Characterizing the Burden and Distribution of Occupational Exposures by Sociodemographic Groups in the United States: A Novel Application of Job-Exposure Matrix (JEM) Data

Thesis (Master's)--University of Washington, 2022Introduction: Occupational exposure surveillance has historically been of limited focus in the United States (US). However, understanding the burden of occupational exposures is critical for the primary prevention of work-related injuries and illnesses. The primary objective of this analysis was two-fold: to estimate the number and prevalence of workers exposed to over 200 occupational hazards in the US, and to explore patterns of exposure across sociodemographic groups. The secondary objective was to identify occupations with the highest exposure burdens for each of the occupational hazards explored in this analysis. Methods: For this analysis, occupational exposure data from the Canadian job-exposure matrix (CANJEM) was combined with worker demographic and wage data from the US Census Bureau and US Bureau of Labor Statistics (BLS) Current Population Survey (CPS) and US BLS Occupational Employment and Wage Statistics (OEWS) survey to characterize the burden and distribution of hazardous exposures by sociodemographic groups in the US. Further, an Exposure Burden Index (EBI) was developed to identify occupations with high burdens of exposure, based on the average of the rank orders for probability of exposure (i.e., likelihood of exposure), frequency-weight intensity of exposure (i.e., magnitude of exposure), and the number of estimated exposed workers (i.e., extent of exposure) for each agent in the analysis. Occupations were additionally characterized by wage and other measures of inequity. Results: Of the occupational hazards examined in this analysis, the most prevalent exposures experienced by workers in the US were cleaning agents (11.8% of workforce exposed), engine emissions (10.9%), organic solvents (10.4%), biocides (8.4%), and PAHs from any source (7.8%). Exposures were found to be unevenly distributed by sociodemographic groups. The majority of exposures in this analysis disproportionately burdened workers who were of color, except those identifying as Asian; male; lower educated; and foreign-born. Conclusions: The findings from this descriptive analysis suggest that the least privileged sociodemographic groups tend to bear the greatest burden of occupational exposures in the US. To our knowledge, this is the first study to combine a population-based job-exposure matrix (JEM) with employment and demographic data to estimate the burden of occupational exposures and characterize exposure disparities among sociodemographic groups in the US. The wealth of data generated in this analysis can help identify the extent of occupational exposures, specific populations disproportionately burdened by exposures and at risk of excess occupational illnesses, and occupations with high exposure burdens that may not otherwise have been identified through current health outcome-based occupational health surveillance systems. This information can be used to target occupational health research, policy, and intervention efforts aimed at reducing occupational illnesses in the US. The incorporation of sociodemographic information can additionally help inform equitable approaches to reduce occupational exposure and health disparities