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Year : 2000  |  Volume : 2  |  Issue : 8  |  Page : 59--70

Individual risk factors in the development of noise-induced hearing loss

Esko Toppila1, Ilmari Pyykko2, Jukka Starck1, Risto Kaksonen2, Hisayoshi Ishizaki2,  
1 Finnish Institute of Occupational Health, Helsinki, Finland
2 Department of Otorhinolaryngology, Karolinska Sjukhuset, Sweden

Correspondence Address:
Ilmari Pyykko
Department of Otolaryngology, Karolinska Hospital, S-171 76 Stockholm
Sweden

Abstract

We have analysed the association of noise-induced hearing loss with various risk factors among 685 workers in forest, shipyard, and paper mills. Occupational histories, health, environmental factors, and noise exposures of each worker were retrieved from the database of NoiseScan, our expert program on hearing. The mean hearing level at 4 kHz was 21.5 dB ± 20.3 dB HL. It correlated significantly with age, noise emission level and noise exposure level. However, these factors could only explain about 2 dB HL of the variation in hearing level. Impulse noise in the shipyard work caused increase in hearing level of 12 dB HL at 4 kHz when compared to steady state noise exposure of forest work. Hearing level correlated with serum cholesterol levels, use of analgesics, blood pressure and smoking. An elevated cholesterol level increased hearing loss in both the high- and low-exposure groups. The use of analgesics did not increase a permanent threshold shift in the low-exposure group, but did in the high-exposure group. Systolic blood pressure, smoking, cholesterol level and the use of painkillers explained 36 % of the variation in hearing level at 4 kHz, whereas noise exposure alone explained 25 % of the corresponding variation.



How to cite this article:
Toppila E, Pyykko I, Starck J, Kaksonen R, Ishizaki H. Individual risk factors in the development of noise-induced hearing loss.Noise Health 2000;2:59-70


How to cite this URL:
Toppila E, Pyykko I, Starck J, Kaksonen R, Ishizaki H. Individual risk factors in the development of noise-induced hearing loss. Noise Health [serial online] 2000 [cited 2021 Nov 28 ];2:59-70
Available from: https://www.noiseandhealth.org/text.asp?2000/2/8/59/31750


Full Text

 Introduction



The equal energy principle is widely adopted in the assessment of noise-induced hearing loss (NIHL). This means that different A-weighted exposures are combined to form a single exposure. For the large population exposed to industrial noise, the use of the equal energy principle gives a good estimate of the distribution of NIHL (ISO 1999-1990). Due to the great variation in NIHL, the equal energy principle cannot be used to predict individual permanent threshold shift (Royster, Lilley & Thomas, 1980; Royster & Royster, 1986).

One reason may be the inaccuracies in the evaluation of the exposure data. Problems arise from sampling techniques, and the selection and duration of samples (ISO 9612.2, 1997). The shortage of information on the use of hearing protective devices (HPDs) may cause inaccuracy in exposure calculation. In the estimation of noise entering the ear, the attenuation performance and the usage rate are recommended to be included (EN 458, 1993). An error in the evaluation of exposure to noise in individual cases may cause errors of up to 10 dB when attenuation data on HPDs is applied, and up to 25 dB if the effect of the usage rate is neglected (Toppila, Starck, Pyykko & Philstrom, 1998).

Advanced permanent threshold shift apart from noise exposure in population surveys has been explained to arise from biological and environmental factors (Hinchcliffe, 1973). Nevertheless, the data on NIHL in carefully controlled studies show considerable case-to­case variation, indicating that individual susceptibility also plays a significant role (Chung, Willson, Cannon & Manson, 1982; Pyykko, Koskimies, Starck, Pekkarinen, Farkkila & Inaba, 1989). Factors such as elevated blood pressure (McCormic, Harris, Hartley & Lassiter, 1982; Pyykko, Koskimies, Starck, Pekkarinen, Farkkila & Inaba, 1989), altered lipid metabolism (Rosen & Olin, 1965), smoking (Zelman, 1973; Pyykko, Starck, Pekkarinen & Farkkila, 1988; Starck, Toppila & Pyykko 1999b), consumption of salicylates (Pyykko, Koskimies, Starck, Pekkarinen, Farkkila & Inaba, 1989), and genetic factors (Hinchcliffe, 1973) are believed to aggregate in NIHL.

