INTRODUCTION
Hearing loss is one of the most frequently occurring chronic conditions worldwide (Mathers et al. 2008 age-related hearing loss (ARHL) are only partly understood. It consists of physiological degenerations related to aging plus the accumulated effects of environmental exposures, medical disorders and their treatment, and hereditary susceptibility (Huang & Tang 2010 Huang & Tang 2010 Mulrow et al. 1990 ; Lin et al. 2013 ; Stam et al. 2016 ; Heywood et al. 2017 ; Thomson et al. 2017
Observed longitudinal changes in pure-tone thresholds provide evidence that ARHL does not increase linearly but accelerates as age progresses (Gates & Cooper 1991 ; Kiely et al. 2012 ; Linssen et al. 2014 Karlsmose et al. 2000 Stam et al. 2015 Pronk et al. 2013 Divenyi et al. 2005 ; Dubno et al. 2008 Dubno (2015) Dubno 2015
Demographic factors such as sex and level of education have shown to be influencing the progression of hearing loss. To illustrate, Gates and Cooper (1991) Lee et al. (2005) Cruickshanks et al. (2010)
Kiely et al. (2012) Kiely et al. 2012 Pronk et al. 2013 ; Stam et al. 2015
Although lifestyle factors such as tobacco smoking and alcohol use have shown to have an effect on hearing loss as well, inconsistent results have been reported so far. Smoking emerged as a risk factor for an increase in the prevalence of hearing loss in a meta-analysis of mainly cross-sectional studies (Nomura et al. 2005 smoking on pure-tone thresholds (Karlsmose et al. 2000 ; Gopinath et al. 2010 ; Kiely et al. 2012 Nakanishi et al. 2000 ; Cruickshanks et al. 2015 smoking has also been found to be associated with the development of (self-reported) hearing loss (Burr et al. 2005 smoking that needs to be considered is time since cessation of smoking . Cruickshanks et al. (2015) Pronk et al. (2013) smoking and speech recognition ability in noise in older adults.
Inconsistent results have also been found for the effect of alcohol consumption on pure-tone thresholds. Cross-sectional studies showed that heavy alcohol use, defined as ≥2 registrations to the Sweden temperance board (Rosenhall et al. 1993 Popelka et al. 2000 alcohol consumption was found to have a protective effect on hearing (Popelka et al. 2000 alcohol use (Brant et al. 1996 ; Karlsmose et al. 2000 ; Gopinath et al. 2010 ; Cruickshanks et al. 2015
Longitudinal changes in hearing have mostly been studied using pure-tone audiometry (Morrell et al. 1996 ; Karlsmose et al. 2000 Cruickshanks et al. 2010 ; Mitchell et al. 2011 ; Kiely et al. 2012 Plomp & Mimpen 1979 ; Kramer et al. 1998 ; Houtgast & Festen 2008
The present study is a continuation on the previously presented 5-year results of the Netherlands Longitudinal Study on Hearing (NL-SH) (Stam et al. 2015 Smits et al. 2006 ; Jansen et al. 2010 ; Watson et al. 2012 ; Potgieter et al. 2015 smoking , and alcohol use on this decline.
MATERIALS AND METHODS
Study Design and Settings
The NL-SH is an ongoing prospective cohort study examining the relationship between hearing ability and different domains of life such as psychosocial health, work status, general health, and health care use. The NL-SH is conducted over the Internet entirely and uses a convenience sampling method. Eligible participants are adults between 18 and 70 years old. Both normal-hearing and hearing-impaired adults are invited to participate. Further details related to the recruitment of participants, data collection, and follow-up measurements are reported by Stam et al. (2015)
Dependent Variable: Speech Recognition Ability in Noise
All NL-SH participants are invited to perform a speech recognition in noise test, known as the National Hearing test (NHT) (Smits et al. 2006 Smits et al. 2006
Instructions on how to perform the hearing test were identical across all three measurements. At T0 and T1, scores were only available for one transducer (either by headphones or by loudspeakers). At T2, additionally to the standard measurement, participants were asked to perform a second measurement with a different kind of transducer (if they used headphones for the first T2 measurement, the second measurement had to be done with loudspeakers and vice versa).
