Noise Exposure History Interview Questions

1. A. How often (never, rarely, sometimes, usually, always) did your military service cause

you to be exposed to loud noise(s) where you would have to shout to be heard? (For

example, loud equipment or trucks, loud ship or jet engines, or the rifle range.)

B. Were you wearing hearing protection when this occurred (never, rarely, sometimes,

usually, always)?

2. A. How often (never, rarely, sometimes, usually, always) did or do non-military jobs

cause you to be exposed to loud noise(s) where you would have to shout to be heard?

B. Were you wearing hearing protection when this occurred (never, rarely, sometimes,

usually, always)?

3. A. How often (never, rarely, sometimes, usually, always) did or do recreational activities

cause you to be exposed to loud noise(s) where you would have to shout to be heard?

(For example, power tools, motorcycles, etc.)

B. Were you wearing hearing protection when this occurred (never, rarely, sometimes,

usually, always)?

4. A. How often (never, rarely, sometimes, usually, always) have you been exposed to

sudden intense noise? (For example, explosions, cannon fire, gun shot, etc.)

B. Were you wearing hearing protection when this occurred (never, rarely, sometimes,

usually, always)?

Noise Exposure Group Assignment Based on Interview Questions

Veterans with a response to 1.A., 2.A., 3.A. or 4.A. of “usually” or “always” with hearing

protection use less than “always” were assigned to the Veteran High Noise group. All other

Veterans were assigned to the Veteran Low Noise group.

Non-Veterans with a response to 2.A. or 3.A. of “usually” or “always” with hearing protection

use less than “always” were excluded. Non-Veterans who did not respond “never” to 4.A. were

excluded.

Lifetime Exposure to Noise and Solvents – Questionnaire (LENS-Q) Example Questions

Sample Calculation of LENS-Q Score

Participant reports:

Working in construction for 2.5 years, with exposure to loud noise daily and use of

hearing protection “some of the time”.

Using a pistol several times a year over 4 years, with hearing protection use “most of the

time”.

Attending rock concerts several times a month over 10 years, with hearing protection use

“never”.

1. Noise exposure activity intensity levels based on publically available measurements (Berger

2015; National Acoustic Laboratories 2015, described in Beach et al. 2013):

Construction – 94 dBA

Pistol – 157 dB SPL

Rock Concert – 104 dBA

2. Intensity levels corrected for reported hearing protection use (“never” = - 0 dB, “some of the

time” = -5 dB, “most of the time” = -10 dB, “always” = -15 dB) – 15 dB was used as a

conservative estimate of the real-world attenuation across frequencies and different types of

hearing protection devices based on Berger 2003:

Construction – 94 - 5 = 89 dBA

Pistol – 157 - 10 = 147 dB SPL

Rock Concert – 104 - 0 = 104 dBA

3. Corrected intensity level assigned a weight starting at a weight of 1 for 80 dB and doubling

with every 3 dB increase (eg. 2 for 83 dB, 4 for 86 dB, 8 for 89 dB, etc.):

Construction – 8

Pistol – 4.39 x 1012

Rock Concert – 256

4. Weight multiplied by duration (in years) and frequency (“never” = 0, “several times a year =

5”, “several times a month” = 30, “several times a week” = 100, “daily” = 300) of exposure:

Construction – 16 x 2.5 x 300 = 12000

Pistol – 4.39 x 1012

x 4 x 5 = 8.78 x 1013

Rock Concert – 256 x 10 x 30 = 76800

5. Exposures are summed: 12000 + 8.78 x 1013

+ 76800 = 8.78 x 1013

6. Final LENS-Q score = log10 (8.78 x 1013

) = 13.94

Detailed Description of Bayesian Regression Analysis

Let yi denote the ith

measurement of the 893 total auditory brainstem response (ABR)

wave I amplitude measurements in Supplemental Data Table 1. ABR wave I amplitudes are by

definition positive numbers (µV), so we assume that the yi are lognormal random variables with

parameters µi and |ξ|. According to this model, the expected ABR wave I amplitude of the ith

observation is exp(µi +ξ2/2). All Normal distribution scale parameters correspond to the standard

deviation in this development.

