Evaluation of the risk of noise-induced hearing loss among ...

Evaluation of the risk of noise-induced hearing loss among unscreened male industrial workers Mary M. Princea) Industrywide Studies Branch, Division of Surveillance, Hazard Evaluations, and Field Studies, National Institute for Occupational Safety and Health, 4676 Columbia Parkway, Cincinnati, Ohio 45226

Stephen J. Gilbert, Randall J. Smith, and Leslie T. Stayner Risk Evaluation Branch, Education and Information Division, National Institute for Occupational Safety and Health, 4676 Columbia Parkway, Cincinnati, Ohio 45226

共Received 4 April 2001; revised 27 August 2002; accepted 8 November 2002兲 Variability in background risk and distribution of various risk factors for hearing loss may explain some of the diversity in excess risk of noise-induced hearing loss 共NIHL兲. This paper examines the impact of various risk factors on excess risk estimates of NIHL using data from the 1968 –1972 NIOSH Occupational Noise and Hearing Survey 共ONHS兲. Previous analyses of a subset of these data focused on 1172 highly ‘‘screened’’ workers. In the current analysis, an additional 894 white males 共609 noise-exposed and 285 controls兲, who were excluded for various reasons 共i.e., nonoccupational noise exposure, otologic or medical conditions affecting hearing, prior occupational noise exposure兲 have been added (n⫽2066) to assess excess risk of noise-induced material impairment in an unscreened population. Data are analyzed by age, duration of exposure, and sound level 共8-h TWA兲 for four different definitions of noise-induced hearing impairment, defined as the binaural pure-tone average 共PTA兲 hearing threshold level greater than 25 dB for the following frequencies: 共a兲 1– 4 kHz (PTA1234), 共b兲 1–3 kHz (PTA123), 共c兲 0.5, 1, and 2 kHz (PTA512), and 共d兲 3, 4, and 6 kHz (PTA346). Results indicate that populations with higher background risks of hearing loss may show lower excess risks attributable to noise relative to highly screened populations. Estimates of lifetime excess risk of hearing impairment were found to be significantly different between screened and unscreened population for noise levels greater than 90 dBA. Predicted age-related risk of material hearing impairment in the ONHS unscreened population was similar to that predicted from Annex B and C of ANSI S3.44 for ages less than 60 years. Results underscore the importance of understanding differential risk patterns for hearing loss and the use of appropriate reference 共control兲 populations when evaluating risk of noise-induced hearing impairment among contemporary industrial populations. 关DOI: 10.1121/1.1536635兴 PACS numbers: 43.50.Qp, 43.64.Wn 关MRS兴

I. INTRODUCTION A. Background

Similar to many chronic diseases, occupational noiseinduced hearing loss 共NIHL兲 has a multi-factorial etiology. Risk factors found to explain most of the variability in hearing loss risk are increasing age and long-term exposure to continuous noise in occupational settings. There is also a large body of scientific literature suggesting that hearing loss risk among human populations may also depend on other endogenous and exogenous factors 共Nakanishi et al., 2000; Fechter, 1999兲 other than age and occupational noise exposure. Intrinsic factors, acting within the body to affect risk, include race, gender, and certain medical conditions 共high blood pressure, diabetes, etc.兲 and family history 共Jerger et al., 1993; Gates et al., 1993; Duck et al., 1997; Brant et al., 1996; Klein et al., 2001; Melamed et al., 2001兲. Conversely, exogenous factors include nonoccupational noise exposure 共hunting, loud musical bands, and other loud hobbies兲, ototoxic chemicals, smoking, social class, education, a兲

Electronic mail: mmp3@cdc.gov

J. Acoust. Soc. Am. 113 (2), February 2003

and certain health-related behavioral factors 关use of hearing protection devices 共HPDs兲, work environmental factors, access to medical care兴. One review of the controlled research concluded that the influence of many intrinsic variables is relatively small and cannot explain the wide range of hearing loss observed in epidemiologic studies 共Henderson et al., 1993兲. In another review of the literature, Ward 共1995兲 concluded that susceptibility has not been clearly shown to be dependent on gender, skin color, any known diseases, mental attitude toward the noise, exposure history, or preexposure hearing loss. Ward further noted that it was possible that uncontrolled variables or unrecognized drug or chemical and noise interaction may obscure the relation between noise exposure and hearing loss. These intrinsic and exogenous risk factors can be difficult to control for in epidemiologic studies because of the inability to statistically separate the effects of highly correlated risk factors over time and/or the mediating effects of noise 共either by decreasing or increasing susceptibility兲 on these factors through an intermediate causal pathway. For example, consider the literature on hypertension and noise, an area with numerous research studies, which, on the

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whole, show contradictory and inconclusive results 共Babisch, 1998; Thompson, 1983; Dijk, 1990; DeJoy, 1984; PasschierVermeer, 1993兲. Much of the literature in this area fails to adequately consider use of HPDs among industrial workers in both earlier and contemporary studies when HPD availability became more prevalent due to government and state regulations 共Davis and Sieber, 1998; Royster and Royster, 1984兲. Proper use of HPDs by individuals exposed to noise is a confounding variable in assessing risk of hearing loss or hypertension due to noise exposure because it is associated with both the exposure of interest 共noise兲 and the outcomes 共hearing loss and possibly hypertension兲. It is also likely to be an effect modifier on the study results 共Talbott et al., 1996兲. In an experimental study 共Ising et al., 1980兲, blood pressure readings and urinary secretion of catecholmaines was lower on days workers wore hearing protection as compared to days they were unprotected during exposures of average noise levels of 95 dBA. The effective reduction in sound pressure level was measured to be between 10 and 16 dBA in this study. Hence, when HPD use is not accounted for in the analysis, misclassification of workers by noise exposure level may occur, thereby affecting inferences regarding exposure-response relationships.

