Efficiency of autocoding programs for converting job descriptors into standard occupational classification (SOC) codes

Skye Buckner-Petty, Ann Marie Dale, Bradley A. Evanoff

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Background: Existing datasets often lack job exposure data. Standard Occupational Classification (SOC) codes can link work exposure data to health outcomes via a Job Exposure Matrix, but manually assigning SOC codes is laborious. We explored the utility of two SOC autocoding programs. Methods: We entered industry and occupation descriptions from two existing cohorts into two publicly available SOC autocoding programs. SOC codes were also assigned manually by experienced coders. These SOC codes were then linked to exposures from the Occupational Information Network (O*NET). Results: Agreement between the SOC codes produced by autocoding programs and those produced manually was modest at the 6-digit level, and strong at the 2-digit level. Importantly, O*NET exposure values based on SOC code assignment showed strong agreement between manual and autocoded methods. Conclusion: Both available autocoding programs can be useful tools for assigning SOC codes, allowing linkage of occupational exposures to data containing free-text occupation descriptors.

Original languageEnglish
Pages (from-to)59-68
Number of pages10
JournalAmerican Journal of Industrial Medicine
Volume62
Issue number1
DOIs
StatePublished - Jan 2019

Keywords

  • NIOCCS
  • O*NET
  • SOCcer
  • industry and occupation coding
  • job exposure matrix

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