Medicine and Health Sciences Commons^™

Adversarial Training Based Domain Adaptation Of Skin Cancer Images, Syed Qasim Gilani, Muhammad Umair, Maryam Naqvi, Oge Marques, Hee-Cheol Kim

Electrical & Computer Engineering Faculty Publications

Skin lesion datasets used in the research are highly imbalanced; Generative Adversarial Networks can generate synthetic skin lesion images to solve the class imbalance problem, but it can result in bias and domain shift. Domain shifts in skin lesion datasets can also occur if different instruments or imaging resolutions are used to capture skin lesion images. The deep learning models may not perform well in the presence of bias and domain shift in skin lesion datasets. This work presents a domain adaptation algorithm-based methodology for mitigating the effects of domain shift and bias in skin lesion datasets. Six experiments were …

The Impact Of Aging And Storage Conditions On The Performance Of Universal Adhesives: A Systematic Review, Maryam Ghamgosar, Mehrsima Ghavami-Lahiji, Sanaz Mihandoust, Enayatollah Homaie Rad, Hassan Salehipour Masooleh, Lobat Tayebi

Electrical & Computer Engineering Faculty Publications

Objective: This systematic review evaluated how different storage times and conditions affect universal adhesives' bond strength and degree of conversion (DC).

Methods: A literature search was conducted in PubMed, Scopus, Web of Science, and Google Scholar databases for articles published from January 1st, 2000, until May 15th, 2022. The researchers comprehensively evaluated the articles using a multi-step process to identify articles relevant to the topic of interest. Quality assessment was performed through the ROBDEMAT tool. Due to the high heterogeneity in the preliminary data, performing a meta-analysis was not feasible.

Results: A total of 3169 records were obtained, and after …

Paper-Based Dna Biosensor For Rapid And Selective Detection Of Mir-21, Alexander Hunt, Sri Ramulu Torati, Gymama Slaughter

Electrical & Computer Engineering Faculty Publications

Cancer is the second leading cause of death globally, with 9.7 million fatalities in 2022. While routine screenings are vital for early detection, healthcare disparities persist, highlighting the need for equitable solutions. Recent advancements in cancer biomarker identification, particularly microRNAs (miRs), have improved early detection. MiR-21 is notably overexpressed in various cancers and can be a valuable diagnostic tool. Traditional detection methods, though accurate, are costly and complex, limiting their use in resource-limited settings. Paper-based electrochemical biosensors offer a promising alternative, providing cost-effective, sensitive, and rapid diagnostics suitable for point-of-care use. This study introduces an innovative electrochemical paper-based biosensor that …

Investigation Of The Effect Of Preparation Parameters On The Structural And Mechanical Properties Of Gelatin/Elastin/Sodium Hyaluronate Scaffolds Fabricated By The Combined Foaming And Freeze-Drying Techniques, Mansour Qamash, S. Misagh Imani, Meisam Omidi, Ciara Glancy, Lobat Tayebi

Electrical & Computer Engineering Faculty Publications

This paper aimed to evaluate the effects of different preparation parameters, including agitation speed, agitation time, and chilling temperature, on the structural and mechanical properties of a novel gelatin/elastin/sodium hyaluronate tissue engineering scaffold, recently developed by our research group. Fabricated using a combination of foaming and freeze-drying techniques, the scaffolds were assessed to understand how these parameters influence their morphology, internal microstructure, porosity, mechanical properties, and degradation behavior. The fabrication process used in this study involved preparing a homogeneous aqueous solution containing 8% gelatin, 2% elastin, and 0.5% sodium hyaluronate (w/v), which was then subjected to mechanical agitation at speeds …

Advancing Chronic Kidney Disease Prediction Through Machine Learning And Deep Learning With Feature Analysis, Shiddarth Dey Tusar, S. M. Ahad Ali Chowdhury, Md. Jalal Uddin Chowdhury, Rana M. Pir, H. M. Nur A. Alam, Muhammad Rezaur Rahman, Md. Nural Absar Siddiky, Muhammad Enayetur Rahman

Electrical & Computer Engineering Faculty Publications

Chronic Kidney Diesease (CKD) is a significant health issue, ranking as the fourth leading cause of mortality worldwide. The traditional diagnosis and treatment process, reliant on medical experts, is time-consuming. Therefore, thereis an urgent need for more efficient diagnostic methods to improve patient outcomes and reduce mortality rates. In this study, we employ Machine Learning (ML) and Deep Learning (DL) techniques to predict CKD based on important features. Feature analysis was performed using a correlation matrix and the LASSO algo-rithm to identify the most relevant features for model training. We evaluated several ML and DL classifiers, including Logistic Regression (LR), …