B. Study relevance and purpose

Current standards 共ANSI S3.44, 1996; ISO-1999, 1990; OSHA, 1983兲 examining the effects of noise on hearing loss are based on surveys conducted during the 1970s when hearing protection was not extensively used in general industry and workers were exposed to steady-state continuous noise environments 共NIOSH, 1972; Baughn, 1966; PasschierVermeer, 1968; Burns and Robinson, 1970兲. The underlying data and models used to develop risk-damage criteria for these standards were based on highly ‘‘screened’’ noiseexposed and control 共exposed to less than 80 dB daily 8-h average兲 populations. The ‘‘screened’’ populations excluded individuals with various risk factors 共medical, otologic兲 associated with hearing loss and nonoccupational noise exposure 共hunters, military service, prior job-related exposure, other sources of recreational noise兲. The exclusion of these workers was deemed necessary to mimic controlled laboratory experiments. However, with the advent of epidemiologic methods, sampling methods, and statistical models for analysis of population-based health data, risk factors associated with hearing loss and nonoccupational noise exposure are routinely adjusted for in analyses. The results of analyses of 1172 共380 controls and 792 noise-exposed兲 white male workers, which represented a population screened to exclude otologic, medical and other sources of noise exposure, has been previously published 共NIOSH, 1972; Lempert and Henderson, 1973; Prince et al., 1997兲. However, the unscreened data that include the previously excluded white males have not been available for analysis until recently. Generalizing prior work 共based on a highly screened population兲 to an unscreened population of workers would allow inferences to be drawn for working populations that are more representative of workers enrolled in industrial hearing conservation programs 共HCPs兲. 872

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The main objectives of this analysis are to examine 共1兲 baseline risk of age-related hearing loss among unscreened low noise-exposed industrial workers; 共2兲 the impact of common risk factors for hearing loss on excess risk estimates of NIHL; and 共3兲 variability in excess risk among unscreened noise-exposed workers relative to the screened subpopulation. Excess risk estimates from this population are calculated to examine variability due to factors other than noise exposure on hearing threshold level. The risk profile of the unscreened population has been compared to those of the screened population in a previous paper 共Prince, 2002兲. In this paper, the ‘‘screened’’ population will refer to the original 1172 workers analyzed previously 共NIOSH, 1972; Prince et al., 1997兲, while those excluded from the original analysis are referred to as the ‘‘excluded’’ population (N ⫽894). The total population of workers examined in this analysis, formed by pooling the ‘‘screened’’ and ‘‘excluded’’ ONHS subpopulations, is referred to as the ‘‘unscreened’’ population. II. METHODS

A comprehensive description of the study methods, population characteristics, and descriptive analysis of hearing levels and impairment rates by age and other risk factors are found in Prince 共2002兲. A. Data analysis

1. Outcome definition

NIOSH 共1972兲 used the term ‘‘material impairment’’ to define its criteria for maximum acceptable hearing loss, and OSHA later used a slightly modified term, ‘‘material impairment of hearing’’ to define the same criteria 共OSHA, 1983兲. In this context, a worker was considered to have a material impairment of hearing when his or her binaural pure-tone average at the audiometric frequencies 1, 2, and 3 kHz exceed 25 dB. NIOSH recently changed its definition of material impairment to include the frequencies 1, 2, 3, and 4 kHz in the binaural pure-tone average 共NIOSH, 1998兲. In this analysis, four definitions of noise-induced material impairment, defined as binaural pure tone averages 共PTA兲 across the following frequencies, were examined: 共a兲 PTA averaged over both ears for 0.5, 1, and 2 kHz 共herein referred to as PTA512); 共b兲 PTA averaged over both ears 1, 2, and 3 kHz (PTA123); 共c兲 PTA averaged over both ears for 1, 2, 3, and 4 kHz (PTA1234); and 共d兲 PTA averaged over both ears for 3, 4, and 6 kHz (PTA346). 2. Covariates

Variables such as age and 8-h time-weighted average 共TWA兲 sound levels were examined as continuous factors while categories of duration of exposure 共2– 4, 5–10, ⬎10 years) were defined in as in previous publications 共Prince et al., 1997; NIOSH, 1972兲 to facilitate comparison of results. Exclusion conditions were coded as originally described by Lempert and Henderson 共1973兲 and later by Prince 共2002兲 to include medical and otologic conditions, previous job noise exposure, and nonoccupational sources of noise 关hunting, loud music, military noise exposure 共weapon Prince et al.: Risk of noise-induced hearing loss

TABLE I. Description of models examined in analysis. Model no.

