Journal of Gerontological Nursing

Feature Article 

Hearing Loss and Communication Among Hospitalized Older Adults: Prevalence and Recognition

Elaine Mormer, PhD, CCC-A; Kelsi Jo Bubb, AuD, CCC-A; Mohammad Alrawashdeh, PhD, MSN; Janet A. Cipkala-Gaffin, DrPH, PMHCNS-BC

Abstract

The current quality improvement study aimed to determine hearing loss (HL) prevalence in older adult patients in a large urban hospital, and the success of current processes to identify its presence during routine admission procedures. Predictors of HL were also evaluated, with the goal of identifying risk factors that might help staff anticipate patient communication challenges. A sample of 162 newly admitted patients, age 70 and older, participated in a hearing/communication assessment that included audiometry and an informal self-report measure about hearing difficulty. Chart review was conducted to assess whether patients with confirmed hearing/communication deficits had been identified as such during the admission or nursing assessments. Results revealed a high prevalence of HL (72.8%) and relatively low sensitivity of routine admission procedures in identifying this communication deficit (14.4% to 43.2%). Age and male gender were found to be predictors of HL. The invisibility of HL poses a challenge to nurses in recognizing when older adult patients are at risk for communication breakdowns. Communication breakdowns associated with HL can potentially impact patients' adherence to treatment plans. [Journal of Gerontological Nursing, 46(6), 34–42.]

Abstract

The current quality improvement study aimed to determine hearing loss (HL) prevalence in older adult patients in a large urban hospital, and the success of current processes to identify its presence during routine admission procedures. Predictors of HL were also evaluated, with the goal of identifying risk factors that might help staff anticipate patient communication challenges. A sample of 162 newly admitted patients, age 70 and older, participated in a hearing/communication assessment that included audiometry and an informal self-report measure about hearing difficulty. Chart review was conducted to assess whether patients with confirmed hearing/communication deficits had been identified as such during the admission or nursing assessments. Results revealed a high prevalence of HL (72.8%) and relatively low sensitivity of routine admission procedures in identifying this communication deficit (14.4% to 43.2%). Age and male gender were found to be predictors of HL. The invisibility of HL poses a challenge to nurses in recognizing when older adult patients are at risk for communication breakdowns. Communication breakdowns associated with HL can potentially impact patients' adherence to treatment plans. [Journal of Gerontological Nursing, 46(6), 34–42.]

Hearing loss (HL) is the third most common chronic condition among older adults in the United States (Blackwell et al., 2014). HL is defined as hearing thresholds in the better hearing ear averaged >25 decibels in hearing level (dB HL) for the frequencies 500 Hz, 1,000 Hz, 2,000 Hz, and 4,000 Hz (World Health Organization [WHO], 2018). As the most prevalent sensory disability (WHO, 2012), it is estimated that HL affects approximately two thirds of people age 70 and older in the United States (Lin et al., 2011). Being male, increasing age, and occupations associated with noise exposure, among other variables, have been associated with HL (Cruickshanks et al., 2003; Lin et al., 2011). Older adults with HL have a greater risk of hospitalization compared to their counterparts with normal hearing (Genther et al., 2015; Reed et al., 2019). According to Weiss and Elixhauser (2014) with the Agency for Healthcare Research and Quality, hospitalization rates increase with age. Several studies have linked untreated HL to increased length of stay, increased 30-day readmission rates, and increased adverse medical events for hospitalized older adults (Barnett et al., 2014; Chang et al., 2018; Deal et al., 2019; Reed et al., 2018).

Patients with HL report not understanding instructions for medications, potential side effects, or plans for treating their medical conditions. In addition, patients with HL experience misdiagnoses, missed appointments, communication challenges during surgeries, and receive insufficient medical information (Cudmore et al., 2017; Iezzoni et al., 2004; Stevens et al., 2019). Patients may give inaccurate health information or omit important health information during conversations with medical staff due to communication breakdowns, which could lead to imprecise diagnoses, avoidable testing, and improper treatment of the medical issue (Barnett et al., 2014). Similarly, patients with HL have a more difficult time understanding conversations with their health care providers, thus limiting their ability to fully participate in health care decisions (Cudmore et al., 2017; Hardin, 2012). This difficulty in understanding conversations with health care providers leads patients with HL to report lower satisfaction with health care provider communication (Barnett et al., 2014; Fook et al., 2000). A study involving review of medical records from Canadian hospitals showed that hospital patients with reported communication problems had a greater risk of experiencing a preventable adverse event compared to those without communication problems (Bartlett et al., 2008). The data in this study came from patients age 18 and older. The authors reported that the two most common outcomes of the resulting adverse events were prolonged hospitalization and readmission (Bartlett et al., 2008). Although the negative consequences of HL have been reported, there are limited published data regarding the actual prevalence of HL in hospitalized older adults or the ability of hospital staff to recognize patients who may be at risk for communication breakdowns secondary to HL.

