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Segmentation of Severe Occupational Incidents in Agribusiness Industries Using Latent Class Clustering
Applied Sciences (2019)
  • Fatemeh Davoudi Kakhki, San Jose State University
  • Steven A. Freeman, Iowa State University
  • Gretchen A. Mosher, Iowa State University
One of the principle objectives in occupational safety analysis is to identify the key factors that affect the severity of an incident. To identify risk groups of occupational incidents and the factors associated with them, statistical analysis of workers’ compensation claims data is performed using latent class clustering, for the segmentation of 1031 severe occupational incidents in agribusiness industries in the Midwest region of the United States between 2008–2016. In this study, severe incidents are those with workers’ compensation costs equal to or greater than $100,000 (USD). Based on the latent class clustering results, three risk groups are identified with injury nature as the most statistically distinctive classifier. The highest cost injuries include strain, tear, fracture, contusion, amputation, laceration, burn, concussion, and crushing. The most prevalent and statistically significant injury type is permanent partial disability. The study introduces a novel application of latent class clustering in the segmentation of high severity occupational incidents. The analytical approach and results of this study will aid safety practitioners in identifying occupational risk groups and analyzing injury patterns, and inform safety intervention plans to avoid the occurrence of similar incidents in agribusiness industries.
  • latent class analysis,
  • occupational injuries,
  • safety management
Publication Date
September 4, 2019
Publisher Statement
This article originally appeared in Applied Sciences, volume 9, issue 18, 2019, published by MDPI. Authors retain copyright. The article can also be found online at this link.
Citation Information
Fatemeh Davoudi Kakhki, Steven A. Freeman and Gretchen A. Mosher. "Segmentation of Severe Occupational Incidents in Agribusiness Industries Using Latent Class Clustering" Applied Sciences Vol. 9 Iss. 18 (2019) p. 3641
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Creative Commons License
This work is licensed under a Creative Commons CC_BY International License.