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Abstract: The current study reanalyzed 250 electrical fatalities in the construction industry from 1996 to 2002 into seven patterns based on source of electricity (power line, energized equipment, improperly installed or damaged equipment), direct contact or indirect contact through some source of injury (boom vehicle, metal bar or pipe, and other conductive material). Each fatality was coded in terms of age, company size, experience, performing tasks, source of injury, accident cause and hazard pattern. The Chi-square Automatic Interaction Detector (CHAID) was applied to the coded data of the fatal electrocution to find a subset of predictors that might derive meaningful classifications or accident scenarios. A series of flow diagrams was constructed based on CHAID result to illustrate the flow of electricity traveling from electrical source to human body. Each of the flow diagrams can be directly linked with feasible prevention strategies by cutting the flow of electricity. [Copyright &y& Elsevier]

This research reanalyzed 1230 work-related single fatalities from the years 1989, 1990, and 1992 with a model free approach. Each single fatality was analyzed in terms of the victim''s age, gender, industry, and the accident type. The standardized mortality ratio (SMR) was calculated to compare the SMR among various demographic groups and to cross validate the results obtained from our previous work using analysis of variance (ANOVA). The Chi-square Automatic Interaction Detector (CHAID) was used to divide the working population into segments based on whether the worker was a victim in any fatality. Predictor variables mainly focus on the demographic variables including the victim''s age, gender and industry. But different from our previous finding [Safety Science 27 (1997) 1] of an insignificant gender effect, gender was found to be the second critical factor and it was nested under the most critical variable, type of industry. Age is the least critical variable and it is nested under the gender effect. Five different interactive patterns were identified correlating the age, gender, and industries of the victims. A comparison among ANOVA, SMR and CHAID proved that CHAID outperforms other methods for it not only reveals the complex and hierarchical interactive patterns but also prioritizes the importance of the predictor variables. [Copyright &y& Elsevier]