Performance of automated and manual coding systems for occupational data: A case study of historical records

Mehul D. Patel, Kathryn M. Rose, Cindy R. Owens, Heejung Bang, Jay S. Kaufman

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

Background: Occupational data are a common source of workplace exposure and socioeconomic information in epidemiologic research. We compared the performance of two occupation coding methods, an automated software and a manual coder, using occupation and industry titles from U.S. historical records. Methods: We collected parental occupational data from 1920-40s birth certificates, Census records, and city directories on 3,135 deceased individuals in the Atherosclerosis Risk in Communities (ARIC) study. Unique occupation-industry narratives were assigned codes by a manual coder and the Standardized Occupation and Industry Coding software program. We calculated agreement between coding methods of classification into major Census occupational groups. Results: Automated coding software assigned codes to 71% of occupations and 76% of industries. Of this subset coded by software, 73% of occupation codes and 69% of industry codes matched between automated and manual coding. For major occupational groups, agreement improved to 89% (kappa=0.86). Conclusions: Automated occupational coding is a cost-efficient alternative to manual coding. However, some manual coding is required to code incomplete information. We found substantial variability between coders in the assignment of occupations although not as large for major groups.

Original languageEnglish (US)
Pages (from-to)228-231
Number of pages4
JournalAmerican Journal of Industrial Medicine
Volume55
Issue number3
DOIs
StatePublished - Mar 2012

Keywords

  • Automatic data processing
  • Computer systems
  • Occupation classification
  • Occupational coding
  • Social class

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

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