The parameter µi is parsed into the sum of group effects and subject effects on the ith

observation. The group effect on the parameter µi is modeled as

𝐺𝑟𝑜𝑢𝑝 𝐸𝑓𝑓𝑒𝑐𝑡 = 𝑎0𝑔[𝑖]+ 𝑎1𝑔[𝑖]

∙ 𝐿𝑣𝑙𝑖 + 𝑎2𝑔[𝑖]∙ 𝐹𝑟𝑒𝑞𝑖 + 𝑎3𝑔[𝑖]

∙ 𝐿𝑣𝑙𝑖 ∙ 𝐹𝑟𝑒𝑞[𝑖] + 𝜆𝐹𝑟𝑒𝑞,𝐿𝑣𝑙[𝑖]. (1)

Equation (1) is two-dimensional response surface showing log mean amplitude as a function of

frequency and level, with the shape of each surface depending on noise-exposure group. The

notation g[i] denotes group membership of the ith

observation in Supplemental Data Table 1

(Gelman et al. 2013; Konrad-Martin et al. 2015), and the notation Freq,Lvl[i] denotes the

frequency-level combination of the ith

observation in Supplemental Data Table 1. The

level/frequency specific intercept λ in equation (1) models non-linearities in µi that are otherwise

poorly characterized by the linear component of the model.

The parameters in equation (1) are hierarchical random effects such that

a0 ~ Normal(β0, |𝜎𝑎0|),

a1 ~ Normal(β1, |𝜎𝑎1|),

a2 ~ Normal(β2, |𝜎𝑎2|),

a3 ~ Normal(β3, |𝜎𝑎3|).

This is a hierarchical model of the group effects centered at β0, β1, β2, and β3, which is the

population two-dimensional response surface of log median wave I amplitude by stimulus level

and frequency (equation 2 in the main text of the article). If the data provide little information

about group variability (i.e. if there is little evidence of noise exposure effects), then the σ

parameters are close to zero, and the regression coefficients in equation (1) are ‘shrunk’ towards

the overall level and frequency effects given by β0, β1, β2, and β3. In this way the hierarchical

model controls against ‘false discoveries’ of important group effects in a manner analogous to,

though more easily interpretable, than classical multiple testing corrections such as Bonferroni

(Gelman et al. 2012).

We expect subjects to vary in their ABR responses, which are modeled with a subject

effects model

𝑆𝑢𝑏𝑗𝑒𝑐𝑡 𝐸𝑓𝑓𝑒𝑐𝑡𝑠 = 𝛿𝑠[𝑖] + 𝛾𝑆𝑒𝑥,𝐿𝑣𝑙,𝐹𝑟𝑒𝑞[𝑖] + 𝜂𝐿𝑣𝑙,𝐹𝑟𝑒𝑞[𝑖] ∙ 𝐷𝑃𝑂𝐴𝐸[𝑖], (2)

where DPOAE[i] is the maximum DPOAE level associated with the ith

observation in

Supplemental Data Table 1. The parameter 𝛿𝑠is a subject-specific random intercept allowing

some subjects, indexed by s, to have more or less robust ABR wave I amplitudes. The

parameters 𝛾 and η are stimulus level- and frequency-specific DPOAE effects (η) and sex effects

(γ) on wave I amplitude. As in the group effects model, the parameters in equation (2) are

hierarchical random effects so that

δ ~ Normal(0, |τδ|),

η ~ Normal(θ, |τη|),

γSex,Lvl,Freq ~ Normal(ψSex, |τγ|).

The level/frequency specific effects of sex are centered at ψSex, which are also random effects

with ψSex~ normal(0, |τψ|). The subject effects model is a hierarchical model with each individual

subject-specific effects centered at their group means given in equation (1). Stimulus level and

frequency-specific DPOAE effects on log wave I amplitude, denoted by η, are centered at the

overall DPOAE effect θ. Stimulus level and frequency-specific effects denoted by γ are similarly

defined.