Parameters included

Description of logistic regression model

1

Intercept ⫹Age 共continuous兲 ⫹(Duration (2 – 4 yrs.)⫻noise level) ␾ ⫹(Duration (5 – 10 yrs.)⫻noise level) ␾ ⫹(Duration (⬎10 yrs.)⫻noise level) ␾ where ␾ ⫽parameter describing shape of dose-response

Parameters describe effects due to age, duration, and noise exposure in the population and assume that risk is independent of screening status. This is the original model developed for 1172 screened ONHS population 共Prince et al., 1997兲.

2

Model 1 parameters ⫹second intercept term for subgroup that failed screening procedure

Second intercept term represents an adjustment to baseline risk due to other risk factors. Model allows for an adjustment in the intercept 共baseline risk兲 for subgroup that failed the screen 共i.e., screened and unscreened ONHS have different intercepts兲. This model best described the relationship of risk of material hearing impairment for three definitions (PTA1234 , PTA123 and PTA346).

3

Model 2 parameters ⫹second age term for subgroup that failed screening procedure

Second age term represents an adjustment to the effect of age due to other risk factors. Model allows for an adjustment in the intercept 共baseline risk兲 and age for subgroup that failed the screen 关i.e., screened and unscreened ONHS have different intercepts 共baseline risk兲 and age effects兴. This model best described the relationship of risk of material hearing impairment for the PTA512 definition.

4

Model 3 parameters ⫹second set of terms for each duration category 共multiplied by noise level to denote noise dose兲 for subgroup that failed screening procedure

Second set of duration terms represents an adjustment to the effect of dose due to other risk factors. Model allows for an adjustment in the intercept 共baseline risk兲, age, and duration exposed for subgroup that failed the screen. Screened and unscreened ONHS have different intercepts 共baseline risk兲, age, and duration effects. The adjustments for duration were not necessary in describing the relationship of risk of material hearing impairment for any of the four definitions examined.

and nonweapon sources兲, and pretest noise兴. For purposes of the risk evaluation, individuals with pretest noise were excluded from the analysis. To ensure adequate sample size for analysis, the other exclusion criteria were collapsed into two groups for risk evaluation: 共a兲 medical conditions and 共b兲 history of noise exposure 共from nonoccupational sources and previously held jobs兲.

3. Research questions

The methods of analysis of these data are addressed within the context of these questions: 共1兲 Does background risk of material hearing impairment in a cross-sectional sample of workers depend on whether the population is screened for conditions not associated with occupational exposure? 共2兲 What is the impact of various factors on the excess risk of material hearing impairment? To evaluate the first question, models allowing an overall adjustment for risk factors other than occupational noise exposure was applied to the unscreened 共combined兲 sample of workers. The second question was evaluated using the model developed for the first question but with systematic inclusion of workers 共with a given set of characteristics兲 to the screened population to examine the impact of various risk factors 共i.e., medical conditions, history of noise exposure兲 on excess risk of noise-induced hearing impairment. J. Acoust. Soc. Am., Vol. 113, No. 2, February 2003

4. Statistical models

The quantitative relationship between material hearing impairment and the covariates 共defined above兲 was modeled using logistic regression methods 共Breslow and Day, 1980兲. These logistic regression models were fit using the nonlinear minimization 共nlminb兲 routine in S-Plus 共MathSoft, 1997兲. This routine was used instead of the usual logistic function to allow for nonlinearity associated with the shape of doseresponse. Further details of model development have been previously published 共Prince et al., 1997兲 and statistical and technical details of the current model will be available in a future technical report. A qualitative description of the various models used in this analysis are shown in Table I. These models differs from previous analyses 共Prince et al., 1997; Model 1 in Table I兲 in that separate parameters were added to test whether risk of material hearing impairment among the screened and excluded subpopulations differed with respect to baseline risk in the population, age, and category of duration of exposure. To examine the impact of different sets of risk factors for hearing loss 关medical conditions, history of noise exposure prior to the study 共past noise兲兴 on population excess risk for a particular definition of impairment, an indicator for these risk factors was added to the model 共Table I兲 to allow for different baseline risks, age, and duration effects between the screened and excluded subpopulations. 5. Excess risk estimation

Excess risk for a particular age is defined as the difference between the prevalence of material hearing impairment Prince et al.: Risk of noise-induced hearing loss

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TABLE II. Prevalence of material hearing impairment 共25-dB fence兲 by age, exposure status, and PTA definition among the screened and unscreened ONHS subgroups.a % Prevalence (⬎25-dB fence兲 by subgroup 关number/total at given age兴 Controls

