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To estimate the prevalence of hearing impairment (HI) and evaluate the cross-sectional associations of environmental and cardiovascular disease risk factors and HI in middle-aged adults.
Data were collected as part of the Beaver Dam Offspring Study (BOSS), an epidemiological cohort study of aging. HI was defined as a pure-tone average (PTA) 0.5,1,2,4kHz >25 db HL in either ear. Word recognition in competing message (WRCM) was measured using the Northwestern University #6 word list. Questionnaire information about behaviors, environmental factors and medical history was collected.
Participants (n=3,285) were offspring of participants of the population-based Epidemiology of Hearing Loss Study, and ranged in age from 21–84 years (mean age=49 years).
The prevalence of HI was 14.1%, and the median WRCM score was 64% (standard deviation=15%). In a multivariate model, controlling for age, sex, education, and occupational noise, a history of ear surgery (Odds Ratio (OR) = 4.11, 95%Confidence Interval (CI) = 2.37, 7.15), larger central retinal venular equivalent (CRVE) (OR = 1.77, 95%CI = 1.20, 2.60; 4th q vs. 1st q), and higher hematocrit percentage (OR = 0.77, 95% CI = 0.63, 0.95; per 5%) were independently associated with HI. Factors associated with lower WRCM scores were similar but also included mean intima-media thickness (mean difference= −0.63% (−1.06, −0.19) P= 0.005; per 0.1mm) and statin use (mean difference= −2.09% (−3.58, −0.60) P=0.005).
HI is a common condition in middle-aged adults. CVD risk factors may be important correlates of age-related auditory dysfunction.
Hearing impairment (HI) is one of the most common chronic conditions in older adults affecting at least 29 million Americans.1 Population-based epidemiological prevalence estimates range from 20.6% in adults ages 48–59 years to 90% in adults over 80 years old.2 The ten-year incidence of HI in these two age groups has been estimated to be 22% and 100% respectively.3 Furthermore, severity of this condition has been shown to be associated with poorer quality of life, communication difficulties, impaired activities of daily living, dementia, and cognitive dysfunction.4,5
Besides older age and male sex, environmental factors such as loud noise, socioeconomic status (SES), and ear infections have been associated with HI.1,3,6–8 Traditional cardiovascular disease (CVD) risk factors may also be important contributors to worse hearing, although the findings from different epidemiological cohorts have been inconsistent. Associations have been found between HI and history of smoking,6,9 CVD including myocardial infarction and stroke,6,10 higher blood pressure or hypertension,10,11 and diabetes.6,12 Most data regarding the association of CVD risk factors and HI have come from the study of cohorts of older persons, after noise and occupational exposures have ceased. Fewer studies have focused on risk factors for HI in younger adults where there may be other competing exposures. Recent reports from the NHANES (age range 20–69 years) have shown that those with increased occupational and firearm noise exposure, increased pack-years of smoking, and the presence of diabetes are more likely to have HI.8,13
Epidemiological data are needed to characterize the burden of HI across all adult age groups, especially in those under age 60 years. In addition, to investigate associations between CVD risk factors and HI, both pure-tone and speech audiometry (word recognition in competing message (WRCM)) were used in analytic modeling as there may be both peripheral and central dysfunction in HI. Identifying possible modifiable risk factors may allow for early interventions to delay the onset of HI and diminish the impact on quality of life.
The Epidemiology of Hearing Loss study (EHLS) is an ongoing population-based cohort study started in 1993 in Beaver Dam, Wisconsin to measure hearing outcomes and their risk factors. The original cohort was made up of 3,753 participants ranging in age from 48–92 years, who were then followed up every 5 years. In 2005 the offspring of the EHLS participants were enrolled in the Beaver Dam Offspring Study (BOSS), a study of multi-sensory impairments and aging. Of the 4,965 eligible offspring, 3,285 participated (66.2%) in the BOSS, 1657 (33.4%) did not participate, and 23 (0.5%) died. Data collection occurred during 2005–2008. The analysis for this report used data from those 2,837 participants with audiometric data (86.4% of participants). BOSS methods were approved by the University of Wisconsin Madison Internal Review Board and all participants provided written informed consent.
The hearing examination included otoscopy, tympanometry, pure-tone air- and bone-conduction audiometry, as well as word recognition in quiet and in competing message (WRCM). All examiners were trained and certified in all study protocols. An abnormal otoscopic exam was defined as drainage, a bulging or retracted eardrum, a visible air-liquid line or perforated ear drum. Consistent with guidelines of the American Speech-Language-Hearing Association14 audiometric testing was conducted in a sound-treated booth (Industrial Acoustics Company, New York, New York) using a GSI-61 clinical audiometer (Grason-Stadler, Eden Prairie, MN). Headphones (TDH-50) were used for air-conduction testing, and insert earphones (E-A_Rtone 3A; Cabot Safety Corp., Indianapolis Indiana) and masking were used when appropriate.
