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. 2020 Feb 3;29(1):59–67. doi: 10.1044/2019_AJA-19-00021

A Novel Method for Classifying Hearing Impairment in Epidemiological Studies of Aging: The Wisconsin Age-Related Hearing Impairment Classification Scale

Karen J Cruickshanks a,b,✉, David M Nondahl a, Mary E Fischer a, Carla R Schubert a, Ted S Tweed a,c
PMCID: PMC7229775  PMID: 32011900

Abstract

Purpose

Longitudinal population-based cohort data were used to develop a standardized classification system for age-related hearing impairment using thresholds for frequencies (0.5–8 kHz) typically measured in cohort studies.

Method

Audiometric testing data collected in the Epidemiology of Hearing Loss Study from participants (n = 1,369) with four visits (1993–1995, 1998–2000, 2003–2005, and 2009–2010) were included (10,952 audiograms). Cluster analyses (Wald's method) were used to identify audiometric patterns. Maximum allowable threshold values were defined for each cluster to create an ordered scale. Progression was defined as a two-step change.

Results

An eight-step scale was developed to capture audiogram shape and severity of hearing impairment. Of the 1,094 participants classified as having normal hearing based on a pure-tone average, only 25% (n = 277) were classified as Level 1 (all thresholds ≤ 20 dB HL) on the new scale, whereas 17% (n = 182) were Levels 4–6. During the 16-year follow-up, 64.9% of those at Level 1 progressed. There was little regression using this scale.

Conclusions

This is the first scale developed from population-based longitudinal cohort data to capture audiogram shape across time. This simple, standardized scale is easy to apply, reduces misclassification of normal hearing, and may be a useful method for identifying risk factors for early, preclinical, age-related changes in hearing.

Age-related hearing impairment is now recognized as an important and common health disorder affecting older adults (National Academies of Sciences, Engineering, and Medicine, 2016). Yet, most people with a hearing impairment are undiagnosed and untreated for years (Nash et al., 2013; National Academies of Sciences, Engineering, and Medicine, 2016; Popelka et al., 1998). Emerging evidence suggests that, similar to other disorders of aging, adiposity, smoking, metabolic dysregulation, vascular disease, and inflammation are associated with increased risk of developing hearing impairment (Cruickshanks et al., 2015; Fischer et al., 2015; National Academies of Sciences, Engineering, and Medicine, 2016).

Longitudinal population-based cohort studies of hearing are needed to better understand how to prevent or delay the onset of hearing impairment in aging and to improve hearing health care for older adults. Typically, in cohort studies, hearing has been measured by pure-tone audiometry, with the average of hearing thresholds across a subset of the frequencies measured (pure-tone average [PTA]) used as the outcome measure (Cruickshanks et al., 2015; Fischer et al., 2015; Gates et al., 1990; Mitchell et al., 2011; Mościcki et al., 1985). However, it is well recognized that hearing changes in aging tend to affect higher frequencies before lower frequencies, resulting in a sloping pattern on a clinical audiogram (Dobie, 2011; Wiley et al., 2008). A PTA metric can obscure this pattern, has the potential to misclassify people as normal who have poor hearing at higher frequencies but good thresholds at lower frequencies, and may not be a good measure of change over time. A better metric for classifying hearing over time would strengthen large-scale longitudinal studies.

Hearing scientists have long been interested in audiogram shape as a measure of underlying pathology and have compared highly selected examples of audiograms with autopsy histopathology to suggest that there are different types of age-related hearing impairment identifiable by audiogram shape (Carhart, 1945; Ciocco, 1932; Guild, 1932; Schuknecht, 1955, 1964; Schuknecht & Gacek, 1993). Similarly, other groups have suggested algorithms for identifying noise-induced hearing loss (Dobie, 2005; McBride & Williams, 2001) based on audiogram shape. However, some studies have suggested that these approaches do not reflect specific cochlear pathology or exposures (Katsarkas & Ayukawa, 1986; Landegger et al., 2016; Nondahl et al., 2009). More recently, one group has used longitudinal audiograms from a convenience sample to identify patterns consistent with known animal models of underlying cochlear pathology (Dubno et al., 2013; Vaden et al., 2017). However, only 20% of audiograms evaluated in the study fit clearly into these patterns, limiting their utility for epidemiological studies.

