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Reliability of interrater occupation coding and potential impact on occupational exposure assessment
Calvin Ge1, Melissa C Friesen2, Sarah Locke2
1Institute for Risk Assessment Sciences, Utrecht University, Yalelaan 2, Utrecht 3584 CM, The Netherlands.
Annals of Work Exposures and Health
|June 11, 2026
Summary
Occupational epidemiology studies rely on accurate job coding for exposure assessment. This study found that while job coding agreement varied, job-exposure matrix estimates showed higher agreement, indicating fewer disagreements impacted exposure assignment.
Area of Science:
- Occupational Epidemiology
- Public Health
- Biostatistics
Background:
- Accurate exposure assessment is crucial in occupational epidemiology.
- Job coding is a primary method for determining occupational exposures.
- Variability in job coding can affect study results.
Purpose of the Study:
- To assess the agreement of occupation codes assigned by different research centers.
- To evaluate the agreement of job-exposure matrix (JEM) estimates derived from these codes.
- To understand the impact of coding discrepancies on exposure assessment in East Asian occupational studies.
Main Methods:
- Utilized the 1988 International Standard Classification of Occupations (ISCO-88) for job classification.
- Employed independent coding by two research centers across Mainland China, Hong Kong, and Taiwan.
- Calculated percent agreement for job codes and weighted Cohen's kappa (κ) for JEM-based exposure estimates.
Main Results:
- Job coding agreement ranged from 51.0% to 77.1% across different center pairings and code granularities.
- JEM-based exposure estimate comparisons yielded κ values from 0.62 to 0.95.
- Agreement levels for both job codes and JEM estimates varied by occupation and industry.
Conclusions:
- Job coding agreement in this study aligns with findings from other occupational research.
- Agreement in JEM-based exposure estimates exceeded job code agreement.
- Discrepancies in job coding did not always result in differing exposure assignments, highlighting the robustness of JEM estimates.
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