Abstract
Background
Hearing loss is highly prevalent among adults and is a major contributor to communication difficulties, particularly in older populations. Pure-tone audiometry (PTA) is routinely used to assess hearing sensitivity; however, threshold measurements alone may not fully reflect functional speech understanding. This study aimed to evaluate speech discrimination performance in a primary care otolaryngology population and to identify baseline factors associated with poor speech discrimination.
Methods
This retrospective observational study included patients who underwent PTA and word recognition score (WRS) testing at a primary care otolaryngology clinic between September 1, 2025, and January 20, 2026. Hearing levels were classified according to the World Health Organization (WHO) grading system based on better ear PTA. Poor speech discrimination was defined as better-ear WRS < 70%. Multivariable logistic regression analysis was performed to identify factors associated with poor speech discrimination.
Results
A total of 503 patients were included, with a median age of 50 years (interquartile range [IQR], 35–63), and 43.7% were male. Twenty patients were excluded from hearing grade analyses due to invalid audiometric results. Poor speech discrimination was observed in 43 patients (8.9%). In multivariable analysis, higher better ear PTA (odds ratio [OR], 1.12 per 1-dB increase; 95% confidence interval [CI], 1.07–1.17; p < 0.001) and older age (OR, 1.09 per 1-year increase; 95% CI, 1.03–1.15; p = 0.003) were independently associated with poor speech discrimination. Sex, noise exposure, and systemic comorbidities were not significantly associated with speech discrimination outcomes. Sensitivity analyses using alternative WRS thresholds yielded consistent results.
Conclusions
In a primary care otolaryngology population, poor speech discrimination is primarily associated with hearing threshold severity and age. Routine assessment of speech discrimination in addition to PTA may improve identification of patients at risk for functional communication impairment and facilitate earlier intervention.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12875-026-03381-8.
Keywords: Primary care, Hearing loss, Pure-tone audiometry, Speech discrimination test, Word recognition score
Introduction
Hearing loss is one of the most prevalent chronic health conditions worldwide and represents a major cause of disability, particularly among older adults (1, ). With population aging, the burden of hearing impairment and its associated social and functional consequences continues to increase, making early detection and appropriate management a growing public health priority [1–3].
Pure-tone audiometry (PTA) is the standard method for assessing hearing sensitivity and is widely used in both primary and tertiary care settings. However, hearing thresholds alone do not fully reflect real-world communication abilities, as patients with similar audiometric profiles may experience markedly different levels of difficulty in speech understanding [4–6]. Speech discrimination testing, commonly assessed using word recognition scores (WRS), provides important complementary information regarding functional auditory performance and communication capacity [5].
Several studies have demonstrated that speech recognition declines with worsening hearing thresholds and advancing age [7–9]. Age-related changes in central auditory processing and cognitive function may further contribute to impaired speech perception, even among individuals with relatively preserved peripheral hearing sensitivity [8, 10]. These findings suggest that both peripheral and central mechanisms play important roles in determining speech discrimination outcomes.
Despite growing recognition of the importance of functional hearing assessment, most existing studies have focused on patients evaluated in tertiary referral centers or specialized audiology clinics [6, 7]. Data describing speech discrimination patterns and their clinical correlations in primary care otolaryngology settings remain limited. Patients presenting to primary care clinics typically exhibit milder degrees of hearing loss and more heterogeneous clinical characteristics compared with those referred to tertiary centers, potentially leading to different determinants of functional hearing impairment.
Understanding factors associated with poor speech discrimination in primary care populations may help clinicians identify patients who would benefit from early intervention and more comprehensive auditory rehabilitation. Therefore, the aim of this study was to evaluate the distribution of hearing grades and speech discrimination performance in a primary care otolaryngology cohort and to identify baseline clinical factors associated with poor speech discrimination.
Materials and methods
Study design and population
This retrospective observational study included patients who underwent PTA and WRS testing at a primary care otolaryngology clinic between September 1, 2025, and January 20, 2026. Patients with complete audiometric data for both ears were eligible for inclusion. Individuals with missing PTA thresholds or unavailable WRS results were excluded from the analysis.
