Predicting changes in PM exposure over time at U.S. trucking terminals using structural equation modeling techniques

A Scoping Review of Technologies and Their Applicability for Exposome-Based Risk Assessment in the Oil and Gas Industry

INTRODUCTION

Oil and gas workers have been shown to be at increased risk of chronic diseases including cancer, asthma, chronic obstructive pulmonary disease, and hearing loss, among others. Technological advances may be used to assess the external (e.g. personal sensors, smartphone apps and online platforms, exposure models) and internal exposome (e.g. physiologically based kinetic modeling (PBK), biomonitoring, omics), offering numerous possibilities for chronic disease prevention strategies and risk management measures. The objective of this study was to review the literature on these technologies, by focusing on: (i) evaluating their applicability for exposome research in the oil and gas industry, and (ii) identifying key challenges that may hamper the successful application of such technologies in the oil and gas industry.

METHOD

A scoping review was conducted by identifying peer-reviewed literature with searches in MEDLINE/PubMed and SciVerse Scopus. Two assessors trained on the search strategy screened retrieved articles on title and abstract. The inclusion criteria used for this review were: application of the aforementioned technologies at a workplace in the oil and gas industry or, application of these technologies for an exposure relevant to the oil and gas industry but in another occupational sector, English language and publication period 2005-end of 2019.

RESULTS

In total, 72 articles were included in this scoping review with most articles focused on omics and bioinformatics (N = 22), followed by biomonitoring and biomarkers (N = 20), external exposure modeling (N = 11), PBK modeling (N = 10), and personal sensors (N = 9). Several studies were identified in the oil and gas industry on the application of PBK models and biomarkers, mainly focusing on workers exposed to benzene. The application of personal sensors, new types of exposure models, and omics technology are still in their infancy with respect to the oil and gas industry. Nevertheless, applications of these technologies in other occupational sectors showed the potential for application in this sector.

DISCUSSION AND CONCLUSION

New exposome technologies offer great promise for personal monitoring of workers in the oil and gas industry, but more applied research is needed in collaboration with the industry. Current challenges hindering a successful application of such technologies include (i) the technological readiness of sensors, (ii) the availability of data, (iii) the absence of standardized and validated methods, and (iv) the need for new study designs to study the development of disease during working life.

Gene expression network analyses in response to air pollution exposures in the trucking industry

BACKGROUND

Exposure to air pollution, including traffic-related pollutants, has been associated with a variety of adverse health outcomes, including increased cardiopulmonary morbidity and mortality, and increased lung cancer risk.

METHODS

To better understand the cellular responses induced by air pollution exposures, we performed genome-wide gene expression microarray analysis using whole blood RNA sampled at three time-points across the work weeks of 63 non-smoking employees at 10 trucking terminals in the northeastern US. We defined genes and gene networks that were differentially activated in response to PM_2.5 (particulate matter ≤ 2.5 microns in diameter) and elemental carbon (EC) and organic carbon (OC).

RESULTS

Multiple transcripts were strongly associated (p_adj < 0.001) with pollutant levels (48, 260, and 49 transcripts for EC, OC, and PM_2.5, respectively), including 63 that were statistically significantly correlated with at least two out of the three exposures. These genes included many that have been implicated in ischemic heart disease, chronic obstructive pulmonary disease (COPD), lung cancer, and other pollution-related illnesses. Through the combination of Gene Set Enrichment Analysis and network analysis (using GeneMANIA), we identified a core set of 25 interrelated genes that were common to all three exposure measures and were differentially expressed in two previous studies assessing gene expression attributable to air pollution. Many of these are members of fundamental cancer-related pathways, including those related to DNA and metal binding, and regulation of apoptosis and also but include genes implicated in chronic heart and lung diseases.

CONCLUSIONS

These data provide a molecular link between the associations of air pollution exposures with health effects.

