Date of Award

January 2021

Document Type

Open Access Thesis

Degree Name

Master of Public Health (MPH)

Department

School of Public Health

First Advisor

Nicole Deziel

Abstract

Background: (1→3)-β-D-glucans, or simply known as glucans, are components of fungal cell walls and are associated with mold. The adverse health effects due to glucan inhalation exposure has not been well studied, but it is known that upper respiratory inflammation has been seen with glucan exposure. Farmers, especially those who work with animals, are exposed to glucans on a daily basis; however, there is no occupational exposure limit to glucans and the full extent of glucan exposure in this population is unclear due to the lack of exposure assessment. This study will utilize decision-rules and a source-specific method to create an algorithm that quantitatively estimates glucan exposure intensity and allows us to identify any farming activities associated with glucan exposure. Methods: The study population consists of farmers in the Biomarkers of Exposure and Effect in Agriculture study (BEEA). Subjects participated in an in-home interview that gathered information on farming activities and the duration and frequencies of these activities. 32 of the BEEA farmers had an additional visit day where an industrial hygienist visited the farm to take personal air samples. Activities performed during each sample were noted by the industrial hygienist. Using this air sampling data, we derived glucan exposure levels and identified certain farming activities that had glucan exposures higher than background. Focusing on these particular tasks, we took the questionnaire data all the farmers in the BEEA population answered to figure out the duration and frequency they did these identified tasks. This information was used to find a glucan intensity score of exposure for the past 30 days, past 7 days, and past 1 day. We calculated the median glucan intensity score and median time spent doing these activities. To see how the activities compared to themselves and to others, we found the correlations between the different time frames and between the different tasks. Results were then compared in a validation assessment using full-shift data of the 32 farmers who participated in the air sampling study. Results: We identified 6 tasks that had glucan exposures greater than background: working around moldy hay, spending time in poultry confinement, spending time in swine confinement, working around stored seed, cleaning grain bins, and working around wood dust. Highest median exposure and time spent was seen for swine confinement across all time frames: 9000 ng/m3-hrs and 30 hours for the past 30 days, 4200 ng/m3-hrs and 4 hours for the past 7 days, and 450 ng/m3-hrs and 1.5 hours in the past 1 day. Within-task correlations comparing the different time frames were the highest in spending time in poultry confinement with a Spearman correlation of 0.94 between the past 30 day and 1 day exposure. The lowest within-task correlation was seen in cleaning grain bins with a Spearman correlation of 0.19 between the past 30 day and 1 day exposure. The correlations between tasks ranged from -0.23 to 0.25. The validation portion of the study showed that our calculated glucan scores for swine confinement (p-value=0.01) and cleaning grain bins (p-value=0.00) were statistically significant in being able to predict full-shift data. Conclusions: From the algorithm, we were able to identify that animal activities, which are typically done on a daily basis, appear to contribute the most to glucan exposure. This study’s usage of decision-rules for assessing glucan exposure has never been done before and begins to fill in the gap for glucan exposure assessment. While this algorithm created in this study is limited to a certain population, it provides the framework for further development of an algorithm for better glucan exposure assessment. This can then be used to link glucan exposure to adverse health effects and the development of an occupational exposure limit.

Comments

This is an Open Access Thesis.

Open Access

This Article is Open Access

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