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Racial-Ethnic Differences in Word Fluency and Auditory Comprehension Among Persons With Poststroke Aphasia
Accepted Manuscript Racial-Ethnic Differences in Word Fluency and Auditory Comprehension Among Persons with Post-Stroke Aphasia Charles Ellis, PhD, Richard K. Peach, PhD PII:
S0003-9993(16)31228-X
DOI:
10.1016/j.apmr.2016.10.010
Reference:
YAPMR 56717
To appear in:
ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION
Received Date: 8 August 2016 Revised Date:
28 September 2016
Accepted Date: 7 October 2016
Please cite this article as: Ellis C, Peach RK, Racial-Ethnic Differences in Word Fluency and Auditory Comprehension Among Persons with Post-Stroke Aphasia, ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION (2016), doi: 10.1016/j.apmr.2016.10.010. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Race and Aphasia Outcomes
Charles Ellis, PhD1 Richard K. Peach, PhD 2
Department of Communication Sciences & Disorders, East Carolina University, Greenville, NC, USA
Department of Communication Disorders and Sciences, Rush University Medical Center, Chicago, IL, USA
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Racial-Ethnic Differences in Word Fluency and Auditory Comprehension Among Persons with Post-Stroke Aphasia
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Corresponding Author: Charles Ellis Ph.D. CCC-SLP Department of Communication Sciences and Disorders Communication Equity and Outcomes Laboratory East Carolina University 3310H Health Sciences Building, MS 668 Greenville, NC 27834 Phone: 252-744-6098 Fax: 252-744-6109 Email: [email protected] Word Count: 2999
Acknowledgements/Conflicts of Interest: None Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
ACCEPTED MANUSCRIPT Race and Aphasia Outcomes 1 2
Racial-Ethnic Differences in Word Fluency and Auditory Comprehension Among Persons with Post-Stroke Aphasia
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes ABSTRACT
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Objective: To examine aphasia outcomes and to determine whether the observed
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language profiles vary by race-ethnicity.
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Design: Retrospective cross sectional study using a convenience sample of persons of
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with aphasia (PWA) obtained from AphasiaBank, a database designed for the study of
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aphasia outcomes.
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Setting: Aphasia research laboratories in the US.
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Participants: 381 (339 Whites and 42 Black) persons with aphasia (PWA).
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Interventions: N/A
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Main Outcomes and Measures: Western Aphasia Battery-Revised (WAB-R) total scale
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score (aphasia quotient) and subtest scores were analyzed for racial-ethnic differences.
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The WAB-R is a comprehensive assessment of communication function designed to
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evaluate PWA in the areas of spontaneous speech, auditory comprehension, repetition
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and naming in addition to reading, writing, apraxia and constructional, visuospatial, and
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calculation skills.
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Results: In univariate comparisons, Black PWA exhibited lower word fluency (5.7 vs
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7.6; p=.004), auditory word comprehension (49.0 vs. 53.0; p=.021) and comprehension
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of sequential commands (44.2 vs. 52.2; p=.012) when compared to White PWA. In
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multivariate comparisons, adjusted for age and years of education, Black PWA
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exhibited lower word fluency (5.5 vs 7.6; p=.015), auditory word recognition (49.3 vs
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53.3; p=.02) and comprehension of sequential commands (43.7 vs 53.2; p=.017) when
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compared to White PWA.
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes Conclusions: This study identified racial-ethnic differences in word fluency and auditory
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comprehension ability among PWA. Both skills are critical to effective communication
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and racial-ethnic differences in outcomes must be considered in treatment approaches
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designed to improve overall communication ability.
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Keywords: Race, Ethnicity, Aphasia
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes INTRODUCTION
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Racial-ethnic differences have been reported consistently in stroke-related outcomes.1-7
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Although the incidence of stroke has been decreasing among White Americans, a
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similar decline has not been observed in Black Americans.8 Blacks are also more likely
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to experience more severe strokes and at a younger age.1 Consequently, studies show
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that Blacks experience higher levels of disability compared to Whites on traditional
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measures of post-stroke disability such as the Functional Independence Measure
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(FIM).2 Greater levels of disability have been observed among Blacks on other
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measures of post-stroke function including the Barthel Index, Modified Rankin Scale
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and Stroke Impact Scale and a questionnaire used to measure performance in activities
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of daily living (ADLs) and instrumental activities of daily living (IADLs).3,4
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Three recent reports including large samples of stroke survivors have highlighted racial
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disparities in post-stroke rehabilitation outcomes.5-7 Ellis and colleagues completed a
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systematic review that included 17 studies involving over 429,000 stroke survivors
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found that Blacks were less likely to achieve the same outcomes as Whites after the
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completion of rehabilitation.5 A second study by Ottenbacher and colleagues including
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161,692 patients from the Uniform Data System for Medical Rehabilitation found that
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Blacks and other minorities had lower admission and discharge functional status
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compared to Whites.6 A third study of over 1000 stroke survivors completing
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community-based inpatient rehabilitation also found that Blacks were less likely to
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achieve the same functional improvement at discharge as Whites.7 To date it is unclear
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why racial-ethnic differences exist in post-stroke outcomes. Reduced access to care
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has been traditionally used as an explanatory factor, however it does not fully explain
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the observed differences.9,10
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Despite reports of racial-ethnic differences in stroke-related outcomes, the same
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association has not been studied or reported in persons with aphasia (PWA). Aphasia
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is a post-stroke condition that is diagnosed in approximately 180,000 individuals in the
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U.S. annually.11 Aphasia is characterized by deficits in listening comprehension, oral
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expression, reading and writing that can result in significant communication limitations
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even in its mildest form.12 Additionally, aphasia contributes to the overall economic and
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societal burden of stroke as it is an independent predictor of longer and more costly
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hospital stays as well as poorer overall stroke-related outcomes.13,14 To date, it is also
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unclear if PWA experience racial-ethnic differences in post-stroke aphasia outcomes in
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concordance with other aspects of post-stroke disability (motor, sensory, cognitive and
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functional outcomes) that have been reported previously. 4,5
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was to examine whether post-stroke aphasia language performance varies by race-
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ethnicity.
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The objective of this study
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For this study, we evaluated a range of expressive language and receptive language
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skills. We utilized total scale scores and subtest scores from the Western Aphasia
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Battery-Revised.15 We hypothesized that Black PWA would exhibit greater deficits
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primarily on measures of auditory comprehension when compared to White PWA. Our
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hypothesis was based on previous reports that have suggested that auditory processes
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are “almost always” (p. 108) impaired in aphasia due to the dependence of language on 5
ACCEPTED MANUSCRIPT Race and Aphasia Outcomes the auditory system.16 While syntactic impairments, lexical retrieval problems, and
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paraphasic errors, among others, may be some of the most obvious signs of aphasia,
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auditory processing impairments are recognized as a primary deficit in aphasia and
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have been linked to concomitant problems in verbal expression.17 As well, the greater
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the auditory comprehension problems in aphasia, the more debilitating they are to
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overall communication ability.18
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METHODS
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Data Source
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This study was submitted to the Institutional Review Board (IRB) for approval and
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determined to be exempt from further review. Data for this project were obtained from
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AphasiaBank, a database designed for the study of aphasia outcomes.19 AphasiaBank
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was established in 2007 and funded by the National Institutes of Health (NIH) National
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Institute of Deafness and Communication Disorders (NIDCD).19 AphasiaBank was
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developed to offer aphasia researchers a large shared database of clinical and aphasia
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data from PWA and individuals without aphasia.19 Researchers involved in the study of
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aphasia upload data to AphasiaBank based on a standard “AphasiaBank” protocol
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which includes a) speech samples, b) picture descriptions, c) story narratives, d)
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procedural discourse samples, e) and standardized test results (Boston Naming Test,
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Verb Naming Test, Western Aphasia Battery, etc).
