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Background: Diesel exhaust is a complex mixture, including polycyclic aromatic hydrocarbons (PAH) and nitrated PAHs (nitro-PAH), many of which are potent mutagens and possible bladder carcinogens. To explore the association between diesel exposure and bladder carcinogenesis, we examined the relationship between exposure and somatic mutations and mutational signatures in bladder tumors. Methods: Targeted sequencing was conducted in bladder tumors from the New England Bladder Cancer Study. Using data on 797 cases and 1,418 controls, two-stage polytomous logistic regression was used to evaluate etiologic heterogeneity between bladder cancer subtypes and quantitative, lifetime estimates of respirable elemental carbon (REC), a surrogate for diesel exposure. Poisson regression was used to evaluate associations between REC and mutational signatures. Results: We observed significant heterogeneity in the diesel-bladder cancer risk relationship, with a strong positive association among cases with high-grade, nonmuscle invasive TP53-mutated tumors compared with controls [ORTop Tertile vs.Unexposed, 4.8; 95% confidence interval (CI), 2.2–10.5; Ptrend Conclusions: The relationship between diesel exhaust and bladder cancer was heterogeneous based on the presence of TP53 mutations in tumors, further supporting the link between PAH exposure and TP53 mutations in carcinogenesis. Future studies that can identify nitro-PAH signatures in exposed tumors are warranted to add human data supporting the link between diesel and bladder cancer. Impact: This study provides additional insight into the etiology and possible mechanisms related to diesel exhaust-induced bladder cancer.

Objectives Automatic job coding tools were developed to reduce the laborious task of manually assigning job codes based on free-text job descriptions in census and survey data sources, including large occupational health studies. The objective of this study is to provide a case study of comparative performance of job coding and JEM (Job-Exposure Matrix)-assigned exposures agreement using existing coding tools. Methods We compared three automatic job coding tools [AUTONOC, CASCOT (Computer-Assisted Structured Coding Tool), and LabourR], which were selected based on availability, coding of English free-text into coding systems closely related to the 1988 version of the International Standard Classification of Occupations (ISCO-88), and capability to perform batch coding. We used manually coded job histories from the AsiaLymph case-control study that were translated into English prior to auto-coding to assess their performance. We applied two general population JEMs to assess agreement at exposure level. Percent agreement and PABAK (Prevalence-Adjusted Bias-Adjusted Kappa) were used to compare the agreement of results from manual coders and automatic coding tools. Results The coding per cent agreement among the three tools ranged from 17.7 to 26.0% for exact matches at the most detailed 4-digit ISCO-88 level. The agreement was better at a more general level of job coding (e.g. 43.8–58.1% in 1-digit ISCO-88), and in exposure assignments (median values of PABAK coefficient ranging 0.69–0.78 across 12 JEM-assigned exposures). Based on our testing data, CASCOT was found to outperform others in terms of better agreement in both job coding (26% 4-digit agreement) and exposure assignment (median kappa 0.61). Conclusions In this study, we observed that agreement on job coding was generally low for the three tools but noted a higher degree of agreement in assigned exposures. The results indicate the need for study-specific evaluations prior to their automatic use in general population studies, as well as improvements in the evaluated automatic coding tools.

Cosine similarities for all signatures

Case-case associations between REC and TP53 mutation, NEBCS

specific TP53 mutations and ever diesel

Background:Diesel exhaust is a complex mixture, including polycyclic aromatic hydrocarbons (PAH) and nitrated PAHs (nitro-PAH), many of which are potent mutagens and possible bladder carcinogens. To explore the association between diesel exposure and bladder carcinogenesis, we examined the relationship between exposure and somatic mutations and mutational signatures in bladder tumors.Methods:Targeted sequencing was conducted in bladder tumors from the New England Bladder Cancer Study. Using data on 797 cases and 1,418 controls, two-stage polytomous logistic regression was used to evaluate etiologic heterogeneity between bladder cancer subtypes and quantitative, lifetime estimates of respirable elemental carbon (REC), a surrogate for diesel exposure. Poisson regression was used to evaluate associations between REC and mutational signatures.Results:We observed significant heterogeneity in the diesel-bladder cancer risk relationship, with a strong positive association among cases with high-grade, nonmuscle invasive TP53-mutated tumors compared with controls [ORTop Tertile vs.Unexposed, 4.8; 95% confidence interval (CI), 2.2–10.5; Ptrend Pheterogeneity = 0.002]. In muscle-invasive tumors, we observed a positive association between diesel exposure and the nitro-PAH signatures of 1,6-dintropyrene (RR, 1.93; 95% CI, 1.28–2.92) and 3-nitrobenzoic acid (RR, 1.97; 95% CI, 1.33–2.92).Conclusions:The relationship between diesel exhaust and bladder cancer was heterogeneous based on the presence of TP53 mutations in tumors, further supporting the link between PAH exposure and TP53 mutations in carcinogenesis. Future studies that can identify nitro-PAH signatures in exposed tumors are warranted to add human data supporting the link between diesel and bladder cancer.Impact:This study provides additional insight into the etiology and possible mechanisms related to diesel exhaust-induced bladder cancer.