Air conduction thresholds were determined for each ear at 0.5,1,2,3,4,6, and 8 kHz. The clinical audiometer was calibrated every six months according to the American National Standards Institute (ANSI) standards.15 Ambient noise levels were routinely measured throughout the study to ensure testing conditions remained within ANSI standards.16 A pure-tone average (PTA) was calculated using the thresholds from the 0.5, 1, 2, and 4 kHz frequencies. HI was defined as a PTA 0.5, 1, 2, and 4 kHz >25 decibels (dB) hearing level (HL) in either ear (worse ear). In our cohort, 93% of the population was concordant for HI status between ears.
Tests of word recognition in quiet and WRCM were conducted in a sound-treated booth using the Northwestern University Auditory Test Number 6 (NU6).17 A 25 word word-list was presented to the better ear at 36 dB HL above the individual’s threshold at 2 kHz (using a single female voice). 17 A competing message (single male talker) was then added at a level 8 dB HL below the speaker’s level in the better ear. 17 WRCM results were reported as percent correct.
Trained interviewers administered a hearing-related medical history and noise exposure questionnaire which included questions about ear-related medical history such as Meniere’s disease, otosclerosis, ear infections, and ear surgery. A positive history of occupational noise exposure was defined as self-reported occupational noise exposure (holding a full-time job that required speaking in a raised voice or louder to be heard), or have driven a farm tractor without a cab.
Data collection included blood pressure measurements using the Dynamap Procare 120 (GE Medical Systems, Milwaukee, WI) system, and hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or self-reported physician diagnosis of hypertension and currently taking anti-hypertension medication. Height and weight were measured to calculate body mass index (BMI) as weight in kilograms divided by height in meters2. Obesity was defined as a BMI ≥ 30 kg/m2.
Serum total cholesterol was measured using a double enzymatic process which produces hydrogen peroxide (Roche/Hitachi 911 System, Indianapolis, IN). After further treatment with peroxidase, 40-aminophenazone and phenol, a colored product was produced which was then measured at 505 nm. Serum high density lipoprotein (HDL) cholesterol was measured the same way after precipitating off the other lipoprotein fractions. White blood cell count and hematocrit percentage were measured in a complete blood cell count at the time of the blood draw. Hemoglobin A1c was measured from whole blood using a A1c 2.2 Plus Glycohemoglobin Analyzer (Tosch, San Francisco, CA). Diabetes status was defined as a self-report of physician diagnosis or elevated hemoglobin A1C level greater than or equal to 6.5% at the time of the exam.
Participants were considered to have a history of CVD if they self-reported having had a physician-diagnosed stroke, myocardial infarction (MI), or angina. Carotid intima-media thickness (IMT) at six sites in each carotid artery was measured using B-mode ultrasonography (Biosound AU4, Biosound Esaote, Indianapolis, IN USA).18,19 Retinal vessel caliber measures, central retinal arteriolar equivalent (CRAE) and the central retinal venular equivalent (CRVE), were obtained using Ivan software (Fundus Photograph Reading Center, Department of Ophthalmology and Visual Sciences, University of Wisconsin) from digital eye fundus images centered on the optic disc (Canon Dgi-45NM Fundus Camera).20 These retinal vessel measures have been associated with cardiovascular and cerebrovascular disease in numerous epidemiological studies (20). It is thought that CRAE and CRVE represent different pathophysiological phenomena with a decreased CRAE linked to increased blood pressure, and an increased CRVE associated with inflammation and endothelial dysfunction.21
The questionnaire captured data on highest education level obtained, longest held job, and water source at age 13 (municipal vs. well water). Smoking was defined as ever having smoked more than 100 cigarettes in his/her lifetime. Pack-years were calculated for smokers (number of cigarettes smoked per day divided by 20 then multiplied by the number of years smoked). A history of heavy drinking was defined as having ever in their life consumed four or more alcoholic beverages daily. Participants were considered physically active if they currently engaged in a regular activity long enough to work up a sweat at least once a week. Participants self-reported use of statins and non-steroidal anti-inflammatory drugs (NSAIDs).