The invisible nature of HL makes its identification challenging for hospital care providers (Taylor, 2016). When asked directly about hearing status, older individuals will, albeit unintentionally, underestimate their own degree of hearing impairment compared to the gold standard of audiometry (Alcock, 2015; Choi et al., 2016; Wallhagen et al., 2006). Identification of HL is the first step to ensuring adequate reception of verbal information during an inpatient stay; however, there are no specific screening protocols for HL mandated in the United States for adults in inpatient settings. The U.S. Preventive Services Task Force determined there is not enough evidence to support a recommendation of screening for HL in adults (age ≥50) who are not seeking help for possible HL (Moyer, 2012). This recommendation has not been updated since its initial publication (Moyer, 2012).

The purpose of the current quality improvement study was to determine the prevalence of HL in newly admitted patients age 70 and older using self-report measures and the gold standard of audiometry. A second goal was to determine whether patients with HL were identified by hospital staff during routine admission and nursing assessments. Lastly, possible predictors of HL were evaluated, to aid staff in identifying patients at greater risk for HL. In addition to these primary objectives, a secondary goal was to quantify the number of patients with HL who accepted the offer of a non-custom amplifier at the bedside. The offer of these amplifiers is an existing routine procedure in the hospital where data were collected. The authors of the current study used this opportunity to quantify the up-take of this existing service.

Method

Recruitment Process

The current quality improvement study was conducted at a large urban hospital in western Pennsylvania. The hospital Quality Review Committee approved the proposal and implementation of the current study before data collection was initiated. Over a 1-year period, approximately 1 day per week, electronic medical records (EMR) of newly admitted patients, age 70 and older, were examined to determine eligibility based on the inclusion and exclusion criteria shown in Table 1.

Inclusion and Exclusion Criteria

Table 1:

Inclusion and Exclusion Criteria

At the bedside, a nurse on the study team identified eligible patients who were medically stable enough to participate. She then informed these patients of the activities and purpose of the study, and each was invited to participate. Given that the project was approved as a quality improvement activity versus a research project, signed informed consent was not required. Patients wishing to participate indicated their willingness via verbal consent.

Bedside Data Collection

Audiometry, the gold standard for measuring HL, was used (Valete-Rosalino & Rozenfeld, 2005). Audiometric thresholds were measured by audiology graduate students under the direct supervision of a licensed audiologist, and all were members of the project team. Audiometry was done using a calibrated Earscan 3 portable audiometer with insert earphones. An audiometric threshold is defined as the softest sound that a person can hear 50% of the time (Walker et al., 2013). Insert earphones were used to prevent the possibility of collapsed ear canals, minimize background noise, and prevent contribution from the ear that was not being tested (Walker et al., 2013). Daily biological calibration at the bedside ensured that noise levels in each hospital room did not exceed the ability to measure thresholds within the normal range. The modified Hughson Westlake procedure was used to establish audiometric thresholds at different pitches (i.e., frequencies) (Carhart & Jerger, 1959). The frequencies 500 Hz, 1,000 Hz, 2,000 Hz, and 4,000 Hz were tested to be consistent with the WHO (2018) definition of HL, which is averaged hearing thresholds in the better-hearing ear >25 dB HL for the frequencies 500 Hz, 1,000 Hz, 2,000 Hz, and 4,000 Hz. Outpatient follow-up with an audiologist was recommended if the patient had HL based on audiometric thresholds.

An informal self-report measure, “Are you having difficulty hearing your doctors and nurses during your stay?” was asked of each patient. Answering yes or sometimes to this question was considered a positive self-report of HL. This question was asked to demonstrate if it could possibly replace the current identification strategies at this hospital. This question was also asked to determine if the patient needed an immediate hearing accommodation to maximize communication potential during the hospital stay. If the patient responded with yes or sometimes, a no-cost, non-custom amplifier was offered. The patient was able to try the personal amplifier at that time. The amplifier supplied to patients is a small hand-held microphone/amplifier unit into which headphones are hard-wired (Sonic Super Ear™ SE4000 Personal Sound Amplifier). The communication partner speaks into the microphone and the amplified (controlled by a simple volume wheel) speech is heard by the listener through the headphones. If an amplifier was desired for the remainder of the patient's stay, it was ordered and delivered to the patient's room by the hospital staff audiologist. The patient was able to keep the personal amplifier for use after discharge. As is policy in this hospital system, there was no patient charge associated with the issuance of an amplifier device.

