Urinary biomonitoring of glyphosate exposure among male farmers and nonfarmers in the Biomarkers of Exposure and Effect in Agriculture (BEEA) study.

Glyphosate is the most widely applied herbicide worldwide. Glyphosate biomonitoring data are limited for agricultural settings. We measured urinary glyphosate concentrations and assessed exposure determinants in the Biomarkers of Exposure and Effect in Agriculture (BEEA) study. We selected four groups of BEEA participants based on self-reported pesticide exposure: recently exposed farmers with occupational glyphosate use in the last 7 days (n = 98), farmers with high lifetime glyphosate use (>80th percentile) but no use in the last 7 days (n = 70), farming controls with minimal lifetime use (n = 100), and nonfarming controls with no occupational pesticide exposures and no recent home/garden glyphosate use (n = 100). Glyphosate was quantified in first morning void urine using ion chromatography isotope-dilution tandem mass spectrometry. We estimated associations between urinary glyphosate concentrations and potential determinants using multivariable linear regression. Glyphosate was detected (≥0.2 µg/L) in urine of most farmers with recent (91 %) and high lifetime (93 %) use, as well as farming (88 %) and nonfarming (81 %) controls; geometric mean concentrations were 0.89, 0.59, 0.46, and 0.39 µg/L (0.79, 0.51, 0.42, and 0.37 µg/g creatinine), respectively. Compared with both control groups, urinary glyphosate concentrations were significantly elevated among recently exposed farmers (P < 0.0001), particularly those who used glyphosate in the previous day [vs. nonfarming controls; geometric mean ratio (GMR) = 5.46; 95 % confidence interval (CI): 3.75, 7.93]. Concentrations among high lifetime exposed farmers were also elevated (P < 0.01 vs. nonfarming controls). Among recently exposed farmers, glyphosate concentrations were higher among those not wearing gloves when applying glyphosate (GMR = 1.91; 95 % CI: 1.17, 3.11), not wearing long-sleeved shirts when mixing/loading glyphosate (GMR = 2.00; 95 % CI: 1.04, 3.86), applying glyphosate exclusively using broadcast/boom sprayers (vs. hand sprayer only; GMR = 1.70; 95 % CI: 1.00, 2.92), and applying glyphosate to crops (vs. non-crop; GMR = 1.72; 95 % CI: 1.04, 2.84). Both farmers and nonfarmers are exposed to glyphosate, with recency of occupational glyphosate use being the strongest determinant of urinary glyphosate concentrations. Continued biomonitoring of glyphosate in various settings is warranted.

Occupational exposure to organic solvents and risk of bladder cancer.

BACKGROUND: Bladder cancer has been linked to several occupations that involve the use of solvents, including those used in the dry-cleaning industry. OBJECTIVES: We evaluated exposure to solvents and risk of bladder cancer in 1182 incident cases and 1408 controls from a population-based study. METHODS: Exposure to solvents was quantitatively assessed using a job-exposure matrix (CANJEM). Exposure to benzene, toluene and xylene often co-occur. Therefore, we created two additional sets of metrics for combined benzene, toluene and xylene (BTX) exposure: (1) CANJEM-based BTX metrics and (2) hybrid BTX metrics, using an approach that integrates the CANJEM-based BTX metrics together with lifetime occupational histories and exposure-oriented modules that captured within-job, respondent-specific details about tasks and chemicals. Adjusted odds ratios (ORs) and 95% confidence intervals (95% CI) were estimated using logistic regression. RESULTS: Bladder cancer risks were increased among those ever exposed to benzene (OR = 1.63, 95% CI: 1.14-2.32), toluene (OR = 1.60, 95% CI: 1.06-2.43), and xylene (OR = 1.67, 95% CI: 1.13-2.48) individually. We further observed a statistically significant exposure-response relationship for cumulative BTX exposure, with a stronger association using the hybrid BTX metrics (ORQ1vsUnexposed = 1.26, 95% CI: 0.83-1.90; ORQ2vsUnexposed = 1.52, 95% CI: 1.00-2.31; ORQ3vsUnexposed = 1.88, 95% CI: 1.24-2.85; and ORQ4vsUnexposed = 2.23, 95% CI: 1.35-3.69) (p-trend=0.001) than using CANJEM-based metrics (p-trend=0.02). IMPACT: There is limited evidence about the role of exposure to specific organic solvents, alone or in combination on the risk of developing bladder cancer. In this study, workers with increasing exposure to benzene, toluene, and xylene as a group (BTX) had a statistically significant exposure-response relationship with bladder cancer. Future evaluation of the carcinogenicity of BTX and other organic solvents, particularly concurrent exposure, on bladder cancer development is needed.