Ever diesel and nitro-PAH signatures adjusted for smoking

REC and nitro-PAH signatures in New England

Genes on panel

study characteristics

Objectives Computer-assisted coding of job descriptions to standardized occupational classification codes facilitates evaluating occupational risk factors in epidemiologic studies by reducing the number of jobs needing expert coding. We evaluated the performance of the 2nd version of SOCcer, a computerized algorithm designed to code free-text job descriptions to US SOC-2010 system based on free-text job titles and work tasks, to evaluate its accuracy. Methods SOCcer v2 was updated by expanding the training data to include jobs from several epidemiologic studies and revising the algorithm to account for nonlinearity and incorporate interactions. We evaluated the agreement between codes assigned by experts and the highest scoring code (a measure of confidence in the algorithm-predicted assignment) from SOCcer v1 and v2 in 14,714 jobs from three epidemiology studies. We also linked exposure estimates for 258 agents in the job-exposure matrix CANJEM to the expert and SOCcer v2-assigned codes and compared those estimates using kappa and intraclass correlation coefficients. Analyses were stratified by SOCcer score, score distance between the top two scoring codes from SOCcer, and features from CANJEM. Results SOCcer’s v2 agreement at the 6-digit level was 50%, compared to 44% in v1, and was similar for the three studies (38%–45%). Overall agreement for v2 at the 2-, 3-, and 5-digit was 73%, 63%, and 56%, respectively. For v2, median ICCs for the probability and intensity metrics were 0.67 (IQR 0.59–0.74) and 0.56 (IQR 0.50–0.60), respectively. The agreement between the expert and SOCcer assigned codes linearly increased with SOCcer score. The agreement also improved when the top two scoring codes had larger differences in score. Conclusions Overall agreement with SOCcer v2 applied to job descriptions from North American epidemiologic studies was similar to the agreement usually observed between two experts. SOCcer’s score predicted agreement with experts and can be used to prioritize jobs for expert review.

Introduction The US Integrated Management Information System (IMIS) contains workplace measurements collected by Occupational Safety and Health Administration (OSHA) inspectors. Its use for research is limited by the lack of record of a value for the limit of detection (LOD) associated with non-detected measurements, which should be used to set censoring point in statistical analysis. We aimed to remedy this by developing a predictive model of the volume of air sampled (V) for the non-detected results of airborne measurements, to then estimate the LOD using the instrument detection limit (IDL), as IDL/V. Methods We obtained the Chemical Exposure Health Data from OSHA’s central laboratory in Salt Lake City that partially overlaps IMIS and contains information on V. We used classification and regression trees (CART) to develop a predictive model of V for all measurements where the two datasets overlapped. The analysis was restricted to 69 chemical agents with at least 100 non-detected measurements, and calculated sampling air flow rates consistent with workplace measurement practices; undefined types of inspections were excluded, leaving 412,201/413,515 records. CART models were fitted on randomly selected 70% of the data using 10-fold cross-validation and validated on the remaining data. A separate CART model was fitted to styrene data. Results Sampled air volume had a right-skewed distribution with a mean of 357 l, a median (M) of 318, and ranged from 0.040 to 1868 l. There were 173,131 measurements described as non-detects (42% of the data). For the non-detects, the V tended to be greater (M = 378 l) than measurements characterized as either ‘short-term’ (M = 218 l) or ‘long-term’ (M = 297 l). The CART models were complex and not easy to interpret, but substance, industry, and year were among the top three most important classifiers. They predicted V well overall (Pearson correlation (r) = 0.73, P < 0.0001; Lin’s concordance correlation (rc) = 0.69) and among records captured as non-detects in IMIS (r = 0.66, P < 0.0001l; rc = 0.60). For styrene, CART built on measurements for all agents predicted V among 569 non-detects poorly (r = 0.15; rc = 0.04), but styrene-specific CART predicted it well (r = 0.87, P < 0.0001; rc = 0.86). Discussion Among the limitations of our work is the fact that samples may have been collected on different workers and processes within each inspection, each with its own V. Furthermore, we lack measurement-level predictors because classifiers were captured at the inspection level. We did not study all substances that may be of interest and did not use the information that substances measured on the same sampling media should have the same V. We must note that CART models tend to over-fit data and their predictions depend on the selected data, as illustrated by contrasting predictions created using all data vs. limited to styrene. Conclusions We developed predictive models of sampled air volume that should enable the calculation of LOD for non-detects in IMIS. Our predictions may guide future work on handling non-detects in IMIS, although it is advisable to develop separate predictive models for each substance, industry, and year of interest, while also considering other factors, such as whether the measurement evaluated long-term or short-term exposure.

The increasing availability of portable air pollution monitoring devices has greatly enhanced the ability to measure personal exposures in real time. However, these devices vary considerably in their cost and specifications, and questions remain as to their reliability and practicality for use in epidemiological investigations. In this field study, three personal PM(2.5) exposure monitors (two nephelometers, one optical particle counter) were compared in an urban setting to assess their feasibility for use in future studies. In total, 3963 1-min measurements were collected over 12 days from locations of several types (e.g., above and below-ground subway stations, sidewalks next to urban traffic, outdoor construction sites) in the Washington, D.C. metropolitan area. Overall, we observed moderate-to-high agreement in pairwise comparisons of PM(2.5) concentrations between devices (R(2) range: 0.37 to 0.75). Bland-Altman plots showed that differences in device agreement varied over the range of mean concentrations. In linear mixed models adjusting for temperature and relative humidity, we saw significant interaction between device and location (p