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes Data Sample
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A sample of PWA available in the AphasiaBank database was utilized for this study.
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Demographic and clinical data available for each participant includes: age at time of
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assessment, gender, race, years of education, occupation, aphasia etiology, duration of
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aphasia, aphasia type and medical diagnosis. The race-ethnicity of participants included
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in the AphasiaBank were defined as White, African American and Hispanic. Whereas
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the ethnic label of African American is utilized in AphasiaBank, the term Non-Hispanic
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Black is used more frequently in the general stroke literature. Therefore, we will refer to
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African Americans as non-Hispanic Blacks (Blacks) to maintain consistency with the
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literature reported in the background. Finally, due to the limited sample of Hispanic
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PWA, we completed comparisons between Whites and Blacks only.
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Outcome Measures
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The primary aphasia outcomes of interest in this study were derived from the Western
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Aphasia Battery-Revised (WAB-R).15 The WAB-R is a comprehensive assessment of
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communication function designed to evaluate PWA. The WAB-R includes subtests of
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spontaneous speech, comprehension, repetition and naming that are used to calculate
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a composite Aphasia Quotient (AQ) which characterizes the individual’s auditory-verbal
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communication ability and severity of aphasia. The AQ scores range from 0-100 with
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lower scores indicating greater language deficits. The WAB AQ and subtest scores
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served as outcome measures for the study.
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes Aphasia Type and Severity
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Aphasia type was identified in AphasiaBank using the Western Aphasia Battery-
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Revised.15 Patients were classified as having one of the following types of aphasia
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anomic, Broca’s, Wernicke’s, global, conduction aphasia, trancortical (motor, sensory,
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isolation) and other. Aphasia severity was graded based on the work of Pederson et al.
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(2004) where WAB-R AQ scores between 0-31.2 indicates severe aphasia, scores
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between 31.3 – 62.5 indicate moderate aphasia, scores between 62.6 – 93.7 indicate
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slight (mild) aphasia, and those between 93.8 – 100 suggest no aphasia.20
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Statistical Analysis
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Baseline demographic and aphasia outcomes were described for the complete sample.
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Comparisons between the two racial-ethnic groups (Whites and Blacks) were completed
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for all continuous variables using independent samples t-tests and Pearson Chi square
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statistics for categorical variables. To compare racial-ethnic differences in language
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performance, the total scale WAB AQ and WAB subtests were compared using
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independent samples t-tests. For statistically significant group differences (racial-
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ethnic) in aphasia outcomes (WAB-R scores), multivariate analysis of covariance
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(MANCOVA) was completed with race group (White vs. Black) as the independent
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factor and WAB-R AQ and subtest scores as the dependent factors controlling for age
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and education. All statistical analyses were completed using IBM SPSS 22.21
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes RESULTS
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AphasiaBank contains data for 444 PWA (397 Whites, 47 Blacks) who were evaluated
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between 2008-2015. After excluding all PWA who did not have WAB-R scores, a final
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sample of 381 patients (339 Whites and 42 Blacks) was available for racial
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comparisons. See Table 1 for the sample characteristics. The mean age of the PWA in
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the sample was 62.8 years, the mean years of education was 15.5 years and the mean
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duration of aphasia was 5.4 years. Racial-ethnic differences were observed in mean
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age (p<.001) and years of education (p<.001). Sixty-one percent of the sample was
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male, 88% were right handed, 94% were born in the US, 11% exhibited dysarthria and
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37% exhibited apraxia of speech. The largest percentage of the sample were
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diagnosed with anomic aphasia (32.7%) and Broca aphasia (30.7%). Approximately
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60% of the sample exhibited mild aphasia and 26% moderate aphasia. No racial-ethnic
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differences were observed in aphasia type, duration of aphasia or aphasia severity.
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Aphasia Outcomes
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See Table 2 for racial-ethnic comparisons of aphasia outcomes. The mean WAB-R AQ
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for the sample was 69.2/100. No racial-ethnic differences were observed in WAB-R
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AQ. In univariate comparisons of the WAB-R subtest scores, Blacks had lower word
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fluency scores compared to Whites (5.7 vs 7.6; p=.004). Blacks also had lower auditory
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word recognition scores (49.0 vs. 53.0; p=.02) and lower scores for comprehension of
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sequential commands (44.2 vs. 52.2; p=.01) than Whites. Racial-ethnic differences
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were not observed in the remaining subtest scores.
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes Post-Hoc Analysis Excluding Individuals with WAB AQ scores >93.8
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The sample included 24 individuals (23 White and 1 Black) who exhibited WAB-R AQ
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scores >93.8 indicating no aphasia. We included these individuals in the initial analyses
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because they originally had a diagnosis of aphasia and the scores reported here
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represented their abilities at the time of testing. WAB-R AQ Scores can fluctuate a few
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points even when assessed within the same week and many individuals with scores
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>93.8 continue to complain of subtle comprehension problems and anomia.
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Nonetheless, we completed post-hoc analyses excluding these participants. No
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differences were observed in WAB-R AQ scores between the two groups, however
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differences in word fluency, auditory word recognition and sequential commands were
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found.
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Multivariate analysis of covariance (MANCOVA) was completed with race group (Whites
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vs. Blacks) as the independent factor and WAB-R word fluency, auditory word
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recognition and sequential commands as dependent variables. The MANCOVA was
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adjusted for the covariates age and years of education which were significant in
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univariate comparisons (see Table 3). A significant main effect for group (race) was
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observed for the three subtests controlling for covariates aged and education (p=.04).
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The adjusted means for WAB-R word fluency were lower for Blacks (5.5 95% CI 3.9-
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7.1) compared to Whites (7.6 95% CI 7.1-8.2) (p=.02). Similarly, auditory word
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recognition scores were lower for Blacks (49.3; 95% CI 46.1-52.3) compared to Whites
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(53.3; 95% CI 52.2-54.4) (p=.02). The adjusted means for WAB-R sequential
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commands were also lower for Blacks (43.7; 95% CI 36.4-51.0) when compared to
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Whites (53.2; 95% CI 50.8-55.6) (p=.01).
