Occupational Stress and Its Adverse Health Effects Among Metal Mining Workers: A Cross-Sectional Study in China

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Methods: A cross-sectional survey of 1,130 metal mining workers in China was conducted from June to November 2023. Occupational stress, depressive symptoms, insomnia, and WMSDs were assessed using validated scales. Logistic regression models examined associations between occupational stress and health outcomes, adjusting for key covariates. Subgroup and mediation analyses assessed effect modification and indirect pathways. Results: The prevalence of occupational stress was 28.8%. Workers with occupational stress had higher odds of depressive symptoms (OR = 4.33, 95% CI: 3.017–6.216), insomnia (OR = 1.91, 95% CI: 1.444–2.524), and WMSDs (OR = 2.071, 95% CI: 1.490–2.879). Younger workers (16–30 years) and those with lower incomes (< 3,000 CNY/month) were at greater risk. Insomnia partially mediated the associations between occupational stress and depressive symptoms (16.0%) and WMSDs (7.7%). A dose-response relationship was observed. Conclusion: Occupational stress is a significant risk factor for mental and physical health issues among metal mining workers, with younger and low-income workers being particularly vulnerable. Insomnia partially mediates these associations, underscoring the need for targeted workplace interventions to mitigate stress-related health consequences and improve worker well-being. Health sciences/Diseases Health sciences/Risk factors Metal mining and beneficiation industry Occupational stress Depression insomnia Musculoskeletal disorders Figures Figure 1 Figure 2 1. Introduction With the rapid development of the global economy, labor-intensive industries and high-intensity work models have become increasingly prevalent. As occupational demands and workplace competition intensify, stress levels among workers continue to rise, contributing to a growing incidence of mental health issues 1 . Studies indicate that over 60% of professionals in the United States identify work-related stress as their primary source of pressure, with more than 40% experiencing chronic workplace stress and anxiety 2 . The “Healthy China Initiative (2019–2030)” recognizes occupational stress as a major workplace health challenge, highlighting the urgent need for effective interventions. Occupational stress, a key concept in occupational psychology, refers to the emotional, cognitive, physiological, and behavioral responses triggered by adverse work-related factors such as job demands, organizational structure, and work environment 1 . When workers struggle to meet job expectations, a mismatch arises between the individual, workplace, and organization, further exacerbating stress levels 3 , 4 . Early symptoms of occupational stress are primarily psychological, including anxiety, irritability, depression, and reduced job satisfaction 5 , 6 . Prolonged exposure to high-stress conditions can lead to severe physical health consequences, including coronary artery disease, hypertension, immune system dysfunction, and musculoskeletal disorders 7 – 9 . Additionally, occupational stress impairs cognitive function and emotional stability, increasing the risk of mental health disorders ranging from mild distress to severe psychiatric conditions 10 , 11 . Beyond its impact on individual health, occupational stress imposes a significant economic burden 12 – 14 . The International Labour Organization (ILO) estimates that stress-related productivity losses and medical expenses result in approximately $ 300 billion in global economic losses annually 15 . In the United States alone, healthcare costs linked to stress-induced illnesses range from $ 500 billion to $ 1 trillion 16 , while in the United Kingdom, stress-related absenteeism accounts for millions of lost workdays each year. Given its widespread consequences, occupational stress is now recognized as a critical public health concern with far-reaching socioeconomic implications 17 , 18 . As a major occupational hazard, it has become a focal point in global research on occupational health, psychology, and disease prevention. The World Health Organization (WHO) has classified occupational stress as a global epidemic, warning of its long-term impact on workers' physical and mental well-being 19 . In China, mining workers face particularly high levels of occupational stress due to the demanding nature of their work environment 20 . Hunan Province, known as the "hometown of nonferrous metals" for its abundant mineral resources, is home to numerous mining sites and a large workforce engaged in metal mining 21 . These workers endure extreme conditions, including high labor intensity, long hours, and irregular shifts, all of which contribute to significant stress levels 20 . Furthermore, limited education, heightened emotional fluctuations, weaker psychological resilience, and low awareness of mental health issues make this group especially vulnerable to the negative effects of occupational stress. While occupational stress has been extensively investigated in professions including educators 22 , healthcare providers 23 , petroleum industry workers 24 , and coal miners 25 , metal mining workers remain an understudied population in this research domain. Current literature exhibits three critical gaps: (1) insufficient epidemiological data on stress prevalence in this high-risk industry; (2) limited understanding of the psychosomatic pathways linking occupational stress to depression, sleep symptoms, WMSDs; (3) absence of evidence-based intervention frameworks tailored to mining operations. Our study establishes the first comprehensive profile of occupational stress patterns in metal mining populations, employing multidimensional assessment to elucidate its detrimental health impacts. These findings directly inform the development of context-specific prevention strategies to enhance both psychological resilience and physical safety in mineral extraction workplaces. 2. Materials and Methods 2.1 Study Population This study utilized data from the 2023 Occupational Health Literacy Monitoring and Intervention Program for Key Populations in Hunan Province, China. Employers in the metal mining and nonferrous metal mining industries were stratified and randomly selected based on enterprise size. Workers aged 16–60 with ≥ 6 months of employment were subsequently enrolled through simple random sampling and stratified proportional sampling. A total of 1,200 employees were surveyed to this cross-sectional study. Following rigorous application of inclusion-exclusion criteria—specifically excluding 70 questionnaires with incomplete responses or inconsistent data patterns—1,130 valid questionnaires were retained, achieving a 94.16% valid response rate. The study flowchart is presented in Supplemental Figure S1 . 2.2 Basic Information Collection Demographic characteristics, basic occupational features, occupational stress, and depressive symptoms were assessed using the Individual Questionnaire of the National Key Population Occupational Health Literacy Monitoring Survey (officially designated as ‌Health Statistics Form 117‌ by the National Bureau of Statistics of China, published in 2022) 2 6 . For brevity, it will be henceforth referred to as "‌the Questionnaire‌" within this context. The demographic section covered gender, age, ethnicity, marital status, education level, and monthly income. Occupational characteristics included weekly working hours, night shifts, and shift patterns. Participants completed the questionnaire via WeChat QR code scanning. 2.3 Occupational Stress Assessment Occupational stress was evaluated using the Core Occupational Stress Scale (COSS) within the Questionnaire, comprising 17 items across four dimensions: social support, organizational rewards, job demands, and autonomy. A 5-point Likert scale (1="strongly disagree" to 5="strongly agree") was employed. Reverse scoring (6 minus raw score) applied to "social support" and "autonomy" dimensions. Higher total scores indicated greater occupational stress severity. Stress levels were categorized: <48 (no stress), 48-<52 (mild), 52-<56 (moderate), and ≥ 56 (severe). Mild to severe cases were combined as "presence of occupational stress." The scale demonstrated good internal consistency (Cronbach's α = 0.761). 2.4 Depressive Symptoms Assessment Depressive symptoms were evaluated using the 9-item Patient Health Questionnaire (PHQ-9). Participants rated symptom frequency over the preceding two weeks on a 4-point scale (0 = Not at all to 3 = Nearly every day). A total score ≥ 10 indicated the presence of depressive symptoms, with the scale showing excellent reliability (Cronbach’s α = 0.872). 2.5 Insomnia Symptoms Assessment Insomnia symptoms were identified through a 3-item sleep module in the Questionnaire, assessing: 1) sleep latency exceeding 30 minutes, 2) persistent difficulty initiating sleep, and 3) early morning awakening. Meeting any criterion defined insomnia presence, supported by satisfactory scale reliability (Cronbach’s α = 0.729). 2.6 Musculoskeletal Disorders Assessment Work-related musculoskeletal disorders (WMSDs) were screened using a 9-body-region inventory (neck, shoulders, back, elbows, lower back, wrists, hips, knees, ankles/feet), excluding symptoms caused by non-work-related injuries. Participants reporting pain or discomfort in any region during the past year (excluding non-work-related injuries) were classified as WMSDs-positive. The instrument showed strong consistency (Cronbach’s α = 0.852). 2.7 Quality Control Quality control measures were implemented throughout the study. The sampling protocol was developed and approved by an expert panel prior to data collection. A standardized implementation plan was established, defining roles for project leaders, coordinators, field investigators, quality control supervisors, and data managers, all of whom received unified training covering the study protocol, questionnaire administration, electronic survey completion guidelines, on-site investigation techniques, and quality assurance procedures. During field surveys, investigators provided real-time guidance to participants on electronic questionnaire completion and addressed technical issues, while quality control supervisors monitored compliance. Collected data underwent centralized management, where data managers conducted rigorous checks, including completeness verification and logical validation of responses. 