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.
Similar content being viewed by others
References
National Institute for Occupational Safety and Health. Workplace Safety and Health Topics: Traumatic Occupational Injuries. [cited May 17, 2014]; Available from: http://www.cdc.gov/niosh/injury/.
Leigh JP, Markowitz SB, Fahs M, Shin C, Landrigan PJ. Occupational injury and illness in the United States. Estimates of costs, morbidity, and mortality. Arch Intern Med. 1997;157(14):1557–68.
Miller TR, Galbraith M. Estimating the costs of occupational injury in the United States. Accid Anal Prev. 1995;27(6):741–7.
Leigh JP. Economic burden of occupational injury and illness in the United States. Milbank Q. 2011;89(4):728–72.
Butterfield PG, Spencer PS, Redmond N, Feldstein A, Perrin N. Low back pain: predictors of absenteeism, residual symptoms, functional impairment, and medical costs in Oregon workers’ compensation recipients. Am J Ind Med. 1998;34(6):559–67.
Cheadle A, Franklin G, Wolfhagen C, Savarino J, Liu PY, Salley C, et al. Factors influencing the duration of work-related disability: a population-based study of Washington State workers’ compensation. Am J Public Health. 1994;84(2):190–6.
Krause N, Frank JW, Dasinger LK, Sullivan TJ, Sinclair SJ. Determinants of duration of disability and return-to-work after work-related injury and illness: challenges for future research. Am J Ind Med. 2001;40(4):464–84.
Shaw WS, Pransky G, Fitzgerald TE. Early prognosis for low back disability: intervention strategies for health care providers. Disabil Rehabil. 2001;23(18):815–28.
Pransky G, Benjamin K, Dembe AE. Performance and quality measurement in occupational health services: current status and agenda for further research. Am J Ind Med. 2001;40(3):295–306.
Sears JM, Bowman SM, Hogg-Johnson S. Using injury severity to improve occupational injury trend estimates. Am J Ind Med. 2014;57(8):928–39.
Sears JM, Blanar L, Bowman SM, Adams D, Silverstein BA. Predicting work-related disability and medical cost outcomes: estimating injury severity scores from workers’ compensation data. J Occup Rehabil. 2013;23(1):19–31.
Association for the Advancement of Automotive Medicine. The abbreviated injury scale, 1990 revision. Des Plaines, IL: AAAM; 1990.
Cryer C, Gulliver P, Langley JD, Davie G. Is length of stay in hospital a stable proxy for injury severity? Inj Prev. 2010;16(4):254–60.
Cryer C, Langley J. Developing indicators of injury incidence that can be used to monitor global, regional and local trends. 2008 [cited May 2, 2014]; Available from: http://ipru3.otago.ac.nz/ipru/ReportsPDFs/OR070.pdf.
National Center for Health Statistics (NCHS) Expert Group on Injury Severity Measurement. Discussion document on injury severity measurement in administrative datasets. 2004 [cited February 11, 2014]; Available from: http://www.cdc.gov/nchs/data/injury/DicussionDocu.pdf.
Stephenson S, Langley J, Cryer C. Effects of service delivery versus changes in incidence on trends in injury: a demonstration using hospitalised traumatic brain injury. Accid Anal Prev. 2005;37(5):825–32.
Baker SP, O’Neill B, Haddon W Jr, Long WB. The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care. J Trauma. 1974;14(3):187–96.
Harwood PJ, Giannoudis PV, Probst C, Van Griensven M, Krettek C, Pape HC. Which AIS based scoring system is the best predictor of outcome in orthopaedic blunt trauma patients? J Trauma. 2006;60(2):334–40.
Osler T, Baker SP, Long W. A modification of the injury severity score that both improves accuracy and simplifies scoring. J Trauma. 1997;43(6):922–5 (discussion 5–6).
Meredith JW, Evans G, Kilgo PD, MacKenzie E, Osler T, McGwin G, et al. A comparison of the abilities of nine scoring algorithms in predicting mortality. J Trauma. 2002;53(4):621–8 (discussion 8–9).
Kilgo PD, Osler TM, Meredith W. The worst injury predicts mortality outcome the best: rethinking the role of multiple injuries in trauma outcome scoring. J Trauma. 2003;55(4):599–606 (discussion -7).
Ruestow PS, Friedman LS. Characterizing the relationship between in-hospital measures and workers’ compensation outcomes among severely injured construction workers using a data linkage strategy. Am J Ind Med. 2013;56(10):1149–56.
Sears JM, Blanar L, Bowman SM. Predicting work-related disability and medical cost outcomes: a comparison of injury severity scores and scoring methods. Injury. 2014;45(1):16–22.
