Lombardi David A, Matz Simon, Brennan Melanye J, Smith Gordon S, Courtney Theodore K
Liberty Mutual Research Institute for Safety, Center for Injury Epidemiology, Hopkinton, Massachusetts 01748, USA.
J Occup Environ Hyg. 2009 Oct;6(10):612-23. doi: 10.1080/15459620903133683.
The purpose of this study was to provide new insight into the etiology of primarily nonfatal, work-related electrical injuries. We developed a multistage, case-selection algorithm to identify electrical-related injuries from workers' compensation claims and a customized coding taxonomy to identify pre-injury circumstances. Workers' compensation claims routinely collected over a 1-year period from a large U.S. insurance provider were used to identify electrical-related injuries using an algorithm that evaluated: coded injury cause information, nature of injury, "accident" description, and injury description narratives. Concurrently, a customized coding taxonomy for these narratives was developed to abstract the activity, source, initiating process, mechanism, vector, and voltage. Among the 586,567 reported claims during 2002, electrical-related injuries accounted for 1283 (0.22%) of nonfatal claims and 15 fatalities (1.2% of electrical). Most (72.3%) were male, average age of 36, working in services (33.4%), manufacturing (24.7%), retail trade (17.3%), and construction (7.2%). Body part(s) injured most often were the hands, fingers, or wrist (34.9%); multiple body parts/systems (25.0%); lower/upper arm; elbow; shoulder, and upper extremities (19.2%). The leading activities were conducting manual tasks (55.1%); working with machinery, appliances, or equipment; working with electrical wire; and operating powered or nonpowered hand tools. Primary injury sources were appliances and office equipment (24.4%); wires, cables/cords (18.0%); machines and other equipment (11.8%); fixtures, bulbs, and switches (10.4%); and lightning (4.3%). No vector was identified in 85% of cases. and the work process was initiated by others in less than 1% of cases. Injury narratives provide valuable information to overcome some of the limitations of precoded data, more specially for identifying additional injury cases and in supplementing traditional epidemiologic data for further understanding the etiology of work-related electrical injuries that may lead to further prevention opportunities.
本研究的目的是为原发性非致命性工作相关电损伤的病因提供新的见解。我们开发了一种多阶段病例选择算法，用于从工伤赔偿申索中识别与电相关的损伤，并开发了一种定制的编码分类法来识别受伤前的情况。我们使用从一家大型美国保险提供商在1年期间定期收集的工伤赔偿申索，通过一种算法来识别与电相关的损伤，该算法评估：编码的损伤原因信息、损伤性质、“事故”描述和损伤描述叙述。同时，针对这些叙述开发了一种定制的编码分类法，以提取活动、来源、起始过程、机制、载体和电压。在2002年报告的586,567份申索中，与电相关的损伤占非致命申索的1283份（0.22%）和15例死亡（占电损伤的1.2%）。大多数（72.3%）为男性，平均年龄36岁，从事服务业（33.4%）、制造业（24.7%）、零售业（17.3%）和建筑业（7.2%）。最常受伤的身体部位是手、手指或手腕（34.9%）；多个身体部位/系统（25.0%）；下/上臂；肘部；肩部和上肢（19.2%）。主要活动是进行体力任务（55.1%）；操作机械、电器或设备；处理电线；操作电动或非电动手动工具。主要损伤来源是电器和办公设备（24.4%）；电线、电缆/软线（18.0%）；机器和其他设备（11.8%）；固定装置、灯泡和开关（10.4%）；以及闪电（4.3%）。85%的病例未识别出载体，不到1%的病例工作过程由他人启动。损伤叙述提供了有价值的信息，以克服预编码数据的一些局限性，特别是用于识别更多的损伤病例，并补充传统的流行病学数据，以进一步了解工作相关电损伤的病因，这可能带来更多的预防机会。
