OBJECTIVES: In attempts to overcome the limitations of self-reported data in occupational health research, job-exposure matrices, which assign exposure by occupation, have emerged as an objective approach for assessing occupational exposures. On the basis of a lung cancer case-control study conducted in the Greater Toronto Area, 1997-2002, assessment of occupational exposure to asbestos was compared using self-reports and a general population job-exposure matrix (DOM-JEM). METHODS: Cases and frequency matched controls provided life-time job histories and self-reported exposures to potential lung carcinogens including asbestos through a detailed questionnaire. Exposure to asbestos was also assigned to each job by linking occupational histories with DOM-JEM. Agreement in classification of exposed and unexposed jobs according to self-reports and DOM-JEM was evaluated using Cohen's κ. Risks for lung cancer were estimated using unconditional logistic regression for each exposure assessment approach. RESULTS: The prevalence of occupational asbestos exposure was greater when based on DOM-JEM than when based on self-reports. Agreement in classifying exposure to jobs between the two assessment approaches was poor. The risk of lung cancer was not elevated among workers who self-reported asbestos exposure, whereas workers considered exposed on the basis of DOM-JEM were almost twice as likely as unexposed workers to be diagnosed with lung cancer (OR 1.9, 95% CI 1.3 to 2.7). CONCLUSIONS: It is generally assumed by epidemiologists that self-reported exposure assessments result in inflated risk estimates. In this study, self-reports found no association with a well-established risk factor, whereas a high-quality job-exposure matrix revealed relative risk estimates that are more consistent with previous findings.
OBJECTIVES: In attempts to overcome the limitations of self-reported data in occupational health research, job-exposure matrices, which assign exposure by occupation, have emerged as an objective approach for assessing occupational exposures. On the basis of a lung cancer case-control study conducted in the Greater Toronto Area, 1997-2002, assessment of occupational exposure to asbestos was compared using self-reports and a general population job-exposure matrix (DOM-JEM). METHODS: Cases and frequency matched controls provided life-time job histories and self-reported exposures to potential lung carcinogens including asbestos through a detailed questionnaire. Exposure to asbestos was also assigned to each job by linking occupational histories with DOM-JEM. Agreement in classification of exposed and unexposed jobs according to self-reports and DOM-JEM was evaluated using Cohen's κ. Risks for lung cancer were estimated using unconditional logistic regression for each exposure assessment approach. RESULTS: The prevalence of occupational asbestos exposure was greater when based on DOM-JEM than when based on self-reports. Agreement in classifying exposure to jobs between the two assessment approaches was poor. The risk of lung cancer was not elevated among workers who self-reported asbestos exposure, whereas workers considered exposed on the basis of DOM-JEM were almost twice as likely as unexposed workers to be diagnosed with lung cancer (OR 1.9, 95% CI 1.3 to 2.7). CONCLUSIONS: It is generally assumed by epidemiologists that self-reported exposure assessments result in inflated risk estimates. In this study, self-reports found no association with a well-established risk factor, whereas a high-quality job-exposure matrix revealed relative risk estimates that are more consistent with previous findings.
Authors: Isabelle Deltour; Amélie Massardier-Pilonchery; Brigitte Schlehofer; Klaus Schlaefer; Martine Hours; Joachim Schüz Journal: Int Arch Occup Environ Health Date: 2019-04-26 Impact factor: 3.015
Authors: Calvin B Ge; Melissa C Friesen; Hans Kromhout; Susan Peters; Nathaniel Rothman; Qing Lan; Roel Vermeulen Journal: Ann Work Expo Health Date: 2018-11-12 Impact factor: 2.179
Authors: Anna Suraya; Dennis Nowak; Astrid Widajati Sulistomo; Aziza Ghanie Icksan; Elisna Syahruddin; Ursula Berger; Stephan Bose-O'Reilly Journal: Int J Environ Res Public Health Date: 2020-01-16 Impact factor: 3.390