Identification of occupations susceptible to high exposure and risk associated with multiple toxicants in an observational study: National Health and Nutrition Examination Survey 1999-2014

Abstract

Occupational exposures to toxicants are estimated to cause over 370 000 premature deaths annually. The risks due to multiple workplace chemical exposures and those occupations most susceptible to the resulting health effects remain poorly characterized. The aim of this study is to identify occupations with elevated toxicant biomarker concentrations and increased health risk associated with toxicant exposures in a diverse working US population. For this observational study of 51 008 participants, we used data from the 1999-2014 National Health and Nutrition Examination Survey. We characterized differences in chemical exposures by occupational group for 131 chemicals by applying a series of generalized linear models with the outcome as biomarker concentrations and the main predictor as the occupational groups, adjusting for age, sex, race/ethnicity, poverty income ratio, study period, and biomarker of tobacco use. For each occupational group, we calculated percentages of participants with chemical biomarker levels exceeding acceptable health-based guidelines. Blue-collar workers from "Construction," "Professional, Scientific, Technical Services," "Real Estate, Rental, Leasing," "Manufacturing," and "Wholesale Trade" have higher biomarker levels of toxicants such as several heavy metals, acrylamide, glycideamide, and several volatile organic compounds (VOCs) compared with their white-collar counterparts. Moreover, blue-collar workers from these industries have toxicant concentrations exceeding acceptable levels: arsenic (16%-58%), lead (1%-3%), cadmium (1%-11%), glycideamide (3%-6%), and VOCs (1%-33%). Blue-collar workers have higher toxicant levels relative to their white-collar counterparts, often exceeding acceptable levels associated with noncancer effects. Our findings identify multiple occupations to prioritize for targeted interventions and health policies to monitor and reduce toxicant exposures.

Keywords: biomonitoring equivalents; environmental chemicals; occupational epidemiology; occupational exposures; risk assessment; unsupervised learning.

Figure 1.

Schematic description on curation of chemical biomarker and inclusion criteria of participants and of the analytical methods used to characterize occupational variations in chemical exposures. Reference group for the analysis on the industry-collar combinations is white collars from public administration.

Figure 2.

Panel of bar plots showing the percentage of participants by (A) age group, (B) sex, (C) race/ethnicity, (D) poverty income ratio (PIR), (E) smoking status, and (F) NHANES cycle for each industry–collar combination and unemployment status. The occupational groups are ordered in ascending order based on percentage of (A) participants who are 28 years and younger, (B) males, (C) Non-Hispanic Whites, (D) PIR = [0,1] (i.e. participants who are below the poverty income line), (E) participants who do not smoke, and (F) participants in 1999–2002. The “NHANES Population” consists of all participants in 1999–2014. The “NHANES 16+ Population” consists of participants in 1999–2014 and are 16 years old or older. Smoking status is defined using serum cotinine levels: no smoking ≤ 1 ng/mL, secondhand smoke 1–3 ng/mL, and active smoking > 3 ng/mL. These individual figures and text for the statistics are available on our interactive app at https://chiragjp.shinyapps.io/nhanes_occupational_exposures/ in option “Bar plot of percentages of demographic categories” under “Choose a plot.”

Figure 3.

Panel of boxplots of chemical distribution for (A) blood lead, (B) m-/p-xylene, (C) cotinine, (D) 2,4-d, (E) glycidamide, and (F) sum of DEHP metabolites. The pink line represents the biomonitoring equivalent of the chemical for noncancer effects. The “NHANES Population” consists of participants in 1999–2014. The “NHANES 16+ Population” consists of participants in 1999–2014 and are 16 years old or older. Percent differences are derived from fully adjusted models, which were adjusted for age, sex, race/ethnicity, poverty income ratio, study period, and serum cotinine (biomarker of smoking). Reference group for the occupational groups is comprised of white collars from Public Administration. Number of asterisks indicate statistical significance of the percent differences: *(P-value ∈ ([0.01, 0.05]), **(P-value ∈ ([0.001, 0.01]), and ***(P-value ≤ 0.001). The P-values corrected for multiple comparison with the Benjamini and Hochberg FDR procedure of 5%. These individual figures and text for the statistics are available on our interactive app at https://chiragjp.shinyapps.io/nhanes_occupational_exposures/ in option “Box and forest plots of differences in chemical concentrations” under “Choose a plot.”

Figure 4.

Heatmap of percent differences in chemical biomarker concentrations by occupational group, relative to white collars from Public Administration. Chemical biomarkers in white color indicate that the concentrations are the same between the given industry–collar combination and the reference group. The color bar for the columns represents the collar categorization and unemployment. The color bar for the rows represents the chemical classes. Blue presents the blue-collar workers. White represents the white-collar workers. Gray presents the unemployed participants. The dendrogram of the occupational groups is defined based on using the average linkage function with Pearson’s correlation-based distance. Results are adjusted for age, sex, race/ethnicity, poverty income ratio, study period, and serum cotinine (biomarker of smoking). Number of asterisks indicate statistical significance of the percent differences: *(P-value ∈ ([0.01, 0.05]), **(P-value ∈ ([0.001, 0.01]), and ***(P-value ≤ 0.001). This figure and text for statistics are available on our interactive app at https://chiragjp.shinyapps.io/nhanes_occupational_exposures/ in option “Heatmap of differences in chemical concentration” under “Choose a plot.”

Figure 5.

Heatmap of percentages of workers with biomarker levels exceeding biomonitoring equivalents for noncancer effects. The numbers in blue text on the dendrogram indicate the clusters of occupational groups. Chemical biomarkers in white color indicate that no worker in a given occupational group has biomarker levels exceeding acceptable guidelines. The color bar for the columns represents the collar categorization and unemployment. Blue presents the blue-collar workers. White represents the white-collar workers. Gray presents the unemployed participants. This figure and text for statistics are available on our interactive app at https://chiragjp.shinyapps.io/nhanes_occupational_exposures/ in option “Heatmap of percentages of workers above biomonitoring equivalents” under “Choose a plot.”