Department of Epidemiology & Environmental Health, 111 Washington Avenue, University of Kentucky, Lexington, KY 40506, United States; Kentucky Injury Prevention and Research Center, 2365 Harrodsburg Road, Southcreek Building B, Suite B475, Lexington, KY 40504, United States.
Department of Civil Engineering, 161 Raymond Building, Lexington, KY 40506, United States.
Accid Anal Prev. 2024 Nov;207:107749. doi: 10.1016/j.aap.2024.107749. Epub 2024 Aug 17.
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.
职业机动车（OMV）事故是美国与职业相关的伤害和死亡的主要原因。全州范围的事故数据库是识别涉及大型商用车辆的事故的良好来源，但对于识别涉及轻型或中型车辆的 OMV 事故则不太理想。这导致各州对 OMV 事故数量的低估，以及对问题严重程度的不完整了解。本研究的目的是开发和试点一种使用州级事故和与健康相关的监测数据库来识别轻型和中型车辆 OMV 事故的系统过程。开发了一个双重过程，包括：1）挖掘事故叙述的机器学习方法，以及 2）与事故州数据和工人补偿（WC）索赔记录以及紧急医疗服务（EMS）数据的确定性数据链接工作，分别进行。总体而言，该综合过程在一年内的事故数据中确定了 5302 起涉及轻型和中型车辆的 OMV 事故。研究结果表明，可以实施和使用多方法方法和多个数据源来改善美国的 OMV 事故监测。