Independent Variables
Demographic data and lifestyle factors were acquired through online questionnaires. Age at baseline was categorized into 5 age groups: 18 to 30, 31 to 40, 41 to 50, 51 to 60, and 61 to 70 years and was analyzed as a time-independent variable. Educational level was divided into low (elementary school or attended high school but no degree), mid (high school graduate or having an associate degree), and high (having a bachelor’s degree, master’s degree, or doctoral degree). The highest obtained educational level reported at T0, T1, or T2 was included in the analyses. Tobacco smoking was measured by asking the participants if and how often they smoked. Answer categories were: “yes. I smoke every day,” “yes, I smoke occasionally,” “no, but I used to smoke every day,” “no, but I used to smoke occasionally,” “no I never smoked.” Participants who had chosen the option “no, I never smoked” were considered having no history of smoking . All others were categorized as having a history of smoking .
Alcohol use was measured by asking how many units of alcohol participants consumed per day. The outcome was used to categorize each participant in one of the following categories: nonuser, mild user (≤1 unit/day), moderate user (2 units/day [women], 2 or 3 units/day [men]), and excessive user (>2 units/day [women], >3 units/day [men]) (Reinhard & Rood-Bakker 1998 smoking and alcohol use were entered in the models as time-dependent variables.
Study Sample
People aged 18 to 70 years at baseline who participated in the NL-SH at T0 and received an invitation to participate in T1 in 2011 to 2012 and in T2 before June 2017 were included in the present study. An invitation for T1 and T2 was sent if at T0 the NHT and the compulsory questionnaires were completed. Participants who consistently (at all measurement rounds) reported to have a congenital hearing loss were excluded from the analyses because the main focus of this study was on ARHL and factors potentially contributing to it. Also cochlear implant recipients were excluded from the study as well as participants who performed near ceiling (those with a NHT score ≥3.0 dB SNR) at baseline. In addition, we excluded aided measurements because unaided measurements provide a more accurate estimate of the actual hearing status. Note that at T1 and T2, hearing aid users were asked to perform the NHT unaided. This was not the case at T0. At T0, participants had been given the option to perform the test aided or unaided. Hence, when only an aided score at T0 was available for a particular participant, the T0 score was regarded as “missing,” while the scores on T1 or T2 for that participant were included in the analyses. Further, changes in SRT between two measurement rounds of >2 SD were checked for inconsistencies on a case-by-case basis. If large fluctuations did not fit with how participants subjectively reported their hearing (defined as >1 point change on a 1 to 9 scale; 1 = very poor, 9 = very good), these measurements were excluded. Measurements were also excluded if the same combination of digits (e.g., 1-2-3) was repetitively entered at each trial of the NHT. After applying the exclusion criteria, the number of measurements available for each participant varied. The minimally required number of completed measurement rounds was 1 for each participant, so they could have completed the NHT at either T0, T1, or T2 only or at two or three measurement rounds.
Statistical Analysis; Descriptive Analysis
Percentages were calculated for categorical variables. Means (M) and SD were calculated for continuous variables. A difference in mean age between participants with a baseline measurement only versus those with complete follow-up data was tested using an independent sample t test. For categorical variables (i.e., sex, educational level, tobacco smoking , and alcohol consumption), differences between participants with a baseline measurement only and participants with complete follow-up data were analyzed using a Pearson Chi-square test.
A paired sample’s t test was used to determine whether the SRTs measured with headphones were significantly different from the SRTs measured with loudspeakers for participants who performed both tests at T2. In the literature, it is shown that performing the NHT using loudspeakers yields slightly higher (worse) SRT scores than using headphones (Smits et al. 2006 Culling et al. 2005 ; Potgieter et al. 2015
Effect Analysis: Change in SRT Over Time
A linear mixed model was built to examine the longitudinal change of SRT over time and to investigate which demographic and lifestyle factors influenced the decline of SRT over 10 years. A maximum likelihood method was chosen with a compound symmetry covariance structure for the repeated measurements within participants. Our base model included measurement round (T0, T1, T2), transducer type (headphones or loudspeakers), and a random intercept for each participant.
Using the base model, we first tested whether a random slope had to be included in the model using the likelihood ratio (LR) test. A random slope accounts for individual variation in the change of the SRT over time. If the LR test results in a significant change (p ≤ 0.05) of the base model, a random slope will be added to the model. Then, we investigated the effect of age group, sex, educational level, smoking , and alcohol use on SRT by adding each covariate separately. The variable with the most significant effect on the baseline SRT value was added first, then the second most significant, and so on in a forward selection procedure with inclusion criterion (p < 0.05). Estimated marginal means (EMMs) for SRTs including their corresponding 95% confidence intervals (CI) were calculated and are presented.
