Estimation of equivalent noise exposure level using hearing threshold levels of a population.
OBJECTIVE
Noise-induced hearing loss (NIHL) is costly in both human and economic terms. One means of reducing NIHL is to apply engineering controls to hazardous noise sources. To trade off the cost of engineering controls against the total direct monetary costs incurred by NIHL, a means of predicting the amount of NIHL that will be incurred over the life-cycle of a hazardous noise source is necessary. A widely known algorithm for the prediction of NIHL is published in ANSI S3.44-1996. However, the algorithm inputs, noise exposure level and duration, may be difficult to determine in some cases. This paper describes the conceptual basis of an approach for using ANSI S3.44-1996 to predict hearing thresholds in a population even when noise exposure levels and durations are not precisely known, and demonstrates the initial application of this approach to a single military population.


METHODS
Retrospective data were obtained on the hearing-threshold levels, demographic characteristics, and noise exposure history of 250 male U.S. Navy machinists' mates. A maximum-likelihood fitting procedure was developed in which the noise level input to the algorithm was varied in order to determine the noise level that best accounted for all of the data.


RESULTS
The maximum likelihood fitting produced a value for the noise level input of approximately 93 dBA, with a standard error of approximately 0.3. The low standard error virtually eliminates any estimate above 94 or below 92 dBA, and indicates that a good fit to the data was achieved.


CONCLUSIONS
This research demonstrates the feasibility of calibrating the algorithm to an individual population, even when noise exposure level or duration is not precisely known. Future work will focus on validating and generalizing this approach so that it may be used to predict hearing-threshold levels in various populations. Such an approach may be used in calculating potential cost savings in compensable hearing loss due to the application of noise control solutions.
Tufts
Jennifer B
JB
Department of Communication Sciences, University of Connecticut, Storrs, Connecticut 06269, USA. jennifer.tufts@uconn.edu
Weathersby
Paul K
PK
Marshall
Lynne
L
D000465:Algorithms; D001309:Auditory Threshold; D017046:Cost Savings; D005240:Feasibility Studies; D017048:Health Care Costs; D006317:Hearing Loss, Noise-Induced / Q000175:diagnosis / Q000191:economics / Q000453:epidemiology; D006801:Humans; D008297:Male; D008889:Military Personnel / Q000706:statistics & numerical data*; D009622:Noise / Q000009:adverse effects; D009623:Noise, Occupational / Q000706:statistics & numerical data*; D016273:Occupational Exposure / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D015995:Prevalence; D012189:Retrospective Studies; D014481:United States; D014493:United States Department of Veterans Affairs / Q000191:economics / Q000706:statistics & numerical data