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

Audiograms and Prevalence of Hearing Loss in U.S. Children and Adolescents 6-19 Years of Age

PURPOSE

The aim of this study was to determine median audiograms and prevalence of hearing loss for U.S. 6- to 19-year-olds.

METHOD

U.S. national audiometric data for 2,709 six- to 19-year-olds from the National Health and Nutrition Examination Survey 2017-2020 were analyzed. Analyses were primarily descriptive and relied on distribution-free medians, cumulative distribution functions (CDFs), and population-weighted prevalence estimates. Differences between CDFs were examined using Cohen's h effect size. Results are presented for the age range of 6-19 years. Main effects of sex, age, and race/ethnicity were examined.

RESULTS

Differences in the audiograms between the sexes and among the various race/ethnicity groups were minor, but age group differences were notable. Pure-tone thresholds at 500 and 8000 Hz for the 6- to 9-year age group were 3-5 dB higher (worse) than those of the other age groups. The overall prevalence of hearing loss among 6- to 19-year-olds, defined as either of the pure-tone averages (PTAs) for 500, 1000, and 2000 Hz (PTA512) or 3000, 4000, 6000, and 8000 Hz (PTA3468k) exceeding 15 dB HL in either ear, was 11.24% (95% confidence interval [10.00%, 12.48%]). Binary logistic regression analyses found the odds of hearing loss to be significantly higher for individuals from families with a low income as well as for those with abnormal otoscopy or abnormal tympanometry.

CONCLUSIONS

For children and adolescents in the United States, hearing was unaffected by differences in sex and race/ethnicity, with only age impacting pure-tone thresholds and only at the lowest (500 Hz) and highest (8000 Hz) frequencies examined. The overall prevalence of hearing loss was about 11%.

Easy as 1-2-3: Development and Evaluation of a Simple yet Valid Audiogram-Classification System

Almost since the inception of the modern-day electroacoustic audiometer a century ago the results of pure-tone audiometry have been characterized by an audiogram. For almost as many years, clinicians and researchers have sought ways to distill the volume and complexity of information on the audiogram. Commonly used approaches have made use of pure-tone averages (PTAs) for various frequency ranges with the PTA for 500, 1000, 2000 and 4000 Hz (PTA4) being the most widely used for the categorization of hearing loss severity. Here, a three-digit triad is proposed as a single-number summary of not only the severity, but also the configuration and bilateral symmetry of the hearing loss. Each digit in the triad ranges from 0 to 9, increasing as the level of the pure-tone hearing threshold level (HTL) increases from a range of optimal hearing (< 10 dB Hearing Level; HL) to complete hearing loss (≥ 90 dB HL). Each digit also represents a different frequency region of the audiogram proceeding from left to right as: (Low, L) PTA for 500, 1000, and 2000 Hz; (Center, C) PTA for 3000, 4000 and 6000 Hz; and (High, H) HTL at 8000 Hz. This LCH Triad audiogram-classification system is evaluated using a large United States (U.S.) national dataset (N = 8,795) from adults 20 to 80 + years of age and two large clinical datasets totaling 8,254 adults covering a similar age range. Its ability to capture variations in hearing function was found to be superior to that of the widely used PTA4.

Estimation of all-cause noise exposure for U.S. adults from national survey data

Millions of adults are at risk of hearing loss resulting from exposure to occupational and recreational noises. Data from the combined National Health and Nutrition Examination Survey (NHANES) 2011-2012 and 2015-2016 datasets were used to establish the prevalence of occupational and recreational noise exposures through self-report questions. For recreational noise exposures, NHANES asked about the use of firearms, including the use of hearing protection devices (HPDs) while shooting, and off-work exposures to very loud noise. For work exposures, NHANES asked about exposures to loud and very loud noise. For four of these five questions, graded responses on a 5- or 7-point scale were available. Receiver-operating-characteristic analyses were used to optimize the criterion response for identification of hearing loss for each question with graded responses using the unweighted data. Correlations among the graded responses supported reduction to two measures: (1) rounds fired combined with use of HPDs while shooting and (2) work exposure to loud and very loud noise combined. Logistic-regression analyses of various measures of pure-tone hearing loss were performed to examine the effects of recreational and occupational noise exposures on hearing loss. The odds of hearing loss were significantly greater for those who reported recreational and combined noise exposures.