Demographic information, including age and sex, and baseline clinical characteristics, including hypertension, diabetes mellitus, dyslipidemia, chronic kidney disease, cardiovascular disease, thyroid disease, and history of occupational or environmental noise exposure, were collected from electronic medical records. Comorbidities were identified based on documented physician diagnoses. Noise exposure was defined based on documentation of occupational or environmental exposure in the electronic medical record. Absence of documentation was considered as no recorded exposure for analytic purposes.
Audiologic assessment
PTA and speech audiometry were performed using a clinical audiometer (AD629; Interacoustics A/S, Denmark) in a sound-treated booth, following standard clinical procedures for diagnostic audiology [5, 11]. Audiologic assessments were performed by trained personnel, including an audiologist and a medical laboratory technologist, using standardized testing protocols with ongoing quality control, under the supervision of a board-certified otolaryngologist.
Air-conduction thresholds were obtained at 0.25, 0.5, 1, 2, 4, and 8 kHz for each ear using standard clinical audiometric procedures [11]. The PTA was expressed as the weighted four-frequency average (W4FA), calculated as: W4FA = (T0.5 + 2T1 + 2T2 + T4) ÷ 6, where T0.5, T1, T2, and T4 denote the air-conduction thresholds (dB HL) at 0.5, 1, 2, and 4 kHz, respectively.
Hearing severity was categorized according to the World Health Organization (WHO) classification based on better-ear W4FA [1]. Speech discrimination was assessed using standardized monosyllabic word recognition testing, and results were recorded as the percentage of correctly recognized words presented at the maximum comfortable listening level [5].
Classification of hearing loss and speech discrimination
Hearing levels were categorized according to the WHO grading system based on better-ear PTA as follows: normal hearing (< 20 dB), mild hearing loss (20 to < 35 dB), moderate hearing loss (35 to < 50 dB), moderately severe hearing loss (50 to < 65 dB), severe hearing loss (65 to < 80 dB), profound hearing loss (80 to < 95 dB), and complete (total) hearing loss (≥ 95 dB) [1]. For analysis, the better ear was defined as the ear with the lower W4FA value, and the worse ear as the contralateral ear. Patients with scale-out PTA values in one ear were excluded from better-ear hearing-grade categorization because valid bilateral thresholds were required to determine the better-ear W4FA. However, for worse-ear analyses, scale-out PTA values were operationally classified as complete hearing loss.
Speech discrimination was dichotomized using a predefined cutoff value of better-ear WRS < 70% to define poor speech discrimination. Although no universally accepted cutoff exists, a WRS ≥ 80% is generally considered within the normal range [5]; therefore, a threshold of 70% was selected to represent clinically meaningful impairment while maintaining adequate separation between groups for statistical analysis. In cases where speech discrimination could not be measured even at the maximum presentation level, WRS was recorded as 0% for analytic purposes.
Statistical analysis
Continuous variables are presented as medians with interquartile ranges (IQR) or as means with standard deviations (SD), as appropriate. Categorical variables are summarized as counts and percentages. Comparisons between patients with poor speech discrimination (better-ear WRS < 70%) and those with good speech discrimination (WRS ≥ 70%) were performed using the Mann–Whitney U test for continuous variables and the chi-square test or Fisher’s exact test for categorical variables, as appropriate.
To identify independent factors associated with poor speech discrimination, multivariable logistic regression analysis was performed with poor WRS as the dependent variable. Variables entered into the model included age, sex, better-ear PTA, noise exposure, hypertension, diabetes mellitus, dyslipidemia, cardiovascular disease, and thyroid disease, based on prior studies reporting associations with hearing outcomes [2, 12–14]. Chronic kidney disease was excluded from the multivariable model because no patients with poor WRS had this condition.
Results are reported as odds ratios (ORs) with corresponding 95% confidence intervals (CIs). Model fit was assessed using McFadden’s pseudo-R². Statistical analyses were performed using Jamovi (version 2.6; The Jamovi Project) based on the R statistical environment (R Foundation for Statistical Computing, Vienna, Austria). A two-sided p value of less than 0.05 was considered statistically significant.
