stress Yes 944 366(38.77) 138(14.62) 440(46.61) 169.059 <0.001 No 524 341(65.08) 115(21.95) 68(12.97) Policy environment Employment contract type Staff on the establishment (in-establishment staff) 733 360(49.11) 127(17.33) 246(33.56) 0.744 0.689 Contractual staff (out-of-establishment staff) 735 374(47.21) 126(17.14) 262(35.65) Binary logistic regression analysis As shown in Table 2 , a binary logistic regression analysis of related factors based on health ecology model was performed. Comorbidity status served as the dependent variable (no = 0, yes = 1). Variables showing statistical significance in the univariate analysis Table 1 were included as independent variables, apart from age, which was excluded due to collinearity. All independent variables were entered simultaneously. The results demonstrated that, compared with medical staff with very bad self-rated health, those with excellent self-rated health were less likely to experience anxiety-depression comorbidity ( OR = 0.074, 95% CI : 0.008 ~ 0.683). Additionally, participants who engaged in leisure-time physical exercise were less likely to have comorbidity ( OR = 0.722, 95% CI : 0.543 ~ 0.962); medical staff were more likely to experience anxiety-depression comorbidity if they have different levels of sleep disorder ( OR = 3.645 ~ 12.084, 95% CI : 2.415 ~ 30.889), more night shift frequency ( OR = 1.168, 95% CI :1.036 ~ 1.317), and higher perceived stress ( OR = 1.199, 95% CI : 1.160 ~ 1.240). Table 2 Binary logistic regression analysis of factors related to anxiety-depression comorbidity in Chinese medical staff ( n = 1468) Variables B SE Wald P OR 95% CI Lower 95% CI Upper Constant -5.319 1.261 17.777 <0.001 0.005 Individual characteristics Self-rated health Very bad 1.000 Bad -0.984 1.148 0.735 0.391 0.374 0.039 3.548 Neither good nor bad -1.486 1.123 1.751 0.186 0.226 0.025 2.044 Good -2.092 1.127 3.445 0.063 0.123 0.014 1.124 Excellent -2.610 1.137 5.267 0.022 0.074 0.008 0.683 Behavioral characteristics Physical exercise No 1.000 Yes -0.325 0.146 4.961 0.026 0.722 0.543 0.962 Sleep disorder No 1.000 Mild 1.293 0.210 37.945 <0.001 3.645 2.415 5.501 Moderate 2.322 0.243 90.985 <0.001 10.197 6.328 16.433 Severe 2.492 0.479 27.079 <0.001 12.084 4.727 30.889 Interpersonal network Having close friends Yes 1.000 No 0.161 0.187 0.739 0.390 1.175 0.814 1.696 Living and working conditions Job position Physician 1.000 Nurse 0.063 0.150 0.174 0.676 1.065 0.793 1.430 Working years 0.001 0.008 0.013 0.908 1.001 0.985 1.017 Night shift frequency 0.156 0.061 6.470 0.011 1.168 1.036 1.317 Perceived stress 0.182 0.017 113.851 <0.001 1.199 1.160 1.240 OR: odds ratio; CI: confidence interval; Nagelkerke R 2 = 0.428; Hosmer-Lemeshow test: χ 2 = 6.652, P = 0.575. Ranking of predictive contribution of predictors Finally, the random forest variable importance was determined on mean decrease in accuracy. As shown in Table 3 , the top five predictors, in descending order of importance, were: perceived stress, sleep disorder, self-rated health, night shift frequency, and physical exercise. Table 3 The rank of importance of influencing factors of anxiety-depression comorbidity in random forest model Rank Variables Mean decrease accuracy 1 Perceived stress 69.00 2 Sleep disorder 55.02 3 Self-rated health 27.74 4 Night shift frequency 5.88 5 Physical exercise 1.47 6 Working years 1.01 7 Having close friends 0.92 8 Job position -0.13 Model evaluation area under the curve (AUC) = 0.994; Accuracy = 95.23%. Discussion This study provides a comprehensive analysis of influencing factors of anxiety-depression comorbidity in Chinese medical staff. By utilizing the health ecology model, we systematically examine factors across five strata: individual characteristics, behavioral characteristics, interpersonal network, living and working conditions, and policy environment factors. Notably, the random forest model identified the top five predictors as perceived stress, sleep disorder, self-rated health, night shift frequency and physical exercise in sequence, with perceived stress having the greatest impact. Prevalence of anxiety-depression comorbidity In the present study, the prevalence of anxiety-depression comorbidity among medical staff in Guangzhou was found to be 34.60%. This rate is notably higher than the 14.2% reported among hospital staff (including administrative personnel) in 304 inland cities in China [ 4 ], despite using the same assessment instruments (GAD-7 and PHQ-9). This variation in prevalence rate could result from diverse participants and hospital locations. First, previous research indicates that clinical medical staff generally exhibit higher rates of anxiety or depression compared to administrative staff [ 4 ]. Furthermore, Guangzhou, as a first-tier economically developed metropolis, likely imposes greater workload pressures and higher patient volumes on staff in university-affiliated hospitals, thereby exacerbating the risk factors associated with anxiety-depression comorbidity. Influence of individual and behavioral factors At the individual strata, self-rated health was significantly associated with comorbidity. Medical staff who perceived their health as poor were more likely to experience anxiety-depression comorbidity. This aligns with previous research suggesting that self-rated health is an important determinant of psychological well-being [ 10 ]. Poor self-perceived health may trigger increased health-related worry, which can contribute to the development of anxiety, depression, and their comorbidity. At the behavioral strata, physical exercise emerged as a significant protective factor, whereas sleep disorder was identified as a risk factor. A systematic review [ 11 ] confirmed that physical exercise reduces symptoms of anxiety and depression, likely by promoting neurogenesis and enhancing mood regulation. Conversely, while [ 3 ] linked sleep disorders to depression in medical staff, few studies have specifically analyzed its impact on comorbidity. Sleep disorders in this population are often attributable to irregular schedules and high occupational stress, which may exacerbate neuroendocrine dysregulation [ 27 ], thereby increasing the risk of mental health issues. Therefore, interventions promoting regular exercise and optimizing sleep hygiene are essential for prevention. Influence of interpersonal networks At the interpersonal strata, the univariate analysis of this study found that medical staff with close friends are at a lower risk of anxiety-depression comorbidity. This finding aligns with the study highlighting social support’s protective role in the mental health of medical staff[ 28 ]. The reason may be that close friends can provide a source of emotional support that can reduce the likelihood of developing anxiety or depression by providing a sense of belonging and understanding. Hospital administrators should strive to build supportive relationships with medical staff and provide psychological support to help them deal with work-life balance and improve the well-being of their staff. Influence of living and working conditions At the living and working strata, the significant influencing factors included job position, night shift frequency and perceived stress. (1) Job Position: In this study, the prevalence of anxiety-depression comorbidity in nurses (36.82%) exhibited significantly higher rates than that in physicians (29.83%). Lu et al[ 3 ] also found that the prevalence of depression in nurses exhibiting significantly higher rates than that in physicians. This may be because nurses are often exposed to a more high-pressure work environment such as direct patient care responsibilities [ 3 , 8 ]. The finding indicated that it is urgent to pay attention to reducing medical staff’s anxiety and depression, especially nurses. (2) Night Shift Frequency: Staff with frequent night shifts showed a higher likelihood of comorbidity. Shift work has been shown to disrupt circadian rhythms and increase the overall risk of anxiety and depression [ 29 ][ 30 ]. Given that night shifts are inherent to medical professions, hospitals should optimize shift scheduling and ensure adequate rest periods for recovery. (3) Perceived Stress: Perceived stress refers to the extent to which situations in one’s life or work are evaluated as stressful [ 25 ]. Among all determinants, perceived stress was identified as the strongest predictor. Higher levels of perceived stress strongly predicted the presence of anxiety-depression comorbidity. This finding aligns with prior study highlighting perceived stress as a critical psychosocial determinant of mental