• Jeanne M Sears, Stephen M Bowman, Mary Rotert, and Sheilah Hogg-Johnson.
• Department of Health Services, School of Public Health, University of Washington, Box 357660, Seattle, WA, 98195, USA. jeannes@uw.edu.
• J Occup Rehabil. 2015 Dec 1; 25 (4): 742-51.

PurposeAcute work-related trauma is a leading cause of death and disability among U.S. workers. Existing methods to estimate injury severity have important limitations. This study assessed a severe injury indicator constructed from a list of severe traumatic injury diagnosis codes previously developed for surveillance purposes. Study objectives were to: (1) describe the degree to which the severe injury indicator predicts work disability and medical cost outcomes; (2) assess whether this indicator adequately substitutes for estimating Abbreviated Injury Scale (AIS)-based injury severity from workers' compensation (WC) billing data; and (3) assess concordance between indicators constructed from Washington State Trauma Registry (WTR) and WC data.MethodsWC claims for workers injured in Washington State from 1998 to 2008 were linked to WTR records. Competing risks survival analysis was used to model work disability outcomes. Adjusted total medical costs were modeled using linear regression. Information content of the severe injury indicator and AIS-based injury severity measures were compared using Akaike Information Criterion and R(2).ResultsOf 208,522 eligible WC claims, 5 % were classified as severe. Among WC claims linked to the WTR, there was substantial agreement between WC-based and WTR-based indicators (kappa = 0.75). Information content of the severe injury indicator was similar to some AIS-based measures. The severe injury indicator was a significant predictor of WTR inclusion, early hospitalization, compensated time loss, total permanent disability, and total medical costs.ConclusionsSevere traumatic injuries can be directly identified when diagnosis codes are available. This method provides a simple and transparent alternative to AIS-based injury severity estimation.

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