Evaluating measurement error in estimates of worker exposure assessed in parallel by personal and biological monitoring

Repeated measurements of 21 urinary metabolites of volatile organic compounds and their associations with three selected oxidative stress biomarkers in 0-7-year-old healthy children from south and central China

Human beings are extensively and concurrently exposed to multiple volatile organic compounds (VOCs, including some Class I human carcinogens), which may induce oxidative stress in human body. Data on urinary metabolites of VOCs (mVOCs) among young children are limited. No studies have examined their inter-day variability of mVOCs and their associations with oxidative stress biomarkers (OSBs) using repeated urine samples from children. In this study, we measured twenty one mVOCs and three OSBs [8-hydroxy-2'-deoxyguanosine (8-OHdG; for DNA), 8-hydroxyguanosine (8-OHG; for RNA], and 4-hydroxy nonenal mercapturic acid (HNEMA; for lipid)] in 390 urine samples of 130 children (three samples on three consecutive days provided by each participant) aged 0-7 years from September 2018 to January 2019 in Shenzhen, south China, and Wuhan, central China. HPMMA (3-hydroxypropyl-1-methyl mercapturic acid/N-Acetyl-S-(3-hydroxypropyl-1-methyl)-l-cysteine), 3HPMA (3-hydroxypropyl mercapturic acid/N-Acetyl-S-(3-hydroxypropyl)-l-cysteine), and ATCA (2-aminothiazoline-4-carboxylic acid) had higher specific gravity-adjusted median concentrations (1 383, 286, and 273 μg/L, respectively) than the others. Intraclass correlation coefficients of mVOCs ranged from 0.29 to 0.71. After false-discovery rate (FDR, defined as FDR q-value < 0.05) adjustment, linear mixed-effects models revealed that 14 mVOCs were positively associated with 8-OHdG (β range: 0.09-0.37), 11 mVOCs were positively associated with 8-OHG (β range: 0.08-0.30), and 11 mVOCs were positively associated with HNEMA (β range: 0.21-0.70) in urine. Considering the weight of the mVOC index accounted for the associations, based on the weighted quantile sum regression model, parent compounds of DHBMA (3,4-dihydroxybutyl mercapturic acid/N-Acetyl-S-(3,4-dihydroxybutyl)-l-cysteine) and t,t-MA (trans,trans-muconic acid) should be listed as priority VOCs for management to mitigate health risks. For the first time, this study characterized the inter-day variability of urinary mVOCs and their associations with selected OSBs (8-OHdG, 8-OHG, and NHEMA) in young, healthy Chinese children.

Estimation of Lead Exposure Intensity by Industry Using Nationwide Exposure Databases in Korea

BACKGROUND

In a previous study, we estimated exposure prevalence and the number of workers exposed to carcinogens by industry in Korea. The present study aimed to evaluate the optimal exposure intensity indicators of airborne lead exposure by comparing to blood lead measurements for the future development of the carcinogen exposure intensity database.

METHODS

Data concerning airborne lead measurements and blood lead levels were collected from nationwide occupational exposure databases, compiled between 2015 and 2016. Summary statistics, including the arithmetic mean (AM), geometric mean (GM), and 95th percentile level (X95) were calculated by industry both for airborne lead and blood lead measurements. Since many measurements were below the limits of detection (LODs), the simple replacement with half of the LOD and maximum likelihood estimation (MLE) methods were used for statistical analysis. For examining the optimal exposure indicator of airborne lead exposure, blood lead levels were used as reference data for subsequent rank correlation analyses.

RESULTS

A total of 19,637 airborne lead measurements and 32,848 blood lead measurements were used. In general, simple replacement showed a higher correlation than MLE. The results showed that AM and X95 using simple replacement could be used as optimal exposure intensity indicators, while X95 showed better correlations than AM in industries with 20 or more measurements.

CONCLUSION

Our results showed that AM or X95 could be potential candidates for exposure intensity indicators in the Korean carcinogen exposure database. Especially, X95 is an optimal indicator where there are enough measurements to compute X95 values.

Comparison of personal air benzene and urine t,t-muconic acid as a benzene exposure surrogate during turnaround maintenance in petrochemical plants

Previous studies have shown that biomarkers of chemicals with long half-lives may be better surrogates of exposure for epidemiological analyses, leading to less attenuation of the exposure-disease association, than personal air samples. However, chemicals with short half-lives have shown inconsistent results. In the present study, we compared pairs of personal air benzene and its short-half-life urinary metabolite trans,trans-muconic acid (t,t-MA), and predicted attenuation bias of theoretical exposure-disease association. Total 669 pairs of personal air benzene and urine t,t-MA samples were taken from 474 male workers during turnaround maintenance operations held in seven petrochemical plants. Maintenance jobs were classified into 13 groups. Variance components were calculated for personal air benzene and urine t,t-MA separately to estimate the attenuation of the theoretical exposure-disease association. Personal air benzene and urine t,t-MA showed similar attenuation of the theoretical exposure-disease association. Analyses for repeated measurements showed similar results, while in analyses for values above the limits of detection (LODs), urine t,t-MA showed less attenuation of the theoretical exposure-disease association than personal air benzene. Our findings suggest that there may be no significant difference in attenuation bias when personal air benzene or urine t,t-MA is used as a surrogate for benzene exposure.

