Use of Logistic Regression to Identify Factors Influencing the Post-Incident State of Occupational Injuries in Agribusiness Operations

Agribusiness industries are among the most hazardous workplaces for non-fatal occupational injuries. The term “post-incident state” is used to describe the health status of an injured person when a non-fatal occupational injury has occurred, in the post-incident period when the worker returns to work, either immediately with zero days away from work (medical state) or after a disability period (disability state). An analysis of nearly 14,000 occupational incidents in agribusiness operations allowed for the classification of the post-incident state as medical or disability (77% and 23% of the cases, respectively). Due to substantial impacts of occupational incidents on labor-market outcomes, identifying factors that influence the severity of such incidents plays a significant role in improving workplace safety, protecting workers, and reducing costs of the post-incident state of an injury. In addition, the average costs of a disability state are significantly higher than those of a medical state. Therefore, this study aimed to identify the contributory factors to such post-incident states with logistic regression using information from workers’ compensation claims recorded between 2008 and 2016 in the Midwest region of the United States. The logistic regression equation was derived to calculate the odds of disability post-incident state. Results indicated that factors influencing the post-incident state included the injured body parts, injury nature, and worker’s age, experience, and occupation, as well as the industry, and were statistically significant predictors of post-incident states. Specific incidents predicting disability outcomes included being caught in/between/under, fall/slip/trip injury, and strain/injury by. The methodology and estimation results provide insightful understanding of the factors influencing medical/disability injuries, in addition to beneficial references for developing effective countermeasures for prevention of occupational incidents