Alysha R Meyers1, Ibraheem S Al-Tarawneh, Steven J Wurzelbacher, P Timothy Bushnell, Michael P Lampl, Jennifer L Bell, Stephen J Bertke, David C Robins, Chih-Yu Tseng, Chia Wei, Jill A Raudabaugh, Teresa M Schnorr. 1. National Institute for Occupational Safety and Health, Division of Surveillance, Hazard Evaluations, and Field Studies, Center for Workers' Compensation Studies, Cincinnati, Ohio (Drs Meyers, Wurzelbacher, Ms Tseng); Ohio Bureau of Workers' Compensation, Division of Safety and Hygiene, Pickerington, Ohio (Dr Al-Tarawneh, Mr Lampl, Mr Robins); National Institute for Occupational Safety and Health, Office of the Director, Economic Research Support Office, Cincinnati, Ohio (Dr Bushnell); National Institute for Occupational Safety and Health, Division of Safety Research, Morgantown, West Virginia (Dr Bell); National Institute for Occupational Safety and Health, Division of Surveillance, Hazard Evaluations, and Field Studies, Cincinnati, Ohio (Dr Bertke, Ms Raudabaugh, Dr Schnorr); Taiwan Centers for Disease Control, Taipei City, Taiwan (Dr Wei).
Abstract
OBJECTIVE: This study leveraged a state workers' compensation claims database and machine learning techniques to target prevention efforts by injury causation and industry. METHODS: Injury causation auto-coding methods were developed to code more than 1.2 million Ohio Bureau of Workers' Compensation claims for this study. Industry groups were ranked for soft-tissue musculoskeletal claims that may have been preventable with biomechanical ergonomic (ERGO) or slip/trip/fall (STF) interventions. RESULTS: On the basis of the average of claim count and rate ranks for more than 200 industry groups, Skilled Nursing Facilities (ERGO) and General Freight Trucking (STF) were the highest risk for lost-time claims (>7 days). CONCLUSION: This study created a third, major causation-specific U.S. occupational injury surveillance system. These findings are being used to focus prevention resources on specific occupational injury types in specific industry groups, especially in Ohio. Other state bureaus or insurers may use similar methods.
OBJECTIVE: This study leveraged a state workers' compensation claims database and machine learning techniques to target prevention efforts by injury causation and industry. METHODS: Injury causation auto-coding methods were developed to code more than 1.2 million Ohio Bureau of Workers' Compensation claims for this study. Industry groups were ranked for soft-tissue musculoskeletal claims that may have been preventable with biomechanical ergonomic (ERGO) or slip/trip/fall (STF) interventions. RESULTS: On the basis of the average of claim count and rate ranks for more than 200 industry groups, Skilled Nursing Facilities (ERGO) and General Freight Trucking (STF) were the highest risk for lost-time claims (>7 days). CONCLUSION: This study created a third, major causation-specific U.S. occupational injury surveillance system. These findings are being used to focus prevention resources on specific occupational injury types in specific industry groups, especially in Ohio. Other state bureaus or insurers may use similar methods.
Authors: Steven J Wurzelbacher; Ibraheem S Al-Tarawneh; Alysha R Meyers; P Timothy Bushnell; Michael P Lampl; David C Robins; Chih-Yu Tseng; Chia Wei; Stephen J Bertke; Jill A Raudabaugh; Thomas M Haviland; Teresa M Schnorr Journal: Am J Ind Med Date: 2016-09-26 Impact factor: 2.214
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Authors: Steven J Wurzelbacher; Alysha R Meyers; Michael P Lampl; P Timothy Bushnell; Stephen J Bertke; David C Robins; Chih-Yu Tseng; Steven J Naber Journal: J Safety Res Date: 2021-09-17