To assess the robustness of the findings, additional sensitivity analyses were performed using alternative thresholds for poor speech discrimination (WRS < 60% and < 80%).
Results
Patient characteristics
A total of 503 patients were included in the study. The median age was 50 years ([IQR], 35–63), and 220 patients (43.7%) were male (Table 1). The largest age group was 60–69 years (19.9%), followed by 30–39 years (18.9%) and 50–59 years (16.3%).
Table 1.
Baseline characteristics of the study population
| Variable | n (%) or Median [IQR] |
|---|---|
| Number of patients | 503 |
| Age, years | 50 [35–63] |
| Age group, n (%) | |
| 10–19 | 14 (2.8) |
| 20–29 | 63 (12.5) |
| 30–39 | 95 (18.9) |
| 40–49 | 78 (15.5) |
| 50–59 | 82 (16.3) |
| 60–69 | 100 (19.9) |
| 70–79 | 46 (9.1) |
| ≥ 80 | 25 (5.0) |
| Sex, n (%) | |
| Male | 220 (43.7) |
| Female | 283 (56.3) |
| Comorbidities, n (%) | |
| Hypertension | 95 (18.9) |
| Diabetes mellitus | 47 (9.3) |
| Dyslipidemia | 74 (14.7) |
| Chronic kidney disease | 0 (0.0) |
| Cardiovascular disease | 16 (3.2) |
| Thyroid disease | 15 (3.0) |
| Noise exposure, n (%) | |
| Yes | 13 (2.6) |
| No recorded exposure | 490 (97.4) |
Noise exposure was defined based on medical record documentation; absence of documentation was treated as no recorded exposure for analytic purposes
Regarding comorbidities, hypertension was present in 95 patients (18.9%), diabetes in 47 (9.3%), and dyslipidemia in 74 (14.7%). Cardiovascular disease and thyroid disease were reported in 16 (3.2%) and 15 (3.0%) patients, respectively, while no patients had chronic kidney disease. Noise exposure was documented in only 13 patients (2.6%), whereas exposure history was unknown in 490 patients (97.4%).
Distribution of WHO hearing grades
Among the 503 patients, 20 were excluded from better-ear hearing-grade analyses because valid PTA thresholds were not available in both ears, leaving 483 patients with valid bilateral PTA for better-ear evaluation (Tables 2 and 3).
Table 2.
Distribution of WHO hearing grades in better and worse ears
| WHO grade | Better ear (n = 483) n (%) |
Worse ear (n = 503) n (%) |
|---|---|---|
| Normal | 298 (61.7) | 232 (46.1) |
| Mild | 107 (22.2) | 116 (23.1) |
| Moderate | 42 (8.7) | 58 (11.5) |
| Moderately severe | 25 (5.2) | 39 (7.8) |
| Severe | 9 (1.9) | 25 (5.0) |
| Profound | 2 (0.4) | 10 (2.0) |
| Complete | 0 (0.0) | 23 (4.6) |
Better-ear analyses: n = 483; 20 patients were excluded because valid PTA thresholds were not available in both ears
Worse-ear analyses: n = 503; cases in which the worse ear had scale-out PTA were classified as complete hearing loss
Table 3.
