Study Explores Exposure Assessment Following Automated Coding of Free-Text Job Descriptions April 7, 2014 Igor Burstyn, PhD, an associate professor in the Department of Environmental and Occupational Health, is the first author of an article entitled “Beyond Crosswalks: Reliability of Exposure Assessment Following Automated Coding of Free-Text Job Descriptions for Occupational Epidemiology,” published in The Annals of Occupational Hygiene, with coauthors Yvonne Michael, ScD, SM, an associate professor in the Department of Epidemiology and Biostatistics, Dr. Yuan An from Drexel’s College of Computing and Informatics, and colleagues from Johns Hopkins and The University of British Columbia. Since coding narrative descriptions of occupational histories can be daunting and prohibitively time-consuming, this article evaluated the performance of a computer algorithm to translate the narrative description of occupational codes into standard classification of jobs (2010 Standard Occupational Classification) in an epidemiological context. In this study the authors found that automated coding of occupations results in assignment of exposures that are in reasonable agreement with results that can be obtained through manual coding. Thus, the authors’ overall conclusion is that automated translation of short narrative descriptions of jobs for exposure assessment is feasible in some settings and essential for large cohorts, especially if combined with manual coding to both assess reliability of coding and to further refine the coding algorithm.
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