2.8 Statistical Analysis‌ Statistical analyses were conducted using R software (version 4.4.2). Socio-demographic and occupational characteristics were summarized with descriptive statistics (means ± SD, proportions, or frequencies). Continuous variables were assessed for normality (Shapiro-Wilk test) and homogeneity of variance (Levene’s test), with Student’s t-tests applied to normally distributed data and Mann-Whitney U tests for nonparametric comparisons; categorical variables were analyzed via chi-square or Fisher’s exact tests. Univariate analysis identified covariates for inclusion in logistic regression models evaluating associations between occupational stress and health outcomes (depressive symptoms, sleep disturbances, WMSDs). Subgroup analyses stratified by age, monthly income, weekly working hours, and night shifts were performed, with interaction effects tested using cross-product terms. Mediation analysis examined whether sleep disturbances mediated the occupational stress–outcome relationships (depressive symptoms, WMSDs), estimating total (TE), direct (DE), and indirect (IE) effects via bootstrapping, with mediation proportions calculated as IE/TE. Statistical significance was defined as two-tailed P < 0.05. 3. Results 3.1 Basic Characteristics of Study Participants From June to November 2023, 1,130 participants were enrolled (930 males, 200 females). Mean age was 45.63 ± 8.89 years, with peak representation in 40–50 (37.6%) and 50–60 (37.3%) age groups. Ethnic Han Chinese comprised 91.9% of participants. Educational attainment was predominantly junior high school level (45.8%), with 45.8% earning monthly incomes of 3,000–4,999 RMB. Notably, 19.0% worked ≥ 55 hours weekly, 50% reported night shifts, and 57.5% engaged in rotational shifts (Table 1 ). Table 1 Demographic and Occupational Characteristics of Metal Mining Workers (N = 1,130). Group Sample size Proportion occupational stress n(%) depressive symptoms n(%) insomnia symptoms n(%) WMSDs n(%) Gender Male 930 82.3% 270(29.0) 137(14.7) 330(35.5) 667(71.7) Female 200 17.7% 55(27.5) 29(14.5) 60(30.0) 147(73.5) χ ² 0.189 0.007 2.190 0.259 P -value 0.664 0.933 0.139 0.611 Age (years) 16 ~ < 30 70 6.2% 14(20.0) 17(24.3) 35(50.0) 42(60.0) 30 ~ < 40 214 18.9% 66(30.8) 32(15.0) 76(35.5) 135(63.1) 40 ~ < 50 425 37.6% 127(29.9) 70(16.5) 147(34.6) 332(78.1) 50 ~ < 60 421 37.3% 118(28.0) 47(11.2) 132(31.4) 305(72.4) χ ² 3.445 10.407 9.383 21.386 P -value 0.328 0.015 0.025 < 0.001 Ethnicity Han 1039 91.9% 310(29.8) 158(15.2) 362(34.8) 755(72.7) Others 91 8.1% 15(16.5) 8(8.8) 28(30.8) 59(64.8) χ ² 7.281 2.748 0.614 2.547 P -value 0.007 0.097 0.433 0.110 Marriage Single 81 7.2% 17(21.0) 16(19.8) 35(43.2) 47(58.0) Married 987 87.3% 291(29.5) 138(14.0) 335(33.9) 717(72.6) Widowed 7 0.6% 0(0.0) 2(28.6) 3(42.9) 5(71.4) Divorced 53 4.7% 16(30.2) 9(17.0) 16(30.2) 43(81.1) Others 2 0.2% 1(50.0) 1(50.0) 1(50.0) 2(100.0) χ 2 5.959 5.340 3.720 11.030 P -value 0.202 0.254 0.445 0.026 Education Illiterate/limited literacy 6 0.5% 5(83.3) 3(50.0) 2(33.3) 4(66.7) Primary 116 10.3% 35(30.2) 20(17.2) 38(32.8) 84(72.4) Middle 517 45.8% 132(25.5) 53(10.3) 150(29.0) 382(73.9) High school/technical school 286 25.3% 98(34.3) 49(17.1) 118(41.3) 202(70.6) Associate degree 130 11.5% 38(29.2) 20(15.4) 47(36.2) 86(66.2) Bachelor’s degree 72 6.4% 15(20.8) 21(29.2) 35(48.6) 53(73.6) Postgraduate or above 3 0.3% 2(66.7) 0(0.0) 0(0.0) 3(100.0) χ ² 20.021 28.668 20.906 4.741 P -value 0.003 < 0.001 0.002 0.577 Monthly Income < 3000 186 16.5% 79(42.5) 51(27.4) 81(43.5) 149(80.1) 3000 ~ 4999 517 45.8% 154(29.8) 69(13.3) 147(28.4) 355(68.7) 5000 ~ 6999 291 25.8% 65(22.3) 29(10.0) 107(36.8) 207(71.1) 7000 ~ 8999 90 8.0% 17(18.9) 12(13.3) 34(37.8) 67(74.4) 9000 ~ 10999 34 3.0% 7(20.6) 4(11.8) 17(50.0) 28(82.4) ≥ 110000 12 1.1% 3(25.0) 1(8.3) 4(33.3) 8(66.7) χ ² 28.668 30.728 19.869 11.276 P -value < 0.001 < 0.001 0.001 0.046 Weekly Working Hours ≤ 40 307 27.2% 63(20.5) 36(11.7) 116(37.8) 207(67.4) 41–44 240 21.2% 69(28.8) 38(15.8) 86(35.8) 175(72.9) 45–48 232 20.5% 68(29.3) 31(13.4) 64(27.6) 180(77.6) 49–54 136 12.0% 44(32.4) 24(17.6) 41(30.1) 98(72.1) ≥ 55 215 19.0% 81(37.7) 37(17.2) 83(38.6) 154(71.6) χ ² 19.401 30.728 9.304 6.896 P -value 0.001 < 0.001 0.054 0.142 Night Shifts Yes 575 50.9% 200(34.8) 105(18.3) 224(39.0) 424(73.7) No 555 49.1% 125(22.5) 61(11.0) 166(29.9) 390(70.3) χ ² 20.718 11.910 10.226 1.687 P -value < 0.001 0.001 0.001 0.194 Shift Work Yes 650 57.5% 232(35.7) 106(16.3) 229(35.2) 476(73.2) No 480 42.5% 93(19.4) 60(12.5) 161(33.5) 338(70.4) χ ² 35.880 3.194 0.349 1.085 P -value < 0.001 0.074 0.555 0.297 3.2 Occupational Stress and Health Status Among Metal Mining and Beneficiation Workers The mean occupational stress score among metal mining workers was 43.37 ± 7.13. Occupational stress prevalence reached 28.8% (325 cases). Significant differences in stress detection rates emerged across ethnicity, education level, income, weekly working hours, night shifts, and shift patterns ( P < 0.05). Depression and insomnia prevalence also varied significantly by age, education, income, working hours, and night shift status ( P < 0.05). WMSDs rates differed significantly across age groups, marital status, and education levels ( P < 0.05, Table 1 ). 3.3 Impact of Occupational Stress on Workers’ Health Among the 1,130 participants, 166 (14.7%) exhibited depressive symptoms, 390 (34.5%) reported insomnia symptoms, and 814 (72.0%) were diagnosed with WMSDs. To assess the impact of occupational stress on health outcomes, logistic regression analysis was conducted. The crude odds ratios (ORs) for the association between occupational stress and depressive symptoms, insomnia symptoms, and WMSDs were 4.825 (95% CI: 3.421–6.806), 1.875 (95% CI: 1.438–2.445), and 2.114 (95% CI: 1.537–2.908), respectively. Given the significant differences in age, ethnicity, education level, monthly income, weekly working hours, night shift, and shift work status among different occupational stress and health condition groups ( P < 0.05, Table 1 ), these variables were considered potential confounders. After adjusting for these confounding factors, the adjusted ORs for the association between occupational stress and depressive symptoms, insomnia symptoms, and WMSDs were 4.330 (95% CI: 3.017–6.216), 1.909 (95% CI: 1.444–2.524), and 2.071 (95% CI: 1.490–2.879), respectively (Table 2 ). Table 2 Associations Between Occupational Stress and Health Outcomes (Depressive Symptoms, Insomnia, and WMSDs). Health Condition Crude Model Adjusted Model a P -value OR (95% CI) P -value OR (95% CI) Depressive Symptoms < 0.001 4.825 (3.421–6.806) < 0.001 4.330 (3.017–6.216) Insomnia Symptoms < 0.001 1.875 (1.438–2.445) < 0.001 1.909 (1.444–2.524) WMSDs < 0.001 2.114 (1.537–2.908) < 0.001 2.071 (1.490–2.879) Note : a Adjusted for age, ethnicity, education, monthly income, weekly working hours, night shifts, and shift work. 3.4 Dose-Response Relationship of Stress Severity When stratifying occupational stress into four severity levels, graded increases in stress intensity corresponded to elevated risks of depression, insomnia, and MSDs. Detailed outcomes are presented in Table 3 and Supplementary Table S1 . Table 3 Dose-Response Relationships: Occupational Stress Severity and Risks of Depression, Insomnia, and WMSDs‌ Outcome Occupational Stress β SE Wald χ ² P -value OR (95% CI) Depressive Symptoms ‌ None‌ - - - - 1.00 (Reference) Mild 1.026 0.223 21.199 < 0.001 2.790 (1.803–4.317) Moderate 1.767 0.265 44.505 < 0.001 5.856 (3.484–9.843) Severe 2.861 0.385 55.233 < 0.001 17.476 (8.218–37.162) Insomnia Symptoms ‌ None‌ - - - - 1.00 (Reference) Mild 0.51 0.169 9.12 0.003 1.665 (1.196–2.319) Moderate 0.685 0.233 8.668 0.003 1.983 (1.257–3.128) Severe 1.359 0.357 14.515 < 0.001 3.893 (1.935–7.835) WMSDs ‌ None‌ - - - - 1.00 (Reference) Mild 0.414 0.188 4.831 0.028 1.513 (1.046–2.189) Moderate 1.428 0.363 15.451 < 0.001 4.169 (2.046–8.496) Severe 1.398 0.54 6.698 0.01 4.046 (1.404–11.659) Note : a Adjusted for age, ethnicity, education, monthly income, weekly working hours, night shifts, and shift work. 3.5 Subgroup analyses Subgroup analyses were conducted to assess potential modifying effects of age, monthly income, weekly working hours, and night shifts on the association between occupational stress and health outcomes (depressive symptoms, sleep disturbances, and WMSDs). As shown in Fig. 1 A, the association between occupational stress and depressive symptoms remained consistent across all covariate strata ( P -interaction > 0.05). In Fig. 1 B, except for age ( P -interaction = 0.009), the association between occupational stress and sleep disturbances showed homogeneity across subgroups ( P -interaction > 0.05). Younger individuals such as 16 ~ < 30 years old (OR = 10.84) and 30 ~ < 40 years old (OR = 3.04) exhibited significantly stronger associations between occupational stress and sleep disturbances. Figure 1 C demonstrates that, apart from monthly income level ( P -interaction = 0.029), the association between occupational stress and WMSDs was consistent across strata ( P -interaction > 0.05). Participants with a monthly income below 3,000 CNY displayed a markedly elevated WMSDs risk (OR = 4.12). 3.6 ‌Mediation Analysis‌ We conducted mediation analyses to assess insomnia symptoms as a mediator between occupational stress and two health outcomes: depressive symptoms and work-related musculoskeletal disorders (WMSDs). For depressive symptoms (Fig. 2 A), occupational stress demonstrated significant total (OR = 1.066, 95% CI [1.056, 1.083], P < 0.001), direct (OR = 1.055, 95% CI [1.046, 1.073], P < 0.001), and indirect effects through insomnia symptoms (OR = 1.010, 95% CI [1.005, 1.020], P = 0.001), with mediation accounting for 16.0% of the total effect. Similarly, for WMSDs (Fig. 2 B), significant total (OR = 1.178, 95% CI [1.094, 1.284], P < 0.001), direct (OR = 1.163, 95% CI [1.078, 1.271], P < 0.001), and indirect effects (OR = 1.013, 95% CI [1.006, 1.030], P = 0.001) were observed, indicating that 7.7% of the occupational stress-WMSDs association was mediated by insomnia symptoms. Bootstrapping with 1500 resamples confirmed the robustness of these partial mediation patterns. 