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DISCUSSION
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The findings reported here demonstrate racial-ethnic differences in aphasia outcomes
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related to word fluency and domains of auditory comprehension. Whereas, the overall
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measure of communication function (WAB-R AQ) as well as measure of aphasia type
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and severity were similar, Black PWA exhibited greater deficits in word fluency, auditory
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word recognition and comprehension of sequential commands after controlling for
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baseline differences in age and education. To our knowledge, this is the first study of
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racial-ethnic differences in aphasia outcomes. No such evidence has emerged in the
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study of aphasia, although the general stroke outcomes literature suggests racial-ethnic
13
differences with generally worse outcomes among racial-ethnic minorities. For
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example, Ottenbacher and colleagues found that Functional Independence Measure
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(FIM) post-rehabilitation discharge scores and efficiency scores (FIM score/LOS) were
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lower among Blacks when compared to Whites.6 Similarly, Black stroke survivors
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reported lower levels of functional independence than White stroke survivors at one
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year despite non-significant differences in stroke severity or utilization of rehabilitation
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services.4
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The underlying cause of the observed differences in word fluency is not clear. Tests of
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word fluency are used in research and clinical practice to support diagnoses of
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language and/or cognitive impairment.22 A greater emphasis has emerged regarding
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes race-based norms of tests such as word fluency to ensure racial-ethnic minorities are
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not misdiagnosed with language and cognitive issues.23 Studies have shown that non-
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neurologically impaired Blacks score lower on measures of word fluency than similar
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cohorts of Whites.24 For example, Molrine and Pierce examined racial-differences in
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WAB subtests among 48 (24 Black and 24 White) non-brain damaged adults and found
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that only WAB word fluency subtest scores differed between Whites and Blacks.25
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What is not entirely clear is the clinical relevance of the approximate 2 word difference
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between the two groups that emerged as statistically significant.
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The underlying cause of the observed differences in auditory comprehension ability is
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also unclear. Observed racial-ethnic differences in stroke-related outcomes can be
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influenced by a wide range of neurological, sociodemographic and clinical management
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factors (differences in amounts of therapy, lack of access to rehabilitation care and
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referrals to post-acute services).26 Putman and colleagues also proposed potential
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differences in the post-discharge environments as contributors to racial-ethnic
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differences in outcomes. They found racial differences in stroke outcomes despite
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Blacks and Whites exhibiting similar outcomes and recovery trajectories after
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participating in inpatient rehabilitation programs.26 Therefore, identifying the underlying
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cause of such differences in outcomes and the specific time points in the recovery
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trajectory where such differences can emerge requires further investigation.
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Because the aphasia data reported here all emerged from individuals experiencing a
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stroke, some consideration must be given to racial differences that have been reported
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes previously in stroke subtypes. Data from the Northern Manhattan Study revealed that
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higher (age adjusted) rates of all stroke subtypes existed among Blacks when
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compared to Whites.27 Of significant interest here was the greater proportion of Blacks
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with small vessel disease. It has been hypothesized that greater cognitive impairment
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exists among individuals with small vessel disease.28 It is tenable then that the
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presence of small vessel disease may be an explanatory factor for the greater racial
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ethnic differences in specific language impairments following aphasia. More
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specifically, it is possible that based on this evidence that Blacks may have greater
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cognitive impairment, in addition to their aphasia that contributes to the racial-ethnic
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differences observed.
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However, stroke subtype is not necessarily related to aphasia type or severity.
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Regardless of stroke subtype, the location of a stroke will dictate presence of aphasia,
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aphasia type and aphasia severity as all are determined by which language related
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structures or combination of structures are damaged or disrupted. Regardless, future
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studies should consider if observed racial-differences in stroke subtypes contribute to
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racial-differences in aphasia outcomes.
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Despite our controlling for age, it is notable that the Blacks were approximately nine
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years younger than the Whites. Age, therefore, might still be a contributing factor.
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Even though atherosclerosis is an underlying cause of stroke in both young and old
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adults, the relative frequency by which it causes stroke in young adults is significantly
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different than in older adults.29 It is tenable that stroke type and younger age may
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combine to influence aphasia type, severity and overall aphasia outcomes.
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Similarly, additional consideration should be given to the impact of aphasia severity in
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these comparisons. Despite a lack of statistically significant differences in WAB-R AQ
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scores, visual inspection of the proportion of individuals at each severity level in these
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racial-ethnic groups suggests a potentially relevant difference. The proportion of Blacks
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with moderate aphasia was approximately 10% greater than Whites. Because severity
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levels range approximately 30 points per severity level, the groups are quite
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heterogeneous in regards to specific impairments. Therefore, it may be that the greater
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number of Blacks with moderate aphasia may account for the observed differences in
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word fluency, auditory word comprehension and comprehension of sequential
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commands.
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It is interesting that the racial-ethnic differences that were observed parallel previously
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reported differences in post-stroke motor function, sensory function, cognitive function
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and overall functional ability.3-5 Currently there is a substantial literature related to
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aphasia treatment outcomes, yet less is known about the impact of different racial-
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ethnic backgrounds on outcomes across patients.30 Consequently, some aphasia
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outcomes may not apply as strongly to different racial-ethnic groups who differ in non-
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clinical factors (e.g., insurance, availability of services, costs of services, access to
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quality equitable care).31 As a result, there is concern regarding the relationship
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between these non-clinical factors and frequently observed disparities in general stroke
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outcomes and additional studies are needed to address these issues.
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STUDY LIMITATATIONS
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The findings reported here are interesting but this study has several limitations. First,
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although we attempted to control for age and years of education, caution should be
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exercised when controlling for these factors as covariates. The total sample included
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here exhibited high levels of education and may not reflect a typical post-stroke aphasia
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population. Years of education is believed to be an inadequate measure of educational
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attainment and quality of education may be a more important factor.32 Further,
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statistical control for age and education assumes a linear relationship between the
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covariate and the dependent variable. Similarly, covariance does not account for
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unmeasured variables that influence test performance.33 Second, the sample size of
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Blacks was approximately 11% of the total sample. A larger sample may yield different
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results. Third, the study was a retrospective data analysis which, by nature, has
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general limitations because the cohort data were collected for purposes other than
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making racial-ethnic comparisons in outcomes. Fourth, the data emerged from
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research centers in different regions of the U.S. Access to quality care can vary greatly
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in different regions of the U.S. and thereby have a significant impact on clinical
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outcomes.34
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CONCLUSIONS
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes Understanding racial-ethnic differences in stroke and stroke-related conditions such as
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aphasia is difficult because of the complex interrelationship among factors that both
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cause stroke and influence outcomes. Access to care issues has been frequently
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identified as a key factor in disparities in outcomes for conditions such as stroke.35
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However, reduced access to care does not consistently explain the nature of racial-
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ethnic differences in outcomes observed in this study. Greater emphasis has been
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placed on the “social determinants of health” or the “conditions in which people are
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born, grow, work, live, and age, and the wider set of forces and systems shaping the
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conditions of daily life.”36 Consequently the social determinants of health should be
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considered in outcomes research investigating health disparities.37 Specifically, the field
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of Speech-Language Pathology should include the racial-ethnic backgrounds of PWA
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consistently in research studies. In the absence of this basic information, drawing
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conclusions from samples that are already heterogeneous (age, education,
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socioeconomic status, social support, aphasia severity, aphasia profiles) becomes even
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more difficult when factors known to be associated with racial-ethnic differences are
16
excluded. Similarly, clinicians engaged in the care of individuals with aphasia must
17
give consideration to the social determinants of health and their potential contribution to
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racial-ethnic differences in outcomes when developing and generating treatment plans
19
designed to improve aphasia outcomes. Systematic study of aphasia outcomes will be
20
required to adequately understand the true impact of race/ethnicity on aphasia
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outcomes. A longitudinal approach should be considered that includes outcomes
22
beyond aphasia impairment scores, functional aphasia scores and services received.
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Post-stroke aphasia outcome measures should also include measures of the PWA’s
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social networks that are known to impact post-stroke outcomes beyond treatment
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settings.38
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Kleindorfer DO, Khoury J, Moomaw CJ, Alwell K, Woo D, Flaherty ML et al. Stroke incidence is decreasing in whites but not in blacks: a population-based
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El Khoury R, Jung R, Nanda A, Sila C, Abraham MF, Castonguay AC, Zaidat OO. Overview of key factors in improving access to acute stroke care.