Admission and Nursing Assessments

Upon patient arrival, the routine admission assessment was completed by the hospital admission team via the EMR. The team asked the patient health-related questions on the form and the patient, or family, provided the answers. Five possible hearing-related fields are included in the current admission assessment dataset, which will be referred to as the “admission assessment.” Once admitted to an inpatient unit, the patient's primary nurse completed the routine physical assessment at the bedside. During this physical assessment, the nurse noted if the patient had HL and/or hearing aids with notations in the EMR. This observation will be referred to as the “nursing assessment.”

For each patient agreeing to participate in the study, the admission and nursing assessments were obtained and evaluated to determine if any hearing-related fields were marked positively, or if text comments were entered. If the hearing-related fields were selected, the patient was identified as having HL by the nurse completing the assessment.

Predictors of Hearing Loss

During review of the EMR, several patient variables were evaluated to determine if they were predictors of HL in the current study's sample of patients. These included: gender, age, race, occupation, occupational noise exposure, hearing protection use at work, alcohol and tobacco use, and medical history. Medical history consisted of diabetes mellitus (DM), chronic obstructive pulmonary disease (COPD), cerebrovascular accident (CVA), renal disease, anxiety, and depression.

Statistical Analysis

Descriptive statistics were used to summarize categorical variables by frequencies and proportions, and continuous variables by means and standard deviations. Distribution differences by HL status were compared using the Student t test for continuous variables and the chi-squared test or its exact counterpart (Fisher's test) for categorical variables. The extent of missing data was assessed and a complete case analysis (van der Heijden et al., 2006) was conducted for the only variable with missing data, hearing protection at work. The sensitivity, specificity, positive predictive value, and negative predictive value for each assessment (self-report, admission assessment, nursing assessment) was evaluated. Logistic regression analysis was used to identify predictors of HL. Univariate regression was first used to identify potential predictors at a significance level of p < 0.10 (Bejanyan et al., 2012). Retained variables were then entered into a multivariate model to identify the final significant predictors (p < 0.05). Effect size in the form of odds ratios (OR) was reported with 95% confidence intervals (CI) and p values. All statistical tests were two-sided and conducted using SPSS version 24.

Results

Patient Characteristics

A total of 214 patients were invited to participate in the current study; 52 patients refused participation. The final sample included 162 (75.7%) in-patients. Demographic information is presented in Table 2. The mean age of the sample was 79.6 (SD = 7.1). The sample was predominately White (79%) and equally representative of both genders (50.6% male). Most patients reported that they had previously worked in labor-related occupations (n = 87, 54.4%) and were not exposed to occupational noise (n = 94, 58%). It was inferred that 68 (42%) patients worked in occupations where they were prone to noise exposure. Of the 61 patients who reported being asked to wear hearing protection in the workplace, the majority (n = 42, 68.9%) reported they did not adhere. More than one half of the sample reported that they did not drink alcohol or smoke tobacco products (n = 93, 57.4% and n = 86, 53.1%, respectively).

Demographic CharacteristicsDemographic Characteristics

Table 2:

Demographic Characteristics

Prevalence of Hearing Loss

Approximately three quarters of the sample (n = 118, 72.8%) had at least some degree of HL based on audiometric testing. The grades of hearing impairment according to WHO criteria are presented in Table 3. Most patients had a slight/mild or moderate degree of HL (37% and 30.2%, respectively). Notably, 35.8% (n = 58) had at least a moderate degree of HL. According to WHO guidelines (2018), this level of HL results in “difficulty hearing regular speech, even at close distances.”

Prevalence of Hearing Loss Using the World Health Organization's (2018) Grades of Hearing Impairment

Table 3:

Prevalence of Hearing Loss Using the World Health Organization's (2018) Grades of Hearing Impairment

Hearing Loss Assessment Methods

To the informal question about difficulty hearing physicians and nurses, 21.6% (n = 35) of patients answered positively (yes or sometimes). Of those with documented HL (72.8%) via audiometry, 28.8% (n = 34) answered positively to the informal question. Of only those with moderate degree HL or worse via audiometry, 43.1% (n = 25) answered positively to the informal question.