4. Discussion This study systematically evaluated the impact of occupational stress on mental health (depression and insomnia) and WMSDs among workers in the metal mining and beneficiation industry. Additionally, it explored the mediating role of insomnia in these associations and examined variations in effects across different subgroups. The findings revealed that occupational stress significantly increased the risk of depression, insomnia, and WMSDs, with younger workers and low-income groups being particularly vulnerable. Moreover, insomnia was identified as a partial mediator between occupational stress and both depression and WMSDs, suggesting that occupational stress may exacerbate psychological and physical health problems through its detrimental effects on sleep. These results not only confirm the widespread adverse health consequences of occupational stress but also highlight the importance of targeted interventions for high-risk groups. The prevalence of occupational stress in this study was 28.8%, lower than reported in some domestic studies (e.g., 42.65% among copper-nickel miners 27 and 33.08% among teachers 28 ) but comparable to that of employees in the genetic sequencing industry (27.40%) 29 . These differences may be attributed to variations in job demands, psychological stressors, and work environments across industries. Despite a relatively lower prevalence, our findings indicate that occupational stress plays a critical role in both mental and physical health outcomes. Notably, a dose-response relationship was observed, where higher levels of occupational stress were associated with increased risks of depression, insomnia, and WMSDs. This trend underscores the importance of addressing psychological stress even in industries primarily characterized by physical labor, emphasizing the need for effective prevention and intervention strategies. Further analysis revealed that the occurrence of occupational stress is influenced by multiple factors. Workers with lower educational attainment, lower monthly income (< 3,000 RMB), night or shift work, and longer working hours were more likely to experience occupational stress. Previous studies have demonstrated that long working hours, high job demands, and low income contribute to heightened psychological pressure and an increased risk of occupational stress 30 , 31 . Our findings align with these observations, suggesting that work scheduling and salary structures should take workers' mental and physical well-being into account. Notably, the relationship between income and occupational stress remains a topic of debate; some studies suggest that higher-income groups experience greater occupational stress 31 , possibly due to different job expectations and responsibilities across income brackets. Subgroup analyses further demonstrated that age and income level significantly moderated the effects of occupational stress on health outcomes. Workers aged 16–30 years exhibited greater susceptibility to the adverse effects of occupational stress, with higher rates of depression, insomnia, and WMSDs. This may be attributed to younger workers facing greater career uncertainty and having less developed coping mechanisms, whereas older workers may possess more experience and psychological resilience to manage stress effectively 32 , 33 . Additionally, low-income workers (< 3,000 RMB per month) exhibited a significantly higher risk of WMSDs compared to their high-income counterparts, suggesting that financial strain may amplify the health consequences of occupational stress. Economic hardship often limits access to health resources, such as flexible work arrangements and mental health services, potentially creating a vicious cycle between stress and poor health 34 , 35 . These findings support the "socioeconomic status-health gradient" theory, which posits that individuals with lower socioeconomic status are at greater risk for adverse health outcomes due to limited protective factors 36 , 37 . Another key finding of this study is the partial mediating role of insomnia in the relationship between occupational stress and both depression and WMSDs. Specifically, insomnia accounted for 16.0% of the total effect of occupational stress on depression and 7.7% of its effect on WMSDs. These align to previous findings that there are two possible pathways through which occupational stress influences health: directly through its immediate psychological and physiological impact, and indirectly by disrupting sleep, which exacerbates health risks 38 , 39 . Research has shown that chronic exposure to high-stress environments can activate the sympathetic nervous system and the hypothalamic-pituitary-adrenal (HPA) axis 40 , leading to elevated stress hormone levels that impair sleep quality and increase susceptibility to depression and WMSDs. Moreover, insomnia may contribute to musculoskeletal damage through mechanisms such as fatigue accumulation and heightened pain sensitivity. This study is among the first to verify this mediating mechanism among workers in the metal mining and beneficiation industry, providing valuable insights for future interventions. Overall, our findings underscore the profound impact of occupational stress on both psychological and physical health and highlight the critical role of sleep disturbances in this process. Given these results, companies and policymakers should implement multi-level intervention strategies, including optimizing work schedules, reducing excessive overtime, and providing psychological support and sleep management programs. Special attention should be given to younger and low-income workers to mitigate the health risks associated with occupational stress. Furthermore, future research should explore the effects of different work environments on occupational stress and adopt longitudinal study designs to establish causal relationships more precisely. Despite the important findings of this study, several limitations should be acknowledged. First, due to the cross-sectional design, causality between occupational stress and health outcomes cannot be determined. Future research should employ longitudinal designs to validate our findings. Second, this study did not account for certain confounding factors, such as individual lifestyle habits and specific job types, which may influence the accuracy of the results. Differences in work environments and personal lifestyles could significantly affect health outcomes, necessitating more detailed occupational classifications and the inclusion of broader psychosocial variables in future studies. Lastly, as the sample was drawn from metal mining and beneficiation workers in Hunan Province, future studies should expand the sample size and geographic scope to enhance external validity. 5. Conclusion In conclusion, this study confirms the significant impact of occupational stress on depression, insomnia, and WMSDs among metal mining and beneficiation workers, and, for the first time, identifies insomnia as a partial mediator in these relationships. Furthermore, the effects of occupational stress vary across different age and income groups, emphasizing the importance of targeted interventions. Future research should continue to explore effective strategies for managing occupational stress to improve workers' mental and physical well-being, ultimately enhancing job satisfaction and productivity. Declarations Declaration of competing interest The authors have no relevant interests to disclose. Ethics approval and consent to participate Written informed consent was obtained from all participants prior to study enrollment. The consent process included full disclosure of research objectives, data confidentiality protocols, and voluntary participation rights. Research procedures strictly adhered to the Declaration of Helsinki principles for non-interventional human study. The Ethics Committee of the Hunan Prevention and Treatment Institute for Occupational Diseases, Affiliated Prevention and Treatment Institute for Occupational Diseases of University of South China approved this study (No. 20230308001). Clinical trial number Not applicable. Funding This study is jointly supported by the Occupational Health Potential Discipline Project of Hunan Prevention and Treatment Institute for Occupational Diseases (Grant No. 20240624-1118) and the Occupational Health Project of the Chinese Center for Disease Control and Prevention (Grant No. 1310311090001). Author Contribution Wenya Liu: Conceptualization, Methodology, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Writing - Review & Editing. Sijia Lvqiu: Conceptualization, Resources, Writing - Review & Editing, Supervision. Duoduo Wang: Writing - Review & Editing, Supervision, Project administration, Funding acquisition. Xinlv Dong: Conceptualization, Writing - Review & Editing, Visualization. Yunfeng Nie: Conceptualization, Writing - Review & Editing. Acknowledgement We express our gratitude to all workers who participated in this study. Data Availability Data supporting the results of this study are available on request from the corresponding author. References Carmona-Barrientos I, Gala-León FJ, Lupiani-Giménez M, Cruz-Barrientos A, Lucena-Anton D, Moral-Munoz JA. Occupational stress and burnout among physiotherapists: a cross-sectional survey in Cadiz (Spain). Human resources for health . Nov 25 2020;18(1):91. https://doi.org/10.1186/s12960-020-00537-0 La Roche MJ, Practice. Changing multicultural guidelines: Clinical and research implications for evidence-based psychotherapies. Professional Psychology . 2021;52(2):111. https://doi.org/10.1186/s12960-020-00537-0 Folkman S, Lazarus RS. Stress-processes and depressive symptomatology. Journal of abnormal psychology . 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Job insecurity and health: A study of 16 European countries. Social Science & Medicine . 