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Journal article
Automated Coding of Job Descriptions From a General Population Study: Overview of Existing Tools, Their Application and Comparison
Annals of work exposures and health, v 67(5), pp 663-672
06 Jun 2023
PMID: 36734402
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Automatic job coding tools were developed to reduce the laborious task of manually assigning job codes based on free-text job descriptions in census and survey data sources, including large occupational health studies. The objective of this study is to provide a case study of comparative performance of job coding and JEM (Job-Exposure Matrix)-assigned exposures agreement using existing coding tools.
We compared three automatic job coding tools [AUTONOC, CASCOT (Computer-Assisted Structured Coding Tool), and LabourR], which were selected based on availability, coding of English free-text into coding systems closely related to the 1988 version of the International Standard Classification of Occupations (ISCO-88), and capability to perform batch coding. We used manually coded job histories from the AsiaLymph case-control study that were translated into English prior to auto-coding to assess their performance. We applied two general population JEMs to assess agreement at exposure level. Percent agreement and PABAK (Prevalence-Adjusted Bias-Adjusted Kappa) were used to compare the agreement of results from manual coders and automatic coding tools.
The coding per cent agreement among the three tools ranged from 17.7 to 26.0% for exact matches at the most detailed 4-digit ISCO-88 level. The agreement was better at a more general level of job coding (e.g. 43.8-58.1% in 1-digit ISCO-88), and in exposure assignments (median values of PABAK coefficient ranging 0.69-0.78 across 12 JEM-assigned exposures). Based on our testing data, CASCOT was found to outperform others in terms of better agreement in both job coding (26% 4-digit agreement) and exposure assignment (median kappa 0.61).
In this study, we observed that agreement on job coding was generally low for the three tools but noted a higher degree of agreement in assigned exposures. The results indicate the need for study-specific evaluations prior to their automatic use in general population studies, as well as improvements in the evaluated automatic coding tools.
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Details
- Title
- Automated Coding of Job Descriptions From a General Population Study: Overview of Existing Tools, Their Application and Comparison
- Creators
- Wenxin Wan - Utrecht UniversityCalvin B Ge - Utrecht UniversityMelissa C Friesen - Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USASarah J Locke - Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USADaniel E Russ - Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USAIgor Burstyn - Drexel University, Environmental and Occupational HealthChristopher J O Baker - University of New BrunswickAnil Adisesh - University of TorontoQing Lan - National Institutes of HealthNathaniel Rothman - National Institutes of HealthAnke Huss - Utrecht UniversityMartie van Tongeren - University of ManchesterRoel Vermeulen - Utrecht UniversitySusan Peters (Corresponding Author) - Utrecht University
- Publication Details
- Annals of work exposures and health, v 67(5), pp 663-672
- Publisher
- Oxford University Press
- Number of pages
- 10
- Grant note
- 874703 / European Union's Horizon 2020 research and innovation programme
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Environmental and Occupational Health
- Web of Science ID
- WOS:000926936300001
- Scopus ID
- 2-s2.0-85162182320
- Other Identifier
- 991019961165504721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Public, Environmental & Occupational Health