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.

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.

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.

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.

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.

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.

Characterization of inhalable endotoxin, glucan, and dust exposures in Iowa farmers.

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 â†’ 3)-ß-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 â†’ 3)-ß-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 â†’ 3)-ß-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 â†’ 3)-ß-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.

Diesel Exhaust Exposure during Farming Activities: Statistical Modeling of Continuous Black Carbon Concentrations.

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.

Estimation of Source-Specific Occupational Benzene Exposure in a Population-Based Case-Control Study of Non-Hodgkin Lymphoma.

OBJECTIVES: Occupational exposures in population-based case-control studies are increasingly being assessed using decision rules that link participants' responses to occupational questionnaires to exposure estimates. We used a hierarchical process that incorporated decision rules and job-by-job expert review to assign occupational benzene exposure estimates in a US population-based case-control study of non-Hodgkin lymphoma. METHODS: We conducted a literature review to identify scenarios in which occupational benzene exposure has occurred, which we grouped into 12 categories of benzene exposure sources. For each source category, we then developed decision rules for assessing probability (ordinal scale based on the likelihood of exposure > 0.02 ppm), frequency (proportion of work time exposed), and intensity of exposure (in ppm). The rules used the participants' occupational history responses and, for a subset of jobs, responses to job- and industry-specific modules. For probability and frequency, we used a hierarchical assignment procedure that prioritized subject-specific module information when available. Next, we derived job-group medians from the module responses to assign estimates to jobs with only occupational history responses. Last, we used job-by-job expert review to assign estimates when job-group medians were not available or when the decision rules identified possible heterogeneous or rare exposure scenarios. For intensity, we developed separate estimates for each benzene source category that were based on published measurement data whenever possible. Frequency and intensity annual source-specific estimates were assigned only for those jobs assigned ≥75% probability of exposure. Annual source-specific concentrations (intensity × frequency) were summed to obtain a total annual benzene concentration for each job. RESULTS: Of the 8827 jobs reported by participants, 8% required expert review for one or more source categories. Overall, 287 (3.3%) jobs were assigned ≥75% probability of exposure from any benzene source category. The source categories most commonly assigned ≥75% probability of exposure were gasoline and degreasing. The median total annual benzene concentration among jobs assigned ≥75% probability was 0.11 ppm (interquartile range: 0.06-0.55). The highest source-specific median annual concentrations were observed for ink and printing (2.3 and 1.2 ppm, respectively). CONCLUSIONS: The applied framework captures some subject-specific variability in work tasks, provides transparency to the exposure decision process, and facilitates future sensitivity analyses. The developed decision rules can be used as a starting point by other researchers to assess occupational benzene exposure in future population-based studies.

Validity of retrospective occupational exposure estimates of lead and manganese in a case-control study.

OBJECTIVES: The 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. METHODS: We 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. RESULTS: We 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 <0.001). Expert estimates were only weakly associated with manganese toenail concentrations. CONCLUSIONS: Our 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.

Case-control investigation of occupational lead exposure and kidney cancer.

OBJECTIVES: Lead 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. METHODS: Study 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 in ALAD (rs1805313, rs1800435, rs8177796, rs2761016), a gene involved in lead toxicokinetics. RESULTS: In 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 evaluated ALAD variants (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. CONCLUSIONS: The findings of this study do not offer clear support for an association between occupational lead exposure and kidney cancer.

Sources of Variability in Real-time Monitoring Data for Fine Particulate Matter: Comparability of Three Wearable Monitors in an Urban Setting.

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 PM2.5 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 PM2.5 concentrations between devices (R2 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<0.05), suggesting that the relationship between devices was not constant in all locations. Our finding of heterogeneity in instrument comparability by location may have important implications in epidemiologic studies incorporating personal PM2.5 measurements.

Activity Tracking with Momentary Assessments.

Task and activity tracking has been an effective industrial management and research technique for generations. It is applied to workflow optimization, group coordination, task sequencing, individual time management and environmental exposures. Appropriately, task tracking technologies are migrating to personal mobile devices. At the same time, individual survey approaches have been advanced tremendously as mobile apps. We report on a method of dynamic task registration with momentary assessment systems in natural environments that apply knowledge of context. We describe how the app was refined by a user acceptance study and its deployment in studies on agricultural exposure and industrial operations.

Decision rule approach applied to estimate occupational lead exposure in a case-control study of kidney cancer.

BACKGROUND: We developed a systematic, data-driven approach to estimate metrics of occupational exposure to lead to aid in epidemiologic analyses in a case-control study of kidney cancer. METHODS: Probability of exposure to ten lead sources was assigned using decision rules developed from an extensive literature review and expert judgement. For jobs with >50% probability of exposure, we assigned source-specific frequency based on subjects' self-reported task frequencies or means of subjects' job-groups and source-specific intensity estimates of blood lead (µg/dL). RESULTS: In our study, 18.7% of employed person-years were associated with high (≥80%) probability of exposure to any lead source. The most common medium (>50%) or high probability source of lead exposure was leaded gasoline (2.5% and 11.5% of employed person-years, respectively). The median blood lead attributed to occupational exposure was 3.1 µg/dL. CONCLUSIONS: These rules can aid in future studies after population-specific adaption for geographic differences and different exposure scenarios.

A task-based analysis of black carbon exposure in Iowa farmers during harvest.

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 min. Of these tasks, 39% involved the presence of a diesel vehicle. Farmers' daily black carbon geometric mean exposures ranged from 0.1-2.3 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.

Evaluating predictors of lead exposure for activities disturbing materials painted with or containing lead using historic published data from U.S. workplaces.

