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The current study analyzed 312 caught-in-between fatalities caused by machinery and vehicles. A comprehensive and mutually exclusive coding scheme was developed to analyze and code each caught-in-between fatality in terms of age, gender, experience of the victim, type of industry, source of injury, and causes for these accidents. Boolean algebra analysis was applied on these 312 caught-in-between fatalities to derive minimal cut set (MCS) causes associated with each source of injury. Eventually, contributing factors and common accident patterns associated with (1) special process machinery including textile, printing, packaging machinery, (2) metal, woodworking, and special material machinery, (3) conveyor, (4) vehicle, (5) crane, (6) construction machinery, and (7) elevator can be divided into three major groups through Boolean algebra and MCS analysis. The MCS causes associated with conveyor share the same primary causes as those of the special process machinery including textile, printing, packaging and metal, woodworking, and special material machinery. These fatalities can be eliminated by focusing on the prevention measures associated with lack of safeguards, working on a running machine or process, unintentional activation, unsafe posture or position, unsafe clothing, and defective safeguards. Other precise and effective intervention can be developed based on the identified groups of accident causes associated with each source of injury., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

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]