There have been a few large cross-sectional studies applying various approaches to describe the distribution of audiogram shapes in the samples (Allen & Eddins, 2010; Ciletti & Flamme, 2008; Demeester et al., 2009; Hannula et al., 2011). The largest population-based study used cluster analyses and data from the National Health and Nutrition Examination Survey and Keokuck County Rural Health Study to generate a description of the range and frequency of various shapes, but this very complex scale has not been applied to longitudinal data (Ciletti & Flamme, 2008).

Standardized grading systems to classify the severity and progression of eye diseases, such as diabetic retinopathy and age-related macular degeneration, have been very useful in advancing research (Bressler et al., 1989; Davis et al., 1969; Klein et al., 1991, 1986; Vitale et al., 2016). These standardized methods, which capture the natural history of disease progression, can facilitate identifying risk factors for early, preclinical stages when intervention may be more effective and risk factors for progression to clinically significant end points. They may provide useful intermediary end points for clinical trials and are useful for comparing results across studies. To our knowledge, there have been no population-based longitudinal studies of audiogram shape as a measure of the severity of hearing impairment. Therefore, we used serial audiograms collected every 5 years in a population-based cohort to develop a simple scale (Wisconsin Age-Related Hearing Impairment Classification Scale [WARHICS]) capturing the patterns of hearing thresholds observed during 15 years of follow-up time.

Materials and Method

Audiometric data were obtained as part of the longitudinal, population-based cohort study, the Epidemiology of Hearing Loss Study (EHLS; Cruickshanks et al., 2015, 1998). EHLS participants were residents of Beaver Dam, Wisconsin, ages 43–84 years, in 1987–1988, who had participated in the Beaver Dam Eye Study. The baseline study for the EHLS occurred in 1993–1995 (EHLS1), with follow-up examinations in 1998–2000 (EHLS2), 2003–2005 (EHLS3), and 2009–2010 (EHLS4). The Health Sciences Institutional Review Board of the University of Wisconsin-Madison approved this study, and written informed consent was obtained at each examination.

Certified examiners used the same standardized methods at each examination. Audiometric testing was conducted in sound-treated booths using clinical audiometers equipped with TDH-50P earphones (Telephonics) and ER-3A insert earphones (Etymōtic Research, Inc.). Pure-tone air-conduction thresholds were obtained for each ear at 0.5, 1, 2, 3, 4, 6, and 8 kHz. Masking was used as necessary. All audiometers were calibrated every 6 months (American National Standards Institute, 1989, 2010). People who were homebound or lived in nursing homes or group homes and were unable to come to the clinic site were tested using a portable audiometer with insert earphones. Ambient noise levels were routinely monitored at the examination site in Beaver Dam and measured at each home or nursing home visit to ensure that testing conditions complied with standards (American National Standards Institute, 1999). At baseline, a word recognition in competing message test that used the Northwestern University Auditory Test No. 6 (Wiley et al., 1998) was administered. A 50-word list was presented to the better ear at 36 dB above the threshold at 2 kHz (single female talker), with the competing message (male talker) presented at 8 dB below this level. Hearing impairment was considered mild if PTA of 0.5–4 kHz was > 25 and ≤ 40 dB HL, moderate if PTA of 0.5–4 kHz was > 40 and ≤ 60 dB HL, and severe if PTA of 0.5–4 kHz was > 60 dB HL.

