Financial Well-Being Among US Adults with Vascular Conditions: Differential Impacts Among Blacks and Hispanics.

Background: The ability to meet current and ongoing financial obligations, known as financial well-being (FWB), is not only associated with the likelihood of adverse health events but is also affected by unexpected health care expenditures. However, the relationship between FWB and common health outcomes is not well understood. Using data available in the Financial Well-Being Scale from the Consumer Financial Protection Bureau, we evaluated the impact of four vascular conditions-cardiovascular disease (CVD), stroke, high blood pressure (BP), and high cholesterol-on FWB and how these impacts varied between racial and ethnic groups. Methods: Using the Understanding America Survey-a nationally representative, longitudinal panel-we identified adults with self-reported diagnoses between 2014 and 2020 of high cholesterol, high BP, stroke, and CVD. We used stratified, longitudinal mixed regression models to assess the association between these diagnoses and FWB. Each condition was modeled separately and included sex, age, marital status, household size, income, education, race/ethnicity, insurance, body mass index, and an indicator of the condition. Racial and ethnic differentials were captured using group-condition interactions. Results: On average, Whites had the highest FWB Scale score (69.0, SD=21.8), followed by other races (66.7, SD=21.0), Hispanics (59.3, SD=21.6), and Blacks (56.2, SD=21.4). In general, FWB of individuals with vascular conditions was lower than that of those without, but the impact varied between racial and ethnic groups. Compared with Whites (the reference group), Blacks with CVD (-7.4, SD=1.0), stroke (-8.1, SD=1.5), high cholesterol (-5.7, SD=0.7), and high BP (6.1, SD=0.7) had lower FWB. Similarly, Hispanics with high BP (-3.0, SD=0.6) and CVD (-6.3, SD=1.3) had lower FWB. Income, education, insurance, and marital status were also correlated with FWB. Conclusions: These results indicated differences in the financial ramifications of vascular conditions among racial and ethnic groups. Findings suggest the need for interventions targeting FWB of individuals with vascular conditions, particularly those from minority groups.

Uncovering Effects of Schizophrenia upon a Maximally Significant, Minimally Complex Subset of Default Mode Network Connectivity Features.

A common analysis approach for resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data involves clustering windowed correlation time-series and assigning time windows to clusters (i.e., states) that can be quantified to summarize aspects of the dFNC dynamics. However, those methods can be dominated by a select few features and obscure key dynamics related to less dominant features. This study presents an iterative feature learning approach to identify a maximally significant and minimally complex subset of dFNC features within the default mode network (DMN) in schizophrenia (SZ). Utilizing dFNC data from individuals with SZ and healthy controls (HC), our approach uncovers a subset of features that has a greater number of dFNC states with disorder-related dynamics than is found when all features are present in the clustering. We find that anterior cingulate cortex/posterior cingulate cortex (ACC/PCC) interactions are consistently related to SZ across the most significant iterations of the feature learning analysis and that individuals with SZ tend to spend more time in states with greater intra-ACC anticorrelation and almost no time in a state of high intra-ACC correlation that HCs periodically enter. Our findings highlight the need for nuanced analyses to reveal disorder-related dynamics and advance our understanding of neuropsychiatric disorders.

Dermoscopy can be safely and reliably used in ophthalmology.

Objective: To determine if dermoscopy, a technique widely utilized in dermatology for improved diagnosis of skin lesions, can be used comfortably for evaluating periorbital, eyelid, and conjunctival lesions. Design: Proof-of-concept study in which a technique for performing dermoscopy near the eye was developed, related educational material was prepared, and a protocol for dermoscopic image capture was created. Methods: Technicians used the developed materials to learn to take high-quality pictures with a 10x dermoscope attached to a standard cell phone camera. The images were assessed for diagnostic utility by an oculoplastic surgeon and two dermatologists. Participants: 115 patients recruited from ophthalmology clinics from July 2021 to April 2023 were photographed, yielding 129 lesions with high-quality dermoscopic images as assessed by an oculoplastic surgeon and two dermatologists. Results: Technicians reported a significant increase in confidence (measured on a 1-10 scale) with dermoscopy after training (pre-instruction mean = 1.72, median = 1, mode = 1, IQR = 1.25 vs mean = 7.69, median = 7.75, mode = 7 and 8, IQR = 1.5 post-instruction. Wilcoxon rank sum test with continuity correction, W = 0, p < 0.001, paired t = 13.95, p < 0.0001). Incorporating a contact plate with a 4 × 4mm reticule on the dermoscope aided in photographing ocular and periocular lesions. Conclusion: Medical support staff in eye-care offices can be taught to use dermoscopes to capture high-quality images of periorbital, eyelid, and conjunctival lesions. Dermoscopy illuminates diagnostic features of lesions and thus offers a new avenue to improve decision-making in ophthalmology. Dermoscopy can be incorporated into telemedicine evaluations by ophthalmologists, oculoplastic surgeons, or affiliated dermatologists for triage of or rendering advice to patients and for planning of surgery if needed.

Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures.

Deep learning methods are increasingly being applied to raw electroencephalogram (EEG) data. However, if these models are to be used in clinical or research contexts, methods to explain them must be developed, and if these models are to be used in research contexts, methods for combining explanations across large numbers of models must be developed to counteract the inherent randomness of existing training approaches. Model visualization-based explainability methods for EEG involve structuring a model architecture such that its extracted features can be characterized and have the potential to offer highly useful insights into the patterns that they uncover. Nevertheless, model visualization-based explainability methods have been underexplored within the context of multichannel EEG, and methods to combine their explanations across folds have not yet been developed. In this study, we present two novel convolutional neural network-based architectures and apply them for automated major depressive disorder diagnosis. Our models obtain slightly lower classification performance than a baseline architecture. However, across 50 training folds, they find that individuals with MDD exhibit higher ß power, potentially higher δ power, and higher brain-wide correlation that is most strongly represented within the right hemisphere. This study provides multiple key insights into MDD and represents a significant step forward for the domain of explainable deep learning applied to raw EEG. We hope that it will inspire future efforts that will eventually enable the development of explainable EEG deep learning models that can contribute both to clinical care and novel medical research discoveries.

Transgender older adults' prior military service: Mental health differences by gender identification.

Despite a proportionally higher likelihood of serving, the role of prior military service in the mental health of transgender individuals is understudied. Research on the impact of military service on mental health tends to be proximal. We examined the distal relationship between prior military service, identity stigma, and mental health among transgender older adults, drawing comparisons between transgender men and women. We conducted a series of weighted multivariate linear models to predict the relationships between prior military service, identity stigma, perceived stress, and depression among 183 transgender women and men aged 51-87 (M = 60.11, SD = 0.668) using 2014 data from the National Health, Aging, and Sexuality/Gender Study. Prior military service was negatively associated with depression and perceived stress; identity stigma was positively associated with both. Prior military service and lower depression and perceived stress were significant for transgender men, but not women. Identity stigma was significant with depression and perceived stress among transgender women, but not transgender men. Our preliminary findings suggest that prior military service may serve as a protective factor for mental health among transgender men, but not transgender women. We need to better understand how military experience interacts with other characteristics, such as differing gender identities influences the mental health of transgender service members. Further research is needed to inform underlying mechanisms whereby military service differentially impacts mental health by gender identity so all active-duty personnel can share in the many benefits that accrue from military service, including protective effects on mental health in later life. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia.

Introduction: Dynamic functional network connectivity (dFNC) analysis of resting state functional magnetic resonance imaging data has yielded insights into many neurological and neuropsychiatric disorders. A common dFNC analysis approach uses hard clustering methods like k-means clustering to assign samples to states that summarize network dynamics. However, hard clustering methods obscure network dynamics by assuming (1) that all samples within a cluster are equally like their assigned centroids and (2) that samples closer to one another in the data space than to their centroids are well-represented by their centroids. In addition, it can be hard to compare subjects, as in some cases an individual may not manifest a state strongly enough to enter a hard cluster. Approaches that allow a dimensional approach to connectivity patterns (e.g., fuzzy clustering) can mitigate these issues. In this study, we present an explainable fuzzy clustering framework by combining fuzzy c-means clustering with several explainability metrics and novel summary features. Methods: We apply our framework for schizophrenia (SZ) default mode network analysis. Namely, we extract dFNC from individuals with SZ and controls, identify 5 dFNC states, and characterize the dFNC features most crucial to those states with a new perturbation-based clustering explainability approach. We then extract several features typically used in hard clustering and further present a variety of unique features specially designed for use with fuzzy clustering to quantify state dynamics. We examine differences in those features between individuals with SZ and controls and further search for relationships between those features and SZ symptom severity. Results: Importantly, we find that individuals with SZ spend more time in states of moderate anticorrelation between the anterior and posterior cingulate cortices and strong anticorrelation between the precuneus and anterior cingulate cortex. We further find that individuals with SZ tend to transition more rapidly than controls between low-magnitude and high-magnitude dFNC states. Conclusion: We present a novel dFNC analysis framework and use it to identify effects of SZ upon network dynamics. Given the ease of implementing our framework and its enhanced insight into network dynamics, it has great potential for use in future dFNC studies.