Lung cancer risk in professional drivers in Korea: A population-based proportionate cancer incidence ratio study

OBJECTIVES

Professional drivers are exposed to diesel engine exhaust and outdoor air pollution while driving. Diesel engine exhaust and outdoor air pollution are known carcinogens causing lung cancer. However, previous epidemiological studies examining lung cancer risk in professional drivers have not shown a consistent association. In the present study, we evaluated lung cancer risk among Korean professional drivers.

METHODS

Subjects consisted of male drivers aged 30-59 registered in the Korea Central Cancer Registry for lung cancer between 1999 and 2011. Proportionate cancer incidence ratios (PCIRs) for lung cancer were calculated and indirectly age standardized with the male general population. Additional PCIRs were calculated by indirectly adjusting for the effect of cigarette smoking.

RESULTS

The PCIR for lung cancer in professional drivers during the study period increased significantly (1.20, 95% CI: 1.13-1.26). The increased risk was generally consistent throughout study years and age categories. Adjusting for the effect of cigarette smoking did not change the significance of the associations (1.09, 95% CI: 1.03-1.15).

CONCLUSIONS

Our findings support an association between lung cancer and driver jobs in the Korean male population. However, the association should be further evaluated in a study with a longitudinal design and a quantitative exposure assessment.

Comparison of algorithm-based estimates of occupational diesel exhaust exposure to those of multiple independent raters in a population-based case-control study

OBJECTIVES

Algorithm-based exposure assessments based on patterns in questionnaire responses and professional judgment can readily apply transparent exposure decision rules to thousands of jobs quickly. However, we need to better understand how algorithms compare to a one-by-one job review by an exposure assessor. We compared algorithm-based estimates of diesel exhaust exposure to those of three independent raters within the New England Bladder Cancer Study, a population-based case-control study, and identified conditions under which disparities occurred in the assessments of the algorithm and the raters.

METHODS

Occupational diesel exhaust exposure was assessed previously using an algorithm and a single rater for all 14 983 jobs reported by 2631 study participants during personal interviews conducted from 2001 to 2004. Two additional raters independently assessed a random subset of 324 jobs that were selected based on strata defined by the cross-tabulations of the algorithm and the first rater's probability assessments for each job, oversampling their disagreements. The algorithm and each rater assessed the probability, intensity and frequency of occupational diesel exhaust exposure, as well as a confidence rating for each metric. Agreement among the raters, their aggregate rating (average of the three raters' ratings) and the algorithm were evaluated using proportion of agreement, kappa and weighted kappa (κw). Agreement analyses on the subset used inverse probability weighting to extrapolate the subset to estimate agreement for all jobs. Classification and Regression Tree (CART) models were used to identify patterns in questionnaire responses that predicted disparities in exposure status (i.e., unexposed versus exposed) between the first rater and the algorithm-based estimates.

RESULTS

For the probability, intensity and frequency exposure metrics, moderate to moderately high agreement was observed among raters (κw = 0.50-0.76) and between the algorithm and the individual raters (κw = 0.58-0.81). For these metrics, the algorithm estimates had consistently higher agreement with the aggregate rating (κw = 0.82) than with the individual raters. For all metrics, the agreement between the algorithm and the aggregate ratings was highest for the unexposed category (90-93%) and was poor to moderate for the exposed categories (9-64%). Lower agreement was observed for jobs with a start year <1965 versus ≥1965. For the confidence metrics, the agreement was poor to moderate among raters (κw = 0.17-0.45) and between the algorithm and the individual raters (κw = 0.24-0.61). CART models identified patterns in the questionnaire responses that predicted a fair-to-moderate (33-89%) proportion of the disagreements between the raters' and the algorithm estimates.

DISCUSSION

The agreement between any two raters was similar to the agreement between an algorithm-based approach and individual raters, providing additional support for using the more efficient and transparent algorithm-based approach. CART models identified some patterns in disagreements between the first rater and the algorithm. Given the absence of a gold standard for estimating exposure, these patterns can be reviewed by a team of exposure assessors to determine whether the algorithm should be revised for future studies.

Lung cancer and elemental carbon exposure in trucking industry workers

BACKGROUND

Diesel exhaust has been considered to be a probable lung carcinogen based on studies of occupationally exposed workers. Efforts to define lung cancer risk in these studies have been limited in part by lack of quantitative exposure estimates.