Background Night shift work may acutely disrupt the circadian rhythm, with possible carcinogenic effects. Prostate cancer has few established risk factors though night shift work, a probable human carcinogen, may increase the risk. We aimed to study the association between night shift work and chlorinated degreasing agents (CDAs) as possible endocrine disrupters in relation to aggressive prostate cancer as verified malignancies. Methods We conducted a case-cohort study on 299 aggressive prostate cancer cases and 2056 randomly drawn non-cases in the Norwegian Offshore Petroleum Workers cohort (1965–98) with linkage to the Cancer Registry of Norway (1953–2019). Work history was recorded as years with day, night, and rollover (rotating) shift work, and CDA exposure was assessed with expert-made job-exposure matrices. Weighted Cox regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for aggressive prostate cancer, adjusted for education and year of first employment, stratified by 10-year birth cohorts, and with 10, 15, and 20 years of exposure lag periods. Results Compared with day work only, an increased hazard of aggressive prostate cancer (HR = 1.86, 95% CI 1.18–2.91; P-trend = 0.046) was found in workers exposed to ≥19.5 years of rollover shift work. This persisted with longer lag periods (HR = 1.90, 95% CI 0.92–3.95; P-trend = 0.007). The exposure-hazard curve for a non-linear model increased linearly (HRs ≥1.00) for 18–26 years of rollover shift work. No association was found with CDA exposure. Conclusions Long-term exposure to rollover shift work may increase the hazard of aggressive prostate cancer in offshore petroleum workers.

Objectives We adapted previously developed decision rules from the New England Bladder Cancer Study (NEBCS) to assign occupational exposure to straight, soluble, and synthetic metalworking fluids (MWFs) to participants of the Spanish Bladder Cancer Study (SBCS). Methods The SBCS and NEBCS are case–control studies that used the same lifetime occupational history and job module questionnaires. We adapted published decision rules from the NEBCS that linked questionnaire responses to estimates of the probability ( Results To assign MWF exposure, we applied decision rules that incorporated participant’s responses and job group patterns for 99% of the jobs and conducted expert review of the remaining 1% (145) jobs. Overall, 14% of the jobs were assessed as having ≥5% probability of exposure to at least one of the three MWFs. Probability of exposure of ≥50% to soluble, straight, and synthetic MWFs was identified in 2.5, 1.7, and 0.5% of the jobs, respectively. To assign mineral oil from non-machining sources, we used module responses for 49% of jobs, a job-exposure matrix for 41% of jobs, and expert review for the remaining 10%. We identified 24% of jobs as possibly exposed to mineral oil from non-machining sources. Conclusions We demonstrated that we could adapt existing decision rules to assess exposure in a new population by deriving population-specific job group patterns.

Objectives Farmers may be exposed to glucans (a cell component of molds) through a variety of tasks. The magnitude of exposure depends on each farmer’s activities and their duration. We developed a task-specific algorithm to estimate glucan exposure that combines measurements of (1→3)-β-D-glucan with questionnaire responses from farmers in the Biomarkers of Exposure and Effect in Agriculture (BEEA) study. Methods To develop the algorithm, we first derived task-based geometric means (GMs) of glucan exposure for farming tasks using inhalable personal air sampling data from a prior air monitoring study in a subset of 32 BEEA farmers. Next, these task-specific GMs were multiplied by subject-reported activity frequencies for three time windows (the past 30 days, past 7 days, and past 1 day) to obtain subject-, task-, and time window-specific glucan scores. These were summed together to obtain a total glucan score for each subject and time window. We examined the within- and between-task correlation in glucan scores for different time frames. Additionally, we assessed the algorithm for the ‘past 1 day’ time window using full-shift concentrations from the 32 farmers who participated in air monitoring the day prior to an interview using multilevel statistical models to compare the measured glucan concentration with algorithm glucan scores. Results We focused on the five highest exposed tasks: poultry confinement (300 ng/m3), swine confinement (300 ng/m3), clean grain bins (200 ng/m3), grind feed (100 ng/m3), and stored seed or grain (50 ng/m3); the remaining tasks were Conclusions This study provides insight into the variability and key sources of glucan exposure in a US farming population. It also provides a framework for better glucan exposure assessment in epidemiologic studies and is a crucial starting point for evaluating health risks associated with glucans in future epidemiologic evaluations of this population.

Objectives Daily driving of diesel-powered tractors has been linked to increased lung cancer risk in farmers, yet few studies have quantified exposure levels to diesel exhaust during tractor driving or during other farm activities. We expanded an earlier task-based descriptive investigation of factors associated with real-time exposure levels to black carbon (BC, a surrogate of diesel exhaust) in Iowa farmers by increasing the sample size, collecting repeated measurements, and applying statistical models adapted to continuous measurements. Methods The expanded study added 43 days of sampling, for a total of 63 sample days conducted in 2015 and 2016 on 31 Iowa farmers. Real-time, continuous monitoring (30-s intervals) of personal BC concentrations was performed using a MicroAeth AE51 microaethelometer affixed with a micro-cyclone. A field researcher recorded information on tasks, fuel type, farmer location, and proximity to burning biomass. We evaluated the influence of these variables on log-transformed BC concentrations using a linear mixed-effect model with random effects for farmer and day and a first-order autoregressive structure for within-day correlation. Results Proximity to diesel-powered equipment was observed for 42.5% of the overall sampling time and on 61 of the 63 sample days. Predicted geometric mean BC concentrations were highest during grain bin work, loading, and harvesting, and lower for soil preparation and planting. A 68% increase in BC concentrations was predicted for close proximity to a diesel-powered vehicle, relative to far proximity, while BC concentrations were 44% higher in diesel vehicles with open cabins compared with closed cabins. Task, farmer location, fuel type, and proximity to burning biomass explained 8% of within-day variance in BC concentrations, 2% of between-day variance, and no between-farmer variance. Conclusion Our findings showed that farmers worked frequently near diesel equipment and that BC concentrations varied between tasks and by fuel type, farmer location, and proximity to burning biomass. These results could support the development of exposure models applicable to investigations of health effects in farmers associated with exposure to diesel engine exhaust.