Exploring the association between social determinants and aphasia impairment: A retrospective data integration approach.

INTRODUCTION: Traditionally, the study of aphasia focused on brain trauma, clinical biomarkers, and cognitive processes, rarely considering the social determinants of health. This study evaluates the relationship between aphasia impairment and demographic, socioeconomic, and contextual determinants among people with aphasia (PWA). METHODS: PWA indexed within AphasiaBank-a database populated by multiple clinical aphasiology centers with standardized protocols characterizing language, neuropsychological functioning, and demographic information-were matched with respondents in the Medical Expenditure Panel Survey based on response year, age, sex, race, ethnicity, time post stroke, and mental health status. Generalized log-linear regression models with bootstrapped standard errors evaluated the association between scores on the Western Aphasia Battery-Revised Aphasia Quotient (WAB-R AQ) and demographic, economic, and contextual characteristics accounting for clustering of respondents and the stratification of data collection. Region, age, and income specific models tested the sensitivity of results. RESULTS: PWA over age 60 had 2.4% (SE = 0.020) lower WAB-R AQ scores compared with younger PWA. Compared to White PWA, Black and Hispanic PWA had 4.7% (SE = 0.03) and 0.81% (SE = 0.06) lower WAB-R AQ scores, respectively, as did those and living in the Southern US (-2.2%, SE = 0.03) even after controlling for age, family size, and aphasia type. Those living in larger families (ß = 0.005, SE = 0.008), with income over $30,000 (ß = 0.017, SE = 0.022), and a college degree (ß = 0.030, SE = 0.035) had higher WAB-R AQ relative to their counterparts. Region-specific models showed that racial differences were only significant in the South and Midwest, while ethnic differences are only significant in the West. Sex differences only appeared in age-specific models. Racial and ethnic differences were not significant in the high-income group regression. CONCLUSION: These findings support evidence that circumstances in which individuals live, work, and age are significantly associated with their health outcomes including aphasia impairment.

Identifying Reproducibly Important EEG Markers of Schizophrenia with an Explainable Multi-Model Deep Learning Approach.

The diagnosis of schizophrenia (SZ) can be challenging due to its diverse symptom presentation. As such, many studies have sought to identify diagnostic biomarkers of SZ using explainable machine learning methods. However, the generalizability of identified biomarkers in many machine learning-based studies is highly questionable given that most studies only analyze explanations from a small number of models. In this study, we present (1) a novel feature interaction-based explainability approach and (2) several new approaches for summarizing multi-model explanations. We implement our approach within the context of electroencephalogram (EEG) spectral power data. We further analyze both training and test set explanations with the goal of extracting generalizable insights from the models. Importantly, our analyses identify effects of SZ upon the α, ß, and θ frequency bands, the left hemisphere of the brain, and interhemispheric interactions across a majority of folds. We hope that our analysis will provide helpful insights into SZ and inspire the development of robust approaches for identifying neuropsychiatric disorder biomarkers from explainable machine learning models.

Looking Upstream to Understand Race/Ethnicity as a Moderator for Poststroke Neuroinflammation and a Social Determinant for Poststroke Aphasia Outcomes.

INTRODUCTION: Over the past decade, the stroke literature has begun to acknowledge and explore explanations for longstanding racial/ethnic differences in stroke outcomes. Poststroke cognitive impairment (PSCI) and poststroke aphasia are two such negative poststroke outcomes where racial/ethnic differences exist. Physiological differences, such as stroke type and lesion size, have been used to partially explain the variation in PSCI and aphasia. However, there is some evidence, although limited, that suggests neuroinflammatory processes as part of allostatic load may be a key contributor to the observed disparities. METHOD: In this tutorial, we explore the influence of race differences in inflammation on poststroke cognitive outcomes. We suggest lifetime stress and other external determinants of health such as neighborhood environment and discriminatory practices through "weathering" explain differences in inflammation. While using an allostatic load framework, we explore the literature focusing specifically on the role of neuroinflammation on poststroke outcomes. CONCLUSIONS: Examination of the immune response poststroke provides a foundation for understanding the mechanisms of PSCI and poststroke aphasia and the potential contributions of neuroinflammatory processes on poststroke cognitive outcomes. Furthermore, understanding of racial differences in those processes may contribute to a better understanding of racial disparities in general stroke outcomes as well as poststroke aphasia.

