BACKGROUND: In epidemiological studies, occupational exposure estimates are often assigned through linkage of job histories to job-exposure matrices (JEMs). However, available JEMs may have a coding system incompatible with the coding system used to code the job histories, necessitating a translation of the originally assigned job codes. Since manual recoding is usually not feasible in large studies, this is often done by use of automated crosswalks translating job codes from one system to another. We set out to investigate whether automatically translating job codes led to different exposure estimates compared with those resulting from manual recoding using the original job descriptions. METHODS: One hundred job histories were randomly drawn from the Netherlands Cohort Study on diet and cancer (NLCS), using a sampling strategy designed to oversample potentially exposed jobs. This resulted in 220 job codes that were automatically translated from the original Dutch coding system to the International Standard Classification of Occupations (ISCO)-68 and ISCO-88 as well as manually recoded from the job descriptions in the original questionnaire by two coders. Exposure to several agents (i.e. chromium, asbestos, silica, pesticides, aromatic solvents, and extremely low-frequency magnetic fields) was assigned by JEMs based on job codes resulting from automatic and manual recodings. RESULTS: The agreement between occupational exposure estimates based on the crosswalk versus those based on manual recoding reached a Cohen's Kappa (κ) of 0.66 or higher and were similar to the agreements between the two coders. CONCLUSIONS: Results of this study indicate that using automated crosswalks to recode job codes from one occupational classification system to another results only in a limited loss in agreement in assigned occupational exposure estimates compared with direct manual recoding. Therefore, in this case, crosswalks provide an efficient alternative to the costly and time-consuming direct manual recoding from job history descriptions from questionnaires.
BACKGROUND: In epidemiological studies, occupational exposure estimates are often assigned through linkage of job histories to job-exposure matrices (JEMs). However, available JEMs may have a coding system incompatible with the coding system used to code the job histories, necessitating a translation of the originally assigned job codes. Since manual recoding is usually not feasible in large studies, this is often done by use of automated crosswalks translating job codes from one system to another. We set out to investigate whether automatically translating job codes led to different exposure estimates compared with those resulting from manual recoding using the original job descriptions. METHODS: One hundred job histories were randomly drawn from the Netherlands Cohort Study on diet and cancer (NLCS), using a sampling strategy designed to oversample potentially exposed jobs. This resulted in 220 job codes that were automatically translated from the original Dutch coding system to the International Standard Classification of Occupations (ISCO)-68 and ISCO-88 as well as manually recoded from the job descriptions in the original questionnaire by two coders. Exposure to several agents (i.e. chromium, asbestos, silica, pesticides, aromatic solvents, and extremely low-frequency magnetic fields) was assigned by JEMs based on job codes resulting from automatic and manual recodings. RESULTS: The agreement between occupational exposure estimates based on the crosswalk versus those based on manual recoding reached a Cohen's Kappa (κ) of 0.66 or higher and were similar to the agreements between the two coders. CONCLUSIONS: Results of this study indicate that using automated crosswalks to recode job codes from one occupational classification system to another results only in a limited loss in agreement in assigned occupational exposure estimates compared with direct manual recoding. Therefore, in this case, crosswalks provide an efficient alternative to the costly and time-consuming direct manual recoding from job history descriptions from questionnaires.
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: Lowiek M Hubers; Alex R Schuurman; Jorie Buijs; Nahid Mostafavi; Marco J Bruno; Roel C H Vermeulen; Anke Huss; Henk R van Buuren; Ulrich Beuers Journal: JHEP Rep Date: 2021-10-09
Authors: Andrea Spinazzè; Dario Consonni; Francesca Borghi; Sabrina Rovelli; Andrea Cattaneo; Carolina Zellino; Barbara Dallari; Angela Cecilia Pesatori; Hans Kromhout; Susan Peters; Luciano Riboldi; Domenico Maria Cavallo; Carolina Mensi Journal: Int J Environ Res Public Health Date: 2022-03-13 Impact factor: 3.390