Development of the Scale

In this study, audiometric results from 1,369 participants with complete threshold data for both ears from all four examinations were evaluated. Analyses were conducted using the SAS System, Version 9.4 (SAS Institute, Inc.). First, cluster analyses (PROC CLUSTER, using Ward's method) were used to explore clusters of right-ear audiometric patterns at EHLS2 and EHLS3 for participants with normal baseline audiograms (all thresholds 20 dB HL or better, n = 284) using PROC TREE to output individual cluster membership. Resulting cluster means were calculated and used as seeds for further examining solutions involving up to six clusters (PROC FASTCLUS, using the k- means method). A three-cluster solution for EHLS2 and a four-cluster solution for EHLS3 were chosen as adequate to represent the main audiometric patterns observed.

Maximum allowable threshold values were defined for each cluster to create an ordered scale with higher-level numbers indicating worsening audiometric patterns. Subsets of audiograms were inspected visually and with cluster analyses to identify outliers, more severe levels, and other patterns. Plots of means and maximums for each threshold, by level and examination phase, aided minor adjustments in definitions. We expanded the scale to include eight levels (see Table 1). In addition, several subcategories were identified: two early “notch” patterns (labeled Levels 1.5 and 3.5) and three “flat” patterns (labeled Levels 4.5, 6.5, and 7.5; see Table 1). Level 1.5 represents audiograms that had thresholds in the normal range for 0.5, 1, 2, and 8 kHz, but at least one threshold in the range of 3, 4, and 6 kHz exceeded the 8-kHz threshold by 15 dB or more. Level 3.5 is similar, but the 8-kHz threshold was not in the normal range (> 20 dB HL). The scale was used to classify left-ear audiograms.

Table 1.

Wisconsin Hearing Impairment Classification Scale.

Maximum allowable thresholds (dB HL) by frequency (kHz)
Level 0.5 1 2 3 4 6 8
 1 ≤ 20 ≤ 20 ≤ 20 ≤ 20 ≤ 20 ≤ 20 ≤ 20
 2 ≤ 25 ≤ 25 ≤ 25 ≤ 25 ≤ 30 ≤ 30 ≤ 40
 3 ≤ 25 ≤ 25 ≤ 25 ≤ 30 ≤ 45 ≤ 55 ≤ 65
 4 ≤ 25 ≤ 25 ≤ 40 ≤ 50 ≤ 70 ≤ 80 ≤ 90
 5 ≤ 40 ≤ 40 ≤ 50 ≤ 60 ≤ 85 ≤ 115 a ≤ 105 a
 6 ≤ 40 ≤ 40 ≤ 65 ≤ 75 ≤ 100 ≤ 115 a ≤ 105 a
 7 Not Levels 1–6 and at least one threshold ≤ 80 dB
 8 > 80 > 80 > 80 > 80 > 80 > 80 > 80
 99 Unclassified
Subclasses (level and definition)
Normal through 2 kHz with “notched pattern” in the higher range
 1.5 (0.5, 1, 2, and 8 kHz ≤ 20 dB) AND (3, 4, or 6 kHz ≥ [8-kHz threshold + 15 dB])
 3.5 (0.5, 1, and 2 kHz ≤ 20 dB) AND 8 kHz > 20 dB AND (3, 4, or 6 kHz ≥ [8-kHz threshold + 15 dB])
Flat audiograms
 4.5 All thresholds are > 20 and ≤ 40 dB
 6.5 All thresholds are > 40 and ≤ 60 dB
 7.5 All thresholds are > 60 and ≤ 80 dB
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a

Output limits of the audiometer.

After the scale was applied to all audiograms, as a final check on consistency, audiograms with rising patterns involving at least a 20-dB improvement in the 0.5- to 4-kHz range and all Level 7 audiograms (the level with the greatest range in thresholds) were reviewed by one author (K. J. C.). Audiograms with unusual rising patterns (e.g., poor low-frequency thresholds and better mid- and/or high-frequency thresholds or fluctuating thresholds) were recoded to unclassified after consensus by two authors (K. J. C. and D. M. N.).

