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Exploring the economic occupational health, safety, and fatal accidents in high-risk industries
樱花视频 volume听25, Article听number:听433 (2025)
Abstract
Despite advancements in occupational health and safety (OHS) management, high-risk industries in China continue to report a significant number of fatal accidents, underscoring systemic challenges in protecting the well-being of workers while supporting economic development. This study analyzed 22 years of historical data on OHS incidents, labor dynamics, and economic growth in China鈥檚 high-risk industries via multiple regression and network analysis methods. The findings reveal hierarchical influence relationships, with coal mine fatalities emerging as critical upstream factors and transportation fatalities and national labor force dynamics emerging as key downstream factors. Notably, the study reveals a negative correlation between GDP and fatal workplace incidents: for every 0.461 trillion CNY increase in GDP, production safety accident deaths decrease by one. Conversely, each safety accident resulted in 1.052 coal mine fatalities and 0.153 cases of occupational disease. These results offer a novel quantitative perspective on the interplay between economic growth and workplace safety. The study鈥檚 models provide practical guidance for enhancing the effectiveness of OHS prevention and control efforts, contributing to sustainable economic and public health outcomes.
Introduction
Every year, millions of workers worldwide die, are injured, or fall ill due to occupational hazards [1]. An EU survey revealed that public awareness of health care system safety has decreased, highlighting the urgent need to raise public awareness of safety concerns [2]. From the perspective of China鈥檚 background, the country鈥檚 total domestic output value reached 126 trillion CNY by 2023 according to China鈥檚 Industry Development Analysis Report. However, as the economy has grown, enterprise safety production remains a severe issue, with high numbers of occupational patients and fatalities from safety accidents. According to data from the National Bureau of Statistics and the Ministry of Housing and Urban鈥扲ural Development, the number of accidents and fatalities in China from 2001to 2022 was unevenly distributed across provinces, especially in the western region, where the number of occupational diseases and accident deaths was very high [3]. The regional differences in accident situations among different provinces in China overall show a decreasing trend from underdeveloped areas in the West to highly developed areas [4]. The relationships among economic development, OHS, and accident fatalities constitute crucial research topics. Therefore, the main purpose of this study is to explore the relationships among occupational health and safety accidents, occupational diseases, and economic development in high-risk industries and make breakthrough contributions to the literature.
From a global perspective, countries worldwide are paying increasing attention to public health management of occupational health and safety, as well as accident-related deaths, while developing their economies. As China has experienced rapid economic growth, the government has proposed a strategy to prevent injuries, promote safety, and set control indicators for the impact on public occupational health in local economic growth targets [5, 6]. The Italian government emphasizes balancing the health and economic development of workers with social security benefits [7, 8]. Global occupational health and the economy not only affect urban areas but also rural areas, where population health is affected by economic development [9,10,11]. Therefore, global engineering or machinery manufacturing industries, especially high-risk industries, urgently need to strengthen employee protection while developing the economy [12].
The classification of risk factors reveals that occupational health and safety risk factors are multifaceted and need effective hierarchical management for enterprise OHS. Scholars have proposed the 鈥淔ive Factor Method鈥 (main, secondary, weak, unrelated, and expectant factors) [13] and the 鈥淔ive Factor Model鈥 (human, material, method, management, and environmental factors) [14,15,16]. Classical theory suggests that human factors can be divided into unsafe behaviors and unsafe object states. Addressing psychosocial hazards in the work environment, which are influenced by physiological, social, and psychological factors, is an increasingly important component of OHS interventions [17,18,19]. The object factor refers to the impact of equipment and materials used at the project site, and the economic investment and cost savings associated with these factors are also key influencing factors for OHS risk [20]. An analysis of management factors suggests that education, publicity, training, and the integration of safety and protection measures with legislation can help reduce the occurrence of occupational injuries [21]. With respect to methodological factors, some scholars have proposed indicator methods to improve enterprise OHS performance, believing that measuring enterprise performance and overall workplace safety management is a key theme in OHS risk management [22,23,24]. In terms of environmental climate factors, the impact of climate change on outdoor workers and their safety is usually crucial [25, 26], especially in extreme weather, exposure to sunlight, and the impact of ultraviolet radiation on worker health. The World Health Organization (WHO) and the International Labor Organization (ILO) are collaborating on a joint approach to estimate the national and global burdens of work-related diseases and injuries [27,28,29,30]. Therefore, the identification and analysis of the sources and outcome factors of various occupational health and safety risks urgently need to be prioritized.