By modeling the parameter µ as the sum of group effects and subject effects we can

separate impacts of noise exposure on wave I amplitude from confounders such as sex and

DPOAE amplitude, as well as ‘unmeasured confounders’ in the subject-specific intercepts.

Model parameters were fit using a Bayesian approach (Gelman et al. 2013). Cauchy (2)

priors were assumed for all σ as well as τδ and τη parameters, so that |σ| are half-Cauchy random

variables, which are weakly informative. τγ is given a more informative Normal (0, 0.5) prior,

which effectively reduces expected variability among the sex by level by frequency interactions.

The τψ and ξ parameters were also given Normal (0, 0.5) priors. The overall DPOAE effect

parameter θ was given a Normal (0.1, 2) prior, which corresponds to roughly 10% increase in

mean wave I amplitude per dB increase in maximum DPOAE level though with a wide margin

of uncertainty.

Priors on the parameters β0, β1, β2, and β3 were particularly important as they describe the

relationship between stimulus effects of level and frequency on the ABR response. We used a

Normal (-2.3, 1.3) prior for β0, which centers the average median wave I amplitude at about 0.1

µV. The β1 parameter, which describes the level effect, was given a Normal (0.35, 0.2 prior). The

β2 parameter was given a Normal (0.8, 0.7) prior, and the interaction effect β3 was given a

Normal (0, 0.1) prior.

One thousand mean response surfaces were generated from these priors and are shown in

Supplemental Data Figure 1, below. The solid line is the prior median in each frequency-level

combination, and the shaded region shows the interquartile range of the prior mean. We

anticipated growth in mean wave I amplitude with increasing level and frequency, and wave I

amplitude was not expected to be more than approximately 1 µV. Otherwise, there was

considerable prior variability in the possible relationship between level, frequency, and mean

wave I amplitude.

The analysis was conducted using SAS software, version 9.4. Gelman-Rubin

convergence diagnostics were computed from five separate chains with random initial values.

Convergence was satisfactorily achieved with 20,000 posterior iterations thinned to every 50th

sample following a 10,000 sample burn-in.

Supplemental Data Table 1. ABR Wave I Amplitude Measurement Data Structure

1 kHz 3 kHz 4 kHz 6 kHz

90

dB

100

dB

110

dB

110

dB

80

dB

90

dB

100

dB

110

dB

110

dB

Non-Veteran Firearms 2 12 23 24 12 24 24 24 24

Non-Veteran 4 20 39 46 26 46 46 46 46

Veteran High Noise 4 20 28 30 14 32 32 32 32

Veteran Low Noise 4 9 24 26 14 26 26 26 26

Each cell shows the number of replications with an identifiable auditory brainstem response

(ABR) wave I amplitude for each group, stimulus frequency, and stimulus level combination.

Supplemental Data Figure 1. Prior Simulations

One thousand mean wave I amplitude response surfaces were generated from the priors. The

solid line shows the prior median for each frequency-level combination and the shaded region

shows the interquartile range of the prior mean wave I amplitude.

References

Beach, E. F., Gilliver, M., Williams, W. (2013). The NOISE (Non-occupational incidents,

situations and events) database: A new research tool. Annals of Leisure Research, 16,

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Berger, E. (2003). Hearing Protection Devices. In E. Berger, L. Royster, J. Royster, et al. (Eds.),

The Noise Manual (pp. 379-454). Fairfax, VA: American Industrial Hygiene Association.

Berger, E. (2015). Noise Navigator sound level database with over 1700 measurement values.

Retrieved December 2015, from http://multimedia.3m.com/mws/media/888553O/noise-

navigator-sound-level-hearing-protection-database.pdf?&fn=Noise%20Navigator.xlsx.

Gelman, A., Carlin, J. B., Stem, H. S., et al. (2013). Bayesian Data Analysis. (3rd ed.). London:

CRC Press.

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Konrad-Martin, D., Billings, C. J., McMillan, G. P., et al. (2015). Diabetes-Associated Changes

in Cortical Auditory-Evoked Potentials in Relation to Normal Aging. Ear Hear.

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http://noisedb.nal.gov.au/.