Evaluation

To evaluate change, a two-step worsening from the baseline level was considered to be progression. When assessing progression of hearing impairment, subgroups were considered within the main level group (e.g., Level 1.5 was grouped with Level 1). Levels 4.5, 6.5, and 7.5 (flat audiograms) were excluded as these were rare atypical patterns, and baseline Levels 7 and 8 also were excluded from the progression calculations as the maximum level did not allow for detecting a two-step change. Discrete-time Cox proportional hazards models were used to evaluate age and sex effects on the risk of progression because of the relatively small number of follow-up intervals (Cox, 1972). Participants who had not progressed at the end of follow-up were treated as censored. Pearson correlation coefficients were computed to compare baseline hearing with word recognition performance. The exact test for the weighted kappa statistic was used to assess the level of agreement on the WARHICS between left and right ears.

Results

The WARHICS was applied to the audiograms (right and left ears) of the 1,369 participants with complete examination data for four time points. Only 1.5% of the 10,952 right and left ear audiograms did not fit any level and were unclassified. The mean baseline age of this sample was 59 years (SD = 7.5), 59.4% were women, and the mean baseline PTA of 0.5–4 kHz was 17.1 dB in the right ear and 18.1 dB in the left ear. The mean follow-up was 15.8 years (SD = 0.5).

As shown in Figures 1a–1d, which illustrates the right ear mean thresholds by frequency and WARHICS level at each visit, the scale results in groups with progressively worse hearing characterized by sloping losses at the higher frequencies. Mean thresholds for audiograms by score level were similar across the study visits. As illustrated, the number of Level 1 (normal) audiograms was highest at Visit 1 (n = 277) and declined at each subsequent visit (138 at Visit 2, 77 at Visit 33, and 24 at Visit 4), consistent with the worsening of hearing with aging. Level 8, the most severe level, was rare at each visit (7, 8, 8, and 13, respectively). Similar results were obtained for the left ear (data not shown).

Figure 1.

Figure 1.

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(a–d) Mean thresholds by the Wisconsin Age-Related Hearing Impairment Classification Scale level and examination, right ear. EHLS = Epidemiology of Hearing Loss Study; EHLS1 = EHLS that occurred in 1993–1995; EHLS2 = EHLS that occurred in 1998–2000; EHLS3 = EHLS that occurred in 2003–2005; EHLS4 = EHLS that occurred in 2009–2010.

Figures 2a–2d show the distribution of participants by WARHICS level and examination phase. The distribution of scores shifted to the right (more severe losses) with each follow-up visit. There were few participants with severe hearing impairments (Levels 7 and 8). The number of participants in the subgroups was also low at each visit, emphasizing that notch and flat audiogram shapes were rare in this population-based cohort. The correlation between ears at each visit ranged from .76 at baseline to .79 at the last visit (EHLS4). The exact agreement between left and right ears at baseline was 50.8%, and 86.3% were within one step. The weighted kappa statistic was .61 (95% confidence interval [CI] [.58, .64], p = .01), indicating substantial agreement.

Figure 2.

Figure 2.

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(a–d) Wisconsin Age-Related Hearing Impairment Classification Scale levels by ear and examination. EHLS = Epidemiology of Hearing Loss Study; EHLS1 = EHLS that occurred in 1993–1995; EHLS2 = EHLS that occurred in 1998–2000; EHLS3 = EHLS that occurred in 2003–2005; EHLS4 = EHLS that occurred in 2009–2010.

Figure 3 shows the final score at EHLS4 compared to the baseline score, illustrating that 189 right ears remained at their original levels (indicated by outlined boxes). There was little regression (improvement) between the scores at these two visits as all improved EHLS4 scores were within one step of the original score (shown below the diagonal in Figure 3). Most ears were scored at a higher level at the fourth visit, with most differing by one to three steps. Similar results were obtained for the left ear (data not shown).

Figure 3.

Figure 3.

