Trends in Catastrophic Occupational Incidents among Electrical Contractors, 2007-2013.

This study used methodologies of descriptive and quantitative statistics to identify the contributing factors most affecting occupational accident outcomes among electrical contracting enterprises, given an accident occurred. Accident reports were collected from the Occupational Safety and Health Administration's fatality and catastrophe database. To ensure the reliability of the data, the team manually codified more than 600 incidents through a comprehensive content analysis using injury-classification standards. Inclusive of both fatal and non-fatal injuries, the results showed that most accidents happened in , , and (i.e., $50,000 or less). The main source of injuries manifested in (46%), followed by (19%), and (16%). The most frequent types of injuries were (31%), (27%), and (14%); the main injured body parts were (25%), (23%), and (18%). Among non-fatal cases, (37%), (36%), and (19%) caused most injuries; among fatal cases, was the leading cause of death (50%), followed by (28%) and (19%). The analysis also investigated the impact of several accident factors on the degree of injuries and found significant effects from such factors such as , , , , , and . In other words, the statistical probability of a fatal accident-given an accident occurrence-changes significantly based on the degree of these factors. The results of this study, as depicted in the proposed decision tree model, revealed that the most important factor for predicting the nature of injury (electrical or non-electrical) is: whether the source of injury is ; followed by whether the source of injury is . In other words, in predicting (with a 94.31% accuracy) the nature of injury as electrical or non-electrical, whether the source of injury is and whether the source of injury is are very important. Seven decision rules were derived from the proposed decision tree model. Beyond these outcomes, the described methodology contributes to the accident-analysis body of knowledge by providing a framework for codifying data from accident reports to facilitate future analysis and modeling attempts to subsequently mitigate more injuries in other fields.