Abstract
Purpose:
Hearing aids are the primary treatment for age-related hearing loss but are vastly underutilized. We explored cross-sectional associations between predisposing, enabling, and need factors and hours of daily hearing aid use.
Method:
In the Atherosclerosis Risk in Communities (ARIC) Study, 764 participants at Visit 6 (2016–2017) reported hearing aid use. Self-reported hours of daily hearing aid use were modeled continuously (hours) and categorically (< 6, 6–12, and > 12 hr). Covariates included predisposing (age, sex, race–center), enabling (education, marital status, years of prior hearing aid use, global cognitive factor score, depressive symptoms, access to health care, health literacy), and need factors (pure-tone average [PTA], Quick Speech-in-Noise Test [QuickSIN] score). Multivariable-adjusted linear and ordinal logistic models were used to examine associations between these factors and daily hearing aid use.
Results:
Every 1-year increase in prior hearing aid use was associated with 0.08-hr increase in daily hearing aid use (95% confidence interval [CI] [0.04, 0.13]); every 10-dB increase in PTA was associated with 0.63-hr increase in daily hearing aid use (95% CI [0.18, 1.08]); every 5-point increase in QuickSIN score was associated with 0.53-hr decrease in daily hearing aid use (95% CI [−0.99, −0.06]). Longer duration of prior hearing aid use and poor PTA and QuickSIN performance were associated with higher odds of being in a higher hearing aid use category.
Conclusions:
Hours of daily hearing aid use is driven primarily by audiometric hearing, SIN performance, and years of prior hearing aid use. Further research into determinants of hours of use can improve precision of hearing care.
Supplemental Material:
Hearing loss is a considerable public health concern. Approximately 38 million Americans have hearing loss (Goman & Lin, 2016), and prevalence increases with age such that half of all adults over the age of 60 years have hearing loss (Goman et al., 2017; Lin et al., 2011). Based on the aging demographics of the United States, the number of Americans with hearing loss is projected to nearly double by 2060 (Goman et al., 2017). Recent epidemiologic literature has linked hearing loss to a number of important health outcomes among older adults including cognitive decline (Lin et al., 2013), dementia (Deal et al., 2017), hospitalization and readmission (Reed et al., 2019), depression (Lawrence et al., 2020), and increased falls (Kamil et al., 2016). Importantly, hearing aid use is associated with improved quality of life (Ferguson et al., 2017) and listening ability (Ferguson et al., 2017). Furthermore, recent randomized controlled trial–level evidence has demonstrated that treatment of hearing loss may reduce cognitive decline among older adults who are at higher risk of dementia (Lin et al., 2013).
Currently, hearing aids represent the gold standard and typical treatment path for age-related hearing loss (Yueh et al., 2003). However, less than 20% of older adults with hearing loss own and use hearing aids (Chien & Lin, 2012). Previous studies suggest that sociodemographic variables such as race, income, and education are significant predictors of hearing aid ownership and self-reported use (McKee et al., 2019; Nieman et al., 2016; Sawyer et al., 2019). Using data from the Health and Retirement study, a nationally representative longitudinal study of older adults, Mckee et al. (2019) found that individuals who were Black (odds ratio [OR] = 0.36, p < .001, reference [Ref] = White), Hispanic (OR = 0.45, p < .001, Ref = White), attained less than a high school education (OR = 0.43, p < .001, Ref = at least some college), and were in the bottom wealth quartile (OR = 0.5, p < .001, Ref = top wealth quartile) demonstrated reduced odds of hearing aid use. Furthermore, in qualitative 1-on-1 semistructured interviews conducted in a community sample, it was found that low-income participants report lack of insurance coverage and high out-of-pocket costs of hearing aids as the primary barriers to procurement and use (McKee et al., 2019), consistent with other literature (Desjardins & Sotelo, 2021). Beyond demographic variables, stigma associated with hearing aid use may also play a role in the limited uptake of hearing aids (David et al., 2018). Notably, it has been reported that stigma may vary by race (McKee et al., 2019; Wallhagen, 2010).