Few studies have evaluated the validity of self-report of work activities because of challenges in obtaining objective measures. In this study, farmers’ recall of the previous day’s agricultural activities was compared to activities observed by field staff during air monitoring. Recall was assessed in 32 farmers from the Biomarkers of Exposure and Effect in Agriculture Study, a subset of a prospective cohort study. The farmers participated in 56 visits that comprised air monitoring the day before an interview. The answers for 14 agricultural activities were compared to activities observed by field staff during air monitoring (median duration 380 minutes, range 129–486). For each task, evaluated as yes/no, overall agreement, sensitivity, specificity, and kappa were calculated. Median prevalence of the 14 activities was 8% from observation and 13% from participants (range: 2–54%). Agreement was generally good to perfect, with a median overall agreement of 95% (range: 89–100%), median sensitivity of 84% (50–100%), median specificity of 95% (88–100%), and median kappa of 0.65 (0.31–1.0). Reasons for disagreement included activities occurring when the field staff was not present (i.e., milking cows), unclear timing notes that made it difficult to determine whether the activity occurred the day of and/or day before the interview, definition issues (i.e., participant included hauling in the definition of harvesting), and difficulty in observing details of an activity (i.e., whether hay was moldy). This study provides support for accurate participant recall the day after activities.

ObjectivesThis study examined the association between night shift work and risk of breast cancer, overall and by hormone receptor subtype, among females in the Norwegian Offshore Petroleum Workers (NOPW) cohort. We also examined the association of coexposure (chlorinated degreasers and benzene) and breast cancer risk, and possible interaction with work schedule.DesignProspectively recruited case-cohort study within the NOPW cohort.SettingFemale offshore petroleum workers active on the Norwegian continental shelf.Participants600 female workers (86 cases and 514 non-cases) were included in the study. We excluded workers that died or emigrated before start of follow-up, had missing work history, were diagnosed with breast cancer or other prior malignancy (except non-melanoma skin cancer) before start of follow-up.ResultsNo overall association was found between breast cancer risk and work schedule (HR 0.87, 95% CI 0.52 to 1.46 for work schedule involving night shift vs day shift only). There was no significant association between work schedule and risk of any breast cancer subtype. No significant interactions were found between work schedule and chemical coexposures (breast cancer overall Pinteraction chlorinated degreasers=0.725 and Pinteraction benzene=0.175).ConclusionsOur results did not provide supporting evidence that work schedule involving night shift affects breast cancer risk in female offshore petroleum workers, but should be considered cautiously due to few cases. Further studies with larger sample sizes are warranted.

ObjectivesThe validity of surrogate measures of retrospective occupational exposure in population-based epidemiological studies has rarely been evaluated. Using toenail samples as bioindicators of exposure, we assessed whether work tasks and expert assessments of occupational metal exposure obtained from personal interviews were associated with lead and manganese concentrations.MethodsWe selected 609 controls from a case–control study of bladder cancer in New England who had held a job for ≥1 year 8–24 months prior to toenail collection. We evaluated associations between toenail metal concentrations and five tasks extracted from occupational questionnaires (grinding, painting, soldering, welding, working near engines) using linear regression models. For 139 subjects, we also evaluated associations between the toenail concentrations and exposure estimates from three experts.ResultsWe observed a 1.9-fold increase (95% CI 1.4 to 2.5) in toenail lead concentrations with painting and 1.4-fold increase (95% CI 1.1 to 1.7) in manganese concentrations with working around engines and handling fuel. We observed significant trends with increasing frequency of both activities. For lead, significant trends were observed with the ratings from all three experts. Their average ratings showed the strongest association, with subjects rated as possibly or probably exposed to lead having concentrations that were 2.0 and 2.5 times higher, respectively, than in unexposed subjects (ptrend ConclusionsOur findings support the ability of experts to identify broad contrasts in previous occupational exposure to lead. The stronger associations with task frequency and expert assessments support using refined exposure characterisation whenever possible.

PURPOSE OF REVIEW: Population-based studies increasingly link task-based occupational questionnaire responses collected from subjects to exposure estimates via transparent, programmable decision rules. We reviewed recent applications and methodological developments of rule-based approaches. RECENT FINDINGS: Agent-specific decision rules require interviews incorporating work-task based questions. Some studies have developed rules before the interviews took place, while others developed rules after the interviews were completed. Agreement between rule-based estimates and exposures assigned using job-by-job expert review were generally moderate to good (Kappa=0.4–0.8). Rules providing quantitative intensity levels using measurement data or that integrate multiple independent exposure sources for the same job represent further advances to improve the characterization of occupational exposures in population studies. SUMMARY: Decision rules have provided transparent and reproducible assessments, reduce job-by-job review, and facilitate sensitivity analyses in epidemiologic studies. Future studies should consider the development of decision rules concurrent with the questionnaire design to facilitate occupational exposure assessment efforts.