Benzene exposure and risk of lung cancer in the Norwegian Offshore Petroleum Worker cohort: a prospective case-cohort study.

OBJECTIVE: The objective of our study was to examine whether occupational exposure to benzene is associated with lung cancer among males in the Norwegian Offshore Petroleum Workers cohort. METHODS: Among 25 347 male offshore workers employed during 1965-1998, we conducted a case-cohort study with 399 lung cancer cases diagnosed between 1999 and 2021, and 2035 non-cases sampled randomly by 5-year birth cohorts. Individual work histories were coupled to study-specific job-exposure matrices for benzene and other known lung carcinogens. Weighted Cox regression was used to estimate HRs and 95% CIs for the associations between benzene exposure and lung cancer, by major histological subtypes, adjusted for age, smoking and occupational exposure to welding fumes, asbestos and crystalline silica. Missing data were imputed. RESULTS: For lung cancer (all subtypes combined), HRs (95% CIs) for the highest quartiles of benzene exposure versus unexposed were 1.15 (0.61 to 2.35) for cumulative exposure, 1.43 (0.76 to 2.69) for duration, and 1.22 (0.68 to 2.18) for average intensity (0.280≤P-trend≤0.741). For 152 adenocarcinoma cases, a positive trend was observed for exposure duration (P-trend=0.044). CONCLUSIONS: In this cohort of offshore petroleum workers generally exposed to low average levels of benzene, we did not find an overall clear support for an association with lung cancer (all subtypes combined), although an association was suggested for duration of benzene exposure and adenocarcinoma. The limited evidence might be due to restricted statistical power.

Artificial intelligence exceeds humans in epidemiological job coding.

BACKGROUND: Work circumstances can substantially negatively impact health. To explore this, large occupational cohorts of free-text job descriptions are manually coded and linked to exposure. Although several automatic coding tools have been developed, accurate exposure assessment is only feasible with human intervention. METHODS: We developed OPERAS, a customizable decision support system for epidemiological job coding. Using 812,522 entries, we developed and tested classification models for the Professions et Catégories Socioprofessionnelles (PCS)2003, Nomenclature d'Activités Française (NAF)2008, International Standard Classifications of Occupation (ISCO)-88, and ISCO-68. Each code comes with an estimated correctness measure to identify instances potentially requiring expert review. Here, OPERAS' decision support enables an increase in efficiency and accuracy of the coding process through code suggestions. Using the Formaldehyde, Silica, ALOHA, and DOM job-exposure matrices, we assessed the classification models' exposure assessment accuracy. RESULTS: We show that, using expert-coded job descriptions as gold standard, OPERAS realized a 0.66-0.84, 0.62-0.81, 0.60-0.79, and 0.57-0.78 inter-coder reliability (in Cohen's Kappa) on the first, second, third, and fourth coding levels, respectively. These exceed the respective inter-coder reliability of expert coders ranging 0.59-0.76, 0.56-0.71, 0.46-0.63, 0.40-0.56 on the same levels, enabling a 75.0-98.4% exposure assessment accuracy and an estimated 19.7-55.7% minimum workload reduction. CONCLUSIONS: OPERAS secures a high degree of accuracy in occupational classification and exposure assessment of free-text job descriptions, substantially reducing workload. As such, OPERAS significantly outperforms both expert coders and other current coding tools. This enables large-scale, efficient, and effective exposure assessment securing healthy work conditions.

Work can expose us to health risks, such as asbestos and constant noise. To study these risks, job descriptions are collected and classified by experts to standard codes. This is time-consuming, expensive, and requires expert knowledge. To improve this coding, we created computer code based on Artificial Intelligence that can both automate this process and suggest codes to experts, who can then check and change it manually if needed. Our system outperforms both expert coders and other available tools. This system could make studying occupational health risks more efficient and accurate, resulting in safer work environments.

Exposure to benzene and other hydrocarbons and risk of bladder cancer among male offshore petroleum workers.