Hazard management is one of the most prominent forms of intervention in occupational health and safety, which can not only prevent occupational diseases but also help reduce the risk of work-related injuries. Its potential social and economic impact is high, and these specific risks require strengthened national-level planning intervention and implementation [31, 32]. Occupational exposure correlates with the health outcomes of exposed workers. The mining industry has the greatest weighted prevalence of workplace noise exposure, and noise-induced hearing loss may also interfere with the safe operation of motor vehicles [33,34,35]. Intermittent chronic diseases, such as musculoskeletal strain and lower back pain, can affect job satisfaction and productivity in high-risk industries [36,37,38]. Exposure to hazards the work environment, such as polychlorinated biphenyls, molybdenum, asbestos, and radiation, in can lead to cancer risk and affect the health status of the population in industrial areas [39,40,41,42]. Therefore, the control and prevention of occupational diseases have become important issues in occupational health and safety management.
High-risk industries typically include enterprises such as coal mines, oil, construction, transportation, fireworks, and electricity. The construction industry is one of the world鈥檚 most dangerous industries, with a greater accident rate than most other industries due to the high risk of accidental death associated with the work in this industry. Therefore, policies to improve the occupational health of construction workers, including accident prevention, need to be implemented [43, 44]. According to statistics from relevant departments in Mexico, the accident rate of smelters and foundries is second only to that of the mining industry, and this rate is almost twice that of all factories [45]. Workers in small manufacturing enterprises are a high-risk group for occupational injuries, and measures such as providing job safety training for young and inexperienced workers can help eliminate or reduce such injuries [46, 47]. Road safety knowledge and risk behaviors impact road traffic injuries, necessitating more injury prevention plans to reduce risky behaviors [48]. Additionally, OHS management in urban waste enterprises and pesticide safety education for farm workers are risks that cannot be overlooked [49,50,51]. Furthermore, ensuring dietary safety is essential for public health, and its risk factors can be categorized as intrinsic, mitigating, and compliance factors [52]. Therefore, more in-depth research is needed on risk factor control methods for occupational health and safety and fatal accidents in high-risk industries and enterprises.
From the perspective of risk factor prevention and control methods, various methods are used for OHS risk prevention and control, such as Structural Equation Modeling (SEM) to explore causal relationships [53, 54] and the CHAID decision tree model for in-depth analysis of key factors and underlying subfactors [55]. Researchers have also explored the influencing mechanisms, relationships, and degrees of influence among various factors and subfactors [56,57,58]. Additionally, NetLogo-based system dynamics modeling can be used to examine the interaction relationships between individual factors and display dynamic processes [59,60,61]. Moreover, statistical analyses of occupational health and safety risk prevention in different countries around the world, such as Sweden and Spain, indicate that national laws have an important effect on reducing safety accidents [62]. Investigations into the implementation of the EU鈥檚 OHS regulations 89/391/EC indicate that collaboration between the government and enterprises is an effective means of risk prevention and control [63]. Empirical analysis of specific high-risk industry cases demonstrates that reasonable design documents and analysis methods are viable risk prevention approaches [64]. This study used a combination of multiple regression and network analysis to analyze the influencing factors.
Based on the above risk prevention and control methods, this study suggests strengthening the analysis and control of the following aspects. First, health care inequalities among workers at different levels and positions need to be reduced, and safety concepts in various high-risk occupations need to be emphasized [65, 66]. A comprehensive OHS hazard intervention approach should be tailored to the socioeconomic development status of rural and urban areas [67, 68]. Second, relevant OHS courses and training that fully consider ethical, legal, and social issues related to occupational health and safety should be developed [69,70,71]. National management and community organizations should be combined to identify serious injury risks, the use of personal protective equipment (PPE) should be strengthened, and prevention efforts should be guided [72,73,74]. Finally, artificial intelligence-driven technologies combined with occupational ergonomics can be leveraged to increase the health and well-being of workers [9, 75].