The Intersection of Social Determinants of Health and Post-stroke Aphasia Outcomes: A Need for Intersectional Analysis.

Aphasia is a disorder that results from damage to portions of the brain that are responsible for language and can vary significantly by type and severity. Differences in aphasia outcomes are influenced by the social determinants of health (SDOH). The SDOH are structural, environmental, and personal determinants that influence health outcomes. Intersectionality, or how one's social and political identities interact to influence individual life outcomes and/or advantage in our society, provides a way to examine the varying levels of the SDOH. However, intersectionality is complex, difficult to measure, and has not yet been explored in post-stroke aphasia outcomes. This article reviews the relationship of race and aphasia outcomes and the SDOH and aphasia outcomes. Additionally, we provide a novel current approach to examine the SDOH and aphasia outcomes. Lastly, we discuss the need for evaluation of intersectionality in aphasia and aim to provide a leveled social-ecological framework to examine aphasia-related outcomes. With notable individual differences among aphasia outcomes, we present a framework to support optimizing research and clinical aphasia care in speech-language pathology.

Neuropsychiatric Disorder Subtyping Via Clustered Deep Learning Classifier Explanations.

Identifying subtypes of neuropsychiatric disorders based on characteristics of their brain activity has tremendous potential to contribute to a better understanding of those disorders and to the development of new diagnostic and personalized treatment approaches. Many studies focused on neuropsychiatric disorders examine the interaction of brain networks over time using dynamic functional network connectivity (dFNC) extracted from resting-state functional magnetic resonance imaging data. Some of these studies involve the use of either deep learning classifiers or traditional clustering approaches, but usually not both. In this study, we present a novel approach for subtyping individuals with neuropsychiatric disorders within the context of schizophrenia (SZ). We trained an explainable deep learning classifier to differentiate between dFNC data from individuals with SZ and controls, obtaining a test accuracy of 79%. We next used cross-validation to obtain robust average explanations for SZ training participants across folds, identifying 5 SZ subtypes that each differed from controls in a distinct manner and that had different degrees of symptom severity. These subtypes specifically differed from one another in their interactions between the visual network and the subcortical, sensorimotor, and auditory networks and between the cerebellar network and the cognitive control and subcortical networks. Additionally, we uncovered statistically significant differences in negative symptom scores between the subtypes. It is our hope that the proposed novel subtyping approach will contribute to the improved understanding and characterization of SZ and other neuropsychiatric disorders.

A Novel Explainable Fuzzy Clustering Approach for fMRI Dynamic Functional Network Connectivity Analysis.

Resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) analysis has illuminated brain network interactions across many neuropsychiatric disorders. A common analysis approach involves using hard clustering methods to identify transitory states of brain activity, and in response to this, other methods have been developed to quantify the importance of specific dFNC interactions to identified states. Some of these methods involve perturbing individual features and examining the number of samples that switch states. However, only a minority of samples switch states. As such, these methods actually identify the importance of dFNC features to the clustering of a subset of samples rather than the overall clustering. In this study, we present a novel approach that more capably identifies the importance of each feature to the overall clustering. Our approach uses fuzzy clustering to output probabilities of each sample belonging to states and then measures their Kullback-Leibler divergence after perturbation. We show the viability of our approach in the context of schizophrenia (SZ) default mode network analysis, identifying significant differences in state dynamics between individuals with SZ and healthy controls. We further compare our approach with an existing approach, showing that it captures the effects of perturbation upon most samples. We also find that interactions between the posterior cingulate cortex (PCC) and the anterior cingulate cortex and the PCC and precuneus are important across methods. We expect that our novel explainable clustering approach will enable further progress in rs-fMRI analysis and to other clustering applications.

A Convolutional Autoencoder-based Explainable Clustering Approach for Resting-State EEG Analysis.

