Objective:To investigate the clinical characteristics of hearing loss in the older people and analyze potential influencing factors associated with its occurrence.Methods:Subjects aged over 60 were recruited from a community in Beijing from 2020 to 2025.Self-perceived hearing status was recorded.After excluding conductive and mixed hearing loss based on pure-tone audiometry and acoustic immittance testing,the included participants were categorized into three groups:normal hearing group,high-frequency hearing loss group,and all-frequency hearing loss group.Comparisons were made regarding basic information,lifestyle,chronic disease history,oto-logical symptoms,cognitive function,and emotional state.Weighted stepwise multiple logistic regression analysis was employed to identify potential factors associated with the occurrence of high-frequency and all-frequency hear-ing loss.Results:① A total of 910 participants were recruited,with 10.3％(94 cases)having normal hearing and 89.7％(816 cases)having hearing loss.Among those with hearing loss,12.7％(104/816)had high-frequency hearing loss,while 87.3％(712/816)had all-frequency hearing loss.Compared to those with all-frequency hearing loss,subjects with high-frequency hearing loss reported a lower rate(19.2％)of self-perceived hearing loss.②Univariate analysis revealed statistically significant differences among the three groups in terms of age,smok-ing,occupational noise exposure,hypertension,diabetes,chronic kidney disease,tinnitus,cognitive decline,and anxiety.③ Weighted stepwise multiple logistic regression analysis indicated that the occurrence of high-frequency hearing loss was associated with smoking,diabetes,chronic kidney disease,and anxiety,while all-frequency hear-ing loss was associated with age,smoking,diabetes,tinnitus,cognitive decline,and anxiety.Conclusion:The in-cidence of hearing loss is high among the older people,with some cases affecting only high frequencies and exhibi-ting a low self-detection rate.The occurrence of hearing loss in the older adults may be related to smoking,diabe-tes,tinnitus,cognitive decline,and anxiety.Additionally,clinicians should be vigilant about high-frequency hear-ing in older individuals with chronic kidney disease and enhance clinical attention.

Background and objective: Auditory brainstem response (ABR) is routinely used in clinical practice for objective hearing assessment, yet its visual interpretation is subjective and time-consuming. This study aims to propose a novel and interpretable deep learning (DL) model, CWT-ViT, based on time-frequency analysis to objectively detect ABR using large-scale and diverse clinical datasets. Methods: ABR waveforms are transformed into time-frequency images via continuous wavelet transform (CWT). The proposed CWT-ViT model captures clinically relevant features for ABR detection, trained and evaluated on a large clinical dataset, and externally validated on an independent cohort with varying equipment and protocols. Gradient-weighted class activation mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP) methods are used to interpret the model's decisions. Results: CWT-ViT achieves 91.83% accuracy and an area under the receiver operating characteristic curve (AUC) of 0.976 on the held-out test set, and 93.54% accuracy and an AUC of 0.976 on the independent dataset, outperforming several state-of-the-art methods. Grad-CAM and SHAP visualizations reveal that peak V and its subsequent negative slope are crucial for ABR detection, while peak III also aids detection in cases where peak V is less evident, aligning with established medical knowledge. Conclusion: The proposed CWT-ViT demonstrates robust performance across diverse clinical settings, providing a reliable and interpretable tool for ABR detection. This framework could streamline clinical workflows and enhance diagnostic accuracy in hearing assessments.

