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OBJECTIVE: To analyze the influencing factors of drinking behavior of manufacturing workers and their impact on their quality of life. METHODS: A total of 594 manufacturing workers were selected as research subjects using stratified cluster sampling method. Their drinking behavior, physical activity status and quality of life were investigated by the Drinking Behavior Questionnaire, International Physical Activity Questionnaire-Short Form and Short Form 36 Health Survey Questionnaire, respectively. RESULTS: The overall drinking rate of the subjects was 32.2%(191/594). Multivariate logistic regression analysis results showed that male workers had higher risk of drinking behavior than female workers(P<0.01). The risk of drinking behavior of smoking workers was higher than that of non-smokers(P<0.01). The risk of drinking behavior of workers with medium or high physical activity levels was higher than that of workers with low activity levels(P<0.01). The results of multiple linear regression analysis showed that after adjusting for the effects of gender, age, marital status, education level, monthly income, household registration, length of service, work shift, weekly work hours, industry type, smoking, physical activity level and other confounding factors, drinking did not affect the physical or psychological quality of life of workers(P>0.05). CONCLUSION: Gender, smoking and physical activity may be important factors influencing the drinking behavior of manufacturing workers. Drinking may have no effect on quality of life of manufacturing workers.
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Objective@#To investigate the current status and influencing factors of occupational stress among couriers.@*Methods@#Couriers (n=925) were selected on this study used cluster sampling method from January to March 2018. They were from SF and Zhongtong Express Co., Ltd., on the Wechat platform, and surveyed by a job stress questionnaire based on a job demand-control model.Valid questionnaires(n=617) were obtained.@*Results@#A total of 418 workers were occupational stress positive (67.7%). The results of Chi-square analysis showed that there were significant differences in occupational stress among workers categorized by job position, working years, mealtime, sleeping time, and weekly work time (P<0.05). The multivariate logistic analysis indicated that non-regular meals, short-term sleep and less than 0.5 working years were risk factors for occupational stress(P<0.05).@*Conclusion@#Couriers generally have occupational stress. The main influencing factors are job position, working years mealtime, sleeping time, and weekly work time. It is necessary to guide healthy lifestyle, rationally organize labor and assign tasks, and improve working environment to relieve their occupational stress.
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OBJECTIVE: To analyze the influencing factors of suspected occupational noise-induced deafness( ONID) in noise-exposed workers. METHODS: A total of 38 770 noise-exposed workers engaged in occupational health examination were collected as the study subjects from 2012-2016 by judgment sampling method. The data of workers' occupational medical examination was collected,and the incidence and influencing factors of suspected ONID were analyzed. RESULTS: A total of 125 cases of suspected ONID were detected and the detection rate was 0. 32%. The result of multivariate Logistic regression showed that male workers exposed to noise had a higher risk of suspected ONID than female workers( P <0. 01). The odds ratio( OR) and 95% confidence interval( CI) were 1. 98( 1. 22-3. 19). The older the age and the longer service length of workers exposed to noise,the higher the risk of suspected ONID( P < 0. 01). The ORs and 95% CIs were 1. 79(1. 43-2. 25) and 1. 84( 1. 47-2. 30) respectively. The noise-exposed workers had a higher risk of suspected ONID in foreign-funded enterprises than domestic-funded enterprises( P < 0. 01). The noise-exposed workers had a higher risk of suspected ONID in metal manufacturing industries than in non-metal manufacturing industries( P < 0. 01). The ORs and 95% CIs were 1. 83(1. 19-2. 82) and 2. 02(1. 40-2. 94) respectively. CONCLUSION: The incidence of suspected ONID is affected by factors of gender,age,length of service,economy type of enterprises and industry type.