Exposed

Material hearing impairment definition

%

No./Total

%

No./Total

%

No./Total

%

No./Total

%

No./Total

%

No./Total

⬍35

PTA512 PTA1234 PTA346

2.1 4.3 9.0

关4/188兴关8/188兴关17/188兴

3.6 7.6 16.3

关10/276兴关21/276兴关45/276兴

6.8 14.8 31.8

关6/88兴关13/88兴关28/88兴

6.5 12.0 22.7

关19/291兴关35/291兴关66/291兴

10.2 16.3 29.0

关47/459兴关75/459兴关133/459兴

16.7 23.8 39.9

关28/168兴关40/168兴关67/168兴

35– 49


5.5 19.5 28.1

关7/128兴关25/128兴关36/128兴

7.9 25.8 45.6

关20/252兴关65/252兴关115/252兴

10.5 32.3 63.7

关13/124兴关40/124兴关79/124兴

14.3 36.0 54.9

关44/308兴关111/308兴关169/308兴

20.5 42.0 59.6

关123/587兴关247/587兴关350/587兴

28.3 48.7 64.9

关79/279兴关136/279兴关181/279兴

⭓50


17.2 35.9 62.5

关11/64兴关23/64兴关40/64兴

25.2 51.1 71.8

关34/135兴关69/135兴关97/135兴

32.4 64.8 80.3

关23/71兴关46/71兴关57/71兴

42.5 60.6 77.7

关82/193兴关117/193兴关150/193兴

40.7 61.1 77.8

关137/336兴关207/336兴关260/336兴

38.5 62.9 76.9

关55/143兴关90/143兴关110/143兴

Age group in years

a

Screened

Unscreened

Excluded

Screened

Unscreened

Excluded

Excluded workers with pretest noise. Subgroups refer to screening and exposure status.

among the noise-exposed population given exposure duration and the sound level, and the corresponding prevalence among controls. The excess risk associated with exposure to noise evaluated at a given age was estimated from logistic models using the following relationship: Excess Risk⫽Pr关 Y ⫽1兩 age, duration, and intensity of noise exposure兴 ⫺Pr关 Y ⫽1兩 age,control兴 ,

共1兲

where Y ⫽1 if material impairment of ⬎25 dB is observed and Y ⫽0 if material impairment of ⭐25 dB is observed. Hence, background risk is assumed to be equivalent to the prevalence of age-related material hearing impairment. Correspondingly, excess risk is assumed to be equivalent to the increase in this background risk associated with occupational noise exposure 共adjusting for other known risk factors in the population兲.

III. RESULTS A. Impairment rates by age and exposure status

The distribution of impairment rates by age and exposure status 共exposed or control兲 for different fences (⬎25 dB, ⬎30 dB, ⬎40 dB) and PTA definitions (PTA1234 , PTA123 , PTA346 , PTA512) was examined for the screened and unscreened 共combined兲 ONHS groups. Table II shows the prevalence using the 25 dB fence by age, exposure status, and subgroup. The prevalence of impairment was highest for the PTA346 definition and lowest for the PTA512 definition irrespective of the fence used. The differences in prevalence between the screened, excluded, and unscreened 共combined兲 groups were most marked among controls. Across all age groups, the highest prevalence of impairment was observed among the excluded control population, followed by the unscreened population and the screened population 共the lowest impairment rates兲. Among exposed subgroups, the difference between the populations becomes smaller with increasing age. 874


B. Risk evaluation

1. Choice of models

The results of model fits and statistical evaluation of nested models indicate differential risk profiles depend on the definition of material hearing impairment. For all definitions examined, separate parameters for the intercept were added to account for differing baseline risks of hearing impairment for the screened and excluded populations 共Model 2, Table I兲. However, the addition of separate age slopes for the screened and excluded subpopulations was significant for the PTA512 definition, suggesting that the effect of age on risk of material hearing impairment for a screened population may differ from that of an unscreened population 共Model 3, Table I兲. The observation that the slope for the effect of age was smaller for the excluded than the screened population can be explained by the fact that a larger percentage of the excluded population had already developed material hearing impairment, leaving a smaller fraction of the population at risk of subsequently developing hearing impairment as they age. Hence, all risk estimates and inferences made in this analysis are based on model 3 共Table I兲 for PTA512 and model 2 共Table I兲 for all other impairment definitions (PTA1234 , PTA123 , PTA346). 2. Background risk of material hearing impairment

Fitted relationships of risk as a function of age among the controls and exposed groups for several definitions of material hearing impairment are shown in Table III. Examination of background risk is based on estimates of risk among controls, adjusting for separate age effects 共for PTA512) and separate intercept 共for all definitions兲 between screened and excluded subpopulations. As shown in Table III, the excluded ONHS population had the highest background risk of material hearing impairment, followed by the unscreened group, and the screened population, which had the lowest background risk at all ages and for all definitions of material impairment. The implications of this difference in background risk of hearing impairment by age is evaluated with respect to noise exposure and duration of exposure. Prince et al.: Risk of noise-induced hearing loss

TABLE III. Comparison of background risk of material impairment for screened, excluded, and unscreened 共combined兲 ONHS population for different impairment definitions. Population by screening status

Background risk 共%兲

Screened (n⫽1172) Unscreened (n⫽2045) a Excluded (n⫽873) a

Age 30 years

Age 45 years

Age 65 years

PTA512

Screened 共model 1兲 Unscreened 共model 3兲 Excluded 共model 3兲

1.9 5.2 8.8

7.46 12.6 17.5

34.6 37.4 37.5

PTA123


2.9 7.3 10.5

10.0 18.6 25.8

38.7 48.3 59.8

PTA1234


6.9 12.3 17.4

19.9 29.7 39.5

55.1 64.3 74.6

PTA346


12.5 23.5 32.8

34.2 48.1 60.8

74.2 80.3 87.8

Definition

a

The unscreened and excluded populations omitted 21 workers with pretest noise. The estimates of background risk for the unscreened population are a weighted average of the screened and excluded population background risk estimates using weights of 1172 and 873, respectively. Estimates for the excluded population were based on fitting models 3 and 2 in Table I with 2045 workers and solving equations for parameters associated with failing the screen.