The purpose of the present study is to evaluate the role of different risk factors in the aetiology of NIHL by using the carefully collected data in the expert program on hearing NoiseScan (Starck, Pyykko, Toppila, Juhola, Philstrom & Auramo, 1998; Starck, Toppila & Pyykko, 1999a). We explored specifically, the possible association between NIHL and lipid metabolism, blood pressure, pain-alleviating medication and tobacco smoking.

 Subjects and Methods



Subjects

The cross-sectional study comprised three different groups of workers in Finland: Forest workers (N = 100) in Northern Finland in Suomussalmi county, shipyard workers (N = 179) in Helsinki in Southern Finland, and paper mill workers (N = 406) in Kuusankoski in Southeast Finland. In all 685 subjects, relevant data were obtained for each variable studied [Table 1]. The Suomussalmi county has only one major forest work employer. The study was carried out during a compulsory medical examination, in which all forest workers with permanent employment participated. The shipyard workers were examined in an annual health examination organised by occupational health care authorities based on occupational health care recommendation and litigation. The paper mill workers were surveyed by the local occupational health care unit and the examination was done in co-operation with occupational health care hygienists and medical personal. As the health care examinations were mandatory for noise exposed workers by the Finnish law the attendance for the survey was very high. The geographical areas of the study populations are covered historically by somewhat different disease profiles as the Northeast part of Finland has been a leading region in cardiovascular mortality (Mahonen & al, 1999). This difference in cardiovascular disease profile has decreased in Northeast Finland but still exceeds the mortality rates in South and Southeast Finland (Mahonen & al, 1999).

The individual lifetime work histories were collected interactively from the forest workers and by questionnaire from the paper mill workers and shipyard workers. The data was fed to expert program, NoiseScan (Starck, Pyykko, Toppila, Juhola, Philstrom & Auramo, 1998). In the study groups the estimation of exposure to noise was based on occupational histories and on-site noise exposure measurements in different occupations and working conditions (Pyykko, Pekkarinen & Starck, 1986).

Audiograms were taken by the local occupational health care centre for the papermill and shipyard workers, and measured in connection with the study for the forest workers. In all the studies a clinical audiometry (Madsen midimate 602, Madsen Corp.) was used allowing measurements with 5 dB intensity resolution according to the IEC 645-1 standard (IEC 645­1,1992). The measurements were made in sound­insulated boost, whose ambient sound pressure levels were lower than the levels specified in ISO 8253-1 (1989). TDH-39 head phones with cushions and standard head bands were used in the hearing measurement. The audiograms were calibrated before measurement in forest workers or annually in paper mill and shipyard to ensure the accuracy of the measurements (ISO 389). In addition to clinical audiometry, spontaneous, transient distortion product otoacoustic emissions were measured in all forest workers. The tests were carried out to examine whether any of the otoacoustic emission parameters would predict NIHL. The results of these studies are published elsewhere (Pyykko, Ishiza, Toppila & Starck. 1999).

The medical histories of the workers, use of drugs, serum cholesterol levels and blood pressure readings of the past three years were retrieved from the charts or queried. The use of analgesics and tobacco smoking was questioned. Special attention was paid to hearing protection - the types of hearing protective device (HPDs) used and the usage rates. All data were input into the database of NoiseScan, and in the case of missing data on previous exposures new questions were sent. The procedure was repeated twice, if needed. The cases with missing exposure were excluded from the database.