Among older adults who own hearing aids, evidence suggests that up to 40% do not use them regularly (Kochkin, 2000). Despite this evidence, there is a paucity of research investigating determinants of hearing aid usage, as previous work has primarily focused on the ownership of hearing aids or examined usage as a binary variable. Research to date that has examined regular hearing aid use has studied it as a function of hearing intervention (e.g., rehabilitative counseling, hearing aid technology level, background noise perceptions), acceptance of hearing loss, and/or preconceived expectations of hearing care (Bertoli et al., 2009; Jerram & Purdy, 2001; Nabelek et al., 2006). The limited research that has attempted to address individual determinants of hearing aid use is relatively outdated, lacks broad sociodemographic information, and has relied heavily on clinical chart-review data in highly specific, homogenous populations (e.g., male veterans; Ho et al., 2018; Mulrow et al., 1992; Surr et al., 1978). To the best of the authors' knowledge, no analyses of prospective, population-based data examining hearing aid usage exist.
The aim of the present study is to explore the association between predisposing, enabling, and need factors, and daily hours of hearing aid use in a cohort of older adults with hearing loss who report using hearing aids. This analysis was performed in the Atherosclerosis Risk in Communities (ARIC) study, a prospective cohort study with a well-characterized, population-based sample of older adults.
Design and Method
Study Population
ARIC is a prospective cohort study that began with 15,792 men and women aged 45–64 years recruited from four communities across the United States (Washington County, Maryland; Forsyth County, North Carolina; Jackson, Mississippi; and Minneapolis suburbs, Minnesota) between 1987 and 1989 (The ARIC Investigators, 1989). The ARIC study protocol was approved by the institutional review board at Johns Hopkins Medicine (IRB00311861). Participants in this study completed up to seven clinic visits over a span of 30 years, with annual/semi-annual follow-up phone call between these visits. During the sixth visit (2016–2017), hearing measures were added to the data collection protocol. Four thousand three participants completed the sixth visit; the present analysis is limited to those who reported hearing aid use. Analysis was restricted by race to include only Black and White participants due to limited subgroup size. A total of 796 Black and White participants reported hearing aid use at Visit 6. Thirty-two participants were excluded from analysis for missing hours of daily use data in one or both ears or for discrepancies in reported hours of use between their left and right ears. The final analytic sample was composed of 764 participants.
Outcome Hours of Daily Hearing Aid Use
ARIC participants who report hearing aid use were asked: “Averaged over the past month, about how many hours per day have you worn your hearing aid or other device in the right/left ear?” We modeled the outcome as a continuous outcome as well as a categorical outcome based on hearing aid use during all waking hours (> 12 hr/day), most of waking hours (6–12 hr/day), and little use during waking hours (< 6 hr/day).
Covariates
Given that there are no established conceptual frameworks for predictors of hearing aid usage, we looked to behavioral models of health care utilization to guide our selection of covariates. We identified the Andersen–Aday behavior model of health care utilization as a guide to select characteristics that may contribute to hours of daily hearing aid use (Aday & Andersen, 1974; see Figure 1). This model categorizes factors that predict the utilization of health care into predisposing, enabling, and need factors. Predisposing characteristics are defined as inherent immutable characteristics of the individual such as age, sex, and race-center. Enabling characteristics are those that help facilitate individual access to health care and include factors such as income, education, employment, and insurance coverage. Need factors are those that indicate the severity of the illness, and hence, the severity of the need for care, for example, measures of the severity of hearing loss in the present study.
Figure 1.
Conceptual framework guided by Andersen behavioral model of health care utilization. PTA = pure-tone average.
Predisposing Factors
Predisposing variables selected included age (continuous), sex (dichotomous: male/female), and race–center (categorical: Minneapolis–White, Jackson–Black, Forsyth–Black, Forsyth–White).
