Severity Analysis for Occupational Heat-related Injury Using the
Multinomial Logit Model
Dublin Core
Title
Severity Analysis for Occupational Heat-related Injury Using the
Multinomial Logit Model
Multinomial Logit Model
Subject
Heat-related injuries
Impact factors
Multinomial logit model
Worker safet
Impact factors
Multinomial logit model
Worker safet
Description
Workers are often exposed to hazardous heat due to their work environment, leading to
various injuries. As a result of climate change, heat-related injuries (HRIs) are becoming more problematic. This study aims to identify critical contributing factors to the severity of occupational HRIs.
Methods: This study analyzed historical injury reports from the Occupational Safety and Health
Administration (OSHA). Contributing factors to the severity of HRIs were identified using text mining and
model-free machine learning methods. The Multinomial Logit Model (MNL) was applied to explore the
relationship between impact factors and the severity of HRIs.
Results: The results indicated a higher risk of fatal HRIs among middle-aged, older, and male workers,
particularly in the construction, service, manufacturing, and agriculture industries. In addition, a higher
heat index, collapses, heart attacks, and fall accidents increased the severity of HRIs, while symptoms
such as dehydration, dizziness, cramps, faintness, and vomiting reduced the likelihood of fatal HRIs.
Conclusions: The severity of HRIs was significantly influenced by factors like workers’ age, gender, industry type, heat index , symptoms, and secondary injuries. The findings underscore the need for tailored
preventive strategies and training across different worker groups to mitigate HRIs risks
various injuries. As a result of climate change, heat-related injuries (HRIs) are becoming more problematic. This study aims to identify critical contributing factors to the severity of occupational HRIs.
Methods: This study analyzed historical injury reports from the Occupational Safety and Health
Administration (OSHA). Contributing factors to the severity of HRIs were identified using text mining and
model-free machine learning methods. The Multinomial Logit Model (MNL) was applied to explore the
relationship between impact factors and the severity of HRIs.
Results: The results indicated a higher risk of fatal HRIs among middle-aged, older, and male workers,
particularly in the construction, service, manufacturing, and agriculture industries. In addition, a higher
heat index, collapses, heart attacks, and fall accidents increased the severity of HRIs, while symptoms
such as dehydration, dizziness, cramps, faintness, and vomiting reduced the likelihood of fatal HRIs.
Conclusions: The severity of HRIs was significantly influenced by factors like workers’ age, gender, industry type, heat index , symptoms, and secondary injuries. The findings underscore the need for tailored
preventive strategies and training across different worker groups to mitigate HRIs risks
Creator
Peiyi Lyu, Siyuan Song
Source
https://pdf.sciencedirectassets.com/287282/1-s2.0-S2093791124X00037/1-s2.0-S2093791124000234/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjED4aCXVzLWVhc3QtMSJHMEUCIQDlH6OX33tCkGtyRzOSMlfYQJXDj7x4ckSlzkYwKbwefAIgA%2BroGj5KxvUxpm1fTWYi18kFA0Rhet6A2NrVthKvbG0qswUIBxAFGgwwNTkwMDM1NDY4NjUiDJDi4lVLTFCPHNiDPSqQBdbMeAJN%2BgexpxtsuXKCPhCJez%2BhfTrfj0VU1IyTrxU%2BoFg%2B%2BtwA2G3OEUzzeiOT4eifFk43MjWJJh0v%2BqMZ%2BI7NBS3Xa7SrFnjgKMtdgKCxtsDOdEVgNnMAlX11qPA4rzCpvUsnSWIR2VUhOfYxrFn%2BXYsyAbAmbkIrWaNBi26qnChbVvi1fuKzg8wdQ787blWQpxbgPOtesfgkf4jAYkUgNXpH6L5Q6mrxhYh%2FdJC1CNyOJpt1rLh9UMqRfMlQ5TnY%2FXWsuj9wSoo4tb6pAmDcCkUy%2BiEQo2Fu0DYROVPMW%2B4b7Dsxw%2F70e0%2FDHubyDAKQTvRnoHwT9L%2BvXPNGyAY1%2B8kfdWljK0K%2FcoC8FZNBpHYZZqK1Qhnm%2BM6B3lfx7PDVjdeXxBEzzoMN6TeXcoNqL98dSLeySMa1Epi%2BkXEjF6kGj%2Br5xOOfiepDmW0LRhZsPXs6OEphiwIVn77pIZEGl%2Bj77LWDjNfd6WJNIm6j%2B%2BH8cplML2Ef0u03jQ3fPgDi1n%2FQvVi4ju1HDPxJ9MnwDYdDUfK0ROQPEBG3xk2WhACjH%2B2YvQvQZUoMK%2Bhn1aJQTV8VwRKL2giZEz%2B6j6OLmTibiiwSgu%2FyGa%2BVJzMlfi2RL7FdVPArjqFr437%2B1CPD%2Fghkpb1tA2hZqpJ%2FrPuBLTQll%2BTK6Y05IbAyZyO7ITjVZpV5equmEnA3bpPaQ9He0%2BeCcAxsnMIruWOA%2Fbq%2Bk16JQBXXtNQiNqYCy%2FOTSBso3g9d6FvteKMhYvAX0lgIselnXrRYY1g5uH%2BS8OiCUX71l5%2BuCtzSoK%2BTwdj43LbP9f3wVnGwgZTH7UsqnIRKpHJiH2YsPhxB7jnYSx9QqRXo8aKKWr8%2FK5H9yd1kMNmX%2BswGOrEBhKY%2BZDdquH1EFLEm%2Bmz5mx3%2BuyWHcPk8dYpPAR%2B9UHyE%2Fk%2Bgu2feAbjV21WJjr6iJk6GXYfkcMmp%2BCogJWC1xVS9O1T1Bh3LenpXeNVdq3S59mCfxWw%2BwDiUwwQqf3JljmlkQy9JTjoKsJuZnGQs0%2Flbm%2FSfX5RQHKwfaDfyA9rStmencC0ehi7cO4O3C2jy1l%2F%2FCiLc9sB9oqpcxrwcygWihRrbyOgR3aJEoRUPeLZN&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20260225T071719Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYYCYX72A2%2F20260225%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=b41bbed500827778bbcbedaffe235dec24d659b88a450138697ca0b8053b3269&hash=05c15ef1259a11435703e33718249ac0a4c8ace2d77047b6f3f79d52d3f72583&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S2093791124000234&tid=spdf-9cd3be57-5523-474f-b800-037402f07706&sid=323f66de8e4980408c0be7b-7fe7e7fe55f2gxrqb&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&rh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=0b015e065457570451&rr=9d356c1b38c1008f&cc=id
Publisher
Safety Automation and Visualization Environment (SAVE) Laboratory, Department of Civil, Construction, and Environmental Engineering, University of
Alabama, Tuscaloosa, USA
Alabama, Tuscaloosa, USA
Date
12 April 2024
Contributor
FAJAR BAGUS W
Format
PDF
Language
ENGLISH
Type
TEXT
Files
Citation
Peiyi Lyu, Siyuan Song, “Severity Analysis for Occupational Heat-related Injury Using the
Multinomial Logit Model,” Repository Horizon University Indonesia, accessed April 11, 2026, https://repository.horizon.ac.id/items/show/11711.
Multinomial Logit Model,” Repository Horizon University Indonesia, accessed April 11, 2026, https://repository.horizon.ac.id/items/show/11711.