Of the 118 (72.8%) patients who had HL based on audiometry, 43.2% were identified by the admission assessment and 14.4% were identified by the nursing assessment. Table 4 shows the sensitivity, specificity, positive predictive values, and negative predictive values for each method in assessing for any degree of HL, and for HL of a moderate degree or worse. Each of the assessments had low sensitivity, but high specificity and positive predictive value.

Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value of the Informal Question, Admission Assessment, and Nursing Assessment for Any and Moderate Degree or Worse of Hearing Loss (HL)

Table 4:

Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value of the Informal Question, Admission Assessment, and Nursing Assessment for Any and Moderate Degree or Worse of Hearing Loss (HL)

Predictors of Hearing Loss

In the univariable analysis, age (OR = 1.11, 95% CI [1.05, 1.18], p = 0.001) and male gender (OR = 1.95, 95% CI [0.96, 3.95], p = 0.06) were identified as the only potential predictors for HL. None of the other explored variables were identified as potential predictors of HL. The multivariable logistic regression indicates that both age and gender were significant predictors of HL (Table 5). Controlling for gender, each year increase in age would result in a 12% increase in the odds of having HL (95% CI [1.05, 1.19], p = 0.001). Male patients were 2.14 times more likely to have HL than female patients, adjusting for age (95% CI [1.02, 4.47], p = 0.04).

Predictors of Hearing Loss

Table 5:

Predictors of Hearing Loss

Personal Amplifiers

If patients answered positively to the question regarding difficulty understanding their physicians and/or nurses, or if communication difficulty was evident as judged by the evaluators, a personal amplifier was offered to the patient. Forty (24.7%) patients were offered a personal amplifier. Eleven (27.5%) of those patients accepted the personal amplifier. Of the 29 patients (72.5%) who did not accept the personal amplifier, six patients were being discharged soon, three had hearing aids at home, and 20 gave no reason for declining the device.

Discussion

The current study evaluated the prevalence of HL in a group of hospitalized older adults and the success of current practice in identifying patients with HL during admission interviews and nursing assessments in a large urban hospital. In addition, possible predictors of HL were examined within this group of patients. The current study revealed that there was a high prevalence of HL among these hospitalized older adults, as approximately three quarters of patients had HL measured through audiometry. Other studies describing the prevalence of HL in hospitalized older adults found relatively similar results (El Kady, 2012; Lim & Yap, 2000). However, the results of the current study are higher than those results found in community-dwelling older adults in the United States. Lin et al. (2011) found HL in 63.1% of adults age 70 and older in the National Health and Nutrition Examination Survey study from 2005 to 2006. Genther et al. (2015) found HL in 59% of community-dwelling Medicare beneficiaries. The informal question did not identify those with HL as accurately as using audiometry. Similarly, Choi et al. (2016) found that people underestimate their own HL. Previous literature, however, has suggested that a global question is sufficient to use in studies with older adults where audiometry is unavailable (Valete-Rosalino & Rozenfeld, 2005).

The second purpose of the study was to determine if patients with HL were being accurately identified as having HL during the admission and/or nursing assessment at this hospital. The current study found that the admission and nursing assessments had low sensitivity. Even when patients had HL that only allowed them to hear loud speech or loud sounds (Table 3), HL was still often unad-dressed by hospital staff and patients, based on EMR documentation. These results are consistent with those of Heron et al. (2000), who conducted a study to determine if nurses were able to recognize hospitalized patients with HL. Those results showed that only 30.6% of nurses were able to correctly identify whether their patient had HL. The invisible and gradual nature of HL makes it difficult for not only hospital staff to determine the presence of HL in their patients, but also for patients to identify it themselves. The informal question, admission assessment, and nursing assessment all had low sensitivity. This finding suggests all three measures were suboptimal in identifying patients with HL.

Increasing age and male gender were the only potential predictors of HL in the current study. This finding is consistent with previous literature (Cruickshanks et al., 2003; Lin et al., 2011; Rigters et al., 2016). Race, occupation, occupational noise exposure, hearing protection use at work, alcohol and tobacco use, and medical history (DM, COPD, CVA, renal disease, anxiety, and depression) were not predictors of HL. Similar to the results of the current study, Lin et al. (2011) did not find tobacco use, DM, or CVA to be significant predictors of HL. Kamenski et al. (2015) did not find a significant relationship between COPD and HL.