All analyses were performed using SAS version 9.2 (SAS Institute, Cary NC) and Stata 11.1 (StataCorp LP, College Station, TX). Participants with audiometric data were compared to non-participants and those without audiometric data using chi-square tests for categorical variables, and t-tests for continuous variables. Participants with hearing impairment that was asymmetric (PTA difference between ears greater than 20 dB) were excluded from models evaluating associations with HI (n=70). Logistic regression was used to estimate odds ratios and examine risk factor associations. Selected risk factors included those associated with HI in published papers as well as well-known markers of or risk factors for CVD. Age-sex adjusted models were first run for each potential individual risk factor. Those factors that were associated with the HI (P< 0.20) were then entered into a multivariable model. At that stage, those with insignificant P-values (P > 0.05) that did not substantially change the other variables coefficients (+/− 20%) when removed were left out of the final model. The associations between exposure variables and HI were examined for possible interactions with age and sex. Multivariable least squares regression was also used to estimate mean WRCM scores and test for associations between exposure variables and WRCM using the same modeling approach.
Analyses were then performed by running the final multivariable model with least squares regression procedures using the worse ear PTA as the outcome to assess risk factors for hearing level overall. To minimize heterogeneity from multiple etiologies of hearing impairment, the final model was rerun excluding participants (n=110) with hearing loss that developed before age 30 years, a history of ear surgery, or conductive hearing loss in addition to the original exclusion criterion based on asymmetric hearing loss. Because there is most likely a heritable component to HI, and participants in BOSS were recruited from families, models were rerun using generalized estimating equations (GEE) to account for reported familial relationships and to determine whether these relationships affected the results of the study.
Participation in the BOSS tended to be higher amongst eligible people living closest to Beaver Dam, WI (p <0.0001). Participants also tended to be slightly older (48 vs. 46 years, respectively, P< 0.0001) and were more likely to be women (54.6% vs. 44.4%, P<0.0001) than non-participants. After adjusting for age, sex and location of residence, there was a statistically significant difference in parental history of HI (Odds Ratio (OR) = 1.21, 95% Confidence Interval (CI) = 1.05, 1.39) between those participants with hearing examination data and those without (including both participants and non-participants).
Participants in the BOSS ranged in age from 21 to 84 years (mean age 49 years), 45.6% of this cohort were men, and 69.9% had more than 12 years of education (table 1). There was a low prevalence of Meniere’s disease, otosclerosis, ear surgery and abnormal otoscopic examinations (0.5%, 0.6%, 4.4%, and 0.8%, respectively). The mean word recognition score in quiet was 89.6% (standard deviation (SD) = 9.2%) and the mean WRCM score was 63.5% (SD = 14.7%).
The overall prevalence of HI was 14.1% (95%CI = 12.9, 15.4) and ranged from 2.9% in those aged 21–34 years to 42.7% in those aged 65–84 years (table 2). When analyses were run using the better ear instead of either ear (bilateral hearing impairment) the prevalence of HI was 6.8% (192/2837) (95%CI = 5.8, 7.7). After controlling for sex, older age was associated with greater odds of HI (OR = 1.58, 95%CI = 1.48, 1.67 per 5 year increase). Men were more likely than women to have HI after controlling for age (OR = 3.0, 95%CI = 2.37, 3.79).
Age-sex-adjusted models for HI are shown in table 3. Several traditional risk factors for CVD were significantly associated with HI including pack-years of smoking (OR = 1.61, 95%CI = 1.16, 2.23; ≥11 vs. 0 pack-years). Hypertension, diabetes, and obesity were not associated with HI in this middle-aged cohort. Several environmental variables were associated with HI in age- and sex-adjusted models such as having a noisy job (OR = 1.67, 95%CI = 1.29, 2.16).
In the final multivariable model, having less education, having a noisy job, history of ear surgery, and larger CRVE were associated with increased odds of HI. Higher hematocrit percentage was associated with decreased odds of HI (table 4). Point estimates and confidence intervals were essentially unchanged when the noisy job variable was limited to noise exposure at their primary job (excluding tractor noise), and in GEE models accounting for familial relationships (results not shown).
Some individuals with HI reported a young age of onset (≤30 years) (n=116), or a history of ear surgery (n=44), or had a measured conductive hearing loss (n=36). When the final model was rerun excluding this group (n=110 with at least one condition) the results were similar to those in the whole cohort (table 5). The main difference was that hematocrit was no longer statistically significantly associated with HI.
Evaluating this model with hearing level as a continuous outcome (mean worse-ear PTA), the results were also similar. Age (mean difference: +2.38, 95%CI +2.17, +2.58; per 5 years), male sex (+5.86, 95%CI +4.93, +6.79), lower levels of education (+2.63, 95%CI +1.65, +3.60; ≤12 vs. ≥16 years), having a noisy job (+1.46, 95%CI +0.64, +2.28), and a history of ear surgery (+8.88, 95%CI = 6.87, 10.89), were associated with larger PTA, while hematocrit (per 5%) (−0.82, 95% CI = −1.44, −0.19) was associated with smaller PTA.
