Melissa C Friesen1, Bryan A Bassig1, Roel Vermeulen2, Xiao-Ou Shu3, Mark P Purdue1, Patricia A Stewart1,4, Yong-Bing Xiang5, Wong-Ho Chow6, Bu-Tian Ji1, Gong Yang3, Martha S Linet7, Wei Hu1, Yu-Tang Gao5, Wei Zheng3, Nathaniel Rothman1, Qing Lan1. 1. Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Rm. 6E634, Rockville, MD 20850, USA. 2. Institute for Risk Assessment Sciences, Utrecht University, Yalelaan 2, Utrecht 3508 TD, The Netherlands. 3. Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, 2525 West End Avenue, Nashville, TN 37203, USA. 4. Stewart Exposure Assessments, LLC, 6045 N 27th St, Arlington, VA 22207, USA. 5. Department of Epidemiology, Shanghai Cancer Institute, 2200 Xietu Road, Xuhui, Shanghai 200032, China. 6. Department of Epidemiology, The University of Texas MD Anderson Cancer Center, 1155 Pressler Street, Houston, TX 77030, USA. 7. Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20850, USA.
Abstract
Objectives: To provide insight into the contributions of exposure measurements to job exposure matrices (JEMs), we examined the robustness of an association between occupational benzene exposure and non-Hodgkin lymphoma (NHL) to varying exposure assessment methods. Methods: NHL risk was examined in a prospective population-based cohort of 73087 women in Shanghai. A mixed-effects model that combined a benzene JEM with >60000 short-term, area benzene inspection measurements was used to derive two sets of measurement-based benzene estimates: 'job/industry-specific' estimates (our presumed best approach) were derived from the model's fixed effects (year, JEM intensity rating) and random effects (occupation, industry); 'calibrated JEM' estimates were derived using only the fixed effects. 'Uncalibrated JEM' (using the ordinal JEM ratings) and exposure duration estimates were also calculated. Cumulative exposure for each subject was calculated for each approach based on varying exposure definitions defined using the JEM's probability ratings. We examined the agreement between the cumulative metrics and evaluated changes in the benzene-NHL associations. Results: For our primary exposure definition, the job/industry-specific estimates were moderately to highly correlated with all other approaches (Pearson correlation 0.61-0.89; Spearman correlation > 0.99). All these metrics resulted in statistically significant exposure-response associations for NHL, with negligible gain in model fit from using measurement-based estimates. Using more sensitive or specific exposure definitions resulted in elevated but non-significant associations. Conclusions: The robust associations observed here with varying benzene assessment methods provide support for a benzene-NHL association. While incorporating exposure measurements did not improve model fit, the measurements allowed us to derive quantitative exposure-response curves. Published by Oxford University Press on behalf of the British Occupational Hygiene Society 2017.
Objectives: To provide insight into the contributions of exposure measurements to job exposure matrices (JEMs), we examined the robustness of an association between occupational benzene exposure and non-Hodgkin lymphoma (NHL) to varying exposure assessment methods. Methods: NHL risk was examined in a prospective population-based cohort of 73087 women in Shanghai. A mixed-effects model that combined a benzene JEM with >60000 short-term, area benzene inspection measurements was used to derive two sets of measurement-based benzene estimates: 'job/industry-specific' estimates (our presumed best approach) were derived from the model's fixed effects (year, JEM intensity rating) and random effects (occupation, industry); 'calibrated JEM' estimates were derived using only the fixed effects. 'Uncalibrated JEM' (using the ordinal JEM ratings) and exposure duration estimates were also calculated. Cumulative exposure for each subject was calculated for each approach based on varying exposure definitions defined using the JEM's probability ratings. We examined the agreement between the cumulative metrics and evaluated changes in the benzene-NHL associations. Results: For our primary exposure definition, the job/industry-specific estimates were moderately to highly correlated with all other approaches (Pearson correlation 0.61-0.89; Spearman correlation > 0.99). All these metrics resulted in statistically significant exposure-response associations for NHL, with negligible gain in model fit from using measurement-based estimates. Using more sensitive or specific exposure definitions resulted in elevated but non-significant associations. Conclusions: The robust associations observed here with varying benzene assessment methods provide support for a benzene-NHL association. While incorporating exposure measurements did not improve model fit, the measurements allowed us to derive quantitative exposure-response curves. Published by Oxford University Press on behalf of the British Occupational Hygiene Society 2017.
Authors: Susan Peters; Roel Vermeulen; Adrian Cassidy; Andrea 't Mannetje; Martie van Tongeren; Paolo Boffetta; Kurt Straif; Hans Kromhout Journal: Occup Environ Med Date: 2010-09-24 Impact factor: 4.402
Authors: Eero Pukkala; Johannes Guo; Pentti Kyyrönen; Marja-Liisa Lindbohm; Markku Sallmén; Timo Kauppinen Journal: Scand J Work Environ Health Date: 2005-04 Impact factor: 5.024
Authors: Melissa C Friesen; Hugh W Davies; Kay Teschke; Aleck S Ostry; Clyde Hertzman; Paul A Demers Journal: Epidemiology Date: 2007-01 Impact factor: 4.822