Download Risk Markers for the Graded Severity of Auditory Processing

J Am Acad Audiol 16:348–356 (2005)
Risk Markers for the Graded Severity of
Auditory Processing Abnormality in an
Older Australian Population: The Blue
Mountains Hearing Study
Maryanne Golding*
Paul Mitchell†
Linda Cupples‡
Abstract
A recent report from a population-based survey of hearing loss in 2015 older
adults showed that the overall prevalence of central auditory processing (CAP)
abnormality was high at 76.4% using abnormality on one or more of seven
speech-based test outcomes as the criteria. The present study grouped these
test outcomes to reflect increasing severity of CAP abnormality and examined
the relationship between this graded dependent variable and 16 independent
variables. Logistic regression modeling suggested that moderate and severe
CAP abnormality increased with age and was associated with increased
hearing handicap, and men were more likely than women to show severe
abnormality. While 98.5% of the population passed a cognitive screening
assessment, declining cognitive function was still associated with the increased
likelihood of CAP abnormality.
Key Words: Aging, central auditory disorders, hearing loss
Abbreviations: AB = Arthur Boothroyd monosyllabic word list materials;
BMES = Blue Mountains Eye Study; BMES-2 = the five-year follow-up
examinations of the Blue Mountains Eye Study; BMHS = Blue Mountains
Hearing Study; CAP = central auditory processing; DSI = Dichotic Sentence
Identification test; HHIE = Hearing Handicap Inventory for the Elderly; HHIES = the shortened version of the Hearing Handicap Inventory for the Elderly;
MDSI = Macquarie Dichotic Sentence Identification test; MMSE = Mini Mental
State Exam; MSSI = Macquarie Synthetic Sentence Identification test; MSSImax
= maximum performance score on the Macquarie Synthetic Sentence
Identification test; PBmax = maximum performance score on the monosyllabic
word list materials; SF-36 = short-form 36 quality-of-life questionnaire;
SPSS(NOMREG) = Statistical Package for Social Sciences (nominal logistic
regression); SPSS(PLUM) = Statistical Package for Social Sciences (ordinal
logistic regression procedure); SSI = Synthetic Sentence Identification test;
SSI:ICM = Synthetic Sentence Identification test with Ipsilateral Competing
Message
*National Acoustic Laboratories, Chatswood, New South Wales, Australia; †Department of Ophthalmology, University of Sydney,
New South Wales, Australia (Westmead Millennium Institute Centre for Vision Research, Westmead Hospital); ‡Speech, Hearing and
Language Research Centre, Department of Linguistics, Macquarie University, New South Wales, Australia
Ms. M. Golding, National Acoustic Laboratories, 126 Greville St., Chatswood, NSW 2067, Australia; Phone: 612-9412-6925;
Fax: 612-9411-8273; E-mail: [email protected]
This paper was presented in part at the XVI National Conference and Workshops of the Audiological Society of Australia, May
17–20, 2004, Brisbane, Queensland, Australia.
This study was supported by the Australian Department of Health and Family Services (RADGAC grant) and the National Health
and Medical Research Council (Grants 974159, 991407).
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Risk Markers for Auditory Processing Abnormality/Golding et al
Sumario
Un reporte reciente de un estudio auditivo poblacional realizado en 2015
adultos mayores mostró que la prevalencia global de las anormalidades de
procesamiento auditivo central (CAP) era alta (76.4%), usando como criterio
la detección de alteraciones en el resultado de una o más de siete pruebas
lingüísticas. El presente estudio agrupó los resultados de estas pruebas para
reflejar la severidad creciente de la anormalidad en el CAP y examinó la
relación entre esta variable dependiente estimada y 16 variables independientes.
Un modelaje por regresión logística sugirió que la anormalidad moderada a
severa en el CAP aumentaba con la edad y que se asociaba con un aumento
en el impedimento auditivo, y que los hombres eran más propensos que las
mujeres a mostrar esta anormalidad en forma severa. Aunque un 98.5% de
las población pasó un evaluación de tamizaje cognitivo, el deterioro de la función
cognitiva fue asociada con un incremento en la posibilidad de hallar
anormalidades en el CAP.
