Evidence map›Paper›PMID 35731645›Full record

ArticleAnnals of work exposures and health2022

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

Melissa C Friesen, Felicia Hung, Shuai Xie, Susan M Viet, Nicole C Deziel, Sarah J Locke, Pabitra R Josse, Jean-François Sauvé, Gabriella Andreotti, Peter S Thorne and 2 more

Open access · greenAbstract read
In one paragraph

Article in Annals of work exposures and health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.6field-weighted citation impact, top 35% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 6 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 4 institutions in 2 countries.

Melissa C FriesenOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.ORCID 0000-0002-1695-3282
Felicia HungDepartment of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA.
Shuai XieOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Susan M VietWestat, Rockville, MD, USA.
Nicole C DezielDepartment of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA.
Sarah J LockeOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Pabitra R JosseOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Jean-François SauvéPollutants Metrology Department, Institut National de Recherche et de Sécurité, Vandoeuvre-lès-Nancy, France.
Gabriella AndreottiOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Peter S ThorneDepartment of Occupational and Environmental Health, University of Iowa, Iowa City, IA, USA.
Laura E Beane-FreemanOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.ORCID 0000-0003-1294-4124
Jonathan N HofmannOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.ORCID 0000-0002-1043-5812
National Cancer Institute · USYale University · USUniversity of Iowa · USWestat (United States) · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Studies of Occupational Cancer (Pesticides)ZIACP010119 · NCI · DIVISION OF CANCER EPIDEMIOLOGY AND GENETICS · PI BEANE FREEMAN, LAURA · 2009 to 2025
$46.1M
Pulmonary Toxicology Facility CoreP30ES005605 · NIEHS · UNIVERSITY OF IOWA · PI Jong Sung Kim · 1990 to 2026
$40.5M
STUDIES OF OCCUPATIONAL CANCER--PESTICIDESZ01CP010119 · NCI · DIVISION OF CANCER EPIDEMIOLOGY AND GENETICS · PI ALAVANJA, MICHAEL · 1996 to 2008
$5.0M
Intramural NIH HHS Z01 CP010119NCATS NIH HHS UL1 TR001863NIEHS NIH HHS P30 ES005605
6 · The paper itself

Abstract

objectivesFarmers 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.

methodsTo 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.

resultsWe 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.

conclusionsThis 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.

Indexed as

Inhalation ExposureOccupational ExposureAgricultureAlgorithmsAnimalsBiomarkersEdible GrainEnvironmental MonitoringFarmersGlucansHumansSwineBiomarkersGlucansbioaerosol exposuresexposure assessmentfarming activitiesoccupational questionnaires

Identifiers

PMID35731645
PMCPMC9551320
OpenAlexW4283270928

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.