Computer-based coding of free-text job descriptions to efficiently identify occupations in epidemiological studies.
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.
Division of Computational Bioscience, Center for Information Technology, NIH, Bethesda, Maryland, USA.
Daniel E
http://orcid.org/0000-0003-4040-4416
DE
Russ
Division of Computational Bioscience, Center for Information Technology, NIH, Bethesda, Maryland, USA.
Kwan-Yuet
KY
Ho
Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA.
Joanne S
JS
Colt
Division of Public Health Services, New Hampshire Department of Health and Human Services, Bureau of Public Health Statistics and Informatics, Concord, New Hampshire, USA.
Karla R
KR
Armenti
Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA.
Dalsu
D
Baris
University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Wong-Ho
WH
Chow
Department of Public Health Sciences, University of Alberta, Edmonton, Alberta, Canada.
Faith
F
Davis
Vermont Cancer Registry, Burlington, Vermont, USA.
Alison
A
Johnson
Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA.
Mark P
MP
Purdue
Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA.
Margaret R
MR
Karagas
Department of Family Medicine and Public Health Sciences, Wayne State University, Detroit, Michigan, USA.
Kendra
K
Schwartz
Maine Cancer Registry, Augusta, Maine, USA.
Molly
M
Schwenn
Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA.
Debra T
DT
Silverman
Division of Computational Bioscience, Center for Information Technology, NIH, Bethesda, Maryland, USA.
Calvin A
CA
Johnson
Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA.
Melissa C
MC
Friesen
D000465:Algorithms; D002292:Carcinoma, Renal Cell; D016022:Case-Control Studies; D004812:Epidemiologic Methods; D016021:Epidemiologic Studies; D006801:Humans; D007221:Industry / Q000145:classification*; D007587:Job Description; D016015:Logistic Models; D009323:Natural Language Processing; D009790:Occupations / Q000145:classification*; D015203:Reproducibility of Results; D012984:Software; D014481:United States; D014489:United States Occupational Safety and Health Administration
Computers and information technology < Methodology; speciality