2010/03/01/ 2010;70(6):867-874. https://doi.org/10.1016/j.socscimed.2009.11.022 O'Connor DB, Thayer JF, Vedhara K. Stress and Health: A Review of Psychobiological Processes. Annual review of psychology . Jan 4 2021;72:663-688. https://doi.org/10.1146/annurev-psych-062520-122331 Hege A, Lemke MK, Apostolopoulos Y, Sönmez S. The Impact of Work Organization, Job Stress, and Sleep on the Health Behaviors and Outcomes of U.S. Long-Haul Truck Drivers. Health education & behavior : the official publication of the Society for Public Health Education . Aug 2019;46(4):626-636. https://doi.org/10.1177/1090198119826232 Clow A, Hucklebridge F, Stalder T, Evans P, Thorn L. The cortisol awakening response: more than a measure of HPA axis function. Neuroscience and biobehavioral reviews . Sep 2010;35(1):97-103. https://doi.org/10.1016/j.neubiorev.2009.12.011 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6851166","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":476013937,"identity":"1a77a56a-eb6c-44c0-a659-6b1a067665fb","order_by":0,"name":"Wenya Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYFAC5sYHH//Z1M9nb2x8+IE4LYzNhjPY0hg39hxuNpYgUkubNA/bYcaGG+ltAjzEaDC4kQi0hecwM+PMh20MEgx2croNhLU0Pvggkc7GLp3Y9qCAIdnY7AABLWZgWwyseRhnJ7YbSDAcSNxGhBagXxKYJRhuHmyT4CFeywFnA4YbjERqsT/zsNlwZkNagmFPIjCQDYjwi2R78sEHHxtsEuTZjz98+KHCTo6gFgaBBGSeASHlIMBP0NBRMApGwSgY8QAADTlI1koORZMAAAAASUVORK5CYII=","orcid":"","institution":"Hunan Prevention and Treatment Institute for Occupational Diseases, Affiliated Prevention and Treatment Institute for Occupational Diseases of University of South China","correspondingAuthor":true,"prefix":"","firstName":"Wenya","middleName":"","lastName":"Liu","suffix":""},{"id":476013938,"identity":"507aaf33-36f0-4ee7-9466-cc8250fc8d87","order_by":1,"name":"Sijia Lvqiu","email":"","orcid":"","institution":"Hunan Prevention and Treatment Institute for Occupational Diseases, Affiliated Prevention and Treatment Institute for Occupational Diseases of University of South China","correspondingAuthor":false,"prefix":"","firstName":"Sijia","middleName":"","lastName":"Lvqiu","suffix":""},{"id":476013939,"identity":"9166170b-fdfe-46d2-af90-a71f6d450b3e","order_by":2,"name":"Duoduo Wang","email":"","orcid":"","institution":"Hunan Prevention and Treatment Institute for Occupational Diseases, Affiliated Prevention and Treatment Institute for Occupational Diseases of University of South China","correspondingAuthor":false,"prefix":"","firstName":"Duoduo","middleName":"","lastName":"Wang","suffix":""},{"id":476013940,"identity":"cacfc0e7-6670-4dd5-bb94-4197471352a2","order_by":3,"name":"Xinlv Dong","email":"","orcid":"","institution":"The 23 rd Metallurgical Construction Group Co., Ltd. of Minmetals","correspondingAuthor":false,"prefix":"","firstName":"Xinlv","middleName":"","lastName":"Dong","suffix":""},{"id":476013941,"identity":"55c303d1-60f3-4a27-aab3-fed2bd491d92","order_by":4,"name":"Yunfeng Nie","email":"","orcid":"","institution":"Hunan Prevention and Treatment Institute for Occupational Diseases, Affiliated Prevention and Treatment Institute for Occupational Diseases of University of South China","correspondingAuthor":false,"prefix":"","firstName":"Yunfeng","middleName":"","lastName":"Nie","suffix":""}],"badges":[],"createdAt":"2025-06-09 06:23:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6851166/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6851166/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-24484-9","type":"published","date":"2025-11-19T15:59:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85466354,"identity":"0534a97f-a2e9-4764-9ae4-69bacf4841e4","added_by":"auto","created_at":"2025-06-26 08:26:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":529245,"visible":true,"origin":"","legend":"\u003cp\u003eForest Plot Illustrating the Associations Between Occupational Stress and Depressive Symptoms(A), Insomnia Symptoms(B), and WMSDs(C) across Subgroups. Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) are shown. (A) No significant interaction effects were observed in depressive symptoms(\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05 for all subgroups). (B) Younger workers (\u0026lt;30 years) exhibited significantly stronger associations (OR = 10.84, 95% CI: 4.56–25.73; \u003cem\u003eP\u003c/em\u003e-interaction = 0.009). (C) Low-income workers (\u0026lt;3,000 CNY/month) had markedly elevated WMSDs risk (OR = 4.12, 95% CI: 2.15–7.89; \u003cem\u003eP\u003c/em\u003e-interaction = 0.029).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6851166/v1/3f3daff6b51cc6f27a61b7f9.png"},{"id":85466355,"identity":"8cbeb185-a69f-420c-8842-05f8987b00c1","added_by":"auto","created_at":"2025-06-26 08:26:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53361,"visible":true,"origin":"","legend":"\u003cp\u003eMediation Effects of Insomnia on Occupational Stress–Depression (A) and Occupational Stress–WMSDs (B) Pathways. Total (TE), direct (DE), and indirect effects (IE) were estimated via bootstrapping (1,500 resamples). Mediation proportions: 16.0% for depression, 7.7% for WMSDs.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6851166/v1/e8a19484fbdfb1214eaa952a.png"},{"id":96650276,"identity":"9031e078-707a-4ed0-a0d3-33f23d7272c1","added_by":"auto","created_at":"2025-11-24 16:10:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2032692,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6851166/v1/3075f43f-a86b-4526-a750-31035bda5a45.pdf"},{"id":85466363,"identity":"36da54f0-6b67-4f27-bfee-d4d2d024c8ba","added_by":"auto","created_at":"2025-06-26 08:26:12","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":386437,"visible":true,"origin":"","legend":"","description":"","filename":"supplementalmaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6851166/v1/3e625304e74b5f1d322c7bcc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Occupational Stress and Its Adverse Health Effects Among Metal Mining Workers: A Cross-Sectional Study in China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWith the rapid development of the global economy, labor-intensive industries and high-intensity work models have become increasingly prevalent. As occupational demands and workplace competition intensify, stress levels among workers continue to rise, contributing to a growing incidence of mental health issues\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Studies indicate that over 60% of professionals in the United States identify work-related stress as their primary source of pressure, with more than 40% experiencing chronic workplace stress and anxiety\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The \u0026ldquo;Healthy China Initiative (2019\u0026ndash;2030)\u0026rdquo; recognizes occupational stress as a major workplace health challenge, highlighting the urgent need for effective interventions.\u003c/p\u003e \u003cp\u003eOccupational stress, a key concept in occupational psychology, refers to the emotional, cognitive, physiological, and behavioral responses triggered by adverse work-related factors such as job demands, organizational structure, and work environment\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. When workers struggle to meet job expectations, a mismatch arises between the individual, workplace, and organization, further exacerbating stress levels\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Early symptoms of occupational stress are primarily psychological, including anxiety, irritability, depression, and reduced job satisfaction\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Prolonged exposure to high-stress conditions can lead to severe physical health consequences, including coronary artery disease, hypertension, immune system dysfunction, and musculoskeletal disorders\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Additionally, occupational stress impairs cognitive function and emotional stability, increasing the risk of mental health disorders ranging from mild distress to severe psychiatric conditions\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e Beyond its impact on individual health, occupational stress imposes a significant economic burden \u003csup\u003e \u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e \u0026ndash; \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e \u003c/sup\u003e . The International Labour Organization (ILO) estimates that stress-related productivity losses and medical expenses result in approximately \u003cspan\u003e$\u003c/span\u003e300\u0026nbsp;billion in global economic losses annually \u003csup\u003e \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e \u003c/sup\u003e . In the United States alone, healthcare costs linked to stress-induced illnesses range from \u003cspan\u003e$\u003c/span\u003e500\u0026nbsp;billion to \u003cspan\u003e$\u003c/span\u003e1 trillion \u003csup\u003e \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e \u003c/sup\u003e , while in the United Kingdom, stress-related absenteeism accounts for millions of lost workdays each year. Given its widespread consequences, occupational stress is now recognized as a critical public health concern with far-reaching socioeconomic implications \u003csup\u003e \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e , \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e \u003c/sup\u003e . As a major occupational hazard, it has become a focal point in global research on occupational health, psychology, and disease prevention. The World Health Organization (WHO) has classified occupational stress as a global epidemic, warning of its long-term impact on workers' physical and mental well-being \u003csup\u003e \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e \u003c/sup\u003e . \u003c/p\u003e \u003cp\u003eIn China, mining workers face particularly high levels of occupational stress due to the demanding nature of their work environment\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Hunan Province, known as the \"hometown of nonferrous metals\" for its abundant mineral resources, is home to numerous mining sites and a large workforce engaged in metal mining\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. These workers endure extreme conditions, including high labor intensity, long hours, and irregular shifts, all of which contribute to significant stress levels\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Furthermore, limited education, heightened emotional fluctuations, weaker psychological resilience, and low awareness of mental health issues make this group especially vulnerable to the negative effects of occupational stress.\u003c/p\u003e \u003cp\u003eWhile occupational stress has been extensively investigated in professions including educators\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, healthcare providers\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, petroleum industry workers\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, and coal miners\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, metal mining workers remain an understudied population in this research domain. Current literature exhibits three critical gaps: (1) insufficient epidemiological data on stress prevalence in this high-risk industry; (2) limited understanding of the psychosomatic pathways linking occupational stress to depression, sleep symptoms, WMSDs; (3) absence of evidence-based intervention frameworks tailored to mining operations. Our study establishes the first comprehensive profile of occupational stress patterns in metal mining populations, employing multidimensional assessment to elucidate its detrimental health impacts. These findings directly inform the development of context-specific prevention strategies to enhance both psychological resilience and physical safety in mineral extraction workplaces.