Machine learning methods have frequently been applied to electroencephalography (EEG) data. However, while supervised EEG classification is well-developed, relatively few studies have clustered EEG, which is problematic given the potential for clustering EEG to identify novel subtypes or patterns of dynamics that could improve our understanding of neuropsychiatric disorders. There are established methods for clustering EEG using manually extracted features that reduce the richness of the feature space for clustering, but only a couple studies have sought to use deep learning-based approaches with automated feature learning to cluster EEG. Those studies involve separately training an autoencoder and then performing clustering on the extracted features, and the separation of those steps can lead to poor quality clustering. In this study, we propose an explainable convolutional autoencoder-based approach that combines model training with clustering to yield high quality clusters. We apply the approach within the context of schizophrenia (SZ), identifying 8 EEG states characterized by varying levels of δ activity. We also find that individuals who spend more time outside of the dominant state tend to have increased negative symptom severity. Our approach represents a significant step forward for clustering resting-state EEG data and has the potential to lead to novel findings across a variety of neurological and neuropsychological disorders in future years.

Comparing Direct-to-Consumer Devices to Hearing Aids: Amplification Accuracy for Three Types of Hearing Loss.

PURPOSE: The U.S. Food and Drug Administration finalized regulations for over-the-counter hearing aids (OTC-HAs) on August 17, 2022. Little is known about the comparative performance of OTC-HAs and prescription HAs. This study compared amplification accuracy of prescription HAs and direct-to-consumer devices (DTCDs, including personal sound amplification products [PSAPs] and OTC-HAs). METHOD: Eleven devices were programmed to meet prescriptive targets in an acoustic manikin for three degrees of hearing loss. Devices consisted of high- and low-end HAs, PSAPS, and OTC-HAs. Each was tested, and deviations from target measured with an HA analyzer at every combination of 10 frequencies and low-, average-, and high-level inputs. Accuracy was compared using a multilevel Poisson model with device-specific intercepts controlling for input level, frequency, and device type. RESULTS: For mild-moderate hearing loss, deviations from targets were not statistically different between high- and low-end HAs, but PSAPs (5.50 dB, SE = 0.92 dB) and OTC-HAs (8.83 dB, SE = 1.10 dB) had larger differentials. For flat moderate hearing loss, compared to high-end HAs, average differentials were larger for all device types at all input levels and frequencies (Low HA: 3.82 dB, SE = 1.10 dB; PSAP: 9.24 dB, SE = 1.22 dB; OTC-HA: 8.61 dB, SE = 1.19 dB). For mild sloping to severe hearing loss, compared to high-end HAs, OTC-HAs (9.72 dB, SE = 1.20 dB) and PSAPs (7.34 dB, SE = 1.07 dB) had larger differentials and significant variability at the highest and lowest frequencies. Half (three) of the PSAPs and OTC-HAs met most targets within ±5 dB. CONCLUSIONS: DTCDs were unable to meet prescriptive targets for severe types of hearing loss but could meet them for mild hearing loss. This study provides an examination of current hearing devices. More research is needed to determine whether meeting prescriptive targets provides any benefit in the outcomes and performance with DTCD devices.

Racial-Ethnic Disparities in Hearing Aid Use: Price Responsiveness Among Older Adults with Hearing Loss.

OBJECTIVE: To explore the role of racial-ethnic background, income, residential context, and historic variation in hearing aid (HA) price HA usage among a nationally representative cohort of older adults with hearing loss. METHODS: Multilevel logistic regression models evaluated data from the 2012 through 2017 Medical Expenditure Panel Survey (MEPS) to 1) compare historic HA use between subgroups, 2) test for differential responsiveness to price changes between racial and ethnic groups, and 3) assess the relative role of demographic characteristics and HA use. RESULTS: Between 2012 and 2017, the price of economy HAs decreased by 5% while HA use among Non-Hispanic (NH) Whites and Hispanics with hearing loss increased by 30% and 20% respectively, but usage among NH-Blacks increased by less than 10%. After controlling for relevant covariates, NH-Blacks were two times less likely than NH-Whites to use a HA. Household income and price were only significant for NH-Whites who showed that a 1% increase in income was associated with a 10% increase in the likelihood of HA use. Calculation of subgroup participation showed that, when the price of HAs dropped by 1%, the likelihood of HA use by NH-Whites increased by 14.2%, Hispanics increased by 13.2%, and Others increased by 14.8%, but only 2.8% among NH-Blacks. CONCLUSION: Results suggest that cost is not the primary barrier to HA utilization among minoritized individuals from racial and ethnic groups. Additional analyses are needed to evaluate the role of social, cultural, and environmental influences on HA utilization.