Distribution of WHO hearing grades according to age group (Better ear)
| WHO grade (better ear) | n (%) | 10–19 | 20–29 | 30–39 | 40–49 | 50–59 | 60–69 | 70–79 | ≥ 80 |
|---|---|---|---|---|---|---|---|---|---|
| Normal | 298 (61.7) | 13 | 60 | 80 | 64 | 47 | 30 | 3 | 1 |
| Mild | 107 (22.2) | 0 | 2 | 10 | 10 | 28 | 37 | 17 | 3 |
| Moderate | 42 (8.7) | 0 | 1 | 1 | 2 | 5 | 20 | 8 | 5 |
| Moderately severe | 25 (5.2) | 0 | 0 | 1 | 0 | 0 | 4 | 9 | 11 |
| Severe | 9 (1.9) | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 3 |
| Profound | 2 (0.4) | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| Complete | 0 (0.0) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Total in age group | 13 | 63 | 92 | 76 | 80 | 94 | 42 | 23 |
n = 483; 20 patients were excluded because valid PTA thresholds were not available in both ears
In the better ear, normal hearing was observed in 298 patients (61.7%), followed by mild hearing loss in 107 (22.2%), moderate hearing loss in 42 (8.7%), moderately severe hearing loss in 25 (5.2%), severe hearing loss in 9 (1.9%), and profound hearing loss in 2 patients (0.4%). No patients were classified as having complete hearing loss in the better ear. In contrast, normal hearing in the worse ear was observed in only 232 patients (46.1%), while higher proportions of moderate to profound hearing loss were noted compared with the better ear (Table 2).
Age-specific distributions of better-ear WHO hearing grades are shown in Table 3. The proportion of moderate to severe hearing loss increased with advancing age, particularly among patients aged 70 years or older. In contrast, normal or mild hearing loss predominated in younger age groups, especially in patients under 50 years of age.
Speech discrimination according to hearing grade
Among the 503 patients included in the analysis, 43 (8.9%) exhibited poor speech discrimination overall. Of these, 37 patients had valid hearing grade classification and were included in the grade-based analyses. Speech discrimination performance varied substantially according to better-ear WHO hearing grade (Table 4). Mean better-ear WRS decreased progressively from 91.2 ± 6.2% in patients with normal hearing to 86.7 ± 8.7% in those with mild hearing loss, 78.3 ± 12.7% in moderate hearing loss, 53.0 ± 25.3% in moderately severe hearing loss, 43.6 ± 27.5% in severe hearing loss, and 16.0 ± 5.7% in profound hearing loss. A similar trend was observed in worse-ear WRS.
Table 4.
Speech discrimination by better-ear WHO hearing grade
| WHO grade (better ear) | n | Better ear WRS mean ± SD |
Better ear WRS median [IQR] |
Worse ear WRS mean ± SD |
Worse ear WRS median [IQR] |
|---|---|---|---|---|---|
| Normal | 298 | 91.2 ± 6.2 | 92 [88–96] | 89.5 ± 11.8 | 92 [88–96] |
| Mild | 107 | 86.7 ± 8.7 | 88 [80–92] | 78.7 ± 21.0 | 84 [76–92] |
| Moderate | 42 | 78.3 ± 12.7 | 78 [73–88] | 67.2 ± 29.5 | 80 [64–88] |
| Moderately severe | 25 | 53.0 ± 25.3 | 52 [36–72] | 44.0 ± 29.8 | 44 [20–68] |
| Severe | 9 | 43.6 ± 27.5 | 40 [20–64] | 32.4 ± 22.8 | 28 [20–48] |
| Profound | 2 | 16.0 ± 5.7 | 16 [14–18] | 20.0 ± 28.3 | 20 [10–30] |
| Complete | 0 | - | - | - | - |
No patients were classified as having complete hearing loss
WRS values of 0% indicate unmeasurable speech discrimination at maximal stimulus levels and were included in the poor WRS calculation
The proportion of patients with poor speech discrimination (better-ear WRS < 70%) increased markedly with worsening hearing grade, from 0% in normal hearing to 4.7% in mild hearing loss, 16.7% in moderate hearing loss, 72.0% in moderately severe hearing loss, 77.8% in severe hearing loss, and 100.0% in profound hearing loss (Table 5).
Table 5.