3. Excess risk of noise-induced hearing impairment by screening status

Figure 1 compares excess risk of material hearing impairment for three definitions (PTA346 PTA512 , PTA1234) by sound level 共dBA兲, age, and duration exposed 共ages 30, 45, and 65 years with 2– 4, 5–10, and ⬎10 years, respectively兲 among the excluded and screened ONHS subpopulations. The curve of excess risk labeled ‘‘combined’’ is a weighted average of the screened and excluded curves with weights equal to the number of screened (N⫽1172) and excluded 关 N⫽(984⫺21)⫽873兴 people, respectively. Patterns of risk depended on age, duration exposed, population, and defini-

tion of impairment. Estimates of lifetime excess risk 共age 65, duration ⬎10 years) become less similar among the excluded and unscreened ‘‘combined’’ groups with the screened group showing somewhat higher excess risks for definitions that include the higher frequencies (PTA346 , PTA1234). For PTA1234 , excess risks begin to differ by screening status at levels greater than 90 dBA, with differences being most marked at older ages, where the screened excess risks are the highest. A similar pattern is observed for PTA346 with no difference by screening status at age 30 and 2– 4 years of exposure and slightly greater differences in the combined 共unscreened兲 versus screened and excluded sub-

FIG. 1. Excess risk of material impairment by age, duration exposed, noise level, and population.


Prince et al.: Risk of noise-induced hearing loss

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TABLE IV. Excess risk percent and 90% confidence limits for PTA-1234 definition of material hearing impairment among the unscreened and screened ONHS population.

8-h TWA sound level 共dBA兲

Unscreened populationa

Screened populationb

Excess risk percent with 90% confidence limits: 共lower, upper limits兲

Excess risk percent with 90% confidence limits: 共upper, lower limits兲

80 85 90 95 100

Age 30, duration exposed 2–4 years 0 共0.0, 0.4兲 0.2 0.5 共0.1, 1.5兲 0.9 3.3 共1.1, 5.8兲 3.3 11.5 共4.5, 19.2兲 10.2 32.0 共14.0, 54.8兲 30.1

80 85 90 95 100

Age 0.0 0.8 5.0 18.7 51.1

30, duration exposed 5–10 years 共0.0, 0.5兲 0.2 共0.0, 0.9兲共0.3, 2.1兲 1.4 共0.4, 2.8兲共2.4, 7.9兲 5.6 共2.6, 8.9兲共9.3, 29.5兲 19.5 共10.2, 31.4兲共28.5, 78.0兲 57.3 共30.3, 86.2兲

80 85 90 95 100

Age 0.0 2.9 18.2 50.9 68.8

45, duration exposed 共0.0, 2.0兲共1.4, 7.2兲共13.2, 23.7兲共45.7, 57.4兲共66.2, 72.0兲

⬎10 years 1.0 共0.0, 3.6兲 5.8 共2.2, 10.6兲 22.1 共14.5, 28.8兲 56.8 共49.4, 65.5兲 78.0 共73.1, 81.9兲

80 85 90 95 100

Age 0.0 3.1 15.5 30.5 35.4

65, duration exposed 共0.0, 2.1兲共1.5, 7.7兲共11.4, 20.5兲共26.7, 35.9兲共31.1, 41.2兲

⬎10 years 1.5 共0.0, 5.7兲 8.1 共2.9, 14.8兲 23.1 共15.1, 30.8兲 39.1 共32.0, 47.2兲 44.5 共36.2, 52.9兲

共0.0, 共0.2, 共0.9, 共2.9, 共8.5,

0.7兲 2.2兲 6.7兲 21.3兲 63.5兲

a

Excess risk estimates were based on model 2 in Table I and are a weighted average of screened and excluded subpopulation excess risk estimates using weights of 1172 and 873, respectively. b Excess risk estimates were based on models described in Prince et al. 共1997兲.

populations at older ages and long exposure durations. For PTA512 , differences are observed at age 30 with 2– 4 years of exposure but not at older age groups 共45 and 65 years兲 and longer durations of exposure 共5–10 and ⬎10 years).