Smith P, Hogg-Johnson S, Mustard C, Chen C, Tompa E. Comparing the risk factors associated with serious versus and less serious work-related injuries in Ontario between 1991 and 2006. Am J Ind Med. 2012;55(1):84–91.
Mann NC, Guice K, Cassidy L, Wright D, Koury J. Are statewide trauma registries comparable? Reaching for a national trauma dataset. Acad Emerg Med. 2006;13(9):946–53.
MacKenzie EJ, Steinwachs DM, Shankar B. Classifying trauma severity based on hospital discharge diagnoses. Validation of an ICD-9CM to AIS-85 conversion table. Med Care. 1989;27(4):412–22.
Clark DE, Osler TM, Hahn DR. ICDPIC: Stata module to provide methods for translating International Classification of Diseases (Ninth Revision) diagnosis codes into standard injury categories and/or scores. 2010 [cited March 2, 2012]; Available from: http://ideas.repec.org/c/boc/bocode/s457028.html.
Cryer C, Samaranayaka A, Langley JD, Davie G. The epidemiology of life-threatening work-related injury—a demonstration paper. Am J Ind Med. 2014;57(4):425–37.
State of Washington. RCW Title 51: Chapter 51.12. Employments and occupations covered. [cited March 10, 2014]; Available from: http://apps.leg.wa.gov/rcw/default.aspx?Cite=51.12.
National Trauma Data Bank. National Trauma Data Standard: Data Dictionary. 2012 Admissions. 2011 [cited August 27, 2012]; Available from: http://www.ntdsdictionary.org/dataElements/documents/NTDS2012_xsd.PDF.
Campbell KM, Deck D, Krupski A. Record linkage software in the public domain: a comparison of Link Plus, The Link King, and a ‘basic’ deterministic algorithm. Health Inform J. 2008;14(1):5–15.
Sears JM, Bowman SM, Silverstein BA, Adams D. Identification of work-related injuries in a state trauma registry. J Occup Environ Med. 2012;54(3):356–62.
Sears JM, Bowman SM, Adams D, Silverstein BA. Occupational injury surveillance using the Washington State Trauma Registry. J Occup Environ Med. 2011;53(11):1243–50.
Council of State and Territorial Epidemiologists (CSTE). Occupational Health Indicators: A guide for tracking occupational health conditions and their determinants. 2014 March 2014 [cited March 24, 2014]; Available from: www.cste.org/resource/resmgr/OHIndicators/2014EditionOHIGuidanceManual.pdf.
Thomsen C, McClain J, Rosenman K, Davis L. Indicators for occupational health surveillance. MMWR Recomm Rep. 2007;56(RR-1):1–7.
Council of State and Territorial Epidemiologists (CSTE). Occupational Health Indicators. [cited March 24, 2014]; Available from: http://www.cste.org/?OHIndicators.
Copes WS, Champion HR, Sacco WJ, Lawnick MM, Keast SL, Bain LW. The Injury Severity Score revisited. J Trauma. 1988;28(1):69–77.
Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159–74.
Pintilie M. Competing risks: a practical perspective. West Sussex: Wiley; 2006.
Cleves M, Gutierrez RG, Gould W, Marchenko YV. An introduction to survival analysis using Stata. 3rd ed. College Station, TX: Stata Press; 2010.
Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc. 1999;94:496–509.
Lumley T, Diehr P, Emerson S, Chen L. The importance of the normality assumption in large public health data sets. Annu Rev Public Health. 2002;23:151–69.
Burnham KP, Anderson DR. Multimodel inference: understanding AIC and BIC in model selection. Sociol Methods Res. 2004;33(2):261–304.
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83.
Dasinger LK, Krause N, Deegan LJ, Brand RJ, Rudolph L. Duration of work disability after low back injury: a comparison of administrative and self-reported outcomes. Am J Ind Med. 1999;35(6):619–31.
Acknowledgments
This study was funded by the National Institute for Occupational Safety and Health (NIOSH), Grant Numbers 1R03OH009883 and 1R21OH010307. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NIOSH. Authors Sears, Bowman, and Hogg-Johnson have no commercial interest related to this research. Author Rotert receives teaching honoraria from the Association for the Advancement of Automotive Medicine, originators of the Abbreviated Injury Scale.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sears, J.M., Bowman, S.M., Rotert, M. et al. A New Method to Classify Injury Severity by Diagnosis: Validation Using Workers’ Compensation and Trauma Registry Data. J Occup Rehabil 25, 742–751 (2015). https://doi.org/10.1007/s10926-015-9582-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10926-015-9582-5