OBJECTIVES:This study aims to develop a deep learning model that directly estimates auditory brainstem response (ABR) thresholds from stacked waveform series across multiple stimulus intensities, and to validate its performance on multicenter, large-scale, diverse human and mouse datasets. METHODS:Independent of traditional binary waveform-by-waveform detection, we propose a transformer-based model that integrates adjacent-level cross-attention, a series encoder, and a supervised contrastive learning branch to capture both intra-waveform temporal structures and inter-series dependencies, aligning with expert thresholding practices. The model was trained and validated on two heterogeneous human datasets (Human Dataset I: 8350 subjects; Human Dataset II: 136 subjects) and two publicly available mouse datasets (Mouse Dataset I: 8259 mice; Mouse Dataset II: 351 mice). Model performance was evaluated using exact-match accuracy and tolerance accuracy within ±5 and ±10 dB. Ablation studies were conducted to assess the contribution of each architectural component. RESULTS:On Human Dataset I, the model achieved 93.07% exact-match accuracy and 99.32% accuracy within ±10 dB under hold-out validation. It maintained strong generalization on the external Human Dataset II (94.24% exact; 99.48% within ±10 dB) and demonstrated robust performance across age groups and hearing conditions. On Mouse Dataset I, the model achieved accuracies of 76.23%, 98.79%, and 99.82% for click-evoked ABRs, and 65.30%, 94.16%, and 99.06% for tone-evoked ABRs at exact-match, ±5 dB, and ±10 dB tolerance levels, respectively. On Mouse Dataset II, accuracies reached 57.53%, 91.51%, and 97.55%. These results surpass those of previously published algorithms on the same datasets. Performance degradation in all ablated variants confirmed the contribution of each model component. CONCLUSIONS:Unlike prior methods focusing on waveform-level detection of the presence or absence of an ABR, this study presents the first deep learning model for automatic thresholding of ABRs from multi-level waveform stacks. It demonstrates strong generalizability across centers, species, and stimulus types, highlighting its potential for clinically applicable, efficient, and automated threshold estimation. Future work should focus on broader external validation and the integration of the model into real-time ABR acquisition workflows, enabling concurrent threshold estimation during ongoing measurements for improved clinical utility.

Objective:To evaluate the hearing health status of the elderly in community settings in Beijing, and to compare the application effectiveness of the Hearing Handicap Inventory for the Elderly-Screening Version（HHIE-S） questionnaire, simple device screening, and audiometer screening. Methods:A total of 722 elderly individuals aged 60 years and above from 5 community health service centers in Beijing were enrolled between October and November 2024. Screening was conducted using the HHIE-S questionnaire, simple devices, and an audiometer. Data were analyzed with SPSS 25.0 software to determine the prevalence of hearing loss among the elderly. The Kappa test was used to assess the consistency between the results of HHIE-S questionnaire screening, simple device screening, and audiometer screening; the McNemar test was applied to analyze the presence of systematic bias. The intraclass correlation coefficient（ICC） and Bland-Altman plots were used to verify the consistency of hearing threshold measurements between simple device screening and audiometer screening. Results:Among the 722 subjects, 53.2% of the elderly had a 4-frequency pure-tone average（4fPTA）≥35 dB HL in the better ear. The McNemar test showed no statistically significant differences between the results of simple device screening（P=0.161） or HHIE-S questionnaire screening（P=0.195） and those of audiometer screening. The Kappa value for consistency between simple device screening and audiometer screening was 0.846（P<0.001）, with a sensitivity of 93.43% and a specificity of 92.00%. In the frequency range of 500-8 000 Hz, 89.7% of measurements showed a threshold difference of ≤10 dB between simple device screening and audiometer screening; the ICC for each frequency was>0.8, and only 3.59% of data points in the Bland-Altman plots fell outside the 95% limits of agreement. The Kappa value for consistency between HHIE-S questionnaire screening and audiometer screening was 0.420（P<0.001）, with a sensitivity of 77.54% and a specificity of 65.20%. Conclusion:The consistency between simple device screening and audiometer screening results is extremely strong, while the consistency between HHIE-S questionnaire screening and audiometer screening is moderate. Both methods can be used for large-scale hearing screening, and the appropriate screening tool can be selected based on the actual resource conditions of the community.

BackgroundThe potential risk factors for concurrent newborn hearing screening and genetic screening remain unclear, posing challenges to the prevention of hearing loss. Although gestational age and birth weight are known to influence hearing development, their associations with newborn hearing screening referral remain controversial due to limited large-scale validation.MethodsThis retrospective population-based cohort study included 76,460 newborns who underwent concurrent newborn and genetic screening. Restricted cubic splines and piecewise linear models were used to identify threshold effects in the associations of birth weight and gestational age with initial hearing screening referral, second hearing screening outcomes, and genetic screening results. Sensitivity analyses were conducted by including sex and Apgar scores.ResultsWe identified non-linear, U-shaped associations between birth weight, gestational age and the odds of referral at initial hearing screening. A birth weight of 3,297 g and gestational age of 39.19 weeks were key change points. Infants above or below these points had increased odds of referral at initial hearing screening. Neither second hearing nor genetic screening results showed significant associations with both factors. Sex-stratified analysis showed male infants had higher odds of referral at initial hearing screening, with similar non-linear patterns observed for gestational age. Adjusting for Apgar scores confirmed these results, with the thresholds at a birth weight of 3,297 g and gestational age of 39.27 weeks.ConclusionBirth weight and gestational age were associated with initial hearing screening referral only. The lowest referral odds occurred around 3.3 kg and 39 weeks of gestation, which may help identify newborns who require closer follow-up after initial screening.

