Dataset Information

Occupational exposure to polycyclic aromatic hydrocarbons and risk of prostate cancer.

ABSTRACT:

Background

Several industries entailing exposure to polycyclic aromatic hydrocarbons (PAHs) are known or suspected carcinogens. A handful of studies have assessed the role of PAHs exposure in prostate cancer risk, but none has examined tumor aggressiveness or the influence of screening practices and detection issues. We aimed to examine the association between lifetime occupational exposure to PAHs and prostate cancer risk.

Methods

Detailed work histories were collected from 1,929 prostate cancer cases (436 aggressive) and 1,994 controls from Montreal, Canada (2005-2012). Industrial hygienists applied the hybrid expert approach to assign intensity, frequency and certainty of exposure to benzo[a]pyrene, PAHs from wood, coal, petroleum, other sources, and any source, in each job held. Odds ratios (ORs) for prostate cancer risk associated with lifetime PAHs exposure, adjusted for age, ancestry, education, lifestyle and occupational factors, and 95% confidence intervals (CI), were estimated using unconditional logistic regression.

Results

After restriction to probable and definite exposures, and application of a 5-year lag, no clear association emerged for any of the PAHs, although small excesses in risk were apparent with 5-year increments in exposure to PAHs from wood (OR = 1.06, 95%CI 0.95 to 1.18). While analyses by cancer aggressiveness suggested no major differences, some elevated risk of high-grade cancer was observed for exposure to PAHs from wood (OR = 1.37, 95%CI 0.65 to 2.89), frequently occurring among firefighters.

Conclusion

Findings provide weak support for an association between occupational exposure to PAHs from wood and prostate cancer risk.

SUBMITTER: Barul C

PROVIDER: S-EPMC8218525 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundAlthough there is evidence in experimental model systems that exposure to polycyclic aromatic hydrocarbons (PAHs) is linked with congenital heart defects (CHDs), few studies have examined the association in humans. We conducted a case-control study to examine the association between maternal exposure to PAHs and CHDs in offspring using data from the National Birth Defects Prevention Study (NBDPS) (1997-2011).MethodsWe obtained detailed information on maternal occupation during the month before to three months after conception. Expert raters, masked to case-control status, assessed job descriptions to assign categorical levels of exposure. Categories were quantitatively mapped to estimate cumulative exposure to PAHs, incorporating exposure intensity, frequency, work duration, and work hours. Quartiles were generated for cumulative maternal exposure to PAHs. Crude and adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were estimated using unconditional logistic regression for quartiles of PAH exposure and six CHD groupings (e.g. conotruncal) and specific subtypes (e.g. tetralogy of Fallot [ToF]). Final models were adjusted for maternal age, race/ethnicity, education, smoking, anticonvulsant use, folic acid supplementation, and study center.ResultsThere were 4,775 case and 7,734 control infants eligible for the study. The prevalence of occupational exposure to PAHs was 10.2% among both case and control mothers. In adjusted analysis, compared to mothers with no occupational PAH exposure, those in the highest quartile of exposure were more likely to have offspring in the conotruncal heart defects group (OR 1.41; 95% CI 1.00-2.00), and with ToF (OR 1.83; 95% CI 1.21-2.78).ConclusionsWomen in the highest quartile of estimated cumulative occupational PAH exposure during early pregnancy were more likely to have offspring with conotruncal heart defects, specifically ToF, compared to women with no occupational PAH exposure. Other comparisons between PAHs and other CHDs subgroups did not show any statistically precise associations.

Project description:IntroductionWhen it is not possible to capture direct measures of occupational exposure or conduct biomonitoring, retrospective exposure assessment methods are often used. Among the common retrospective assessment methods, assigning exposure estimates by multiple expert rater review of detailed job descriptions is typically the most valid, but also the most time-consuming and expensive. Development of screening protocols to prioritize a subset of jobs for expert rater review can reduce the exposure assessment cost and time requirement, but there is often little data with which to evaluate different screening approaches. We used existing job-by-job exposure assessment data (assigned by consensus between multiple expert raters) from a large, population-based study of women to create and test screening algorithms for polycyclic aromatic hydrocarbons (PAHs) that would be suitable for use in other population-based studies.MethodsWe evaluated three approaches to creating a screening algorithm: a machine-learning algorithm, a set of a priori decision rules created by experts based on features (such as keywords) found in the job description, and a hybrid algorithm incorporating both sets of criteria. All coded jobs held by mothers of infants participating in National Birth Defects Prevention Study (NBDPS) (n = 35,424) were used in developing or testing the screening algorithms. The job narrative fields considered for all approaches included job title, type of product made by the company, main activities or duties, and chemicals or substances handled. Each screening approach was evaluated against the consensus rating of two or more expert raters.ResultsThe machine-learning algorithm considered over 30,000 keywords and industry/occupation codes (separate and in combination). Overall, the hybrid method had a similar sensitivity (87.1%) as the expert decision rules (85.5%) but was higher than the machine-learning algorithm (67.7%). Specificity was best in the machine-learning algorithm (98.1%), compared to the expert decision rules (89.2%) and hybrid approach (89.1%). Using different probability cutoffs in the hybrid approach resulted in improvements in sensitivity (24-30%), without the loss of much specificity (7-18%).ConclusionBoth expert decision rules and the machine-learning algorithm performed reasonably well in identifying the majority of jobs with potential exposure to PAHs. The hybrid screening approach demonstrated that by reviewing approximately 20% of the total jobs, it could identify 87% of all jobs exposed to PAHs; sensitivity could be further increased, albeit with a decrease in specificity, by adjusting the algorithm. The resulting screening algorithm could be applied to other population-based studies of women. The process of developing the algorithm also provides a useful illustration of the strengths and potential pitfalls of these approaches to developing exposure assessment algorithms.