Exposure Models for the Prior Distribution in Bayesian Decision Analysis for Occupational Hygiene Decision Making

Details

• Alternative Title:
  J Occup Environ Hyg
• Personal Author:
  Lee, Eun Gyung ; Kim, Seung Won ; Feigley, Charles E. ; Harper, Martin
• Description:
  This study introduces two semi-quantitative methods, Structured Subjective Assessment (SSA) and Control of Substances Hazardous to Health (COSHH) Essentials, in conjunction with two-dimensional Monte Carlo simulations for determining prior probabilities. Prior distribution using expert judgment was included for comparison. Practical applications of the proposed methods were demonstrated using personal exposure measurements of isoamyl acetate in an electronics manufacturing facility and of isopropanol in a printing shop. Applicability of these methods in real workplaces was discussed based on the advantages and disadvantages of each method. Although these methods could not be completely independent of expert judgments, this study demonstrated a methodological improvement in the estimation of the prior distribution for the Bayesian decision analysis tool. The proposed methods provide a logical basis for the decision process by considering determinants of worker exposure.
• Subjects:
  2-Propanol AIHA Exposure Rating Scheme Article Bayesian Decision Analysis Bayes Theorem COSHH Essentials Scheme Decision Making Decision Support Techniques Electronics Hazardous Substances Humans Models, Theoretical Monte Carlo Method Occupational Exposure Occupational Health Pentanols Printing Prior Decision Distribution Risk Assessment Structured Subjective Assessment (SSA) Two-dimensional (2-D) Monte Carlo Simulation Workplace

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• Source:
  J Occup Environ Hyg. 10(2):97-108.
• Pubmed ID:
  23252451
• Pubmed Central ID:
  PMC4665102
• Document Type:
  Journal Article
• Funding:
  CC999999/Intramural CDC HHS/United States
• Volume:
  10
• Issue:
  2
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:1e5032f0bb59236530ee92274c1491c641e9574de12e01b9eb145d1d2419c881
• Download URL:
  https://stacks.cdc.gov/view/cdc/36731/cdc_36731_DS1.pdf
• File Type:
  [PDF - 868.10 KB ]