Subsequently, to assess whether change in SRT over time was different for different categories of a covariate, all covariates were tested for effect modification. Therefore, the covariate and its interaction with time were added to the base model (e.g., Time × Age group). A covariate was considered an effect modifier when the interaction with time was found to be statistically significant. When multiple significant effect modifiers were found, three-way interactions were analyzed too (e.g., Time × Age Group × Sex). In case of significant 3-way interactions, we tested effect modification within each stratum of effect modifiers to identify within which subgroups of the effect modifiers the change of SRT over time was different. We performed a sensitivity analysis with the group of participants who had complete data (i.e., those who participated in all three measurement rounds). We used the same analysis protocol for that purpose.
All statistical analyses were performed using SPSS version 22.0 (IBM Corp., Armonk, NY). P values <0.05 were considered to indicate statistical significance.
RESULTS
Participants
After applying the exclusion criteria, data of 1349 out of 1555 participants were included and analyzed. Figure 1 shows the number of participants not eligible according to the exclusion criteria in each measurement round. In total, 23.8% of the participants had a complete 10-year follow-up with complete scores across the 3 measurement rounds (T0–T1–T2). Participant with a complete follow-up (N = 321) were significantly older (p < 0.001) (M = 47.0 years, SD = 13.1 years, range = 18 to 69 years) compared with participants who dropped out after T0 (N = 642, M = 44.5 years, SD = 11.2 years, range = 18 to 70 years), and had a significantly higher level of education (p < 0.001). No significant differences between participants with a complete follow-up and those who only participated at baseline were seen for sex, smoking , or alcohol use. Participant characteristics at each measurement round are shown in Table 1 . In all, 61% of participants had good hearing at baseline as indicated by the SRT (Table 1 ). Participants who performed two hearing tests at T2, one with loudspeakers and one with headphones, (N = 38) had significantly lower (i.e., more favorable) SRT scores, −1.19 dB SNR (95% CI = −0.49 to −1.89), when using headphones compared with loudspeakers.
TABLE 1.: Participant characteristics for each measurement round
Fig. 1.: Flowchart of participant numbers after applying inclusion criteria per measurement cycle. The number of measurements excluded per measurement round is given in the dashed line rectangle. SNR indicates signal to noise ratio; SRT, speech reception threshold; T0, baseline measurement; T1, 5-year follow-up measurement; T2, 10-yr follow-up measurement.
Decline in SRT Over Time
The first aim of the present study was to examine the deterioration of the SRT over 10 years. The LR test indicated that adding a random slope did not improve the base model significantly (χ2 (1) = 1.22, p = 0.27). This demonstrated that the variance in the rate of change of the SRT over time between participants was explained by natural variation and did not need to be corrected for. Tests performed with headphones yielded a lower SRT than tests performed with loudspeakers, [F (1, 1833) = 224.51, p < 0.001]. The SRT decreased significantly over time [F (2, 1205) = 49.23, p < 0.001]. EMM of the base model, corrected for transducer type, indicated that the mean decrease in SRT over 10 years was 0.93 dB SNR (95% CI = 0.73 to 1.12, p < 0.001) (Table 2 ).
TABLE 2.: Estimated marginal means (95% CIs) from the linear mixed model for SRT dB SNR (signal to noise ratio) over 10 yr in the total sample
Variables that significantly affected the baseline SRTs were the following: age group [F (4, 1109) = 17.66, p < 0.001], history of smoking [F (1, 1759) = 5.27, p = 0.022], and sex [F (1, 1110) = 13.66, p < 0.001]. Participants in older age groups, participants with a history of smoking , and females had a higher (i.e., more unfavorable) SRT at baseline than the youngest age group, participants without a history of smoking , and males, respectively. Educational level [F (2, 1936) = 1.72, p = 0.18] and alcohol use [F (3, 1875) = 0.58, p = 0.63] did not influence the baseline SRTs. The EMM of the base model—adjusted for age group, history of smoking , and sex—indicated that the mean decrease in SRT over 10 years was 0.89 dB SNR (95% CI = 0.69 to 1.09, p < 0.001) (Table 2 ).
