Developing a Multi-Variate Logistic Regression Model to Analyze Accident Scenarios: Case of Electrical Contractors

Details

• Personal Author:
  Esmaeili B ; Gholizadeh P
• Description:
  The ability to identify factors that influence serious injuries and fatalities would help construction firms triage hazardous situations and direct their resources towards more effective interventions. Therefore, this study used odds ratio analysis and logistic regression modeling on historical accident data to investigate the contributing factors impacting occupational accidents among small electrical contracting enterprises. After conducting a thorough content analysis to ensure the reliability of reports, the authors adopted a purposeful variable selection approach to determine the most significant factors that can explain the fatality rates in different scenarios. Thereafter, this study performed an odds ratio analysis among significant factors to determine which factors increase the likelihood of fatality. For example, it was found that having a fatal accident is 4.4 times more likely when the source is a "vehicle" than when it is a "tool, instrument, or equipment". After validating the consistency of the model, 105 accident scenarios were developed and assessed using the model. The findings revealed which severe accident scenarios happen commonly to people in this trade, with nine scenarios having fatality rates of 50% or more. The highest fatality rates occurred in "fencing, installing lights, signs, etc." tasks in "alteration and rehabilitation" projects where the source of injury was "parts and materials". The proposed analysis/modeling approach can be applied among all specialty contracting companies to identify and prioritize more hazardous situations within specific trades. The proposed model-development process also contributes to the body of knowledge around accident analysis by providing a framework for analyzing accident reports through a multivariate logistic regression model. [Description provided by NIOSH]
• Subjects:
  Accident Prevention Accidents Accidents, Occupational Construction Industry Mining Occupational Health Safety
• Keywords:
  Author Keywords: Occupational Accident Analysis Electrical Contractors Electrical Fields Electrical Industry Electrical Workers Electricity Fatalities Hazard Identification Hazard Recognition Hazards Injuries Models Morbidity Rates Mortality Rates Multi-variate Logistic Regression Predicting Fatality Rates Statistical Analysis Traumatic Injuries Work Environment Workers

  More +
• ISSN:
  1660-4601
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Journal Article
• Place as Subject:
  Maryland ; OSHA Region 3 ; Virginia
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  17
• Issue:
  13
• NIOSHTIC Number:
  nn:20060236
• Citation:
  Int J Environ Res Public Health 2020 Jul; 17(13):4852
• Contact Point Address:
  Pouya Gholizadeh, Sid and Reva Dewberry Department of Civil Environmental and Infrastructure Engineering, Volgenau School of Engineering, George Mason University, 4400 University Drive, MS 6C1, Fairfax, VA 22030
• Email:
  pgholiz@masonlive.gmu.edu
• Federal Fiscal Year:
  2020
• Performing Organization:
  CPWR - The Center for Construction Research and Training, Silver Spring, Maryland
• Peer Reviewed:
  True
• Start Date:
  20090901
• Source Full Name:
  International Journal of Environmental Research and Public Health
• End Date:
  20240831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:154e0c902a360e913db716940ef70d6a22b4af47266ab7340cd5eac46a17cc619d6830a844a6ee62c83c56916aa9a744a2cc9773e355b188fa7378cd0254e0c6
• Download URL:
  https://stacks.cdc.gov/view/cdc/224200/cdc_224200_DS1.pdf
• File Type:
  [PDF - 1.74 MB ]