Palabras Clave: Envejecimiento, trastornos auditivos centrales, pérdida
auditiva
Abreviaturas: AB = Listas de palabras monosilábicas de Arthur Boothroyd;
BMES = Estudio Visual de las Montañas Azules; BMES-2 = el examen de
seguimiento a cinco años del Estudio Visual de las Montañas Azules; BMHS
= Estudio Auditivo de las Montañas Azules; CAP = procesamiento auditivo
central; DSI = Prueba de Identificación de Frases Dicóticas; HHIE = Inventario
del Impedimento Auditivo del Anciano; HHIE-S = versión acortada del Inventario
del Impedimento Auditivo del Anciano; MDSI = Prueba de Identificación de
Frases Dicóticas de Macquarie; MMSE = Mini-Examen del Estado Mental; MSSI
= Prueba de Identificación de Frases Sintéticas de Macquarie; MSSImax =
puntaje máximo de desempeño de la Prueba de Identificación de Frases
Sintéticas de Macquarie; PBmax = puntaje máximo de desempeño con listas
de palabras monosilábicas; SF-36 = Cuestionario de calidad de vida 36 de
fórmula corta; SPSS(NOMREG) = Paquetes Estadísticos para Ciencias
Sociales (regresión logística nominal); SPSS(PLUM) = Paquete Estadístico para
Ciencias Sociales (procedimiento de regresión logística ordinal); SSI = Prueba
de Identificación de Frases Sintéticas; SSI:ICM = Prueba de Identificación de
Frases Sintéticas con Mensaje Competitivo Ipsilateral
A
ge-related central auditory processing
(CAP) abnormality has been described
in many studies with wide variations
in reported prevalence (Shiranian and Arnst,
1982; Jerger et al, 1989; Stach et al, 1990;
Cooper and Gates, 1991). In a recent report
from the population-based survey of hearing
loss in older Australians, known as the Blue
Mountains Hearing Study (BMHS), the
overall prevalence of CAP abnormality among
persons aged 55 years or older was found to
be high at 76.4%, using abnormality on one
or more of seven speech-based test outcomes
as the criterion (Golding et al, 2004).
These measures arose from the
administration of newly developed Australian
versions of the original Synthetic Sentence
Identification (SSI) test and the Dichotic
Sentence Identification (DSI) test, known as
the Macquarie Synthetic Sentence
Identification (MSSI) test and the Macquarie
Dichotic Sentence Identification (MDSI) test
(Golding, 2001), together with the Arthur
Boothroyd (AB) Monosyllabic Word Lists
recording from the National Acoustic
Laboratories (Travers, 1990). As the MSSI
and MDSI emulate the performance
characteristics of the original versions
(Golding, 2001), participants were categorized
as showing evidence of CAP abnormality
using the following criteria: (1) if the
maximum performance score for the AB Word
Lists (PBmax) minus the maximum
performance score for the MSSI (MSSImax)
was >20% in the right ear or in the left ear
(Hannley et al, 1983), or (2) if the MSSImax
ear score for the right ear or for the left ear
was poorer than expected given the previously
reported performance scores for normal
hearers and cochlear hearing loss (Yellin et
al, 1989), or (3) if the MDSI ear score for the
right ear or for the left ear was poorer than
expected given the previously reported
performance scores for normal hearers and
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Journal of the American Academy of Audiology/Volume 16, Number 6, 2005
cochlear hearing loss (Fifer et al, 1983; Jerger
et al, 1989), or (4) if the difference between
ears on the MDSI test was greater than
expected given the previously reported
performance scores when hearing loss is
evident and asymmetric (Fifer et al, 1983;
Jerger et al, 1989; Chmiel and Jerger, 1996).