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Baseline versus final WARHICS level (right ears). The numbers of right ears are shown by Wisconsin Age-Related Hearing Impairment Classification Scale (WARHICS) level at the baseline (EHLS1) and final visits (EHLS4). Numbers in outlined boxes represent ears that remained at the same level during the 15-year follow-up period. Shaded area represents the number of right ears that demonstrated a change (progression) of two or more steps on the scale. EHLS = Epidemiology of Hearing Loss Study; EHLS1 = EHLS that occurred in 1993–1995; EHLS4 = EHLS that occurred in 2009–2010.

During the 16-year follow-up, the cumulative progression rate (two or more steps) was 49.7% in right ears and 46.5% in left ears (see Table 2). Comparing right and left ears, 33.7% progressed in both ears, 19.2% in right ears only, and 15.9% in left ears only. Among the right ears at Level 1 at baseline, the cumulative progression rate was 61.4% by the last visit, and none of the Level 6 ears progressed. Older age was associated with an increased odds of progressing during the 16-year follow-up period (odds ratio = 1.14, 95% CI [1.08, 1.21] for each 5 years of age). Men were less likely to progress than women (odds ratio = 0.77, 95% CI [0.64, 0.92]).

Table 2.

Sixteen-year cumulative progression of hearing level by baseline level and ear.

Baseline level At risk at baseline 5 Years
 10 Years
 16 Years
Cases Rate per 100 (95% CI) a Cases Cumulative rate per 100 (95% CI) b Cases Cumulative rate per 100 (95% CI) b
Right ears
 1 277 22 7.9 [5.0, 11.8] 79 28.6 [23.3, 34.0] 169 61.4 [55.6, 67.2]
 2 283 26 9.2 [6.1, 13.2] 81 29.6 [24.3, 35.0] 160 58.1 [52.3, 63.9]
 3 228 25 11.0 [7.2, 15.8] 52 22.9 [17.4, 28.4] 101 44.5 [38.0, 51.0]
 4 200 8 4.0 [1.7, 7.7] 32 16.0 [10.9, 21.1] 82 41.2 [34.3, 48.0]
 5 144 13 9.0 [4.9, 14.9] 38 26.6 [19.3, 33.8] 68 48.0 [39.7, 56.2]
 6 48 0 0.0 [0.0, 7.4] 0 0.0 [––] 0 0.0 [––]
 Total 1,180 94 8.0 [6.5, 9.7] 284 24.2 [21.7, 26.6] 582 49.7 [46.9, 52.6]
Left ears
 1 225 11 4.9 [2.5,8.6] 56 25.0 [19.8, 31.2] 133 59.6 [53.2, 66.0]
 2 282 24 8.5 [50.5,12.4] 71 25.4 [20.7, 30.9] 166 59.5 [53.8, 65.2]
 3 250 12 4.8 [2.5,8.2] 46 18.4 [14.1, 23.8] 100 40.0 [34.2, 46.4]
 4 215 11 5.1 [2.6, 9.0] 36 16.7 [12.4, 22.4] 91 42.3 [36.0, 49.2]
 5 145 12 8.3 [4.4, 14.0] 24 16.8 [11.6, 24.0] 54 38.5 [31.0, 47.1]
 6 63 0 0.0 [0.0, 5.7] 0 0.0 [––] 0 0.0 [––]
 Total 1,180 70 5.9 [4.7, 7.4] 233 19.8 [17.7, 22.2] 544 46.5 [43.7, 49.4]
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Note. Em dashes indicate data not available. CI = confidence interval.

a

Exact binomial confidence intervals.

b

Kaplan–Meier estimates.