Poor speech discrimination by better-ear WHO hearing grade
| WHO grade (better ear) | Total patients | Poor WRS (< 70%) n (%) |
|---|---|---|
| Normal | 298 | 0 (0.0) |
| Mild | 107 | 5 (4.7) |
| Moderate | 42 | 7 (16.7) |
| Moderately severe | 25 | 18 (72.0) |
| Severe | 9 | 7 (77.8) |
| Profound | 2 | 2 (100.0) |
Factors associated with poor speech discrimination
Multivariable logistic regression analysis was performed in 483 patients to identify baseline factors associated with poor speech discrimination (better-ear WRS < 70%) (Table 6). Increasing age and higher better-ear PTA were independently associated with poor WRS. Each 1-year increase in age was associated with a 9% increase in the odds of poor speech discrimination ([OR], 1.09; 95% [CI], 1.03–1.15; p = 0.003), and each 1-dB increase in better-ear PTA was associated with a 12% increase in odds (OR, 1.12; 95% CI, 1.07–1.17; p < 0.001).
Table 6.
Multivariable logistic regression for poor WRS (< 70%)
| Variable | OR (95% CI) | p-value |
|---|---|---|
| Age (per 1-year increase) | 1.09 (1.03–1.15) | 0.003 |
| Better ear PTA (per 1 dB increase) | 1.12 (1.07–1.17) | < 0.001 |
| Male sex (vs. female) | 0.66 (0.21–2.11) | 0.481 |
| Noise exposure (yes vs. no) | 0.42 (0.02–11.44) | 0.605 |
| Hypertension (yes vs. no) | 2.22 (0.51–9.58) | 0.286 |
| Diabetes mellitus (yes vs. no) | 1.08 (0.29–4.01) | 0.911 |
| Dyslipidemia (yes vs. no) | 0.21 (0.04–1.05) | 0.057 |
| Cardiovascular disease (yes vs. no) | 1.09 (0.13–8.86) | 0.935 |
| Thyroid disease (yes vs. no) | 6.51 (0.72–58.68) | 0.095 |
Odds ratios (ORs) were adjusted for all variables listed in the table
Model fit: McFadden R² = 0.64; N = 483
Chronic kidney disease was not included in the multivariable model because no patients with poor WRS had chronic kidney disease
Sex, noise exposure, hypertension, diabetes, dyslipidemia, cardiovascular disease, and thyroid disease were not significantly associated with poor speech discrimination in the multivariable model. Chronic kidney disease was not included in the multivariable model because no patients with poor WRS had chronic kidney disease. The overall model demonstrated good explanatory power (McFadden R² = 0.64).
Sensitivity analyses using alternative thresholds for poor speech discrimination (WRS < 60% and < 80%) yielded similar results (Table S1). Better-ear PTA remained significantly associated with poor speech discrimination, and age also showed an independent association across all models.
Discussion
Principal findings
In this primary care otolaryngology cohort, poorer speech discrimination was independently associated with higher hearing thresholds and older age. Better-ear PTA was the strongest predictor of poor word recognition, with increasing PTA associated with progressively worse speech discrimination. This finding aligns with recent population-based and clinical studies demonstrating a strong relationship between pure-tone thresholds and speech perception performance across a broad age range [6, 7].
Age remained an independent predictor of poor speech discrimination even after adjustment for hearing thresholds and comorbidities. This suggests that age-related declines in speech perception are not fully explained by peripheral hearing sensitivity alone. Previous studies have reported that aging affects both peripheral and central auditory processing, leading to reduced speech recognition, particularly in complex listening environments [8, 9, 15, 16]. Our findings support the concept of central presbycusis contributing to functional hearing impairment in older adults.
Comparison with previous studies
Most previous studies evaluating speech discrimination have been conducted in tertiary referral centers, often focusing on patients with moderate-to-severe sensorineural hearing loss [6, 7]. In contrast, the present study reflects a primary care population in which the majority of patients had normal or mild hearing loss. Despite this, speech discrimination declined substantially once hearing thresholds reached moderate levels, highlighting that functional hearing impairment may already be present at moderate hearing loss levels in a primary care population. This pattern highlights the potential value of speech discrimination as a functional measure of hearing, reflecting real-world communication ability beyond pure-tone thresholds.
Several recent investigations have also reported that speech recognition performance declines with age even among individuals with clinically normal hearing thresholds, supporting the notion that central auditory dysfunction and cognitive factors play important roles in speech perception [10, 15–17]. The persistence of an age effect in our multivariable model is consistent with these observations.
