Agricultural and Biosystems Engineering Publications

Campus Units

Agricultural and Biosystems Engineering

Document Type

Article

Publication Version

Published Version

Publication Date

9-4-2019

Journal or Book Title

Applied Sciences

Volume

9

Issue

18

First Page

3641

Research Focus Area(s)

Occupational Safety Engineering

DOI

10.3390/app9183641

Abstract

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.

Comments

This article is published as Davoudi Kakhki, Fatemeh, Steven A Freeman, and Gretchen A Mosher. "Segmentation of Severe Occupational Incidents in Agribusiness Industries Using Latent Class Clustering." Applied Sciences 9, no. 18 (2019): 3641. DOI: 10.3390/app9183641. Posted with permission.

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Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Copyright Owner

The Author(s)

Language

en

File Format

application/pdf

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