Aude Lacourt1,2, France Labrèche3,4, Mark S Goldberg5,6, Jack Siemiatycki1,7,8, Jérôme Lavoué1,4. 1. University of Montreal Hospital Research Center (CRCHUM), Montreal, QC, Canada. 2. Univ. Bordeaux, INSERM, BPH U1219-EPICENE, ISPED, Bordeaux, France. 3. Institut de recherche Robert-Sauvé en santé et en sécurité du travail (IRSST), Montreal, QC, Canada. 4. Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, QC, Canada. 5. Department of Medicine, McGill University, Montreal, QC, Canada. 6. Centre for Outcomes Research and Evaluation, Research Institute, McGill University Health Centre, Montreal, QC, Canada. 7. Department of Social and Preventive Medicine, School of Public Health, University of Montreal, Montreal, QC, Canada. 8. Guzzo-Cancer Research Society Chair on Environment and Cancer, School of Public Health, University of Montreal, Montreal, QC, Canada.
Abstract
Objectives: To estimate the level of agreement and identify notable differences in occupational exposures (agents) between men and women from retrospective assessments by expert coders. Methods: Lifetime occupational histories of 1657 men and 2073 women from two case-control studies, were translated into exposure estimates to 243 agents, from data on 13882 jobs. Exposure estimates were summarized as proportions and frequency-weighted intensity of exposure for 59 occupational codes by sex. Agreement between metrics of exposure in men's and women's jobs was determined with intraclass correlation coefficients (ICC) and weighted Kappa coefficients, using as unit of analysis ('cell') a combination of occupational code and occupational agent. 'Notable' differences between men and women were identified for each cell, according to a Bayesian hierarchical model for both proportion and frequency-weighted intensity of exposure. Results: For cells common to both men and women, the ICC for continuous probability of exposure was 0.84 (95% CI: 0.83-0.84) and 7.4% of cells showed notable differences with jobs held by men being more often exposed. A weighted kappa of 0.67 (95% CI: 0.61-0.73) was calculated for intensity of exposure, and an ICC of 0.67 (95% CI: 0.62-0.71) for frequency-weighted intensity of exposure, with a tendency of higher values of exposure metrics in jobs held by men. Conclusions: Exposures were generally in agreement between men and women. Some notable differences were identified, most of them explained by differential sub-occupations or industries or dissimilar reported tasks within the studied occupations.
Objectives: To estimate the level of agreement and identify notable differences in occupational exposures (agents) between men and women from retrospective assessments by expert coders. Methods: Lifetime occupational histories of 1657 men and 2073 women from two case-control studies, were translated into exposure estimates to 243 agents, from data on 13882 jobs. Exposure estimates were summarized as proportions and frequency-weighted intensity of exposure for 59 occupational codes by sex. Agreement between metrics of exposure in men's and women's jobs was determined with intraclass correlation coefficients (ICC) and weighted Kappa coefficients, using as unit of analysis ('cell') a combination of occupational code and occupational agent. 'Notable' differences between men and women were identified for each cell, according to a Bayesian hierarchical model for both proportion and frequency-weighted intensity of exposure. Results: For cells common to both men and women, the ICC for continuous probability of exposure was 0.84 (95% CI: 0.83-0.84) and 7.4% of cells showed notable differences with jobs held by men being more often exposed. A weighted kappa of 0.67 (95% CI: 0.61-0.73) was calculated for intensity of exposure, and an ICC of 0.67 (95% CI: 0.62-0.71) for frequency-weighted intensity of exposure, with a tendency of higher values of exposure metrics in jobs held by men. Conclusions: Exposures were generally in agreement between men and women. Some notable differences were identified, most of them explained by differential sub-occupations or industries or dissimilar reported tasks within the studied occupations.
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