Clinical implications in primary care
These results have important implications for primary care otolaryngology practice. PTA remains essential for quantifying hearing sensitivity; however, threshold measurements alone may not adequately reflect real-world communication difficulties. Prior studies have suggested that speech perception testing captures aspects of functional hearing ability that are not fully reflected by pure-tone thresholds [4, 5]. Although no universally accepted cutoff exists for defining poor speech discrimination, reduced WRS values have consistently been linked to clinically meaningful communication difficulties in daily listening environments.
Routine assessment of speech discrimination in primary care settings may facilitate earlier identification of patients who are likely to experience significant communication difficulties, even when hearing thresholds appear relatively preserved. Early recognition of poor speech discrimination may prompt timely referral for hearing rehabilitation, hearing aid evaluation, or further diagnostic assessment.
Systemic comorbidities and noise exposure were not significantly associated with poor speech discrimination in the multivariable model. Although some population studies have reported associations between chronic medical conditions and hearing loss [2, 12, 13], our findings should be interpreted with caution, as the lack of statistical significance may reflect limited statistical power or measurement limitations rather than a true absence of effect. Overall, functional speech perception in this primary care population may be more strongly influenced by auditory factors, while the contribution of systemic comorbidities remains uncertain. The consistency of the results across different WRS thresholds further supports the robustness of the observed associations.
Limitations
This study has several limitations. First, its retrospective and cross-sectional design, with no systematic follow-up data, precludes evaluation of longitudinal changes in speech discrimination or treatment-related improvement. Second, the study was conducted at a single primary care otolaryngology clinic, which may limit generalizability to other clinical settings or populations; multicenter studies would be valuable to validate and extend these findings. Third, detailed data regarding cognitive status, educational level, and hearing aid use were unavailable, all of which may influence speech recognition performance [16–18]. Fourth, information on noise exposure was unavailable for the majority of patients. In this study, absence of documentation was treated as absence of exposure, which may have resulted in misclassification and information bias. This limitation may have attenuated a true association and should be considered when interpreting the findings. Fifth, although the multivariable model demonstrated a high explanatory power, the number of outcome events relative to the number of covariates was limited, which may increase the risk of model overfitting. Accordingly, the regression estimates should be interpreted with appropriate caution. Despite these limitations, the study provides real-world data from a primary care setting, contributing to the limited literature on speech discrimination in this population.
Conclusions
In a primary care otolaryngology population, speech discrimination is primarily associated with hearing threshold severity and age. Incorporating routine speech discrimination testing alongside PTA may improve identification of patients at risk for functional communication impairment and facilitate earlier intervention.
Supplementary Information
Supplementary Material 1. Table S1. Sensitivity analyses of multivariable logistic regression using alternative WRS thresholds.
Acknowledgements
The authors thank the clinical staff for their assistance in audiologic testing and data management.
Abbreviations
- WHO
World Health Organization
- PTA
Pure-tone average
- WRS
Word recognition score
- W4FA
Weighted four-frequency average
- OR
Odds ratio
- CI
Confidence interval
- IQR
Interquartile range
- SD
Standard deviation
Authors’ contributions
CHL and SCY conceived and designed the study. CHL and SCY supervised audiologic testing and quality control. ESK and YJL performed audiologic assessments and contributed to the clinical data used in this study. CHL, WJ, MK, NS, HJK and SCY interpreted the audiologic data. CHL drafted the manuscript. All authors critically revised the manuscript and approved the final version.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability
The datasets supporting the conclusions of this article are available from the corresponding author on reasonable request, in accordance with institutional data protection regulations.
Declarations
Ethics approval and consent to participate
This study was reviewed and confirmed to be exempt from full review by a Ministry of Health and Welfare–designated Public Institutional Review Board in the Republic of Korea (Exemption No. P01-202602-01-037; Administrative No. 2026-0213-001), in accordance with the Bioethics and Safety Act and its Enforcement Regulations.
