Applying Machine Learning to Workers’ Compensation Data to Identify Industry-Specific Ergonomic and Safety Prevention Priorities: Ohio, 2001 to 2011

Details

• Personal Author:
  Al-Tarawneh IS ; Bell JL ; Bertke SJ ; Bushnell PT ; Lampl MP ; Meyers, Alysha R. ; Raudabaugh JA ; Robins DC ; Schnorr, Teresa M. ; Tseng C-Y ; Wei C ; Wurzelbacher, Steven J.
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Ergonomics Musculoskeletal Diseases Occupational Health Risk Assessment Safety
• Keywords:
  Artificial Intelligence Future Of Work Lost Work Days Machine Learning MSD Musculoskeletal Disorders Occupations Risk Analysis Surveillance Workers Compensation
• ISSN:
  1076-2752
• Document Type:
  Text
• Genre:
  Journal Article
• Place as Subject:
  Ohio ; OSHA Region 3 ; OSHA Region 5 ; West Virginia
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  DSHEFS - Division of Surveillance, Hazard Evaluations, and Field Studies ; OD - Office of the Director ; DSR - Division of Safety Research
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  55-73
• Volume:
  60
• Issue:
  1
• NIOSHTIC Number:
  nn:20050490
• Citation:
  J Occup Environ Med 2018 Jan; 60(1):55-73
• Contact Point Address:
  Alysha R. Meyers, PhD, National Institute for Occupational Safety and Health, Division of Surveillance, Hazard Evaluations, and Field Studies, Center for Workers' Compensation Studies, 1090 Tusculum Ave., MS R-15, Cincinnati, OH 45226
• Email:
  armeyers@CDC.gov
• Federal Fiscal Year:
  2018
• NORA Priority Area:
  Wholesale and Retail Trade
• Peer Reviewed:
  True
• Source Full Name:
  Journal of Occupational and Environmental Medicine
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:392f82aa8654fe9d8ca3b8fe5d3344ecb60aef425a89b82c08db3c802e2a3b7712a254e7ff892e50280449135b913d8f25a1872771ae76d89cd1d43b2fad51cc
• Download URL:
  https://stacks.cdc.gov/view/cdc/210809/cdc_210809_DS1.pdf
• File Type:
  [PDF - 698.91 KB ]