Epidemiology of Age-related Hearing Loss from 1990 to 2021: Global Burden of Disease and Forecasted Trends

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The aim of this study was to investigate the epidemiologic characteristics of ARHL from 1990 to 2021. Methods: We collected hearing data from the elderly using the National Health and Nutrition Examination Survey (NHANES) database from 2005 to 2018. Prevalence and disability-adjusted life years (DALYs) for ARHL were obtained from the Global Burden of Disease (GBD) 2021. Trends in ARHL burden were assessed using Joinpoint regression analysis. The Slope Inequality Index (SII) and Concentration Index (CI) were calculated to quantify absolute and relative cross-country inequalities in ARHL burden. Bayesian age-period cohort (BAPC) modeling was used to predict trends in ARHL prevalence and DALY over the next 30 years. Results: Mild to moderate hearing loss predominated among older adults in the U.S. between 2005 and 2018. In 2021, there will be more than 700 million cases of ARHL globally, increasing by 137.43% from 300 million cases in 1990. The age-standardized rate (ASR) has also increased, with an estimated annual percentage change of 16%. According to the Joinpoint regression analysis, the upward trend in the age-standardized prevalence rate (ASPR) for males intensified after 2010. In contrast, the upward trend in the ASPR for females slowed between 2000 and 2010. As the Socio-Demographic Index (SDI) rises, the ASR of DALYs and ASPR show a downward trend. Notably, as of the latest data, 204 countries and 21 regions globally still have significant health inequalities, although the slope index of inequality has declined over time. Projections of the global burden of ARHL over the next 30 years show a gradual increase in the ASR of DALYs and ASPR. For DALYs affecting ARHL the main factors include environmental risks, occupational risks, and occupational noise. Conclusions: The burden of ARHL varies by gender, age group, and geographic region. ASR has been on the rise over time and the burden of disease is high, particularly in low- and middle-income areas. Health sciences/Diseases Health sciences/Medical research Age-related hearing loss GBD NHANES Prediction Risk Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Age-related hearing loss (ARHL) is increasingly recognized as a significant public health concern and ranks as the third leading cause of chronic disability among the elderly 1 , 2 . ARHL is characterized by a progressive decline in hearing, particularly at high frequencies, which often results in communication difficulties, social isolation, and a reduced quality of life among the elderly 3 – 5 . In addition to auditory impairment, ARHL is associated with various adverse consequences, such as emotional distress, cognitive decline, and an increased risk of dementia 6 , 7 . These outcomes highlight the importance of understanding the epidemiological characteristics and trends of ARHL to inform public health strategies and interventions. Over the past few decades, the prevalence of ARHL has increased due to factors such as global population aging and increased exposure to environmental and occupational hazards 8 . Despite the increasing impact of ARHL, comprehensive data on its epidemiology and burden are still limited, particularly regarding long-term trends and cross-national comparisons. Understanding these aspects is crucial for developing targeted interventions and policies to mitigate the impact of ARHL on affected individuals and healthcare systems. The Global Burden of Disease (GBD) study offers comprehensive data on the epidemiology and burden of ARHL. Disability-adjusted life years (DALYs) serve as a crucial metric for assessing the impact of diseases on health and socioeconomic status. DALYs combine years of life lost due to premature mortality (YLLs) and years of healthy life lost due to disability (YLDs), offering a holistic view of the burden of ARHL on individual health and socioeconomic conditions 9 . Through the calculation of DALYs, the impact of ARHL on healthy life expectancy can be more intuitively assessed, as well as its distribution across different regions and populations. This not only helps to identify priorities for public health interventions but also provides a scientific basis for the formulation of health policies and resource allocation 10 . Epidemiological studies, including those utilizing data from the National Health and Nutrition Examination Survey (NHANES), indicate that ARHL is highly prevalent among older adults in the United States 11 . The burden of ARHL varies by gender, age group, and geographic region, with higher prevalence rates observed in low- and middle-income areas 12 . Environmental and occupational risks, including noise exposure, are significant factors in the development of ARHL 13 . Understanding these factors is essential for formulating targeted interventions and policies to reduce the impact of ARHL. This study employs the global disease assessment model to provide data support for global health decision-making by quantitatively analyzing disease risks, characteristics, and burdens. The study integrates data on disease burden and socioeconomic development to construct quantitative indicators of cross-national health inequalities 14 . The research findings will guide the development of prevention and treatment strategies and the optimization of resource allocation to improve global health levels 15 . The objectives of this study are to: 1) comprehensively describe the disease burden of ARHL at the global, regional, and national levels; 2) assess the inequality in the burden of ARHL at the global and regional levels; 3) analyze the temporal trends in the burden and inequality of ARHL from 1990 to 2021; and 4) predict the disease burden and inequality of global ARHL over the next 30 years, providing a scientific basis for policy-making and resource allocation. Methods Research data The NHANES survey is a cross-sectional study based on a complex multistage probabilistic design, recruiting a representative sample of participants from the U.S. population. The project collects demographic, clinical, and laboratory test data from survey participants. The NHANES database is publicly available from the National Center for Health Statistics of the Centers for Disease Control and Prevention ( https://www.cdc.gov/nchs/nhanes/index.htm ). A total of 4025 participants from the 2005–2018 dataset were included for further analysis. The investigation protocol was approved by the NCHS Research Ethics Review Approval Board and all participants provided written informed consent. The study was in line with the Declaration of Helsinki. GBD 2021 provides a comprehensive assessment of health loss for 371 diseases and injuries and 88 risk factors worldwide, using the latest epidemiological data and improved standardized methods 9 . In this study, the number of cases and age-standardized rates (ASR) of ARHL were collected by gender, region, and country for the period from 1990 to 2021. Also, prevalence estimates and their 95% uncertainty intervals (UI) were extracted. The socio-demographic development of a country or region is quantified through the sociodemographic index (SDI), which is a composite average of income, educational attainment, and fertility status 16 . Data were downloaded from the Global Health Data Exchange (GHDx) query tool ( https://vizhub.healthdata.org/gbd-results/ ). Specifically, in the GHDx query tool, we selected "Cause" as "Age-related and other hearing loss," "Measure" as "Prevalence" and "DALY (Disability Adjusted Life Year)," and "Metric" as "Number" and "Rate" to obtain the prevalence data for ARHL, with the search period from 1990 to 2021. Data Analysis Trends in ARHL incidence are quantified using ASR and estimated annual percentage change (EAPC). In the GBD study, the Estimated Annual Percentage Change (EAPC) is a key metric for measuring the trend of a specific health indicator over time. The EAPC is calculated using the following formula: EAPC = 100×(exp(β)-1). where β is the slope obtained through a linear regression model that uses log-transformed morbidity (or mortality, DALY rate, etc.) as the dependent variable and year as the independent variable. This approach assumes a linear relationship between log-transformed incidence and year, i.e., ln(Rate) = α + β × Year + ϵ, where α is the intercept and ϵ is the error term 17 . The Joinpoint regression model is a statistical method used to characterize trend changes in time series data by segmenting the time series into different phases