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Population\u003c/h2\u003e \u003cp\u003eThis study utilized data from the 2023 Occupational Health Literacy Monitoring and Intervention Program for Key Populations in Hunan Province, China. Employers in the metal mining and nonferrous metal mining industries were stratified and randomly selected based on enterprise size. Workers aged 16\u0026ndash;60 with \u0026ge;\u0026thinsp;6 months of employment were subsequently enrolled through simple random sampling and stratified proportional sampling. A total of 1,200 employees were surveyed to this cross-sectional study. Following rigorous application of inclusion-exclusion criteria\u0026mdash;specifically excluding 70 questionnaires with incomplete responses or inconsistent data patterns\u0026mdash;1,130 valid questionnaires were retained, achieving a 94.16% valid response rate. The study flowchart is presented in \u003cb\u003eSupplemental Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Basic Information Collection\u003c/h2\u003e \u003cp\u003eDemographic characteristics, basic occupational features, occupational stress, and depressive symptoms were assessed using the Individual Questionnaire of the National Key Population Occupational Health Literacy Monitoring Survey (officially designated as \u0026zwnj;Health Statistics Form 117\u0026zwnj; by the National Bureau of Statistics of China, published in 2022)\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e6\u003c/sup\u003e. For brevity, it will be henceforth referred to as \"\u0026zwnj;the Questionnaire\u0026zwnj;\" within this context. The demographic section covered gender, age, ethnicity, marital status, education level, and monthly income. Occupational characteristics included weekly working hours, night shifts, and shift patterns. Participants completed the questionnaire via WeChat QR code scanning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Occupational Stress Assessment\u003c/h2\u003e \u003cp\u003eOccupational stress was evaluated using the Core Occupational Stress Scale (COSS) within the Questionnaire, comprising 17 items across four dimensions: social support, organizational rewards, job demands, and autonomy. A 5-point Likert scale (1=\"strongly disagree\" to 5=\"strongly agree\") was employed. Reverse scoring (6 minus raw score) applied to \"social support\" and \"autonomy\" dimensions. Higher total scores indicated greater occupational stress severity. Stress levels were categorized: \u0026lt;48 (no stress), 48-\u0026lt;52 (mild), 52-\u0026lt;56 (moderate), and \u0026ge;\u0026thinsp;56 (severe). Mild to severe cases were combined as \"presence of occupational stress.\" The scale demonstrated good internal consistency (Cronbach's α\u0026thinsp;=\u0026thinsp;0.761).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Depressive Symptoms Assessment\u003c/h2\u003e \u003cp\u003eDepressive symptoms were evaluated using the 9-item Patient Health Questionnaire (PHQ-9). Participants rated symptom frequency over the preceding two weeks on a 4-point scale (0\u0026thinsp;=\u0026thinsp;Not at all to 3\u0026thinsp;=\u0026thinsp;Nearly every day). A total score\u0026thinsp;\u0026ge;\u0026thinsp;10 indicated the presence of depressive symptoms, with the scale showing excellent reliability (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.872).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Insomnia Symptoms Assessment\u003c/h2\u003e \u003cp\u003eInsomnia symptoms were identified through a 3-item sleep module in the Questionnaire, assessing: 1) sleep latency exceeding 30 minutes, 2) persistent difficulty initiating sleep, and 3) early morning awakening. Meeting any criterion defined insomnia presence, supported by satisfactory scale reliability (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.729).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Musculoskeletal Disorders Assessment\u003c/h2\u003e \u003cp\u003eWork-related musculoskeletal disorders (WMSDs) were screened using a 9-body-region inventory (neck, shoulders, back, elbows, lower back, wrists, hips, knees, ankles/feet), excluding symptoms caused by non-work-related injuries. Participants reporting pain or discomfort in any region during the past year (excluding non-work-related injuries) were classified as WMSDs-positive. The instrument showed strong consistency (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.852).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Quality Control\u003c/h2\u003e \u003cp\u003eQuality control measures were implemented throughout the study. The sampling protocol was developed and approved by an expert panel prior to data collection. A standardized implementation plan was established, defining roles for project leaders, coordinators, field investigators, quality control supervisors, and data managers, all of whom received unified training covering the study protocol, questionnaire administration, electronic survey completion guidelines, on-site investigation techniques, and quality assurance procedures. During field surveys, investigators provided real-time guidance to participants on electronic questionnaire completion and addressed technical issues, while quality control supervisors monitored compliance. Collected data underwent centralized management, where data managers conducted rigorous checks, including completeness verification and logical validation of responses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Statistical Analysis\u0026zwnj;\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using R software (version 4.4.2). Socio-demographic and occupational characteristics were summarized with descriptive statistics (means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, proportions, or frequencies). Continuous variables were assessed for normality (Shapiro-Wilk test) and homogeneity of variance (Levene\u0026rsquo;s test), with Student\u0026rsquo;s t-tests applied to normally distributed data and Mann-Whitney U tests for nonparametric comparisons; categorical variables were analyzed via chi-square or Fisher\u0026rsquo;s exact tests. Univariate analysis identified covariates for inclusion in logistic regression models evaluating associations between occupational stress and health outcomes (depressive symptoms, sleep disturbances, WMSDs). Subgroup analyses stratified by age, monthly income, weekly working hours, and night shifts were performed, with interaction effects tested using cross-product terms.\u003c/p\u003e \u003cp\u003eMediation analysis examined whether sleep disturbances mediated the occupational stress\u0026ndash;outcome relationships (depressive symptoms, WMSDs), estimating total (TE), direct (DE), and indirect (IE) effects via bootstrapping, with mediation proportions calculated as IE/TE. Statistical significance was defined as two-tailed \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Basic Characteristics of Study Participants\u003c/h2\u003e \u003cp\u003eFrom June to November 2023, 1,130 participants were enrolled (930 males, 200 females). Mean age was 45.63\u0026thinsp;\u0026plusmn;\u0026thinsp;8.89 years, with peak representation in 40\u0026ndash;50 (37.6%) and 50\u0026ndash;60 (37.3%) age groups. Ethnic Han Chinese comprised 91.9% of participants. Educational attainment was predominantly junior high school level (45.8%), with 45.8% earning monthly incomes of 3,000\u0026ndash;4,999 RMB. Notably, 19.0% worked\u0026thinsp;\u0026ge;\u0026thinsp;55 hours weekly, 50% reported night shifts, and 57.5% engaged in rotational shifts (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and Occupational Characteristics of Metal Mining Workers (N\u0026thinsp;=\u0026thinsp;1,130).\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eoccupational stress\u003c/p\u003e \u003cp\u003en(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003edepressive symptoms\u003c/p\u003e \u003cp\u003en(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003einsomnia symptoms\u003c/p\u003e \u003cp\u003en(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWMSDs\u003c/p\u003e \u003cp\u003en(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\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\u003e930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e270(29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e137(14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e330(35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e667(71.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55(27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29(14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60(30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e147(73.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\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\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\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\u003e16\u0026thinsp;~\u0026thinsp;\u0026lt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17(24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42(60.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026thinsp;~\u0026thinsp;\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66(30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32(15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e76(35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e135(63.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026thinsp;~\u0026thinsp;\u0026lt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e127(29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70(16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e147(34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e332(78.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026thinsp;~\u0026thinsp;\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e118(28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47(11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e132(31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e305(72.