Enabling Factors
Education (categorical: less than completed high school/completed high school or equivalent/at least some college), marital status (dichotomous: married/not married), and duration of hearing aid use in years (continuous) were included as enabling factors. In addition, cognition was assessed using a global cognitive factor score derived from a factor analysis using latent variable methods, which is the weighted sum of standardized scores across neurocognitive tests in language, executive function, and memory domains (Gross et al., 2015). Depressive symptomatology was assessed using the Center for Epidemiologic Studies Depression (CES-D) Scale 11-item questionnaire and reported as a continuous variable (Kohout et al., 1993; Radloff, 1977). Ease of access to health care was assessed subjectively using two questionnaire items: (a) perceived difficulty obtaining appointment on short notice and (b) difficulty reaching health care provider by phone. Responses to both questions were reported as a categorical variable (not at all difficult, somewhat difficult, quite difficult, very difficult). Wide Range Achievement Test 3 (WRAT-3) score was added as a continuous measure of health literacy.
Need Factors
Need-based factors included better-ear pure-tone average (PTA) and speech-in-noise performance. Audiometric hearing was assessed by presenting tones at various frequencies in a quiet sound-treated booth using best practice methodology (Carhart & Jerger, 1959). PTA was calculated by averaging hearing thresholds in decibels hearing level (dB HL) at 0.5, 1, 2, and 4 kHz frequencies in the better-hearing ear and higher values indicate poorer hearing. For analysis, PTA was scaled to every 10 dB HL. Speech-in-noise performance was assessed using the Quick Speech-in-Noise Test (QuickSIN; Killion et al., 2004). This test involves playing tracks of six sentences with five key words in the presence of background noise, and participants are asked to repeat the sentences. The task increases in difficulty as with each subsequent sentence, the background noise increases until it is equal to the intensity level of the presented sentence. Scoring is based on the number of key words participants correctly repeated. Total scores range from 0 to 30; higher scores indicate better speech-in-noise performance. For analysis, the QuickSIN score was scaled to every 5 points. Both PTA and the QuickSIN score were modeled continuously.
Statistical Analysis
Characteristics of participants were compared across categories of daily hours of hearing aid use (< 6 hr; 6–12 hr; > 12 hr per day) using analysis of variance for continuous variables and Pearson's chi-squared test for binary and categorical variables. Missing covariates were imputed with 20 sets of multiple imputation using chain equations. Missing values imputed include education (n = 1), duration of hearing aid use (n = 19), global cognitive factor score (n = 6), CES-D score (n = 37), difficulty obtaining appointment on short notice (n = 46), difficulty reaching health care provider by phone (n = 66), WRAT-3 score (n = 34), better-ear PTA (n = 94), and QuickSIN score (n = 102). The imputation model included all covariates in our model as well as ancillary variables including (a) other hearing-related variables: noise exposure, self-reported hearing status, total score of Hearing Handicap Inventory for the Elderly, audiology exam location (clinic vs. home); (b) Mini-Mental State Examination score; and (c) annual follow-up variables: self-reported health when compared to others (closest to Visit 6) and hospitalization counts between Visit 4 and Visit 6.
The associations between predisposing, enabling, and need factors and continuous hours of hearing aid use per day was analyzed using multivariable-adjusted linear regression. Multivariable-adjusted ordinal logistic regression was used to model the association between these factors and categories of hearing aid use per day (< 6 hr; 6–12 hr; > 12 hr), with ORs denoting the odds of being in a higher hearing aid use category (greater use).
Results
Characteristics of Participants
Characteristics of participants by categories of hearing aid use per day are summarized in Table 1. Among 542 participants, 128 (23.6%) reported less than 6 hr of hearing aid use per day, 242 (44.6%) reported hearing aid use between 6 and 12 hr per day, and 172 (31.7%) reported greater than 12 hr of hearing aid use per day. Participants in the lowest category of daily hearing aid use were less likely to be married, had shorter duration of hearing aid use (years), and better audiometric hearing as well as speech-in-noise performance.
