Influencing factors of occupational injury in construction workers of European Union based on Boruta algorithm and logistic regression
- VernacularTitle:基于Boruta算法和logistic回归模型的欧盟建筑业员工职业伤害影响因素分析
- Author:
Zhian LI
1
;
Lin ZHANG
1
;
Peng ZHANG
2
;
Xiaojun ZHU
2
,
3
Author Information
- Publication Type:Specialcolumn:Occupationalinjuriesandtheirinfluencingfactors
- Keywords: construction worker; occupational injury; influencing factor; Boruta algorithm; logistic regression
- From: Journal of Environmental and Occupational Medicine 2025;42(2):151-156
- CountryChina
- Language:Chinese
-
Abstract:
Background Construction workers represent a high risk group for occupational injuries. Currently, domestic and international studies examining the factors affecting occupational injuries among construction workers focus on demographic and behavioural characteristics. However, there is limited attention to psychosocial, use of digital technology, and health status of workers. Objective To analyze the occurrence of occupational injuries among workers in the construction industry, explore impacts of psychosocial risk, use of digital technology, health status, and preventive measures at the workplace on occupational injuries, and provide a basis for the development of preventive measures. Methods Publicly available data from the European Union Occupational Safety and Health Administration were retrieved, comprising a sample of
2167 construction workers. The outcome indicator was the presence of occupational injuries among workers. A total of 25 variables in the dimensions of psychosocial risk, use of digital technology, health status, and preventive measures at the workplace were extracted after Chi-square test, and then a combination of Boruta's algorithm and logistic regression was applied to identify the key factors affecting occupational injuries. Results Among the2167 construction workers surveyed, 182 (8.6%) reported experiencing occupational injuries. The Boruta algorithm identified eight characteristics which in descending order of importance were musculoskeletal disorders, job type, depression and anxiety, level of completed education, use of electronic smart products, timely solution of safety problems, overall fatigue, and age. The logistic regression results indicated that six variables had statistically significant effects on occupational injuries: age, job type, level of completed education, musculoskeletal disorders, overall fatigue, and timely solution of safety problems (P<0.05). Conclusion Occupational injuries in construction workers are influenced by a variety of factors, including age, job type, level of completed education, musculoskeletal disorders, overall fatigue, and timely solution of safety problems. Companies and workers should take targeted measures to reduce the incidence of occupational injuries.
