Predictive Model for Industrial Accident: Based on a Case Study

Falahati  M; Dehghani  M; Yasi  Y; Zokaei  M

doi:10.18502/tkj.v17i2.19118

Falahati M Associate Professor, Social Determinants of Health Research Center, Saveh University of Medical Sciences, Saveh, Iran.
Dehghani M MS student HSE, Energy Institute of Higher Education
Yasi Y Assistant Professor Energy Institute of Higher Education
Zokaei M Assistant Professor, Social Determinants of Health Research Center, Saveh University of Medical Sciences, Saveh, Iran

DOI: https://doi.org/10.18502/tkj.v17i2.19118

Keywords: Accident, Perdition Model, SEM

Abstract

Introduction: Each year, numerous workers worldwide lose their lives due to workplace accidents, leading not only to significant economic consequences for countries but also to social effects on the families involved. Consequently, identifying the influencing factors and predicting their occurrence of accidents can significantly reduce their frequency. This study aimed to provide a predictive model for workplace accidents.

Materials and Methods: This research gathered data on workplace accidents from industries that agreed to participate over the last three years. Among the recorded incidents, the research team concentrated on those classified as reportable events under OSHA guidelines. Accordingly, 1,734 accidents met the conditions for analysis. After further examination, several incidents were excluded from the study due to insufficient information and lack of appropriate analysis, leading to a final total of 1011 accidents included in the study. Structural Equation Modeling (SEM) was employed to predict and determine the impact of each variable influencing accident occurrences. .

Results: The result from the first hypothesis test (individual and demographic variables affect the types of accidents that occur) showed a significant negative impact of individual and demographic characteristics on the type of accidents, with a path coefficient of -0.720 and a t-value of -7.27. In testing the second hypothesis (demographic factors influence occupational factors), a path coefficient of 0.812 and a t-value of 35.37 indicated a strong and significant effect of demographic factors on occupational factors.

Conclusion: The findings of this research indicate that path analysis utilizing the SEM approach is effective for analyzing the severity of injuries resulting from workplace accidents. The results from SEM clearly show that demographic indicators, organizational factors, timing, and causes leading to accidents are indirectly related to the severity of occupational injuries, whereas the type of accidents has a direct correlation with occupational injury severity in various industries.