ObjectivesLead is a suspected carcinogen that has been inconsistently associated with kidney cancer. To clarify this relationship, we conducted an analysis of occupational lead exposure within a population-based study of kidney cancer using detailed exposure assessment methods.MethodsStudy participants (1217 cases and 1235 controls), enrolled between 2002 and 2007, provided information on their occupational histories and, for selected lead-related occupations, answered questions regarding workplace tasks, and use of protective equipment. Industrial hygienists used this information to develop several estimates of occupational lead exposure, including probability, duration and cumulative exposure. Unconditional logistic regression was used to compute ORs and 95% CIs for different exposure metrics, with unexposed subjects serving as the reference group. Analyses were also conducted stratifying on several factors, including for subjects of European ancestry only, single nucleotide polymorphisms inALAD(rs1805313, rs1800435, rs8177796, rs2761016), a gene involved in lead toxicokinetics.ResultsIn our study, cumulative occupational lead exposure was not associated with kidney cancer (OR 0.9, 95% CI 0.7 to 1.3 for highest quartile vs unexposed; ptrend=0.80). Other lead exposure metrics were similarly null. We observed no evidence of effect modification for the evaluatedALADvariants (subjects of European ancestry only, 662 cases and 561 controls) and most stratifying factors, although lead exposure was associated with increased risk among never smokers.ConclusionsThe findings of this study do not offer clear support for an association between occupational lead exposure and kidney cancer.

ObjectiveTo estimate the association between occupational polycyclic aromatic hydrocarbon (PAH) exposure and female breast cancer.MethodsLifetime work histories for 1130 cases and 1169 controls from British Columbia and Ontario (Canada) were assessed for PAH exposure using a job-exposure matrix based on compliance measurements obtained during US Occupational Safety and Health Administration workplace safety inspections.ResultsExposure to any level of PAHs was associated with an increased risk of breast cancer (OR=1.32, 95% CI: 1.10 to 1.59), as was duration at high PAH exposure (for >7.4 years: OR=1.45, 95% CI: 1.10 to 1.91; ptrend=0.01), compared with women who were never exposed. Increased risk of breast cancer was most strongly associated with prolonged duration at high occupational PAH exposure among women with a family history of breast cancer (for >7.4 years: OR=2.79, 95% CI: 1.25 to 6.24; ptrendConclusionsOur study suggests that prolonged occupational exposure to PAH may increase breast cancer risk, especially among women with a family history of breast cancer.

ObjectivesEstablished prostate cancer (PCa) risk factors include age, family history of PCa and African ancestry. Studies, mostly among highly screened, predominantly European ancestral populations, suggest that employment in certain occupations (eg, farming, military) may also have an increased risk for PCa. Here, we evaluated the association between usual adult occupation and PCa risk in Ghanaian men, a population with historically low rates of PCa screening.MethodsThe Ghana Prostate Study is a case-control study of PCa that was conducted from 2004 to 2012 in 749 cases and 964 controls. In-person interviews were conducted to collect information from participants, including longest held job. Industrial hygienists classified job titles into occupational categories. Unconditional logistic regression was used to calculate ORs and 95% CIs for the association between longest held job and PCa risk (overall, aggressive (Gleason≥7)), controlling for potential confounders.ResultsRisk was increased among men in management (overall PCa OR=2.2, 95% CI 1.4 to 3.2; aggressive PCa OR=2.2, 95% CI 1.3 to 3.5) and military occupations (overall PCa OR=3.4, 95% CI 1.7 to 7.0; aggressive PCa OR=3.5, 95% CI 1.5 to 8.3). Risks were also elevated for management and military-specific jobs based on 3-digit level Standard Occupational Classification definitions. Sensitivity analyses accounting for access to medical care did not show significant differences.ConclusionsOur study provides some evidence for increased risk of PCa among men in management and military occupations, which is consistent with the published literature. Additional research is needed to clarify the drivers of the associations between these occupations and PCa.

Background Most methods for treating left-censored data assume the analyte is present but not quantified. Biased estimates may result if the analyte is absent such that the unobserved data represents a mixed exposure distribution with an unknown proportion clustered at zero. Objective We used semi-continuous models to identify time and industry trends in 52,457 OSHA inspection lead sample results. Method The first component of the semi-continuous model predicted the probability of detecting concentrations ≥ 0.007 mg/m3 (highest estimated detection limit, 62% of measurements). The second component predicted the median concentration of measurements ≥ 0.007 mg/m3. Both components included a random-effect for industry and fixed-effects for year, industry group, analytical method, and other variables. We used the two components together to predict median industry- and time-specific lead concentrations. Results The probabilities of detectable concentrations and the median detected concentrations decreased with year; both were also lower for measurements analyzed for multiple (vs. one) metals and for those analyzed by inductively-coupled plasma (vs. atomic absorption spectroscopy). The covariance was 0.30 (standard error = 0.06), confirming the two components were correlated. Significance We identified determinants of exposure in data with over 60% left-censored, while accounting for correlated relationships and without assuming a distribution for the censored data.

