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Efficiency of autocoding programs for converting job descriptors into standard occupational classification (SOC) codes
Author(s) -
BucknerPetty Skye,
Dale Ann Marie,
Evanoff Bradley A.
Publication year - 2019
Publication title -
american journal of industrial medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.7
H-Index - 104
eISSN - 1097-0274
pISSN - 0271-3586
DOI - 10.1002/ajim.22928
Subject(s) - job exposure matrix , medicine , zip code , data mining , computer science , occupational exposure , database , environmental health
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.