Interactions Between Demographic and Life Style Factors and SRT Over Time
The covariates “age group” and “history of smoking ” also modified the decline in SRT over time. Speech recognition in noise of participants aged 51 to 60 and 61 to 70 years deteriorated significantly faster compared with younger age groups [F (8, 1214) = 4.37, p < 0.001] (Fig. 2 and Table 3 ). The estimated 10-year decline in SRT of the participants aged 51 to 60 years was 1.37 dB SNR (95% CI = 1.03 to 1.72). The eldest age group had an estimated 10-year decline in SRT of 1.69 dB SNR (95% CI = 1.09 to 2.29). Participants aged 18 to 30 years or 31 to 40 years at baseline maintained their good hearing ability during a 10-year follow-up time interval (Fig. 2 and Table 3 ). Average SRTs of participants aged 41 to 50 years at baseline gradually declined from good to insufficient.
TABLE 3.: Estimated marginal means of speech recognition in noise over 10 yr per age group at baseline using the base model
Fig. 2.: Estimated marginal means of speech recognition in noise over 10 yr per age group at baseline using the base model. SNR indicates signal to noise ratio; SRT, speech reception threshold.
In addition, participants with a history of smoking deteriorated faster compared with participants who had never smoked [F (2, 1158) = 5.7, p = 0.003] (Fig. 3 and Table 4 ). No significant 3-way interaction Time × History of Smoking × Age Group [F (12, 1430) = 1.38, p < 0.17] was found.
TABLE 4.: Estimated marginal means of speech recognition in noise over 10 yr for participants with a history of smoking and participants without a history of smoking using the base model
Fig. 3.: Estimated marginal means of speech recognition in noise over 10 yr for participants who have a history of smoking and for participants who never smoked using the base model. SNR indicates signal to noise ratio; SRT, speech reception threshold.
Finally, using the base model, no significant interactions between Time × Sex [F (2, 1206) = 1.60, p = 0.20], Time × Education [F (4, 1164) = 1.56, p = 0.18], and Time × Alcohol Use [F (6, 1144) = 0.78, p = 0.59] were found.
A sensitivity analysis was additionally performed including the participants with complete data (i.e., those who participated in all 3 measurement rounds; N = 321). Again, a significant decline in SRTs over time was observed [F (2, 672) = 16.19, p < 0.001]. EMMs of the baseline model were −5.86 dB SNR (95% CI = −6.19 to −5.53) at T0, −5.37 dB SNR (95% CI = −5.70 to −5.04) at T1, and −5.13 dB SNR (95% CI = −5.46 to −4.81) at T2. Age group significantly affected SRTs at baseline [F (4, 319) = 6.21, p < 0.001]. History of smoking [F (1, 547) = 3.44, p = 0.064], sex [F (1, 324) = 1.87, p = 0.17], education [F (2, 740) = 0.32, p = 0.73], and alcohol use [F (3, 903) = 1.32, p =0.27] did not significantly affect baseline SRTs. The same effect modifiers as in the main analyses were found. Older age groups declined faster compared with younger age groups [F (8, 672) = 2.20, p = 0.025]. Participants with a history of smoking had a faster decline in SRTs compared with participants who never smoked [F (2, 675) = 4.89, p = 0.008].
DISCUSSION
Identifying factors that contribute to a decline in speech recognition ability in noise over time is highly valuable. This knowledge may be used for prevention and timely interventions to avert hearing loss and its broad psychosocial and daily life consequences. The present study reported on the analyses of longitudinal data from an online prognostic cohort study using a speech in noise test to assess an individual’s hearing status. We examined the 10-year change in speech recognition ability in noise in adults aged 18 to 70 years. In addition, we investigated the effects of age, sex, educational level, smoking , and alcohol use on this decline.
On average, the SRT deteriorated by 0.89 dB SNR over 10 years. This is a significant difference over time. It corresponds to an 18% decrease in speech understanding in specific difficult listening situations (Smits et al. 2004 Smits et al. 2004 Stam et al. 2015 Moore et al. 2014 Smits et al. 2006 Smits et al. 2006 Stam et al. 2016
The estimated decrease in SRT between T1 and T2 was smaller compared with that between T0 and T1. This might partly be explained by a ceiling effect caused by the maximum score that can be obtained with the NHT. The maximum score is set at 4.0 dB SNR. This finding could also be the result of selective attrition at T2, loss of those participant with a more unfavorable SRT threshold. If selective attrition would have occurred at T2, the actual deterioration in hearing would have been underestimated for the T1–T2 interval. However, this trend was also observed in the sensitivity analysis, arguing against selective attrition. Another possibility is that the relatively small sample size at T2 let to a sampling error causing an underestimation of the actual change in SRT.