Research literature found differing results from the current study regarding predictors of HL. Of note, these earlier studies were conducted on very large sample sizes, which could account for the differing findings. Lin et al. (2011) found race to be a significant predictor of HL. Cruickshanks et al. (2003) found that occupation was associated with incidental HL, with those working in occupations with a high likelihood of being exposed to noise having more HL. In the current study, no significant association was found between occupation, occupational noise exposure, and HL. Unlike the current study, Rigters et al. (2016) found that HL was associated with DM, smoking, and alcohol consumption. As noted, the Rigters et al. (2016) data were based on a large sample of >3,300 people. The current study's significantly smaller sample size could have obscured similar findings. Li et al. (2014) found that in adults age 70 and older, there was a significant association between measured hearing thresholds and depression in women, but not in men. Seo et al. (2015) found that chronic kidney disease (renal disease) independently affects hearing impairment. Lasisi et al. (2007) found mean differences in hearing thresholds between a group of patients with chronic renal failure and a control group of healthy individuals.

More than one third of patients in the current study had moderate or worse HL on audiometry. Although 40 (24.7%) patients were offered a personal amplifier device, only 11 patients accepted the device. The offer of a device was based on the patient's self-reported hearing difficulty or evaluator judgment. Thus, a small number of patients who could have benefited from the device accepted one. Of note, patients with the same audiometric profile do not always function in the same way or perceive the same amount of difficulty receiving verbal communication. Given the high prevalence of HL and the difficulty patients have in recognizing their own communication challenges, it was recommended that hospital staff make patients aware of the importance of communication while in the hospital, and the potential negative consequences of not using an amplification device. Although only 11 patients accepted the device, those who did were afforded increased communication access for the remainder of the hospital stay.

Strengths & Limitations

There were several strengths of the current quality improvement project. First, the sample was obtained from all patient units, excluding patients from the intensive care unit (ICU) and those who did not meet the inclusion criteria, in a large hospital over a 1-year period. The authors believe this sample accurately represented the majority of the patient population. Second, the current study's multidisciplinary team worked during the creation and execution of this project to ensure all considerations from members' respective fields were discussed. This effort was to make certain that all aspects of this project were clear and to give this project the best chance at success from all perspectives. Third, the gold standard, audiometry, was used as one of the measures while determining if inpatients have HL, and to what degree.

The current study also had some limitations. Patients who did not meet the inclusion criteria were excluded from the study. Thus, the current study's results do not capture the HL of the excluded patients and likely underestimates overall HL in this hospital. Of note, given that the ultimate purpose of a quality improvement study is local practice improvement, results cannot be generalized to other hospital patients. Recommendations associated with this study included suggested improvements to the admission assessment, enabling clearer identification of patients with HL. A cost-effective hearing screening during the admission assessment and nursing assessment that successfully identifies patients with HL was recommended so that hospital staff can be made aware of patients who have HL and the accompanying communication breakdowns. After identification, communication strategies can be used, including the offer of an amplifier, allowing patients access to conversations and the ability to participate in health care decisions. Aside from improving quality of care, hospitals are mandated to provide adequate and appropriate accommodations to all patients with disabilities through the Americans with Disabilities Act (ADA). In addition, hospitals are required by The Joint Commission (2019) to provide the most effective means possible for clear patient–provider communication during care, treatment services, and the exchange of information.

Practice Implications

Future actions should focus on implementation of a cost-effective method to screen for HL and communication challenges in hospitalized older adults. For example, Strawbridge and Wallhagen (2017) found that simple low-tech hearing screening procedures had acceptable sensitivity and specificity to be of use in primary care offices. Their results could be tested for reliability and validity in the in-patient setting. In addition, methods to increase the number of patients who will accept use of an amplifier should be examined. Finally, it would be useful to have research results examining the impact of identification and amplification of hearing loss on outcomes such as patient satisfaction, misdiagnosis, unnecessary testing, adverse events, and other variables relating to health outcomes.

Conclusion

The current study found a high prevalence of HL in a hospitalized older adult population and that HL was often invisible to the patient and health care providers. Modifications should be made to improve identification to accommodate patients with HL during their hospital stay. It is critical for patients to understand conversations with hospital staff to make appropriate decisions about their health care and have accurate discharge and follow-up information.