In summary, this study constructs an economic relationship analysis model between OHS and fatal accidents in high-risk industries from the perspective of public health intervention and policy. It explores the causal relationships and degree of influence between various factors, providing guidance information for best practices or comparative research in international public health [76]. The greatest difference between this study and previous studies is the combination of a single multiple regression model and network analysis methods and the more intuitive presentation of the impact relationships between factors.
Methods
Data source and method
The aims of this study are to explore the current status of public health and OHS in China, analyze the underlying reasons, and provide policy guidance for public health departments. Rapid economic growth in China from 2001 to 2022 has led to an increasing emphasis on public health and OHS, accompanied by various intervention measures and prevention strategies. These policy changes and economic transformations make this an appropriate time frame for the study. This study searched and organized the 22-year data of ten indicators officially released by the National Bureau of Statistics of China () and relevant industry ministries () from 2001 to 2022 (see Supplementary material). These data are sourced from all the high-risk industries in China over the past 22 years, and the data sample is representative and complete, ensuring reliability.
The stepwise multiple regression method can not only construct corresponding models for historical data but also be used to predict future trends, which plays an important role in guiding future policy interventions in the public health sector. The greatest advantage of network analysis methods is the intuitive visualization of complex causal relationships, which can also dynamically display the occupational health and safety risks, fatal accidents, and economic development status of high-risk industries in the future. The combination of multiple regression and network methods presents the causal relationships and weights between various related factors in a visually intuitive manner as a whole. The key control variables and other variables directly related to them can be found in the diagram, which is more convenient for providing recommendations for intervention policies. Therefore, this study combines two methods to make breakthrough contributions to the selection of key intervention indicators and methods in the field of public health based on previous research. However, this method also has certain potential limitations, such as sensitivity to outliers in regression or complexity in interpreting network analysis.
Variable design
The variable table design is shown in Table听1. The four columns are Classification, No., Name, Code, and Description. This variable table was used for subsequent analysis. Notably, the variable values in the variable table are annual totals. Because the annual total OP comprises nearly 90% of NOP, this table focuses on OP and does not include other diseases. Because the PDCI directly affects the investment in occupational health and safety protection for each resident, it is an important economic indicator related to occupational health and safety.
Results
Statistical analysis
To identify potential biases or limitations in the data, descriptive statistics and correlation analyses were conducted on the official occupational health statistics to increase the reliability of the data and report regional differences. The supplementary data table reflects the summary data of various regions in the country for each year, and regional differences can be ignored; that is, regional differences do not affect the statistical analysis results of the annual summary data. Although data from various departments predating 2001 were lacking, the public data of each department from 2001 to 2022 are complete. The descriptive statistics in Table听2 show that the range of statistical values for each variable in 2022, such as the NA statistical value range, is [26257, 1070000]. The mean NA is 397555.86, the std. error is 72260.402, and the std. deviation is 338931.331. The high standard deviation of the NA indicates that the number of accidents has changed over the years, which increases the uncertainty of occupational health and safety interventions. In addition, the high standard deviation of GDP also indicates that the Chinese economy has developed rapidly over the past 22 years but has not effectively reduced the number of accidents and fatalities. This discrepancy has placed enormous pressure on occupational health, safety and hygiene management. The descriptive values of the other variables are analyzed in sequence.
For the convenience of the subsequent correlation analysis, the correlation table is shown in Table听3. NA is significantly correlated with all other variables (p鈥>鈥0.05), and DT is also significantly correlated with other variables (excluding NOP), indicating that the number of accidents and deaths in high-risk industries are not only related but also significant to other factors, which poses challenges for the formulation of occupational health and safety and occupational health intervention policies. The correlations among the other variables are analyzed in sequence.
Stepwise multiple regression analysis
Starting from the reasons for using stepwise multiple regression in the above method section, the following section analyzes the multiple regression results of ten variables in this dataset from three aspects. First, the summary index values R, R Square, and Durbin Watson of all regression models were measured to assess their effectiveness and reliability. Subsequently, the significance and multicollinearity of all the regression models were analyzed. Finally, the statistical standard errors of all regression models were analyzed to determine the effectiveness of the constructed regression model.