 Methods



Noise measurements were taken simultaneously outside and inside the HPDs. Inside the HPD, a miniature microphone was attached to the centre of the ear canal entrance. The microphone signal was fed to a signal analyser by a thin 0.3 mm thick cable to minimise leakage between the skin and cushion ring of the HPD (Pekkarinen, 1987). At different work sites, 10-min samples were recorded for the analysis of the A-weighted equivalent level and impulsiveness of the noise. A crest factor method was applied for the measurement of impulsiveness. The cumulative distribution function was analysed for the crest factor to determine the impulse noise index (Starck & Pekkarinen, 1987; Starck & Pekkarinen, 1988). The results from the questionnaire on occupational histories and usage of hearing protectors were used for the calculation of lifetime exposure to noise. The exposure data were used to calculate the A­weighted noise exposure level both without (LANO ) and with (LANI ) the protection efficiency of hearing protectors for each worker, applying the formulas:

[INLINE:1]

Where L'Ex = mean noise level inside the HPD

L Ex = mean noise level outside the HPD

t u = the time that the subject is not using HPDs in hours

T = length of working day

t E = length of work periods in years

A= mean real attenuation of the protector measured at workplace

Statistics

Linear regression analysis was used to search for the relationship between different variables. When the correlation with categorical variables was examined, Spearman's test was used, otherwise Pearson's correlation moment was used. When modelling noise-induced hearing loss, a stepwise linear regression analysis was made first. After finding relevant combinations, the model was tested with a general linear modelling technique in which various interactions were searched. In comparing risk factors within groups, the data were analysed after dividing the subjects into low-risk and high-risk groups based on the median value of the variable. When studying the effect of impulse noise on hearing, the matched pair technique was used to avoid the effect of confounding factors. LANI was matched with an accuracy of 2.5 dB

and age with an accuracy of 2 years. Altogether 39 matched pairs were selected. Student's t-test was used for comparing permanent threshold shift between the groups in order to evaluate the effect of impulse noise.

 Results



Factors linked to exposure

[Figure 1] shows the relationship between the noise exposure levels (L ANI ) of the workers and their age. Linear regression lines with 95 % confidence limits have been displayed for reference. The regression line agrees well with the energy concept for the whole group, as the doubling of exposure duration corresponds to a 3 dB increase in exposure level. The regression coefficient for the line gives 4 dB HL for the group. The correlation between exposure and age was statistically significant (r = 0.452, p ANO and L ANI for the workers were 107 dB 6 dB HL and 99 dB 9 dB HL, respectively. In L ANO the distribution was two peaked bars, illustrating the presence of two populations. [Figure 2]a The higher exposure level consists of forest and shipyard workers, and the lower exposure level of paper mill workers. In L ANI the difference in exposure between populations disappears due to differences in the usage rates and attenuation of HPDs [Figure 2]b.

A significant correlation existed between hearing level and L ANI (r = 0.316, p ANO (r = 0.305, p ANI could explain about 2 dB HL of permanent threshold shift and the L ANO about 1.8 dB HL at 4 kHz. The total permanent threshold shift of the subjects was 21.5 dB 20.3 dB HL at 4 kHz.

The impulse noise group consisted of shipyard workers, and the steady state noise group of forest workers and paper mill workers. The impulsiveness of noise caused an extra 12 dB HL permanent threshold shift (t=2.97, pANI level was 100.2 dB [Figure 3].

Factors linked to individual susceptibility Serum cholesterol level:

We found a significant correlation between the serum cholesterol level and hearing level (r = 0.194, p Blood pressure: Both the systolic and diastolic blood pressures correlated significantly with hearing level at all frequencies studied, and the highest correlation was observed at 4 kHz for systolic (r = 0.249, p Smoking: Effects of smoking were confounded by subjects who had given up smoking. Current smoking did not correlate with hearing level. We reclassified the subjects into never-smokers and those who had given up smoking, and current smokers. This analysis indicated that never­smokers had significantly better hearing at 4 kHz (r = 0.138, p Analgesics: The use of analgesics was analysed separately for prescription-free drugs and prescribed drugs. Also the frequency of use was analysed. The total use of analgesics correlated with hearing level at 4 kHz (r = 0.331, p Interaction of risk factors

We looked for combined interaction between total cholesterol, blood pressure and noise exposure. In the low-exposure group, the subjects with high cholesterol had significantly worse hearing at 4 kHz (F=14.2, p Modelling NIHL

A general linear model analysis was applied. In the model we used the mean hearing of both ears at 4 kHz as a dependent variable. The independent variables included were L ANI (p ANI , systolic blood pressure and total cholesterol and (b) LANI , tobacco smoking, and regular use of analgesics. It explained 36.4% of the variance of the HL . The model was statistically significant (F=14.42, p<0.001).