health outcomes in nurses [ 31 ] and medical workers [ 32 ]. This is because high levels of perceived stress can overwhelm people's coping mechanisms and increase the risk of anxiety or depression [ 33 ]. Strategies such as offering support services or mindfulness training [ 34 , 35 ], may be effective in reducing the risk of anxiety-depression comorbidity among medical staff. Limitations Several limitations of this study should be acknowledged. First, the cross-sectional design limits causal inference, future longitudinal studies are needed to explore the causal relationships between these factors and anxiety-depression comorbidity. Second, the reliance on self-reported measures may introduce recall bias, future studies incorporating objective measures may provide more accurate and reliable data. Third, although the health ecology model was employed to capture a wide range of factors, it is possible that certain unmeasured confounding variables were omitted due to the complexity of the system. Future research should consider including a broader range of variables to enhance model explanatory power. By addressing these limitations, future research can generate more comprehensive insights, ultimately providing a solid theoretical basis for the management of anxiety-depression comorbidity among medical staff. Conclusions While previous research has extensively examined anxiety and depression in isolation, the potential impact of anxiety-depression comorbidity cannot be overlooked. The current study reveals a substantial prevalence of this comorbidity among medical staff in China. This study provides a comprehensive analysis of the influencing factors of anxiety-depression comorbidity among medical staff, highlighting the complexity of this issue within the context of the health ecology model. Consequently, hospital administrators should not only prioritize the management of occupational stressors, such as night shifts and perceived stress, but also address behavioral and interpersonal factors. Comprehensive intervention strategies, ranging from promoting physical exercise and optimizing rest environments to enhancing social and emotional support, are essential to effectively mitigate the risk of anxiety-depression comorbidity and improve the overall well-being of medical staff. Declarations Ethics approval and consent to participate Ethical approval was obtained from the Research Ethics Committee of The Second Affiliated Hospital of Guangzhou Medical University (Approval No. : 2020-hs-57), and all participants provided informed consent prior to data collection. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding The study was supported by a grant from the Clinical Research Project of New Technology and New Service of the Second Affiliated Hospital of Guangzhou Medical University (Grant Number: 2020-LCYJ-XJS-06). Author Contribution All authors have made substantial contributions to the work and have approved the final version of the manuscript:YYC (Co-first Author): Formal analysis and original draftYF (Co-first Author): Formal analysis and original draftWXY: Investigation; Review and editingLWY: Investigation; Review and editingYYC: Investigation; Review and editingZWL (Corresponding Author): Conceptualization and Methodology; Investigation; Formal analysis and original draft. Acknowledgments We gratefully acknowledge all medical staff members from the six tertiary hospitals in Guangzhou who participated in this survey for their time and valuable contributions to this study. Data Availability The data presented in this study are available from the corresponding author upon reasonable request. References Sovold LE, Naslund JA, Kousoulis AA, Saxena S, Qoronfleh MW, Grobler C, Munter L. Prioritizing the Mental Health and Well-Being of Healthcare Workers: An Urgent Global Public Health Priority. 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Xu H, Chen ZH, Ji JJ, Qian HY, She J, Hou CT, Zhang YH. Behavioral and psychosocial factors associated with suicidal ideation in adolescents with depression: An ecological model of health behavior. J Psychiatr Res. 2025;181:411–6. https://10.1016/j.jpsychires.2024.12.014.org*10.1016/j.jpsychires.2024.12.014 . Choi K, Marden J. A multivariate version of kendall’s τ. J NONPARAMETRIC Stat. 1998;9(3):261–93. 10.1080/10485259808832746.org*https://doi.org/10.1080/10485259808832746 . https://https://doi.org/ . Spitzer RL, Kroenke K, Williams JB, Lowe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092–7. https://10.1001/archinte.166.10.1092.org*10.1001/archinte.166.10.1092 . Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13. https://10.1046/j.1525-1497.2001.016009606.x.org*10.1046/j.1525-1497.2001.016009606.x . Wang M, Mou X, Li T, Zhang Y, Xie Y, Tao S, Wan Y, Tao F, Wu X. Association Between Comorbid Anxiety and Depression and Health Risk Behaviors Among Chinese Adolescents: Cross-Sectional Questionnaire Study. JMIR Public Health Surveillance. 2023;9:e46289. https://10.2196/46289.org*10.2196/46289 . MADDOX GL. Some correlates of differences in self-assessment of health status among the elderly. J Gerontol. 1962;17:180–5. https://10.1093/geronj/17.2.180.org*10.1093/geronj/17.2.180 . Gu DF, Wen JP, Lu XF. Chinese Guideline on Healthy Lifestyle to Prevent Cardiometabolic Diseases. Chin Circulation J. 2020;35:209–30. https://10.3969/j.issn.1000-3614.2020.03.001.org*10.3969/j.issn.1000-3614.2020.03.001 . Zhao W, Li KJ, Wang YY, Wang JZ, Liu AL. Physical Activity Guidelines for Chinese (2021). Chin J Public Health 2022; 38(2): 129–130. https://10.11847/zgggws1137503.org*10.11847/zgggws1137503 Buysse DJ, Reynolds CR, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193–213. https://10.1016/0165-1781(89)90047-4.org*10.1016/0165-1781(89)90047-4 . Liu XC, Tang MQ, Hu L, Wang AZ. Reliability and validity of the Pittsburgh sleep quality index. Chin J Psychiatry. 1996;29:103–7. COHEN S, KAMARCK T. A global measure of perceived stress. J Health Soc Behav. 1983;24(4):385–96. Yang TZ, Huang HT. An epidemiological study on stress among urban residents in social transition period. Chin J Epidemiol. 2003;24:760–4. https://10.3760/j.issn:0254-6450.2003.09.004.org*10.3760/j.issn:0254-6450.2003.09.004 . King JD, Yang M, Tyrer H, Tyrer P. Sleep Disturbance in People with Anxiety or Depressive Disorders over 30 Years, and the Influence of Personality Disorder. Behav sleep Med 2024: 1–13. https://10.1080/15402002.2024.2441795.org*10.1080/15402002.2024.2441795 Wu J, Dou J, Wang D, Wang L, Chen F, Lu G, Sun L, Liu J. The empathy and stress mindset of healthcare workers: the chain mediating roles of self-disclosure and social support. Front Psychiatry. 2024;15:1399167. https://10.3389/fpsyt.2024.1399167.org*10.3389/fpsyt.2024.1399167 . Li Y, Wang Y, Lv X, Li R, Guan X, Li L, Li J, Cao Y. Effects of Factors Related to Shift Work on Depression and Anxiety in Nurses. Front Public Health. 2022;10:926988. https://10.3389/fpubh.2022.926988.org*10.3389/fpubh.2022.926988 . Torquati L, Mielke GI, Brown WJ, Burton NW, Kolbe-Alexander TL. Shift Work and Poor Mental Health: A Meta-Analysis of Longitudinal Studies. Am J Public Health. 2019;109(11):e13–20. https://10.2105/AJPH.2019.305278.org*10.2105/AJPH.2019.305278 . Liu XK, Huang DL, Cheng W, Xu BC, Luo XY, Liu SR, Yuan TC, Yu LY, Wang TX, Sun Y, Zhang H. Psychometric properties and measurement invariance of the medical staff occupational stress scale among Chinese clinical nurses. BMC Nurs. 2025;24(1):185. https://10.1186/s12912-025-02780-z.org*10.1186/s12912-025-02780-z . Meng R, Luo X, Du S, Luo Y, Liu D, Chen J, Li Y, Zhang W, Li J, Yu C. The Mediating Role of Perceived Stress in Associations Between Self-Compassion and Anxiety and Depression: Further Evidence from Chinese Medical Workers. Risk Manage Healthc Policy. 2020;13:2729–41. https://10.2147/RMHP.S261489.org*10.2147/RMHP.S261489 . He J, Tu S, Zhao H, He Q. Transitioning from perceived stress to mental health: The mediating role of self-control in a longitudinal investigation with MRI scans. Int J Clin Health Psychol. 2025;25(1):100539. https://10.1016/j.ijchp.2024.100539.org*10.1016/j.ijchp.2024.100539 . Melnyk BM, Kelly SA, Stephens J, Dhakal K, McGovern C, Tucker S, Hoying J, McRae K, Ault S, Spurlock E, Bird SB. Interventions to Improve Mental Health, Well-Being, Physical Health, and Lifestyle Behaviors in Physicians and Nurses: A Systematic Review. Am J HEALTH PROMOTION. 2020;34(8):929–41. https://10.1177/0890117120920451.org*10.1177/0890117120920451 . Watanabe N, Horikoshi M, Shinmei I, Oe Y, Narisawa T, Kumachi M, Matsuoka Y, Hamazaki K, Furukawa TA. Brief mindfulness-based stress management program for a better mental state in working populations - Happy Nurse Project: A randomized controlled trial. J Affect Disord. 