In a multivariable linear regression model with WRCM as a continuous outcome (table 6), age, sex, education, PTA, having had a longest held job in labor, production, and farming, statin use, greater CRVE, and greater mean IMT were statistically associated with lower mean WRCM scores. Those who had municipal water as a child had higher scores than those who reported well water sources. Results were similar in GEE models accounting for familial relationships (results not shown).
The prevalence of HI was 14.1% in this population of middle-aged adults. Although the prevalence of HI in the BOSS was less than 10% among those less than 45 years of age, it was substantially greater in older adults. HI was more likely in men, participants with lower education levels, and in those working in noisy occupations or with a history of ear surgery. Other factors associated with HI that could be considered risk factors for CVD were larger CRVE, and larger hematocrit percentage. CRVE, a microvascular measure, IMT, a macrovascular measure, and statin use, a possible indicator of a clinicians concern about high CVD risk, were associated with hearing measured by WRCM. These results suggest that there may be cardiovascular antecedents of HI, as measured by pure tone or speech audiometry, which are detectable even in middle age.
The overall prevalence of HI was similar although somewhat lower than in the NHANES (16.1%), a nationwide estimate from adults under the age of 65 years.1 The small difference between this estimate of prevalence and ours could possibly be explained by differences in the distributions of age, gender, race/ethnicity, other characteristics related to HI, or sampling variability. The EHLS, Framingham Heart Study, and Blue Mountains Hearing Study (BMHS) cohorts had similar prevalence estimates of 46% in adults aged 48–92 years, 47% in adults 57–89 years, and 44.6% in adults aged >49 years, respectively.2,7,22
The most consistent cardiovascular factors associated with worse hearing measured by PTA or WRCM in the BOSS were micro- and macro-vasculature measurements. Larger CRVE was associated with both HI and WRCM and larger carotid artery IMT was associated with WRCM. Few studies have examined relationships between micro- and macro-vascular factors and HI. In the BMHS, retinopathy was associated with HI in women, but wider retinal venular diameter and narrower retinal arteriolar diameter were not associated with HI in either men or women.23 It is not known what accounts for the differences between studies. CRVE is associated with retinal tissue hypoxia, systemic inflammation, and high lipid and glucose levels. Some of these measures have been hypothesized to be involved in the pathogenesis of HI.12,13 IMT is a subclinical measure of atherosclerosis which predicts adverse cardiovascular outcomes.24 Therefore, these results may add support to a possible cardiovascular link to HI.
Participants taking statin drugs had lower mean WRCM scores then those not taking statins. Statin drugs are mostly prescribed to those with high cholesterol levels. Although serum cholesterol levels themselves were not associated with WRCM scores in BOSS, those on statins may represent those with the worst cholesterol level profiles. Lipids have been implicated in the atherosclerotic process, and may indirectly influence blood flow to both the brain and the microvasculature of the ear. The WRCM task may be capturing age-related changes in the central auditory cortex.
Participants with a higher hematocrit percentage were less likely to have HI, contrary to the expectation that hematocrit is strongly correlated with and can act as a marker of blood viscosity.25 Increased viscosity has been shown to be associated with increased blood pressure, ischemic heart disease, as well as diminished oxygen levels in the cochlea in animal models.25–27 Gates et al. found no association between hematocrit and hearing level in the Framingham cohort.10 Other studies have shown that those with worse hearing or hearing loss had increased blood viscosity or hematocrit levels.28 The contradictory results of this study could be due to the use of hematocrit percentage as an imperfect surrogate for blood viscosity, the fact that in middle-aged adults blood vessels may be less atherosclerotic and therefore more resilient to increases in viscosity, or that hematocrit in people of this age-range may be more a reflection of diet or vitamin usage. Alternatively, low hematocrit levels could also damage hearing, because of associated impairment in oxygen transport. Low hematocrit and anemia have been linked to cardiovascular disease and cardiomyopathy.29–30
SES was associated with worse hearing measured either by PTA (education) or by WRCM (longest held job, municipal water as a child). Municipal water as child (as opposed to well water) could be a marker of either increased SES, or decreased exposure to substances such as pesticides in drinking water. Prevalence and incidence studies from the Epidemiology of Hearing Loss Study2,3 as well as national prevalence estimates from NHANES have shown associations with HI and level of education and occupation.1 Although those with lower levels of education may tend to work in occupations (such as production/labor/manufacturing) that have higher levels of noise exposure, the education-HI association remained controlling for occupation. Low SES may be associated with less healthy behaviors, less access to health care, and has been consistently shown to be associated with CVD and therefore may be associated with HI through a CVD pathway.31
Although several CVD risk factors were associated with HI in this study, smoking, diabetes and hypertension were not. The lack of associations may be due to the younger age of this cohort or the fairly low prevalence of prevalent CVD (3.4%), diabetes (6.3%) and smoking exposure (17.7% current smokers; 22.6% >11 pack-years). Agrawal et al. used a much higher cut-point for pack-years smoking and had a higher prevalence of diabetes which may have allowed them increased power for detecting an association in the NHANES.8 Although 38.4% of the current cohort was considered hypertensive, data were not available to assess duration of hypertension, and due to the young mean age in the BOSS these participants may not had had hypertension long enough to have affected hearing.
