ABSTRACT
The new business forms are the new patterns of the job market extended by the use of mobile internet, big data, cloud computing, and other information technologies in the context of a new round of information technology development. In recent years, under the catalysis of the new employment model, a large number of new business employees have emerged such as food delivery riders, couriers, and online car-hailing drivers, whose employment forms are flexible and don’t fully conform to the establishment of labor relations with the employers, and whose problem of overtime work is common. The employment characteristics of new business forms of "focusing on performance and ignoring responsibility" make workers at great risks of occupational injuries and occupational health problems such as work-related musculoskeletal disorders, mental disorders, and overwork. However, the current research on occupational safety and health of workers in new business forms is insufficient, and the experience of institutional practice and supervision is also limited. Therefore, based on relevant research progress at home and abroad, this paper discussed the health injuries of workers in new business forms, summarized associated influencing factors from four aspects: employment form, employment affiliation, long working hours exposure, and institutional guarantee, and then proposed countermeasures to strengthen occupational safety and health protection of workers. For the current problems in the employment of new business forms, the government, enterprises, and workers need to work together to improve the legal and regulatory system for employment of new business forms, and standardize occupational safety and health management, thus effectively protect the occupational safety and health rights and interests of workers in new business forms.
ABSTRACT
In order to promote the development of China's occupational injury surveillance system, this paper presented the legal basis, project overview, reporting procedures, definitions and stati statistical scope, data sources and collection standards, statistical data management and analysis points of the European Statistics on Accidents at Work (ESAW), and combined with existing research and related surveillance management system in China, five key points were proposed for constructing China's occupational injury surveillance system: 1) Establish and improve laws and regulations related to occupational injury surveillance; 2) Promote utilization of nation-level data systems; 3) Establish and optimize a sound national occupational injury surveillance system; 4) Provide standardized protocols for data collection and data application of occupational injury statistics; 5) Strengthen supervision and law enforcement targeting industries and enterprises.
ABSTRACT
Background The severity of occupational injury in countries such as the United Kingdom, the United States, and Germany is usually analyzed using lost workdays, but in existing occupational injury surveillance research in China, the application of this index is rare. Objective To evaluate the application value of lost workdays in non-fatal occupational injury surveillance, and provide a reference for the construction of occupational injury surveillance index system. Methods The public data of European Statistics on Accidents at Work (ESAW) from 2010 to 2019 on non-fatal injury accidents in 27 member states of the European Union were used. Non-fatal occupational injury is defined as an injury event during occupational activities or at work resulting a victim's absence from work for ≥4 d. According to the European Statistics on Accidents at Work-Summary methodology, the lost workdays were divided into 8 categories (4-6 d, 7-13 d, 14-20 d, 21-30 d, 31-91 d, 92-182 d, 183 d and above, and unknown). Annual percentage change (APC) and the average annual percentage change (AAPC) were used to evaluate the overall trend changes in the incidence rate of non-fatal occupational injury accidents in different lost workdays from 2010 to 2019, and the non-fatal occupational injury accidents in key industries. The characteristics of the occurrence of non-fatal occupational injuries were analyzed in conjunction with the changes in non-fatal occupational injuries in different lost workdays in the industry. Results From 2010 to 2019, the overall incidence of non-fatal occupational injury accidents in the European Union showed a downward trend, and the AAPC was −1.0% (P<0.05). The accident rates of lost workdays of 4-6 d and 92-182 d showed an upward trend, and the AAPC were 7.9% and 5.8% respectively (P<0.05). The average annual accident rates of non-fatal occupational injuries (≥4 d) in Categories C (manufacturing industry), E (water supply, sewage treatment, waste management and remediation), and F (construction industry) showed a linear downward trend, and the AAPC were −3.0%, −2.5%, and −1.5%, respectively (P<0.05). However, among them, the rate of non-fatal occupational injury accidents with 92-182 d of lost workdays in the manufacturing industry showed a significant upward trend, with an AAPC of 3.7% (P<0.001). Conclusion Using lost workdays combined with APC and AAPC by Join-point linear regression analysis can measure the severity and trend changes of non-fatal occupational injury accidents in different industries and different lost workdays. This indicator has an important practical significance in evaluating the effectiveness of occupational injury prevention and control strategies adopted by countries and enterprises.