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\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\u003eHan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e310(29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e158(15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e362(34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e755(72.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15(16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8(8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28(30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e59(64.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarriage\u003c/b\u003e\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\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17(21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16(19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35(43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47(58.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e291(29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e138(14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e335(33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e717(72.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2(28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3(42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5(71.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16(30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9(17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16(30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43(81.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2(100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\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\u003eIlliterate/limited literacy\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\u003e0.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5(83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2(33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4(66.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35(30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20(17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38(32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e84(72.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e132(25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53(10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e150(29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e382(73.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school/technical school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98(34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49(17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e118(41.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e202(70.6)\u003c/p\u003e \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\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38(29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20(15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47(36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e86(66.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor\u0026rsquo;s degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15(20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21(29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35(48.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53(73.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostgraduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2(66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3(100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly Income\u003c/b\u003e\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\u003e\u0026lt;\u0026thinsp;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79(42.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51(27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e81(43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e149(80.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u0026thinsp;~\u0026thinsp;4999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e154(29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69(13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e147(28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e355(68.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5000\u0026thinsp;~\u0026thinsp;6999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65(22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29(10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e107(36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e207(71.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7000\u0026thinsp;~\u0026thinsp;8999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17(18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12(13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34(37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e67(74.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9000\u0026thinsp;~\u0026thinsp;10999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7(20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4(11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28(82.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;110000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3(25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1(8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4(33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8(66.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeekly Working Hours\u003c/b\u003e\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\u003e\u0026le;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63(20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36(11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e116(37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e207(67.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69(28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38(15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e86(35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e175(72.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68(29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31(13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64(27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e180(77.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e49\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44(32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24(17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41(30.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e98(72.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81(37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37(17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e83(38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e154(71.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNight Shifts\u003c/b\u003e\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\u003e575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e200(34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e105(18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e224(39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e424(73.7)\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\u003e555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e125(22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61(11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e166(29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e390(70.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eShift Work\u003c/b\u003e\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\u003e650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e232(35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e106(16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e229(35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e476(73.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93(19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60(12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e161(33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e338(70.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Occupational Stress and Health Status Among Metal Mining and Beneficiation Workers\u003c/h2\u003e \u003cp\u003eThe mean occupational stress score among metal mining workers was 43.37\u0026thinsp;\u0026plusmn;\u0026thinsp;7.13. Occupational stress prevalence reached 28.8% (325 cases). Significant differences in stress detection rates emerged across ethnicity, education level, income, weekly working hours, night shifts, and shift patterns (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Depression and insomnia prevalence also varied significantly by age, education, income, working hours, and night shift status (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). WMSDs rates differed significantly across age groups, marital status, and education levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Impact of Occupational Stress on Workers\u0026rsquo; Health\u003c/h2\u003e \u003cp\u003eAmong the 1,130 participants, 166 (14.7%) exhibited depressive symptoms, 390 (34.5%) reported insomnia symptoms, and 814 (72.0%) were diagnosed with WMSDs. To assess the impact of occupational stress on health outcomes, logistic regression analysis was conducted. The crude odds ratios (ORs) for the association between occupational stress and depressive symptoms, insomnia symptoms, and WMSDs were 4.825 (95% CI: 3.421\u0026ndash;6.806), 1.875 (95% CI: 1.438\u0026ndash;2.445), and 2.114 (95% CI: 1.537\u0026ndash;2.908), respectively.\u003c/p\u003e \u003cp\u003eGiven the significant differences in age, ethnicity, education level, monthly income, weekly working hours, night shift, and shift work status among different occupational stress and health condition groups (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), these variables were considered potential confounders. After adjusting for these confounding factors, the adjusted ORs for the association between occupational stress and depressive symptoms, insomnia symptoms, and WMSDs were 4.330 (95% CI: 3.017\u0026ndash;6.216), 1.909 (95% CI: 1.444\u0026ndash;2.524), and 2.071 (95% CI: 1.490\u0026ndash;2.879), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations Between Occupational Stress and Health Outcomes (Depressive Symptoms, Insomnia, and WMSDs).