Occupational stress and associated risk factors among 13,867 industrial workers in China
ObjectiveOccupational stress is a critical global public health problem. We aimed to evaluate the prevalence of occupational stress among the workers in the electricity, heat, gas, water production and supply (EHGWPS), manufacturing, and transportation industries in Beijing, China. We explored the d...
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-11-01
|
| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2022.945902/full |
| _version_ | 1811307825474830336 |
|---|---|
| author | Tenglong Yan Fang Ji Mingli Bi Huining Wang Xueting Cui Baolong Liu Dongsheng Niu Leilei Li Tian Lan Tingting Xie Jie Wu Jue Li Xiaowen Ding |
| author_facet | Tenglong Yan Fang Ji Mingli Bi Huining Wang Xueting Cui Baolong Liu Dongsheng Niu Leilei Li Tian Lan Tingting Xie Jie Wu Jue Li Xiaowen Ding |
| author_sort | Tenglong Yan |
| collection | DOAJ |
| description | ObjectiveOccupational stress is a critical global public health problem. We aimed to evaluate the prevalence of occupational stress among the workers in the electricity, heat, gas, water production and supply (EHGWPS), manufacturing, and transportation industries in Beijing, China. We explored the demographic differences in occupational stress status among workers in industrial enterprises.MethodsA cross-sectional study was conducted on 13,867 workers. The self-administered New Brief Job Stress Questionnaire was used to evaluate high occupational stress status, which includes four sub-dimensions (job stressors, stress response, social support, job stressors & social support). Multiple regression and logistic regression models were used to estimate the association between high occupational stress and the four occupational stress sub-dimensions with risk factors.ResultsA total of 13,867 workers were included. The prevalence of high occupational stress was 3.3% in the EHGWPS industries, 10.3% in manufacturing, and 5.8% in transportation. The prevalence of high occupational stress was higher than in the other two categories (p < 0.05) in manufacturing industries. Logistic regression analysis showed that male workers with lower educational status, more job experience, and working in manufacturing were vulnerable to high occupational stress. Further analysis of the four occupational stress sub-dimensions showed that male workers, older adult workers, workers with lower educational levels, and longer working time were associated with higher scores in job stressors, stress response, social support, and job stress & social support (all p < 0.05). Moreover, divorced or widowed workers had higher occupational stress scores.ConclusionMale workers with lower educational levels and longer working time may have an increased risk of occupational stress. |
| first_indexed | 2024-04-13T09:11:21Z |
| format | Article |
| id | doaj.art-75d92dad28504b43a0b7f4f02fff4c43 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-13T09:11:21Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-75d92dad28504b43a0b7f4f02fff4c432022-12-22T02:52:52ZengFrontiers Media S.A.Frontiers in Public Health2296-25652022-11-011010.3389/fpubh.2022.945902945902Occupational stress and associated risk factors among 13,867 industrial workers in ChinaTenglong Yan0Fang Ji1Mingli Bi2Huining Wang3Xueting Cui4Baolong Liu5Dongsheng Niu6Leilei Li7Tian Lan8Tingting Xie9Jie Wu10Jue Li11Xiaowen Ding12Beijing Institute of Occupational Disease Prevention and Treatment, Beijing, ChinaBeijing Institute of Occupational Disease Prevention and Treatment, Beijing, ChinaBeijing Institute of Occupational Disease Prevention and Treatment, Beijing, ChinaBeijing Institute of Occupational Disease Prevention and Treatment, Beijing, ChinaSchool of Public Health, North China University of Science and Technology, Tangshan, ChinaBeijing Institute of Occupational Disease Prevention and Treatment, Beijing, ChinaBeijing Institute of Occupational Disease Prevention and Treatment, Beijing, ChinaBeijing Institute of Occupational Disease Prevention and Treatment, Beijing, ChinaBeijing Institute of Occupational Disease Prevention and Treatment, Beijing, ChinaBeijing Institute of Occupational Disease Prevention and Treatment, Beijing, ChinaCanvard College, Beijing Technology and Business University, Beijing, ChinaBeijing Institute of Occupational Disease Prevention and Treatment, Beijing, ChinaBeijing Institute of Occupational Disease