More important than identification of
prevalence, however, is the functional
significance of CAP abnormality in this age
group. If CAP abnormality is found to have
no association with other subject variables
and if there is no demonstrable disadvantage
suffered by its presence, then identifying
intervention and preventive strategies
becomes redundant. Reports of associations
between CAP abnormality and risk markers
are limited in the literature to date. Cooper
and Gates (1991) used linear regression
modeling to examine the relationship between
performance scores from their chosen CAP
measures and age. They reported that age
accounted for a maximum of only 15% of the
variability in test scores and postulated that
the remainder of this variation arose from
other factors that occur as a consequence of
living and aging (Cooper and Gates, 1991).
They also subsequently reported that
performance scores on the Synthetic Sentence
Identification test with ipsilateral competing
message (SSI:ICM) were significantly
decreased in women who had a history of
heart attack or stroke but found no such
association in men (Gates et al, 1993).
A relationship between CAP abnormality
and cognitive decline in an older population
has also been studied with evidence to date
suggesting that these conditions may be
concomitant in some but certainly not all
cases (Jerger et al, 1989; Gates et al, 1995;
Strouse et al, 1995). CAP abnormality has also
been reported to precede the onset of
dementia in some cases (Gates et al, 1996).
More recently, Wiley et al (1998) reported
that after adjusting for hearing loss,
deteriorating age-related performance scores
for word recognition in competing noise were
observed, a finding more evident in men than
in women. The authors indicated that the
combined effects of age, gender, and hearing
loss for frequencies up to 8000 Hz only
accounted for approximately 50% of the
variability in participant word recognition
scores. Other factors such as age-related
changes in central auditory processing
pathways should therefore be considered as
350
possible contributors.
CAP abnormality, defined as reduced
performance on specific measures of auditory
processing, was found associated with an
increased perceived hearing handicap as
measured by the Hearing Handicap Inventory
for the Elderly (HHIE) (Ventry and Weinstein,
1982) when using the DSI test (Jerger et al,
1990; Chmiel and Jerger, 1996). There was,
however, no association found between CAP
abnormality and hearing handicap when the
SSI test was used (Strouse et al, 1995).
Other factors such as increased systolic
blood pressure, or history of hypertension,
cardiovascular disease, smoking and alcohol
consumption, diabetes and serum lipid levels
have been associated with peripheral hearing
loss in some but not all studies (Weston,
1964; Rosen et al, 1970; Gates et al, 1993;
Brant et al, 1996; Cruikshanks et al, 1998;
Dalton et al, 1998; Popelka et al, 2000; Itoh
et al, 2001; Mitchell, 2002). Whether these
factors are also associated with CAP
abnormality, however, is not known.
We aimed in the present study to
examine the relationship between the dichotic
speech-based CAP test outcomes stratified
into four groups (i.e., all normal, 1–2 test
abnormalities, 3–4 test abnormalities, 5–7
test abnormalities) and selected possible risk
markers including self-reported hearing loss
and selected lifestyle variables. We assumed
for the purpose of our study that the four-way
stratification reflected increasing severity of
CAP abnormality. The groups were thus:
normal (i.e., no abnormality), mild, moderate,
and severe CAP abnormality. Two maineffects multinomial logistic regression models
were used to assess the predictive power of
the independent variables to identify category
of membership correctly.
METHODS
Subjects
The BMHS, conducted during 1997–1999,
is a population-based survey of age-related
hearing loss in a representative older
Australian urban community (Sindhusake
et al, 2001). Participants are members of the
larger Blue Mountains Eye Study (BMES),
which is a widely reported longitudinal study
of eye and health-related changes in an older
population (Mitchell et al, 1995; Attebo et
Risk Markers for Auditory Processing Abnormality/Golding et al
al, 1996). The BMHS, conducted in
conjunction with the five-year follow-up
examinations of the BMES (known as BMES2), examined 2015 people aged 54–99 years
(mean 69.84 years; SD 8.56). Of the 2015
participants, 439 were excluded from taking
the CAP test battery for the following reasons:
(1) by the application of hearing loss criteria
(N = 216); (2) because no hearing thresholds
were obtained, thus negating calculation of
speech presentation levels (N = 12); (3) poor
eyesight or written/spoken language
difficulties (N = 55); (4) incomplete
assessments (N = 56); (5) frailty (N = 31); (6)
inadequate test time (N = 25); or (7) because
speech measures were unable to be completed
(N = 44). While all 1576 participants who
were administered the CAP abnormality test
battery were eligible for the analysis
described here, a complete dataset was
available for only 1192 participants (75.6%)
because of missing data on certain
independent variables. The 1192 participants
with complete data included 696 women
(mean age 68.2 years, SD 7.6) and 496 men
(mean age 68.3 years, SD 7.7). There was no
significant age difference between
participants with and without complete data
using an independent samples t-test (t[1572]
= 1.56; p > 0.05). No significant gender
difference between the two groups was
observed (χ2 = 0.144; df = 1; p > 0.05). There
were 273 participants with normal test
outcomes, 345 with mild CAP abnormality
(i.e., 1–2 test abnormalities), 311 with
moderate abnormality (i.e., 3–4 test
abnormalities), and 263 with severe
abnormality (i.e., 5–7 test abnormalities).
