Seyed Shamseddin Alizadeh1, Seyed Bagher Mortazavi2, Mohammad Mehdi Sepehri3. 1. Department of Occupational Health Engineering, , Tabriz University of Medical Sciences, Tabriz, Iran. 2. Department of Occupational Health Engineering, Tarbiat Modares University, Tehran, Iran. 3. Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran.
Abstract
BACKGROUND: Falls from height are one of the main causes of fatal occupational injuries. The objective of this study was to present a model for estimating occurrence probability of falling from height. METHODS: In order to make a list of factors affecting falls, we used four expert group's judgment, literature review and an available database. Then the validity and reliability of designed questionnaire were determined and Bayesian networks were built. The built network, nodes and curves were quantified. For network sensitivity analysis, four types of analysis carried out. RESULTS: A Bayesian network for assessment of posterior probabilities of falling from height proposed. The presented Bayesian network model shows the interrelationships among 37 causes affecting the falling from height and can calculate its posterior probabilities. The most important factors affecting falling were Non-compliance with safety instructions for work at height (0.127), Lack of safety equipment for work at height (0.094) and Lack of safety instructions for work at height (0.071) respectively. CONCLUSION: The proposed Bayesian network used to determine how different causes could affect the falling from height at work. The findings of this study can be used to decide on the falling accident prevention programs.
BACKGROUND: Falls from height are one of the main causes of fatal occupational injuries. The objective of this study was to present a model for estimating occurrence probability of falling from height. METHODS: In order to make a list of factors affecting falls, we used four expert group's judgment, literature review and an available database. Then the validity and reliability of designed questionnaire were determined and Bayesian networks were built. The built network, nodes and curves were quantified. For network sensitivity analysis, four types of analysis carried out. RESULTS: A Bayesian network for assessment of posterior probabilities of falling from height proposed. The presented Bayesian network model shows the interrelationships among 37 causes affecting the falling from height and can calculate its posterior probabilities. The most important factors affecting falling were Non-compliance with safety instructions for work at height (0.127), Lack of safety equipment for work at height (0.094) and Lack of safety instructions for work at height (0.071) respectively. CONCLUSION: The proposed Bayesian network used to determine how different causes could affect the falling from height at work. The findings of this study can be used to decide on the falling accident prevention programs.
Authors: Katherine Pérez; Ana M Novoa; Elena Santamariña-Rubio; Yislenz Narvaez; Vita Arrufat; Carme Borrell; Elena Cabeza; Eva Cirera; Josep Ferrando; Anna García-Altés; Juan Carlos Gonzalez-Luque; Vicenta Lizarbe; Carlos Martin-Cantera; María Seguí-Gómez; Josep M Suelves Journal: Accid Anal Prev Date: 2012-01-20