The requirement for informed consent was waived by the Ministry of Health and Welfare-designated Public Institutional Review Board because this retrospective study analyzed de-identified clinical data obtained during routine care and involved no additional interventions or specimen collection for research purposes.
The study was conducted in accordance with the Declaration of Helsinki. All data were anonymized prior to analysis, and patient confidentiality was strictly maintained.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.World Health Organization. World report on hearing. Geneva; 2021.
- 2.Xue Y, Ma D, Zhang Y, Dong C, Ma J, Guan L. The global burden of hearing loss and its comorbidity with chronic diseases: a comprehensive analysis from 1990 to 2021. Sci Rep. 2025;15(1):38899. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Donahue A, Dubno JR, Beck L. Guest editorial: accessible and affordable hearing health care for adults with mild to moderate hearing loss. Ear Hear. 2010;31(1):2–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Clark JG. Uses and abuses of hearing loss classification. Asha. 1981;23(7):493–500. [PubMed] [Google Scholar]
- 5.Naqvi Y, Sutton AE. Speech Audiometry. StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025. [PubMed] [Google Scholar]
- 6.Wasano K, Nakagawa T, Kaga K, Ogawa K. The effect of aging and asymmetrical hearing on speech discrimination. Commun Med (Lond). 2024;4(1):166. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Choi J, Kim T, Kim S, Kim Y, Kim DK. Effect of Aging on Speech Discrimination. J Audiol Otol. 2022;26(4):198–201. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Rahne T, Wagner TM, Kopsch AC, Plontke SK, Wagner L. Influence of Age on Speech Recognition in Noise and Hearing Effort in Listeners with Age-Related Hearing Loss. J Clin Med. 2023;12(19):6133. [DOI] [PMC free article] [PubMed]
- 9.An H, Lee J, Park Y-j, Suh M-W, Lim Y. Effects of Age on the Neural Tracking of Speech in Noise. Brain Sci. 2025;15(8):874. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Bugannim Y, Roziner I, Kishon-Rabin L. Speech recognition in noise across the life span with cognition and hearing sensitivity as mediators of age effects. Sci Rep. 2025;15(1):20575. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Carl AC, Hohman MH, Cornejo J. Audiology Pure Tone Evaluation. StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025. [PubMed] [Google Scholar]
- 12.Genther DJ, Frick KD, Chen D, Betz J, Lin FR. Association of hearing loss with hospitalization and burden of disease in older adults. JAMA. 2013;309(22):2322–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Lisan Q, Goldberg M, Lahlou G, Ozguler A, Lemonnier S, Jouven X, et al. Prevalence of Hearing Loss and Hearing Aid Use Among Adults in France in the CONSTANCES Study. JAMA Netw Open. 2022;5(6):e2217633. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Deal JA, Reed NS, Kravetz AD, Weinreich H, Yeh C, Lin FR, et al. Incident Hearing Loss and Comorbidity: A Longitudinal Administrative Claims Study. JAMA Otolaryngol Head Neck Surg. 2019;145(1):36–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Gordon-Salant S, Fitzgibbons PJ, Yeni-Komshian GH. Auditory Temporal Processing and Aging: Implications for Speech Understanding of Older People. Audiol Res. 2011;1(1):e4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Windle R, Dillon H, Heinrich A. A review of auditory processing and cognitive change during normal ageing, and the implications for setting hearing aids for older adults. Front Neurol. 2023;14:1122420. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Humes LE. Associations Between Measures of Auditory Function and Brief Assessments of Cognition. Am J Audiol. 2020;29(4):825–37. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Humes LE, Kidd GR, Lentz JJ. Auditory and cognitive factors underlying individual differences in aided speech-understanding among older adults. Front Syst Neurosci. 2013;7:55. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Material 1. Table S1. Sensitivity analyses of multivariable logistic regression using alternative WRS thresholds.
Data Availability Statement
The datasets supporting the conclusions of this article are available from the corresponding author on reasonable request, in accordance with institutional data protection regulations.