Table IV shows excess risk estimates with 90% confidence limits for the PTA1234 definition among the screened and unscreened populations for different ages and duration categories. In general, the screened population has higher excess risks than the unscreened population, but these differences do not appear to be significant among younger workers 共30 years of age兲 with short exposure durations 2– 4 years, 5–10 years or among all workers with noise levels less than 95 dBA. The underlying model used in evaluating excess risk among the unscreened population assumed different background risk by screening status. Based on the 90% confidence limits for lifetime excess risk estimates 共age 65, ⬎10 years duration exposed兲, there appears to be significant differences in excess risk estimates between the screened and unscreened group for 8-h time-weighed average exposures greater than 90 dBA. Due to data sparseness for levels 85 dBA and lower, it is likely that increased variability and greater uncertainty in estimating excess risk would make it difficult to discern differences in risk at lower levels of exposure and among younger workers.

4. Effect of different risk factors on excess risk

The additional risk due to various risk factors among the ‘‘screened’’ population is shown in Fig. 2. The curves define the following populations: 共i兲共ii兲共iii兲

Screened—original 1172 screened workers with no known risk factors for hearing loss other than occupational noise. Past noise—original 1172 screened workers plus those who failed screening because they had a history of noise exposure before the study. Medical—original 1172 screened workers plus those who failed screening due to medical conditions that might affect risk of hearing loss.

Patterns of excess risk were similar for the PTA1234 and PTA123 definitions so results are only presented for three

FIG. 2. Excess risk of material impairment for different definitions by age, duration, sound level, and exclusion criteria.

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FIG. 3. Background risks by age and impairment definition for ONHS unscreened controls compared to Annex B and Annex C, ANSI S3.44 共1996兲.

definitions (PTA1234 , PTA512, PTA346). The screened population curve represents the dose-response associated with occupational noise. The curves associated with past noise and medical conditions among the unscreened population represent the additional risk over and above cumulative noise among the screened population for each risk factor examined. As shown in Fig. 2, the impact of certain risk factors depends on age and the definition of impairment used. Among younger workers 共age 30兲 with shorter exposure durations 共2– 4 years兲, background risk of hearing loss due to age is small 关see Figs. 2共a兲–共c兲兴, and it is assumed that any observed excess is attributed to the extra risk associated with factors other than age. For these workers 共age 30, 2– 4 years exposed兲, the largest effect on excess risk appears to be medical conditions for all definitions. In contrast, the effect of past noise tends to lower excess risk of material impairment using the PTA346 definition, which includes the most sensitive hearing threshold frequencies for noise, whereas there is no effect on excess risk for the PTA1234 definition from this factor. As the exposed population reaches middle age 关see age 45, Figs. 2共d兲–共f兲兴, the effects on excess risk due to past noise are nominal for most exposures ranges using the PTA1234 and PTA512 definitions, while distinctly lower excess risks due to this factor are observed for the PTA346 definition. Compared to the screened population, medical conditions generally increased excess risk for the PTA512 definition 共for levels below 100 dBA兲 and decreased excess risk associated with the PTA346 definition. The increased excess risk of hearing impairment for the PTA512 definition may be due to lowfrequency hearing losses caused by medical conditions or unknown etiological factors other than noise and age. For estimates of lifetime excess risk 关Figs. 2共g兲–共i兲兴, the effects of factors such as medical conditions and past noise exposure become nominal for the PTA512 definition. However, these risk factors tend to lower lifetime excess risks estimates when using definitions that include 3 or 4 kHz (PTA1234 , PTA346). Although data are not shown in this report, the effect of pretest noise on excess risk is noteworthy in that its impact J. Acoust. Soc. Am., Vol. 113, No. 2, February 2003

on excess risk is greatest for the PTA346 definition. Since contamination of audiometric test results by pretest noise exposure might be assumed to occur periodically over time, its effect on risk estimates would affect the frequencies most sensitive to noise 共i.e., 3, 4, and 6 kHz兲, resulting in artificially inflated excess risk estimates. 5. Comparison of ONHS background risk to ANSI S3.44 (Annex B and C)

The purpose of this analysis is to compare background risk of material impairment among controls from the unscreened 共combined兲 ONHS data to Annex B and Annex C 共ANSI S3.44-1996兲, which also represent unscreened populations. The background risk among controls from the ONHS unscreened population represent a ‘‘weighted’’ risk and is calculated as follows: Control 共 background兲 Risk ⫽ 关380共 Background RiskPassed兲 ⫹283共 Background RiskFailed兲兴 /665,

共2兲

where Passed⫽Screened population 共 380 is number of controls passing the screen兲 , Failed⫽Excluded population 共 283 is the number of controls who failed the screen兲 . As shown in Fig. 3, the risks generated from the ONHS control population are similar to those obtained from Annex B and C of ANSI S3.44 共1996兲, for the PTA1234 , PTA123 and PTA346 definitions for most age groups. Risks associated with the PTA512 definition are more variable by age with the ONHS population having higher risks than those generated from Annex B and Annex C of ANSI S3.44 共1996兲. The ONHS population risks are more similar to Annex C estimates of risk for most definitions, except PTA512 and PTA346 at age 50. For the PTA512 and PTA1234 definitions, there apPrince et al.: Risk of noise-induced hearing loss

