Sarah J Locke1, Joanne S Colt1, Patricia A Stewart2, Karla R Armenti3, Dalsu Baris1, Aaron Blair1, James R Cerhan4, Wong-Ho Chow5, Wendy Cozen6, Faith Davis7, Anneclaire J De Roos8, Patricia Hartge1, Margaret R Karagas9, Alison Johnson10, Mark P Purdue1, Nathaniel Rothman1, Kendra Schwartz11, Molly Schwenn12, Richard Severson11, Debra T Silverman1, Melissa C Friesen1. 1. Occupational and Environmental Epidemiology, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA. 2. Stewart Exposure Assessments, LLC, Arlington, Virginia, USA. 3. New Hampshire Department of Health and Human Services, Division of Public Health Services, Bureau of Public Health Statistics and Informatics, Concord, New Hampshire, USA. 4. Mayo Clinic College of Medicine, Rochester, Minnesota, USA. 5. The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. 6. Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, USA. 7. Department of Public Health Sciences, University of Alberta, Edmonton, Alberta, Canada. 8. Department of Environmental and Occupational Health, Drexel University School of Public Health, Philadelphia, Pennsylvania, USA. 9. Department of Community and Family Medicine, Dartmouth Medical School, Lebanon, New Hampshire, USA. 10. Vermont Department of Health, Burlington, Vermont, USA. 11. Department of Family Medicine and Public Health Sciences, Wayne State University, Detroit, Michigan, USA. 12. Maine Cancer Registry, Augusta, Maine, USA.
Abstract
OBJECTIVES: Growing evidence suggests that gender-blind assessment of exposure may introduce exposure misclassification, but few studies have characterised gender differences across occupations and industries. We pooled control responses to job-specific, industry-specific and exposure-specific questionnaires (modules) that asked detailed questions about work activities from three US population-based case-control studies to examine gender differences in work tasks and their frequencies. METHODS: We calculated the ratio of female-to-male controls that completed each module. For four job modules (assembly worker, machinist, health professional, janitor/cleaner) and for subgroups of jobs that completed those modules, we evaluated gender differences in task prevalence and frequency using χ(2) and Mann-Whitney U tests, respectively. RESULTS: The 1360 female and 2245 male controls reported 6033 and 12 083 jobs, respectively. Gender differences in female:male module completion ratios were observed for 39 of 45 modules completed by ≥20 controls. Gender differences in task prevalence varied in direction and magnitude. For example, female janitors were significantly more likely to polish furniture (79% vs 44%), while male janitors were more likely to strip floors (73% vs 50%). Women usually reported more time spent on tasks than men. For example, the median hours per week spent degreasing for production workers in product manufacturing industries was 6.3 for women and 3.0 for men. CONCLUSIONS: Observed gender differences may reflect actual differences in tasks performed or differences in recall, reporting or perception, all of which contribute to exposure misclassification and impact relative risk estimates. Our findings reinforce the need to capture subject-specific information on work tasks. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
OBJECTIVES: Growing evidence suggests that gender-blind assessment of exposure may introduce exposure misclassification, but few studies have characterised gender differences across occupations and industries. We pooled control responses to job-specific, industry-specific and exposure-specific questionnaires (modules) that asked detailed questions about work activities from three US population-based case-control studies to examine gender differences in work tasks and their frequencies. METHODS: We calculated the ratio of female-to-male controls that completed each module. For four job modules (assembly worker, machinist, health professional, janitor/cleaner) and for subgroups of jobs that completed those modules, we evaluated gender differences in task prevalence and frequency using χ(2) and Mann-Whitney U tests, respectively. RESULTS: The 1360 female and 2245 male controls reported 6033 and 12 083 jobs, respectively. Gender differences in female:male module completion ratios were observed for 39 of 45 modules completed by ≥20 controls. Gender differences in task prevalence varied in direction and magnitude. For example, female janitors were significantly more likely to polish furniture (79% vs 44%), while male janitors were more likely to strip floors (73% vs 50%). Women usually reported more time spent on tasks than men. For example, the median hours per week spent degreasing for production workers in product manufacturing industries was 6.3 for women and 3.0 for men. CONCLUSIONS: Observed gender differences may reflect actual differences in tasks performed or differences in recall, reporting or perception, all of which contribute to exposure misclassification and impact relative risk estimates. Our findings reinforce the need to capture subject-specific information on work tasks. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
Authors: G A Hansson; I Balogh; J U Byström; K Ohlsson; C Nordander; P Asterland; S Sjölander; L Rylander; J Winkel; S Skerfving Journal: Scand J Work Environ Health Date: 2001-02 Impact factor: 5.024
Authors: Karen Messing; Laura Punnett; Meg Bond; Kristina Alexanderson; Jean Pyle; Shelia Zahm; David Wegman; Susan R Stock; Sylvie de Grosbois Journal: Am J Ind Med Date: 2003-06 Impact factor: 2.214
Authors: Aude Lacourt; France Labrèche; Mark S Goldberg; Jack Siemiatycki; Jérôme Lavoué Journal: Ann Work Expo Health Date: 2018-11-12 Impact factor: 2.179
Authors: Thomas A Perry; Xia Wang; Lucy Gates; Camille M Parsons; Maria T Sanchez-Santos; Cesar Garriga; Cyrus Cooper; Michael C Nevitt; David J Hunter; Nigel K Arden Journal: Semin Arthritis Rheum Date: 2020-08-08 Impact factor: 5.431