Standard Audiograms for Koreans Derived through Hierarchical Clustering Using Data from the Korean National Health and Nutrition Examination Survey 2009–2012

Abstract

Assessments of standardized region/population-specific audiological characteristics are needed for provision of effective rehabilitative services through reducing costs associated with hearing aids. This study aims to propose a set of standard audiograms representing the Korean population that were derived by analyzing data from the 2009–2012 Korea National Health and Nutrition Examination Survey (KNHANES), a nationwide epidemiologic study conducted by Korean government organizations. Standard audiograms were derived by applying a hierarchical clustering method from recorded audiologic data that were obtained independently at 6 frequencies for each ear: 0.5, 1.0, 2.0, 3.0, 4.0, and 6.0 kHz (in dB HL). To derive the optimal number of clusters of the desired standard audiograms, cubic clustering criterion, pseudo-F-, and pseudo-t2-statistics were calculated. These analyses resulted in 29 clusters representing a standard audiogram of the South Korean population. Eighteen of the clusters represented normal hearing audiograms (73.11%), while 11 represented hearing-impaired (HI) standard audiograms (27.89%). Of the 11 HI audiograms, 7 were defined as flat-type (17.81%), while the remaining 4 were defined as sloping-type (9.08%). In conclusion, 29 audiograms representing standard audiograms for the Korean population have been derived using KNHANES data. Improved understanding of the characteristics of each cluster may be helpful for development of more personalized, fixed-setting hearing aids.

Robust data-driven auditory profiling towards precision audiology

“…There have been some attempts to stratify HI listeners based on the shapes of their audiograms. Several classification schemes have been proposed in earlier studies, some of which were based on data-driven approaches (Bisgaard, Vlaming and Dahlquist 2010; Chang, Yoon, Kim, Baek, Cho, Hong, Kim and Moon 2019; Parthasarathy, Romero Pinto, Lewis, Goedicke and Polley 2020), where computational methods for data analysis were used for identifying the most common audiometric profiles. Based on results from human temporal bone studies, Schuknecht and Gacek (1993) proposed four different types of age-related hearing loss: sensory presbycusis, neural presbycusis, metabolic presbycusis and mechanical presbycusis.…”

Further Evaluation and Application of the Wisconsin Age-Related Hearing Impairment Classification System

“…Figure 10, for example, shows the results of the application of the WARHICS to two large data sets with no a priori restrictions on age range. In the top panel, data from Chang et al (2019) for 18,425 individuals (36,828 ears) in the Korean NHANES (KNHANES) are shown. The KNHANES is a nationwide survey conducted annually by the Korea Centers for Disease Control and Prevention to investigate the health and nutritional status of a representative Korean population.…”

“…The applicability of the WARHICS or a WARHICS-WHO hybrid to population data sets that included younger adults suggests that this classification system has the potential to be applied even more broadly than just to age-related hearing loss. E stablishing the severity of hearing impairment (HI) for individuals with sloping sensorineural hearing loss, perhaps the most common audiometric configuration among adults (Bisgaard et al, 2010;Chang et al, 2019;Ciletti & Flamme, 2008), has proven to be challenging. Doing so is important for many reasons including possible compensation for disability by governmental agencies or third-party payers, meeting fit-for-duty criteria for a variety of occupations, determination of candidacy for various types of intervention, and effective counseling of patients, among others.…”

Speech Perception Across The Lifespan by Means of Artificial Intelligence

“…A model may provide more insights into hearing-loss-related pathologies amongst hearing aid users, especially in the cases in which the scores with a hearing aid are far below the WRS max or if the WRS max is unexpectedly low with respect to clinical experience or model prediction. Furthermore, epidemiological studies [34][35][36][37][38] on hearing loss can be complemented, providing indirect functional assessment.…”