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHealth Condition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCrude Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eAdjusted Model\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepressive Symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.825 (3.421\u0026ndash;6.806)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.330 (3.017\u0026ndash;6.216)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsomnia Symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.875 (1.438\u0026ndash;2.445)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.909 (1.444\u0026ndash;2.524)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWMSDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.114 (1.537\u0026ndash;2.908)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.071 (1.490\u0026ndash;2.879)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote\u003c/b\u003e: \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e Adjusted for age, ethnicity, education, monthly income, weekly working hours, night shifts, and shift work.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Dose-Response Relationship of Stress Severity\u003c/h2\u003e \u003cp\u003eWhen stratifying occupational stress into four severity levels, graded increases in stress intensity corresponded to elevated risks of depression, insomnia, and MSDs. Detailed outcomes are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\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\u003eDose-Response Relationships: Occupational Stress Severity and Risks of Depression, Insomnia, and WMSDs\u0026zwnj;\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccupational Stress\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWald \u003cem\u003eχ\u003c/em\u003e\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDepressive Symptoms\u003c/b\u003e\u0026zwnj;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u0026zwnj;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.790 (1.803\u0026ndash;4.317)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.856 (3.484\u0026ndash;9.843)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.476 (8.218\u0026ndash;37.162)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsomnia Symptoms\u003c/b\u003e\u0026zwnj;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u0026zwnj;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.665 (1.196\u0026ndash;2.319)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.983 (1.257\u0026ndash;3.128)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.893 (1.935\u0026ndash;7.835)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWMSDs\u003c/b\u003e\u0026zwnj;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u0026zwnj;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.513 (1.046\u0026ndash;2.189)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.169 (2.046\u0026ndash;8.496)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.046 (1.404\u0026ndash;11.659)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote\u003c/b\u003e: \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e Adjusted for age, ethnicity, education, monthly income, weekly working hours, night shifts, and shift work.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Subgroup analyses\u003c/h2\u003e \u003cp\u003eSubgroup analyses were conducted to assess potential modifying effects of age, monthly income, weekly working hours, and night shifts on the association between occupational stress and health outcomes (depressive symptoms, sleep disturbances, and WMSDs).\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, the association between occupational stress and depressive symptoms remained consistent across all covariate strata (\u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, except for age (\u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;=\u0026thinsp;0.009), the association between occupational stress and sleep disturbances showed homogeneity across subgroups (\u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Younger individuals such as 16\u0026thinsp;~\u0026thinsp;\u0026lt;\u0026thinsp;30 years old (OR\u0026thinsp;=\u0026thinsp;10.84) and 30\u0026thinsp;~\u0026thinsp;\u0026lt;\u0026thinsp;40 years old (OR\u0026thinsp;=\u0026thinsp;3.04) exhibited significantly stronger associations between occupational stress and sleep disturbances. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC demonstrates that, apart from monthly income level (\u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;=\u0026thinsp;0.029), the association between occupational stress and WMSDs was consistent across strata (\u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Participants with a monthly income below 3,000 CNY displayed a markedly elevated WMSDs risk (OR\u0026thinsp;=\u0026thinsp;4.12).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.6 \u0026zwnj;Mediation Analysis\u0026zwnj;\u003c/h2\u003e \u003cp\u003eWe conducted mediation analyses to assess insomnia symptoms as a mediator between occupational stress and two health outcomes: depressive symptoms and work-related musculoskeletal disorders (WMSDs). For depressive symptoms (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), occupational stress demonstrated significant total (OR\u0026thinsp;=\u0026thinsp;1.066, 95% CI [1.056, 1.083], \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001), direct (OR\u0026thinsp;=\u0026thinsp;1.055, 95% CI [1.046, 1.073], \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001), and indirect effects through insomnia symptoms (OR\u0026thinsp;=\u0026thinsp;1.010, 95% CI [1.005, 1.020], \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.001), with mediation accounting for 16.0% of the total effect. Similarly, for WMSDs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), significant total (OR\u0026thinsp;=\u0026thinsp;1.178, 95% CI [1.094, 1.284], \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001), direct (OR\u0026thinsp;=\u0026thinsp;1.163, 95% CI [1.078, 1.271], \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001), and indirect effects (OR\u0026thinsp;=\u0026thinsp;1.013, 95% CI [1.006, 1.030], \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.001) were observed, indicating that 7.7% of the occupational stress-WMSDs association was mediated by insomnia symptoms. Bootstrapping with 1500 resamples confirmed the robustness of these partial mediation patterns.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study systematically evaluated the impact of occupational stress on mental health (depression and insomnia) and WMSDs among workers in the metal mining and beneficiation industry. Additionally, it explored the mediating role of insomnia in these associations and examined variations in effects across different subgroups. The findings revealed that occupational stress significantly increased the risk of depression, insomnia, and WMSDs, with younger workers and low-income groups being particularly vulnerable. Moreover, insomnia was identified as a partial mediator between occupational stress and both depression and WMSDs, suggesting that occupational stress may exacerbate psychological and physical health problems through its detrimental effects on sleep. These results not only confirm the widespread adverse health consequences of occupational stress but also highlight the importance of targeted interventions for high-risk groups.\u003c/p\u003e \u003cp\u003eThe prevalence of occupational stress in this study was 28.8%, lower than reported in some domestic studies (e.g., 42.65% among copper-nickel miners\u003csup\u003e27\u003c/sup\u003e and 33.08% among teachers\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e) but comparable to that of employees in the genetic sequencing industry (27.40%)\u003csup\u003e29\u003c/sup\u003e. These differences may be attributed to variations in job demands, psychological stressors, and work environments across industries. Despite a relatively lower prevalence, our findings indicate that occupational stress plays a critical role in both mental and physical health outcomes. Notably, a dose-response relationship was observed, where higher levels of occupational stress were associated with increased risks of depression, insomnia, and WMSDs. This trend underscores the importance of addressing psychological stress even in industries primarily characterized by physical labor, emphasizing the need for effective prevention and intervention strategies.\u003c/p\u003e \u003cp\u003eFurther analysis revealed that the occurrence of occupational stress is influenced by multiple factors. Workers with lower educational attainment, lower monthly income (\u0026lt;\u0026thinsp;3,000 RMB), night or shift work, and longer working hours were more likely to experience occupational stress. Previous studies have demonstrated that long working hours, high job demands, and low income contribute to heightened psychological pressure and an increased risk of occupational stress\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Our findings align with these observations, suggesting that work scheduling and salary structures should take workers' mental and physical well-being into account. Notably, the relationship between income and occupational stress remains a topic of debate; some studies suggest that higher-income groups experience greater occupational stress\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, possibly due to different job expectations and responsibilities across income brackets.