2019;251:186–94. https://10.1016/j.jad.2019.03.067.org*10.1016/j.jad.2019.03.067 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers invited by journal 06 Mar, 2026 Editor invited by journal 11 Feb, 2026 Editor assigned by journal 10 Feb, 2026 Submission checks completed at journal 10 Feb, 2026 First submitted to journal 06 Feb, 2026 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8806075","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":601849874,"identity":"9b09d400-6ec3-41fd-9313-cd91643ad27e","order_by":0,"name":"Yichen YE","email":"","orcid":"","institution":"Second Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yichen","middleName":"","lastName":"YE","suffix":""},{"id":601849875,"identity":"32bbacfb-430e-4a4b-8999-be0ec6d55023","order_by":1,"name":"Fan YANG (co-first author)","email":"","orcid":"","institution":"Second Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"YANG (co-first","lastName":"author)","suffix":""},{"id":601849876,"identity":"7fc1d213-b1dd-4b0a-b926-5eb83540f205","order_by":2,"name":"Xiaoyi WU","email":"","orcid":"","institution":"Second Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyi","middleName":"","lastName":"WU","suffix":""},{"id":601849877,"identity":"92748653-217a-4fb3-b895-a392a060211f","order_by":3,"name":"Weiying LIANG","email":"","orcid":"","institution":"Second Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weiying","middleName":"","lastName":"LIANG","suffix":""},{"id":601849878,"identity":"e28b29bc-c998-4f58-9997-44e0b0814606","order_by":4,"name":"Yongchang YANG","email":"","orcid":"","institution":"Second Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yongchang","middleName":"","lastName":"YANG","suffix":""},{"id":601849879,"identity":"c322360f-e8dd-4744-b123-2b5f76cb47da","order_by":5,"name":"Wenli ZHOU","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYDACCQglw8bewMAMYScQp4WHjecAqVoYJBKI1CI/u/mYNE/FHR4+yeeXPxfmHGbgZ88xYPi5A7cWxjnH0qR5zjzjYZPOKTCeue0wg2TPGwPG3jO4tTBL5JhJ57YdBmlJSOYFajG4kWPAzNiGWwsbWMs/oBbJMwmHQVrsCWnhAWtpAGqRYD/YDLZFgoAWCYm0ZOs/x4BaeHKYmXm3pfNInHlWcLAXjxb5GckHb86oOSwn33788WfebdZy/O3JGx/8xKMF2Y0GYBJEHCBKAwMD+wMiFY6CUTAKRsFIAwDed0f0fE+O0wAAAABJRU5ErkJggg==","orcid":"","institution":"Second Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Wenli","middleName":"","lastName":"ZHOU","suffix":""}],"badges":[],"createdAt":"2026-02-06 10:56:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8806075/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8806075/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104412564,"identity":"eeb239a9-c0e7-411b-93d1-a959241f45e7","added_by":"auto","created_at":"2026-03-11 12:59:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":140088,"visible":true,"origin":"","legend":"\u003cp\u003eThe Health Ecology Model of anxiety-depression comorbidity.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8806075/v1/20360598a04fd37259bf2170.png"},{"id":104415723,"identity":"b24ac47d-5f60-44ab-88a2-37b35c58a886","added_by":"auto","created_at":"2026-03-11 13:11:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1291833,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8806075/v1/642fbcd0-174f-40cf-a079-d3773f56536c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictors of anxiety-depression comorbidity in Chinese medical staff: A Random Forest analysis based on the Health Ecology Model","fulltext":[{"header":"Background","content":"\u003cp\u003eMedical staff\u0026rsquo;s mental health has emerged as a public health issue globally, especially in the context of escalating healthcare demands [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Numerous studies have investigated anxiety and depression among medical professionals [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], consistently reporting prevalence rates higher than those of the general population in countries such as China and the United States [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Globally, the prevalence of anxiety among physicians ranges from 8% to 78.9%, while depression ranges from 4.8% to 66.5% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Similarly, recent research indicates that the prevalence of anxiety and depression in clinical nurses can reach as high as 67.3% and 67.2%, respectively [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These statistics underscore the significant mental health challenges inherent in the demanding healthcare environment.\u003c/p\u003e \u003cp\u003eAnxiety-depression comorbidity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] refers to the co-occurrence of anxiety and depressive symptoms. This condition involves shared risk factors and often leads to the exacerbation of symptoms. Such comorbidity not only diminishes the well-being of medical staff [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] but also compromises healthcare service quality and patient outcomes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Consequently, the anxiety-depression comorbidity of medical staff has received increasing attention from researchers. However, few studies have specifically focused on this comorbidity within the medical workforce. Previous studies have estimated the prevalence of anxiety-depression comorbidity among Chinese medical staff ranging from 14.2% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] to 23% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Furthermore, while many studies have explored the factors influencing anxiety or depression in isolation, which often focus on single variables such as self-rated health [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], physical activity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], or sleep disorders [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], few have analyzed medical staff\u0026rsquo;s anxiety-depression comorbidity using a comprehensive, multi-dimensional analytical approach.\u003c/p\u003e \u003cp\u003eThe Social Ecological Model of Health [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], often referred to as the Health Ecology Model, emphasizes both individual and social environmental factors. In this framework, health outcomes are viewed as the product of dynamic interactions across five strata: individual characteristics, behavioral characteristics, interpersonal networks, living and working conditions, and the policy environment [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While this model has been widely utilized in chronic disease management, it provides a novel theoretical framework for addressing the gaps in the current study. By facilitating multi-level and multi-factor analysis, this model allows for a more accurate identification of high-risk medical staff. Nevertheless, its application in research on anxiety-depression comorbidity remains limited.\u003c/p\u003e \u003cp\u003eBased on this model [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and previously identified risk factors [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], this study aimed to explore individual (e.g., sex, age, self-rated health), behavioral (e.g., smoking, excessive alcohol consumption, physical exercise, sleep disorder), interpersonal (e.g., marital status, number of children, close friends), living and working conditions (e.g., job position, working years, education level, night shift frequency, perceived stress), and policy environment (e.g., employment contract type) factors that influence anxiety-depression comorbidity in Chinese medical staff (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, determining the relative importance of these predictors is crucial for prioritizing interventions. To address this, a Random Forest Model (RFM) was employed to rank the influencing factors. This approach is significant for constructing tailored and effective intervention regimens. The findings are expected to provide valuable insights and a theoretical basis for hospital administrators to create supportive environments and targeted psychological interventions, thereby improving the mental health of medical staff in China and enhancing the overall quality of medical services.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe findings are expected to provide valuable insights and theoretical basis for hospital administrators to create supportive environments and targeted psychological interventions, so as to improve the mental health of medical staff in China and further improve the quality of medical services.