Auditory brainstem response (ABR) is widely used in clinical practice to assess hearing, with thresholds typically determined by clinicians via visual inspection—a process prone to human bias. Various statistical methods have therefore been developed to assist clinicians with this task and improve test accuracy and efficiency. However, existing methods primarily focus on determining the presence or absence of an ABR at a single stimulus level, thereby neglecting inter-level correlations that hold valuable information. This work proposes the Dynamic Time Warping (DTW)-Aware Series-Temporal Transformer (DTWA-STformer), a novel deep learning framework for automated ABR threshold estimation from a series of ABR waveforms. DTWA-STformer employs a DTW similarity-aware Series Transformer to capture inter-waveform dependencies, enhanced by stimulus level-informed positional encodings. A hierarchical multi-scale Temporal Transformer then extracts rich temporal representations, followed by a multi-class classifier for threshold prediction. The model was trained and evaluated on three pre-recorded datasets, including two large-scale human datasets (Dataset I: 8,350 subjects; Dataset II: 136 subjects) and a public mouse dataset (8,259 mice). The model achieved exact-match/±10 dB accuracies of 92.08%/99.31% (Dataset I), 90.35%/98.75% (Dataset II), 73.45%/99.27% (mouse click ABRs), and 60.85%/98.82% (mouse tone-pip ABRs), outperforming existing state-of-the-art methods for threshold estimation across both human and mouse data. Results show that DTWA-STformer provides accurate and objective threshold estimation from pre-recorded waveforms and may serve as a post hoc tool for verifying examiner-estimated thresholds. In future work, the approach could be integrated with active learning rules for dynamically selecting stimulus levels and more efficiently homing in on hearing threshold.

Accurate recognition of auditory brainstem response (ABR) wave latencies is essential for clinical practice but remains a subjective and time-consuming process. Existing AI approaches face challenges in generalization, complexity, and semantic sparsity due to single sampling-point analysis. This study introduces the Derivative-Guided Patch Dual-Attention Transformer (Patch-DAT), a novel, lightweight, and generalizable deep learning (DL) model for the automated recognition of latencies for waves I, III, and V. Patch-DAT divides the ABR time series into overlapping patches to aggregate semantic information, better capturing local temporal patterns. Meanwhile, leveraging the fact that ABR waves occur at the zero crossing of the first derivative, Patch-DAT incorporates a first derivative-guided dual-attention mechanism to model global dependencies. Trained and validated on large-scale, diverse datasets from two hospitals, Patch-DAT(with a size of 0.36 MB) achieves accuracies of 92.29% and 98.07% at 0.1 ms and 0.2 ms error scales, respectively, on a held-out test set. It also performs well on an independent dataset with accuracies of 88.50% and 95.14%, demonstrating strong generalization across clinical settings. Ablation studies highlight the contributions of the patching strategy and dual-attention mechanisms. Compared to previous state-of-the-art DL models, Patch-DAT shows superior accuracy and reduced complexity, making it a promising solution for object recognition of ABR latencies. Additionally, we systematically investigate how sample size and data heterogeneity affect model generalization, indicating the importance of large, diverse datasets in training robust DL models. Future work will focus on expanding dataset diversity and improving model interpretability to further improve clinical relevance.

