Developing a job-exposure matrix with exposure uncertainty from expert elicitation and data modeling

Area of Science:

• Occupational Health
• Epidemiology
• Risk Assessment

Background:

• Job exposure matrices (JEMs) are commonly used to estimate occupational exposures when individual data is unavailable.
• Existing JEMs often lack systematic quantification of exposure uncertainty stemming from variations in work practices and data quality.

Purpose of the Study:

• To develop and present a novel methodology for constructing JEMs that incorporates continuous exposure scales and quantifies uncertainty.
• To create a consensus-based JEM for electric shock exposure using expert elicitation.

Main Methods:

• Developed a methodology for JEM creation defining occupational exposures on a continuous scale.
• Utilized formal expert elicitation methods to quantify exposure uncertainty via probability distributions.
• Incorporated expert knowledge to develop mathematical models using surrogate data and adjust model outputs.

Main Results:

• Successfully created a population-based electric shock JEM using the described methodology.
• The new JEM provides transparent estimates of occupational exposure with quantified uncertainty.

Conclusions:

• The proposed methodology offers a systematic approach to quantify exposure uncertainty in JEMs.
• This approach enhances the reliability and transparency of occupational exposure assessments, particularly for electric shock risks.