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Background As traditional chemical and physical hazards as well as associated adverse health outcomes in workplace were wildly controlled in the past half century, the prevalence and disease burden of low back pain (LBP) have drawn more and more attention and become one of the important public health problems in the world. Objective To analyze the health loss and attributable disease burden of ergonomic risk factors for LBP in two major categories of occupations in China, aiming to provide evidence for formulating effective prevention and control policies of LBP in the workplace. Methods Based on the methodological framework of the Global Burden of Disease Study (GBD), a meta-analysis was firstly applied to summarize relevant literature results and estimate the prevalence of LBP in two occupational groups (including technicians and associate professionals and machine operators and assemblers) by different age groups in China. Then important epidemiologic parameters (including disability weight, remission rate, and incidence) from GBD 2019 were used to estimate mean duration of disease and age at onset using DisMod II software, and to calculate health loss indexes in the selected occupational groups in China in 2013, such as years lived with disability (YLD) and disability-adjusted life year (DALY) of LBP and its attributable fractions by ergonomic risk factors, which were compared to the outcome of GBD 2013. Results After the adjustment by DisMod II, the prevalence rate of LBP was 13.00% in technicians and associate professionals (11.25% for males and 14.84% for females) and 14.80% in machine operators and assemblers (13.56% for males and 16.10% for females) in 2013, which increased with age. The DALY rate of LBP was 8.02‰ in technicians and associate professionals (7.68‰ for males and 8.33‰ for females) and 10.34‰ in machine operators and assemblers (10.30‰ for males and 10.44‰ for females), which also showed an overall increasing trend with age. In 2013, the population attributable fraction (PAF) of ergonomic risk factors to LBP was 11.42% in technicians and associate professionals and 29.17% in machine operators and assemblers. The DALY of LBP attributable to ergonomics risk factors was 4498 person-years (2108 person-years for males), with the highest DALY in the 45-49 year group (951 person-years), and the attributable DALY rate was 0.92‰ in technicians and associate professionals. The DALY of LBP attributable to ergonomics risk factors was 48529 person-years (33046 person-years for males), with the highest DALY in the 40-44 year group (10852 person-years), and the attributable DALY rate was 3.02‰ in machine operators and assemblers. Regarding LBP-associated DALY rate, in the 20 years of age and above group, both occupational groups (technicians and associate professionals: 8.06‰, machine operators and assemblers: 10.66‰) showed higher values than the general population (3.55‰). In the 20 years of age and above group, the DALY rates attributable to ergonomic risk factors with the order from high to low were machine operators and assemblers (3.11‰), general population (1.10‰) and technicians and associate professionals (0.92‰). Conclusion The LBP-associated disease burden is heavier in the two Chinese occupational groups than in general population. Reducing the disease burden of LBP by interventions targeting ergonomic risk factors in machine operators and assemblers is more effective than that in technicians and associate professionals as the results of attributable burden of disease suggest.
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Background Nursing staff are often exposed to a variety of occupational risk factors in the working environment, such as long working hours and heavy workload, which associated with adverse mental health outcomes. And these factors may not be randomly distributed across different levels. Objective To explore mental health risk factors of nursing staff by multilevel analysis. Methods A cross-sectional survey of nursing staff in Shanghai Municipality, Zhejiang Province, Guangxi Zhuang Autonomous Region, and Xinjiang Uygur Autonomous Region was conducted through convenience sampling from 2018 to 2021. Data were collected by self-report questionnaires. The mental component summaries of 12-Iitem Short Form Health Survey were used to evaluate the mental health status of nursing staff, and related factors were collected atindividual level, including gender, body mass index (BMI), smoking status, drinking status, working years, pain intensity of musculoskeletal disorders, and working hours per week, and at regional level, including gross domestic product (GDP) level of each province. A two-level model was established by incorporating both individual and regional factors, and deviance was used to test the goodness of fit of the model. A traditional generalized linear model was also established, and then compared with the multilevel model. Results A total of 567 nurses participated in this study, and the valid rate of questionnaire was 80.08%. The results of the multilevel model showed that the regional factor contributed 12.1% to the mental component summaries. As to the regional factor, GDP was negatively correlated with mental health of nursing staff, the adjusted OR (AOR) was −0.53 (95%CI: −0.66-−0.28). Among the factors at individual level, the mental component summaries of females were lower than those of males (AOR=−3.25, 95%CI: −4.73-−0.35); the longer the working years, the higher the mental health score (AOR=0.11, 95%CI: 0.06-0.20); working hours per week (AOR=−0.10, 95%CI: −0.14-−0.03) and pain intensity of musculoskeletal disorders (AOR=−0.05, 95%CI: −0.06-−0.03) were negatively correlated with mental component summaries. The results of the generalized linear model included the same factors as the multilevel model, but the 95%CIs of AOR of the factors in the multilevel model were narrower, and the deviation value of the multilevel model was the smallest, indicating that the goodness of fit of the multilevel model was better than that of the traditional linear model. Conclusion The mental health of nursing staff is not only affected by individual level factors, but also affected by regional level factors. It suggests that combining different levels of intervention measures can upscale the effect of improving mental health in nursing staff.
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Background In recent years, road traffic injury (RTI) has become a serious public health problem in China, and the factors affecting deaths caused by RTI are also complicated. Objective This study is designed to identify factors of RTI fatality risk and establish a road user fatality risk prediction model. Methods The data of traffic accident casualties in Pudong New Area of Shanghai from 2010 to 2016 were collected retrospectively, and the related impact factors of RTI were collected. Logistic regression was used to screen the selected factors of RTI fatality risk. A nomogram of RTI fatality risk was established, the consistency and accuracy of the model was evaluated by C-index and bootstrap internal verification, and a sensitivity analysis was also conducted. Results A total of 3521 casualties in traffic accidents were included in the study. The logistic regression results showed that age of victims, medical rescue distance, road type, transport means, injured body part, time of accident, and weekday/weekend affected RTI death risk (P<0.05). The nomogram model for RTI death risk showed that the most affecting factors were injured body part (especially head and neck injury), followed by age, transportation means, medical rescue distance, road type, time of accident, and weekday/weekend. The C-index of the model was 0.790, indicating high prediction accuracy and good fitness. The nomogram model for RTI death risk of head and neck injury showed that the score scales of all included factors expanded, the most prominent (most affecting) one was age; the RTI fatality risk of different road types changed, where urban road became the most dangerous road type; in addition, walking was the transportation means with the greatest risk of RTI fatality from head and neck injury. The results of the sensitivity analysis on accidents with varied casualties confirmed the robustness of the model. Conclusion The road user fatality risk of RTI is affected by many factors. As a simple tool to predict fatality risk of RTI, the nomogram based on logistic regression has certain reference significance for road traffic safety.