Speech recognition in participants with a history of tobacco smoking deteriorated faster compared with participants who had never smoked. The adjusted model with baseline corrections for sex, age group, and history of smoking showed that participants with a higher age and with a history of smoking had higher (i.e., more unfavorable) SRTs. History of smoking and age group each showed a significant interaction with time but no significant three-way interaction was observed between these two variables and time. This could be explained by the fact that the power was too low for testing three-way interactions. Another possibility is that, given that participants with a history of tobacco smoking were on average older compared with nonsmokers, the increased deterioration of SRT in participants with a history of smoking was partly an effect of age.
Other studies also demonstrated an association between tobacco smoking and hearing loss. To illustrate, a meta-analysis by Nomura et al. (2005) smoking and decreased pure-tone thresholds. Also in longitudinal studies, the detrimental effect of smoking on pure-tone thresholds and self-reported hearing loss has been reported (Nakanishi et al. 2000 , Burr et al. 2005 , Cruickshanks et al. 2015 Dawes et al. 2014 Pronk et al. 2013 smoking and hearing loss by the observation of a steeper decline in speech recognition in noise in participants with a history of smoking .
Several hypotheses on the ototoxic effects of tobacco smoking on cochlear function have been published. For instance, (a) Toxins such as nicotine, carbon monoxide, and hydrogen cyanide, all present in cigarette smoke might cause hearing loss (Chen & Fechter 1999 ; Harkrider et al. 2001 ; Fechter et al. 2002a smoking is associated with hearing loss, and (c) hypoxia induced by tobacco smoking has been associated with hearing loss (Howard et al. 1998 ; Chen 2002 Harkrider et al. 2001 Chen & Fechter 1999 ; Fechter et al. 2002a, b smoking and noise-induced hearing loss has been observed in humans as well (Mizoue et al. 2003 ; Ferrite & Santana 2005 ; Wang et al. 2017 Chen 2002
Smoking may also induce atherosclerosis which could add to cochlear hypoxia (Howard et al. 1998 Vinay 2010 smoking affects central auditory pathways. An increasing amount of research suggests that smoking is related to neurobiological abnormalities and cognitive impairment (Durazzo et al. 2014 Durazzo et al. 2014 Durazzo et al. (2014) smoking on the accelerated decline in speech recognition in noise observed in (ex-)smokers is unclear. Future studies are recommended to confirm these hypotheses.
Tobacco smoking is known to have a variety of other deleterious effects on health causing diseases such as CVD, chronic obstructive pulmonary disease, and various types of cancer. Increased hearing loss in people with a history of smoking might therefore also be a secondary effect caused by other diseases associated with smoking such as CVD and its treatments. In addition, it is important to mention that the effect of smoking on the progression of hearing loss, as shown in this study, adds to healthcare expenditures (via the treatment of hearing loss).
Our results demonstrated that females had a higher SRT, but no longitudinal differences in the decline of SRTs between sexes were observed. Other longitudinal studies reported contradictory results with respect to gender. Using pure-tone audiometry, some studies reported a faster progression of hearing loss in male participants than in female participants (Karlsmose et al. 2000 ; Cruickshanks et al. 2010 ; Linssen et al. 2014 Mitchell et al. 2011 ; Kiely et al. 2012 Dubno et al. (2008) Pronk et al. (2013) Moore et al. (2014)
In the present study, no association between educational level and a 10-year change in speech recognition in noise was found. Though lower education has been shown to be associated with a higher prevalence of hearing loss (Kiely et al. 2012 Kiely et al. 2012 ; Pronk et al. 2013 alcohol consumption in this study seems to be consistent with previous research in this domain. To date, there are no longitudinal studies in which a significant association between hearing loss and the use of alcohol was observed (Brant et al. 1996 ; Karlsmose et al. 2000 ; Gopinath et al. 2010 ; Cruickshanks et al. 2015
Strengths and Limitations
Strengths of the present study are the longitudinal data collection with a long follow-up, and the use of a speech recognition in noise test in a diverse study sample, including normal-hearing as well as hearing-impaired adults with a wide age range (18 to 70 years). The online data collection setting allowed people to easily participate in all measurements. This way we created a large dataset with a 10-year follow-up. By using longitudinal data, participants served as their own controls, thereby minimizing the effect of individual differences introducing selection bias as is often the case in cross-sectional studies.
