Etiology of work-related electrical injuries: a narrative analysis of workers' compensation claims

J Occup Environ Hyg. 2009 Oct;6(10):612-23. doi: 10.1080/15459620903133683.

Abstract

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.

MeSH terms

  • Accidents, Occupational* / classification
  • Accidents, Occupational* / economics
  • Accidents, Occupational* / statistics & numerical data
  • Adolescent
  • Adult
  • Aged
  • Algorithms
  • Demography
  • Electric Injuries / classification
  • Electric Injuries / economics
  • Electric Injuries / etiology*
  • Female
  • Humans
  • Male
  • Middle Aged
  • Occupations / statistics & numerical data
  • United States / epidemiology
  • Workers' Compensation* / statistics & numerical data
  • Young Adult