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design, participants and data collection\u003c/p\u003e \u003cp\u003eIn this multi-center cross-sectional study, a convenience sampling method was adopted to recruit medical staff from 6 university affiliated hospitals in Guangzhou, China in May 2022. The inclusion criteria were: (1) possessing a Physician or Nursing License; (2) having been working in the hospital for over one year. The exclusion criteria were: (1) reported a history of malignant tumor or mental illness; (2) on a formal leave during the data collection period.\u003c/p\u003e \u003cp\u003eAccording to Kendall\u0026rsquo;s sample size determination for multi-factor analysis[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], the sample size should be 10\u0026ndash;20 times the number of variables. As 16 independent variables were used in this study, the required sample size was calculated to be 160 cases. Considering a potential 20% invalid response rate, the required sample size was adjusted accordingly, yielding a minimum target of 200 completed questionnaires.\u003c/p\u003e \u003cp\u003eAll data were collected via Wenjuanxing platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.wjx.cn\" target=\"_blank\"\u003ewww.wjx.cn\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.wjx.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and distributed through WeChat by trained investigators at the research hospitals. Participants were instructed to complete the questionnaire anonymously and independently. A total of 1,590 questionnaires were collected. After excluding invalid responses, 1,468 valid questionnaires were included in the final analysis, yielding an effective response rate of 92.33%.\u003c/p\u003e \u003cp\u003eDependent variable and measures\u003c/p\u003e \u003cp\u003eThe dependent variable was anxiety-depression comorbidity. (1) Anxiety: anxiety symptoms were assessed using the questionnaire of Generalized Anxiety Disorder-7 (GAD-7) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].The frequency of anxiety symptoms in the past 2 weeks were measured in 7 items using a 4-level Likert scoring method, ranging from 0 to 3 for \u0026ldquo;not at all\u0026rdquo; to \u0026ldquo;almost every day\u0026rdquo;. A higher score indicates a higher level of anxiety symptoms. A total score\u0026thinsp;\u0026ge;\u0026thinsp;5 points was suggested for identifying the presence of anxiety symptoms. The Cronbach\u0026rsquo;s α reported in previous studies was 0.920 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and it was 0.959 in the current study. (2) Depression: depression was assessed using the questionnaire of Patient Health Questionnaire-9 (PHQ-9) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The frequency of depressive symptoms over the past two weeks was measured across 9 items using a 4-point Likert scale, ranging from 0 to 3 for \u0026ldquo;not at all\u0026rdquo; to \u0026ldquo;almost every day\u0026rdquo;. A higher score indicates a higher level of depression symptoms. A total score\u0026thinsp;\u0026ge;\u0026thinsp;5 points was suggested for diagnosis of depression disorder. The Cronbach\u0026rsquo;s α was 0.890 in previous studies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and 0.930 in the current study. (3) Anxiety-depression comorbidity: The simultaneous presence of a GAD-7 score 5 and a PHQ-9 score\u0026thinsp;\u0026ge;\u0026thinsp;5 indicated the presence of anxiety-depression comorbidity [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The prevalence of anxiety-depression comorbidity is the proportion of the number of participants with anxiety-depression comorbidity in the total number of participants.\u003c/p\u003e \u003cp\u003eIndependent variables and measures\u003c/p\u003e \u003cp\u003eThe independent variables were selected based on the health ecology model [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and a review of relevant literature [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] including the following: (1) Self-rated health: the Self-rated Health Scale [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] was used to assess medical staff\u0026rsquo;s self-rated health through a single question. participants were asked \" How do you think your health is at present?\". 5-level Likert scoring method was adopted with 1\u0026ndash;5 points assigned to \"very bad to excellent\". (2) Smoking: smoking is defined as smoking an average of more than one cigarette per day in the last one month[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]; (3) Excessive alcohol consumption: the daily intake of \u0026ge;\u0026thinsp;50 mL of distilled spirits or \u0026ge;\u0026thinsp;100 mL of wine [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] in the last one month; (4) Physical exercise: based on the physical activity guideline for the Chinese population [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], the questionnaire was designed to investigate the physical activity items that individuals carried out for at least 10 minutes during their leisure time in the latest week, such as running, swimming, cycling, playing ball, climbing, etc. (5) Sleep disorder: the Chinese version of sleep disorder dimension in Pittsburgh Sleep Quality Index (PSQI)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] was used. It consists of 9 items. 4-level Likert scoring method was adopted with 0\u0026ndash;3 points assigned to 4 choices: no, \u0026lt;once a week, once to twice a week, \u0026ge;3times per week. A cumulative score of \"0\" indicates no sleep disorders, \u0026ldquo;1ཞ9\u0026rdquo; indicates mild sleep disorder, \"10\u0026ndash;18\" indicates moderate sleep disorder, and \"19\u0026ndash;27\" indicates severe sleep disorder. The Cronbach's α of this scale was 0.83 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and the Cronbach's α measured in this study was 0.857. (6) Night shift frequency: participants were asked \"On average, how many night shifts do you typically work per week?\u0026rdquo;. (7) Perceived stress: the Chinese Version Perceived Stress Scale (CPSS) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] was used to assess the degree to which one\u0026rsquo;s life situations were appraised as stressful in the past month. The scale comprises 14 items using a 5-point Likert scoring method, with response options ranging from 0 (never) to 4 (very often) points. A higher score indicates a higher level of perceived stress. A total score\u0026thinsp;\u0026ge;\u0026thinsp;26 points was classified as perceived health risk stress (HRS). The CPSS has demonstrated good reliability and validity, with reported Cronbach\u0026rsquo;s \u003cem\u003eα\u003c/em\u003e 0.780 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In the present study, the Cronbach's α was 0.816.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using SPSS (version 27.0) and R software (version 4.4.3). Results with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistically significant differences. Count data were reported as the frequency and percentage. Measurement data were expressed as the mean and standard deviation (\u003cem\u003eSD\u003c/em\u003e). The \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e test was used to compare the difference in the prevalence of anxiety-depression comorbidity among medical staff with different characteristics. Binary logistic regression analysis was used to explore the influencing factors of anxiety-depression comorbidity. The odds ratio (\u003cem\u003eOR\u003c/em\u003e) and 95% confidence interval (\u003cem\u003eCI\u003c/em\u003e) were calculated. Finally, a random forest model (RFM) was conducted to estimate the importance rank of each influencing factor. The variable importance was quantified by the mean decrease in accuracy when that variable was permuted, with higher values indicating a greater influence on predicting the outcome.\u003c/p\u003e \u003cp\u003eEthical considerations\u003c/p\u003e \u003cp\u003e This study was approved by the Institutional Review Board of the university affiliated hospital (NO.2020-hs-57). All participants provided informed consent.