based on identified joinpoints and analyzing the trend of each phase separately. This method is particularly suitable for analyzing the trends of health indicators, such as morbidity and mortality, over time. The core idea of the Joinpoint model is to establish segmented regression based on the temporal characteristics of the disease distribution, partition the study time into different intervals through a number of joinpoints, and fit and optimize the trend for each interval, thus evaluating the disease changes specific to different intervals in the global time scale in more detail. Characterization. This approach has been widely used in the field of tumor epidemiology since it was proposed in 1998 by Kim et al 18 . In GBD research, the Joinpoint model is used to analyze and predict the impact of different diseases and risk factors on health, helping researchers to identify changes in health trends. We disaggregated the raw number of cases by age, demographic and epidemiologic changes (including age- and population-standardized prevalence rates) to examine the extent to which population growth, aging, and epidemiologic changes have affected ARHL over the past 30 years. Cross-national inequality analysis In GBD studies, cross-country inequality analysis is typically quantified using the Slope Inequality Index (SII) and the Concentration Index (CI) 19 . SII: The SII measures absolute inequality between countries with different Socio-Demographic Index (SDI) levels. Its formula is: \(\:\text{S}\text{I}\text{I}=\frac{\sum\:_{i=1}^{n}\:({R}_{i}-{R}_{\text{m}\text{e}\text{a}\text{n}})\cdot\:{W}_{i}}{\sum\:_{i=1}^{n}\:{W}_{i}}\) , where \(\:{R}_{i}\) is the rate for the ith country (e.g., the DALY rate), \(\:{R}_{\text{m}\text{e}\text{a}\text{n}}\) is the average of the rates for all countries, \(\:{W}_{i}\) is the weight of the ith country (usually the size of the population), and \(\:n\) is the number of countries. The value of SII can be positive or negative, with positive values indicating a higher burden for higher SDI countries and negative values indicating a higher burden for lower SDI countries. CI: CI is used to measure relative inequality and is calculated as follows: \(\:\text{C}\text{I}=\frac{2}{\mu\:}\sum\:_{i=1}^{n}\:({r}_{i}-\stackrel{-}{r})\cdot\:{R}_{i}\) . \(\:\:\) Where \(\:{r}_{i}\:\) is the rank of the ith country among all the countries (in ascending order of SDI), \(\:\stackrel{-}{r}\) is the average of the rankings of all the countries, \(\:{R}_{i}\:\) is the rate for the ith country, and µ is the average of the rates of all the countries. CI values range from − 1 to 1, with positive values indicating a higher burden for higher SDI countries and negative values indicating a higher burden for lower SDI countries. ASPR and ASR of DALYs were analyzed in relation to SDI for 21 regions globally and for ARHL in 204 countries. Predicting trends The Bayesian age-period cohort (BAPC) model was employed to predict trends in the age-standardized prevalence rate (ASPR) and ASR of DALYs for ARHL over the next 30 years 20 , 21 . BAPC modeling is a statistical method used to analyze and predict the impact of age, period, and cohort effects on health outcomes. The model utilizes a Bayesian framework for parameter estimation by integrating a priori information and sample data. In this paper, we use the R package “BAPC” (“ http://R-Forge.R-project.org ”) for simulation. Risk factor assessment The 2021 GBD analysis employed seven interrelated methodological steps to assess the burden of risk factors 22 . The first step involves estimating effect sizes: the impact of specific risk factors on health outcomes is assessed by calculating relative risks (RR). Second: Exposure data collection and analysis: the distribution of exposure to risk factors is assessed using Bayesian statistical methods. Third, TMREL establishment: based on epidemiologic studies, the theoretical minimum risk exposure level (TMREL) for each risk factor was determined. Fourth, calculation of PAF: the population attributable fraction (PAF) was calculated for each risk-outcome pair as an indicator of the potential health benefits of reducing risk factors to the TMREL. Fifth, calculation of age-specific exposure values (sev): adjusting for age-specific risk factors to reflect the prevalence of exposure. Sixth, estimation of mediating factors: addressing possible overestimation in PAF. Seventh, attributable burden determination: derives estimates of the burden of disease attributable to specific risk factors by multiplying PAF values with deaths or DALYs for specific age groups, sexes, geographic locations, and time points. This approach provides a comprehensive risk factor assessment framework that helps to identify and quantify the main drivers of the global burden of disease. In the GBD study, risk factors were categorized into four classes, and this study focused on the third class of risk factors. Analysis software For statistical analysis and modeling, we utilized RStudio, an integrated development environment (IDE) for R. We performed the analysis and modeling using RStudio. All data manipulation, model fitting and visualization were performed using R (4.4.1). Results Trend analysis The hearing data of the elderly population aged 60 years and older were obtained from the National Health and Nutrition Examination Survey (NHANES) database for the period 2005–2018, totaling 4,025 cases. The results (Fig. 1A, B) indicated that hearing loss in the elderly population increases with age. The severity of hearing loss was dominated by mild and moderate hearing loss. In 2021, there were approximately 700 million cases of ARHL, with a 95% UI range of 680.22×10 6 -754.27×10 6 , representing a significant increase of 137.43% from 1990. The ASPR for ARHL in 2021 was recorded at 18070.26 per 100,000 (95% UI 17299.37 per 100,000–18923.79 per 100,000) as compared to 17106.88 per 100,000 (95% UI 16319.2 per 100,000–18004.88 per 100,000) in 1990. For the period 1990 to 2021, the EAPC is 16%. The EAPC for females is 20%, much higher than the 13% for males. Among female individuals, the prevalence of ARHL in 2021 was 373.57×106 (95% UI 353.91×10 6 -392.46×10 6 ) compared to 345.35×10 6 (95% UI 326.30×10 6 -361.81×10 6 ) in males, which is significantly more prevalent in females than in males. However, the ASR for females was 16978.94 (95% UI 16246.61-17776.49), significantly lower than that for males at 19196.56 (95% UI 18396.99-20115.85).In 2021, the DALY for ARHL was 23.97 × 10 6 (95% UI 16.85 × 10 6 -32.84 × 10 6 ), which is significantly lower than that of the 9.85×10 6 (95% UI 5.24×10 6 -13.53×10 6 ) in 1990, an increase of 143.30%. Similar to the number of people with the disease, the number of women with DALY in 2021 was 12.61×10 6 (95% UI 8.85×10 6 -17.22×10 6 ), more than the number of men with DALY, which was 11.36×10 6 (95% UI 7.99×10 6 -15.61×10 6 ). The ASR for DALY in 2021 was 525.87/100,000 (95% UI 364.24-731.97), a 5.31% increase from 499.37/100,000 (95% UI 346.66-694.02) in 1990. The ASRs for DALY in females were all smaller than those for males. The EAPC for DALY is 17% for the period 1990 to 2021(Table 1). As shown in Fig. 1c-f, the prevalence rate is higher in men than women, but the number of prevalent cases is consistently greater in women than in men, except for those aged 60–64. There is no significant difference between men and women in the rate of DALYs, which is greater in men than in women in the 60-69-year-olds, and the opposite is true for the remaining age groups. The geographic distribution of prevalence and DALYs rates across 204 countries is well characterized. According to the data in Fig. 1G-J, high-income North America, Tropical Latin America, East Asia, and East Africa had higher ARHL DALYs rates in 1990. By 2021, Central Africa, Tropical Latin America, and Asia have significantly higher rates of ARHL DALYs to become the highest. Regarding prevalence, North America, Tropical Latin America, East Africa, East Asia, South Asia, and Oceania regions had relatively high rates in 1990. However, over time, by 2021, prevalence rates have remained relatively stable with little change, except in high-income North America and South Asia, where prevalence rates have decreased. Joinpoint regression analysis of ASPR for ARHL from 1990–2021 showed different segmentation for men and women (Fig. 1K-L). The ASPR for males was in a significant upward trend from 1990–1993, with an annual percentage change (APC) of 0.19%. The prevalence growth slowed down during 1993–2000, with an APC of 0.12%. By 2000–2010, the increasing trend continued but the rate of increase decreased further with an APC of 0.09%. After 2010, the prevalence increased significantly with an APC of 0.18%. The ASPR in females differs from that of males. 