OBJECTIVE:This study aimed to provide normative ranges of Chinese smartphone-based digits-in-noise (DIN) test results, to explore the association between speech reception threshold (SRT) and pure-tone average (PTA) threshold, and to verify the effectiveness and feasibility of the Chinese DIN. DESIGN:Chinese-speaking adult subjects with varying types, symmetry and degrees of hearing loss, were recruited. All participants completed a pure-tone audiometric hearing test and DIN test with dichotic and antiphasic stimulus presentation. STUDY SAMPLE:The overall sample consisted of 191 subjects, 24 with normal hearing and 167 with hearing loss. RESULTS:There was a positive correlation between antiphasic DIN SRTs and PTA thresholds. Receiver operating characteristic curves (ROC) were calculated based on the classification of poorer ears. When SRT was ≥ -15.8 dB, it suggested the possible presence of mild or more severe hearing loss; when SRT was ≥ -14.2 dB, it suggested the possible presence of moderate or more severe hearing loss. CONCLUSION:The Chinese DIN SRT showed a highly positive correlation with PTA and exhibited high sensitivity and specificity in detecting hearing loss. Poorer ear PTA was the primary predictor of the antiphasic DIN SRT. The present results verify the validity of the Chinese DIN in its purpose of hearing screening.

This study aims to evaluate sociodemographic information, lifestyle, physical and mental health status, and otological symptoms factors associated with the accuracy of the Hearing Handicap Inventory for the Elderly Screening version (HHIE-S) in older people, which helps to increase the efficiency of hearing screening. Participants aged over 60 years who had not undergone professional hearing examinations were recruited from July 2023 to November 2024. The assessments consisted of age, sex, body mass index, living alone, education background, occupation, history of smoking, alcohol consumption, noise exposure, hypertension, diabetes, coronary heart disease, cerebrovascular disease, cognitive function, anxiety, depression, tinnitus and vertigo, along with HHIE-S and pure tone audiometry. The rate of total accuracy, total inaccuracy, false negative, and false positive were calculated, and factors associated with the false negatives and false positives of the HHIE-S were analyzed via multivariate logistic regression analysis. The results revealed that 773 participants (aged 60-93 years) were included, and the total inaccuracy rate of the HHIE-S was 33.11%. Among individuals with normal hearing, the false positive rate of the HHIE-S was 13.64%, while the false negative rate was 36.35% among individuals with hearing loss. Tinnitus (OR = 24.77, 95% CI 4.91-124.89) was the main factor contributing to false positives of HHIE-S. However, the significantly associated factors with false negatives of HHIE-S were living alone (OR = 1.96, 95% CI 1.21-3.17), smoking (OR = 1.83, 95% CI 1.23-2.70), cognitive decline (OR = 1.54, 95% CI 1.11-2.16), anxiety (OR = 1.51, 95% CI 1.02-2.22) and hypertension (OR = 0.65, 95% CI 0.46-0.91). Therefore, even when the HHIE-S ≤ 8 during hearing screening for the older people, there is still a possibility of hearing loss. Vigilance should be maintained associated factors such as living alone, smoking, cognitive decline and anxiety.

Auditory brainstem response (ABR) interpretation in clinical practice often relies on visual inspection by audiologists, which is prone to inter-practitioner variability. While deep learning (DL) algorithms have shown promise in objectifying ABR detection in controlled settings, their applicability to real-world clinical data is hindered by small datasets and insufficient heterogeneity. This study evaluates the generalizability of nine DL models for ABR detection using large, multicenter datasets. The primary dataset analyzed, Clinical Dataset I, comprises 128,123 labeled ABRs from 13,813 participants across a wide range of ages and hearing levels, and was divided into a training set (90%) and a held-out test set (10%). The models included convolutional neural networks (CNNs; AlexNet, VGG, ResNet), transformer-based architectures (Transformer, Patch Time Series Transformer [PatchTST], Differential Transformer, and Differential PatchTST), and hybrid CNN-transformer models (ResTransformer, ResPatchTST). Performance was assessed on the held-out test set and four external datasets (Clinical II, Southampton, PhysioNet, Mendeley) using accuracy and area under the receiver operating characteristic curve (AUC). ResPatchTST achieved the highest performance on the held-out test set (accuracy: 91.90%, AUC: 0.976). Transformer-based models, particularly PatchTST, showed superior generalization to external datasets, maintaining robust accuracy across diverse clinical settings. Additional experiments highlighted the critical role of dataset size and diversity in enhancing model robustness. We also observed that incorporating acquisition parameters and demographic features as auxiliary inputs yielded performance gains in cross-center generalization. These findings underscore the potential of DL models-especially transformer-based architectures-for accurate and generalizable ABR detection, and highlight the necessity of large, diverse datasets in developing clinically reliable systems.