WARHICS Versus PTA and Word Recognition Performance

The WARHICS was compared to a typical hearing impairment scale based on PTA of 0.5–4 kHz used in epidemiological studies. Table 3 shows the distribution of the right ear WARHICS level at EHLS1 versus baseline (EHLS1) hearing impairment grouped as none, mild, moderate, and severe. Among those previously considered to have normal hearing based on this PTA approach, the WARHICS level ranged from 1 (normal) to 6, indicating that the PTA may misclassify as normal people with sloping hearing impairment with abnormal thresholds at higher frequencies. Similarly, the group with mild hearing impairment based on PTA of 0.5–4 kHz had WARHICS levels of 3–7. As the severity of hearing impairment increased, there was less spread in WARHICS level. None of the audiograms classified as mild, moderate, or severe hearing impairment by PTA of 0.5–4 kHz had a WARHICS level of 1 or 2. The correlation between the WARHICS level and performance of the word recognition in competing message task (see Figure 4; r = −.61, p < .0001) was stronger than the correlation between PTA of 0.5–4 kHz and word recognition in competing message (r = −.51, p < .0001).

Table 3.

Baseline Wisconsin Age-Related Hearing Impairment Classification Scale (WARHICS) level versus pure-tone average hearing impairment classification: right ears.

WARHICS level Hearing impairment a
None Mild Moderate Severe Total
1 277 277
1.5 77 77
2 283 283
3 226 2 228
3.5 49 8 57
4 144 56 200
4.5 6 6
5 33 101 10 144
6 5 24 19 48
6.5 6 6
7 4 15 6 25
7.5 2 2
8 7 7
All 1,094 201 50 15 1,360 b
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a

Hearing impairment was defined based on the baseline pure-tone average (PTA) of 0.5–4 kHz in the right ear. None = PTA ≤ 25 dB HL; Mild = PTA > 25 and ≤ 40 dB HL; Moderate = PTA > 40 and ≤ 60 dB HL; Severe = PTA > 60 dB HL.

b

Nine right ear audiograms were unclassified at baseline.

Figure 4.

Figure 4.

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Scatter plot and regression line showing relation between Wisconsin Age-Related Hearing Impairment Classification Scale (WARHICS) level and word recognition in competing message score, r = −.61, p < .0001.

Discussion

The WARHICS captures audiogram shape throughout the range of frequencies measured in epidemiological studies of hearing. It describes the sloping pattern typical of age-related changes in hearing with elevated high-frequency thresholds when lower frequencies are still normal. Unlike PTA approaches that may obscure high-frequency losses in the presence of good thresholds in the lower frequencies and require decisions about which frequencies to average (Dobie, 2011), this scale considers the entire spectrum of frequencies. Using the WARHICS likely will reduce the misclassification inherent in averaging approaches. The scale supports longitudinal analyses as it shows high progression rates in keeping with expectations, and there was little regression (Cruickshanks et al., 2003). The incidence rate of 61% for Level 1 compares favorably to the 57% incidence rate reported among those with a normal PTA, and age and sex effects are similar to those seen with traditional classification systems, suggesting that this system is useful for modeling change over time (Cruickshanks et al., 2015, 2003). This scale that differentiates people with mild early losses from normal has the potential to be useful in studies of risk factors for early changes, monitoring ototoxic effects of drugs, and as an intermediary outcome for clinical trials. In addition, it may be useful as an early indication of neuronal changes in the brain, which may precede cognitive decline and dementia (Schubert et al., 2019). Although designed as a research tool, this scale may be useful clinically in discussions with patients about the severity of their hearing loss and in monitoring patients over time.

More than 98% of audiograms were classified automatically. Other scales have fit a smaller proportion of audiograms automatically (Dubno et al., 2013; Vaden et al., 2017). The WARHICS has the advantage of being based on population-based data, rather than select groups, and is agnostic as to cause, unlike attempts to match cochlear histopathology or identify noise-induced hearing loss based on various notch algorithms (Dobie, 2005; McBride & Williams, 2001; Schuknecht, 1955, 1964; Schuknecht & Gacek, 1993). Flat audiograms are rare on a population basis, but through the subcategories, this system allows the flexibility to consider these patterns, as well as audiograms that are normal through 2 kHz and then exhibit a “notched pattern” in the higher range, separately, or combined in the major grouping. Cluster analyses were used to identify major patterns that occurred over time, and only minor adjustments were needed to define levels and subcategories that can be easily scored with a simple program. Because each ear is categorized individually, the scale has the flexibility to support analyses using the most appropriate approach for the specific research question. Scores for one ear (better, worse, left, or right ear) or a concatenated score (1/1, 1/2, etc.) can be used as the outcome, or both ears can be included in statistical models that adjust for the correlation between ears. The high level of symmetry between ears and rates of progression suggests that results may be similar across these approaches because presbycusis is usually symmetrical.