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eParticipants\u0026rsquo; characteristics\u003c/p\u003e \u003cp\u003eA total of 1,468 Chinese medical staff were included in this study. The sample comprised 466 physicians (31.74%) and 1,002 nurses (68.26%). There were 305 males (20.78%) and 1,163 females (79.22%). Regarding marital status, 1,056 (71.93%) married, while 412 (28.07%) were unmarried or widowed. Participants\u0026rsquo; ages ranged from 20 to 59 years, with a mean age of 34.71\u0026thinsp;\u0026plusmn;\u0026thinsp;8.61 years. Working experience ranged from 1 to 39 years, with a mean of 12.08\u0026thinsp;\u0026plusmn;\u0026thinsp;9.00 years. In terms of education, 297 (20.23%) held an associate degree, 951 (64.78%) held a bachelor\u0026rsquo;s degree, and 220 (14.99%) held a master\u0026rsquo;s degree or above.\u003c/p\u003e \u003cp\u003ePrevalence of anxiety-depression comorbidity in medical staff with different characteristics\u003c/p\u003e \u003cp\u003eOf the 1,468 participants, 508 (34.60%) exhibited anxiety-depression comorbidity. Additionally, 253 (17.24%) had either anxiety or depression alone, while 707 (48.16%) were free of both disorders. Among them, the prevalence of anxiety disorder was 561 (38.22%) and depression disorder was 708 (48.23%). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e compared the prevalence of anxiety-depression comorbidity in medical staff with different characteristics in five strata of health ecology model. The groups differed significantly for the following variables: individual characteristics (age, self-rated health), behavioral characteristics (physical exercise, sleep disorder), interpersonal network (close friends), living and working conditions (job position, working years, night shift frequency, perceived stress), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\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\u003ePrevalence of anxiety-depression comorbidity among medical staff with different characteristics (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1468)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\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\u003eNo anxiety or depression disorder\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnxiety or depression disorder\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnxiety-depression comorbidity\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e༰\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\u003eIndividual characteristics\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 \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 \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\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e151(49.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57(18.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97(31.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.475\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\u003e1163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e556(47.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e196(16.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e411(35.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20ཞ29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e240(49.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88(18.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e158(32.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30ཞ39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e264(43.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113(18.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e228(37.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40ཞ49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131(50.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34(13.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92(35.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50ཞ59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72(60.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(15.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30(25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-rated health\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery bad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(9.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10(90.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e274.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11(11.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(14.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71(73.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeither good nor bad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e194(32.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125(21.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e275(46.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e287(57.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92(18.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e117(23.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e214(78.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(8.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35(12.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral characteristics\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 \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 \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\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20(57.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(20.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8(22.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.335\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\u003e1433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e687(47.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e246(17.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e500(34.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcessive alcohol consumption\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 \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\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31(40.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(24.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27(35.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.160\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\u003e1391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e676(48.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e234(16.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e481(34.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical 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 \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\u003e942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e415(44.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157(16.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e370(39.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\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\u003e526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e292(55.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96(18.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e138(26.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep disorder\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 \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\u003e312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e244(78.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(10.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35(11.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e313.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e424(50.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e158(18.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e259(30.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34(12.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59(21.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e181(66.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5(12.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(7.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33(80.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterpersonal network\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e509(48.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e181(17.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e366(34.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried or widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e198(48.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72(17.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e142(34.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of children\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e243(48.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92(18.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e162(32.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e263(48.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87(16.