Association of self-reported noise exposure and audiograms processed with algorithms proposed to quantify noise-induced hearing loss. (3rd October 2022)

• Record Type: Journal Article
• Authors: Pudrith, Charles; Phillips, Susan; Labban, Jeffrey
• Is Part Of: International journal of audiology. Volume 61:Number 10(2022)
• Page Start: 809
• Page End: 817
• Publication Date: 2022-10-03
• Subjects: Noise-induced hearing loss -- age-related hearing loss -- area under the curve; Audiology -- Periodicals; Hearing disorders -- Periodicals; Deafness -- Prevention -- Periodicals; Hearing Disorders -- Periodicals; Hearing -- Periodicals; 617.8
• DOI: 10.1080/14992027.2021.1983216
• Languages: English
• ISSNs: 1499-2027

Abstract

Abstract: Objective: The objective of this study was to assess the association of self-reported noise exposure and audiograms processed with ten algorithms proposed to quantify noise-induced hearing loss using receiver operating characteristic (ROC) curves. Design: Participants were placed into groups based on self-reported noise exposure. Self-reported noise exposure served as a predictor for noise-induced hearing loss (NIHL). Audiograms were analysed with ten algorithms: The Guidelines, Brewster's Rules, two versions of military Noise-induced Hearing Loss, the Bulge Depth, the age-adjusted 8 kHz threshold and four versions of a new algorithm called the Adjusted Notch Depth (AND). The area under the ROC curves were calculated for each algorithm. Study Sample: Data were collected from three cycles of the National Health and Nutrition Examination Survey. Results: Only one version the AND significantly identified those with self-reported noise exposure with an area under the curve of 0.562. Conclusions: The association between the AND and self-reported noise exposure was marginally better than the previous algorithms in identifying those with self-reported noise exposure. These findings do not support using puretone thresholds for identifying those with NIHL in a cross-sectional research study without stratifying the participants. More research is needed to determine how the AND can be applied to stratified designs.