OBJECTIVE:This study aimed to identify ear and hearing healthcare professionals' current knowledge, attitudes, and practices in caring for people with cognitive impairment and dementia in China. DESIGN:A cross-sectional survey was distributed to eligible ear and hearing professionals in China. Knowledge, attitudes, and practices were compared among demographic factors. Linear regression assessed association between demographic information and survey responses. Structural equation model was used to identify mediation effects between knowledge, attitudes, and practices. STUDY SAMPLE:334 ear and hearing healthcare professionals. RESULTS:Respondents showed high knowledge and positive attitude towards providing ear and hearing care to patients with potential cognitive impairment, but lower practice scores. Multiple regression analysis showed that professional role significantly influenced practice behaviors. Mediation analysis demonstrated that around 25% of the effect of knowledge on practice was mediated by attitude. CONCLUSION:This study found significant differences between knowledge, attitudes, and practice among Chinese ear and hearing professionals regarding cognitive impairment and hearing loss. There is a need for practice guidelines and training to enhance care for patients with comorbid hearing and cognitive impairments. Addressing the knowledge-practice gap will also require system-level supports, including multidisciplinary referral pathways and closer integration of hearing and cognitive health services.

BackgroundThere is little information on whether video gaming might be a modifiable risk factor for hearing loss and/or tinnitus, despite the plausibility of these relationships given that video games are often played at high-intensity sound levels and for long periods of time.ObjectiveTo synthesise current evidence related to relationships between gaming and the potential risk of hearing loss and/or tinnitus.DesignSystematic scoping reviewData sourcesWe searched three databases (PubMed, Scopus, Ovid MEDLINE) in January 2023 for peer-reviewed articles, and searched grey literature sources, from inception to 2023.Eligibility criteriaObservational, mixed-methods, trials, or case studies published in (or that could be translated into) English, Spanish or Chinese were eligible for inclusion. Studies were included if they evaluated relationships of gaming with hearing loss and/or tinnitus.Data extraction and synthesisTwo reviewers extracted and verified study data, which are synthesised in tables and in the text.ResultsFourteen peer-reviewed studies were included, 11 of which were cohort studies and 3 of which were non-cohort observational studies. Across studies, the prevalence of gaming ranged from 20% to 78%. In general, the average measured sound levels of video games nearly exceeded, or exceeded, permissible sound exposure limits, and on average, individuals played video games for approximately 3 hours per week. Among the five peer-reviewed studies that evaluated associations or correlations of gaming with hearing loss or tinnitus, four reported significant associations or correlations with gaming and hearing loss or tinnitus.ConclusionsThe limited available evidence suggests that gaming may be a common source of unsafe listening, which could place many individuals worldwide at risk of permanent hearing loss and/or tinnitus. Additional research on these relationships is needed along with steps to promote safe listening among gamers.

First-generation bone bridges (BBs) have demonstrated favorable safety and audiological benefits in patients with conductive hearing loss. However, studies on the effects of second-generation BBs are limited, especially among children. In this study, we aimed to explore the surgical and audiological effects of second-generation BBs in patients with bilateral congenital microtia. This single-center prospective study included nine Mandarin-speaking patients with bilateral microtia. All the patients underwent BCI Generation 602 (BCI602; MED-EL, Innsbruck, Austria) implant surgery between September 2021 and June 2023. Audiological and sound localization tests were performed under unaided and BB-aided conditions. The transmastoid and retrosigmoid sinus approaches were implemented in three and six patients, respectively. No patient underwent preoperative planning, lifts were unnecessary, and no sigmoid sinus or dural compression occurred. The mean function gain at 0.5–4.0 kHz was 28.06 ± 4.55-dB HL. The word recognition scores improved significantly in quiet under the BB aided condition. Signal-to-noise ratio reduction by 10.56 ± 2.30 dB improved the speech reception threshold in noise. Patients fitted with a unilateral BB demonstrated inferior sound source localization after the initial activation. Second-generation BBs are safe and effective for patients with bilateral congenital microtia and may be suitable for children with mastoid hypoplasia without preoperative three-dimensional reconstruction.