The stepwise multiple regression method was then used to construct models for the ten variables mentioned above. The summary table of the models is shown in Table听4, where Model NA (DT, LF, OP, and PCDI) was constructed using NA as the dependent variable and DT, LF, OP, and PCDI as the independent variables. Other models were constructed in sequence. Table听4 shows that the R square values of all the models exceed 0.9 (with an R square of DBC鈥>鈥0.7, indicating that the fit is slightly lower than that of the other variables but still indicating good fit), the sig. F value is less than 0.05, and the Durbin鈥揥atson value is between 0.7 and 2. Typically, the Durbin鈥揥atson value is between 0 and 4. A value close to the middle of 2 indicates that no correlation in the model error term. A value close to the lower limit of 0indicates a positive correlation, whereas a value close to 4 indicates a negative correlation. All the models are statistically significant, with R square values above 0.9, indicating strong explanatory power. Therefore, all the constructed models are reliable and significantly effective.
The coefficients of all the constructed multiple regression models are shown in Table听5, and the sig. values of the nonsignificant variables in all the models are less than 0.05, which indicates a significant correlation. The VIF values of all the models are less than 10 [3]. The tolerance values are all greater than 0.1; therefore, the constructed model is significant, and the risk of multicollinearity is very small. The standardized regression coefficients in Table听5 were used to construct the regression equation, indicating that the presentation of policy significance is closely related to the standardized regression coefficients.
Table听6 shows that the statistical standard deviation of all the models is between 0.92 and 0.98, indicating that the models are stable and can be used for subsequent predictive analysis.
Network relationship diagram analysis
Starting from the reasons for selecting network analysis methods in the above methods section, the following four types of network analysis graphs were constructed for 10 factors. The first type is the graph of the maximum output and input degree results of analyzing the 10 factors. The second type is the resulting graph of analyzing 10 factors with output and input degrees one level lower than the first type of factor. The third type is the maximum input degree result chart for analyzing 10 factors. The fourth category is the maximum output degree result chart for analyzing 10 factors. By combining the input and output degrees of these network analysis graphs, 10 factors were classified, compared, and analyzed to identify the key factors that affect the public health intervention measures required for high-risk industries.
To explore the causal relationships between variables in the regression model mentioned above, a network relationship diagram was constructed via Cytoscape 3.9 software (Figs.听1, 2, 3 and 4). The construction method uses the dependent variable NA in Model NA in Table听5 as the endpoint node and the other two independent variables, DCM and OP, as the source nodes to construct trimming lines. The standardized coefficients corresponding to the independent variables were used as trimming weights. Network diagrams were constructed for other groups, such as Model DBC and DTI, based on the same principle.
Figure听1 shows hierarchical influence relationships among all node factors at different levels. Moreover, 5 connection lines exist for nodes OP, DTI, and DBC, all of which are 5 degrees, and all other nodes have degrees less than 5. Therefore, these three nodes can be considered the most important nodes. Similarly, in Fig.听2, NA, DCM, and LF are all 4 degrees, and these three nodes are the second most important.
Figure听3 shows that DTI and LF are the nodes with a maximum input degree of 3, which indicates that DTI and LF are the dependent variables most affected by other independent variable nodes. Similarly, as shown in Fig.听4, node DBC (with a maximum output degree of 4) is the independent variable that has the greatest impact on other dependent variable nodes. Therefore, nodes DTI, LF, and DBC can be considered more critical than other nodes in the entire network.
In summary, the first type of nodes OP, DTI, and DBC are the most important nodes; the second type of nodes NA, DCM, and LF are the second most important nodes; and the other nodes are general nodes. Network analysis can be used to elucidate the interrelationships among countless factors that shape OHS, which is a novel and unique feature of this study.
Modeling and predictive analysis
This section uses two regression models to simulate and predict the number of safety accidents and deaths in high-risk industries and explore the intervention measures of public health departments in occupational health and safety, occupational diseases, and economic development in the future.
Based on the above analysis, Table听5 was first combined to construct a regression model for NA in the second type of node as follows (Eq.听1), and other models can be constructed and analyzed in the same manner:
Combining NA with Tables听5 and 6, the residual analysis in Figs.听5 and 6 above can test the hypothesis of the regression model, that is, whether the random error term is independent and identically distributed. The fitting values in Fig.听5 show that all the points are randomly scattered between two parallel lines with y-axis values of -1 and +鈥1, indicating that the random error term has homoscedasticity. Figure听5 also indicates that the random error term follows a normal distribution, as the normal Q鈥扱 graph (Fig.听6) can be approximated as a straight line.
