Computer-based coding of free-text job descriptions to efficiently identify occupations in epidemiological studies. Issue 6 (21st April 2016)

• Record Type: Journal Article
• Authors: Russ, Daniel E; Ho, Kwan-Yuet; Colt, Joanne S; Armenti, Karla R; Baris, Dalsu; Chow, Wong-Ho; Davis, Faith; Johnson, Alison; Purdue, Mark P; Karagas, Margaret R; Schwartz, Kendra; Schwenn, Molly; Silverman, Debra T; Johnson, Calvin A; Friesen, Melissa C
• Is Part Of: Occupational and environmental medicine. Volume 73:Issue 6(2016)
• Page Start: 417
• Page End: 424
• Publication Date: 2016-04-21
• Subjects: Computers and information technology < Methodology -- speciality; Medicine, Industrial -- Periodicals; Environmental health -- Periodicals; 616.980305
• DOI: 10.1136/oemed-2015-103152
• Languages: English
• ISSNs: 1351-0711

Abstract

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.