Gene therapy for monogenic auditory neuropathy (AN) has successfully improved hearing function in target gene -deficient mice. Accurate genetic diagnosis can not only clarify the etiology but also accurately locate the lesion site, providing a basis for gene therapy and guiding patient intervention and management strategies. In this study, we collected data from a family with a pair of sisters with prelingual deafness. According to their auditory tests, subject II -1 was diagnosed with profound sensorineural hearing loss (SNHL), II -2 was diagnosed with AN, I-1 was diagnosed with highfrequency SNHL, and I-2 had normal hearing. Using whole-exome sequencing (WES), one nonsense mutation, c.4030C>T (p.R1344X), and one missense mutation, c.5000C>A (p.A1667D), in the OTOF (NM_001287489.1) gene were identified in the two siblings. Their parents were heterozygous carriers of c.5000C>A (father) and c.4030C>T (mother). We hypothesized that c.5000C>A is a novel pathogenic mutation. Thus, subject II -1 should also be diagnosed with AN caused by OTOF mutations. These findings not only expand the OTOF gene mutation spectrum for AN but also indicate that WES is an effective approach for accurately diagnosing AN.

OBJECTIVES:Age-related speech perception difficulties may be related to a decline in central auditory processing abilities, particularly in noisy or challenging environments. However, how the activation patterns related to speech stimulation in different noise situations change with normal aging has yet to be elucidated. In this study, we aimed to investigate the effects of noisy environments and aging on patterns of auditory cortical activation.DESIGN:We analyzed the functional near-infrared spectroscopy signals of 20 young adults, 21 middle-aged adults, and 21 elderly adults, and evaluated their cortical response patterns to speech stimuli under five different signal to noise ratios (SNRs). In addition, we analyzed the behavior score, activation intensity, oxyhemoglobin variability, and dominant hemisphere, to investigate the effects of aging and noisy environments on auditory cortical activation.RESULTS:Activation intensity and oxyhemoglobin variability both showed a decreasing trend with aging at an SNR of 0 dB; we also identified a strong correlation between activation intensity and age under this condition. However, we observed an inconsistent activation pattern when the SNR was 5 dB. Furthermore, our analysis revealed that the left hemisphere may be more susceptible to aging than the right hemisphere. Activation in the right hemisphere was more evident in older adults than in the left hemisphere; in contrast, younger adults showed leftward lateralization.CONCLUSIONS:Our analysis showed that with aging, auditory cortical regions gradually become inflexible in noisy environments. Furthermore, changes in cortical activation patterns with aging may be related to SNR conditions, and that understandable speech with a low SNR ratio but still understandable may induce the highest level of activation. We also found that the left hemisphere was more affected by aging than the right hemisphere in speech perception tasks; the left-sided dominance observed in younger individuals gradually shifted to the right hemisphere with aging.

Healthy aging leads to complex changes in the functional network of speech processing in a noisy environment. The dual-route neural architecture has been applied to the study of speech processing. Although evidence suggests that senescent increases activity in the brain regions across the dorsal and ventral stream regions to offset reduced periphery, the regulatory mechanism of dual-route functional networks underlying such compensation remains largely unknown. Here, by utilizing functional near-infrared spectroscopy (fNIRS), we investigated the compensatory mechanism of the dual-route functional connectivity, and its relationship with healthy aging by using a speech perception task at varying signal-to-noise ratios (SNR) in healthy individuals (young adults, middle-aged adults, and older adults). Results showed that the speech perception scores showed a significant age-related decrease with the reduction of the SNR. The analysis results of dual-route speech processing networks showed that the functional connection of Wernicke's area and homolog Wernicke's area were age-related increases. Further to clarify the age-related characteristics of the dual-route speech processing networks, graph-theoretical network analysis revealed an age-related increase in the efficiency of the networks, and the age-related differences in nodal characteristics were found both in Wernicke's area and homolog Wernicke's area under noise environment. Thus, Wernicke's area might be a key network hub to maintain efficient information transfer across the speech process network with healthy aging. Moreover, older adults would recruit more resources from the homologous Wernicke's area in a noisy environment. The recruitment of the homolog of Wernicke's area might provide a means of compensation for older adults for decoding speech in an adverse listening environment. Together, our results characterized dual-route speech processing networks at varying noise environments and provided new insight for the compensatory theories of how aging modulates the dual-route speech processing functional networks.

