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Published on: May 9, 2018
A comparison of exposure assessment approaches: lung cancer and occupational asbestos exposure in a population-based
Jill S Hardt1, Roel Vermeulen, Susan Peters
1Occupational Cancer Research Centre, Cancer Care Ontario, Toronto, Ontario, Canada.
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.
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