RT Journal Article
SR Electronic
T1 Computer-based coding of free-text job descriptions to efficiently identify occupations in epidemiological studies
JF Occupational and Environmental Medicine
JO Occup Environ Med
FD BMJ Publishing Group Ltd
SP 417
OP 424
DO 10.1136/oemed-2015-103152
VO 73
IS 6
A1 Daniel E Russ
A1 Kwan-Yuet Ho
A1 Joanne S Colt
A1 Karla R Armenti
A1 Dalsu Baris
A1 Wong-Ho Chow
A1 Faith Davis
A1 Alison Johnson
A1 Mark P Purdue
A1 Margaret R Karagas
A1 Kendra Schwartz
A1 Molly Schwenn
A1 Debra T Silverman
A1 Calvin A Johnson
A1 Melissa C Friesen
YR 2016
UL http://oem.bmj.com/content/73/6/417.abstract
AB 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.