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e187(34.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e199(46.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73(17.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e156(36.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2(33.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(16.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3(50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaving close friends\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 \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\u003e1256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e638(50.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e208(16.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e410(32.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\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\u003e212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69(32.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45(21.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98(46.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving and working conditions\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob position\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e241(51.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86(18.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e139(29.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e466(46.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167(16.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e369(36.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking years\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1ཞ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162(54.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49(16.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89(29.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5ཞ9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e188(44.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90(21.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e148(34.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10ཞ14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e138(44.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49(15.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e125(40.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15ཞ19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52(42.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(22.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43(35.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20ཞ24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46(50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(9.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37(40.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25ཞ29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57(47.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(13.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46(38.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30ཞ41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64(65.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(13.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20(20.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssociate degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e145(48.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53(17.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e99(33.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e442(46.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162(17.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e347(36.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster degree or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120(54.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38(17.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62(28.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNight shift frequency\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 \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\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e215(56.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68(17.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97(25.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than once a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52(47.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(14.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42(38.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbout once a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e198(48.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74(18.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e133(32.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce to twice a week or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e242(42.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95(16.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e236(41.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived health risk stress\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 \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\u003e944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e366(38.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138(14.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e440(46.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e169.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\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\u003e524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e341(65.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115(21.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68(12.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolicy environment\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment contract type\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStaff on the establishment (in-establishment staff)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e360(49.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127(17.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e246(33.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContractual staff (out-of-establishment staff)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e374(47.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126(17.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e262(35.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBinary logistic regression analysis\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, a binary logistic regression analysis of related factors based on health ecology model was performed. Comorbidity status served as the dependent variable (no\u0026thinsp;=\u0026thinsp;0, yes\u0026thinsp;=\u0026thinsp;1). Variables showing statistical significance in the univariate analysis Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e were included as independent variables, apart from age, which was excluded due to collinearity. All independent variables were entered simultaneously. The results demonstrated that, compared with medical staff with very bad self-rated health, those with excellent self-rated health were less likely to experience anxiety-depression comorbidity (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.074, 95% \u003cem\u003eCI\u003c/em\u003e: 0.008\u0026thinsp;~\u0026thinsp;0.683). Additionally, participants who engaged in leisure-time physical exercise were less likely to have comorbidity (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.722, 95% \u003cem\u003eCI\u003c/em\u003e: 0.543\u0026thinsp;~\u0026thinsp;0.962); medical staff were more likely to experience anxiety-depression comorbidity if they have different levels of sleep disorder (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.645\u0026thinsp;~\u0026thinsp;12.084, 95% \u003cem\u003eCI\u003c/em\u003e: 2.415\u0026thinsp;~\u0026thinsp;30.889), more night shift frequency (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.168, 95% \u003cem\u003eCI\u003c/em\u003e:1.036\u0026thinsp;~\u0026thinsp;1.317), and higher perceived stress (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.199, 95% \u003cem\u003eCI\u003c/em\u003e: 1.160\u0026thinsp;~\u0026thinsp;1.240).