OBJECTIVES: We evaluated predictors of differences in published occupational lead concentrations for activities disturbing material painted with or containing lead in U.S. workplaces to aid historical exposure reconstruction. METHODS: For the aforementioned tasks, 221 air and 113 blood lead summary results (1960-2010) were extracted from a previously developed database. Differences in the natural log-transformed geometric mean (GM) for year, industry, job, and other ancillary variables were evaluated in meta-regression models that weighted each summary result by its inverse variance and sample size. RESULTS: Air and blood lead GMs declined 5%/year and 6%/year, respectively, in most industries. Exposure contrast in the GMs across the nine jobs and five industries was higher based on air versus blood concentrations. For welding activities, blood lead GMs were 1.7 times higher in worst-case versus non-worst case scenarios. CONCLUSIONS: Job, industry, and time-specific exposure differences were identified; other determinants were too sparse or collinear to characterize. Am. J. Ind. Med. 60:189-197, 2017. © 2017 Wiley Periodicals, Inc.

Occupational exposure to chlorinated solvents and kidney cancer: a case-control study.

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.

Smoking status, usual adult occupation, and risk of recurrent urothelial bladder carcinoma: data from The Cancer Genome Atlas (TCGA) Project.

PURPOSE: Tobacco smoking and occupational exposures are the leading risk factors for developing urothelial bladder carcinoma (UBC), yet little is known about the contribution of these two factors to risk of UBC recurrence. We evaluated whether smoking status and usual adult occupation are associated with time to UBC recurrence for 406 patients with muscle-invasive bladder cancer submitted to The Cancer Genome Atlas (TCGA) project. METHODS: Kaplan-Meier and Cox proportional hazard methods were used to assess the association between smoking status, employment in a high-risk occupation for bladder cancer, occupational diesel exhaust exposure, and 2010 Standard Occupational Classification group and time to UBC recurrence. RESULTS: Data on time to recurrence were available for 358 patients over a median follow-up time of 15 months. Of these, 133 (37.2%) experienced a recurrence. Current smokers who smoked for more than 40 pack-years had an increased risk of recurrence compared to never smokers (HR 2.1, 95% CI 1.1, 4.1). Additionally, employment in a high-risk occupation was associated with a shorter time to recurrence (log-rank p = 0.005). We found an increased risk of recurrence for those employed in occupations with probable diesel exhaust exposure (HR 1.8, 95% CI 1.1, 3.0) and for those employed in production occupations (HR 2.0, 95% CI 1.1, 3.6). CONCLUSIONS: These findings suggest smoking status impacts risk of UBC recurrence, although several previous studies provided equivocal evidence regarding this association. In addition to the known causal relationship between occupational exposure and bladder cancer risk, our study suggests that occupation may also be related to increased risk of recurrence.

Evaluation of Automatically Assigned Job-Specific Interview Modules.

OBJECTIVE: In community-based epidemiological studies, job- and industry-specific 'modules' are often used to systematically obtain details about the subject's work tasks. The module assignment is often made by the interviewer, who may have insufficient occupational hygiene knowledge to assign the correct module. We evaluated, in the context of a case-control study of lymphoid neoplasms in Asia ('AsiaLymph'), the performance of an algorithm that provided automatic, real-time module assignment during a computer-assisted personal interview. METHODS: AsiaLymph's occupational component began with a lifetime occupational history questionnaire with free-text responses and three solvent exposure screening questions. To assign each job to one of 23 study-specific modules, an algorithm automatically searched the free-text responses to the questions 'job title' and 'product made or services provided by employer' using a list of module-specific keywords, comprising over 5800 keywords in English, Traditional and Simplified Chinese. Hierarchical decision rules were used when the keyword match triggered multiple modules. If no keyword match was identified, a generic solvent module was assigned if the subject responded 'yes' to any of the three solvent screening questions. If these question responses were all 'no', a work location module was assigned, which redirected the subject to the farming, teaching, health professional, solvent, or industry solvent modules or ended the questions for that job, depending on the location response. We conducted a reliability assessment that compared the algorithm-assigned modules to consensus module assignments made by two industrial hygienists for a subset of 1251 (of 11409) jobs selected using a stratified random selection procedure using module-specific strata. Discordant assignments between the algorithm and consensus assignments (483 jobs) were qualitatively reviewed by the hygienists to evaluate the potential information lost from missed questions with using the algorithm-assigned module (none, low, medium, high). RESULTS: The most frequently assigned modules were the work location (33%), solvent (20%), farming and food industry (19%), and dry cleaning and textile industry (6.4%) modules. In the reliability subset, the algorithm assignment had an exact match to the expert consensus-assigned module for 722 (57.7%) of the 1251 jobs. Overall, adjusted for the proportion of jobs in each stratum, we estimated that 86% of the algorithm-assigned modules would result in no information loss, 2% would have low information loss, and 12% would have medium to high information loss. Medium to high information loss occurred for <10% of the jobs assigned the generic solvent module and for 21, 32, and 31% of the jobs assigned the work location module with location responses of 'someplace else', 'factory', and 'don't know', respectively. Other work location responses had ≤8% with medium to high information loss because of redirections to other modules. Medium to high information loss occurred more frequently when a job description matched with multiple keywords pointing to different modules (29-69%, depending on the triggered assignment rule). CONCLUSIONS: These evaluations demonstrated that automatically assigned modules can reliably reproduce an expert's module assignment without the direct involvement of an industrial hygienist or interviewer. The feasibility of adapting this framework to other studies will be language- and exposure-specific.