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Background Identification and analysis of influencing factors of occupational injury is an important research content of feature selection. In recent years, with the rise of machine learning algorithms, feature selection combined with Boosting algorithm provides a new analysis idea to construct occupational injury prediction models. Objective To evaluate applicability of Boosting algorithm-based model in predicting severity of miners' non-fatal occupational injuries, and provide a basis for rationally predicting the severity level of miners' non-fatal occupational injuries. Methods The publicly available data of the US Mine Safety and Health Administration (MSHA) from 2001 to 2021 on metal miners' non-fatal occupational injuries were used, and the outcome variables were lost working days < 105 d (minor injury) and ≥ 105 d (serious injury). Four different feature sets were screened out by four feature selection methods including least absolute shrinkage and selection operator (Lasso) regression, stepwise regression, single factor + Lasso regression, and single factor + stepwise regression. Logistic regression, gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost) were selected to construct prediction models by training with the four feature sets. A total of 12 prediction models of severity of miners' non-fatal occupational injuries were built and their area under the curve (AUC), sensitivity, specificity, and Youden index were calculated for model evaluation. Results According to the results of four feature selection methods, age, time of accident occurrence, total length of service, cause of injury, activities that triggered injury occurrence, body part of injury, nature of injury, and outcome of injury were identified as influencing factors of non-fatal occupational injury severity in miners. Feature set 4 was the optimal set screened out by single factor+stepwise regression and the GBDT model presented the best predictive performance in predicting the severity of non-fatal occupational injuries. The associated specificity, sensitivity, and Youden index were 0.7530, 0.9490, and 0.7020, respectively. The AUC values of logistic regression, GBDT, and XGBoost models trained by feature set 4 were 0.8526 (95%CI: 0.8387, 0.8750), 0.8640 (95%CI: 0.8474, 0.8806), and 0.8603 (95%CI: 0.8439, 0.8773), respectively, higher than the AUC values trained by feature set 2 [0.8487 (95%CI: 0.8203, 0.8669), 0.8110 (95%CI: 0.8012, 0.8344), and 0.8439 (95%CI: 0.8245, 0.8561), respectively] . The AUC values of GBDT and XGBoost models trained by feature set 4 were higher than that of logistic regression model. Conclusion The performance of the prediction models constructed by predictors screened out by two feature selection methods is better than those by single feature selection methods. At the same time, under the condition of optimal feature set, the performance of model prediction based on Boosting is better than that of traditional logistic regression model.
ABSTRACT
Occupational injuries cause a large number of personal injuries, illnesses, or deaths, resulting in a huge burden of disease, and has become an important global occupational safety and health problem. Developed countries in Europe and the United States have provided strong support for occupational injury prevention and control by establishing continuous and stable occupational injury surveillance systems. The occupational injury problem has not attracted enough attention and concern in China, with few relevant research reports, and the current occupational injury surveillance system is far from perfection. From the perspective of protecting workers' occupational health, this paper analyzed and compared the classification and scope of occupational injuries at home and abroad, as well as the status quo of occupational injury surveillance and assessment, and proposed to set up an occupational injury surveillance system with multiple surveillance methods and multiple data sources that complement with each other, so as to strengthen the continuity of surveillance activities, consistency of data formats, and comparability of assessment indicators. Step by step, we can set up a surveillance system covering the whole process of surveillance, assessment, intervention, and evaluation of intervention effects.
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Since its founding 100 years ago, the Communist Party of China (CPC) has attached great importance to the occupational health of workers, and closely linked the safeguard of occupational safety and health of workers with the missions of leading the people to stand up, get rich, and become strong. Based on this, the achievements of occupational health work made under the leadership of the CPC since the new democratic revolution, socialist revolution and construction, the new period of reform and opening up and socialist modernization, and socialism with Chinese characteristics for a new era were summarized, so as to provide reference for understanding the legacy of China's occupational health undertaking, and innovating to create the future.
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Objective@#To evaluate the performence of GeneXpert MTB/RIF and BACTEC-MGIT 960 on detecting Mycobacterium tuberculosis and rifampicin resistance for pneumoconiosis-associated tuberculosis patients.@*Methods@#The recruited 133 suspected active pneumoconiosis-associated tuberculosis hospitalized cases, morning sputum samples were collected to do modified L-J culture, conventional proportion method drug susceptibility test, GeneXpert MTB/RIF and BACTEC-MGIT 960. Analyze the sensitivity and specificity of the 133 sputum from patients, the positive rates of patients with tuberculosis in GeneXpert MTB/RIF test, BACTEC-MGIT 960 and modified L-J culture were 37.59%, 34.59% and 30.08% respectively. There was no significant difference among the three tests respectively (P>0.05) . According to the modified L-J culture, the sensitivity of GeneXpert MTB/RIF and BACTEC-MGIT 960 in detecting tuberculosis were 92.5% and 95.0% respectively, and specificity in rifampicin resistance were 86.0% and 91.4% respectively. There was no significant difference between GeneXpert MTB/RIF and BACTEC-MGIT 960 (P>0.05) . According to conventional proportion method drug susceptibility test, the sensitivity of GeneXpert MTB/RIF and BACTEC-MGIT 960 in detecting rifampicin resistance were 90.0% and 100%, and specificity were 92.6% and 96.4%. There was no significant difference between GeneXpert MTB/RIF and BACTEC-MGIT 960 (P>0.05) .@*Conclusion@#The GeneXpert MTB/RIF has good performence of detecting tuberculosis and rifampicin resistance. It has good application value among pneumoconiosis-associated tuberculosis patients.