CROSS-SAMPLING RATE TRANSFER LEARNING FOR ENHANCED RAW EEG DEEP LEARNING CLASSIFIER PERFORMANCE IN MAJOR DEPRESSIVE DISORDER DIAGNOSIS.

Transfer learning offers a route for developing robust deep learning models on small raw electroencephalography (EEG) datasets. Nevertheless, the utility of applying representations learned from large datasets with a lower sampling rate to smaller datasets with higher sampling rates remains relatively unexplored. In this study, we transfer representations learned by a convolutional neural network on a large, publicly available sleep dataset with a 100 Hertz sampling rate to a major depressive disorder (MDD) diagnosis task at a sampling rate of 200 Hertz. Importantly, we find that the early convolutional layers contain representations that are generalizable across tasks. Moreover, our approach significantly increases mean model accuracy from 82.33% to 86.99%, increases the model's use of lower frequencies, (θ-band), and increases its robustness to channel loss. We expect this analysis to provide useful guidance and enable more widespread use of transfer learning in EEG deep learning studies.

Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data.

As the field of deep learning has grown in recent years, its application to the domain of raw resting-state electroencephalography (EEG) has also increased. Relative to traditional machine learning methods or deep learning methods applied to manually engineered features, there are fewer methods for developing deep learning models on small raw EEG datasets. One potential approach for enhancing deep learning performance, in this case, is the use of transfer learning. While a number of studies have presented transfer learning approaches for manually engineered EEG features, relatively few approaches have been developed for raw resting-state EEG. In this study, we propose a novel EEG transfer learning approach wherein we first train a model on a large publicly available single-channel sleep stage classification dataset. We then use the learned representations to develop a classifier for automated major depressive disorder diagnosis with raw multichannel EEG. Statistical testing reveals that our approach significantly improves the performance of our model (p < 0.05), and we also find that the performance of our approach exceeds that of many previous studies using both engineered features and raw EEG. We further examine how transfer learning affected the representations learned by the model through a pair of explainability analyses, identifying key frequency bands and channels utilized across models. Our proposed approach represents a significant step forward for the domain of raw resting-state EEG classification and has broader implications for use with other electrophysiology and time-series modalities. Importantly, it has the potential to expand the use of deep learning methods across a greater variety of raw EEG datasets and lead to the development of more reliable EEG classifiers.

Stroke in women between 2006 and 2018: Demographic, socioeconomic, and age disparities.

BACKGROUND: Black Americans have a higher prevalence of stroke and stroke-related deaths than any other racial group. Racial disparities in stroke outcomes are even wider among women than men. Conventional studies have cited differences in lifestyle (i.e. smoking, alcohol consumption, etc.) and vascular risk factors between races as the source of these disparities. However, these studies fail to account for the higher prevalence of minoritized populations at the lower end of the socioeconomic distribution. OBJECTIVES: This study explores differences in stroke risk factors across age and socioeconomic cohorts to determine whether comorbidities can sufficiently explain disparities at all ages and income levels. DESIGN: Using the 2006-2018 National Health Interview Survey data, statistical analysis evaluated differences in risk factors among a full sample cohort (aged 18-85 years; n = 131,091) and a "young" subsample cohort (aged 18-59 years; n = 6183) of women. METHODS: Logistics and unconditional quantile regression models assessed the relationship between stroke and comorbid, demographic, and behavioral characteristics across socioeconomic classes. RESULTS: Results suggest that Black women had a 1.415-fold (confidence interval = 1.259, 1.591) higher likelihood of stroke compared with White women after controlling for age, behavior, and comorbidities. Racial disparities were not statistically significant at the higher income ranges for either the full (odds ratio = 1.404, p = 0.3114) or young samples (odds ratio = 1.576, p = 0.7718). However, Blacks had significantly higher odds of stroke in the lower quartiles (lower odds ratio: 1.329, p = 0.0242; lower middle odds ratio: 1.233, p = 0.0486; and upper middle odds ratio: 1.994, p = 0.0005). Disparities were larger among young women (odds ratio = 1.449, confidence interval = 1.211, 1.734). CONCLUSION: While comorbidities were highly associated with stroke prevalence in all socioeconomic cohorts, Blacks only had higher relative odds in the lower income classes. Lack of biological or behavioral explanations for these findings suggests that unobserved or uncontrolled factors such as systemic racism, prejudicial institutions, or differential treatment may contribute to this.