Evaluating predictors of lead exposure for activities disturbing materials painted with or containing lead using historic published data from U.S. workplaces

OBJECTIVES

We evaluated predictors of differences in published occupational lead concentrations for activities disturbing material painted with or containing lead in U.S. workplaces to aid historical exposure reconstruction.

METHODS

For the aforementioned tasks, 221 air and 113 blood lead summary results (1960-2010) were extracted from a previously developed database. Differences in the natural log-transformed geometric mean (GM) for year, industry, job, and other ancillary variables were evaluated in meta-regression models that weighted each summary result by its inverse variance and sample size.

RESULTS

Air and blood lead GMs declined 5%/year and 6%/year, respectively, in most industries. Exposure contrast in the GMs across the nine jobs and five industries was higher based on air versus blood concentrations. For welding activities, blood lead GMs were 1.7 times higher in worst-case versus non-worst case scenarios.

CONCLUSIONS

Job, industry, and time-specific exposure differences were identified; other determinants were too sparse or collinear to characterize. Am. J. Ind. Med. 60:189-197, 2017. © 2017 Wiley Periodicals, Inc.

Evaluating uncertainty to strengthen epidemiologic data for use in human health risk assessments

BACKGROUND

There is a recognized need to improve the application of epidemiologic data in human health risk assessment especially for understanding and characterizing risks from environmental and occupational exposures. Although there is uncertainty associated with the results of most epidemiologic studies, techniques exist to characterize uncertainty that can be applied to improve weight-of-evidence evaluations and risk characterization efforts.

METHODS

This report derives from a Health and Environmental Sciences Institute (HESI) workshop held in Research Triangle Park, North Carolina, to discuss the utility of using epidemiologic data in risk assessments, including the use of advanced analytic methods to address sources of uncertainty. Epidemiologists, toxicologists, and risk assessors from academia, government, and industry convened to discuss uncertainty, exposure assessment, and application of analytic methods to address these challenges.

SYNTHESIS

Several recommendations emerged to help improve the utility of epidemiologic data in risk assessment. For example, improved characterization of uncertainty is needed to allow risk assessors to quantitatively assess potential sources of bias. Data are needed to facilitate this quantitative analysis, and interdisciplinary approaches will help ensure that sufficient information is collected for a thorough uncertainty evaluation. Advanced analytic methods and tools such as directed acyclic graphs (DAGs) and Bayesian statistical techniques can provide important insights and support interpretation of epidemiologic data.

CONCLUSIONS

The discussions and recommendations from this workshop demonstrate that there are practical steps that the scientific community can adopt to strengthen epidemiologic data for decision making.

Evaluation of occupational exposure: comparison of biological and environmental variabilities using physiologically based toxicokinetic modeling

PURPOSE

Few studies compare the variabilities that characterize environmental (EM) and biological monitoring (BM) data. Indeed, comparing their respective variabilities can help to identify the best strategy for evaluating occupational exposure. The objective of this study is to quantify the biological variability associated with 18 bio-indicators currently used in work environments.

METHOD

Intra-individual (BV(intra)), inter-individual (BV(inter)), and total biological variability (BV(total)) were quantified using validated physiologically based toxicokinetic (PBTK) models coupled with Monte Carlo simulations. Two environmental exposure profiles with different levels of variability were considered (GSD of 1.5 and 2.0).

RESULTS

PBTK models coupled with Monte Carlo simulations were successfully used to predict the biological variability of biological exposure indicators. The predicted values follow a lognormal distribution, characterized by GSD ranging from 1.1 to 2.3. Our results show that there is a link between biological variability and the half-life of bio-indicators, since BV(intra) and BV(total) both decrease as the biological indicator half-lives increase. BV(intra) is always lower than the variability in the air concentrations. On an individual basis, this means that the variability associated with the measurement of biological indicators is always lower than the variability characterizing airborne levels of contaminants. For a group of workers, BM is less variable than EM for bio-indicators with half-lives longer than 10-15 h.

CONCLUSION

The variability data obtained in the present study can be useful in the development of BM strategies for exposure assessment and can be used to calculate the number of samples required for guiding industrial hygienists or medical doctors in decision-making.