building a model using bayesian network for assessment of posterior probabilities of falling from height at workplaces

• DOI: 10.5681/hpp.2014.025
• Publication Date: 2014-01-01
• Authors: Mohammad Mehdi Se...; Seyed Shamseddin ...; Seyed Bagher Mort...
• Keywords: Nutrition. Foods and food supply; 05 social sciences; Posterior probabilities; 610 Medical sciences; Medicine; Falling; Bayesian networks; Accident; 0502 economics and business; 8. Economic growth; TX341-641; Public aspects of medicine; RA1-1270

Health Promotion Perspectives; EISSN 2228-6497<br/>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 inter-relationships 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.<br/>