1990–1995: significant increase in prevalence, with an APC of 0.19%. 1995–2006: slower increase, with an APC of 0.12%. 2006–2016: continued trend of increase in prevalence but at a lower rate of growth, with an APC of 0.09%. 2016–2021: significant increase in prevalence, with an APC of 0.29%. Overall, there is an increasing trend in the prevalence of ARHL in both males and females from 1990 to 2021. The increase in males accelerated during 2010–2021, while the increase in females was more significant during 2016–2021. The average annual percentage change (AAPC) in prevalence for males was 0.14 (95% UI 0.13–0.14), which was lower than that for females, which was 0.21 (95% UI 0.21–0.22). Cross-country inequality analysis Between 1990 and 2021, SII for DALYs declined from 121.48 in 1990 to -48.8 in 2021 (Fig. 2A), showing a downward trend, with groups with lower SDI bearing an increasing health burden. The concentration index for ARHL increased from 0.0078 in 1990 to 0.0508 in 2021 (Fig. 2B). The pattern of change from 1990 to 2021 is diverse across regions, but most regions show worsening inequality in lower SDI countries in both the SII and the concentration index. Globally, the ASPR and ASR of DALYs are higher at lower and intermediate SDI levels, and these indicators decline at higher SDI levels. In 21 regions (Fig. 2C, E), ASPR and SDI showed an “M” correlation, with a gradual increase in ASPR at SDI < 0.4 and 0.6 < SDI < 0.7, and a mild increase in ASR of DALYs at 0.6 < SDI < 0.7, with an overall decreasing trend. In 204 countries (Fig. 2D, F), ASPR, ASR of DALYs and SDI showed an “M” correlation, with a fluctuating state of first increasing and then decreasing. Predicted trends Trends in prevalence and DALYs of ARHL over the next 30 years were predicted using the BAPC model (Fig. 3A-D). It can be seen that there will be an increase in the trend over the next 30 years. In 2051, the prevalence and DALYs will be 741.75 x106 and 24.8 x106 for females and 688.41 x106 and 22.51 x106 for males, respectively, and the burden of the disease will further increase. Risk Factors The main contributor to the disease burden of ARHL is occupational noise. Both the percentage and rate of DALYs for ARHL due to occupational noise decrease with age, and are consistently higher in men than in women. In terms of temporal development, the ARHL disease burden due to occupational noise increased significantly more in women than in men from 1990 to 2021, and even developed a negative growth rate, especially for men of advanced age(Fig. 4A-C).. Discussion This study describes the growth rate of the global burden of ARHL over three decades, with cases surging by 137.43% from 1990 to 2021. The age-standardized prevalence rate ASPR has risen with an EAPC of 16%, reflecting the aging population and epidemiological changes. Notably, although the number of cases among females is larger, males have higher prevalence rates and age-standardized rates of DALYs, highlighting gender disparities. Geographical inequalities persist, with low- and middle-income regions shouldering a disproportionate burden, exemplified by the elevated DALY rates in Central Africa and South Asia. Bayesian projections indicate that ARHL cases will continue to rise over the next 30 years, expected to exceed 1.4 billion, demanding urgent global attention. According to statistical results from the NHANES database, mild to moderate hearing loss predominates among the elderly, and our data uniquely quantify the growing disability burden in an aging society. For mild-to-moderate hearing loss, the use of hearing aids and other assistive devices can provide some therapeutic benefits for the elderly, but this also increases the burden on society. Moreover, hearing loss in the elderly is primarily characterized by high-frequency hearing decline, often manifested as reduced speech recognition rates, and may lead to cognitive dysfunction 23 , 24 . These issues severely impact the quality of life of the elderly and place both economic and emotional burdens on their families. Therefore, actively taking preventive measures to reduce the risk of hearing loss in the elderly is of great significance for improving their quality of life and alleviating the burden on families and society. The paradox of higher prevalence in females but higher ASR in males may stem from the interplay of demographic and occupational factors. The longer life expectancy of females, which is well-documented in aging populations 25 leads to a larger proportion of females with ARHL. Additionally, studies have shown that hormonal changes during menopause may predispose women to a higher risk of hearing loss 26 . In contrast, the higher ASR in males is consistent with historical patterns of occupational noise exposure, particularly in industries such as manufacturing and construction, where male workers have traditionally dominated 27 . This explains the higher ASR observed in males. Moreover, our statistical findings indicate that occupational noise plays a significant role in ARHL. Therefore, measures to reduce the impact of occupational noise can significantly decrease the prevalence of ARHL and are crucial for reducing the socio-economic burden. However, we also observe an accelerating increase in the prevalence among females, which contrasts sharply with previous studies emphasizing male dominance. This may reflect changes in the occupational structure, such as increased participation of women in high-noise industries, as well as the extended life expectancy of women in developing countries. The inverse relationship between the SDI and the burden of ARHL further highlights systemic inequalities, consistent with previous studies 12 . Our statistical results show that the global prevalence of ARHL is on the rise, but the upward trend varies with SDI. Countries with higher SDI benefit from advanced hearing health care and noise regulation policies, while regions with lower SDI often face scarce medical resources and fragmented access to hearing aids and preventive care 28 . Additionally, studies have shown that malnutrition is a potential risk factor for hearing loss 29 . Economic growth in some regions may improve dietary quality and thereby reduce the incidence of hearing loss 30 , 31 . These findings collectively emphasize that ARHL is not only a biological consequence of aging but also a condition determined by socio-economic factors. Therefore, it is crucial for countries and societies to develop targeted guidelines for impoverished areas to reduce the global burden of hearing loss among the elderly, which is key to achieving sustainable development goals. The increasing burden of ARHL calls for a shift in global health priorities. Interventions such as pharmacological treatments 32 , 33 , lifestyle changes 34 , 35 , cellular therapies 36 , 37 , and cochlear implants 38 can have some impact on ARHL. However, early intervention through routine hearing screening incorporated into elderly health care can mitigate cognitive dysfunction and social sequelae 23 . We can also implement policies to reduce the prevalence of ARHL: (1) Enforce occupational noise regulations and provide subsidies for hearing protection in high-risk industries; (2) Reduce the cost of hearing devices through public-private partnerships to expand accessibility and benefit more patients; (3) Prioritize ARHL in universal health insurance agendas, especially in low-SDI regions where most affected individuals still lack access to hearing aids. Additionally, efforts should be intensified to conduct public awareness campaigns targeting modifiable risk factors. Similarly, this study has certain limitations. First, reliance on data from the NHANES database and the GBD may introduce reporting biases or limitations in data collection. Second, in regions with low hearing loss indices, the scarcity of audiometric infrastructure and cultural biases against hearing loss may lead to underreporting and thus underestimate the true prevalence. Third, the COVID-19 pandemic has introduced significant uncertainty in estimating mortality rates for all diseases, especially in the most severely affected regions. Fourth, relying solely on GBD data is insufficient to unravel the complex impact of diseases on health outcomes. Fifth, the definition of age-related hearing loss provided by GBD may underestimate the disease burden due to its limitations. Lastly, our risk factor analysis of ARHL, influenced by GBD