 Discussion



In Finland, NIHL was the leading occupational disease in the 1980s, accounting for about 2000 new cases every year, i.e. 0.08 % of the working population (Toikkanen, Vaaranen, Vasama, Jolanki & Kauppinen, 1997). In the 1990s, the number of new cases has been decreasing to about 1000 new cases annually, representing an incidence of 0.03 % . In Sweden NIHL is the third largest group of all occupational diseases. Approximately 3000 new cases of occupational hearing loss have been reported annually, indicating that every year 0.01-0.02 % of the labour force are injured by noise (Arbetslivsinstititutet, 1996) [Figure 6]. The development of hearing loss takes at least 10-20 years, which is also the time it takes for the controlling measures to become visible in statistics. Some other explanation must therefore lie behind the different structures of the curves. In Sweden, new rules for recording cases, more stringent rules for compensation, and seemingly reduced efforts in local hearing conservation programs, are factors causing the observed variability.

The present study showed that in a heterogeneous population consisting of forest workers, shipyard workers and paper mill workers with relatively different occupational noise profiles and living environment, the noise exposure (LANI), systolic blood pressure, serum

cholesterol level and use of painkillers correlated with permanent threshold shift separately and in combination. The connection between smoking and hearing level was confounded by cessation of smoking. Tobacco smoking is widely accepted as a risk factor related to vascular diseases, but its role in NIHL is controversial. Some authors (Drettner, 1975; Rosen, Pleste, Al­Mofty & Rosen, 1964) were not able to demonstrate that smoking could be a risk factor in NIHL, while some have demonstrated a correlation between smoking and HL ( Starck, Toppila & Pyykko 1999b).

The findings of Rosen et al (Rosen, Pleste Ef­Mofty & Rosen, 1964), and Rosen and Olin (1965) suggested that major cardiovascular risk factors, such as elevated total cholesterol, promote NIHL. The present study confirmed that total cholesterol significantly correlates with hearing level. The effect of total cholesterol on hearing may be mediated as a "small vessel disease", as indicated by Pillsbury (1986) but may also have a direct effect on outer hair cells in the cochlea. Oghlai et al (Oghlai, Nakagawa, Patell & Brownell, 1997) demonstrated cholesterol in the lateral wall of the outer hair cells and Nguyen and Brownell (1997) observed that elevated total cholesterol reduces the stiffness of the outer hair cells leading to deterioration of hearing.

Animal studies have indicated that hypertension accelerates age-related hearing loss (McCormic, Harris, Hartley & Lassiter, 1982; Borg, 1982). In man an association has been suggested between elevated blood pressure and hearing level (Johnsson & Hansson, 1977; Andren, 1980) but this result has also been disputed (Drettner, Hedstrand, Klockhoff & Svedberg & 1975). In the present study we found that elevated systolic blood pressure significantly correlates with the hearing level. Antihypertensive medication may partly mask the effect of elevated blood pressure on hearing, as in antihypertensive subjects under medication normal or near normal blood pressure values can be recorded (Pyykko, Koskimies, Starck, Pekkarinen, Farkkila & Inaba, 1989).

