US OCCUPATIONAL BURDEN ESTIMATES, 2021

Here, you can view the burden estimates disaggregated at the specific occupation level. Explore the dataset by selecting the inputs below. You can view the generated table or figure by toggling between the tabs below. Occupations are organized using the 2018 Census occupation code structure. See the ABOUT page to learn more about the methods, limitations, and definitions in this dataset

Citation: Stephan-Recaido S, Peckham T, Lavoué, J, Baker M. US Occupational Exposure Burden Estimates, 2021 [Internet]. 2023. Available: https://shiny.deohs.washington.edu/app_direct_i/us-exposures-app/_/

Page last updated: 10 January 2024

AVERAGE EMPLOYMENT ESTIMATES IN THE US BY SOCIODEMOGRAPHIC GROUP: 2021 CURRENT POPULATION SURVEY

OCCUPATIONS WITH NO EXPOSURE INFORMATION

The table below lists the Census occupation codes in which no exposure information is available, as well as the average number of workers counted within the codes in 2021. This portion of the workforce is disproportionately employed in white collar occupations in which chemical exposures are less likely (e.g., Software developers), and are thus expected to contribute only small numbers of exposed workers to the overall burden estimates. However, workers in some of the excluded occupations likely do experience relevant chemical exposures (e.g., Home health aides). It is therefore important to consider that although these occupations only contribute small numbers to the overall occupational exposure burden in the US, they are still amenable to intervention.

Note. Employee counts are rounded to the nearest thousand.

Data Sources: Workforce demographic data were obtained from the National Institute for Occupational Safety and Health (NIOSH) Employed Labor Force (ELF) query system, which provides estimates based on a subset of the joint US Census Bureau and the US Bureau of Labor Statistics (BLS) Current Population Survey (CPS).

Citation: Stephan-Recaido S, Peckham T, Lavoué, J, Baker M. US Occupational Exposure Burden Estimates, 2021 [Internet]. 2023. Available: https://shiny.deohs.washington.edu/app_direct_i/us-exposures-app/_/

Page last updated: 10 January 2024

ABOUT

This dataset was created as part of a study by researchers in the Department of Environmental and Occupational Health Sciences at the University of Washington and Hazardous Waste Management Program in King County. The study aims were to estimate the number and prevalence of United States (US) workers exposed to over 240 occupational hazards and examine exposure inequities across sociodemographic groups. The data generated by the study can be used to help address gaps in occupational exposure surveillance in the US and inform priorities for research, policy, and intervention efforts to improve worker health and safety. The full dataset has been provided here for occupational and public health researchers, policymakers, government employees, practitioners, and others to use.

This project was led by Marissa Baker, Trevor Peckham and Shelley Stephan-Recaido. The Canadian job-exposure matrix (CANJEM) was developed by Jérôme Lavoué, who also collaborated on this project.

METHODS AND DATA SOURCES

We combined occupational exposure data from CANJEM spanning 1985-2005 with worker demographic data from 2021 from the US Census Bureau and US Bureau of Labor Statistics (BLS) joint Current Population Survey (CPS) to characterize the burden and distribution of exposure to occupational hazards by sociodemographic groups in the US.

DATA SOURCES

CURRENT POPULATION SURVEY

The CPS is a monthly survey of households used to generate employment statistics on the civilian, non-institutionalized labor force in the US. We obtained 2021 employment and worker demographic data from the Employed Labor Force (ELF) query system developed by the National Institute for Occupational Safety and Health (NIOSH) Division of Safety Research, which generates estimates based on the CPS. We utilized employment counts by detailed occupation and the following sociodemographic groups:

• Race and ethnicity: American Indian or Alaska Native (AIAN); Asian; Black or African American; multiracial; Native Hawaiian or other Pacific Islander (NHPI); White, non-Hispanic; Hispanic or Latino
• Sex: male; female
• Education: less than high school diploma or equivalent (<high school); high school diploma or equivalent (high school); some college or associate degree (some college/associate); bachelor’s or advanced degree (≥bachelor's)
• Nativity and citizenship status: native-born; foreign-born, citizen; foreign-born, noncitizen

These categories represent axes of social and health inequity and allow us to examine how occupational segregation across sociodemographic characteristics may contribute to unequal occupational exposure burdens.

