[The mediating effect of anxiety on occupational stress and sleep quality in local occupational population]

Objective: To screen the influencing factors of sleep quality among the occupational population in Ningxia Hui Autonomous Region (Ningxia), and to analyze the mediating effect of anxiety between occupational stress and sleep quality. Methods: From April 25 to May 1, 2024, a cluster random sampling method was used to survey 4, 106 occupational workers in a city of Ningxia. A total of 3, 837 valid questionnaires were collected (valid response rate 93.4%). The survey instruments included a general demographic and occupational characteristics questionnaire, the Generalized Anxiety Disorder-7 (GAD-7), the Patient Health Questionnaire-9 (PHQ-9), the Job Content Questionnaire (JCQ; occupational stress was defined as the ratio of job demands to job control, D/C>1, where D represents the job demands score and C represents the job control score), and the Pittsburgh Sleep Quality Index (PSQI; poor sleep quality was defined as a total PSQI score ≥11). LASSO regression was used to screen the influencing factors of sleep quality. Spearman's rank correlation analysis was used to examine the correlations among occupational stress, anxiety, and sleep quality. A linear regression model combined with the Bootstrap method (5000 resamples) was employed to test the mediating effect of anxiety between occupational stress and sleep quality, controlling for occupation, average monthly income, shift pattern, length of service, alcohol consumption, physical exercise, and depression. Results: The detection rate of occupational stress among Ningxia occupational workers was 70.91% (2721/3837), the detection rate of anxiety was 61.82% (2372/3837), and the detection rate of poor sleep quality was 41.54% (1594/3837). LASSO regression identified nine influencing factors: occupation, average monthly income, shift pattern, length of service, alcohol consumption, physical exercise, depression, anxiety, and occupational stress. Spearman correlation analysis showed that occupational stress was positively correlated with poor sleep quality (r(s)=0.12, P<0.001), and anxiety was positively correlated with both occupational stress and poor sleep quality (r(s)= 0.12 and 0.40, both P<0.001). After adjusting for the above covariates, linear regression-based mediating effect analysis revealed that the direct effect of occupational stress on sleep quality was 0.92 (95%CI: 0.61-1.23), and the total effect was 2.26 (95%CI: 1.90-2.63). The indirect effect mediated by anxiety was 1.34 (95%CI: 1.08-1.60). Collectively, anxiety exerted a statistically significant mediating effect between occupational stress and sleep quality. Conclusion: Sleep quality of occupational workers in Ningxia is affected by multiple factors including occupation, shift pattern, length of service, occupational stress and anxiety symptoms. It is essential to alleviate occupational stress in working populations, reduce their anxiety levels, and improve sleep quality.