The acute, toxic effects of non-steroidal, anti­inflammatory type analgesics on hearing loss are well documented in the literature (Myers & Bersntein, 1965; Jung, Rhee, Lee, Park & Choi, 1993), but no studies have been published on its long-term effects. The present results indicate that the use of prescribed painkillers correlates with hearing level. This effect seems to be independent of the noise exposure of the subject. Salicylates were the most commonly used prescription-free drugs in the present study. After high doses of salicylates, very few morphological changes occurred in the inner ear (Myers & Bernstein, 1965). Hawkins (1967) was one of the first to demonstrate that salicylates reduce cochlear blood flow by inducing capillary narrowing. The narrowing of vessels appears to be caused by swollen endothelial cells and possibly pericyte contraction (Smith, Lawrence & Hawkins, 1985). In man, the critical ototoxic salicylemic level is high (Graham & Parker, 1948), corresponding to the ingestion of 10-15 g of salicylic acid in a day (Grifo, 1975). Eddy et al (Eddy, Morgan & Carney, 1976) demonstrated in experiments on chinchillas, that a temporary threshold shift produced by combined noise (85 dB) and salicylates (20-40 mg/100 g of animal tissue) was significantly greater (55 dB) than that produced by noise (35 dB) or salicylates (30 dB) alone. In the present study, we observed only a tendency that chronic and moderate use of salicylates promotes NIHL. Even if the effect of salicylates on hearing was less than 1 dB for the whole study population, such an increase may be substantial in some subjects and supports the idea that even moderate use of salicylates in conjunction with environmental noise may be hazardous to cochlear function.

The risk of NIHL was higher in occupations in which workers were exposed to impulsive noise. In the present study we found a 12 dB at 4 kHz difference in hearing level in an age- and exposure-matched population. This finding confirms our earlier study in which we demonstrated that shipyard workers had a 10 dB higher permanent threshold shift than could be predicted by the model, whereas the observed hearing levels were very consistent for forest workers (Starck & Pekkarinen, 1988). In many occupations the impulses are so rapid that their contribution to the energy content of noise is minimal.

The risk factors and their interactions explained 36 % of the permanent threshold shift. In our earlier studies we could explain about 50 % of the permanent threshold shift among paper mill workers (Nieminen, Pyykko, Starck, Toppila & Iki, 1998) and 40 % among forest workers (Starck, Pyykko, Toppila, Nieminen & Pekkarinen, 1995). The decrease can be explained by several factors. In this study the group was heterogeneous, which lead to greater variation. The highly impulsive noise in the shipyard also decreased the power of the model. The free time exposure to noise was not taken into account in this model. Many of the forest workers had hunted in their free time, which caused an additional exposure to shooting noise that could not yet be modelled. Military service which is mandatory in Finland exposes men to gun fire noise that was not taken into account in present model. Earlier we have shown that a recruit exposed to heavy weapon noise has an average 5 dB worse hearing at 4 kHz than a conscript exposed to hand held weapons (Pekkarinen, Iki, Starck, Pyykko, 1993). These changes were not included in the model and reduce the accuracy of the model. Also the factors linked to measurement of audiometry in the health care examinations may cause variability. As the vast majority has been measured repeatedly during the years the learning effect seems to be minimal (Royster and Royster 1986). The drop in the power of the model emphasises the need to take into account the above-mentioned factors. In the present study we did not study the interaction of occupational noise and free-time noise since there is no accepted method, which combines shooting noise with steady-state noise. The risk assessment of impulse noise lacks a generally accepted method.

The development of a sophistcatedsophisticated model is a complicated task and requires a well­controlled epidemiological data collection of different noise profiles and risk factors. Possibly joint European action against noise would provide a base for generation of such large database for more complete models as in the present work.

 Conclusion



A detailed analysis of various factors contributing to NIHL was carried out using 685 subjects. A significant contribution was de­monstrated by LANI, impulsiveness of noise, serum cholesterol level, elevated blood pressure, tobacco smoking, and use of analgesics. At high exposure levels, the use of analgesics and elevated systolic blood pressure have a significant influence. Using a linear model, 36 % of the variation in hearing loss at 4 kHz could be explained. In order to increase the power of the model, free time exposure and the effect of noise characteristics must be included to the model.[48]

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