For the race and ethnicity categories, persons within each race category are of any ethnicity, except for persons who identify as non-Hispanic White, and persons of Hispanic/Latino ethnicity are also counted in their preferred race category. Consequently, estimates provided for the race and ethnicity categories are non-mutually exclusive. American Indian/Alaska Native, Asian, Black/African American, multiracial, Native Hawaiian/Pacific Islander, and Hispanic/Latino workers were aggregated into an additional category of all racial and ethnic minoritized groups.

CANADIAN JOB-EXPOSURE MATRIX

CANJEM is an occupational exposure information system that provides estimates of the probability, intensity, and frequency of exposure to 258 occupational hazards. Briefly, it is a semi-quantitative job-exposure matrix (JEM) based on over 40-person years of expert assessment of occupational exposures from over 30,000 jobs described by nearly 9,000 subjects in four case control studies of various cancers conducted in Canada from 1979 to 2004. Additional details on its development, including definitions of occupational agents, can be viewed here: http://canjem.ca.

We used versions of CANJEM coded into 3-, 5-, and 6- digit 2010 SOC codes that summarized data collected from jobs held between 1985 and 2005 (closest to present day) and included occupations with a sample size of ≥ 5 jobs from ≥ 5 subjects. We included 247 occupational agents from CANJEM, of which three are radiation hazards (e.g., ionizing radiation) and the remaining 244 are specific chemicals (e.g., formaldehyde), mixtures (e.g., gasoline), classes of chemicals (e.g., aliphatic aldehydes), or chemical groups based on use (e.g., cleaning and antimicrobial agents).

ANALYTIC APPROACH

ESTIMATES OF EXPOSURE BURDEN

For each occupational exposure, we estimated the number of exposed workers in the US, with separate estimates for those experiencing any exposure (frequency-weighted intensity [FWI] ≥ 0.05) and a high level of exposure (FWI ≥ 5). This was calculated by multiplying the agent-occupation specific probability of exposure by the number of workers in each occupation and summing the estimated number of exposed workers across all occupations.

We calculated the prevalence of workers estimated to have any exposure or a high level of exposure to each agent by dividing the number of exposed workers by the total number of workers in the US. All estimates were calculated for all workers and separately for each sociodemographic group.

ESTIMATES OF EXPOSURE DISPROPORTIONALITY

Estimates of exposure disproportionality reflect the extent to which sociodemographic groups are over- or underrepresented in exposure burden. We calculated estimates of exposure disproportionality by finding the absolute and relative differences between the number of workers estimated to be exposed and the number of workers expected to be exposed based on their overall proportion of the total workforce. Overrepresentation occurs when the number of exposed workers in a particular sociodemographic group exceeds the number of workers expected to be exposed based on their overall proportion of the total workforce.

LIMITATIONS

This study has several limitations which are important to understand when interpreting the results. First, it is important to acknowledge occupational hazards and populations that are not covered in this analysis. Military and institutionalized workers are excluded from the CPS, so these populations were not covered. Approximately a quarter of the counted workforce did not have exposure information because of employment in occupations either missing from CANJEM or excluded on the basis of our selection criteria. The portion of the workforce without CANJEM-provided exposure information was disproportionately employed in white-collar and service occupations (88% employed within white-collar and service occupations) in which chemical exposures are less likely, and would thus be expected to contribute minimally to the overall burden estimates. In assuming workers without exposure information had no occupational exposure, we therefore expect that our results may slightly underestimate true exposure prevalence. Furthermore, the portion of the workforce with CANJEM provided exposure information was slightly more likely to be from historically marginalized groups, including REM groups as a whole; Hispanic/Latino, Black/African American, and foreignborn noncitizen persons; and persons with lower educational attainment. We therefore expect that our results may slightly overestimate exposure disparities for those groups overrepresented in the sample with CANJEM-provided information. Overall, we feel our primary estimates balance adequate coverage of the workforce with valid estimates of exposure and reduced misclassification, while being specific enough to inform future research, practice, and policy. It is still important to consider that workers in some of the excluded occupations likely do experience relevant chemical exposures (e.g., home health aides) and are amenable to intervention efforts. Furthermore, while our analysis includes many chemicals, it does not cover other important occupational hazards, including psychosocial, biological, physical, and other chemical hazards.

