BACKGROUND: Occupational data are a common source of workplace exposure and socioeconomic information in epidemiologic research. We compared the performance of two occupation coding methods, an automated software and a manual coder, using occupation and industry titles from U.S. historical records. METHODS: We collected parental occupational data from 1920-40s birth certificates, Census records, and city directories on 3,135 deceased individuals in the Atherosclerosis Risk in Communities (ARIC) study. Unique occupation-industry narratives were assigned codes by a manual coder and the Standardized Occupation and Industry Coding software program. We calculated agreement between coding methods of classification into major Census occupational groups. RESULTS: Automated coding software assigned codes to 71% of occupations and 76% of industries. Of this subset coded by software, 73% of occupation codes and 69% of industry codes matched between automated and manual coding. For major occupational groups, agreement improved to 89% (kappa = 0.86). CONCLUSIONS: Automated occupational coding is a cost-efficient alternative to manual coding. However, some manual coding is required to code incomplete information. We found substantial variability between coders in the assignment of occupations although not as large for major groups.
BACKGROUND: Occupational data are a common source of workplace exposure and socioeconomic information in epidemiologic research. We compared the performance of two occupation coding methods, an automated software and a manual coder, using occupation and industry titles from U.S. historical records. METHODS: We collected parental occupational data from 1920-40s birth certificates, Census records, and city directories on 3,135 deceased individuals in the Atherosclerosis Risk in Communities (ARIC) study. Unique occupation-industry narratives were assigned codes by a manual coder and the Standardized Occupation and Industry Coding software program. We calculated agreement between coding methods of classification into major Census occupational groups. RESULTS: Automated coding software assigned codes to 71% of occupations and 76% of industries. Of this subset coded by software, 73% of occupation codes and 69% of industry codes matched between automated and manual coding. For major occupational groups, agreement improved to 89% (kappa = 0.86). CONCLUSIONS: Automated occupational coding is a cost-efficient alternative to manual coding. However, some manual coding is required to code incomplete information. We found substantial variability between coders in the assignment of occupations although not as large for major groups.
Authors: Thomas Rémen; Lesley Richardson; Corinne Pilorget; Gilles Palmer; Jack Siemiatycki; Jérôme Lavoué Journal: Ann Work Expo Health Date: 2018-08-13 Impact factor: 2.179
Authors: Daniel E Russ; Kwan-Yuet Ho; Joanne S Colt; Karla R Armenti; Dalsu Baris; Wong-Ho Chow; Faith Davis; Alison Johnson; Mark P Purdue; Margaret R Karagas; Kendra Schwartz; Molly Schwenn; Debra T Silverman; Calvin A Johnson; Melissa C Friesen Journal: Occup Environ Med Date: 2016-04-21 Impact factor: 4.402
Authors: A J Agopian; Jihye Kim; Peter H Langlois; Laura Lee; Lawrence W Whitehead; Elaine Symanski; Michele L Herdt; George L Delclos Journal: Am J Ind Med Date: 2017-05-19 Impact factor: 2.214