Abstract
Occupational motor vehicle (OMV) crashes are a leading cause of occupation-related injury and fatality in the United States. Statewide crash databases provide a good source for identifying crashes involving large commercial vehicles but are less optimal for identifying OMV crashes involving light or medium vehicles. This has led to an underestimation of OMV crash counts across states and an incomplete picture of the magnitude of the problem. The goal of this study was to develop and pilot a systematic process for identifying OMV crashes in light and medium vehicles using both state crash and health-related surveillance databases. A two-fold process was developed that included: 1) a machine learning approach for mining crash narratives and 2) a deterministic data linkage effort with crash state data and workers compensation (WC) claims records and emergency medical service (EMS) data, independently. Overall, the combined process identified 5,302 OMV crashes in light and medium vehicles within one year's worth of crash data. Findings suggest the inclusion of multi-method approaches and multiple data sources can be implemented and used to improve OMV crash surveillance in the United States.