Using Automatic Speech Recognition to Optimize Hearing-Aid Time Constants

Automatic speech recognition (ASR), when combined with hearing-aid (HA) and hearing-loss (HL) simulations, can predict aided speech-identification performances of persons with age-related hearing loss. ASR can thus be used to evaluate different HA configurations, such as combinations of insertion-gain functions and compression thresholds, in order to optimize HA fitting for a given person. The present study investigated whether, after fixing compression thresholds and insertion gains, a random-search algorithm could be used to optimize time constants (i.e., attack and release times) for 12 audiometric profiles. The insertion gains were either those recommended by the CAM2 prescription rule or those optimized using ASR, while compression thresholds were always optimized using ASR. For each audiometric profile, the random-search algorithm was used to vary time constants with the aim to maximize ASR performance. A HA simulator and a HL simulator simulator were used, respectively, to amplify and to degrade speech stimuli according to the input audiogram. The resulting speech signals were fed to an ASR system for recognition. For each audiogram, 1,000 iterations of the random-search algorithm were used to find the time-constant configuration yielding the highest ASR score. To assess the reproducibility of the results, the random search algorithm was run twice. Optimizing the time constants significantly improved the ASR scores when CAM2 insertion gains were used, but not when using ASR-based gains. Repeating the random search yielded similar ASR scores, but different time-constant configurations.

OPRA-RS: A Hearing-Aid Fitting Method Based on Automatic Speech Recognition and Random Search

Hearing-aid (HA) prescription rules (such as NAL-NL2, DSL-v5, and CAM2) are used by HA audiologists to define initial HA settings (e.g., insertion gains, IGs) for patients. This initial fitting is later individually adjusted for each patient to improve clinical outcomes in terms of speech intelligibility and listening comfort. During this fine-tuning stage, speech-intelligibility tests are often carried out with the patient to assess the benefits associated with different HA settings. As these tests tend to be time-consuming and performance on them depends on the patient's level of fatigue and familiarity with the test material, only a limited number of HA settings can be explored. Consequently, it is likely that a suboptimal fitting is used for the patient. Recent studies have shown that automatic speech recognition (ASR) can be used to predict the effects of IGs on speech intelligibility for patients with age-related hearing loss (ARHL). The aim of the present study was to extend this approach by optimizing, in addition to IGs, compression thresholds (CTs). However, increasing the number of parameters to be fitted increases exponentially the number of configurations to be assessed. To limit the number of HA settings to be tested, three random-search (RS) genetic algorithms were used. The resulting new HA fitting method, combining ASR and RS, is referred to as "objective prescription rule based on ASR and random search" (OPRA-RS). Optimal HA settings were computed for 12 audiograms, representing average and individual audiometric profiles typical for various levels of ARHL severity, and associated ASR performances were compared to those obtained with the settings recommended by CAM2. Each RS algorithm was run twice to assess its reliability. For all RS algorithms, ASR scores obtained with OPRA-RS were significantly higher than those associated with CAM2. Each RS algorithm converged on similar optimal HA settings across repetitions. However, significant differences were observed between RS algorithms in terms of maximum ASR performance and processing costs. These promising results open the way to the use of ASR and RS algorithms for the fine-tuning of HAs with potential speech-intelligibility benefits for the patient.

Older adult recognition error patterns when listening to interrupted speech and speech in steady-state noise

This study examined sentence recognition errors made by older adults in degraded listening conditions compared to a previous sample of younger adults. We examined speech recognition errors made by older normal-hearing adults who repeated sentences that were corrupted by steady-state noise (SSN) or periodically interrupted by noise to preserve 33%, 50%, or 66% of the sentence. Responses were transcribed and coded for the number and type of keyword errors. Errors increased with decreasing preservation of the sentence. Similar sentence recognition was observed between SSN and the greatest amount of interruption (33%). Errors were predominately at the word level rather than at the phoneme level and consisted of omission or substitution of keywords. Compared to younger listeners, older listeners made more total errors and omitted more whole words when speech was highly degraded. They also made more whole word substitutions when speech was more preserved. In addition, the semantic relatedness of the substitution errors to the sentence context varied according to the distortion condition, with greater context effects in SSN than interruption. Overall, older listeners made errors reflecting poorer speech representations. Error analyses provide a more detailed account of speech recognition by identifying changes in the type of errors made across listening conditions and listener groups.