Prevention and Treatment, Beijing, ChinaObjectiveOccupational stress is a critical global public health problem. We aimed to evaluate the prevalence of occupational stress among the workers in the electricity, heat, gas, water production and supply (EHGWPS), manufacturing, and transportation industries in Beijing, China. We explored the demographic differences in occupational stress status among workers in industrial enterprises.MethodsA cross-sectional study was conducted on 13,867 workers. The self-administered New Brief Job Stress Questionnaire was used to evaluate high occupational stress status, which includes four sub-dimensions (job stressors, stress response, social support, job stressors & social support). Multiple regression and logistic regression models were used to estimate the association between high occupational stress and the four occupational stress sub-dimensions with risk factors.ResultsA total of 13,867 workers were included. The prevalence of high occupational stress was 3.3% in the EHGWPS industries, 10.3% in manufacturing, and 5.8% in transportation. The prevalence of high occupational stress was higher than in the other two categories (p < 0.05) in manufacturing industries. Logistic regression analysis showed that male workers with lower educational status, more job experience, and working in manufacturing were vulnerable to high occupational stress. Further analysis of the four occupational stress sub-dimensions showed that male workers, older adult workers, workers with lower educational levels, and longer working time were associated with higher scores in job stressors, stress response, social support, and job stress & social support (all p < 0.05). Moreover, divorced or widowed workers had higher occupational stress scores.ConclusionMale workers with lower educational levels and longer working time may have an increased risk of occupational stress.https://www.frontiersin.org/articles/10.3389/fpubh.2022.945902/fulloccupational stressindustrial enterprisesrisk factorsworkersthe new brief job stress questionnaire |
| spellingShingle | Tenglong Yan Fang Ji Mingli Bi Huining Wang Xueting Cui Baolong Liu Dongsheng Niu Leilei Li Tian Lan Tingting Xie Jie Wu Jue Li Xiaowen Ding Occupational stress and associated risk factors among 13,867 industrial workers in China Frontiers in Public Health occupational stress industrial enterprises risk factors workers the new brief job stress questionnaire |
| title | Occupational stress and associated risk factors among 13,867 industrial workers in China |
| title_full | Occupational stress and associated risk factors among 13,867 industrial workers in China |
| title_fullStr | Occupational stress and associated risk factors among 13,867 industrial workers in China |
| title_full_unstemmed | Occupational stress and associated risk factors among 13,867 industrial workers in China |
| title_short | Occupational stress and associated risk factors among 13,867 industrial workers in China |
| title_sort | occupational stress and associated risk factors among 13 867 industrial workers in china |
| topic | occupational stress industrial enterprises risk factors workers the new brief job stress questionnaire |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2022.945902/full |
| work_keys_str_mv | AT tenglongyan occupationalstressandassociatedriskfactorsamong13867industrialworkersinchina AT fangji occupationalstressandassociatedriskfactorsamong13867industrialworkersinchina AT minglibi occupationalstressandassociatedriskfactorsamong13867industrialworkersinchina AT huiningwang occupationalstressandassociatedriskfactorsamong13867industrialworkersinchina AT xuetingcui occupationalstressandassociatedriskfactorsamong13867industrialworkersinchina AT baolongliu occupationalstressandassociatedriskfactorsamong13867industrialworkersinchina AT dongshengniu occupationalstressandassociatedriskfactorsamong13867industrialworkersinchina AT leileili occupationalstressandassociatedriskfactorsamong13867industrialworkersinchina AT tianlan occupationalstressandassociatedriskfactorsamong13867industrialworkersinchina AT tingtingxie occupationalstressandassociatedriskfactorsamong13867industrialworkersinchina AT jiewu occupationalstressandassociatedriskfactorsamong13867industrialworkersinchina AT jueli occupationalstressandassociatedriskfactorsamong13867industrialworkersinchina AT xiaowending occupationalstressandassociatedriskfactorsamong13867industrialworkersinchina |