Background The observed deficit of lung cancer in farmers has been partly attributed to exposure to organic dusts and endotoxins based largely on surrogate metrics. To move beyond these surrogates for etiological studies, we characterized task-based and time-weighted average (TWA) exposure to inhalable endotoxin, (1 → 3)-β-D-glucan, and dust in Iowa farmers. Methods We collected 320 personal inhalable dust samples from 32 farmers during 69 sample days in 2015 and 2016. Samples were collected using Button aerosol samplers and analyzed for endotoxin using a kinetic chromogenic amebocyte lysate assay, and for (1 → 3)-β-D-glucan using a Limulus endpoint assay. We assessed relationships between bioaerosol concentrations and selected tasks and farm characteristics using linear mixed-effects models. Results Bedding work, hog handling, and working in barn/confinement buildings, grain bins, and grain elevators were associated with higher endotoxin exposure. We found a monotonic trend between higher endotoxin concentrations and increasing number of animals. Bedding work, cleaning, and feed/grain storage work were associated with higher (1 → 3)-β-D-glucan concentrations. The median concentrations by task spanned one order of magnitude for inhalable dust and two orders of magnitude for endotoxin and (1 → 3)-β-D-glucan. Pearson correlations between endotoxin and glucan concentrations were 0.22 for TWA exposure and 0.56 for task samples. Conclusions This characterization of exposure factors that influence bioaerosol concentrations can support the development of refined bioaerosol exposure metrics for future etiologic analyses of cancer and other health outcomes in farmers.

publishedVersion

Residents of agricultural areas experience pesticide exposures from sources other than direct agricultural work. We developed a quantitative, active ingredient-specific algorithm for cumulative (adult, married lifetime) non-occupational pesticide exposure intensity for spouses of farmers who applied pesticides in the Agricultural Health Study (AHS). The algorithm addressed three exposure pathways: take-home, agricultural drift, and residential pesticide use. Pathway-specific equations combined (i) weights derived from previous meta-analyses of published pesticide exposure data and (ii) information from the questionnaire on frequency and duration of pesticide use by applicators, home proximity to treated fields, residential pesticide usage (e.g., termite treatments), and spouse's off-farm employment (proxy for time at home). The residential use equation also incorporated a published probability matrix that documented the likelihood active ingredients were used in home pest treatment products. We illustrate use of these equations by calculating exposure intensities for the insecticide chlorpyrifos and herbicide atrazine for 19,959 spouses. Non-zero estimates for ≥1 pathway were found for 78% and 77% of spouses for chlorpyrifos and atrazine, respectively. Variability in exposed spouses' intensity estimates was observed for both pesticides, with 75th to 25th percentile ratios ranging from 7.1 to 7.3 for take-home, 6.5 to 8.5 for drift, 2.4 to 2.8 for residential use, and 3.8 to 7.0 for the summed pathways. Take-home and drift estimates were highly correlated (≥0.98), but were not correlated with residential use (0.01‒0.02). This algorithm represents an important advancement in quantifying non-occupational pesticide relative exposure differences and will facilitate improved etiologic analyses in the AHS spouses. The algorithm could be adapted to studies with similar information.

Objectives Use of cleaning and disinfecting products is associated with work-related asthma among healthcare workers, but the specific levels and factors that affect exposures remain unclear. The objective of this study was to evaluate the determinants of selected volatile organic compound (VOC) exposures in healthcare settings. Methods Personal and mobile-area air measurements (n = 143) from 100 healthcare workers at four hospitals were used to model the determinants of ethanol, acetone, 2-propanol, d-limonene, α-pinene, and chloroform exposures. Hierarchical cluster analysis was conducted to partition workers into groups with similar cleaning task/product-use profiles. Linear mixed-effect regression models using log-transformed VOC measurements were applied to evaluate the association of individual VOCs with clusters of task/product use, industrial hygienists' grouping (IH) of tasks, grouping of product application, chemical ingredients of the cleaning products used, amount of product use, and ventilation. Results Cluster analysis identified eight task/product-use clusters that were distributed across multiple occupations and hospital units, with the exception of clusters consisting of housekeepers and floor strippers/waxers. Results of the mixed-effect models showed significant associations between selected VOC exposures and several clusters, combinations of IH-generated task groups and chemical ingredients, and product application groups. The patient/personal cleaning task using products containing chlorine was associated with elevated levels of personal chloroform and α-pinene exposures. Tasks associated with instrument sterilizing and disinfecting were significantly associated with personal d-limonene and 2-propanol exposures. Surface and floor cleaning and stripping tasks were predominated by housekeepers and floor strippers/waxers, and use of chlorine-, alcohol-, ethanolamine-, and quaternary ammonium compounds-based products was associated with exposures to chloroform, α-pinene, acetone, 2-propanol, or d-limonene. Conclusions Healthcare workers are exposed to a variety of chemicals that vary with tasks and ingredients of products used during cleaning and disinfecting. The combination of product ingredients with cleaning and disinfecting tasks were associated with specific VOCs. Exposure modules for questionnaires used in epidemiologic studies might benefit from seeking information on products used within a task context.