Secondly, the probabilities of exposures of the CANJEM data are static and based on jobs held by an urban Canadian population between 1985 and 2005, which is geographically and temporally different than the US population in 2021 and may not account for differences in industries, occupations, and regulations between the 2 populations or changes in exposures over time. These limitations could potentially lead to an over- or underestimation of exposures for some agents attributable to, for example, changes in federal and state regulations (e.g., crystalline silica), increased knowledge and awareness of certain occupational hazards (e.g., phthalates), changes in protective technologies (e.g., carbon monoxide), and changes in work practices (e.g., cleaning and antimicrobial agents because of the COVID-19 pandemic30). It is therefore important to consider the historical context of these agents when interpreting the study’s findings. Despite these limitations, CANJEM is the most comprehensive JEM available for a wide range of occupations and chemical exposures in North America.

Thirdly, another important limitation is exposure misclassification associated with the use of a JEM. All individuals within an occupation code were assigned the same probability of exposure, and we were therefore unable to account for intraoccupational exposure differences across individuals or groups that may exist because of differences in assigned tasks or other occupational inequities. Exposure disparities identified in our analysis can only be attributed to the differential distribution of workers across occupations (i.e., occupational segregation). Misclassification may have also been introduced from the use of crosswalks needed to merge the datasets by a common occupational classification system.

DEFINITIONS

Agent: Definitions of occupational agents can be viewed on the CANJEM website.

Any exposure: An exposure with an FWI ≥ 0.05, which corresponds to low exposure for two hours per week.

Frequency-weighted intensity(FWI): A measure of intensity of exposure to the agent (i.e., low = 1, medium = 5, high = 25) averaged over a 40-hour workweek (FWI = exposure intensity*frequency of exposure in hours worked per week/40 hours). A higher FWI indicates increased exposure, either in terms of frequency of exposure or intensity of exposure.

High exposure: An exposure with an FWI ≥ 5, which corresponds to medium exposure for 40 hours per week or a high exposure for eight or more hours per week.

Overrepresentation: Overrepresentation occurs when the number of exposed workers in a particular sociodemographic group exceeds the number of workers expected to be exposed based on their overall proportion of the total workforce (e.g., workers identifying as Hispanic/Latino compose 17.6% of the overall US workforce, but 31.2% of the workforce exposed to cleaning agents. They are therefore overrepresented in exposure to cleaning agents.)

Racial and ethnic minoritized groups: Racial and ethnic minoritized groups include persons who identify as American Indian or Alaska Native, Asian, Black or African American, multiracial, Native Hawaiian or other Pacific Islander, or Hispanic or Latino.

Sociodemographic group: A specific population defined by a combination of social and demographic factors. Here, we looked at sociodemographic groups as defined by race and ethnicity, sex, educational attainment, and nativity and citizenship status.

PUBLICATIONS

Shelley C. Stephan-Recaido, Trevor K. Peckham, Jérôme Lavoué, Marissa G. Baker, “Characterizing the Burden of Occupational Chemical Exposures by Sociodemographic Groups in the United States, 2021”, American Journal of Public Health 114, no. 1 (January 1, 2024): pp. 57-67. https://doi.org/10.2105/AJPH.2023.307461

FUNDING SOURCES

This project was supported by the National Institute for Occupational Safety and Health (NIOSH) under Federal Training Grant T42OH008433, and the National Institute of Environmental Health Sciences (NIEHS) under award P30ES00703. Additional funding was provided by the Hazardous Waste Fund, administered by the Hazardous Waste Management Program in King County, Washington. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIOSH, NIEHS, or King County.

CONTACT INFORMATION

Please contact Marissa Baker at bakermg@uw.edu for any questions.

Citation: Stephan-Recaido S, Peckham T, Lavoué, J, Baker M. US Occupational Exposure Burden Estimates, 2021 [Internet]. 2023. Available: https://shiny.deohs.washington.edu/app_direct_i/us-exposures-app/_/

Page last updated: 10 January 2024