Moreover, Table听4 shows that the R-square of NA is approximately 0.938; consequently, most of the model can be explained by independent variables. Therefore, the model (Eq.听(1)) can be used to predict the number of accidents that occur. Equation听(1) shows that every year in China, a production safety accident occurs for every 1.052 deaths in coal mines and a production safety accident occurs for every 0.153 occupational diseases. Conversely, every production safety accident that occurs nationwide every year leads to 1.052 deaths in coal mines and 0.153 occupational diseases.
Similarly, the model that incorporates DT, NA, and GDP was constructed as follows (Eq.听2):
Equation听(2) shows that a negative relationship between economic development (GDP) and the overall number of deaths from production safety accidents in the country. That is, the greater the GDP development is, the lower the number of deaths from safety accidents. Specifically, for every 0.461 trillion yuan increase in GDP, the number of deaths from safety accidents decreases by one person. Moreover, Eq.听2 shows that for every 0.557 safety accidents, one person will die. The quantitative relationship of the above equation is linear and tends to stabilize at higher GDP levels, which can be used to guide policy-making.
According to the above regression prediction model, the impact relationship between production safety accidents in high-risk industries and the number of deaths and occupational diseases, as well as the impact relationship between GDP development and the overall number of deaths in the country, undoubtedly provides direction for public health intervention measures in high-risk industries and can guide the formulation of public health policies.
Discussion
The main purpose of this study was to explore the relationships among fatal accidents, occupational diseases, and economic development in occupational health and safety (OHS) in high-risk industries. Previous scholars have studied the relationship between economic development and fatal accidents in specific industries, but none have taken a comprehensive nationwide approach. This study began with a nationwide investigation of all production safety accidents and then focused on analyzing the number of production safety accidents and deaths in multiple high-risk industries, as well as occupational diseases [3, 77]. These research results enhance the contribution of this manuscript to the field of public health in terms of research methods, model construction, and policy guidance compared with similar studies in other countries or regions [21, 78]. The method of combining stepwise multiple regression and network analysis has made several breakthroughs [15, 16]. First, few studies have closely integrated and applied these two methods. Second, the model results are visually presented through graphical means, and the impact relationships and weight coefficients are also displayed together.
The above research results were used to construct models for ten research subjects. Table听4 combines multiple models, and each research subject was used to successfully construct a multiple regression model, indicating inherent connections between these factors. To further explore the causal relationships and importance levels among these factors, a network relationship analysis model was constructed via Cytoscape software to better reveal the causal relationships and importance levels. This analysis is another innovative achievement. The research results can be applied to the study of the relationship between death accidents and occupational diseases related to occupational health and safety in the context of economic development in high-risk industries and further demonstrate the inherent influence relationship between various factors.
In addition, the importance of the economic indicators GDP and PDCI selected in network relationships is not prominent, as GDP only has a quantitative relationship with overall safety production accidents. Furthermore, per capita, the disposable income of residents has no direct quantitative relationship with indicators other than GDP. These findings indicate that the GDP and PDCI indirectly affect the occurrence of production safety accidents and occupational diseases. This finding also reflects the potential impact of economic growth on the safety of high-risk industries. However, the relationships between the workforce and various factors are closely linked. The GDP and PDCI indicators were not directly correlated with other factors and should be related to the use of multiple regression methods because these two factors have multicollinearity with other factors, which is consistent with previous research results [3]. The relationship between GDP and security is not unique to China but is also consistent with the trends in other countries, especially developing countries.
Therefore, the influencing factors need to be classified and managed according to their importance. Specifically, the first type of factors, OP, DBC, and DTI, which include occupational diseases such as pneumoconiosis, coal mine deaths, and traffic accidents, should be a focus. The factors related to the coefficients of negative weights in the network analysis graphs should also be considered; some factors negatively correlate. This study provides corresponding guidance for the implementation of intervention policies. The results address two main gaps in previous research. One was the combination of a single multiple regression model with network analysis methods to more intuitively demonstrate the impact relationships between factors. Second, for the study shows that government management departments can conveniently adopt classified management methods to intervene in the formulation of policies related to factors such as accidents, deaths, and occupational diseases affecting occupational health and safety in high-risk industries.