IntroductionPrevious longitudinal studies indicate that hearing loss and cognitive impairment are associated in non-tonal language-speaking older adults. This study aimed to investigate whether there is a longitudinal association between hearing loss and cognitive decline in older adults who speak a tonal language.MethodsChinese-speaking older adults aged 60 years and above were recruited for baseline and 12 month follow-up measurements. All participants completed a pure tone audiometric hearing test, Hearing Impaired-Montreal Cognitive Assessment Test (HI-MoCA), and a Computerized Neuropsychological Test Battery (CANTAB). The De Jong Gierveld Loneliness Scale was used to measure loneliness, and the 21-item Depression Anxiety Stress Scale (DASS-21) was used to measure aspects of mental health. Associations between baseline hearing loss and various cognitive, mental and psychosocial measures were evaluated using logistic regression.ResultsA total of 71 (29.6%) of the participants had normal hearing, 70 (29.2%) had mild hearing loss, and 99 (41.2%) had moderate or severe hearing loss at baseline, based on mean hearing thresholds in the better ear. After adjusting for demographic and other factors, baseline moderate/severe audiometric hearing loss was associated with an increased risk of cognitive impairment at follow-up (OR: 2.20, 95% CI: 1.06, 4.50). When pure-tone average (PTA) was modeled continuously, an average difference of 0.24 in HI-MoCA scores for every 10 dB increase in BE4FA existed, and an average difference of 0.07 in the change of HI-MoCA scores in a 12 month period.DiscussionThe results revealed a significant longitudinal relationship between age-related hearing loss and cognitive decline in this cohort of tonal language-speaking older adults. Steps should also be taken to incorporate hearing assessment and cognitive screening in clinical protocols for older adults 60 years and above in both hearing and memory clinics.

Universal newborn hearing screening (UNHS) and audiological diagnosis are crucial for children with congenital hearing loss (HL). The objective of this study was to analyze hearing screening techniques, audiological outcomes and risk factors among children referred from a UNHS program in Beijing. A retrospective analysis was performed in children who were referred to our hospital after failing UNHS during a 9-year period. A series of audiological diagnostic tests were administered to each case, to confirm and determine the type and degree of HL. Risk factors for HL were collected. Of 1839 cases, 53.0% were referred after only transient evoked otoacoustic emission (TEOAE) testing, 46.1% were screened by a combination of TEOAE and automatic auditory brainstem response (AABR) testing, and 1.0% were referred after only AABR testing. HL was confirmed in 55.7% of cases. Ears with screening results that led to referral experienced a more severe degree of HL than those with results that passed. Risk factors for HL were identified in 113 (6.1%) cases. The main risk factors included craniofacial anomalies (2.7%), length of stay in the neonatal intensive care unit longer than 5 days (2.4%) and birth weight less than 1500 g (0.8%). The statistical data showed that age (P < 0.001) and risk factors, including craniofacial anomalies (P < 0.001) and low birth weight (P = 0.048), were associated with the presence of HL. This study suggested that hearing screening plays an important role in the early detection of HL and that children with risk factors should be closely monitored.

The onset of hearing loss can lead to altered brain structure and functions. However, hearing restoration may also result in distinct cortical reorganization. A differential pattern of functional remodeling was observed between post- and prelingual cochlear implant users, but it remains unclear how these speech processing networks are reorganized after cochlear implantation. To explore the impact of language acquisition and hearing restoration on speech perception in cochlear implant users, we conducted assessments of brain activation, functional connectivity, and graph theory-based analysis using functional near-infrared spectroscopy. We examined the effects of speech-in-noise stimuli on three groups: postlingual cochlear implant users (n = 12), prelingual cochlear implant users (n = 10), and age-matched individuals with hearing controls (HC) (n = 22). The activation of auditory-related areas in cochlear implant users showed a lower response compared with the HC group. Wernicke's area and Broca's area demonstrated differences network attributes in speech processing networks in post- and prelingual cochlear implant users. In addition, cochlear implant users maintain a high efficiency of the speech processing network to process speech information. Taken together, our results characterize the speech processing networks, in varying noise environments, in post- and prelingual cochlear implant users and provide new insights for theories of how implantation modes impact remodeling of the speech processing functional networks.