Modeling Causal Factors of Occupational Accidents in Chemical Industries: A 10-Year Field Study in Iran

Abstract

Analyzing and modeling causal factors of occupational accidents play an important role in planning prevention programs. The study aimed to explore the causal factors of occupational accidents in chemical industries in Iran. The reports of 1322 accidents from 2007 to 2016 were gathered from 22 chemical industries. First, the Accident Frequency Rate (AFR) was calculated and all the effective factors were reviewed and labeled as “dependent” and “independent” factors. Second, feature selection was conducted to find the important causal factors. Then, multiple linear regression analysis was employed to model the most frequent causal factors in accident occurrence. The average accident frequency rate (AFR) was 87.75±74.82. A total of 30 independent casual factors were identified to be eligible for further analysis. Results of multiple linear regression analysis showed that 21 out of 30 important casual factors had the most important role in occupational accidents which are categorized into personal factors, organizational factors, HSE training, risk management, unsafe acts, and conditions as well as the type of accident occurrence. This study revealed that any preventive program should consider the importance of each casual factor in occurring occupational accidents. The higher level of importance needs the higher level of attention.

Keywords

• chemical industry
• occupational accident modeling
• accident frequency rate (afr)
• causal factor
• feature selection
• multiple linear regression