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Neurology 2012; 79: S26-34. 10.
geographic and racial differences in stroke. Stroke 2013; 44: 1930-1935.
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11.
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Armstrong E, Fox S, Wilkinson R. Mild aphasia: is this the place for an argument? Am J Speech Lang Pathol. 2013; 22(2):S268-78.
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National Aphasia Association. Aphasia FAQs. 2016. Available at: http://www.aphasia.org/aphasia-faqs/.
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Disparities in evaluation at certified primary stroke care centers: Reasons for
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Mullen MT, Judd S, Howard VJ, Kasner SE, Branas CC, Albright KC.
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13.
Guyomard V, Fulcher RA, Redmayne O, Metcalf AK, Potter JF, Myint PK. Effect of dysphasia and dysphagia on inpatient mortality and hospital
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length of stay: A database study. J Am Geriatr Soc. 2009. 57:2101–2106. 14.
attributable cost of poststroke aphasia. Stroke; 2012; 43(5): 1429-31.
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15.
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Kertesz, A. The Western Aphasia Battery – Revised . Texas: Harcourt Assessments. 2006.
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Ellis C, Simpson AN, Bonilha H, Mauldin PD, Simpson KN. The one-year
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Jenkins, J.J., Jimenez-Pabon, E., Shaw, R.E., & Sefer, J.W. (1975). Schuell’s aphasia in adults (2nd ed.). Hagerstown, MD: Harper & Row.
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17.
TX: ProEd; 1989.
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Rosenbek JC, LaPointe LL, Wertz RT. Aphasia: A Clinical Approach. Austin,
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Morris J, Franklin S. Disorders of auditory comprehension. In I. Papathanasiou & P. Coppens (Eds.), Aphasia and Related Neurogenic
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Communication Disorders (p.152). Burlington, MA: Jones & Bartlett Learning:
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2017.
Forbes MM, Fromm D, MacWhinney. AphasiaBank: A resource for clinicians.
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Semin Speech Lang. 2012; 33(30):217-222.
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20.
Pedersen PM, Vinter K, Olsen TS. Aphasia after stroke: type, severity and
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4
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prognosis. The Copenhagen aphasia study. Cerebrovasc Dis. 2004;17:35–
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43. 21.
IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp.
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22.
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Shao Z, Janse E, Visser K, Meyet AS, What do verbal fluency tasks measure? Predictors doi: 10.3389/fpsyg.2014.00772of verbal fluency
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performance in older adults. Front Psychol. 2014; 5, 772:
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Gasquoine PG. Race-norming of neuropsychological tests. Neuropsychol Rev. 2009; 19: 250-262.
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Heaton, R. K., Miller, S. W., Taylor, M. J., & Grant, I. (2004). Revised comprehensive norms for an expanded Halstead–Reitan battery:
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Demographically adjusted neuropsychological norms for African
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American and Caucasian Adults. Odessa, FL: Psychological
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Assessment Resources.
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25.
Molrine CJ, Pierce RS. Black and white adults’ expressive language
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performance on three tests of aphasia. Am J Speech-Lang Path. 2002;
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11:139-150. 26.
Putman K, Horn S, Smout R, DeJong G, Deutscher D, Tian W, Hsieh C-H.
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Racial disparities in stroke functional outcomes upon discharge from inpatient
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rehabilitation facilities. Dis Rehabil, 2010; 32(19): 1604-1611. 27.
White H, Boden-Albala B, Wang C, Elkind MSV, Rundek T, Wright CB, Sacco
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RL. Ischemic stroke subtype incidence among Whites, Blacks and Hispanics.
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The Northern Manhattan Study. Circulation. 2005; 111: 1327-1331.
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28.
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Makin SDJ, Turpin S, Dennis MS, Wardlaw JM. Cognitive impairment after lacunar stroke: systematic review and meta-analysis of incidence, prevalence
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and comparison with other stroke subtypes. J Neurol Neurosurg Psychiatry
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2013, 84(8): 893-900.
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Griffiths, D Strum J. Epidemiology and etiology of young stroke. Stroke Res Treat. 2011. Article ID 209370, 9 pages, 2011. doi:10.4061/2011/209370.
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Brady MC, Kelly H, Godwin J, Enderby P, Campbell P. Speech and language
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therapy for aphasia following stroke. Cochrane Database of Systematic
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10.1002/14651858.CD000425.pub4
31.
Cruz-Flores S, Rabinstein A, Biller J, Elkind MSV, Griffith P, Gorelick PB, et
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al. Racial-ethnic disparities in stroke care: The American experience: A
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statement for healthcare professionals from the American Heart
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Association/American Stroke Association. Stroke. 2011; 42: 2091-2116.
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32.
Manly JJ, Jacobs DM, Touradji P, Small SA, Stern Y. Reading level
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attenuates differences in neuropsychological test performance between
3
Blacks and White elders. JINS. 2002; 8: 341-348. 33.
Manly JJ, Jacobs DM, Sano M, Bell K, Merchant CA, Small SA, Stern Y.
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Cognitive test performance among nondemented elderly Blacks and Whites.
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Neurology. 1998; 50: 1238-1245. 34.
Mullen MT, Wiebe D, Bowman A, Wolff CS, Albright KC, Roy J, et al.
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Disparities in Accessibility of Certified Primary Stroke Centers. Stroke. 2014;
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45(11): 3381–3388.
10
35.
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Mullen MT, Judd S, Howard VJ, Kasner SE, Branas CC, Albright KC et al
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(2013) Disparities in evaluation at certified primary stroke centers: Reasons
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for geographic and racial differences in stroke. Stroke 44:1930-1935. 36.
June 15, 2016 from: http://www.who.int/social_determinants/en/.
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World Health Organization (2016) Social determinants of health. Retreived
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Frier A, Barnett F, Devine S. (2016) The relationship between social determinants of health, and rehabilitation of neurological conditions: A
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systematic review. Disability and Rehabilitation. Epub ahead of print.
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38.
Northcutt S, Marshall J, Hilari K. What factors predict who will have a strong
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social network following a stroke? J Speech Lang Hear Res. 59: 772–783.