Table 1.
Characteristics of hearing aid users by hours of use per day in the Atherosclerosis Risk in Communities Study Visit 6 (2016–2017).
| Variable | Total |
< 6 hr |
6–12 hr |
> 12 hr |
p valuea |
|---|---|---|---|---|---|
| N = 542 | n = 128 | n = 242 | n = 172 | ||
| Predisposing factors | |||||
| Age (years), M (SD) | 80.7 (4.8) | 80.4 (4.8) | 80.5 (4.9) | 81.2 (4.7) | .27 |
| Female, n (%) | 219 (40.4) | 51 (39.8) | 96 (39.7) | 72 (41.9) | .89 |
| Race–center, n (%) | .10 | ||||
| Minneapolis–White | 213 (39.3) | 47 (36.7) | 95 (39.3) | 71 (41.3) | |
| Jackson–Black | 17 (3.1) | 6 (4.7) | 11 (4.5) | 0 (0.0) | |
| Washington County–White | 187 (34.5) | 40 (31.2) | 78 (32.2) | 69 (40.1) | |
| Forsyth County–Black | 4 (0.7) | 1 (0.8) | 2 (0.8) | 1 (0.6) | |
| Forsyth County–White | 121 (22.3) | 34 (26.6) | 56 (23.1) | 31 (18.0) | |
| Enabling factors | |||||
| Education, n (%) | .36 | ||||
| Less than completed high school | 53 (9.8) | 14 (10.9) | 23 (9.5) | 16 (9.3) | |
| Completed high school or equivalent | 223 (41.1) | 55 (43.0) | 89 (36.8) | 79 (45.9) | |
| At least some college | 266 (49.1) | 59 (46.1) | 130 (53.7) | 77 (44.8) | |
| Marital status (married), n (%) | 359 (66.2) | 76 (59.4) | 177 (73.1) | 106 (61.6) | .01 |
| Years of prior hearing aid use (years), M (SD) | 7.6 (7.5) | 5.3 (5.5) | 7.4 (7.7) | 9.7 (7.8) | < .001 |
| Global Cognition Factor Score, M (SD) | 0.1 (0.8) | 0.1 (0.8) | 0.1 (0.8) | 0.2 (0.8) | .36 |
| 11-item CES-D score, M (SD) | 2.6 (2.8) | 3.0 (3.3) | 2.5 (2.6) | 2.5 (2.6) | .23 |
| Difficulty to get appointment on short notice, n (%) | .88 | ||||
| Not at all difficult | 258 (47.6) | 59 (46.1) | 116 (47.9) | 83 (48.3) | |
| Not too difficult | 197 (36.3) | 47 (36.7) | 83 (34.3) | 67 (39.0) | |
| Somewhat difficult | 67 (12.4) | 17 (13.3) | 33 (13.6) | 17 (9.9) | |
| Very difficult | 20 (3.7) | 5 (3.9) | 10 (4.1) | 5 (2.9) | |
| Difficulty to talk over phone to provider, n (%) | .37 | ||||
| Not at all difficult | 258 (47.6) | 60 (46.9) | 112 (46.3) | 86 (50.0) | |
| Not too difficult | 185 (34.1) | 40 (31.2) | 91 (37.6) | 54 (31.4) | |
| Somewhat difficult | 73 (13.5) | 22 (17.2) | 31 (12.8) | 20 (11.6) | |
| Very difficult | 26 (4.8) | 6 (4.7) | 8 (3.3) | 12 (7.0) | |
| Wide Range Achievement Test, M (SD) | 46.7 (6.5) | 46.1 (7.0) | 46.7 (6.4) | 47.2 (6.3) | .31 |
| Need factors | |||||
| Better-ear PTA (dB HL), M (SD) | 47.9 (11.6) | 43.2 (11.5) | 47.9 (11.1) | 51.5 (11.2) | < .001 |
| QuickSIN score, M (SD) | 13.4 (6.1) | 15.3 (5.2) | 13.4 (6.2) | 12.0 (6.4) | < .001 |
Note. CES-D = Center for Epidemiologic Studies Depression Scale; PTA = pure-tone average; QuickSIN = Quick Speech-in-Noise Test.
p values were calculated by analysis of variance for continuous variables and Pearson chi-squared test for categorical variables.