There are several limitations of our study that need to be mentioned. The first is the known measurement error of the NHT (i.e., approximately 1 dB SNR)(Smits & Houtgast 2005
The convenience sampling method we used may also have resulted in sampling error expressed as differences for specific variables at baseline. For example, selection bias could have caused cross-sectional effects of age on SRT at baseline compromising the possibility to compare age groups (Morrell et al. 2009
Also, conductive hearing losses are not perceived by the NHT, because the NHT only detects a sensorineural type of hearing loss. Nevertheless, we argue that this has had a negligible effect on our results. We allowed participants to adapt and increase the volume of the sound to a preferred level before testing, minimizing possible effects of a conductive hearing loss on the SRT.
Furthermore, we used self-reports to measure alcohol use. Although self-reported alcohol use has been demonstrated to be reasonably reliable and valid (Del Boca & Darkes 2003 Del Boca & Darkes 2003 smoking was not quantified but categorized into “having a history of smoking ” and “nonsmokers.” For participants with a history of tobacco smoking , we did not have data on how many years before study entry they stopped smoking . Also, we only had a small sample of participants who stopped smoking during follow-up, making it impossible to accurately estimate the effect of smoking cessation. In addition, more details about tobacco smoking such as pack-years and information about the exact moment of smoking cessation would be needed to further substantiate our knowledge on the relation between tobacco smoking , smoking cessation, and hearing loss.
CONCLUSIONS
This longitudinal study revealed that the average 10-year decline in speech recognition in noise in a population aged 18 to 70 years at baseline is 0.89 dB SNR. This change may seem small, but it has a significant impact on a person’s ability to understand speech in challenging acoustic situations. The decline in speech recognition in noise was significantly larger in groups aged 51 to 60 and 61 to 70 years compared with younger age groups (18 to 30, 31 to 40, and 41 to 50 years) (p < 0.001). Speech recognition in noise in participants with a history of smoking declined significantly faster during the 10-year follow-up interval (p = 0.003). We did not find any differences in the decline of speech recognition in noise with regard to sex, educational level, and alcohol use.
Our findings confirm that ARHL is not a process limited to old age (i.e., >70 years), but occurs much earlier in life. Identification of factors associated with the decline in speech recognition in noise is indispensable for further actions such as preventive measures. Further longitudinal research is needed to investigate the interplay between a range of factors related to smoking and hearing decline, such as CVDs, their treatment, and other risk factors .
ACKNOWLEDGMENTS
The authors thank the participants on the Netherlands Longitudinal Study on Hearing (NL-SH). The authors also thank the assistance of Celina Henke in managing the database.
REFERENCES
Brant L. J., Gordon-Salant S., Pearson J. D., et al.
Risk factors related to age-associated hearing loss in the speech frequencies. J Am Acad Audiol, (1996). 7, 152–160.
Burr H., Lund S. P., Sperling B. B., et al.
Smoking and height as
risk factors for prevalence and 5-year incidence of hearing loss. A questionnaire-based follow-up study of employees in Denmark aged 18-59 years exposed and unexposed to noise. Int J Audiol, (2005). 44, 531–539.
Chen G. D. Effect of hypoxia on noise-induced auditory impairment. Hear Res, (2002). 172, 186–195.
Chen G. D., Fechter L. D. Potentiation of octave-band noise induced auditory impairment by carbon monoxide. Hear Res, (1999). 132, 149–159.
Cruickshanks K. J., Nondahl D. M., Tweed T. S., et al. Education, occupation, noise exposure history and the 10-yr cumulative incidence of hearing impairment in older adults. Hear Res, (2010). 264, 3–9.
Cruickshanks K. J., Nondahl D. M., Dalton D. S., et al.
Smoking , central adiposity, and poor glycemic control increase risk of hearing impairment. J Am Geriatr Soc, (2015). 63, 918–924.
Culling J. F., Zhao F., Stephens D. The viability of speech-in-noise audiometric screening using domestic audio equipment. Int J Audiol, (2005). 44, 691–700.
Dawes P., Cruickshanks K. J., Moore D. R., et al. Cigarette
smoking , passive
smoking ,
alcohol consumption, and hearing loss. J Assoc Res Otolaryngol, (2014). 15, 663–674.