data, did not include all risk factors for the disease. Conclusion In summary, this study has revealed the significant growth trend of global ARHL over the past three decades and has emphasized the profound impact of gender, regional, and socioeconomic factors on the burden of ARHL. Although early intervention and policy support can effectively mitigate the burden of ARHL, data limitations and regional disparities still need to be further addressed. Therefore, a shift in global health priorities is needed to respond to the ongoing increase in ARHL and to achieve sustainable improvements in hearing health through multidisciplinary collaboration and policy innovation. Declarations C ontributions. Conceptualization, JK, and YL; methodology, JK; software, JK and XJ; formal analysis, JK; investigation, YS, XJ, and YL; writing-original draft preparation, JK; writing-review and editing, WY. All authors have read and agreed to the published version of the manuscript. Conflicts of interest. The authors declare no conflicts of interest. Funding. This study was funded by a grant from Chongqing Municipal Human Resources and Social Security Bureau. The project numbers are 2022062802 and YWZJGZS. This research was also supported by a grant from the Chongqing Municipal Health Commission and Science and Technology Bureau. The project number is W2022DBXM006. This research was also supported by a grant from the Natural Science Foundation of the Chongqing Municipality. The project number is cstc2021jcyj-msxmX0128. Availability of data and materials. Data in this manuscript were obtained from online databases and no new data were generated (https://www.cdc.gov/nchs/nhanes/ and https://ghdx.healthdata.org/gbd-2021). References Collins JG. Prevalence of selected chronic conditions: United States, 1990-1992[J]. Vital and health statistics Series 10, Data from the National Health Survey, 1997(194):1-89. Loughrey DG, Kelly ME, Kelley GA, et al. Association of Age-Related Hearing Loss With Cognitive Function, Cognitive Impairment, and Dementia: A Systematic Review and Meta-analysis[J]. JAMA otolaryngology-- head & neck surgery, 2018,144(2):115-126. Kramer SE, Kapteyn TS, Kuik DJ, et al. The association of hearing impairment and chronic diseases with psychosocial health status in older age[J]. Journal of aging and health, 2002,14(1):122-37. 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Survey of otolaryngology services in Central America: need for a comprehensive intervention[J]. Otolaryngol Head Neck Surg, 2013,149(5):674-8. Sardone R, Lampignano L, Guerra V, et al. Relationship between Inflammatory Food Consumption and Age-Related Hearing Loss in a Prospective Observational Cohort: Results from the Salus in Apulia Study[J]. Nutrients, 2020,12(2). Gutiérrez-Camacho C, Méndez-Sánchez L, Klünder-Klünder M, et al. Association between Sociodemographic Factors and Dietary Patterns in Children Under 24 Months of Age: A Systematic Review[J]. Nutrients, 2019,11(9). Rodrigo L, Campos-Asensio C, Rodríguez M, et al. Role of nutrition in the development and prevention of age-related hearing loss: A scoping review[J]. Journal of the Formosan Medical Association = Taiwan yi zhi, 2021,120(1 Pt 1):107-120. Peixoto Pinheiro B, Müller M, Bös M, et al. A potassium channel agonist protects hearing function and promotes outer hair cell survival in a mouse model for age-related hearing loss[J]. Cell death & disease, 2022,13(7):595. Cassinotti LR, Ji L, Borges BC, et al. Cochlear Neurotrophin-3 overexpression at mid-life prevents age-related inner hair cell synaptopathy and slows age-related hearing loss[J]. Aging cell, 2022,21(10):e13708. Han C, Ding D, Lopez MC, et al. Effects of Long-Term Exercise on Age-Related Hearing Loss in Mice[J]. The Journal of neuroscience : the official journal of the Society for Neuroscience, 2016,36(44):11308-11319. Miwa T. Protective Effects of N(1)-Methylnicotinamide Against High-Fat Diet- and Age-Induced Hearing Loss via Moderate Overexpression of Sirtuin 1 Protein[J]. Frontiers in cellular neuroscience, 2021,15:634868. Walters BJ, Coak E, Dearman J, et al. In Vivo Interplay between p27(Kip1), GATA3, ATOH1, and POU4F3 Converts Non-sensory Cells to Hair Cells in Adult Mice[J]. Cell Rep, 2017,19(2):307-320. Cheng YF. Atoh1 regulation in the cochlea: more than just transcription[J]. Journal of Zhejiang University Science B, 2019,20(2):146-155. Gurgel RK, Duff K, Foster NL, et al. Evaluating the Impact of Cochlear Implantation on Cognitive Function in Older Adults[J]. Laryngoscope, 2022,132 Suppl 7(Suppl 7):S1-s15. Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6161836","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":427861901,"identity":"c68e7539-9fcb-4f43-a172-9a19d9b3737c","order_by":0,"name":"Jing Ke","email":"","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Ke","suffix":""},{"id":427861902,"identity":"0fe86388-3ad7-474a-a0ad-cac34de142a4","order_by":1,"name":"Yiting Liu","email":"","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yiting","middleName":"","lastName":"Liu","suffix":""},{"id":427861903,"identity":"5152493a-f5fb-40e6-8300-d0148918dc65","order_by":2,"name":"Ya Shi","email":"","orcid":"","institution":"Chongqing General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ya","middleName":"","lastName":"Shi","suffix":""},{"id":427861904,"identity":"755293fb-cddf-43fe-b69f-1a5d38eb1b26","order_by":3,"name":"Xu Jiang","email":"","orcid":"","institution":"Chongqing General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Jiang","suffix":""},{"id":427861906,"identity":"1a25f6ae-635e-4f19-b9eb-a18462f0a363","order_by":4,"name":"Wei Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYPACCTnStRiTbk1iA9FKDW4kP3vMU2GRvuH44ccfGGrsGPhnE9BtcCPN3JjnjETuhjNpZhIMx5IZJO4cIKQlwUw6tw2o5QaDGQMD2wEGA4kEQlrSv0nn/pNIN7jB/vkDwz+itOQAbWmQSDC4wWMgwdhGhBbJM2/KpP8ckzCceSanTCKxL5lH4gYBLXzH07dJzqipk+c7fnzzhw/f7OT4ZxDQonAAmQdUzINfPRDINxBUMgpGwSgYBSMeAAClr0AqN3Pp1wAAAABJRU5ErkJggg==","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2025-03-05 10:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6161836/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6161836/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78691243,"identity":"bcfa3b64-acbd-4c83-9ed1-1fc64cd5e1e4","added_by":"auto","created_at":"2025-03-17 16:17:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":503054,"visible":true,"origin":"","legend":"\u003cp\u003e(A-B) Hearing among elderly people in the USA, 2005-2018:(A) Hearing condition in the Elderly;(B) Distribution of the Number of Elderly with Different Levels of Hearing Loss. (C-F) Prevalence and DALYs for ARHL in the elderly population, by age group and sex, 1990 and 2021: (C, D) Prevalence;(E, F) DALYS.(G-J)World map of ARHL Prevalence and DALYs rate, 1990 and 2021, by 204 country:(G, H) Prevalence;(I, J) DALYs. (K-L) Jionpoint regression analysis of global ASPR of ARHL, 1990-2021:(K) Age-standardized prevalence in males. (L)Age-standardized prevalence in females.*:P\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6161836/v1/ce95a6aea3f1a0b4c037ea78.png"},{"id":78692543,"identity":"7185e097-d862-4f75-b96f-340b370e2cf1","added_by":"auto","created_at":"2025-03-17 16:25:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":333695,"visible":true,"origin":"","legend":"\u003cp\u003e(A-B) 1990-2021 Relationship between the ASR of DALYs and SDl concerning total ARHL: (A) Heath inequality regression curves for the ASR of DALYs of total ARHL; (B) Concentration curves for the ASR of DALYs of total AHL. (C-F)Age-standardized burden rates of SDI due to ARHL for 21 regions and 204 countries in GBD 1990-2021: (C, D)ASPR; (E, F)ASR of DALYs. The black line is based on the adaptive association of all data points with adaptive Loess regression fit.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6161836/v1/aa5595c447251af53c94aabd.png"},{"id":78691235,"identity":"c2115864-d50a-4035-a9e8-1e54cb0a53e8","added_by":"auto","created_at":"2025-03-17 16:17:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":144783,"visible":true,"origin":"","legend":"\u003cp\u003eASPR and the ASR of DALYs trends in ARHL over the next 30 years as predicted by the bayesian age-period cohort (BAPC) model: (A, B) Prevalence; (C, D) DALYs.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6161836/v1/b8183a2eb27300e03beb1eac.png"},{"id":78692545,"identity":"4538c08c-f86d-47d4-b197-f5089e70dd95","added_by":"auto","created_at":"2025-03-17 16:25:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":102625,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in Percentage and Rate of DALYs in ARHL under the Influence of Occupational Noise, grouped by age and sex, 1990-2021:(A) 1990;(B) 2021;(C) Annual changes, 1990-2021.