OBJECTIVE

We conducted a retrospective cohort study to assess lung cancer mortality risk among U.S. trucking industry workers. Elemental carbon (EC) was used as a surrogate of exposure to engine exhaust from diesel vehicles, traffic, and loading dock operations.

METHODS

Work records were available for 31,135 male workers employed in the unionized U.S. trucking industry in 1985. A statistical model based on a national exposure assessment was used to estimate historical work-related exposures to EC. Lung cancer mortality was ascertained through the year 2000, and associations with cumulative and average EC were estimated using proportional hazards models.

RESULTS

Duration of employment was inversely associated with lung cancer risk consistent with a healthy worker survivor effect and a cohort composed of prevalent hires. After adjusting for employment duration, we noted a suggestion of a linear exposure-response relationship. For each 1,000-µg/m³ months of cumulative EC, based on a 5-year exposure lag, the hazard ratio (HR) was 1.07 [95% confidence interval (CI): 0.99, 1.15] with a similar association for a 10-year exposure lag [HR = 1.09 (95% CI: 0.99, 1.20)]. Average exposure was not associated with relative risk.

CONCLUSIONS

Lung cancer mortality in trucking industry workers increased in association with cumulative exposure to EC after adjusting for negative confounding by employment duration.

Comparing gravimetric and real-time sampling of PM(2.5) concentrations inside truck cabins

As part of a study on truck drivers' exposure and health risk, pickup and delivery (P&D) truck drivers' on-road exposure patterns to PM(2.5) were assessed in five, weeklong sampling trips in metropolitan areas of five U.S. cities from April to August of 2006. Drivers were sampled with real-time (DustTrak) and gravimetric samplers to measure average in-cabin PM(2.5) concentrations and to compare their correspondence in moving trucks. In addition, GPS measurements of truck locations, meteorological data, and driver behavioral data were collected throughout the day to determine which factors influence the relationship between real-time and gravimetric samplers. Results indicate that the association between average real-time and gravimetric PM(2.5) measurements on moving trucks was fairly consistent (Spearman rank correlation of 0.63), with DustTrak measurements exceeding gravimetric measurements by approximately a factor of 2. This ratio differed significantly only between the industrial Midwest cities and the other three sampled cities scattered in the South and West. There was also limited evidence of an effect of truck age. Filter samples collected concurrently with DustTrak measurements can be used to calibrate average mass concentration responses for the DustTrak, allowing for real-time measurements to be integrated into longer-term studies of inter-city and intra-urban exposure patterns for truck drivers.

A retrospective assessment of occupational exposure to elemental carbon in the U.S. trucking industry

BACKGROUND

Despite considerable epidemiologic evidence about the health effects of chronic exposure to vehicle exhaust, efforts at defining the extent of risk have been limited by the lack of historical exposure measurements suitable for use in epidemiologic studies and for risk assessment.

OBJECTIVES

We sought to reconstruct exposure to elemental carbon (EC), a marker of diesel and other vehicle exhaust exposure, in a large national cohort of U.S. trucking industry workers.

METHODS

We identified the predictors of measured exposures based on a statistical model and used this information to extrapolate exposures across the cohort nationally. These estimates were adjusted for changes in work-related conditions over time based on a previous exposure assessment of this industry, and for changes in background levels based on a trend analysis of historical air pollution data, to derive monthly estimates of EC exposure for each job and trucking terminal combination between 1971 and 2000.

RESULTS

Occupational exposure to EC declined substantially over time, and we found significant variability in estimated exposures both within and across job groups, trucking terminals, and regions of the United States. Average estimated EC exposures during a typical work shift ranged from < 1 μg/m³ in the lowest exposed category in the 1990s to > 40 μg/m³ for workers in the highest exposed jobs in the 1970s.

CONCLUSIONS

Our results provide a framework for understanding changes over time in exposure to EC in the U.S. trucking industry. Our assessment should minimize exposure misclassification by capturing variation among terminals and across U.S. regions, and changes over time.