Using workers’ compensation claims data to characterize occupational injuries in the biofuels industry

Abstract

Biofuels production is a fast growing and emerging industry. Occupational injuries are a serious problem due to their human, financial and social costs, yet little research has been published on injuries in the biofuels industry. Learning from past injuries are essential for preventing future occurrences, but the lack of injury information hinders this effort in the biofuels industry. The present study addresses this knowledge gap by utilizing data from over 900 workers' compensation claims reported from 2008 to 2016 by ethanol and biodiesel facilities in the U.S. to characterize injury costs and severity. The total amount paid for each claim was used as a measure of injury severity, and the effects of age, tenure, type of claim, body part injured, nature and, cause of injury on the cost of work-related injuries were investigated. Contingency tables were used to classify the variable pairs, chi-square test and chi-square residuals were employed to evaluate the relation between the variable pairs and identify the at-risk groups. Results showed age, tenure of employee, type of claim, body part injured, nature of injury and, cause of injury have a significant influence in determining the claim amount. Age group 46-50, tenure group 1-2 years, strain and fractures injuries, slips, falls, or trips and, injuries to lower extremities were some of the at-risk groups identified. The findings from the study will assist biofuel producers to develop precisely targeted safety interventions that are effective in preventing worker injuries and also mitigating the financial and social losses from occupational injuries.

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

“…The analysis showed that higher age of workers affects the probability of disability (corresponding with the positive coefficient β = 0.02 from the model). The trend in this study agreed with previous literature on the effect of older ages on higher frequency and severity level of incidents, specifically when the occupation includes manufacturing operations, due to the effect of age on the physical activity and attention of the workers [1,[36][37][38][39]. Although the experience showed no specific upward or downward trend, workers with fewer years of experience were prone to higher frequencies (yet, with a lower probability β = −0.02) of a disability post-incident state.…”

“…Analysis of the incidents helps occupational risk managers identify which hazards have contributed and led to the most frequent occupational accidents, and thus determine appropriate preventative actions [43]. Understanding the relationships between individual predictors of occupational incidents contributes to the development of focused mitigation strategies to prevent workers' injuries [39]. Analyzing empirical data to extract risk indicators adds predictivity to risk scenarios and helps to efficiently plan and modify loss approaches in the agribusiness industries [44].…”

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

“…Although the experience and age of the worker are not the most significant contributors to the probability of a specific safety risk from the RBF model, they both show a high impact on the sensitivity analysis of the MLP model. This also agrees with the previous literature about the importance of workers' age and their years on the job in the occurrence of a specific incident [68][69][70].…”

Examination the Influence of Human Factors in Indian Building Construction Sites

“…To find the significant relationship between the categorical values, chi squaregoodness of fit test is used. In order to know the relationship between the hypothesis previous researchers adopted chi square test to determine the significance between the company and the contractor workers with respect to safety in firework industries [25] fatal and nonfatal accidents in construction industries [26], injured worker & claimable fund in the biofuels industry [27]. In this research chi square test as shown in Table III is used to analyze the relationship between the human factors with respect to non-fatal injuries.…”