@article{f8d8dc1bc7eb4bb2bfc216f22d129e97,
title = "Efficiency of autocoding programs for converting job descriptors into standard occupational classification (SOC) codes",
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.",
keywords = "NIOCCS, O*NET, SOCcer, industry and occupation coding, job exposure matrix",
author = "Skye Buckner-Petty and Dale, {Ann Marie} and Evanoff, {Bradley A.}",
note = "Funding Information: Grant sponsor: National Institute for Occupational Safety and Health; Grant number: R01OH011076. SB-P designed and executed the analytic strategy for this study. He was also the primary writer of the manuscript. AMD conducted the manual job coding and assisted with writing. BAE provided guidance, assisted with writing, is the PI on the grant that funded this project, and is the PI on the grant that funded one of the two cohort studies that provided source data. All authors approved the final draft of the manuscript. The authors would like to thank the following groups and individuals whose contributions were necessary for this research: NIOSH Division of Surveillance, Hazard Evaluations, and Field Studies for providing guidance and troubleshooting for use of NIOCCS, Dr Daniel Russ from the NIH for providing guidance and troubleshooting for use of SOCcer, and investigators from the SHOW-ME study (U54 CA155496) and the NIOSH Upper Extremity Musculoskeletal Disorders Consortium (R01OH009712) for access to data from these two studies. Grant sponsor: National Institute for Occupational Safety and Health; Grant number: R01OH011076. The analysis was completed using secondary data, which was de-identified; IRB review was not required. The authors declare no conflicts of interest. Steven B. Markowitz declares that he has no competing or conflicts of interest in the review and publication decision regarding this article. Publisher Copyright: {\textcopyright} 2018 Wiley Periodicals, Inc.",
year = "2019",
month = jan,
doi = "10.1002/ajim.22928",
language = "English",
volume = "62",
pages = "59--68",
journal = "American Journal of Industrial Medicine",
issn = "0271-3586",
number = "1",
}