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6161836/v1/67b8d120fae17a92393a2ae5.png"},{"id":96917627,"identity":"aa794b1a-75ab-42d4-9285-3cb1f1210733","added_by":"auto","created_at":"2025-11-27 14:10:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1614270,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6161836/v1/1f3949fb-9e5b-48c9-86f8-e42d0aa8d99c.pdf"},{"id":78691250,"identity":"1e9aadba-4edd-4eba-ac8c-dd416a1647db","added_by":"auto","created_at":"2025-03-17 16:17:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":503932,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6161836/v1/e92169a11edfcaf95af38e7a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epidemiology of Age-related Hearing Loss from 1990 to 2021: Global Burden of Disease and Forecasted Trends","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAge-related hearing loss (ARHL) is increasingly recognized as a significant public health concern and ranks as the third leading cause of chronic disability among the elderly\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. ARHL is characterized by a progressive decline in hearing, particularly at high frequencies, which often results in communication difficulties, social isolation, and a reduced quality of life among the elderly\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In addition to auditory impairment, ARHL is associated with various adverse consequences, such as emotional distress, cognitive decline, and an increased risk of dementia\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. These outcomes highlight the importance of understanding the epidemiological characteristics and trends of ARHL to inform public health strategies and interventions.\u003c/p\u003e \u003cp\u003eOver the past few decades, the prevalence of ARHL has increased due to factors such as global population aging and increased exposure to environmental and occupational hazards\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Despite the increasing impact of ARHL, comprehensive data on its epidemiology and burden are still limited, particularly regarding long-term trends and cross-national comparisons. Understanding these aspects is crucial for developing targeted interventions and policies to mitigate the impact of ARHL on affected individuals and healthcare systems.\u003c/p\u003e \u003cp\u003eThe Global Burden of Disease (GBD) study offers comprehensive data on the epidemiology and burden of ARHL. Disability-adjusted life years (DALYs) serve as a crucial metric for assessing the impact of diseases on health and socioeconomic status. DALYs combine years of life lost due to premature mortality (YLLs) and years of healthy life lost due to disability (YLDs), offering a holistic view of the burden of ARHL on individual health and socioeconomic conditions\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Through the calculation of DALYs, the impact of ARHL on healthy life expectancy can be more intuitively assessed, as well as its distribution across different regions and populations. This not only helps to identify priorities for public health interventions but also provides a scientific basis for the formulation of health policies and resource allocation\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEpidemiological studies, including those utilizing data from the National Health and Nutrition Examination Survey (NHANES), indicate that ARHL is highly prevalent among older adults in the United States\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The burden of ARHL varies by gender, age group, and geographic region, with higher prevalence rates observed in low- and middle-income areas\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Environmental and occupational risks, including noise exposure, are significant factors in the development of ARHL\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Understanding these factors is essential for formulating targeted interventions and policies to reduce the impact of ARHL.\u003c/p\u003e \u003cp\u003eThis study employs the global disease assessment model to provide data support for global health decision-making by quantitatively analyzing disease risks, characteristics, and burdens. The study integrates data on disease burden and socioeconomic development to construct quantitative indicators of cross-national health inequalities\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The research findings will guide the development of prevention and treatment strategies and the optimization of resource allocation to improve global health levels\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe objectives of this study are to: 1) comprehensively describe the disease burden of ARHL at the global, regional, and national levels; 2) assess the inequality in the burden of ARHL at the global and regional levels; 3) analyze the temporal trends in the burden and inequality of ARHL from 1990 to 2021; and 4) predict the disease burden and inequality of global ARHL over the next 30 years, providing a scientific basis for policy-making and resource allocation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch data\u003c/h2\u003e \u003cp\u003eThe NHANES survey is a cross-sectional study based on a complex multistage probabilistic design, recruiting a representative sample of participants from the U.S. population. The project collects demographic, clinical, and laboratory test data from survey participants. The NHANES database is publicly available from the National Center for Health Statistics of the Centers for Disease Control and Prevention (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A total of 4025 participants from the 2005\u0026ndash;2018 dataset were included for further analysis. The investigation protocol was approved by the NCHS Research Ethics Review Approval Board and all participants provided written informed consent. The study was in line with the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eGBD 2021 provides a comprehensive assessment of health loss for 371 diseases and injuries and 88 risk factors worldwide, using the latest epidemiological data and improved standardized methods\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In this study, the number of cases and age-standardized rates (ASR) of ARHL were collected by gender, region, and country for the period from 1990 to 2021. Also, prevalence estimates and their 95% uncertainty intervals (UI) were extracted. The socio-demographic development of a country or region is quantified through the sociodemographic index (SDI), which is a composite average of income, educational attainment, and fertility status\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Data were downloaded from the Global Health Data Exchange (GHDx) query tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd-results/\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd-results/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Specifically, in the GHDx query tool, we selected \"Cause\" as \"Age-related and other hearing loss,\" \"Measure\" as \"Prevalence\" and \"DALY (Disability Adjusted Life Year),\" and \"Metric\" as \"Number\" and \"Rate\" to obtain the prevalence data for ARHL, with the search period from 1990 to 2021.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eTrends in ARHL incidence are quantified using ASR and estimated annual percentage change (EAPC). In the GBD study, the Estimated Annual Percentage Change (EAPC) is a key metric for measuring the trend of a specific health indicator over time. The EAPC is calculated using the following formula: EAPC\u0026thinsp;=\u0026thinsp;100\u0026times;(exp(β)-1). where β is the slope obtained through a linear regression model that uses log-transformed morbidity (or mortality, DALY rate, etc.) as the dependent variable and year as the independent variable. This approach assumes a linear relationship between log-transformed incidence and year, i.e., ln(Rate) = α\u0026thinsp;+\u0026thinsp;β\u0026thinsp;\u0026times;\u0026thinsp;Year + ϵ, where α is the intercept and ϵ is the error term\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The Joinpoint regression model is a statistical method used to characterize trend changes in time series data by segmenting the time series into different phases based on identified joinpoints and analyzing the trend of each phase separately. This method is particularly suitable for analyzing the trends of health indicators, such as morbidity and mortality, over time. The core idea of the Joinpoint model is to establish segmented regression based on the temporal characteristics of the disease distribution, partition the study time into different intervals through a number of joinpoints, and fit and optimize the trend for each interval, thus evaluating the disease changes specific to different intervals in the global time scale in more detail. Characterization. This approach has been widely used in the field of tumor epidemiology since it was proposed in 1998 by Kim et al\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In GBD research, the Joinpoint model is used to analyze and predict the impact of different diseases and risk factors on health, helping researchers to identify changes in health trends. We disaggregated the raw number of cases by age, demographic and epidemiologic changes (including age- and population-standardized prevalence rates) to examine the extent to which population growth, aging, and epidemiologic changes have affected ARHL over the past 30 years.