Predisposing Factors
Among the predisposing factors (age, sex, race), female sex was associated with 0.96 hr (95% confidence interval [CI] [0.17, 1.74]) greater daily hearing aid use (see Table 2: Predisposing factors) as well as 46% higher (95% CI [1.07, 2.00]) odds of being in a higher daily hearing aid use category (greater use; see Table 3: Predisposing factors). In available case analyses, only borderline significance was found (linear regression: estimate [95% CI]: 0.84 [−0.08, 1.76], see Supplemental Material S2; ordinal logistic regression: OR [95% CI]: 1.33 [0.92, 1.91], see Supplemental Material S3). Age and race were not associated with hours of daily hearing aid usage.
Table 2.
Associations of predisposing, enabling and need factors with hours of hearing aid use per daya in the Atherosclerosis Risk in Communities (ARIC) Study Visit 6 (2016–2017) (N = 764).
| Variable | Hours of hearing aid use |
|
|---|---|---|
| Estimate [95% CI] | p value | |
| Predisposing factors | ||
| Age | −0.06 [−0.15, 0.02] | .14 |
| Female | 0.96 [0.17, 1.74] | .02 |
| Race–center | ||
| Minneapolis–White | Ref. | Ref. |
| Jackson–Black | −2.19 [−4.15, −0.23] | .03 |
| Washington County–White | −0.32 [−1.23, 0.58] | .49 |
| Forsyth County–Black | 3.55 [−0.90, 8.00] | .12 |
| Forsyth County–White | −0.21 [−1.16, 0.73] | .66 |
| Enabling factors | ||
| Education | ||
| Less than completed high school | Ref. | Ref. |
| Completed high school or equivalent | −0.63 [−1.93, 0.67] | .34 |
| At least some college | −0.48 [−1.94, 0.97] | .52 |
| Marital status (married) | 0.39 [−0.45, 1.23] | .36 |
| Years of prior hearing aid use | 0.08 [0.04, 0.13] | < .001 |
| Global cognition factor score | 0.34 [−0.24, 0.92] | .25 |
| 11-item CES-D score | −0.14 [−0.28, −0.00] | .05 |
| Difficulty to get appointment on short notice | ||
| Not at all difficult | Ref. | Ref. |
| Not too difficult | −0.31 [−1.16, 0.54] | .48 |
| Somewhat difficult | −1.05 [−2.35, 0.26] | .12 |
| Very difficult | −1.15 [−3.28, 0.99] | .29 |
| Difficulty to talk over phone to provider | ||
| Not at all difficult | Ref. | Ref. |
| Not too difficult | 0.01 [−0.87, 0.89] | .99 |
| Somewhat difficult | 0.30 [−0.92, 1.52] | .63 |
| Very difficult | 0.79 [−0.97, 2.55] | .38 |
| Wide Range Achievement Test | 0.07 [−0.01, 0.14] | .08 |
| Need factors | ||
| Better-ear PTA, per 10 dB HL | 0.63 [0.18, 1.08] | .01 |
| QuickSIN score, per 5 points | −0.53 [−0.99, −0.06] | .03 |
Note. CI = confidence interval; Ref = reference; CES-D = Center for Epidemiologic Studies Depression Scale; PTA = pure-tone average; QuickSIN = Quick Speech-in-Noise Test.
Linear regression with continuous outcome based on hours of hearing aid use per day. Estimate stands for differences in hours of hearing aid use associated with the factor. Missing factors were imputed with 20 sets of multiple imputation using chain equations.
Table 3.
