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Computer-based coding of free-text job descriptions to efficiently identify occupations in epidemiological studies
  1. Daniel E Russ1,
  2. Kwan-Yuet Ho1,
  3. Joanne S Colt2,
  4. Karla R Armenti3,
  5. Dalsu Baris2,
  6. Wong-Ho Chow4,
  7. Faith Davis5,
  8. Alison Johnson6,
  9. Mark P Purdue2,
  10. Margaret R Karagas7,
  11. Kendra Schwartz8,
  12. Molly Schwenn9,
  13. Debra T Silverman2,
  14. Calvin A Johnson1,
  15. Melissa C Friesen2
  1. 1Division of Computational Bioscience, Center for Information Technology, NIH, Bethesda, Maryland, USA
  2. 2Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
  3. 3Division of Public Health Services, New Hampshire Department of Health and Human Services, Bureau of Public Health Statistics and Informatics, Concord, New Hampshire, USA
  4. 4University of Texas MD Anderson Cancer Center, Houston, Texas, USA
  5. 5Department of Public Health Sciences, University of Alberta, Edmonton, Alberta, Canada
  6. 6Vermont Cancer Registry, Burlington, Vermont, USA
  7. 7Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
  8. 8Department of Family Medicine and Public Health Sciences, Wayne State University, Detroit, Michigan, USA
  9. 9Maine Cancer Registry, Augusta, Maine, USA
  1. Correspondence to Dr Daniel E Russ, Division of Computational Bioscience, Center for Information Technology, NIH, Bethesda 20892, MD, USA; druss{at}mail.nih.gov

Abstract

Background Mapping job titles to standardised occupation classification (SOC) codes is an important step in identifying occupational risk factors in epidemiological studies. Because manual coding is time-consuming and has moderate reliability, we developed an algorithm called SOCcer (Standardized Occupation Coding for Computer-assisted Epidemiologic Research) to assign SOC-2010 codes based on free-text job description components.

Methods Job title and task-based classifiers were developed by comparing job descriptions to multiple sources linking job and task descriptions to SOC codes. An industry-based classifier was developed based on the SOC prevalence within an industry. These classifiers were used in a logistic model trained using 14 983 jobs with expert-assigned SOC codes to obtain empirical weights for an algorithm that scored each SOC/job description. We assigned the highest scoring SOC code to each job. SOCcer was validated in 2 occupational data sources by comparing SOC codes obtained from SOCcer to expert assigned SOC codes and lead exposure estimates obtained by linking SOC codes to a job-exposure matrix.

Results For 11 991 case–control study jobs, SOCcer-assigned codes agreed with 44.5% and 76.3% of manually assigned codes at the 6-digit and 2-digit level, respectively. Agreement increased with the score, providing a mechanism to identify assignments needing review. Good agreement was observed between lead estimates based on SOCcer and manual SOC assignments (κ 0.6–0.8). Poorer performance was observed for inspection job descriptions, which included abbreviations and worksite-specific terminology.

Conclusions Although some manual coding will remain necessary, using SOCcer may improve the efficiency of incorporating occupation into large-scale epidemiological studies.

  • Computers and information technology < Methodology
  • speciality

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