There were also no significant differences
between the two groups with respect to
grouped test outcomes (χ2 = 2.608; df = 1; p
> 0.05). While imputation of the missing
independent variable data was considered, we
felt this step was unnecessary because of the
relatively large size of the study sample.
Procedures
There were 11 categorical independent
variables entered to the models: (1) “Do you
feel you have a hearing loss?” (Yes, n = 529;
No, n = 663), (2) “gender,” (3) “history of
stroke” (Yes, n = 75; No, n = 1117), (4)
“diabetes” as diagnosed by a doctor and/or
evident by fasting glucose levels >7 mmol/L
(Yes, n = 114; No, n = 1078), (5) “employment”
(still employed, n = 232; Other, n = 960), (6)
“living alone” (Yes, n = 866; No, n= 326), (7)
“hypertension,” systolic blood pressure >160
mmHg or diastolic blood pressure >90 mmHg
or a history of high blood pressure, treated
with medication (Yes, n = 466; No, n = 726),
(8) “cardiovascular disease,” history of angina
and/or heart attack (Yes, n = 181; No, n =
1011), (9) “never smoked” (True, n = 623;
Other, n = 569), (10) “current smoker” (Yes,
n = 96; Other, n = 1096), and (11) “alcohol
intake” (None, n = 251; Light 1–6 drinks/week,
n = 499; Moderate 7–27 drinks/week, n =
410; Heavy ≥ 28 drinks/week, n = 32).
The five continuous variables entered
into the model were age, the score from the
shortened version of the Hearing Handicap
Inventory for the Elderly (HHIE-S) (mean
5.95, SD 7.89), the cognitive screening test
score using the Mini Mental State
Examination (MMSE) (Folstein et al, 1975)
(mean score 28.7, SD 1.6), systolic blood
pressure (mean 147.9, SD 20.5 mmHg), and
the summary score from the Short Form 36
(SF-36) quality-of-life questionnaire (SansonFisher and Perkins, 1998; Ware and Gandek,
1998) (mean score 74.4, SD 19.4).
Ordinal logistic regression (SPSS
[PLUM] version 11.5) models a graded
dichotomous dependent variable on a set of
predictors using a series of equations (P [Y
< j] = eu / 1 + eu; where “u” is the usual linear
regression equation, “Y” is the outcome, and
“j” indicates the category) (Tabachnick and
Fidell, 2001) with the first equation finding
the probability that a participant is below the
highest category (severe abnormality) and
the second equation finding the probability
that the participant is below the next category
(moderate abnormality). An equation is solved
for each category except the lowest, as it is
not possible for a case to be below that level.
As this form of modeling has limitations in
determining whether a significant predictor
variable is equally associated across all
categories of the dependant variable, a
nominal logistic regression model (SPSS
[NOMREG]) that provides this detail and
that does not assume increasing severity of
the condition was also used.