Associations of predisposing, enabling, and need factors with hours of hearing aid use per day categoriesa in the Atherosclerosis Risk in Communities Study Visit 6 (2016–2017) (N = 764).
| Variable | Hearing aid use categories (< 6 hr; 6–12 hr; > 12 hr per day) |
|
|---|---|---|
| OR [95% CI] | p value | |
| Predisposing factors | ||
| Age | 0.99 [0.96, 1.02] | .53 |
| Female | 1.46 [1.07, 2.00] | .02 |
| Race–center | ||
| Minneapolis–White | Ref. | Ref. |
| Jackson–Black | 0.49 [0.23, 1.03] | .06 |
| Washington County–White | 0.81 [0.57, 1.15] | .23 |
| Forsyth County–Black | 1.80 [0.31, 10.45] | .51 |
| Forsyth County–White | 0.80 [0.55, 1.15] | .23 |
| Enabling factors | ||
| Education | ||
| Less than completed high school | Ref. | Ref. |
| Completed high school or equivalent | 1.00 [0.60, 1.66] | .99 |
| At least some college | 1.02 [0.58, 1.79] | .95 |
| Marital status (married) | 1.22 [0.87, 1.70] | .25 |
| Years of prior hearing aid use | 1.03 [1.01, 1.05] | .01 |
| Global Cognition Factor Score | 1.19 [0.95, 1.48] | .13 |
| 11-item CES-D score | 0.95 [0.90, 1.01] | .08 |
| Difficulty to get appointment on short notice | ||
| Not at all difficult | Ref. | Ref. |
| Not too difficult | 0.95 [0.68, 1.34] | .79 |
| Somewhat difficult | 0.81 [0.48, 1.34] | .41 |
| Very difficult | 0.72 [0.32, 1.64] | .43 |
| Difficulty to talk over phone to provider | ||
| Not at all difficult | Ref. | Ref. |
| Not too difficult | 0.98 [0.69, 1.38] | .89 |
| Somewhat difficult | 1.04 [0.64, 1.69] | .88 |
| Very difficult | 1.25 [0.62, 2.53] | .53 |
| Wide Range Achievement Test | 1.03 [1.00, 1.06] | .10 |
| Need Factors | ||
| Better-ear PTA, per 10 dB HL | 1.32 [1.11, 1.59] | .002 |
| QuickSIN score, per 5 points | 0.80 [0.67, 0.96] | .02 |
Note. OR = odds ratio; CI = confidence interval; Ref = reference; CES-D = Center for Epidemiologic Studies Depression Scale; PTA = pure-tone average; QuickSIN = Quick Speech-in-Noise Test.
Ordinal logistic regression with categorical outcome based on hours of hearing aid use per day (< 6 hr/day; 6–12 hr/day; > 12 hr/day). Odds ratio stands for being in a higher hearing aid use category (greater use) compared to a lower hearing aid use category. Missing factors were imputed with 20 sets of multiple imputation using chain equations.
Enabling Factors
Duration of hearing aid use was the only enabling factor associated with daily hearing aid usage. Every 1-year increase in duration of hearing aid use was associated with 0.08-hr (95% CI [0.04, 0.13]) increase in daily hearing aid use (see Table 2: Enabling factors) and higher odds (OR = 1.03, 95% CI [1.01, 1.05]) of being in a category with greater daily hearing aid use (see Table 3: Enabling factors). Available case analyses showed consistent results (linear regression: estimate [95% CI]: 0.11 [0.05, 0.17], see Supplemental Material S2; ordinal logistic regression: OR [95% CI]: 1.03 [1.01, 1.06], see Supplemental Material S3).
