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BACKGROUND: Acute work-related trauma is a leading cause of death and disability among U.S. workers. More severe trauma is associated with poorer long-term outcomes, but better severity measures are needed for occupational health research. Existing methods to estimate Abbreviated Injury Scale (AIS)-based injury severity from International Classification of Diseases (ICD-9-CM) diagnosis codes have important limitations, e.g., requiring use of out-of-date platforms or proprietary software, not being current to the most recent coding revisions, or not allowing for transparent updating. This study assessed whether a list of severe traumatic injury diagnosis codes that had been developed for surveillance purposes could usefully classify injury severity for other purposes, e.g., control of confounding in occupational injury intervention or outcome studies. Study objectives were to: (1) describe the degree to which a binary indicator based on the severe traumatic injury diagnosis list predicts work disability and medical cost outcomes; (2) assess whether this indicator can adequately substitute for estimating AIS-based injury severity from workers’ compensation (WC) billing data; and (3) assess concordance between severe injury indicators constructed from trauma registry clinical diagnoses versus from WC billing diagnoses.
METHODS: Compensable nonfatal WC claims for workers injured in Washington State from 1998-2008 were linked to Washington State Trauma Registry (WTR) records. AIS was estimated from ICD-9-CM codes using Stata’s user-written -icdpic- program. The severe traumatic injury list was converted to a binary indicator (set to 1 in the presence of any listed diagnosis; 0 otherwise), and constructed using: (1) WC billing diagnoses (for all WC claims) and (2) WTR clinical diagnoses (for the linked subset). Information content was compared using Akaike Information Criterion and R2. Competing risks survival analysis was used to evaluate work disability outcomes. Adjusted total medical costs were modeled using linear regression.
RESULTS: Of 208,522 eligible WC claims, 5% were classified as severe. For the 4,302 WC claims linked to the WTR, there was substantial agreement between the WC-based and WTR-based severe injury indicators (kappa=0.75). The severe injury indicator was a significant predictor of WTR inclusion, early hospitalization, compensated time loss, total permanent disability, and total medical costs. Information content of the severity indicator was similar to other binary AIS-based measures.
CONCLUSIONS: The severe injury indicator was a significant predictor of work disability, medical intensity, and medical costs. Severe traumatic injuries can be directly identified when ICD-9-CM codes are available. This method provides a simple and transparent alternative to AIS-based injury severity estimation.