RESULTS
T
he adequacy of the observed and expected
frequencies for all independent variable
categorical data was confirmed using a series
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Journal of the American Academy of Audiology/Volume 16, Number 6, 2005
of cross tabulations. Less than 20% of cells
had an observed frequency less than five,
with no cells having expected outcomes less
than one, and therefore, there was no
restriction on the goodness-of-fit criteria used
to evaluate the model. There were no
violations of the linearity of the logit for any
continuous variables used in the model.
Using the ordinal logistic regression
technique, the -2 log likelihood final model
value of 2905.3 shows a significant difference
from that of the intercept only model (χ2 [18]
= 385; p < .001). Goodness-of-fit criteria, where
observed and predicted frequencies are
compared (and all independent variables were
included), showed an excellent fit (χ2 [3555]
= 2906; p = 1.00) by the Deviance and Pearson
criteria (χ2 [3555] = 3492; p = 0.771). The
Nagelkerke R2 of 0.295 suggests that 29.5%
of the variability in the dependent variable
(i.e., severity of CAP abnormality) could be
explained by this logistic regression model.
The Wald test of significance on
parameter estimates evaluated the
contribution of individual predictors to the
final model. Table 1 shows that of the 16
independent variables used to predict
membership within the four-category
dependent variable, only the HHIE-S score
(mean 6.0, SD 7.9), the MMSE score (mean
28.7, SD 1.6), age (mean 68.3, SD 7.6), and
gender were significant predictors using a p
≤. 005 significance level, chosen because of the
large number of predictor variables.
Table 2 shows significant parameter
estimates arising from the nominal logistic
regression model. The only significant
variable across all categories was the MMSE
score while HHIE-S score and age were
significant predictors of the moderate and
Table 1. Parameter Estimates for the Main-Effects Ordinal Logistic Regression Model
Correlation
estimate
SE
Wald test
df
Significance
Systolic BP
-0.00
.003
0.38
1
.537
HHIE-S
-0.03
.009
10.62
1
.001
SF-36
0.00
.003
4.99
1
.025
MMSE
0.38
.039
95.58
1
<.001
Age (per year)
-0.08
.009
78.76
1
<.001
Alcohol (none)
Alcohol (light)
Alcohol (mod.)*
-0.02
-0.10
0.29
.362
.350
.349
0.01
0.09
0.71
1
1
1
.946
.766
.401
Stroke
-0.08
.230
0.12
1
.733
Social
-0.36
.158
5.15
1
.023
Hypertension
0.22
.141
2.45
1
.118
Cardiovascular
0.11
.160
0.47
1
.495
Lives alone
0.16
.127
1.60
1
.206
Diabetes
-0.14
.185
0.53
1
.467
Never smoke
-0.09
.123
0.48
1
.490
Current smoke
-0.04
.213
0.04
1
.852
0.19
.139
1.80
1
.179
-0.66
.128
27.06
1
<.001
Hearing loss
Gender (male)
* When multiple categories exist, dummy variables were formed for all but one level of the predictor: in this case “heavy alcohol
intake.”
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Risk Markers for Auditory Processing Abnormality/Golding et al
Table 2. Significant Parameter Estimates from the Nominal Logistic Regression Model for Each
Category Except “Normal,” Which Is the Reference Category
Category
B
SE
Wald test
df
Significance
Odds Ratio (95% CI)
Severe abnormality
HHIE-S
MMSE
Age (per yr.)
Gender (male)
SF-36*
0.06
-0.72
0.13
1.39
-0.02
.02
.09
.02
.25
.01
8.54
72.26
59.71
32.04
7.59
1
1
1
1
1
.003
<.001
<.001
<.001
.006
1.06 (1.02–1.10)
0.49 (0.41–0.57)
1.14 (1.10–1.18)
4.02 (2.48–6.51)
0.98 (0.97–0.10)
Moderate abnormality
HHIE-S
MMSE
Age (per yr.)
0.05
-0.48
0.08
.02
.08
.02
9.05
34.01
27.96
1
1
1
.003
<.001
<.001
1.05 (1.02–1.09)
0.62 (0.53–0.73)
1.09 (1.05–1.12)
Mild abnormality
MMSE
-0.29
.08
12.20
1
<.001
0.75 (0.64–0.88)
* Borderline significant.
severe categories, and gender was significant
for the severe category only.