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Table 1. Aphasia Sample Characteristics White (N=339) 63.8 (11.6)
Black (N=42) 54.7 (12.3)
pvalue <.001
Education (Mean/SD)
15.5 (2.9)
15.7 (2.9)
14.1 (1.9)
<.001
Gender # male (%)
237 (61.1)
216 (62.4)
21 (50.0)
.119
Handedness # (%) • Right • Left • Ambidextrous/Unknown
343 (88.4) 34 (8.8) 11 (2.8)
308 (89.0) 30 (8.7) 8(2.3)
35 (83.3) 4 (9.5) 3 (7.1)
.197
Primary Language English # (%)
357 (92.0)
37 (88.1)
320 (92.5)
.322
Born in the US # (%)
365 (94.1)
325 (93.9)
40 (95.2)
.122
5.4 (4.9)
5.2 (4.7)
6.6 (5.9)
.151
127 (32.7) 119 (30.7) 25 (6.4) 21 (5.4) 40 (10.3) 17 (4.8) 38 (9.0)
116 (33.5) 101 (29.2) 23 (6.6) 19 (5.5) 35 (10.1) 16 (5.2) 36 (9.3)
11 (26.2) 18 (42.9) 2 (4.8) 2 (4.8) 5 (11.9) 1 (2.4) 3 (7.1)
.807
26 (6.7) 103 (26.6) 22 (58.7) 31 (8.0)
24 (7.0) 88 (25.5) 203 (58.8) 30 (8.7)
2 (4.8) 15 (35.7) 24 (57.1) 1 (2.4)
Motor Speech Disorders # (%) • Dysarthria 44 (11.3) • Apraxia 143 (36.9) *Education – 12 years = high school education
35 (10.1) 129 (37.3)
9 (21.4) 14 (33.3)
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Aphasia Type # (%) • Anomic • Broca • Wernicke • Global • Conduction • Transcortical • Other
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Aphasia Duration (Mean/SD)
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Aphasia Severity # (%) • Severe Aphasia • Moderate Aphasia • Mild Aphasia • No Aphasia
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Total (N=381) 62.8 (12.0)
.306
.033 .181
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Table 2. Univariate Comparisons of WAB-R Aphasia Quotient (AQ) and Subtest Scores By Race White (N=339)
Black (N=42)
pvalue
69.2 (20.7)
69.7 (21.0)
65.1 (17.6)
.118
7.5 (2.4) 6.0 (2.6)
7.6 (2.4) 6.1 (2.6)
7.3 (2.2) 5.4 (2.4)
.502 .061
Repetition (Mean/SD)
64.2 (27.7)
64.4 (28.2)
63.1 (22.8)
.739
Naming (Mean/SD) • Object Naming • Word Fluency • Sentence Completion • Responsive Speech
43.3 (17.6) 7.4 (4.9) 7.8 (2.9) 7.7 (3.2)
43.3 (17.7) 7.6 (5.1) 7.8 (2.9) 7.7 (3.2)
42.1 (17.5) 5.7 (3.6) 7.2 (2.8) 7.1 (3.1)
.648 .004 .200 .230
55.3 (5.8) 53.0 (9.9) 52.2 (22.5)
55.5 (5.9) 53.5 (9.6) 53.2 (22.4)
54.4 (4.8) 49.0 (1.8) 44.2 (21.1)
.184 .021 .012
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Comprehension (Mean/SD) • Yes/No • Auditory Word Recognition • Sequential Commands
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Spontaneous Speech (Mean/SD) • Information Content • Fluency
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Table 3. Estimated Marginal Means of Multivariate Comparisons of Western Aphasia Battery Subtest Scores By Race Mean (95% Confidence Interval) White Black 5.5 (3.9 – 7.1)
WAB-R Auditory Word Recognition
53.3 (52.2 – 54.4)
49.3 (46.1 – 52.3)
53.2 (50.8-55.6)
43.70 (36.4- 51.0)
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7.6 (7.1 – 8.2)
*MANCOVA model controlling for age and years of education
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.015
.018
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WAB-R Word Fluency
p-value
.014
S0003-9993(16)31228-X
DOI:
10.1016/j.apmr.2016.10.010
Reference:
YAPMR 56717
To appear in:
ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION
Received Date: 8 August 2016 Revised Date:
28 September 2016
Accepted Date: 7 October 2016
Please cite this article as: Ellis C, Peach RK, Racial-Ethnic Differences in Word Fluency and Auditory Comprehension Among Persons with Post-Stroke Aphasia, ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION (2016), doi: 10.1016/j.apmr.2016.10.010. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Race and Aphasia Outcomes
Charles Ellis, PhD1 Richard K. Peach, PhD 2
Department of Communication Sciences & Disorders, East Carolina University, Greenville, NC, USA
Department of Communication Disorders and Sciences, Rush University Medical Center, Chicago, IL, USA
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Racial-Ethnic Differences in Word Fluency and Auditory Comprehension Among Persons with Post-Stroke Aphasia
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Corresponding Author: Charles Ellis Ph.D. CCC-SLP Department of Communication Sciences and Disorders Communication Equity and Outcomes Laboratory East Carolina University 3310H Health Sciences Building, MS 668 Greenville, NC 27834 Phone: 252-744-6098 Fax: 252-744-6109 Email: [email protected] Word Count: 2999
Acknowledgements/Conflicts of Interest: None Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
ACCEPTED MANUSCRIPT Race and Aphasia Outcomes 1 2
Racial-Ethnic Differences in Word Fluency and Auditory Comprehension Among Persons with Post-Stroke Aphasia
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes ABSTRACT
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Objective: To examine aphasia outcomes and to determine whether the observed
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language profiles vary by race-ethnicity.
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Design: Retrospective cross sectional study using a convenience sample of persons of
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with aphasia (PWA) obtained from AphasiaBank, a database designed for the study of
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aphasia outcomes.
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Setting: Aphasia research laboratories in the US.
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Participants: 381 (339 Whites and 42 Black) persons with aphasia (PWA).
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Interventions: N/A
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Main Outcomes and Measures: Western Aphasia Battery-Revised (WAB-R) total scale
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score (aphasia quotient) and subtest scores were analyzed for racial-ethnic differences.
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The WAB-R is a comprehensive assessment of communication function designed to
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evaluate PWA in the areas of spontaneous speech, auditory comprehension, repetition
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and naming in addition to reading, writing, apraxia and constructional, visuospatial, and
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calculation skills.
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Results: In univariate comparisons, Black PWA exhibited lower word fluency (5.7 vs
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7.6; p=.004), auditory word comprehension (49.0 vs. 53.0; p=.021) and comprehension
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of sequential commands (44.2 vs. 52.2; p=.012) when compared to White PWA. In
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multivariate comparisons, adjusted for age and years of education, Black PWA
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exhibited lower word fluency (5.5 vs 7.6; p=.015), auditory word recognition (49.3 vs
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53.3; p=.02) and comprehension of sequential commands (43.7 vs 53.2; p=.017) when
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compared to White PWA.
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes Conclusions: This study identified racial-ethnic differences in word fluency and auditory
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comprehension ability among PWA. Both skills are critical to effective communication
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and racial-ethnic differences in outcomes must be considered in treatment approaches
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designed to improve overall communication ability.
