Suchergebnisse

Abstract

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.

AbstractObjectivesOccupational 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.MethodsWe 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.ResultsOf 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).ConclusionsThe 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.

Abstract

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.

Abstract

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 <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.

Abstract

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.