Adjusting for all other variables in the
model, the odds in favor of moderate and
severe CAP abnormality increased with each
additional year of age (for severe: OR = 1.14,
95% CI 1.102–1.178; and for moderate: OR =
1.086, 95% CI 1.053–1.119) and with
increasing HHIE-S score (for severe: OR =
1.06, 95% CI 1.02–1.10; and for moderate: OR
1.05, 95% CI 1.02–1.09). Gender was a
significant predictor for the severe category
only. Our data suggested that men were
substantially more likely to have severe CAP
abnormality than women (OR = 4.02, 95% CI
2.48–6.51). The MMSE score was a significant
predictor for all categories. For every point
increase in MMSE score, the odds in favor of
CAP abnormality decreased, with the greatest
change in odds occurring for the severe
category and the least for the mild category
(for severe: OR = 0.49, 95% CI 0.41–0.57; and
for moderate: OR = 0.62, CI 0.53–0.73; and for
mild: OR = 0.75, CI 0.64–0.88).
In summary, nominal and ordinal logistic
regression modeling indicated that moderate
or severe CAP abnormality was more likely
with increasing age; severe CAP abnormality
was more likely in men than women;
increasing hearing handicap was associated
with moderate and severe CAP abnormality;
and finally, declining cognitive function
increased the likelihood of CAP abnormality
at all severity levels.
DISCUSSION
I
n this study, we examined the relationship
between CAP abnormality and selected
subject variables. The independent variables
were selected because they had either been
previously associated with poor performance
on measures of CAP abnormality or were
common risk factors associated with
peripheral hearing loss but not previously
investigated for association with CAP
abnormality. Hearing loss was not included
as a predictor even though it is often identified
as a probable confounder in the interpretation
of CAP abnormality. The BMHS population
who underwent CAP testing had good hearing
with a mean pure-tone average better than
25 dB HL in both ears across all age groups,
as reported previously (Golding et al, 2004).
While the results from sentence-based CAP
measures are resistant to the influence of
peripheral hearing loss (Strouse et al, 1995),
and indeed the test outcomes utilized in this
study were corrected for the level of hearing
before classification, a recent study confirmed
that mild hearing impairment has no effect
on the outcomes of sentence-in-noise tests
provided that presentation levels are
adequate (Neijenhuis et al, 2004). The
addition of measured hearing as a predictor
was therefore considered unnecessary and
inappropriate.
Variables found to be significantly
associated with CAP abnormality were
limited to age, MMSE score, HHIE-S score,
and gender. Our failure to find significant
associations with CAP abnormality among
353
Journal of the American Academy of Audiology/Volume 16, Number 6, 2005
risk factors associated with peripheral
hearing loss, such as history of stroke,
cardiovascular disease, hypertension, systolic
blood pressure, alcohol, smoking, and
diabetes, could suggest that behavioral tests
of central auditory processes in general, or
perhaps those used in this study, were
insensitive to changes at the vascular level.
Alternately, the complexities and
redundancies within the central auditory
pathways might compensate for vascular
influences. Only one previous study reported
an association between decreased
performance scores on a measure of CAP
abnormality and history of heart attack or
stroke, with the association restricted to
women (Gates et al, 1993). Although this
relationship was not demonstrated in the
present study, it should be noted first that
only 6% of participants reported having
suffered a stroke and, second, that the results
presented here were dichotic outcomes arising
from raw test performance scores corrected
for hearing loss. This method of reporting
test outcomes was then very different to the
raw test scores used in the study reported by
Cooper and Gates (1991).
