A statistical learning model for predicting noise-induced hearing loss in humans

点击翻译按钮获取中文摘要

DESCRIPTION (provided by applicant): Existing damage risk criteria for noise exposure are based entirely on an energy metric. Our animal model experiments have shown that energy alone is not sufficient to characterize a complex noise for hearing conservation purposes. These data suggest that energy and the statistical metric kurtosis may constitute necessary and sufficient metrics for the prediction of hearing loss that will result from long-term industrial exposures. This proposal is a continuation of this effort but focuses on the human condition. It is important to verify the animal results with comparable human exposure and hearing loss data. The main aim is to develop a noise measurement/analysis strategy that can be used to estimate the hearing loss that will develop in workers exposed to complex industrial noise* environments. Statistical learning models will be developed to achieve this goal. Model development will require that the following data be obtained: (1) Stable audiograms on a population of highly screened workers exposed to Gaussian and nonGaussian noise environments. (2) Record, archive and analyze the noise waveforms that each subject is exposed to over the course of an entire work shift. The models developed from this database will identify the noise variables that are important in the production of hearing loss and can also be used to develop exposure criteria. In addition, in response to ethical concerns of collecting data from unprotected workers, elements of a hearing conservation program will be introduced. The experimental paradigm will generate data needed for the development of an empirical basis for using in addition to an energy metric to predict the hazards of an exposure, variables that quantify the temporal and peak characteristics of a noise such as the kurtosis, impact interval and peak histograms, and transient durations. Data will be collected in collaboration with researchers at Peking University and include a population of at least 1,250 workers employed in various complex, high noise level industrial settings in China. This noise exposure and cross-sectional audiometric data will constitute a unique database that will fill a NIOSH acknowledged void in the data available for developing exposure criteria for hearing conservation purposes. [*Throughout this proposal our use of the term 'complex noise' refers to non-Gaussian noise, which in the course of a work cycle, may be intermittent, interrupted and of variable level. Non-Gaussian noise is very common in industry and the military where it consists of a background Gaussian noise with embedded high- level transients (impacts or noise bursts).] Existing safety criteria for noise exposure are based entirely on an energy metric. New data indicate that energy alone is not a suitable metric to use for this purpose. The main aim of this proposal is to develop a noise measurement/analysis strategy that can be used to estimate the hearing loss that will develop in workers exposed to complex industrial noise environments. Statistical learning models will be developed to achieve this goal.