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Keywords: Race, Ethnicity, Aphasia
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes INTRODUCTION
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Racial-ethnic differences have been reported consistently in stroke-related outcomes.1-7
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Although the incidence of stroke has been decreasing among White Americans, a
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similar decline has not been observed in Black Americans.8 Blacks are also more likely
5
to experience more severe strokes and at a younger age.1 Consequently, studies show
6
that Blacks experience higher levels of disability compared to Whites on traditional
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measures of post-stroke disability such as the Functional Independence Measure
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(FIM).2 Greater levels of disability have been observed among Blacks on other
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measures of post-stroke function including the Barthel Index, Modified Rankin Scale
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and Stroke Impact Scale and a questionnaire used to measure performance in activities
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of daily living (ADLs) and instrumental activities of daily living (IADLs).3,4
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Three recent reports including large samples of stroke survivors have highlighted racial
14
disparities in post-stroke rehabilitation outcomes.5-7 Ellis and colleagues completed a
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systematic review that included 17 studies involving over 429,000 stroke survivors
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found that Blacks were less likely to achieve the same outcomes as Whites after the
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completion of rehabilitation.5 A second study by Ottenbacher and colleagues including
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161,692 patients from the Uniform Data System for Medical Rehabilitation found that
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Blacks and other minorities had lower admission and discharge functional status
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compared to Whites.6 A third study of over 1000 stroke survivors completing
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community-based inpatient rehabilitation also found that Blacks were less likely to
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achieve the same functional improvement at discharge as Whites.7 To date it is unclear
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why racial-ethnic differences exist in post-stroke outcomes. Reduced access to care
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes 1
has been traditionally used as an explanatory factor, however it does not fully explain
2
the observed differences.9,10
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Despite reports of racial-ethnic differences in stroke-related outcomes, the same
5
association has not been studied or reported in persons with aphasia (PWA). Aphasia
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is a post-stroke condition that is diagnosed in approximately 180,000 individuals in the
7
U.S. annually.11 Aphasia is characterized by deficits in listening comprehension, oral
8
expression, reading and writing that can result in significant communication limitations
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even in its mildest form.12 Additionally, aphasia contributes to the overall economic and
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societal burden of stroke as it is an independent predictor of longer and more costly
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hospital stays as well as poorer overall stroke-related outcomes.13,14 To date, it is also
12
unclear if PWA experience racial-ethnic differences in post-stroke aphasia outcomes in
13
concordance with other aspects of post-stroke disability (motor, sensory, cognitive and
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functional outcomes) that have been reported previously. 4,5
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was to examine whether post-stroke aphasia language performance varies by race-
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ethnicity.
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The objective of this study
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For this study, we evaluated a range of expressive language and receptive language
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skills. We utilized total scale scores and subtest scores from the Western Aphasia
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Battery-Revised.15 We hypothesized that Black PWA would exhibit greater deficits
21
primarily on measures of auditory comprehension when compared to White PWA. Our
22
hypothesis was based on previous reports that have suggested that auditory processes
23
are “almost always” (p. 108) impaired in aphasia due to the dependence of language on 5
ACCEPTED MANUSCRIPT Race and Aphasia Outcomes the auditory system.16 While syntactic impairments, lexical retrieval problems, and
2
paraphasic errors, among others, may be some of the most obvious signs of aphasia,
3
auditory processing impairments are recognized as a primary deficit in aphasia and
4
have been linked to concomitant problems in verbal expression.17 As well, the greater
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the auditory comprehension problems in aphasia, the more debilitating they are to
6
overall communication ability.18
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METHODS
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Data Source
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This study was submitted to the Institutional Review Board (IRB) for approval and
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determined to be exempt from further review. Data for this project were obtained from
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AphasiaBank, a database designed for the study of aphasia outcomes.19 AphasiaBank
13
was established in 2007 and funded by the National Institutes of Health (NIH) National
14
Institute of Deafness and Communication Disorders (NIDCD).19 AphasiaBank was
15
developed to offer aphasia researchers a large shared database of clinical and aphasia
16
data from PWA and individuals without aphasia.19 Researchers involved in the study of
17
aphasia upload data to AphasiaBank based on a standard “AphasiaBank” protocol
18
which includes a) speech samples, b) picture descriptions, c) story narratives, d)
19
procedural discourse samples, e) and standardized test results (Boston Naming Test,
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Verb Naming Test, Western Aphasia Battery, etc).
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A sample of PWA available in the AphasiaBank database was utilized for this study.
3
Demographic and clinical data available for each participant includes: age at time of
4
assessment, gender, race, years of education, occupation, aphasia etiology, duration of
5
aphasia, aphasia type and medical diagnosis. The race-ethnicity of participants included
6
in the AphasiaBank were defined as White, African American and Hispanic. Whereas
7
the ethnic label of African American is utilized in AphasiaBank, the term Non-Hispanic
8
Black is used more frequently in the general stroke literature. Therefore, we will refer to
9
African Americans as non-Hispanic Blacks (Blacks) to maintain consistency with the
10
literature reported in the background. Finally, due to the limited sample of Hispanic
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PWA, we completed comparisons between Whites and Blacks only.
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Outcome Measures
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The primary aphasia outcomes of interest in this study were derived from the Western
15
Aphasia Battery-Revised (WAB-R).15 The WAB-R is a comprehensive assessment of
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communication function designed to evaluate PWA. The WAB-R includes subtests of
17
spontaneous speech, comprehension, repetition and naming that are used to calculate
18
a composite Aphasia Quotient (AQ) which characterizes the individual’s auditory-verbal
19
communication ability and severity of aphasia. The AQ scores range from 0-100 with
20
lower scores indicating greater language deficits. The WAB AQ and subtest scores
21
served as outcome measures for the study.
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Aphasia type was identified in AphasiaBank using the Western Aphasia Battery-
3
Revised.15 Patients were classified as having one of the following types of aphasia
4
anomic, Broca’s, Wernicke’s, global, conduction aphasia, trancortical (motor, sensory,
5
isolation) and other. Aphasia severity was graded based on the work of Pederson et al.
6
(2004) where WAB-R AQ scores between 0-31.2 indicates severe aphasia, scores
7
between 31.3 – 62.5 indicate moderate aphasia, scores between 62.6 – 93.7 indicate
8
slight (mild) aphasia, and those between 93.8 – 100 suggest no aphasia.20
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Statistical Analysis
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Baseline demographic and aphasia outcomes were described for the complete sample.
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Comparisons between the two racial-ethnic groups (Whites and Blacks) were completed
13
for all continuous variables using independent samples t-tests and Pearson Chi square
14
statistics for categorical variables. To compare racial-ethnic differences in language
15
performance, the total scale WAB AQ and WAB subtests were compared using
16
independent samples t-tests. For statistically significant group differences (racial-
17
ethnic) in aphasia outcomes (WAB-R scores), multivariate analysis of covariance
18
(MANCOVA) was completed with race group (White vs. Black) as the independent
19
factor and WAB-R AQ and subtest scores as the dependent factors controlling for age
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and education. All statistical analyses were completed using IBM SPSS 22.21
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes RESULTS
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AphasiaBank contains data for 444 PWA (397 Whites, 47 Blacks) who were evaluated
3
between 2008-2015. After excluding all PWA who did not have WAB-R scores, a final
4
sample of 381 patients (339 Whites and 42 Blacks) was available for racial
5
comparisons. See Table 1 for the sample characteristics. The mean age of the PWA in
6
the sample was 62.8 years, the mean years of education was 15.5 years and the mean
7
duration of aphasia was 5.4 years. Racial-ethnic differences were observed in mean
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age (p<.001) and years of education (p<.001). Sixty-one percent of the sample was
9
male, 88% were right handed, 94% were born in the US, 11% exhibited dysarthria and
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37% exhibited apraxia of speech. The largest percentage of the sample were
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diagnosed with anomic aphasia (32.7%) and Broca aphasia (30.7%). Approximately
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60% of the sample exhibited mild aphasia and 26% moderate aphasia. No racial-ethnic
13
differences were observed in aphasia type, duration of aphasia or aphasia severity.
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See Table 2 for racial-ethnic comparisons of aphasia outcomes. The mean WAB-R AQ
17
for the sample was 69.2/100. No racial-ethnic differences were observed in WAB-R
18
AQ. In univariate comparisons of the WAB-R subtest scores, Blacks had lower word
19
fluency scores compared to Whites (5.7 vs 7.6; p=.004). Blacks also had lower auditory
20
word recognition scores (49.0 vs. 53.0; p=.02) and lower scores for comprehension of
21
sequential commands (44.2 vs. 52.2; p=.01) than Whites. Racial-ethnic differences
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were not observed in the remaining subtest scores.
