TY - JOUR
T1 - Identifying gender differences in reported occupational information from three US population-based case-control studies
AU - Locke, Sarah J.
AU - Colt, Joanne S.
AU - Stewart, Patricia A.
AU - Armenti, Karla R.
AU - Baris, Dalsu
AU - Blair, Aaron
AU - Cerhan, James R.
AU - Chow, Wong Ho
AU - Cozen, Wendy
AU - Davis, Faith
AU - De Roos, Anneclaire J.
AU - Hartge, Patricia
AU - Karagas, Margaret R.
AU - Johnson, Alison
AU - Purdue, Mark P.
AU - Rothman, Nathaniel
AU - Schwartz, Kendra
AU - Schwenn, Molly
AU - Severson, Richard
AU - Silverman, Debra T.
AU - Friesen, Melissa C.
PY - 2014/12/1
Y1 - 2014/12/1
N2 - Objectives: Growing evidence suggests that genderblind 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-speci fic 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 χ2 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.
AB - Objectives: Growing evidence suggests that genderblind 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-speci fic 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 χ2 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.
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U2 - 10.1136/oemed-2013-101801
DO - 10.1136/oemed-2013-101801
M3 - Article
C2 - 24683012
AN - SCOPUS:84908616016
SN - 1351-0711
VL - 71
SP - 855
EP - 864
JO - Occupational and Environmental Medicine
JF - Occupational and Environmental Medicine
IS - 12
ER -