OBJECTIVES: The Integrated Management Information System (IMIS) is the largest multi-industry source of exposure results available in North America. In 2010, the Occupational Safety and Health Administration (OSHA) released the Chemical Exposure Health Data (CEHD) that contains analytical results of samples collected by OSHA inspectors. However, the two databanks only partially overlap, raising suspicion of bias in IMIS data. We investigated the factors associated with selective recording of CEHD results into the IMIS databank. METHODS: This analysis was based on personal exposure measurements of 24 agents from 1984 to 2009. The association between nine variables (level of exposure coded as detected versus non-detected (ND), whether a sampling result was part of a panel of chemicals, duration of sampling, issuance of a citation, presence of other detected levels during the same inspection, year, OSHA region, amount of penalty, and establishment size) and a CEHD sampling result being reported in IMIS was analyzed using modified Poisson regression. RESULTS: A total of 461900 CEHD sampling results were examined. The proportion of CEHD sampling results recorded into IMIS was 38% (51% for detected and 28% for ND measurements). In the models, the detected sampling results were associated with a higher probability of recording into IMIS than ND sampling results, and this difference was similar for panel versus non-panel samples. Probability of recording remained constant from 1984 to 2009 for sampling results measured on panels but increased for sampling results of single determinations of an agent. Some OSHA regions had probability of recording two times higher than others. No other variables that we examined were associated with a CEHD sampling result being reported in IMIS. CONCLUSIONS: Our results indicate that the under-reporting of sampling results in IMIS is differential: ND results (especially those determined from the panels) seem less likely to be recorded in IMIS than other results. It is important to consider both IMIS and CEHD data in order to reduce bias in evaluation of exposures in workplaces inspected by OSHA.

Diesel exhaust has been associated with adverse human health effects. Farmers are often exposed to diesel exhaust; however, their diesel exposure has not been well characterized. In this descriptive study, we measured black carbon concentrations as a proxy for diesel exhaust exposure in 16 farmers over 20 sampling days during harvest in southeast Iowa. Farmers wore a personal aethalometer which measured real-time black carbon levels throughout the working day, and their activities were recorded by a field researcher. Black carbon concentrations were characterized for each farmer, and by activity, vehicle fuel type, and microenvironment. Overall, 574 discrete tasks were monitored with a median task duration of 5.5 minutes. Of these tasks, 39% involved the presence of a diesel vehicle. Farmers’ daily black carbon geometric mean exposures ranged from 0.1 to 2.3 μg/m3, with a median daily geometric mean of 0.3 μg/m3. The highest black carbon concentrations were measured on farmers who used or worked near diesel vehicles (geometric mean ranged from 0.5 μg/m3 while harvesting to 4.9 μg/m3 during animal work). Higher geometric means were found for near vs. far proximity to diesel-fueled vehicles and equipment (2.9 vs. 0.3 μg/m3). Indoor, bystander proximity to diesel-operated vehicles resulted in the highest geometric mean black carbon concentrations (18 μg/m3). Use of vehicles with open cabs had higher mean black carbon concentrations than closed cabs (2.1–3.2 vs. 0.4–0.9 μg/m3). In summary, our study provided evidence that farmers were frequently exposed to black carbon associated with diesel-related activities at levels above urban ambient concentrations in their daily work during harvest.

Exposure assessment efforts in population-based studies are increasingly incorporating measurements. The published literature was reviewed to identify the measurement sources and the approaches used to incorporate measurements into these efforts. The variety of occupations and industries in these studies made collecting participant-specific measurements impractical. Thus, the starting point was often the compilation of large databases of measurements from inspections, published literature, and other exposure surveys. These measurements usually represented multiple occupations, industries, and worksites, and spanned multiple decades. Measurements were used both qualitatively and quantitatively, dependent on the coverage and quality of the data. Increasingly, statistical models were used to derive job-, industry-, time period-, and other determinant-specific exposure concentrations. Quantitative measurement-based approaches are increasingly replacing expert judgment, which facilitates the development of quantitative exposure-response associations. Evaluations of potential biases in these measurement sources, and their representativeness of typical exposure situations, warrant additional examination.

Background: Increased pesticide concentrations in house dust in agricultural areas have been attributed to several exposure pathways, including agricultural drift, para-occupational, and residential use. Objective: To guide future exposure assessment efforts, we quantified relative contributions of these pathways using meta-regression models of published data on dust pesticide concentrations. Methods: From studies in North American agricultural areas published from 1995 to 2015, we abstracted dust pesticide concentrations reported as summary statistics [e.g., geometric means (GM)]. We analyzed these data using mixed-effects meta-regression models that weighted each summary statistic by its inverse variance. Dependent variables were either the log-transformed GM (drift) or the log-transformed ratio of GMs from two groups (para-occupational, residential use). Results: For the drift pathway, predicted GMs decreased sharply and nonlinearly, with GMs 64% lower in homes 250 m versus 23 m from fields (interquartile range of published data) based on 52 statistics from seven studies. For the para-occupational pathway, GMs were 2.3 times higher [95% confidence interval (CI): 1.5, 3.3; 15 statistics, five studies] in homes of farmers who applied pesticides more recently or frequently versus less recently or frequently. For the residential use pathway, GMs were 1.3 (95% CI: 1.1, 1.4) and 1.5 (95% CI: 1.2, 1.9) times higher in treated versus untreated homes, when the probability that a pesticide was used for the pest treatment was 1–19% and ≥ 20%, respectively (88 statistics, five studies). Conclusion: Our quantification of the relative contributions of pesticide exposure pathways in agricultural populations could improve exposure assessments in epidemiologic studies. The meta-regression models can be updated when additional data become available. Citation: Deziel NC, Beane Freeman LE, Graubard BI, Jones RR, Hoppin JA, Thomas K, Hines CJ, Blair A, Sandler DP, Chen H, Lubin JH, Andreotti G, Alavanja MC, Friesen MC. 2017. Relative contributions of agricultural drift, para-occupational, and residential use exposure pathways to house dust pesticide concentrations: meta-regression of published data. Environ Health Perspect 125:296–305; http://dx.doi.org/10.1289/EHP426

