Institutional members access full text with Ovid®

Share this article on:

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

Meyers Alysha R. PhD; Al-Tarawneh, Ibraheem S. PhD; Wurzelbacher, Steven J. PhD; Bushnell, P. Timothy PhD, MPA; Lampl, Michael P. MS; Bell, Jennifer L. PhD, MS; Bertke, Stephen J. PhD; Robins, David C. AAS; Tseng, Chih-Yu MS; Wei, Chia PhD; Raudabaugh, Jill A. MPH; Schnorr, Teresa M. PhD
Journal of Occupational and Environmental Medicine: Post Author Corrections: September 25, 2017
doi: 10.1097/JOM.0000000000001162
Original Article: PDF Only

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.

Address correspondence to: 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 (armeyers@CDC.gov).

SJW, IST, ARM, PTB, MPL, JLB, and TMS made substantial contributions to study conception and design. IST, DCR, MPL, JAR, SJW, and ARM made substantial contributions to data acquisition. SJB, CYT, ARM, SJW, PTB, and DCR made substantial contributions to data analysis. ARM, PTB, IST, and SJW made substantial contributions to data interpretation. SJW, ARM, SJB, IST, PTB, MPL, DCR, and JLB made substantial contributions to methods development. JAR, CYT, DCR, CW, SJB, ARM, SJW were key contributors for data management. SJW, ARM, and JLB manually coded intervention category for the testing and training data set. ARM, IST, SJW, PTB, JLB, and TMS made major contributions in writing the manuscript. ARM drafted the manuscript and led the coauthor and peer review process. All authors were involved in drafting the manuscript or revising it critically for important intellectual content. All authors read and approved the final manuscript and agreed to be accountable for all aspects of the work.

This research was supported by intramural funds, awarded as part of a competitive process within the U.S. National Institute for Occupational Safety and Health.

The findings and conclusions in this paper are those of the authors and do not necessarily represent the views of the National Institute for Occupational Safety and Health or the Ohio Bureau of Workers’ Compensation.

The authors have no conflicts of interest.

Supplemental digital contents are available for this article. Direct URL citation appears in the printed text and is provided in the HTML and PDF versions of this article on the journal's Web site (www.joem.org).

Copyright © 2017 by the American College of Occupational and Environmental Medicine