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 𝛿𝑠 is 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
90
100
110
110
80
90
dB
dB
dB
dB
dB
Non-Veteran Firearms
2
12
23
24
Non-Veteran
4
20
39
Veteran High Noise
4
20
Veteran Low Noise
4
9
6 kHz
110
dB
110
dB
100
dB
12
24
24
24
24
46
26
46
46
46
46
28
30
14
32
32
32
32
24
26
14
26
26
26
26
dB
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/noisenavigator-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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comparisons. Journal of Research on Educational Effectiveness, 5, 189-211.
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/.