Del Boca F. K., Darkes J. The validity of self-reports of
alcohol consumption: state of the science and challenges for research. Addiction, (2003). 98(Suppl 2), 1–12.
Divenyi P. L., Stark P. B., Haupt K. M. Decline of speech understanding and auditory thresholds in the elderly. J Acoust Soc Am, (2005). 118, 1089–1100.
Dubno J. R. Speech recognition across the lifespan: Longitudinal changes from middle age to older adults. Am J Audiol, (2015). 24, 84–87.
Dubno J. R., Lee F. S., Matthews L. J., et al. Longitudinal changes in speech recognition in older persons. J Acoust Soc Am, (2008). 123, 462–475.
Durazzo T. C., Mattsson N., Weiner M. W; Alzheimer’s Disease Neuroimaging Initiative. (
Smoking and increased Alzheimer’s disease risk: A review of potential mechanisms. Alzheimers Dement, (2014). 10(3 Suppl), S122–S145.
Fechter L. D., Chen G. D., Johnson D. L. Potentiation of noise-induced hearing loss by low concentrations of hydrogen cyanide in rats. Toxicol Sci, (2002a). 66, 131–138.
Fechter L. D., Chen G. D., Rao D. Chemical asphyxiants and noise. Noise Health, (2002b). 4, 49–61.
Ferrite S., Santana V. Joint effects of
smoking , noise exposure and age on hearing loss. Occup Med (Lond), (2005). 55, 48–53.
Gates G. A., Cooper J. C. Incidence of hearing decline in the elderly. Acta Otolaryngol, (1991). 111, 240–248.
Gopinath B., Flood V. M., McMahon C. M., et al. The effects of
smoking and
alcohol consumption on
age-related hearing loss : The Blue Mountains Hearing Study. Ear Hear, (2010). 31, 277–282.
Harkrider A. W., Champlin C. A., McFadden D. Acute effect of nicotine on non-smokers: I. OAEs and ABRs. Hear Res, (2001). 160, 73–88.
Heywood R., Gao Q., Nyunt M. S. Z., et al. Hearing loss and risk of mild cognitive impairment and dementia: Findings from the Singapore Longitudinal Ageing Study. Dement Geriatr Cogn Disord, (2017). 43, 259–268.
Houtgast T., Festen J. M. On the auditory and cognitive functions that may explain an individual’s elevation of the speech reception threshold in noise. Int J Audiol, (2008). 47, 287–295.
Howard G., Wagenknecht L. E., Burke G. L., et al. Cigarette
smoking and progression of atherosclerosis: The Atherosclerosis Risk in Communities (ARIC) Study. JAMA, (1998). 279, 119–124.
Huang Q., Tang J.
Age-related hearing loss or presbycusis. Eur Arch Otorhinolaryngol, (2010). 267, 1179–1191.
Jansen S., Luts H., Wagener K. C., et al. The French digit triplet test: A hearing screening tool for speech intelligibility in noise. Int J Audiol, (2010). 49, 378–387.
Karlsmose B., Lauritzen T., Engberg M., et al. A five-year longitudinal study of hearing in a Danish rural population aged 31-50 years. Br J Audiol, (2000). 34, 47–55.
Kiely K. M., Gopinath B., Mitchell P., et al. Cognitive, health, and sociodemographic predictors of longitudinal decline in hearing acuity among older adults. J Gerontol A Biol Sci Med Sci, (2012). 67, 997–1003.
Kramer S. E., Kapteyn T. S., Festen J. M. The self-reported handicapping effect of hearing disabilities. Audiology, (1998). 37, 302–312.
Lee F. S., Matthews L. J., Dubno J. R., et al. Longitudinal study of pure-tone thresholds in older persons. Ear Hear, (2005). 26, 1–11.
Lin F. R., Yaffe K., Xia J., et al.; Health ABC Study Group. (Hearing loss and cognitive decline in older adults. JAMA Intern Med, (2013). 173, 293–299.
Linssen A. M., van Boxtel M. P., Joore M. A., et al. Predictors of hearing acuity: Cross-sectional and longitudinal analysis. J Gerontol A Biol Sci Med Sci, (2014). 69, 759–765.
Mathers C., Fat D. M., Boerma J. T. World Health Organization. The Global Burden of Disease: 2004 Update. (2008). Geneva: World Health Organization.