This study is subject to two main limitations. First, the data were collected from official websites and association yearbooks, and the public disclosure period was insufficient, resulting in insufficient historical data for 22 years. However, the limitations of this dataset can be further addressed in the future, and existing datasets can meet the stability and reliability requirements of current research models. Second, in the stepwise multiple regression analysis, some indicators were found to have multicollinearity, and they were excluded consequently from the model. Excluding these indicators significantly increased the robustness of the conclusion. In the future, other models can be used to attempt more comprehensive quantitative analysis. For example, using decision trees or other multifactor analysis methods should be a viable option. The greatest advantage of decision trees is that they can further demonstrate the hierarchical relationships between related factors. Moreover, based on existing findings, this study can provide a useful framework for future research. This includes longitudinal surveys of occupational health and safety (OHS) practices and economic indicators over time, as well as comparative studies of different countries or regions to determine best practices. These results indicate that the potential of incorporating occupational health and safety policies into broader economic planning is that economic development cannot ignore intervention measures for occupational health and safety but should be implemented synchronously and should focus on safety.
Conclusion
This study analyzed 22 years of historical data on occupational health and safety (OHS) accidents, labor, and economic development in China鈥檚 high-risk industries via multiple regression and network analysis methods.
The findings reveal hierarchical influence relationships, with occupational pneumoconiosis, coal mine accidents, and traffic accidents being the most significant influencing factors. The death toll in coal mines is the primary source factor, whereas the death toll in the transportation industry and the national labor force are the greatest outcome factors.
The study also revealed a negative correlation between China鈥檚 GDP and the number of deaths from production safety accidents. For every 0.461 trillion CNY increase in GDP, the number of production safety accident deaths decreases by one. Conversely, every 0.557 production safety accidents lead to one death. Furthermore, each production safety accident can result in 1.052 coal mine deaths and 0.153 occupational diseases.
Therefore, the constructed series of models provides practical guidance for the study of the economic relationship between occupational health and safety and fatal accidents in high-risk industries through intervention measures. First, the departments responsible for formulating public health policies and occupational health and safety regulations need to focus on the impact of high-risk factors, such as occupational pneumoconiosis, coal mine accidents, and traffic accidents, in the design of intervention measures for high-risk industries. Second, while promoting economic development, prevention and intervention measures need to be strengthened to reduce the harm caused by fatal accidents and occupational diseases. In addition, efficient hazard prevention and control models and methods should be appropriately adopted to improve the efficiency of public health and occupational health and safety prevention and control.
Data availability
All data, models, and code generated or used during the study appear in the submitted article.
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Acknowledgements
The authors would like to acknowledge all the colleagues who provided the questionnaire survey for this research, especially Dr. Yuqing Cao from the University of Hong Kong provides language support.
Funding
This study was funded by the 2023 Hunan Social Science Achievement Evaluation Committee Project (XSP2023GLC113), the Research Achievements in the Stage of Education Science Planning in Hunan Province, Research on the Path of Occupational Health and Safety Education for College Students under the Overall National Security Concept (XJK23BGD042), and the University level project of Hunan University of Science and Engineering (2022028), and the Key Project of Hubei Provincial Education Science Plan for 2023(2023GA084), and the 2024 Hunan Province Undergraduate Teaching Reform Research Project (202401001394), and the 2024 Hunan Provincial Department of Education Scientific Research Project (24A0596).
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Conceptualization, methodology, writing of original draft preparation, supervision, and funding acquisition: Z.H. Cao and S.Y. Miao; formal analysis, data curation, visualization, and project administration: Z.H. Cao and T. Zhou; validation: Z.H. Cao and Z.Z. Wang; software: Z.H. Cao and L.F. Wang; writing of review and editing: Z.H. Cao and T. Zhou.
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The studies involving participants were reviewed and approved by the Research Ethics Committee of Hunan University of Science and Engineering. The studies were conducted in accordance with the Declaration of Helsinki.
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Not applicable. This study uses retrospective data and does not require participant consent.
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Cao, Z., Zhou, T., Miao, S. et al. Exploring the economic occupational health, safety, and fatal accidents in high-risk industries. 樱花视频 25, 433 (2025). https://doi.org/10.1186/s12889-025-21583-0
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DOI: https://doi.org/10.1186/s12889-025-21583-0