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pears to be more divergence in risk estimates for ages 50 years and above. This is consistent with the predicted mean HTL generated from the ONHS data and the Annex B and C of ANSI S3.44 共Prince, 2002兲. The higher risks for the PTA512 definition among the ONHS population are likely due to artificially higher 0.5 kHz HTLs due to high background audiometric test booth levels. Other possible explanations for the difference in risk predicted from the ANSI S3.44 models and the ONHS unscreened data are discussed below. IV. DISCUSSION

Variability in background risk patterns across populations may explain some of the diversity in risk due to noise. This analysis suggests that populations with lower background risks of material hearing impairment tend to have greater excess risks of noise-induced hearing impairment. We found that 共a兲 background risk increases with increasing age and test frequency; 共b兲 excess risk of noise-induced impairment was generally higher in the screened population than the unscreened population, especially at higher noise levels, longer durations of noise exposure, and older ages; and 共c兲 the impact of medical conditions and past noise exposure affect baseline risk in the population but may not contribute greatly to lifetime excess risk of occupational noise exposure. Noise-induced damage to hearing is cumulative and increases with increasing duration of exposure and intensity. If background risk of hearing impairment is relatively high due to prior noise exposure or to other risk factors for hearing loss, the maximum excess risk due to subsequent noise should be smaller. The modeling results support the conclusion that background risks among screened and unscreened populations are different for all definitions examined. The coefficients for the intercepts for the screened population were smaller than for the excluded population for most definitions except PTA512 . This suggests that the screened population background risk is lower than the unscreened population. In addition to different intercepts, the results for PTA512 also suggest that the excluded population has a smaller slope for age than the screened population. It is possible that the apparent diminished effect of age on excess risk may be due to a greater burden of background risk among workers in the excluded population at these lower frequencies. Similarly, the proportion of the total risk attributable to noise is less easily detected in the unscreened population. Although the magnitude of excess risk varies by whether a population is screened or not 共i.e., has a smaller or larger burden of nonoccupational causes兲, the overall patterns of excess risk by sound level, age, duration exposed, and all definitions of material impairment are qualitatively similar for the screened and unscreened populations. For definitions that include the frequencies most sensitive to noise damage 共3, 4, and 6 kHz兲, a plateau in excess risk is observed after 10 years of exposure among workers older than 45 years, which is most marked at or above 95 dBA 共Fig. 1兲. This plateau occurs because the expected proportion of the population with HTLs exceeding 25 dB 共i.e., beginning material impairment兲 becomes relatively large as duration and intensity of sound exposure increases. Further878


more, the impact of pretest noise over time may artificially inflate excess risks with increasing age and duration of exposure. These results underscore the importance of understanding differential risk patterns for hearing loss in working populations. The choice of reference populations for comparing risk of noise-induced hearing among industrial populations should carefully consider risk factors for hearing loss unrelated to noise exposure, other sources of noise exposure, or medical conditions associated with hearing loss. The dearth of such comparison populations for epidemiological studies of hearing loss underscores the need to utilize the data that is currently available. However, there are certain caveats to their use that should be considered. A. Strengths and weaknesses of the underlying data and models

The current data analysis is limited to white males because raw data collected on white female workers has been lost. Summary hearing threshold level statistics for the female ONHS population are referenced in two government documents 共NIOSH, 1972; OSHA, 1983兲. The models used in this analysis extend those used and developed for the screened ONHS population 共Prince et al., 1997兲 by evaluating differential risk patterns depending on screening status, age, duration of exposure, and baseline risk. Therefore, other functional forms for describing the relationship of noise and hearing loss risk in an unscreened population have not been considered or evaluated in this analysis. The outcome for analyses was defined as the probability of hearing loss greater than 25 dB, which limits exploration of whether the effect of noise on hearing loss becomes more or less severe as the threshold fence exceeds 25 dB. An alternative approach would be to express the outcome as a change in the distribution of hearing thresholds due to noise exposure 共i.e., noise-induced permanent threshold shift or NIPTS兲 to explore implications for relevant etiology. For example, if a large proportion of the population has risk factors that interact synergistically with noise, thereby increasing susceptibility, the excess risk due to occupational noise among an unscreened population might increase in some cases. In addition, analysis of hearing threshold levels as a continuous variable in the model would provide more statistical power in evaluating whether the risk of crossing a 35- or 40-dB fence is as important as the risk associated with crossing a 25-dB fence. Nonetheless, hearing impairment definitions using a 25-dB fence remain valid hearing health outcomes for purposes of identifying risk of early damage to hearing when medical or public health interventions would be most likely to have the greatest impact on preventing moderate to severe hearing loss in the population. 1. Assessments of exposure and outcome

The underlying data were collected using audiometric testing procedures and noise measuring instruments available in the 1970s. Therefore, care must be taken when attempting to directly compare the distribution of noise exposure and hearing loss from the ONHS population to contemporary populations 共post-1990兲. Prince et al.: Risk of noise-induced hearing loss