The categorical variables reflecting
general quality-of-life issues (i.e., living alone
and employment) also showed no significant
association with CAP abnormality in this
study. This nonsignificant finding may have
been due to the vague nature of these
questions that did not indicate how long the
person had been retired or lived alone. The
summary score of the SF-36 questionnaire,
on the other hand, demonstrated borderline
significance (B = -.016, p = .006) as a predictor
of severe CAP abnormality in the nominal
logistic regression model but was not
statistically significant (p = 0.025) in the
ordinal logistic regression model. This
apparent contradiction was not surprising
as the ordinal logistic regression model
examined the overall significance of variables
in predicting category membership whereas
the nominal model examined whether
significant predictors were equally significant
across all categories of severity. The borderline
association between severe CAP abnormality
and the SF-36 summary variable may suggest
that severe interference with auditory
processing may disrupt communication and
lead to a subsequent decline in quality of
life. While the detrimental effects of
increasing measured hearing loss on quality
354
of life have recently been reported (Dalton et
al, 2003), the possible association found
between severe CAP abnormality and quality
of life, independent of measured hearing loss,
has not been previously reported. Further
clinical studies of this relationship may thus
be warranted.
The HHIE-S score, which aims to assess
the impact of hearing loss on the emotional
and social needs of the individual (Ventry
and Weinstein, 1982, 1983), was a significant
predictor when CAP abnormality was at least
moderate (abnormality observed for three or
more test outcomes). It has previously been
shown that hearing handicap is a predictor
for auditory processing outcomes (Jerger and
Chmiel, 1997) with other studies suggesting
that this relationship may be evident only
when participants are drawn from a clinical
population where a high rate of reported
hearing difficulties is expected (Strouse et al,
1995). In the present study, 44.4% of
participants felt they had a hearing loss, yet
the mean pure-tone average was better than
25 dB HL in the poorer ear. The predictive
relationship between the HHIE-S score and
CAP abnormality therefore suggests that
participants feel the impact of CAP
abnormality on their emotional and social
needs when it is of at least moderate degree.
These patients should therefore be assisted
in finding appropriate rehabilitative
strategies to ensure a better quality of life.
Our study also showed that age was a
significant predictor for both moderate and
severe CAP abnormality but was not related
to mild CAP abnormality. Age has already
been identified as a risk factor for measured
hearing loss in population studies (Brant et
al, 1996; Klein et al, 2001; Mitchell, 2002) and
has been considered to account for around
15% of the variability in CAP test scores
(Cooper and Gates, 1991). However, age has
not previously been identified as a risk factor
for moderate and severe CAP abnormality
independent of other frequently identified
confounding factors such as hearing loss and
cognitive change.
Our measure of cognitive function
(MMSE score) was a significant independent
predictor for all categories of CAP
abnormality. An orderly progression in this
relationship by severity was seen with the
regression coefficient becoming more negative
with increasing CAP severity (mild CAP
abnormality: B = -.286; moderate CAP
Risk Markers for Auditory Processing Abnormality/Golding et al
abnormality: B = -.479; severe CAP
abnormality: B = -.721). Although the mean
MMSE score for BMHS participants was well
within the normal range at 28.7 (SD 1.6),
subtle cognitive decline must be considered
a risk marker for CAP abnormality.
Gender was the final significant predictor
of CAP abnormality identified in this study,
with men more likely than women to show
severe CAP abnormality. Gender, however, did
not predict mild or moderate CAP
abnormality. This pattern could suggest that
men are predisposed to more severe CAP
abnormality, or it could simply reflect the
underlying nature of the tests used to
establish abnormality, some of which were
dichotic and known to show gender bias
(Jerger et al, 1994). As gender differences
have also been described for measured
hearing loss independent of other risk factors
(Pearson et al, 1995; Jonsson and Rosenhall,
1998; Mitchell, 2002), this finding warrants
further investigation.
This study represents the first
comprehensive review of risk markers for
CAP abnormality. After adjusting for all other
variables in the models, gender, cognitive
decline, age, and HHIE-S score were the only
CAP abnormality risk markers identified in
this population with the impact of these
variables most evident for severe CAP
abnormality. It should be acknowledged that
these models, although relatively
comprehensive in terms of the number and
range of independent variables entered,
explained only 29.5% of the variability of
CAP abnormality with other predictor
variables remaining unidentified. Further
investigation of these factors and the
influences of specific tests used to categorize
CAP abnormality will be important to
understand more fully the significance of
these predictor variables.
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