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Inclusion and Exclusion Criteria

Inclusion CriteriaExclusion Criteria
Admitted to a non-ICUUnable to follow directions for participation
Length of stay <24 hoursContact or droplet isolation precautions
Age ≥70Neutropenia
No apparent cognitive deficitsReadmitted patients with prior participation
Proficient in the English language

Demographic Characteristics

CharacteristicAudiometric Hearing Loss, n (%)p Value
Total (N = 162)No (n = 44)Yes (n = 118)
Age (years) (mean, SD)79.6 (7.1) (70 to 102)76.4 (5.1) (70 to 90)80.7 (7.3) (70 to 102)<0.10
Race0.16
  White128 (79)31 (70.5)97 (82.2)
  Other34 (21)13 (29.5)21 (17.8)
Gender0.06
  Male82 (50.6)17 (38.6)65 (55.1)
  Female80 (49.4)27 (61.4)53 (44.9)
Occupationa0.60
  Laborer87 (54.4)20 (46.5)67 (57.3)
  Professional41 (25.6)14 (32.6)27 (23.1)
  Other18 (11.3)5 (11.6)13 (11.1)
  Homemaker14 (8.8)4 (9.3)10 (8.5)
Occupational noise exposure0.38
  No94 (58)28 (63.6)66 (55.9)
  Yes68 (42)16 (36.4)52 (44.1)
Hearing protection at workb0.12
  No42 (31.1)12 (34.3)30 (30)
  Sometimes10 (7.4)010 (10)
  Yes9 (6.7)4 (11.4)5 (5)
  N/A for occupation74 (54.8)19 (54.3)55 (55)
Alcohol consumption history0.29
  Never93 (57.4)26 (59.1)67 (56.8)
  Former26 (16)4 (9.1)22 (18.6)
  Current43 (26.5)14 (31.8)29 (24.6)
Smoking history0.68
  Never86 (53.1)26 (59.1)60 (50.8)
  Former64 (39.5)15 (34.1)49 (41.5)
  Current12 (7.4)3 (6.8)9 (7.6)

Prevalence of Hearing Loss Using the World Health Organization's (2018) Grades of Hearing Impairment

Grade of ImpairmentAudiometric ISO Value (Better Ear)a (dB HL)Impairment DescriptionFrequency (%)
0 (no impairment)≤25No or very slight hearing problems. Able to hear whispers.44 (27.2)
1 (slight/mild)26 to 40Difficulty hearing and understanding soft speech, speech from a distance, or speech against a background of noise.60 (37)
2 (moderate)41 to 60Difficulty hearing regular speech, even at close distances.49 (30.2)
3 (severe)61 to 80Hears only very loud speech or loud sounds in the environment. Most conversational speech is not heard.9 (5.6)
4 (profound)>81May perceive loud sounds as vibrations.0

Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value of the Informal Question, Admission Assessment, and Nursing Assessment for Any and Moderate Degree or Worse of Hearing Loss (HL)

Outcome VariableSensitivitySpecificityPositive Predictive ValueNegative Predictive Value
Any HL
  Informal question28.8197.7397.1433.86
  Admission assessment43.2289.4792.7333.66
  Nursing assessment14.4195.4589.4729.37
Moderate degree HL or worse
  Informal question43.190.2971.4373.29
  Admission assessment58.6279.6161.8277.36
  Nursing assessment25.4297.0983.3369.93

Predictors of Hearing Loss

CharacteristicAOR95% CIp Value
Age (years)1.12[1.05, 1.19]0.001
Gender, male2.14[1.02, 4.47]0.04
Authors

Dr. Mormer is Associate Professor, Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania; Dr. Bubb is Audiologist, Cranberry Hearing & Balance, Cranberry Township, Pennsylvania; Dr. Alrawashdeh is Postdoctoral Research Fellow, Department of Population Medicine, Harvard Medical School & Harvard Pilgrim Health Care Institute, Boston, Massachusetts; and Dr. Cipkala-Gaffin is Manager, Behavioral Health Education, Allegheny Health Network, Pittsburgh, Pennsylvania. Dr. Alrawashdeh is also Assistant Professor, Jordan University of Science and Technology, Jordan.

The authors have disclosed no potential conflicts of interest, financial or otherwise. Financial support for this project was provided by the Shadyside Hospital.

The authors acknowledge contributions of expertise to this project from Shadyside Hospital nurses Kelly S. Neal and Bridget Recznik. The authors also appreciate help with data collection provided by students from the University of Pittsburgh AuD program.

Address correspondence to Elaine Mormer, PhD, CCC-A, Associate Professor, Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, 6035 Forbes Tower, Pittsburgh, PA 15217; e-mail: emormer@pitt.edu.

Received: May 14, 2019
Accepted: January 03, 2020

10.3928/00989134-20200316-03

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