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCross-national inequality analysis\u003c/h3\u003e\n\u003cp\u003eIn GBD studies, cross-country inequality analysis is typically quantified using the Slope Inequality Index (SII) and the Concentration Index (CI)\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. SII: The SII measures absolute inequality between countries with different Socio-Demographic Index (SDI) levels. Its formula is: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{S}\\text{I}\\text{I}=\\frac{\\sum\\:_{i=1}^{n}\\:({R}_{i}-{R}_{\\text{m}\\text{e}\\text{a}\\text{n}})\\cdot\\:{W}_{i}}{\\sum\\:_{i=1}^{n}\\:{W}_{i}}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the rate for the ith country (e.g., the DALY rate), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{\\text{m}\\text{e}\\text{a}\\text{n}}\\)\u003c/span\u003e\u003c/span\u003e is the average of the rates for all countries, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{W}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the weight of the ith country (usually the size of the population), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e\u003c/span\u003e is the number of countries. The value of SII can be positive or negative, with positive values indicating a higher burden for higher SDI countries and negative values indicating a higher burden for lower SDI countries. CI: CI is used to measure relative inequality and is calculated as follows: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{C}\\text{I}=\\frac{2}{\\mu\\:}\\sum\\:_{i=1}^{n}\\:({r}_{i}-\\stackrel{-}{r})\\cdot\\:{R}_{i}\\)\u003c/span\u003e\u003c/span\u003e .\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\)\u003c/span\u003e\u003c/span\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003eis the rank of the ith country among all the countries (in ascending order of SDI), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{r}\\)\u003c/span\u003e\u003c/span\u003e is the average of the rankings of all the countries, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003eis the rate for the ith country, and \u0026micro; is the average of the rates of all the countries. CI values range from \u0026minus;\u0026thinsp;1 to 1, with positive values indicating a higher burden for higher SDI countries and negative values indicating a higher burden for lower SDI countries. ASPR and ASR of DALYs were analyzed in relation to SDI for 21 regions globally and for ARHL in 204 countries.\u003c/p\u003e\n\u003ch3\u003ePredicting trends\u003c/h3\u003e\n\u003cp\u003eThe Bayesian age-period cohort (BAPC) model was employed to predict trends in the age-standardized prevalence rate (ASPR) and ASR of DALYs for ARHL over the next 30 years\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. BAPC modeling is a statistical method used to analyze and predict the impact of age, period, and cohort effects on health outcomes. The model utilizes a Bayesian framework for parameter estimation by integrating a priori information and sample data. In this paper, we use the R package \u0026ldquo;BAPC\u0026rdquo; (\u0026ldquo;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://R-Forge.R-project.org\u003c/span\u003e\u003cspan address=\"http://R-Forge.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u0026rdquo;) for simulation.\u003c/p\u003e\n\u003ch3\u003eRisk factor assessment\u003c/h3\u003e\n\u003cp\u003eThe 2021 GBD analysis employed seven interrelated methodological steps to assess the burden of risk factors\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The first step involves estimating effect sizes: the impact of specific risk factors on health outcomes is assessed by calculating relative risks (RR). Second: Exposure data collection and analysis: the distribution of exposure to risk factors is assessed using Bayesian statistical methods. Third, TMREL establishment: based on epidemiologic studies, the theoretical minimum risk exposure level (TMREL) for each risk factor was determined. Fourth, calculation of PAF: the population attributable fraction (PAF) was calculated for each risk-outcome pair as an indicator of the potential health benefits of reducing risk factors to the TMREL. Fifth, calculation of age-specific exposure values (sev): adjusting for age-specific risk factors to reflect the prevalence of exposure. Sixth, estimation of mediating factors: addressing possible overestimation in PAF. Seventh, attributable burden determination: derives estimates of the burden of disease attributable to specific risk factors by multiplying PAF values with deaths or DALYs for specific age groups, sexes, geographic locations, and time points. This approach provides a comprehensive risk factor assessment framework that helps to identify and quantify the main drivers of the global burden of disease. In the GBD study, risk factors were categorized into four classes, and this study focused on the third class of risk factors.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis software\u003c/h2\u003e \u003cp\u003eFor statistical analysis and modeling, we utilized RStudio, an integrated development environment (IDE) for R. We performed the analysis and modeling using RStudio. All data manipulation, model fitting and visualization were performed using R (4.4.1).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003eTrend analysis\u003c/h2\u003e\n \u003cp\u003eThe hearing data of the elderly population aged 60 years and older were obtained from the National Health and Nutrition Examination Survey (NHANES) database for the period 2005–2018, totaling 4,025 cases. The results (Fig. 1A, B) indicated that hearing loss in the elderly population increases with age. The severity of hearing loss was dominated by mild and moderate hearing loss.\u003c/p\u003e\n \u003cp\u003eIn 2021, there were approximately 700\u0026nbsp;million cases of ARHL, with a 95% UI range of 680.22×10\u003csup\u003e6\u003c/sup\u003e-754.27×10\u003csup\u003e6\u003c/sup\u003e, representing a significant increase of 137.43% from 1990. The ASPR for ARHL in 2021 was recorded at 18070.26 per 100,000 (95% UI 17299.37 per 100,000–18923.79 per 100,000) as compared to 17106.88 per 100,000 (95% UI 16319.2 per 100,000–18004.88 per 100,000) in 1990. For the period 1990 to 2021, the EAPC is 16%. The EAPC for females is 20%, much higher than the 13% for males. Among female individuals, the prevalence of ARHL in 2021 was 373.57×106 (95% UI 353.91×10\u003csup\u003e6\u003c/sup\u003e-392.46×10\u003csup\u003e6\u003c/sup\u003e) compared to 345.35×10\u003csup\u003e6\u003c/sup\u003e (95% UI 326.30×10\u003csup\u003e6\u003c/sup\u003e-361.81×10\u003csup\u003e6\u003c/sup\u003e) in males, which is significantly more prevalent in females than in males. However, the ASR for females was 16978.94 (95% UI 16246.61-17776.49), significantly lower than that for males at 19196.56 (95% UI 18396.99-20115.85).In 2021, the DALY for ARHL was 23.97 × 10\u003csup\u003e6\u003c/sup\u003e (95% UI 16.85 × 10\u003csup\u003e6\u003c/sup\u003e-32.84 × 10\u003csup\u003e6\u003c/sup\u003e), which is significantly lower than that of the 9.85×10\u003csup\u003e6\u003c/sup\u003e (95% UI 5.24×10\u003csup\u003e6\u003c/sup\u003e-13.53×10\u003csup\u003e6\u003c/sup\u003e) in 1990, an increase of 143.30%. Similar to the number of people with the disease, the number of women with DALY in 2021 was 12.61×10\u003csup\u003e6\u003c/sup\u003e (95% UI 8.85×10\u003csup\u003e6\u003c/sup\u003e-17.22×10\u003csup\u003e6\u003c/sup\u003e), more than the number of men with DALY, which was 11.36×10\u003csup\u003e6\u003c/sup\u003e (95% UI 7.99×10\u003csup\u003e6\u003c/sup\u003e-15.61×10\u003csup\u003e6\u003c/sup\u003e). The ASR for DALY in 2021 was 525.87/100,000 (95% UI 364.24-731.97), a 5.31% increase from 499.37/100,000 (95% UI 346.66-694.02) in 1990. The ASRs for DALY in females were all smaller than those for males. The EAPC for DALY is 17% for the period 1990 to 2021(Table 1). As shown in Fig. 1c-f, the prevalence rate is higher in men than women, but the number of prevalent cases is consistently greater in women than in men, except for those aged 60–64. There is no significant difference between men and women in the rate of DALYs, which is greater in men than in women in the 60-69-year-olds, and the opposite is true for the remaining age groups.