Mitchell P., Gopinath B., Wang J. J., et al. Five-year incidence and progression of hearing impairment in an older population. Ear Hear, (2011). 32, 251–257.
Mizoue T., Miyamoto T., Shimizu T. Combined effect of
smoking and occupational exposure to noise on hearing loss in steel factory workers. Occup Environ Med, (2003). 60, 56–59.
Moore D. R., Edmondson-Jones M., Dawes P., et al. Relation between speech-in-noise threshold, hearing loss and cognition from 40-69 years of age. PLoS One, (2014). 9, e107720.
Morrell C. H., Brant L. J., Ferrucci L. Model choice can obscure results in longitudinal studies. J Gerontol A Biol Sci Med Sci, (2009). 64, 215–222.
Morrell C. H., Gordon-Salant S., Pearson J. D., et al. Age- and gender-specific reference ranges for hearing level and longitudinal changes in hearing level. J Acoust Soc Am, (1996). 100(4 Pt 1), 1949–1967.
Mulrow C. D., Aguilar C., Endicott J. E., et al. Quality-of-life changes and hearing impairment. A randomized trial. Ann Intern Med, (1990). 113, 188–194.
Nakanishi N., Okamoto M., Nakamura K., et al. Cigarette
smoking and risk for hearing impairment: A longitudinal study in Japanese male office workers. J Occup Environ Med, (2000). 42, 1045–1049.
Nomura K., Nakao M., Morimoto T. Effect of
smoking on hearing loss: Quality assessment and meta-analysis. Prev Med, (2005). 40, 138–144.
Plomp R., Mimpen A. M. Speech-reception threshold for sentences as a function of age and noise level. J Acoust Soc Am, (1979). 66, 1333–1342.
Popelka M. M., Cruickshanks K. J., Wiley T. L., et al. Moderate
alcohol consumption and hearing loss: A protective effect. J Am Geriatr Soc, (2000). 48, 1273–1278.
Potgieter J. M., Swanepoel d. e. W., Myburgh H. C., et al. Development and validation of a smartphone-based digits-in-noise hearing test in South African English. Int J Audiol, (2015). 55, 405–411.
Pronk M., Deeg D. J., Festen J. M., et al. Decline in older persons’ ability to recognize speech in noise: The influence of demographic, health-related, environmental, and cognitive factors. Ear Hear, (2013). 34, 722–732.
Reinhard O. P. M., Rood-Bakker D. S.
Alcohol Gebruik in Beeld. Standaard Meetlat. (1998). Rotterdam, the Netherlands.Nederlands Economisch Instituut.
Rosenhall U., Sixt E., Sundh V., et al. Correlations between
presbyacusis and extrinsic noxious factors. Audiology, (1993). 32, 234–243.
Smits C., Houtgast T. Results from the Dutch speech-in-noise screening test by telephone. Ear Hear, (2005). 26, 89–95.
Smits C., Kapteyn T. S., Houtgast T. Development and validation of an automatic speech-in-noise screening test by telephone. Int J Audiol, (2004). 43, 15–28.
Smits C., Merkus P., Houtgast T. How we do it: The Dutch functional hearing-screening tests by telephone and internet. Clin Otolaryngol, (2006). 31, 436–440.
Stam M., Smits C., Twisk J. W., et al. Deterioration of speech recognition ability over a period of 5 years in adults ages 18 to 70 years: Results of the Dutch Online Speech-in-Noise Test. Ear Hear, (2015). 36, e129–e137.
Stam M., Smit J. H., Twisk J. W., et al. Change in psychosocial health status over 5 years in relation to adults’ hearing ability in noise. Ear Hear, (2016). 37, 680–689.
Thomson R. S., Auduong P., Miller A. T., et al. Hearing loss as a risk factor for dementia: A systematic review. Laryngoscope Investig Otolaryngol, (2017). 2, 69–79.
Vinay. Effect of
smoking on transient evoked otoacoustic emissions and contralateral suppression. Auris Nasus Larynx, (2010). 37, 299–302.
Wang D., Wang Z., Zhou M., et al. The combined effect of cigarette
smoking and occupational noise exposure on hearing loss: Evidence from the Dongfeng-Tongji Cohort Study. Sci Rep, (2017). 7, 11142.
Watson C. S., Kidd G. R., Miller J. D., et al. Telephone screening tests for functionally impaired hearing: current use in seven countries and development of a US version. J Am Acad Audiol, (2012). 23, 757–767.