Those with a history of a noisy job, and a history of ear surgery, had increased odds of having HI. Relationships between occupational noise and worsening hearing have been shown in many contexts, including large population-based cohort studies.2,6,7 A report using NHANES data showed that those with occupational loud noise exposure had a 60% increased odds of HI.8 Our assessment of occupational noise exposure was based on self-report which may result in an under-estimate of the effects of noise exposure. It is possible that there remained some residual confounding due to noise in our multivariate models. Those with ear surgery reported procedures such as tympanoplasty and mastoidectomy, which can have clear direct effects on hearing levels.
In this study, HI was defined by the PTA in the worse ear to avoid underestimating the prevalence of HI. Although this definition may increase the heterogeneity of types of HI, it is a useful measure of the number of people with HI in the cohort which has been used in other epidemiological studies of HI.1–3,8,9,12 In this cohort, 93% were concordant between ears. Other studies have used other combinations of frequencies, or other audiogram patterns, to reduce the heterogeneity inherent in cross-sectional studies of HI. However, we have previously shown in population-based cohorts, that most HI has a typical sensorineural pattern with high frequencies affected more than, and before, lower frequencies.32 There were too few people with a flat hearing loss in the low range to support subset analyses (n=16).
To assess the impact of heterogeneity of HI, we conducted subset analyses which excluded cases where the HI might be due to trauma or middle ear problems. In the subset of participants (n=2,514) excluding those with an onset of HI before age 30, a history of ear surgery, conductive or asymmetrical hearing loss the risk factor model results were similar to those in the whole population.
There are few current epidemiological studies measuring hearing in middle-aged adults.33 The BOSS cohort had a large sample size, data were collected using standardized protocols and accepted methodologies, and multiple hearing endpoints allows for a clearer understanding of factors associated with HI. Using both pure tone and speech audiometry measures, we found consistent evidence of associations between cardiovascular risk factors and hearing impairment. Responses to tone and speech audiometric tests reflect the function of the auditory system, including central processing, but the additional complexity of speech understanding may make these tasks better indicators of central processing. Our findings with tones and WRCM, suggest that age-related changes in the cochlear and central auditory association areas may contribute to HI in adults.
This study was cross-sectional and could not demonstrate causal relationships. As described in the results section, participants in the BOSS had significantly higher odds of parental history of HI than non-participants, and it has been shown that HI is highly heritable with estimates ranging from 47% to 68% depending on the statistical adjustments for confounders.34 Therefore, this study may have an overestimated prevalence of HI in White adults. This was probably unlikely, however, since the prevalence of HI in BOSS (14.1%) was close to and even lower than a national estimate from the NHANES for non-Hispanic Whites (18.0%).1 Furthermore, when GEE models were employed to allow for clustering of participants based on family structure, associations and their standard errors between risk factors and HI differed only slightly. Despite biological plausibility and pre-defined statistical procedures, some of the associations found in multivariable modeling may have been due to type 1 errors.
This study described the prevalence of HI in a cohort of adults ranging in age from 21 to 84 years and identified possible modifiable correlates (CRVE, exercise, hematocrit percentage, statin use, mean IMT) of auditory dysfunction measured by HI and WRCM, suggesting that HI, if detected early, may be a preventable chronic disease.
The project described was supported by R01AG021917 from the National Institute on Aging, National Eye Institute, and National Institute on Deafness and Other Communication Disorders. The content is solely the responsibility of the authors and does not necessarily reflect the official views of the National Institute on Aging or the National Institutes of Health.
All authors had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Presented at the annual American Auditory Society meeting, Scottsdale AZ, March 5–7, 2009