\u003c/p\u003e\n \u003cp\u003eThe geographic distribution of prevalence and DALYs rates across 204 countries is well characterized. According to the data in Fig. 1G-J, high-income North America, Tropical Latin America, East Asia, and East Africa had higher ARHL DALYs rates in 1990. By 2021, Central Africa, Tropical Latin America, and Asia have significantly higher rates of ARHL DALYs to become the highest. Regarding prevalence, North America, Tropical Latin America, East Africa, East Asia, South Asia, and Oceania regions had relatively high rates in 1990. However, over time, by 2021, prevalence rates have remained relatively stable with little change, except in high-income North America and South Asia, where prevalence rates have decreased.\u003c/p\u003e\n \u003cp\u003eJoinpoint regression analysis of ASPR for ARHL from 1990–2021 showed different segmentation for men and women (Fig. 1K-L). The ASPR for males was in a significant upward trend from 1990–1993, with an annual percentage change (APC) of 0.19%. The prevalence growth slowed down during 1993–2000, with an APC of 0.12%. By 2000–2010, the increasing trend continued but the rate of increase decreased further with an APC of 0.09%. After 2010, the prevalence increased significantly with an APC of 0.18%. The ASPR in females differs from that of males. 1990–1995: significant increase in prevalence, with an APC of 0.19%. 1995–2006: slower increase, with an APC of 0.12%. 2006–2016: continued trend of increase in prevalence but at a lower rate of growth, with an APC of 0.09%. 2016–2021: significant increase in prevalence, with an APC of 0.29%. Overall, there is an increasing trend in the prevalence of ARHL in both males and females from 1990 to 2021. The increase in males accelerated during 2010–2021, while the increase in females was more significant during 2016–2021. The average annual percentage change (AAPC) in prevalence for males was 0.14 (95% UI 0.13–0.14), which was lower than that for females, which was 0.21 (95% UI 0.21–0.22).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eCross-country inequality analysis\u003c/h2\u003e\n \u003cp\u003eBetween 1990 and 2021, SII for DALYs declined from 121.48 in 1990 to -48.8 in 2021 (Fig. 2A), showing a downward trend, with groups with lower SDI bearing an increasing health burden. The concentration index for ARHL increased from 0.0078 in 1990 to 0.0508 in 2021 (Fig. 2B). The pattern of change from 1990 to 2021 is diverse across regions, but most regions show worsening inequality in lower SDI countries in both the SII and the concentration index.\u003c/p\u003e\n \u003cp\u003eGlobally, the ASPR and ASR of DALYs are higher at lower and intermediate SDI levels, and these indicators decline at higher SDI levels. In 21 regions (Fig. 2C, E), ASPR and SDI showed an “M” correlation, with a gradual increase in ASPR at SDI \u0026lt; 0.4 and 0.6 \u0026lt; SDI \u0026lt; 0.7, and a mild increase in ASR of DALYs at 0.6 \u0026lt; SDI \u0026lt; 0.7, with an overall decreasing trend. In 204 countries (Fig. 2D, F), ASPR, ASR of DALYs and SDI showed an “M” correlation, with a fluctuating state of first increasing and then decreasing.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003ePredicted trends\u003c/h2\u003e\n \u003cp\u003eTrends in prevalence and DALYs of ARHL over the next 30 years were predicted using the BAPC model (Fig. 3A-D). It can be seen that there will be an increase in the trend over the next 30 years. In 2051, the prevalence and DALYs will be 741.75 x106 and 24.8 x106 for females and 688.41 x106 and 22.51 x106 for males, respectively, and the burden of the disease will further increase.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eRisk Factors\u003c/h2\u003e\n \u003cp\u003eThe main contributor to the disease burden of ARHL is occupational noise. Both the percentage and rate of DALYs for ARHL due to occupational noise decrease with age, and are consistently higher in men than in women. In terms of temporal development, the ARHL disease burden due to occupational noise increased significantly more in women than in men from 1990 to 2021, and even developed a negative growth rate, especially for men of advanced age(Fig. 4A-C)..\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study describes the growth rate of the global burden of ARHL over three decades, with cases surging by 137.43% from 1990 to 2021. The age-standardized prevalence rate ASPR has risen with an EAPC of 16%, reflecting the aging population and epidemiological changes. Notably, although the number of cases among females is larger, males have higher prevalence rates and age-standardized rates of DALYs, highlighting gender disparities. Geographical inequalities persist, with low- and middle-income regions shouldering a disproportionate burden, exemplified by the elevated DALY rates in Central Africa and South Asia. Bayesian projections indicate that ARHL cases will continue to rise over the next 30 years, expected to exceed 1.4\u0026nbsp;billion, demanding urgent global attention.\u003c/p\u003e \u003cp\u003eAccording to statistical results from the NHANES database, mild to moderate hearing loss predominates among the elderly, and our data uniquely quantify the growing disability burden in an aging society. For mild-to-moderate hearing loss, the use of hearing aids and other assistive devices can provide some therapeutic benefits for the elderly, but this also increases the burden on society. Moreover, hearing loss in the elderly is primarily characterized by high-frequency hearing decline, often manifested as reduced speech recognition rates, and may lead to cognitive dysfunction\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. These issues severely impact the quality of life of the elderly and place both economic and emotional burdens on their families. Therefore, actively taking preventive measures to reduce the risk of hearing loss in the elderly is of great significance for improving their quality of life and alleviating the burden on families and society.\u003c/p\u003e \u003cp\u003eThe paradox of higher prevalence in females but higher ASR in males may stem from the interplay of demographic and occupational factors. The longer life expectancy of females, which is well-documented in aging populations \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e leads to a larger proportion of females with ARHL. Additionally, studies have shown that hormonal changes during menopause may predispose women to a higher risk of hearing loss \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In contrast, the higher ASR in males is consistent with historical patterns of occupational noise exposure, particularly in industries such as manufacturing and construction, where male workers have traditionally dominated\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This explains the higher ASR observed in males. Moreover, our statistical findings indicate that occupational noise plays a significant role in ARHL. Therefore, measures to reduce the impact of occupational noise can significantly decrease the prevalence of ARHL and are crucial for reducing the socio-economic burden. However, we also observe an accelerating increase in the prevalence among females, which contrasts sharply with previous studies emphasizing male dominance. This may reflect changes in the occupational structure, such as increased participation of women in high-noise industries, as well as the extended life expectancy of women in developing countries.