An inherent assumption made in the evaluation of exposure-response relationships is that errors in outcome and exposure measures are nominal, or at best, distributed in such a manner that they do not bias estimates of effect. Exposure misclassification is therefore a valid concern in any analysis involving exposed human populations. In the ONHS survey, noise measurements were based on sound level meter readings 共using state-of-the-art monitoring equipment of the 1970s兲 and involved taking representative samples of tasks within a job to calculate an 8-h time-weighted average 共TWA兲 exposure. For some jobs, relatively short duration 共about 10–15 min兲 samples were taken to estimate 8-h TWA noise levels. Such short sampling periods would be of concern if there is considerable job mobility and the number and types of tasks and noise levels vary on a daily basis. However, the ONHS study focused on sampling stable jobs with continuous, steady-state noise exposures. If there was any indication of high variability during the day, NIOSH investigators either observed the worker for longer periods or excluded the data from analysis 共Lempert and Henderson, 1973兲. While some degree of exposure misclassification cannot be ruled out, such misclassification is expected to be small and effects on risk estimates would be limited. Application of these models for populations exposed to intermittent or highly variable exposure conditions should be conducted with caution because the underlying data and models assume that workers stayed in the same job for the entire period of employment and that they were exposed to steady-state noise. Comparison of noise measurement data 共8-h TWAs兲 from this study to contemporary populations using dosimetry-based 8-h TWA estimates should also be conducted with care due to differences in the precision of instrumentation over time 共Earshen, 2000兲. Direct comparisons of risks across exposed populations from the same time period 共ISO-1999, 1990; ANSI S3.44-1996, 1996兲 remain valid. Audiometric testing was conducted using a mobile test booth in conformance with ANSI S3.1-1969 共ANSI, 1960兲 and manual audiometers that were calibrated under ANSI S3.6-1969 共ANSI, 1969兲. Comparison of hearing threshold data collected using automated audiometers and more recent ANSI calibration standards may require that adjustments be made to standardize measures when comparing audiometric data across study populations. Hearing threshold levels obtained by self-recording techniques are generally slightly better than those obtained with manual techniques 共Burns and Hinchliffe, 1975; Knight, 1996; Harris, 1980兲. Nonetheless, risk estimates across populations can be validly compared if control and exposed populations are drawn from the same sample population 共e.g., internal comparison group兲 and study procedures for measuring hearing and noise are consistent between exposure and control groups within the population. B. Reference „control… populations: Implications for risk evaluation

There is increasing interest in assessing hearing change trends for groups of noise-exposed workers enrolled in industrial HCPs who are followed longitudinally over time in comparison to age-adjusted reference 共control兲 populations. J. Acoust. Soc. Am., Vol. 113, No. 2, February 2003

These populations can be compared to ANSI S3.44-1996 共ANSI, 1996兲 modeled predictions of hearing loss by age, to the ONHS control data, or to the Baltimore Longitudinal Study of Aging 共BLSA兲共Morrell et al., 1991兲. If observed changes in hearing are no greater than that expected due to age, then it is often assumed that the program is effectively protecting workers. While this approach has the advantage of simplicity, further analysis would be necessary to identify whether observed differences 共elevated HTLs above those expected from age兲 actually reflect variability due to factors other than a poor HCP. For example, if there are workers with intermittent exposure to noise, then multiple comparison populations may be examined, such as 共a兲 nonindustrial control populations 关Morrell et al., 1991; Royster and Thomas, 1979; NCHS, 2001 共future NHANES III adult audiometry data兲兴 or 共b兲 unscreened industrial control populations 共ANSI S3.44, 1996兲 as a means of defining a range of acceptable values from which HCP data may be compared. V. CONCLUSIONS

Quantitative assessments of the relationship and magnitude of NIHL that use screened populations have some advantages including improved identification of susceptible worker populations, better estimation of their risks associated with noise, and a reduction of variability in non-noise related factors for hearing loss. However, unscreened populations can provide valuable information on the burden of hearing loss in the general population, particularly among non-noise exposed industrial populations. Moreover, use of unscreened low noise-exposed data are useful in comparing HCP databases, particularly if they can be drawn from populations likely to have similar risk factor profiles for other causes of hearing loss. For example, many chronic disease studies in human populations draw comparison populations from individuals living in the same geographical area as the exposed workers 共i.e., community controls兲 or from workers in the same or similar plants having low or no exposure as a means for partially controlling for unknown risk factors that might increase the prevalence of disease in the population. Nevertheless, the present analysis suggests that differences in the distribution of background risk factors between screened and unscreened industrial populations should be controlled for in analyses of exposure-response relationships to avoid bias in risk estimates. ACKNOWLEDGMENTS

The authors wish to thank the journal peer reviewers for their insightful review and comments. We also acknowledge the technical support of Bing Xue, Xiangdong Zhou, and Ruishan Wu in producing graphical displays of the data and to Ryan Elmore for his assistance in developing the statistical models for this paper. ANSI 共1960兲. ANSI S3.1-1960, ‘‘American National Standard Maximum Permissible Ambient Noise Levels for Audiometric Test Rooms’’ 共American National Standards Institute, New York兲. ANSI 共1969兲. ANSI S3.44-1969, ‘‘American National Standard Determination of Occupational Noise Exposure and Estimation of Noise-induced Hearing Impairment’’ 共American National Standards Institute, New York兲. Prince et al.: Risk of noise-induced hearing loss

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