BACKGROUND: Occupational exposures constitute the second leading cause of urinary bladder cancer after tobacco smoking. Increased risks have been found in the petroleum industry, but high-quality exposure data are needed to explain these observations. METHODS: Using a prospective case-cohort design, we analysed 189 bladder cancer cases (1999-2017) and 2065 randomly drawn non-cases from the Norwegian Offshore Petroleum Workers cohort. Cases were identified in the Cancer Registry of Norway, while work histories (1965-1998) and lifestyle factors were recorded by questionnaire at baseline (1998). Occupational petroleum-related hydrocarbon exposures were assessed by expert-developed job-exposure matrices. Hazard ratios were estimated by weighted Cox-regressions, adjusted for age, tobacco smoking, education, and year of first employment, and with lagged exposures. RESULTS: Increased risks were found in benzene-exposed workers, either long-term exposure (≥18.8 years, HR = 1.89, 95% CI: 1.14-3.13; p-trend = 0.044) or high-level cumulative benzene exposure (HR = 1.60, 95% CI: 0.97-2.63; p-trend = 0.065), compared with the unexposed. Associations persisted with 20-year exposure lag. No associations were found with skin or inhalation exposure to crude oil, mineral oil (lubrication, hydraulics, turbines, drilling), or diesel exhaust. CONCLUSIONS: The results suggest that exposures in the benzene fraction of the petroleum stream may be associated with increased bladder cancer risk.

Using a smartphone application to capture daily work activities: a longitudinal pilot study in a farming population.

OBJECTIVES: Smartphones are increasingly used to collect real-time information on time-varying exposures. We developed and deployed an application (app) to evaluate the feasibility of using smartphones to collect real-time information on intermittent agricultural activities and to characterize agricultural task variability in a longitudinal study of farmers. METHODS: We recruited 19 male farmers, aged 50-60 years, to report their farming activities on 24 randomly selected days over 6 months using the Life in a Day app. Eligibility criteria include personal use of an iOS or Android smartphone and >4 h of farming activities at least two days per week. We developed a study-specific database of 350 farming tasks that were provided in the app; 152 were linked to questions that were asked when the activity ended. We report eligibility, study compliance, number of activities, duration of activities by day and task, and responses to the follow-up questions. RESULTS: Of the 143 farmers we reached out to for this study, 16 were not reached by phone or refused to answer eligibility questions, 69 were ineligible (limited smartphone use and/or farming time), 58 met study criteria, and 19 agreed to participate. Refusals were mostly related to uneasiness with the app and/or time commitment (32 of 39). Participation declined gradually over time, with 11 farmers reporting activities through the 24-week study period. We obtained data on 279 days (median 554 min/day; median 18 days per farmer) and 1,321 activities (median 61 min/activity; median 3 activities per day per farmer). The activities were predominantly related to animals (36%), transportation (12%), and equipment (10%). Planting crops and yard work had the longest median durations; short-duration tasks included fueling trucks, collecting/storing eggs, and tree work. Time period-specific variability was observed; for example, crop-related activities were reported for an average of 204 min/day during planting but only 28 min/day during pre-planting and 110 min/day during the growing period. We obtained additional information for 485 (37%) activities; the most frequently asked questions were related to "feed animals" (231 activities) and "operate fuel-powered vehicle (transportation)" (120 activities). CONCLUSIONS: Our study demonstrated feasibility and good compliance in collecting longitudinal activity data over 6 months using smartphones in a relatively homogeneous population of farmers. We captured most of the farming day and observed substantial heterogeneity in activities, highlighting the need for individual activity data when characterizing exposure in farmers. We also identified several areas for improvement. In addition, future evaluations should include more diverse populations.

Evaluation of the updated SOCcer v2 algorithm for coding free-text job descriptions in three epidemiologic studies.

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.

An algorithm for quantitatively estimating occupational endotoxin exposure in the biomarkers of exposure and effect in agriculture study: II. Application to the study population.

BACKGROUND: We developed an algorithm to quantitatively estimate endotoxin exposure for farmers in the Biomarkers of Exposure and Effect in Agriculture (BEEA) Study. METHODS: The algorithm combined task intensity estimates derived from published data with questionnaire responses on activity duration to estimate task-specific cumulative endotoxin exposures for 13 tasks during four time windows, ranging from "past 12 months" to "yesterday/today." We applied the algorithm to 1681 participants in Iowa and North Carolina. We examined correlations in endotoxin metrics within- and between-task. We also compared these metrics to prior day full-shift inhalable endotoxin concentrations from 32 farmers. RESULTS: The highest median task-specific cumulative exposures were observed for swine confinement, poultry confinement, and grind feed. Inter-quartile ranges showed substantial between-subject variability for most tasks. Time window-specific metrics of the same task were moderately-highly correlated. Between-task correlation was variable, with moderately-high correlations observed for similar tasks (e.g., between animal-related tasks). Prior day endotoxin concentration increased with the total metric and with task metrics for swine confinement, clean other animal facilities, and clean grain bins. SIGNIFICANCE: This study provides insight into the variability and sources of endotoxin exposure among farmers in the BEEA study and summarizes exposure estimates for future investigations in this population.

