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COVID-10 GPH

Last data update: Jan 21, 2025. (Total: 48615 publications since 2009)
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Query Trace: Raudabaugh JA[original query]
Applying Machine Learning to Workers' Compensation Data to Identify Industry-Specific Ergonomic and Safety Prevention Priorities: Ohio, 2001 to 2011.
Meyers AR , Al-Tarawneh IS , Wurzelbacher SJ , Bushnell PT , Lampl MP , Bell JL , Bertke SJ , Robins DC , Tseng CY , Wei C , Raudabaugh JA , Schnorr TM .
J Occup Environ Med 2017 60 (1) 55-73 type type

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.
Development of methods for using workers' compensation data for surveillance and prevention of occupational injuries among state-insured private employers in Ohio
Wurzelbacher SJ , Al-Tarawneh IS , Meyers AR , Bushnell PT , Lampl MP , Robins DC , Tseng CY , Wei C , Bertke SJ , Raudabaugh JA , Haviland TM , Schnorr TM .
Am J Ind Med 2016 59 (12) 1087-1104

BACKGROUND: Workers' compensation (WC) claims data may be useful for identifying high-risk industries and developing prevention strategies. METHODS: WC claims data from private-industry employers insured by the Ohio state-based workers' compensation carrier from 2001 to 2011 were linked with the state's unemployment insurance (UI) data on the employer's industry and number of employees. National Labor Productivity and Costs survey data were used to adjust UI data and estimate full-time equivalents (FTE). Rates of WC claims per 100 FTE were computed and Poisson regression was used to evaluate differences in rates. RESULTS: Most industries showed substantial claim count and rate reductions from 2001 to 2008, followed by a leveling or slight increase in claim count and rate from 2009 to 2011. Despite reductions, there were industry groups that had consistently higher rates. CONCLUSION: WC claims data linked to employment data could be used to prioritize industries for injury research and prevention activities among State-insured private employers.
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