\u003c/p\u003e \u003cp\u003eThe inverse relationship between the SDI and the burden of ARHL further highlights systemic inequalities, consistent with previous studies \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Our statistical results show that the global prevalence of ARHL is on the rise, but the upward trend varies with SDI. Countries with higher SDI benefit from advanced hearing health care and noise regulation policies, while regions with lower SDI often face scarce medical resources and fragmented access to hearing aids and preventive care\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Additionally, studies have shown that malnutrition is a potential risk factor for hearing loss\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Economic growth in some regions may improve dietary quality and thereby reduce the incidence of hearing loss\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. These findings collectively emphasize that ARHL is not only a biological consequence of aging but also a condition determined by socio-economic factors. Therefore, it is crucial for countries and societies to develop targeted guidelines for impoverished areas to reduce the global burden of hearing loss among the elderly, which is key to achieving sustainable development goals.\u003c/p\u003e \u003cp\u003eThe increasing burden of ARHL calls for a shift in global health priorities. Interventions such as pharmacological treatments\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, lifestyle changes\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, cellular therapies\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, and cochlear implants\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e can have some impact on ARHL. However, early intervention through routine hearing screening incorporated into elderly health care can mitigate cognitive dysfunction and social sequelae\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. We can also implement policies to reduce the prevalence of ARHL: (1) Enforce occupational noise regulations and provide subsidies for hearing protection in high-risk industries; (2) Reduce the cost of hearing devices through public-private partnerships to expand accessibility and benefit more patients; (3) Prioritize ARHL in universal health insurance agendas, especially in low-SDI regions where most affected individuals still lack access to hearing aids. Additionally, efforts should be intensified to conduct public awareness campaigns targeting modifiable risk factors.\u003c/p\u003e \u003cp\u003eSimilarly, this study has certain limitations. First, reliance on data from the NHANES database and the GBD may introduce reporting biases or limitations in data collection. Second, in regions with low hearing loss indices, the scarcity of audiometric infrastructure and cultural biases against hearing loss may lead to underreporting and thus underestimate the true prevalence. Third, the COVID-19 pandemic has introduced significant uncertainty in estimating mortality rates for all diseases, especially in the most severely affected regions. Fourth, relying solely on GBD data is insufficient to unravel the complex impact of diseases on health outcomes. Fifth, the definition of age-related hearing loss provided by GBD may underestimate the disease burden due to its limitations. Lastly, our risk factor analysis of ARHL, influenced by GBD data, did not include all risk factors for the disease.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study has revealed the significant growth trend of global ARHL over the past three decades and has emphasized the profound impact of gender, regional, and socioeconomic factors on the burden of ARHL. Although early intervention and policy support can effectively mitigate the burden of ARHL, data limitations and regional disparities still need to be further addressed. Therefore, a shift in global health priorities is needed to respond to the ongoing increase in ARHL and to achieve sustainable improvements in hearing health through multidisciplinary collaboration and policy innovation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003eontributions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, JK, and YL; methodology, JK; software, JK and XJ; formal analysis, JK; investigation, YS, XJ, and YL; writing-original draft preparation, JK; writing-review and editing, WY. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by a grant from Chongqing Municipal Human Resources and Social Security Bureau. The project numbers are 2022062802 and YWZJGZS. This research was also supported by a grant from the Chongqing Municipal Health Commission and Science and Technology Bureau. The project number is W2022DBXM006. This research was also supported by a grant from the Natural Science Foundation of the Chongqing Municipality. The project number is cstc2021jcyj-msxmX0128.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData in this manuscript were obtained from online databases and no new data were generated (https://www.cdc.gov/nchs/nhanes/ and https://ghdx.healthdata.org/gbd-2021).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCollins JG. Prevalence of selected chronic conditions: United States, 1990-1992[J]. Vital and health statistics Series 10, Data from the National Health Survey, 1997(194):1-89.\u003c/li\u003e\n\u003cli\u003eLoughrey DG, Kelly ME, Kelley GA, et al. Association of Age-Related Hearing Loss With Cognitive Function, Cognitive Impairment, and Dementia: A Systematic Review and Meta-analysis[J]. JAMA otolaryngology-- head \u0026amp; neck surgery, 2018,144(2):115-126.\u003c/li\u003e\n\u003cli\u003eKramer SE, Kapteyn TS, Kuik DJ, et al. 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Laryngoscope, 2022,132 Suppl 7(Suppl 7):S1-s15.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Age-related hearing loss, GBD, NHANES, Prediction, Risk","lastPublishedDoi":"10.21203/rs.3.rs-6161836/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6161836/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eAge-related hearing loss (ARHL) is a prevalent progressive hearing loss that can lead to emotional impairment and cognitive decline in older adults. The aim of this study was to investigate the epidemiologic characteristics of ARHL from 1990 to 2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe collected hearing data from the elderly using the National Health and Nutrition Examination Survey (NHANES) database from 2005 to 2018. Prevalence and disability-adjusted life years (DALYs) for ARHL were obtained from the Global Burden of Disease (GBD) 2021. Trends in ARHL burden were assessed using Joinpoint regression analysis. The Slope Inequality Index (SII) and Concentration Index (CI) were calculated to quantify absolute and relative cross-country inequalities in ARHL burden. Bayesian age-period cohort (BAPC) modeling was used to predict trends in ARHL prevalence and DALY over the next 30 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Mild to moderate hearing loss predominated among older adults in the U.S. between 2005 and 2018. In 2021, there will be more than 700 million cases of ARHL globally, increasing by 137.43% from 300 million cases in 1990. The age-standardized rate (ASR) has also increased, with an estimated annual percentage change of 16%. According to the Joinpoint regression analysis, the upward trend in the age-standardized prevalence rate (ASPR) for males intensified after 2010. In contrast, the upward trend in the ASPR for females slowed between 2000 and 2010. As the Socio-Demographic Index (SDI) rises, the ASR of DALYs and ASPR show a downward trend. Notably, as of the latest data, 204 countries and 21 regions globally still have significant health inequalities, although the slope index of inequality has declined over time. Projections of the global burden of ARHL over the next 30 years show a gradual increase in the ASR of DALYs and ASPR. For DALYs affecting ARHL the main factors include environmental risks, occupational risks, and occupational noise.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e The burden of ARHL varies by gender, age group, and geographic region. ASR has been on the rise over time and the burden of disease is high, particularly in low- and middle-income areas.\u003c/p\u003e","manuscriptTitle":"Epidemiology of Age-related Hearing Loss from 1990 to 2021: Global Burden of Disease and Forecasted Trends","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-17 16:17:49","doi":"10.21203/rs.3.rs-6161836/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"39c99144-3c1d-4c07-b86d-eacba72a094b","owner":[],"postedDate":"March 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45585365,"name":"Health sciences/Diseases"},{"id":45585366,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-11-26T20:06:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-17 16:17:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6161836","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6161836","identity":"rs-6161836","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}