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Automated Coding of Job Descriptions From a General Population Study: Overview of Existing Tools, Their Application

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Automatic job coding tools showed low agreement for detailed job codes but better agreement for exposure assignments. Further evaluation and tool improvement are needed for occupational health studies.

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Area of Science:

  • Occupational Health
  • Data Science
  • Epidemiology

Background:

  • Manual job coding in large occupational health studies is time-consuming.
  • Automatic job coding tools aim to streamline this process.
  • Evaluating the performance of these tools is crucial for reliable data analysis.

Purpose of the Study:

  • To compare the performance of three automatic job coding tools: AUTONOC, CASCOT, and LabourR.
  • To assess the agreement between automatic job coding and Job-Exposure Matrix (JEM)-assigned exposures.
  • To evaluate these tools using manually coded job histories from the AsiaLymph case-control study.

Main Methods:

  • Three automatic job coding tools (AUTONOC, CASCOT, LabourR) were selected based on criteria including ISCO-88 compatibility and batch coding capability.
  • Manually coded job histories from the AsiaLymph study were translated to English for auto-coding.
  • Percent agreement and Prevalence-Adjusted Bias-Adjusted Kappa (PABAK) were used to compare manual coding, auto-coding, and JEM-assigned exposures.

Main Results:

  • Agreement for exact 4-digit ISCO-88 job codes was low (17.7-26.0%) across the three tools.
  • Agreement improved at more general job coding levels (1-digit ISCO-88: 43.8-58.1%).
  • CASCOT demonstrated superior performance with 26% 4-digit agreement and a median kappa of 0.61 for exposure assignment.

Conclusions:

  • Agreement on detailed job coding using automatic tools was generally low.
  • Higher agreement was observed for JEM-assigned exposures.
  • Study-specific evaluations and tool enhancements are recommended before widespread adoption in general population studies.