Efficiency of Autocoding Programs for Converting Job Descriptors into Standard Occupational Classification (SOC) Codes

Details

• Personal Author:
  Buckner-Petty S ; Dale, Ann M. ; Evanoff BA
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Database Occupational Exposure Occupational Health Safety Software
• Keywords:
  Author Keywords: Industry And Occupation Coding Computer Software Databases JEM Job Analysis Job Exposure Matrix NIOCCS O*NET Occupational Exposure Occupations SOCcer

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• ISSN:
  0271-3586
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Missouri ; OSHA Region 7
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  59-68
• Volume:
  62
• Issue:
  1
• NIOSHTIC Number:
  nn:20053955
• Citation:
  Am J Ind Med 2019 Jan; 62(1):59-68
• Contact Point Address:
  Skye Buckner-Petty, MPH, Division of General Medical Sciences, Washington University School of Medicine, 4523 Clayton Avenue, Campus Box 8005, St. Louis, MO 63110
• Email:
  skye.skye@wustl.edu
• Federal Fiscal Year:
  2019
• Performing Organization:
  Washington University, St. Louis, Missouri
• Peer Reviewed:
  True
• Start Date:
  20160901
• Source Full Name:
  American Journal of Industrial Medicine
• End Date:
  20190831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:ef2f414ca301d8a2ac9979fff57185e83c73a5c5c8e5e5aebfcc28e198d37d43afd8d6f23fe351c7630ba9dca6e96a20afbb8db768c495ca91adf30c2604e626
• Download URL:
  https://stacks.cdc.gov/view/cdc/216612/cdc_216612_DS1.pdf
• File Type:
  [PDF - 715.24 KB ]