Graduation Year

2015

Document Type

Thesis

Degree

M.S.P.H.

Degree Name

MS in Public Health (M.S.P.H.)

Degree Granting Department

Public Health

Major Professor

Steve Mlynarek, Ph.D.

Committee Member

Thomas E. Bernard, Ph.D.

Committee Member

Rene R. Salazar, Ph.D.

Keywords

food-service workers, hazardous noise, multi-level modeling, occupational exposures in the service industry, risk of noise-induced hearing loss

Abstract

Occupational hearing loss resulting from noise exposures encountered in the workplace affects millions of workers and costs hundreds of millions of dollars annually in Workers’ Compensation costs in the United States alone. Some industries have been well studied, and the presence of hazardous noise in the work environment established and documented. The restaurant industry is one in which little current data exists, but in which there may be cause for concern.

This work sought to quantify noise exposures for cooks, servers, and dishwashers and to determine whether or not any of these workers are at risk for Noise Induced Hearing Loss. Further, the researchers wanted to know what environmental factors present in the restaurants had the greatest impact on noise exposures for each exposure group.

Statistical analysis was conducted on selected factors, and while nearly all were found to have significant effects on noise exposure for servers, only the number of minutes worked explained variance in exposures for cooks and dishwashers when all factors were included in analysis. These two groups are the ones most likely to be overexposed and they typically worked more than 480 minutes on the day the sample was collected. Efforts to control exposure must take these extended shifts into careful account.

The study was limited by relatively small sample size, with 124 cooks, 119 servers, and 91 dishwashers employed at nine different restaurants participating. Future efforts to explain and characterize the sources of variation in noise exposure for these three groups should include greater numbers of participants and structure the data in a way that allows the effects of selected factors to be more clearly seen.

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