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Workers' compensation injury claims among firefighters in Ohio, 2001-2017
Tyler D Quinn1, Suzanne M Marsh2, Kierstyn Oldham3
1Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, National Personal Protective Technology Laboratory, Pittsburgh, PA, United States; West Virginia University, Department of Biostatistics and Epidemiology, Morgantown, WV, United States.
Background:
Firefighters are at high risk for nonfatal and fatal occupational injuries. While some past research has quantified firefighter injuries using various data sources, Ohio workers' compensation injury claims data largely have not been used.
Methods:
Public and private firefighter claims, including volunteer and career firefighters, from Ohio's workers' compensation data for 2001-2017 were identified based on occupational classification codes and manual review of the occupation title and injury description. The task during injury (firefighting, patient care, training, other/unknown, etc.) was manually coded based on the injury description. Injury claim counts and proportions were described across claim type (medical-only or lost-time), worker demographics, task during injury, injury events, and principal diagnoses.
Results:
33,069 firefighter claims were identified and included. Most claims were medical-only (66.28%, <8 days away from work) and involved males (93.81%) aged 25-54 years (86.54%). While the task during injury could not be categorized for many narratives (45.96%), the largest percentage that could be categorized occurred during firefighting (20.48%) and patient care (17.60%). The most common injury events were overexertion involving outside sources (31.33%) and struck by objects or equipment (12.68%). The most frequent principal diagnoses were back, lower extremity, and upper extremity sprains (16.02%, 14.46%, and 11.98%, respectively).
Conclusions:
This study provides a preliminary basis for the development of focused firefighter injury prevention programming and training. Obtaining denominator data, enabling rate calculation, would strengthen the risk characterization. Based on the current data, prevention efforts focusing on the most frequent injury events and diagnoses may be warranted.
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