\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\u003eBinary logistic regression analysis of factors related to anxiety-depression comorbidity in Chinese medical staff (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1468)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \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\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eWald\u003c/em\u003e\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\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e Lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e Upper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual characteristics\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-rated health\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery bad\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeither good nor bad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral characteristics\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical 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 \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\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 \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\u003e-0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep disorder\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 \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16.433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30.889\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterpersonal network\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaving close friends\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 \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\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\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 \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\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving and working conditions\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob position\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysician\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNight shift frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.240\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\u003eOR: odds ratio; CI: confidence interval; Nagelkerke \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.428; Hosmer-Lemeshow test: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;6.652, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.575.\u003c/p\u003e \u003cp\u003eRanking of predictive contribution of predictors\u003c/p\u003e \u003cp\u003eFinally, the random forest variable importance was determined on mean decrease in accuracy. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the top five predictors, in descending order of importance, were: perceived stress, sleep disorder, self-rated health, night shift frequency, and physical exercise.\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\u003eThe rank of importance of influencing factors of anxiety-depression comorbidity in random forest model\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean decrease accuracy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-rated health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNight shift frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorking years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHaving close friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJob position\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.13\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 \u003cstrong\u003eModel evaluation\u003c/strong\u003e \u003cp\u003earea under the curve (AUC)\u0026thinsp;=\u0026thinsp;0.994; Accuracy\u0026thinsp;=\u0026thinsp;95.23%.\u003c/p\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides a comprehensive analysis of influencing factors of anxiety-depression comorbidity in Chinese medical staff. By utilizing the health ecology model, we systematically examine factors across five strata: individual characteristics, behavioral characteristics, interpersonal network, living and working conditions, and policy environment factors. Notably, the random forest model identified the top five predictors as perceived stress, sleep disorder, self-rated health, night shift frequency and physical exercise in sequence, with perceived stress having the greatest impact.\u003c/p\u003e \u003cp\u003ePrevalence of anxiety-depression comorbidity\u003c/p\u003e \u003cp\u003eIn the present study, the prevalence of anxiety-depression comorbidity among medical staff in Guangzhou was found to be 34.60%. This rate is notably higher than the 14.2% reported among hospital staff (including administrative personnel) in 304 inland cities in China [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], despite using the same assessment instruments (GAD-7 and PHQ-9). This variation in prevalence rate could result from diverse participants and hospital locations. First, previous research indicates that clinical medical staff generally exhibit higher rates of anxiety or depression compared to administrative staff [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Furthermore, Guangzhou, as a first-tier economically developed metropolis, likely imposes greater workload pressures and higher patient volumes on staff in university-affiliated hospitals, thereby exacerbating the risk factors associated with anxiety-depression comorbidity.\u003c/p\u003e \u003cp\u003eInfluence of individual and behavioral factors\u003c/p\u003e \u003cp\u003eAt the individual strata, self-rated health was significantly associated with comorbidity. Medical staff who perceived their health as poor were more likely to experience anxiety-depression comorbidity. This aligns with previous research suggesting that self-rated health is an important determinant of psychological well-being [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Poor self-perceived health may trigger increased health-related worry, which can contribute to the development of anxiety, depression, and their comorbidity.\u003c/p\u003e \u003cp\u003eAt the behavioral strata, physical exercise emerged as a significant protective factor, whereas sleep disorder was identified as a risk factor. A systematic review [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] confirmed that physical exercise reduces symptoms of anxiety and depression, likely by promoting neurogenesis and enhancing mood regulation. Conversely, while [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] linked sleep disorders to depression in medical staff, few studies have specifically analyzed its impact on comorbidity. Sleep disorders in this population are often attributable to irregular schedules and high occupational stress, which may exacerbate neuroendocrine dysregulation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], thereby increasing the risk of mental health issues. Therefore, interventions promoting regular exercise and optimizing sleep hygiene are essential for prevention.\u003c/p\u003e \u003cp\u003eInfluence of interpersonal networks\u003c/p\u003e \u003cp\u003eAt the interpersonal strata, the univariate analysis of this study found that medical staff with close friends are at a lower risk of anxiety-depression comorbidity. This finding aligns with the study highlighting social support\u0026rsquo;s protective role in the mental health of medical staff[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The reason may be that close friends can provide a source of emotional support that can reduce the likelihood of developing anxiety or depression by providing a sense of belonging and understanding. Hospital administrators should strive to build supportive relationships with medical staff and provide psychological support to help them deal with work-life balance and improve the well-being of their staff.