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Occupational exposure to diesel motor exhaust and risk of lung cancer by histological subtype: a population-based case-control study in Swedish men.

ABSTRACT: We investigated occupational exposure to diesel motor exhaust (DME) and the risk of lung cancer by histological subtype among men, using elemental carbon (EC) as a marker of DME exposure. 993 cases and 2359 controls frequency-matched on age and year of study inclusion were analyzed by unconditional logistic regression in this Swedish case-control study. Work and smoking histories were collected by a questionnaire and telephone interviews. DME was assessed by a job-exposure matrix. We adjusted for age, year of study inclusion, smoking, occupational exposure to asbestos and combustion products (other than motor exhaust), residential exposure to radon and exposure to air pollution from road traffic. The OR for lung cancer for ever vs. never exposure to DME was 1.15 (95% CI 0.94-1.41). The risk was higher for squamous and large cell, anaplastic or mixed cell carcinoma than for alveolar cell cancer, adenocarcinoma and small cell carcinoma. The OR in the highest quartile of exposure duration (?34 years) vs. never exposed was 1.66 (95% CI 1.08-2.56; p for trend over all quartiles: 0.027) for lung cancer overall, 1.73 (95% CI 1.00-3.00; p: 0.040) for squamous cell carcinoma and 2.89 (95% CI 1.37-6.11; p: 0.005) for the group of undifferentiated, large cell, anaplastic and mixed cell carcinomas. We found no convincing association between exposure intensity and lung cancer risk. Long-term DME exposure was associated with an increased risk of lung cancer, particularly to squamous cell carcinoma and the group of undifferentiated, large cell, anaplastic or mixed carcinomas.

SUBMITTER: Ilar A

PROVIDER: S-EPMC5591361 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Occupational exposure to diesel motor exhaust and risk of lung cancer by histological subtype: a population-based case-control study in Swedish men.

Ilar Anna A Plato Nils N Lewné Marie M Pershagen Göran G Gustavsson Per P

European journal of epidemiology 20170605 8

We investigated occupational exposure to diesel motor exhaust (DME) and the risk of lung cancer by histological subtype among men, using elemental carbon (EC) as a marker of DME exposure. 993 cases and 2359 controls frequency-matched on age and year of study inclusion were analyzed by unconditional logistic regression in this Swedish case-control study. Work and smoking histories were collected by a questionnaire and telephone interviews. DME was assessed by a job-exposure matrix. We adjusted fo ...[more]

PMID: 28585123

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Project description:PurposeUnderground diesel exhaust exposure is an occupational health risk. It is not known how recent intensified emission legislation and use of renewable fuels have reduced or altered occupational exposures. We characterized these effects on multipollutant personal exposure to diesel exhaust and underground ambient air concentrations in an underground iron ore mine.MethodsFull-shift personal sampling (12 workers) of elemental carbon (EC), nitrogen dioxide (NO2), polycyclic aromatic hydrocarbons (PAHs), and equivalent black carbon (eBC) was performed. The study used and validated eBC as an online proxy for occupational exposure to EC. Ambient air sampling of these pollutants and particle number size distribution and concentration were performed in the vicinity of the workers. Urine samples (27 workers) were collected after 8 h exposure and analyzed for PAH metabolites and effect biomarkers (8-oxodG for DNA oxidative damage, 4-HNE-MA for lipid peroxidation, 3-HPMA for acrolein).ResultsThe personal exposures (geometric mean; GM) of the participating miners were 7 µg EC m-3 and 153 µg NO2 m-3, which are below the EU occupational exposure limits. However, exposures up to 94 µg EC m-3 and 1200 µg NO2 m-3 were observed. There was a tendency that the operators of vehicles complying with sharpened emission legislation had lower exposure of EC. eBC and NO2 correlated with EC, R = 0.94 and R = 0.66, respectively. No correlation was found between EC and the sum of 16 priority PAHs (GM 1790 ng m-3). Ratios between personal exposures and ambient concentrations were similar and close to 1 for EC and NO2, but significantly higher for PAHs. Semi-volatile PAHs may not be effectively reduced by the aftertreatment systems, and ambient area sampling did not predict the personal airborne PAHs exposure well, neither did the slightly elevated concentration of urinary PAH metabolites correlate with airborne PAH exposure.ConclusionMiners' exposures to EC and NO2 were lower than those in older studies indicating the effect of sharpened emission legislation and new technologies. Using modern vehicles with diesel particulate filter (DPF) may have contributed to the lower ambient underground PM concentration and exposures. The semi-volatile behavior of the PAHs might have led to inefficient removal in the engines aftertreatment systems and delayed removal by the workplace ventilation system due to partitioning to indoor surfaces. The results indicate that secondary emissions can be an important source of gaseous PAH exposure in the mine.

Project description:ObjectivesAlgorithm-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.MethodsOccupational 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.ResultsFor 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.DiscussionThe 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.

Project description:BackgroundMost studies of the association between diesel exhaust exposure and lung cancer suggest a modest, but consistent, increased risk. However, to our knowledge, no study to date has had quantitative data on historical diesel exposure coupled with adequate sample size to evaluate the exposure-response relationship between diesel exhaust and lung cancer. Our purpose was to evaluate the relationship between quantitative estimates of exposure to diesel exhaust and lung cancer mortality after adjustment for smoking and other potential confounders.MethodsWe conducted a nested case-control study in a cohort of 12?315 workers in eight non-metal mining facilities, which included 198 lung cancer deaths and 562 incidence density-sampled control subjects. For each case subject, we selected up to four control subjects, individually matched on mining facility, sex, race/ethnicity, and birth year (within 5 years), from all workers who were alive before the day the case subject died. We estimated diesel exhaust exposure, represented by respirable elemental carbon (REC), by job and year, for each subject, based on an extensive retrospective exposure assessment at each mining facility. We conducted both categorical and continuous regression analyses adjusted for cigarette smoking and other potential confounding variables (eg, history of employment in high-risk occupations for lung cancer and a history of respiratory disease) to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Analyses were both unlagged and lagged to exclude recent exposure such as that occurring in the 15 years directly before the date of death (case subjects)/reference date (control subjects). All statistical tests were two-sided.ResultsWe observed statistically significant increasing trends in lung cancer risk with increasing cumulative REC and average REC intensity. Cumulative REC, lagged 15 years, yielded a statistically significant positive gradient in lung cancer risk overall (P (trend) = .001); among heavily exposed workers (ie, above the median of the top quartile [REC ? 1005 ?g/m(3)-y]), risk was approximately three times greater (OR = 3.20, 95% CI = 1.33 to 7.69) than that among workers in the lowest quartile of exposure. Among never smokers, odd ratios were 1.0, 1.47 (95% CI = 0.29 to 7.50), and 7.30 (95% CI = 1.46 to 36.57) for workers with 15-year lagged cumulative REC tertiles of less than 8, 8 to less than 304, and 304 ?g/m(3)-y or more, respectively. We also observed an interaction between smoking and 15-year lagged cumulative REC (P (interaction) = .086) such that the effect of each of these exposures was attenuated in the presence of high levels of the other.ConclusionOur findings provide further evidence that diesel exhaust exposure may cause lung cancer in humans and may represent a potential public health burden.

Dataset Information

Occupational exposure to diesel motor exhaust and risk of lung cancer by histological subtype: a population-based case-control study in Swedish men.

Publications

Occupational exposure to diesel motor exhaust and risk of lung cancer by histological subtype: a population-based case-control study in Swedish men.

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