Need Factors
Both need factors (PTA, QuickSIN score) demonstrated associations between worse hearing (greater need) and greater daily hearing aid usage. Every 10 dB HL worse PTA was associated with both 0.63 hr greater (95% CI [0.18, 1.18]) hearing aid use (see Table 2: Need factors) and 1.32 times (95% CI [1.11, 1.59]) the odds of being in a category with greater daily hearing aid use (see Table 3: Need factors). Every 5-point better QuickSIN score was associated with 0.53 hr shorter (95% CI [−0.99, −0.06]) daily hearing aid use (see Table 2: Need factors), as well as a 20% decrease (95% CI [0.67, 0.96]) in the odds of being in a greater daily hearing aid use category (see Table 3: Need factors). We found similar results in available case analyses (linear regression: estimate [95% CI]: 1.36 [1.12, 1.66], see Supplemental Material S1; ordinal logistic regression: OR [95% CI]: 0.81 [0.66, 0.98], see Supplemental Material S2).
Discussion
In our study, we examined predisposing, enabling, and need factors and their associations with hours of daily hearing aid usage among older adults with hearing loss who reported using hearing aids at Visit 6 (2016–2017) of the ARIC study. Of the enabling factors, we found that longer duration of hearing aid use was associated with greater daily use. Furthermore, our study found that need-based factors are important determinants of hours of daily hearing aid usage; individuals with poorer audiometric hearing and speech-in-noise performance were more likely to have higher daily hearing aid use across both models.
Our finding of increased daily use in individuals with increased severity of hearing loss is consistent with prior literature. Ho et al. demonstrated that higher PTA and younger age were associated with increased odds of using hearing aids for > 4 hr/day in 1,068 older Singaporean adults (Ho et al., 2018). In a study of 1,874 adults in the United Kingdom, Aazh et al. also found that the risk of nonregular (< 4 hr per day) was reduced in participants with moderate or severe hearing loss in the better ear (Aazh et al., 2015). However, Mulrow et al. (1992) performed similar analyses in a smaller clinical setting and did not find an association between degree of usage and degree of hearing loss. In the aforementioned papers, however, social and demographic factors were given relatively little consideration, and rather focused on characteristics of the hearing loss and the hearing aid. In a recent analysis of 300 Australian older adults with dual sensory impairment, it was also noted that individuals with moderate–severe hearing loss (> 40 dB PTA) were much more likely to report “regular” use, defined as 1–4 hr of use per day (Schneider et al., 2014). This analysis did include demographic information as well as information about participants living situations but did not examine other socioeconomic or race factors. Duration of prior hearing aid use was found to be associated with increased daily use as well in our study. We included this measure in our analysis in the hopes that it would provide insight into the participant's experience and familiarity with hearing aids. The association of female sex with increased daily use is only suggestive in our study, but has been reported in the literature before. Staehelin et al. studied rates of nonregular use of hearing aids by sex and found that females were on average more likely to report regular use (Staehelin et al., 2011). Primary reasons for nonregular use in their study was consistent with prior research but males were more likely to report poor perceived benefit as a reason for nonregular use.
A main limitation of our study is that it does not control for patient satisfaction with or attitudes toward hearing aids. The literature pertaining to hearing aid satisfaction and its association with increased daily hours of use is extensive and highlights the importance of the subjective user experience on their adherence to hearing aids (Kochkin, 2000; McCormack & Fortnum, 2013; Wong et al., 2003). Issues such as poor hearing aid fit, increased level of background noise with amplification, and dissatisfaction were not considered in our analysis. While there is certainly evidence that increased daily use is associated with hearing aid satisfaction, the directionality of the relationship is unclear. Furthermore, the generalizability of our study is limited by the demographics and health status of the cohort studied. Our analytic cohort was predominantly White with an average age of around 80 years. ARIC participants who survived and were willing and able to participate in Visit 6 are also known to be a self-selected group who are likely healthier than the general population and potentially more prone to exhibit healthy behavioral patterns. We evaluate duration of prior use among those that reported current hearing aid use at Visit 6, but our study does not consider people who may have previously used hearing aids but have since stopped using them. Regardless, we believe that our comparisons are valid and provide important information regarding determinants of regular hearing aid usage.