\u003c/p\u003e \u003cp\u003eInfluence of living and working conditions\u003c/p\u003e \u003cp\u003eAt the living and working strata, the significant influencing factors included job position, night shift frequency and perceived stress. (1) Job Position: In this study, the prevalence of anxiety-depression comorbidity in nurses (36.82%) exhibited significantly higher rates than that in physicians (29.83%). Lu et al[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] also found that the prevalence of depression in nurses exhibiting significantly higher rates than that in physicians. This may be because nurses are often exposed to a more high-pressure work environment such as direct patient care responsibilities [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The finding indicated that it is urgent to pay attention to reducing medical staff\u0026rsquo;s anxiety and depression, especially nurses. (2) Night Shift Frequency: Staff with frequent night shifts showed a higher likelihood of comorbidity. Shift work has been shown to disrupt circadian rhythms and increase the overall risk of anxiety and depression [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e][\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Given that night shifts are inherent to medical professions, hospitals should optimize shift scheduling and ensure adequate rest periods for recovery. (3) Perceived Stress: Perceived stress refers to the extent to which situations in one\u0026rsquo;s life or work are evaluated as stressful [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Among all determinants, perceived stress was identified as the strongest predictor. Higher levels of perceived stress strongly predicted the presence of anxiety-depression comorbidity. This finding aligns with prior study highlighting perceived stress as a critical psychosocial determinant of mental health outcomes in nurses [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and medical workers [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This is because high levels of perceived stress can overwhelm people's coping mechanisms and increase the risk of anxiety or depression [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Strategies such as offering support services or mindfulness training [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], may be effective in reducing the risk of anxiety-depression comorbidity among medical staff.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eSeveral limitations of this study should be acknowledged. First, the cross-sectional design limits causal inference, future longitudinal studies are needed to explore the causal relationships between these factors and anxiety-depression comorbidity. Second, the reliance on self-reported measures may introduce recall bias, future studies incorporating objective measures may provide more accurate and reliable data. Third, although the health ecology model was employed to capture a wide range of factors, it is possible that certain unmeasured confounding variables were omitted due to the complexity of the system. Future research should consider including a broader range of variables to enhance model explanatory power. By addressing these limitations, future research can generate more comprehensive insights, ultimately providing a solid theoretical basis for the management of anxiety-depression comorbidity among medical staff.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWhile previous research has extensively examined anxiety and depression in isolation, the potential impact of anxiety-depression comorbidity cannot be overlooked. The current study reveals a substantial prevalence of this comorbidity among medical staff in China. This study provides a comprehensive analysis of the influencing factors of anxiety-depression comorbidity among medical staff, highlighting the complexity of this issue within the context of the health ecology model. Consequently, hospital administrators should not only prioritize the management of occupational stressors, such as night shifts and perceived stress, but also address behavioral and interpersonal factors. Comprehensive intervention strategies, ranging from promoting physical exercise and optimizing rest environments to enhancing social and emotional support, are essential to effectively mitigate the risk of anxiety-depression comorbidity and improve the overall well-being of medical staff.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e Ethical approval was obtained from the Research Ethics Committee of The Second Affiliated Hospital of Guangzhou Medical University (Approval No. : 2020-hs-57), and all participants provided informed consent prior to data collection.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe study was supported by a grant from the Clinical Research Project of New Technology and New Service of the Second Affiliated Hospital of Guangzhou Medical University (Grant Number: 2020-LCYJ-XJS-06).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors have made substantial contributions to the work and have approved the final version of the manuscript:YYC (Co-first Author): Formal analysis and original draftYF (Co-first Author): Formal analysis and original draftWXY: Investigation; Review and editingLWY: Investigation; Review and editingYYC: Investigation; Review and editingZWL (Corresponding Author): Conceptualization and Methodology; Investigation; Formal analysis and original draft.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003e We gratefully acknowledge all medical staff members from the six tertiary hospitals in Guangzhou who participated in this survey for their time and valuable contributions to this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data presented in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSovold LE, Naslund JA, Kousoulis AA, Saxena S, Qoronfleh MW, Grobler C, Munter L. 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J Affect Disord. 2019;251:186\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://10.1016/j.jad.2019.03.067.org*10.1016/j.jad.2019.03.067\u003c/span\u003e\u003cspan address=\"https://10.1016/j.jad.2019.03.067.org*10.1016/j.jad.2019.03.067\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anxiety, Depression, Comorbidity, Medical staff, Health ecology model","lastPublishedDoi":"10.21203/rs.3.rs-8806075/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8806075/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAnxiety-depression comorbidity is a serious issue among medical staff, yet few studies have analyzed its influencing factors using a comprehensive approach. This study aims to explore the prevalence and key predictors of this comorbidity among Chinese medical staff based on the health ecology model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA multi-center cross-sectional study was conducted with 1,468 medical staff from 6 hospitals in China. Self-measures including Generalized Anxiety Disorder 7-item (GAD-7), Patient Health Questionnaire-9 (PHQ-9), Self-rated Health Scale, Physical Exercise Questionnaire, Pittsburgh Sleeping Quality Index (PSQI), Night Shift frequency Questionnaire and the Chinese Version Perceived Stress Scale were used to evaluate participants' prevalence of anxiety-depression comorbidity and its influencing factors. The chi-squared test and binary logistic regression were used to analyze the influencing factors of anxiety-depression comorbidity, the random forest model (RFM) was used to determine the importance of the predictors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of anxiety-depression comorbidity was 34.60%. The individual (self-rated health), behavioral (physical exercise, sleep disorder), and living and working (night shift frequency, perceived stress) factors were significantly associated with anxiety-depression comorbidity (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Based on the RFM, the top five predictors were higher perceived stress, sleep disorder, poor self-rated health, high night shift frequency and less physical exercise in sequence.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAnxiety-depression comorbidity is highly prevalent among Chinese medical staff. Perceived stress acts as the strongest predictor. Effective interventions should prioritize stress management, sleep improvement, reasonable shift scheduling, and physical exercise to mitigate this mental health burden.\u003c/p\u003e","manuscriptTitle":"Predictors of anxiety-depression comorbidity in Chinese medical staff: A Random Forest analysis based on the Health Ecology Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-11 12:03:42","doi":"10.21203/rs.3.rs-8806075/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-16T02:06:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177140410771696577112654036617741456058","date":"2026-03-12T07:40:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-06T09:47:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-11T17:39:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-11T04:43:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-11T04:42:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2026-02-06T09:58:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1b9a2883-4e9a-414e-9020-bae3c3c22a64","owner":[],"postedDate":"March 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T12:03:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-11 12:03:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8806075","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8806075","identity":"rs-8806075","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}