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes Post-Hoc Analysis Excluding Individuals with WAB AQ scores >93.8
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The sample included 24 individuals (23 White and 1 Black) who exhibited WAB-R AQ
3
scores >93.8 indicating no aphasia. We included these individuals in the initial analyses
4
because they originally had a diagnosis of aphasia and the scores reported here
5
represented their abilities at the time of testing. WAB-R AQ Scores can fluctuate a few
6
points even when assessed within the same week and many individuals with scores
7
>93.8 continue to complain of subtle comprehension problems and anomia.
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Nonetheless, we completed post-hoc analyses excluding these participants. No
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differences were observed in WAB-R AQ scores between the two groups, however
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differences in word fluency, auditory word recognition and sequential commands were
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found.
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Multivariate analysis of covariance (MANCOVA) was completed with race group (Whites
14
vs. Blacks) as the independent factor and WAB-R word fluency, auditory word
15
recognition and sequential commands as dependent variables. The MANCOVA was
16
adjusted for the covariates age and years of education which were significant in
17
univariate comparisons (see Table 3). A significant main effect for group (race) was
18
observed for the three subtests controlling for covariates aged and education (p=.04).
19
The adjusted means for WAB-R word fluency were lower for Blacks (5.5 95% CI 3.9-
20
7.1) compared to Whites (7.6 95% CI 7.1-8.2) (p=.02). Similarly, auditory word
21
recognition scores were lower for Blacks (49.3; 95% CI 46.1-52.3) compared to Whites
22
(53.3; 95% CI 52.2-54.4) (p=.02). The adjusted means for WAB-R sequential
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes 1
commands were also lower for Blacks (43.7; 95% CI 36.4-51.0) when compared to
2
Whites (53.2; 95% CI 50.8-55.6) (p=.01).
3
DISCUSSION
5
The findings reported here demonstrate racial-ethnic differences in aphasia outcomes
6
related to word fluency and domains of auditory comprehension. Whereas, the overall
7
measure of communication function (WAB-R AQ) as well as measure of aphasia type
8
and severity were similar, Black PWA exhibited greater deficits in word fluency, auditory
9
word recognition and comprehension of sequential commands after controlling for
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baseline differences in age and education. To our knowledge, this is the first study of
11
racial-ethnic differences in aphasia outcomes. No such evidence has emerged in the
12
study of aphasia, although the general stroke outcomes literature suggests racial-ethnic
13
differences with generally worse outcomes among racial-ethnic minorities. For
14
example, Ottenbacher and colleagues found that Functional Independence Measure
15
(FIM) post-rehabilitation discharge scores and efficiency scores (FIM score/LOS) were
16
lower among Blacks when compared to Whites.6 Similarly, Black stroke survivors
17
reported lower levels of functional independence than White stroke survivors at one
18
year despite non-significant differences in stroke severity or utilization of rehabilitation
19
services.4
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21
The underlying cause of the observed differences in word fluency is not clear. Tests of
22
word fluency are used in research and clinical practice to support diagnoses of
23
language and/or cognitive impairment.22 A greater emphasis has emerged regarding
11
ACCEPTED MANUSCRIPT Race and Aphasia Outcomes race-based norms of tests such as word fluency to ensure racial-ethnic minorities are
2
not misdiagnosed with language and cognitive issues.23 Studies have shown that non-
3
neurologically impaired Blacks score lower on measures of word fluency than similar
4
cohorts of Whites.24 For example, Molrine and Pierce examined racial-differences in
5
WAB subtests among 48 (24 Black and 24 White) non-brain damaged adults and found
6
that only WAB word fluency subtest scores differed between Whites and Blacks.25
7
What is not entirely clear is the clinical relevance of the approximate 2 word difference
8
between the two groups that emerged as statistically significant.
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The underlying cause of the observed differences in auditory comprehension ability is
11
also unclear. Observed racial-ethnic differences in stroke-related outcomes can be
12
influenced by a wide range of neurological, sociodemographic and clinical management
13
factors (differences in amounts of therapy, lack of access to rehabilitation care and
14
referrals to post-acute services).26 Putman and colleagues also proposed potential
15
differences in the post-discharge environments as contributors to racial-ethnic
16
differences in outcomes. They found racial differences in stroke outcomes despite
17
Blacks and Whites exhibiting similar outcomes and recovery trajectories after
18
participating in inpatient rehabilitation programs.26 Therefore, identifying the underlying
19
cause of such differences in outcomes and the specific time points in the recovery
20
trajectory where such differences can emerge requires further investigation.
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21 22
Because the aphasia data reported here all emerged from individuals experiencing a
23
stroke, some consideration must be given to racial differences that have been reported
12
ACCEPTED MANUSCRIPT Race and Aphasia Outcomes previously in stroke subtypes. Data from the Northern Manhattan Study revealed that
2
higher (age adjusted) rates of all stroke subtypes existed among Blacks when
3
compared to Whites.27 Of significant interest here was the greater proportion of Blacks
4
with small vessel disease. It has been hypothesized that greater cognitive impairment
5
exists among individuals with small vessel disease.28 It is tenable then that the
6
presence of small vessel disease may be an explanatory factor for the greater racial
7
ethnic differences in specific language impairments following aphasia. More
8
specifically, it is possible that based on this evidence that Blacks may have greater
9
cognitive impairment, in addition to their aphasia that contributes to the racial-ethnic
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differences observed.
11
However, stroke subtype is not necessarily related to aphasia type or severity.
13
Regardless of stroke subtype, the location of a stroke will dictate presence of aphasia,
14
aphasia type and aphasia severity as all are determined by which language related
15
structures or combination of structures are damaged or disrupted. Regardless, future
16
studies should consider if observed racial-differences in stroke subtypes contribute to
17
racial-differences in aphasia outcomes.
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19
Despite our controlling for age, it is notable that the Blacks were approximately nine
20
years younger than the Whites. Age, therefore, might still be a contributing factor.
21
Even though atherosclerosis is an underlying cause of stroke in both young and old
22
adults, the relative frequency by which it causes stroke in young adults is significantly
13
ACCEPTED MANUSCRIPT Race and Aphasia Outcomes 1
different than in older adults.29 It is tenable that stroke type and younger age may
2
combine to influence aphasia type, severity and overall aphasia outcomes.
3
Similarly, additional consideration should be given to the impact of aphasia severity in
5
these comparisons. Despite a lack of statistically significant differences in WAB-R AQ
6
scores, visual inspection of the proportion of individuals at each severity level in these
7
racial-ethnic groups suggests a potentially relevant difference. The proportion of Blacks
8
with moderate aphasia was approximately 10% greater than Whites. Because severity
9
levels range approximately 30 points per severity level, the groups are quite
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heterogeneous in regards to specific impairments. Therefore, it may be that the greater
11
number of Blacks with moderate aphasia may account for the observed differences in
12
word fluency, auditory word comprehension and comprehension of sequential
13
commands.
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It is interesting that the racial-ethnic differences that were observed parallel previously
16
reported differences in post-stroke motor function, sensory function, cognitive function
17
and overall functional ability.3-5 Currently there is a substantial literature related to
18
aphasia treatment outcomes, yet less is known about the impact of different racial-
19
ethnic backgrounds on outcomes across patients.30 Consequently, some aphasia
20
outcomes may not apply as strongly to different racial-ethnic groups who differ in non-
21
clinical factors (e.g., insurance, availability of services, costs of services, access to
22
quality equitable care).31 As a result, there is concern regarding the relationship
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes 1
between these non-clinical factors and frequently observed disparities in general stroke
2
outcomes and additional studies are needed to address these issues.