Hearing aids are a well-established therapy that enhance the communicative ability of older adults with hearing loss (Ferguson et al., 2017); however, evidence regarding what degree of daily use is considered optimal is scarce. Presumably, adherence to hearing aids and regular daily use is needed to experience benefit. Nevertheless, it is possible that prioritizing more meaningful or higher quality hours of use (e.g., during participation in cognitively engaging situations) might carry greater significance than the absolute quantity of hours of daily use. This may be of particular relevance in the context of long-term aging outcomes. Evidence from a study of 28 individuals with mild to moderate age-related hearing loss showed that regular use of > 5 hr a day over a 6-month period improved speech-in-noise performance and cognitive ability, and maladaptive neurophysiologic changes known to be associated with hearing loss were reversed (Glick & Sharma, 2020). Determinants of daily hours of use are therefore important to understand to identify populations at risk for nonadherence and improve the precision of hearing care.
To further illustrate the importance of understanding these factors and how they can provide actionable materials to improve the state of hearing care, we can look to the field of medication adherence. In 2011, the American College of Preventive Medicine published a report identifying five key factors that determine adherence to prescription medication (American College of Preventive Medicine, 2011). The problem of medication adherence was framed as a multifaceted problem that includes patient, therapy, health care system, and socioeconomic factors. The various dimensions of the model they built were based on a wealth of epidemiologic research that characterized the determinants of medication nonadherence and evaluated the value of various interventions (Haynes et al., 2002; Kardas et al., 2013; Sokol et al., 2005; Zolnierek & Dimatteo, 2009). Given the vast impact this problem has on the health care system, the Food and Drug Administration developed several key programs with a multitude of collaborators to tackle these issues in an evidence-based manner (Ferdinand et al., 2017). The challenge of improving hearing aid adherence is similarly complex, requiring a multifaceted and multidisciplinary effort to improve hearing aid technology, target at-risk populations, mitigate economic barriers, and improve education. Policy efforts that aim to tackle these issues need rigorous epidemiologic evidence to support them. Understanding the parameters of hearing aid usage and their determinants in population-based studies is therefore an important endeavor that we contribute to with this study.
Summary/Conclusion
Investigations of determinants of hearing aid use and ownership have gained increased significance considering epidemiologic research that highlights unfavorable long-term sequelae of untreated hearing loss. Socioeconomic factors are highly relevant to hearing aid ownership and use due to the significant cost barrier in the United States' hearing care system. Unfortunately, even among individuals who have the means to purchase hearing aids, regular use of the device after acquisition is suboptimal. In our analysis, we explored factors associated with hours of daily hearing aid use guided by the Andersen–Aday model of health care utilization. Poor audiometric hearing, poor speech-in-noise performance, and longer duration of hearing aid use were associated with greater daily usage of hearing aids. Further research into determinants of regular hearing aid use are essential to improve hearing care precision.
Data Availability Statement
The data sets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
Supplementary Material
Acknowledgments
The Atherosclerosis Risk in Communities (ARIC) study has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under Contract Nos. 75N92022D00001, 75N92022D00002, 75N92022D00003, 75N92022D00004, and 75N92022D00005. The authors thank the staff and participants of the ARIC study for their important contributions. Nicholas S. Reed additionally reports funding support from National Institutes of Health/National Institute on Aging (NIA) Grant K23AG065443. Sarah Y. Bessen was funded by the National Institute on Deafness and Other Communication Disorders (1R25DC021243-01). Jennifer A. Deal was funded by National Institute on Aging Grant K01AG054693.
Funding Statement
The Atherosclerosis Risk in Communities (ARIC) study has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under Contract Nos. 75N92022D00001, 75N92022D00002, 75N92022D00003, 75N92022D00004, and 75N92022D00005. Nicholas S. Reed additionally reports funding support from National Institutes of Health/National Institute on Aging (NIA) Grant K23AG065443. Sarah Y. Bessen was funded by the National Institute on Deafness and Other Communication Disorders (1R25DC021243-01). Jennifer A. Deal was funded by National Institute on Aging Grant K01AG054693.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data sets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.