Applying a scale that better captures the natural history of threshold changes in hearing may facilitate advances in our understanding of the causes of hearing impairment in adults and testing interventions to reduce the burden of communication problems for older adults. The development and adoption of the Airlie House Classification system for diabetic retinopathy led to an explosion of rigorous research, which ultimately led to new treatments that reduced blindness in people with diabetes (Davis et al., 1969; The Diabetes Control and Complications Trial Research Group, et al., 1993; Klein et al., 1986). Similarly, systems for classifying age-related macular degeneration have contributed to cross-population comparisons and the identification of genetic and behavioral risk factors as well as supported clinical trials testing nutritional supplements (Age-Related Eye Disease Study Research Group, 2001; Klein et al., 2014). In large cohort studies of hearing, a wide array of PTA classifications using different ears and frequencies has made comparisons across difficult studies. Standardized approaches to monitoring patterns of changes across frequencies have been useful for identifying otoxicity (Konrad-Martin et al., 2010), and audiometric phenotype changes over time may prove useful in studies of the mechanisms of aging changes (Vaden et al., 2017). Widespread use of standardized scales would facilitate comparisons across studies of hearing and the search for modifiable risk factors.

In addition, this scale, which is correlated with word recognition in competing message (a measure of central auditory function; Wiley et al., 1998), may be useful in studies of hearing as a predictor of cognitive changes or in studies of the brain changes underlying these functional declines (Schubert et al., 2019). Incorporating information from the whole audiogram may help detect preclinical subtle changes in hearing.

The lack of external validation is a limitation, but the serial audiograms per person provided some internal validity. Also, although the Beaver Dam population has been shown to be similar to the U.S. non-Hispanic White population at the baseline, it lacked racial/ethnic diversity, which may be a limitation if the patterns of hearing or the changes over time differ by race/ethnicity (Cruickshanks et al., 1998). It is possible that there are geographic differences in the patterns or severity of hearing loss across countries that may require adaptations. However, other epidemiological scales have performed well across racial/ethnic groups or countries and enhanced comparisons across groups (Bressler et al., 1989; Cruickshanks et al., 1997; Davis et al., 1969; Klein et al., 1991, 1986, 2014; Vitale et al., 2016).

The WARHICS requires little training to use, as the standardized automated coding scheme is easily applied, and the majority of audiograms do not require human review. We recommend a manual review of Level 7 audiograms and those with a rising pattern (at least a 20-dB improvement in the 0.5- to 4-kHz range) to identify the few that should be set to unclassified (e.g., poor low-frequency thresholds with better mid- and/or high-frequency thresholds or fluctuating patterns). Adopting this system in epidemiological studies could facilitate cross-study collaborations and comparisons, consistency that is needed to advance the discovery of modifiable determinants of hearing impairment in aging adults.

Conclusions

This simple standardized scale for audiometric data may be useful in epidemiological studies of hearing in aging.

Acknowledgments

The project was supported by Grant R37AG011099 (awarded to K. J. C.) from the National Institute on Aging and an unrestricted grant from Research to Prevent Blindness to the Department of Ophthalmology and Visual Sciences at the University of Wisconsin–Madison. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Aging or the National Institutes of Health. The funding agencies had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the article; and decision to submit the article for publication.

Funding Statement

The project was supported by Grant R37AG011099 (awarded to K. J. C.) from the National Institute on Aging and an unrestricted grant from Research to Prevent Blindness to the Department of Ophthalmology and Visual Sciences at the University of Wisconsin–Madison.

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