An algorithm for quantitatively estimating occupational endotoxin exposure in the Biomarkers of Exposure and Effect in Agriculture (BEEA) study: I. Development of task-specific exposure levels from published data.

BACKGROUND/OBJECTIVE: Farmers conduct numerous tasks with potential for endotoxin exposure. As a first step to characterize endotoxin exposure for farmers in the Biomarkers of Exposure and Effect in Agriculture (BEEA) Study, we used published data to estimate task-specific endotoxin concentrations. METHODS: We extracted published data on task-specific, personal, inhalable endotoxin concentrations for agricultural tasks queried in the study questionnaire. The data, usually abstracted as summary measures, were evaluated using meta-regression models that weighted each geometric mean (GM, natural-log transformed) by the inverse of its within-study variance to obtain task-specific predicted GMs. RESULTS: We extracted 90 endotoxin summary statistics from 26 studies for 9 animal-related tasks, 30 summary statistics from 6 studies for 3 crop-related tasks, and 10 summary statistics from 5 studies for 4 stored grain-related tasks. Work in poultry and swine confinement facilities, grinding feed, veterinarian services, and cleaning grain bins had predicted GMs > 1000 EU/m3 . In contrast, harvesting or hauling grain and other crop-related tasks had predicted GMs below 100 EU/m3 . SIGNIFICANCE: These task-specific endotoxin GMs demonstrated exposure variability across common agricultural tasks. These estimates will be used in conjunction with questionnaire responses on task duration to quantitatively estimate endotoxin exposure for study participants, described in a companion paper.

Elevated 2,4-dichlorophenoxyacetic acid (2,4-D) herbicide concentrations in the household dust of farmers with recent occupational use.

Pesticide dust concentrations in homes have been previously associated with occupational and home/garden use of pesticides, hygiene practices, and other factors. This study evaluated the relationship between self-reported use of 2,4-dichlorophenoxyacetic acid (2,4-D) and house dust concentrations and these factors in the Biomarkers of Exposure and Effect in Agriculture (BEEA) Study, a molecular epidemiologic study of farmers in Iowa and North Carolina. The vacuum dust from the homes of 35 BEEA participants was analyzed for the presence of 2,4-D. Participants provided detailed information on occupational and home/garden pesticide use during the past 12 months and reported household characteristics via questionnaires. Linear regression models were used to examine the association between 2,4-D concentrations and four exposure metrics for occupational use in the last 12 months (yes/no, days since last use, days of use, intensity-weighted days of use), home/garden use (yes/no), as well as several household characteristics. 2,4-D was detected in all homes and was used occupationally by 54% of the participants. In a multi-variable model, compared to homes with no occupational or home/garden 2,4-D use reported in the past 12 months, concentrations were 1.6 (95% confidence interval (CI): 0.5, 4.9) times higher in homes with low occupational 2,4-D use (intensity-weighted days < median) and 3.1 (95% CI: 1.0, 9.8) times higher in homes of participants with high use (≥median intensity-weighted days) (p-trend = 0.06). Similar patterns were observed with other occupational metrics. Additionally, 2,4-D dust concentrations were non-significantly elevated (relative difference (RD) = 1.8, 95% CI: 0.5, 6.2) in homes with home/garden use and were significantly lower in homes that did not have carpets (RD = 0.20, 95% CI: 0.04, 0.98). These analyses suggest that elevated 2,4-D dust concentrations were associated with several metrics of recent occupational use and may be influenced by home/garden use and household characteristics.

Nitrated Polycyclic Aromatic Hydrocarbon (Nitro-PAH) Signatures and Somatic Mutations in Diesel Exhaust-Exposed Bladder Tumors.

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 < 0.001; 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.

Prerequisite for Imputing Non-detects among Airborne Samples in OSHA's IMIS Databank: Prediction of Sample's Volume.

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.

Glyphosate in house dust and risk of childhood acute lymphoblastic leukemia in California.