Parental occupational exposures to pesticides, animals and organic dust have been associated with an increased risk of childhood cancer based mostly on case–control studies. We prospectively evaluated parental occupational exposures and risk of childhood leukemia and central nervous system (CNS) tumors in the International Childhood Cancer Cohort Consortium. We pooled data on 329,658 participants from birth cohorts in five countries (Australia, Denmark, Israel, Norway and United Kingdom). Parental occupational exposures during pregnancy were estimated by linking International Standard Classification of Occupations-1988 job codes to the ALOHA+ job exposure matrix. Risk of childhood (

Asthma is a heterogeneous disease with varying severity and subtypes. Recent reviews of epidemiologic studies have identified cleaning and disinfecting activities (CDAs) as important risk factors for asthma-related outcomes among healthcare workers. However, the complexity of CDAs in healthcare settings has rarely been examined. This study utilized a complex survey dataset and data reduction approaches to identify and group healthcare workers with similar patterns of asthma symptoms, and then explored their associations with groups of participants with similar patterns of CDAs. Self-reported information on asthma symptoms/care, CDAs, demographics, smoking status, allergic status, and other characteristics were collected from 2030 healthcare workers within nine selected occupations in New York City. Hierarchical clustering was conducted to systematically group participants based on similarity of patterns of the 27 asthma symptom/care variables, and 14 product applications during CDAs, separately. Word clouds were used to visualize the complex information on the resulting clusters. The associations of asthma health clusters (HCs) with exposure clusters (ECs) were evaluated using multinomial logistic regression. Five HCs were identified (HC-1 to HC-5), labelled based on predominant features as: “no symptoms”, “winter cough/phlegm”, “mild asthma symptoms”, “undiagnosed/untreated asthma”, and “asthma attacks/exacerbations”. For CDAs, five ECs were identified (EC-1 to EC-5), labelled as: “no products”, “housekeeping/chlorine”, “patient care”, “general cleaning/laboratory”, and “disinfection products”. Using HC-1 and EC-1 as the reference groups, EC-2 was associated with HC-4 (odds ratio (OR) = 3.11, 95% confidence interval (95% CI) = 1.46–6.63) and HC-5 (OR = 2.71, 95% CI = 1.25–5.86). EC-3 was associated with HC-5 (OR = 2.34, 95% CI = 1.16–4.72). EC-4 was associated with HC-5 (OR = 2.35, 95% CI = 1.07–5.13). EC-5 was associated with HC-3 (OR = 1.81, 95% CI = 1.09–2.99) and HC-4 (OR = 3.42, 95% CI = 1.24–9.39). Various combinations of product applications like using alcohols, bleach, high-level disinfectants, and enzymes to disinfect instruments and clean surfaces captured by the ECs were identified as risk factors for the different asthma symptoms clusters, indicating that prevention efforts may require targeting multiple products. The associations of HCs with EC can be used to better inform prevention strategies and treatment options to avoid disease progression. This study demonstrated hierarchical clustering and word clouds were useful techniques for analyzing and visualizing a complex dataset with a large number of potentially correlated variables to generate practical information that can inform prevention activities.

Objectives Trichloroethylene, a chlorinated solvent widely used for metal degreasing, is classified by the International Agency for Research on Cancer as a kidney carcinogen. Other chlorinated solvents are suspected carcinogens, most notably the cleaning solvent perchloroethylene, although it is unclear whether they are associated with kidney cancer. We investigated kidney cancer associations with occupational exposure to 6 chlorinated solvents (trichloroethylene, perchloroethylene, 1,1,1-trichloroethane, carbon tetrachloride, chloroform, and methylene chloride) within a case–control study using detailed exposure assessment methods. Methods Cases (n=1217) and controls (n=1235) provided information on their occupational histories and, for selected occupations, on tasks involving potential exposure to chlorinated solvents through job-specific interview modules. Using this information, an industrial hygienist assessed potential exposure to each solvent. We computed ORs and 95% CIs for different exposure metrics, with unexposed participants as the referent group. Results 1,1,1-trichloroethane, carbon tetrachloride, chloroform, and methylene chloride were not associated with kidney cancer. Among jobs with high exposure intensity, high cumulative hours exposed to perchloroethylene was associated with increased risk, both overall (third tertile vs unexposed: OR 3.1, 95% CI 1.3 to 7.4) and after excluding participants with ≥50% exposure probability for trichloroethylene (OR 3.0, 95% CI 0.99 to 9.0). A non-significant association with high cumulative hours exposed to trichloroethylene was observed (OR 1.7, 95% CI 0.8 to 3.8). Conclusions In this study, high exposure to perchloroethylene was associated with kidney cancer, independent of trichloroethylene. Additional studies are needed to further investigate this finding.