• Lisa Lewandowski-Romps, Christopher Peterson, Patricia A Berglund, Stacey Collins, Kenneth Cox, Keith Hauret, Bruce Jones, Ronald C Kessler, Colter Mitchell, Nansook Park, Michael Schoenbaum, Murray B Stein, Robert J Ursano, Steven G Heeringa, and Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) Collaborators.
• Institute for Social Research, University of Michigan, Ann Arbor, Michigan.
• Am J Prev Med. 2014 Dec 1; 47 (6): 745753745-53.

BackgroundAccidents are one of the leading causes of death among U.S. active-duty Army soldiers. Evidence-based approaches to injury prevention could be strengthened by adding person-level characteristics (e.g., demographics) to risk models tested on diverse soldier samples studied over time.PurposeTo identify person-level risk indicators of accident deaths in Regular Army soldiers during a time frame of intense military operations, and to discriminate risk of not-line-of-duty from line-of-duty accident deaths.MethodsAdministrative data acquired from multiple Army/Department of Defense sources for active duty Army soldiers during 2004-2009 were analyzed in 2013. Logistic regression modeling was used to identify person-level sociodemographic, service-related, occupational, and mental health predictors of accident deaths.ResultsDelayed rank progression or demotion and being male, unmarried, in a combat arms specialty, and of low rank/service length increased odds of accident death for enlisted soldiers. Unique to officers was high risk associated with aviation specialties. Accident death risk decreased over time for currently deployed, enlisted soldiers and increased for those never deployed. Mental health diagnosis was associated with risk only for previous and never-deployed, enlisted soldiers. Models did not discriminate not-line-of-duty from line-of-duty accident deaths.ConclusionsAdding more refined person-level and situational risk indicators to current models could enhance understanding of accident death risk specific to soldier rank and deployment status. Stable predictors could help identify high risk of accident deaths in future cohorts of Regular Army soldiers.Copyright © 2014 American Journal of Preventive Medicine. All rights reserved.

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