Latent Class Analysis of Shift Work Patterns Among Nurses and Its Impact on Anxiety-Depression Comorbidity: A Multicenter Cross-Sectional Study.
Relatively few population-based studies have explored the relationship between shift work patterns and anxiety-depression comorbidity among nurses. This study aimed to identify different patterns of nurse shift work and assess their association with anxiety-depression comorbidity.
This study was based on the Nurses' Environmental and Occupational Exposure and Health Nexus (NEON) cohort. A self-designed questionnaire was used to collect general demographic information and shift work details. Anxiety and depressive symptoms were evaluated using the generalized anxiety disorder-7 (GAD-7) and the patient health questionnaire-9 (PHQ-9), respectively. Latent class analysis (LCA) was performed to identify homogeneous groups of shift work. A three-step approach of LCA, involving logistic regression models, was applied to examine associations between various shift patterns and anxiety-depression comorbidity. Sleep quality served as a stratification factor for the subgroups.
Three latent classes were identified among 2791 shift nurses: Class 1: high-frequency, high-seniority group (N = 441, 15.80%); Class 2: low-frequency, low-seniority group (N = 835, 29.92%); Class 3: low-frequency, high-seniority group (N = 1515, 54.28%). Binary logistic regression analysis showed that, compared with Class 2, Class 3 had increased risks of anxiety, depression, and comorbid anxiety and depression by 54.8% (OR = 1.548, 95% CI 1.304-1.838), 30.6% (OR = 1.306, 95% CI 1.096-1.555), and 47.6% (OR = 1.476, 95% CI 1.245-1.749), respectively. Moreover, Class 1 had the highest risk of these conditions in both unadjusted and adjusted models. Sleep quality had no significant modifying effect.
This study identified distinct patterns in nurses' shift work. Different patterns were significantly associated with anxiety-depression comorbidity. Targeted interventions addressing specific shift patterns may improve nurses' mental health and contribute to achieving broader health goals.
This study was based on the Nurses' Environmental and Occupational Exposure and Health Nexus (NEON) cohort. A self-designed questionnaire was used to collect general demographic information and shift work details. Anxiety and depressive symptoms were evaluated using the generalized anxiety disorder-7 (GAD-7) and the patient health questionnaire-9 (PHQ-9), respectively. Latent class analysis (LCA) was performed to identify homogeneous groups of shift work. A three-step approach of LCA, involving logistic regression models, was applied to examine associations between various shift patterns and anxiety-depression comorbidity. Sleep quality served as a stratification factor for the subgroups.
Three latent classes were identified among 2791 shift nurses: Class 1: high-frequency, high-seniority group (N = 441, 15.80%); Class 2: low-frequency, low-seniority group (N = 835, 29.92%); Class 3: low-frequency, high-seniority group (N = 1515, 54.28%). Binary logistic regression analysis showed that, compared with Class 2, Class 3 had increased risks of anxiety, depression, and comorbid anxiety and depression by 54.8% (OR = 1.548, 95% CI 1.304-1.838), 30.6% (OR = 1.306, 95% CI 1.096-1.555), and 47.6% (OR = 1.476, 95% CI 1.245-1.749), respectively. Moreover, Class 1 had the highest risk of these conditions in both unadjusted and adjusted models. Sleep quality had no significant modifying effect.
This study identified distinct patterns in nurses' shift work. Different patterns were significantly associated with anxiety-depression comorbidity. Targeted interventions addressing specific shift patterns may improve nurses' mental health and contribute to achieving broader health goals.
Authors
Zhao Zhao, Shi Shi, Fan Fan, Huang Huang, Yang Yang, Niu Niu, Zhang Zhang, Xu Xu, Deng Deng, Jin Jin, Hu Hu, Tang Tang, Jiang Jiang, Tao Tao, Wang Wang, Wu Wu
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