A new method to classify injury severity by diagnosis: validation using workers' compensation and trauma registry data

Publication type
Journal article
Authors
Sears JM, Bowman SM, Rotert M, Hogg-Johnson S
Date published
2015 Dec 01
Journal
Journal of Occupational Rehabilitation
Volume
25
Issue
4
Pages
742-751
Open Access?
No
Abstract

Purpose Acute 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. Methods WC 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 R2. Results Of 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. Conclusions Severe 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