@article{ref1,
title="Severity analysis for occupational heat-related injury using the multinomial logit model",
journal="Safety and health at work",
year="2024",
author="Lyu, Peiyi and Song, Siyuan",
volume="15",
number="2",
pages="200-207",
abstract="BACKGROUND: 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. <br><br>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. <br><br>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. <br><br>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.<p /> <p>Language: en</p>",
language="en",
issn="2093-7911",
doi="10.1016/j.shaw.2024.03.005",
url="http://dx.doi.org/10.1016/j.shaw.2024.03.005"
}