BACKGROUND: Residential use of pesticides has been associated with increased risk of childhood acute lymphoblastic leukemia (ALL). We evaluated determinants of glyphosate concentrations in house dust and estimated ALL risk in the California Childhood Leukemia Study (CCLS). METHODS: The CCLS is a population-based case-control study of childhood leukemia in California. Among those < 8-years (no move since diagnosis/reference date), we collected dust (2001-2007) from the room where the child spent the most time while awake and measured > 40 pesticides. Three-to-eight years later, we collected a second sample from non-movers. We used Ultra-Performance Liquid Chromatography Tandem Mass Spectrometry to measure glyphosate (µg/g dust) for 181 ALL cases and 225 controls and for 45 households with a second dust sample. We used multivariable Tobit regression to evaluate determinants of glyphosate concentrations. Odds ratios (ORs) and 95 % confidence intervals (CI) were calculated for ALL and quartiles of the concentration (first samples) using unconditional logistic regression. We computed the within- and between-home variance and intraclass correlation coefficient (ICC). RESULTS: Glyphosate was frequently detected (cases: 98 %; controls: 99 %). Higher concentrations were associated with occupational pesticide exposure, nearby agricultural use, treatment for lawn weeds and bees/wasps, and sampling season. Increasing concentrations were not associated with ALL risk (adjusted ORQ4vsQ1 = 0.8, CI: 0.4-1.4). We observed similar null associations for boys and girls, Hispanics and non-Hispanic whites, and among those who resided in their home since birth (76 cases/117 controls) or age two (130 cases/176 controls). The ICC was 0.32 indicating high within-home temporal variability during the years of our study. CONCLUSIONS: We observed higher concentrations in homes associated with expected predictors of exposure but no association with childhood ALL risk. Due to continuing use, potential exposure to young children is high. It will be important to evaluate risk in future studies with multiple dust measurements or biomarkers of exposure.

Automated Coding of Job Descriptions From a General Population Study: Overview of Existing Tools, Their Application and Comparison.

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.

Night shift work and risk of aggressive prostate cancer in the Norwegian Offshore Petroleum Workers (NOPW) cohort.

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.

A Task-Specific Algorithm to Estimate Occupational (1→3)-ß-D-glucan Exposure for Farmers in the Biomarkers of Exposure and Effect in Agriculture Study.

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)-ß-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 <50 ng/m3 and had similar concentrations to each other. Overall, 67% of the participants reported at least one of these tasks. The most prevalent task was stored seed or grain (64%). The highest median glucan scores were observed for poultry confinement and swine confinement; these tasks were reported by 2% and 8% of the participants, respectively. The correlation between scores for the same task but different time windows was high for swine confinement and poultry confinement, but low for clean grain bins. Task-specific scores had low correlation with other tasks. Prior day glucan concentration was associated with the total glucan 'past 1 day' score and with swine confinement and clean grain bin task scores. 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.

Night shift work, chemical coexposures and risk of female breast cancer in the Norwegian Offshore Petroleum Workers (NOPW) cohort: a prospectively recruited case-cohort study.

OBJECTIVES: This 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. DESIGN: Prospectively recruited case-cohort study within the NOPW cohort. SETTING: Female offshore petroleum workers active on the Norwegian continental shelf. PARTICIPANTS: 600 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. RESULTS: No 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). CONCLUSIONS: Our 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.

Adapting Decision Rules to Estimate Occupational Metalworking Fluid Exposure in a Case-Control Study of Bladder Cancer in Spain.

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 (<5, ≥5 to <50, ≥50 to <100, and 100%), frequency (in h week-1), and intensity (in mg m-3) of exposure to each of the three broad classes of MWFs to assign exposure to 10 182 reported jobs in the SBCS. The decision rules used the participant's module responses to MWF questions wherever possible. We then used these SBCS module responses to calculate job-, industry-, and time-specific patterns in the prevalence and frequency of MWF exposure. These estimates replaced the NEBCS-specific estimates in decision rules applied to jobs without MWF module responses. Intensity estimates were predicted using a previously developed statistical model that used the decade, industry (three categories), operation (grinding versus machining), and MWF type extracted from the SBCS questionnaire responses. We also developed new decision rules to assess mineral oil exposure from non-machining sources (possibly exposed versus not exposed). The decision rules for MWF and mineral oil identified questionnaire response patterns that required job-by-job expert review. 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.

Observed vs. self-reported agricultural activities: Evaluating 24-hr recall in a pilot study.

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 min, 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.