3
STUDY LIMITATATIONS
5
The findings reported here are interesting but this study has several limitations. First,
6
although we attempted to control for age and years of education, caution should be
7
exercised when controlling for these factors as covariates. The total sample included
8
here exhibited high levels of education and may not reflect a typical post-stroke aphasia
9
population. Years of education is believed to be an inadequate measure of educational
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attainment and quality of education may be a more important factor.32 Further,
11
statistical control for age and education assumes a linear relationship between the
12
covariate and the dependent variable. Similarly, covariance does not account for
13
unmeasured variables that influence test performance.33 Second, the sample size of
14
Blacks was approximately 11% of the total sample. A larger sample may yield different
15
results. Third, the study was a retrospective data analysis which, by nature, has
16
general limitations because the cohort data were collected for purposes other than
17
making racial-ethnic comparisons in outcomes. Fourth, the data emerged from
18
research centers in different regions of the U.S. Access to quality care can vary greatly
19
in different regions of the U.S. and thereby have a significant impact on clinical
20
outcomes.34
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CONCLUSIONS
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes Understanding racial-ethnic differences in stroke and stroke-related conditions such as
2
aphasia is difficult because of the complex interrelationship among factors that both
3
cause stroke and influence outcomes. Access to care issues has been frequently
4
identified as a key factor in disparities in outcomes for conditions such as stroke.35
5
However, reduced access to care does not consistently explain the nature of racial-
6
ethnic differences in outcomes observed in this study. Greater emphasis has been
7
placed on the “social determinants of health” or the “conditions in which people are
8
born, grow, work, live, and age, and the wider set of forces and systems shaping the
9
conditions of daily life.”36 Consequently the social determinants of health should be
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considered in outcomes research investigating health disparities.37 Specifically, the field
11
of Speech-Language Pathology should include the racial-ethnic backgrounds of PWA
12
consistently in research studies. In the absence of this basic information, drawing
13
conclusions from samples that are already heterogeneous (age, education,
14
socioeconomic status, social support, aphasia severity, aphasia profiles) becomes even
15
more difficult when factors known to be associated with racial-ethnic differences are
16
excluded. Similarly, clinicians engaged in the care of individuals with aphasia must
17
give consideration to the social determinants of health and their potential contribution to
18
racial-ethnic differences in outcomes when developing and generating treatment plans
19
designed to improve aphasia outcomes. Systematic study of aphasia outcomes will be
20
required to adequately understand the true impact of race/ethnicity on aphasia
21
outcomes. A longitudinal approach should be considered that includes outcomes
22
beyond aphasia impairment scores, functional aphasia scores and services received.
23
Post-stroke aphasia outcome measures should also include measures of the PWA’s
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes 1
social networks that are known to impact post-stroke outcomes beyond treatment
2
settings.38
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes 1 2
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ACCEPTED MANUSCRIPT
Table 1. Aphasia Sample Characteristics White (N=339) 63.8 (11.6)
Black (N=42) 54.7 (12.3)
pvalue <.001
Education (Mean/SD)
15.5 (2.9)
15.7 (2.9)
14.1 (1.9)
<.001
Gender # male (%)
237 (61.1)
216 (62.4)
21 (50.0)
.119
Handedness # (%) • Right • Left • Ambidextrous/Unknown
343 (88.4) 34 (8.8) 11 (2.8)
308 (89.0) 30 (8.7) 8(2.3)
35 (83.3) 4 (9.5) 3 (7.1)
.197
Primary Language English # (%)
357 (92.0)
37 (88.1)
320 (92.5)
.322
Born in the US # (%)
365 (94.1)
325 (93.9)
40 (95.2)
.122
5.4 (4.9)
5.2 (4.7)
6.6 (5.9)
.151
127 (32.7) 119 (30.7) 25 (6.4) 21 (5.4) 40 (10.3) 17 (4.8) 38 (9.0)
116 (33.5) 101 (29.2) 23 (6.6) 19 (5.5) 35 (10.1) 16 (5.2) 36 (9.3)
11 (26.2) 18 (42.9) 2 (4.8) 2 (4.8) 5 (11.9) 1 (2.4) 3 (7.1)
.807
26 (6.7) 103 (26.6) 22 (58.7) 31 (8.0)
24 (7.0) 88 (25.5) 203 (58.8) 30 (8.7)
2 (4.8) 15 (35.7) 24 (57.1) 1 (2.4)
Motor Speech Disorders # (%) • Dysarthria 44 (11.3) • Apraxia 143 (36.9) *Education – 12 years = high school education
35 (10.1) 129 (37.3)
9 (21.4) 14 (33.3)
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Aphasia Type # (%) • Anomic • Broca • Wernicke • Global • Conduction • Transcortical • Other
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Aphasia Duration (Mean/SD)
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Aphasia Severity # (%) • Severe Aphasia • Moderate Aphasia • Mild Aphasia • No Aphasia
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Age (Mean/SD)
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Total (N=381) 62.8 (12.0)
.306
.033 .181
ACCEPTED MANUSCRIPT
Table 2. Univariate Comparisons of WAB-R Aphasia Quotient (AQ) and Subtest Scores By Race White (N=339)
Black (N=42)
pvalue
69.2 (20.7)
69.7 (21.0)
65.1 (17.6)
.118
7.5 (2.4) 6.0 (2.6)
7.6 (2.4) 6.1 (2.6)
7.3 (2.2) 5.4 (2.4)
.502 .061
Repetition (Mean/SD)
64.2 (27.7)
64.4 (28.2)
63.1 (22.8)
.739
Naming (Mean/SD) • Object Naming • Word Fluency • Sentence Completion • Responsive Speech
43.3 (17.6) 7.4 (4.9) 7.8 (2.9) 7.7 (3.2)
43.3 (17.7) 7.6 (5.1) 7.8 (2.9) 7.7 (3.2)
42.1 (17.5) 5.7 (3.6) 7.2 (2.8) 7.1 (3.1)
.648 .004 .200 .230
55.3 (5.8) 53.0 (9.9) 52.2 (22.5)
55.5 (5.9) 53.5 (9.6) 53.2 (22.4)
54.4 (4.8) 49.0 (1.8) 44.2 (21.1)
.184 .021 .012
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Comprehension (Mean/SD) • Yes/No • Auditory Word Recognition • Sequential Commands
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Spontaneous Speech (Mean/SD) • Information Content • Fluency
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ACCEPTED MANUSCRIPT Race and Aphasia Outcomes 1 2
Table 3. Estimated Marginal Means of Multivariate Comparisons of Western Aphasia Battery Subtest Scores By Race Mean (95% Confidence Interval) White Black 5.5 (3.9 – 7.1)
WAB-R Auditory Word Recognition
53.3 (52.2 – 54.4)
49.3 (46.1 – 52.3)
53.2 (50.8-55.6)
43.70 (36.4- 51.0)
3
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WAB-R Sequential Commands
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7.6 (7.1 – 8.2)
*MANCOVA model controlling for age and years of education
4
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5
.015
.018
SC
WAB-R Word Fluency
p-value
.014