\u003c/p\u003e \u003cp\u003eSubgroup analyses further demonstrated that age and income level significantly moderated the effects of occupational stress on health outcomes. Workers aged 16\u0026ndash;30 years exhibited greater susceptibility to the adverse effects of occupational stress, with higher rates of depression, insomnia, and WMSDs. This may be attributed to younger workers facing greater career uncertainty and having less developed coping mechanisms, whereas older workers may possess more experience and psychological resilience to manage stress effectively\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Additionally, low-income workers (\u0026lt;\u0026thinsp;3,000 RMB per month) exhibited a significantly higher risk of WMSDs compared to their high-income counterparts, suggesting that financial strain may amplify the health consequences of occupational stress. Economic hardship often limits access to health resources, such as flexible work arrangements and mental health services, potentially creating a vicious cycle between stress and poor health\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. These findings support the \"socioeconomic status-health gradient\" theory, which posits that individuals with lower socioeconomic status are at greater risk for adverse health outcomes due to limited protective factors\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAnother key finding of this study is the partial mediating role of insomnia in the relationship between occupational stress and both depression and WMSDs. Specifically, insomnia accounted for 16.0% of the total effect of occupational stress on depression and 7.7% of its effect on WMSDs. These align to previous findings that there are two possible pathways through which occupational stress influences health: directly through its immediate psychological and physiological impact, and indirectly by disrupting sleep, which exacerbates health risks\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Research has shown that chronic exposure to high-stress environments can activate the sympathetic nervous system and the hypothalamic-pituitary-adrenal (HPA) axis \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, leading to elevated stress hormone levels that impair sleep quality and increase susceptibility to depression and WMSDs. Moreover, insomnia may contribute to musculoskeletal damage through mechanisms such as fatigue accumulation and heightened pain sensitivity. This study is among the first to verify this mediating mechanism among workers in the metal mining and beneficiation industry, providing valuable insights for future interventions.\u003c/p\u003e \u003cp\u003eOverall, our findings underscore the profound impact of occupational stress on both psychological and physical health and highlight the critical role of sleep disturbances in this process. Given these results, companies and policymakers should implement multi-level intervention strategies, including optimizing work schedules, reducing excessive overtime, and providing psychological support and sleep management programs. Special attention should be given to younger and low-income workers to mitigate the health risks associated with occupational stress. Furthermore, future research should explore the effects of different work environments on occupational stress and adopt longitudinal study designs to establish causal relationships more precisely.\u003c/p\u003e \u003cp\u003eDespite the important findings of this study, several limitations should be acknowledged. First, due to the cross-sectional design, causality between occupational stress and health outcomes cannot be determined. Future research should employ longitudinal designs to validate our findings. Second, this study did not account for certain confounding factors, such as individual lifestyle habits and specific job types, which may influence the accuracy of the results. Differences in work environments and personal lifestyles could significantly affect health outcomes, necessitating more detailed occupational classifications and the inclusion of broader psychosocial variables in future studies. Lastly, as the sample was drawn from metal mining and beneficiation workers in Hunan Province, future studies should expand the sample size and geographic scope to enhance external validity.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this study confirms the significant impact of occupational stress on depression, insomnia, and WMSDs among metal mining and beneficiation workers, and, for the first time, identifies insomnia as a partial mediator in these relationships. Furthermore, the effects of occupational stress vary across different age and income groups, emphasizing the importance of targeted interventions. Future research should continue to explore effective strategies for managing occupational stress to improve workers' mental and physical well-being, ultimately enhancing job satisfaction and productivity.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eThe authors have no relevant interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e Written informed consent was obtained from all participants prior to study enrollment. The consent process included full disclosure of research objectives, data confidentiality protocols, and voluntary participation rights. Research procedures strictly adhered to the Declaration of Helsinki principles for non-interventional human study. The Ethics Committee of the Hunan Prevention and Treatment Institute for Occupational Diseases, Affiliated Prevention and Treatment Institute for Occupational Diseases of University of South China approved this study (No. 20230308001).\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study is jointly supported by the Occupational Health Potential Discipline Project of Hunan Prevention and Treatment Institute for Occupational Diseases (Grant No. 20240624-1118) and the Occupational Health Project of the Chinese Center for Disease Control and Prevention (Grant No. 1310311090001).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWenya Liu: Conceptualization, Methodology, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Writing - Review \u0026amp; Editing. Sijia Lvqiu: Conceptualization, Resources, Writing - Review \u0026amp; Editing, Supervision. Duoduo Wang: Writing - Review \u0026amp; Editing, Supervision, Project administration, Funding acquisition. Xinlv Dong: Conceptualization, Writing - Review \u0026amp; Editing, Visualization. Yunfeng Nie: Conceptualization, Writing - Review \u0026amp; Editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe express our gratitude to all workers who participated in this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData supporting the results of this study are available on request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCarmona-Barrientos I, Gala-Le\u0026oacute;n FJ, Lupiani-Gim\u0026eacute;nez M, Cruz-Barrientos A, Lucena-Anton D, Moral-Munoz JA. 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Sep 2010;35(1):97-103. https://doi.org/10.1016/j.neubiorev.2009.12.011\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metal mining and beneficiation industry, Occupational stress, Depression, insomnia, Musculoskeletal disorders","lastPublishedDoi":"10.21203/rs.3.rs-6851166/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6851166/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective:\u003c/h2\u003e \u003cp\u003eThis study investigates the prevalence of occupational stress among metal mining workers in China and its impact on depressive symptoms, insomnia, and work-related musculoskeletal disorders (WMSDs), while also exploring insomnia as a potential mediator and identifying effect modifiers.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eA cross-sectional survey of 1,130 metal mining workers in China was conducted from June to November 2023. Occupational stress, depressive symptoms, insomnia, and WMSDs were assessed using validated scales. Logistic regression models examined associations between occupational stress and health outcomes, adjusting for key covariates. Subgroup and mediation analyses assessed effect modification and indirect pathways.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe prevalence of occupational stress was 28.8%. Workers with occupational stress had higher odds of depressive symptoms (OR\u0026thinsp;=\u0026thinsp;4.33, 95% CI: 3.017\u0026ndash;6.216), insomnia (OR\u0026thinsp;=\u0026thinsp;1.91, 95% CI: 1.444\u0026ndash;2.524), and WMSDs (OR\u0026thinsp;=\u0026thinsp;2.071, 95% CI: 1.490\u0026ndash;2.879). Younger workers (16\u0026ndash;30 years) and those with lower incomes (\u0026lt;\u0026thinsp;3,000 CNY/month) were at greater risk. Insomnia partially mediated the associations between occupational stress and depressive symptoms (16.0%) and WMSDs (7.7%). A dose-response relationship was observed.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eOccupational stress is a significant risk factor for mental and physical health issues among metal mining workers, with younger and low-income workers being particularly vulnerable. Insomnia partially mediates these associations, underscoring the need for targeted workplace interventions to mitigate stress-related health consequences and improve worker well-being.\u003c/p\u003e","manuscriptTitle":"Occupational Stress and Its Adverse Health Effects Among Metal Mining Workers: A Cross-Sectional Study in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-26 08:26:07","doi":"10.21203/rs.3.rs-6851166/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-05T05:51:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-04T09:37:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-28T06:08:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248047086465094261690065961127696764103","date":"2025-07-25T09:35:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85041668238833246805857821107121298881","date":"2025-07-23T09:47:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-25T12:17:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159745811902656585308752062321248672909","date":"2025-06-24T02:58:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-24T01:00:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-24T00:56:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-12T09:28:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-11T09:04:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-09T06:07:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1bf70b67-f460-4af4-9399-05d4527fb0f8","owner":[],"postedDate":"June 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":50546484,"name":"Health sciences/Diseases"},{"id":50546485,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-11-24T16:05:27+00:00","versionOfRecord":{"articleIdentity":"rs-6851166","link":"https://doi.org/10.1038/s41598-025-24484-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-19 15:59:11","publishedOnDateReadable":"November 19th, 2025"},"versionCreatedAt":"2025-06-26 08:26:07","video":"","vorDoi":"10.1038/s41598-025-24484-9","vorDoiUrl":"https://doi.org/10.1038/s41598-025-24484-9","workflowStages":[]},"version":"v1","identity":"rs-6851166","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6851166","identity":"rs-6851166","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}