Comparative Analysis Of Machine Learning Models For Predicting Healthcare Traffic: Insights For Optimized Emergency Response, Shadman Mahmood Khan Pathan, Sakan Binte Imran, M. M. Shabab Iqbal, Muhammad Enayetur Rahman, Md. Nurul Absar Siddiky, Muhammad Rezaur Rahman, Md Rafid Hasan, Nondon Lal Dey, Md Sobuj Hossain

Electrical & Computer Engineering Faculty Publications

Efficient management of healthcare traffic is crucial for ensuring timely access to medical services, particularly in emergency situations where delays can have severe consequences. This study presents a comparative analysis of three widely used machine learning models—Linear Regression, Decision Trees, and Random Forests—aimed at predicting healthcare-related traffic volumes. A large dataset from a metropolitan traffic system was used to train and evaluate the models based on key performance indicators, including Mean Squared Error (MSE), R² Score, and computational efficiency. The results reveal that the Random Forest model offers the best performance, achieving higher predictive accuracy and faster execution times compared …

Mesoporous Silica Administration As A New Strategy In The Management Of Warfarin Toxicity: An In-Vitro And In-Vivo Study, Fatemeh Farjadian, Fatemeh Parsi, Reza Heidari, Khatereh Zarkesh, Hamid Reza Mohammadi, Soliman Mohammadi-Samani, Lobat Tayebi

Electrical & Computer Engineering Faculty Publications

Purpose: Warfarin is one of the most widely used anticoagulants that functions by inhibiting vitamin K epoxide reductase. Warfarin overdose, whether intentional or unintentional, can cause life-threatening bleeding. Here, we present a novel warfarin adsorbent based on mesoporous silica that could serve as an antidote to warfarin toxicity.

Method: Amino-functionalized mesoporous silica (MS-NH₂) was synthesized based on the cocondensation method through a soft template technique followed by template removal. The prepared structure and functional group were studied by Fourier transform infrared spectroscopy (FT-IR), and X-ray diffraction (XRD). Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) checked the morphology. The …

Integrated Machine Learning And Deep Learning Models For Cardiovascular Disease Risk Prediction: A Comprehensive Comparative Study, Shadman Mahmood Khan Pathan, Sakan Binte Imran

Electrical & Computer Engineering Faculty Publications

Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the …

Is The Job Performance Of Tourism Academicians Related To Their Emotional Intelligence?, Leyla Tokgöz, Zennube Işık, Mehmet Fatih Işık

Khazar Journal of Humanities and Social Sciences

The aim of the research is to determine whether the job performances of tourism academics in Türkiye are related to emotional intelligence. Further, this research is an endeavor to investigate whether emotional intelligence and job performance differ according to demographic variables. “Job Performance Scale” adapted by Yang and Hwang (2014) was used to measure the job performance of academicians. In addition to that “Rotterdam Emotional Intelligence Scale” developed by Pekaar, Bakker, Linden, and Born (2017) and adapted to Turkish by Tanrıöğen and Türker (2019) was used to measure their emotional intelligence. The research sample consists of academicians working in Tourism …

Magnetic Resonance Imaging Findings In Kenyans And South Africans With Active Convulsive Epilepsy: An Observational Study, Symon M. Kariuki, Ryan Wagner, Roxana Gunny, Felice D'Arco, Martha Kombe, Anthony Ngugi, Steven White, Rachel Odhiambo, Helen Cross, Josemir Sander

Population Health, East Africa

Objective: Focal epilepsy is common in low- and middle-income countries. The frequency and nature of possible underlying structural brain abnormalities have, however, not been fully assessed.

Methods: We evaluated the possible structural causes of epilepsy in 331 people with epilepsy (240 from Kenya and 91 from South Africa) identified from community surveys of active convulsive epilepsy. Magnetic resonance imaging (MRI) scans were acquired on 1.5-Tesla scanners to determine the frequency and nature of any underlying lesions. We estimated the prevalence of these abnormalities using Bayesian priors (from an earlier pilot study) and observed data (from this study). We used a …

Implementation Framework For Income Generating Activities Identified By Community Health Volunteers (Chvs): A Strategy To Reduce Attrition Rate In Kilifi County, Kenya., Roselyter Riang’A, Njeri Nyanja, Adelaide Lusambili, Eunice Muthoni, Cyprian Mostert, Anthony Ngugi, Joshua Ehrlich, Paul Clyde

Population Health, East Africa

Background; Despite the proven efficacy of Community Health Volunteers (CHVs) in promoting primary healthcare in low- and middle-income countries (LMICs), they are not adequately fnanced and compensated. The latter contributes to the challenge of high attrition rates observed in many settings, highlighting an urgent need for innovative compensation strategies for CHVs amid budget constraints experienced by healthcare systems. This study sought to identify strategies for implementing Income-Generating Activities (IGAs) for CHVs in Kilif County in Kenya to improve their livelihoods, increase motivation, and reduce attrition.

Methods; An exploratory qualitative research study design was used, which consisted of Focus group discussions …

Post-Mortem Investigation Of Deaths Due To Pneumonia In Children Aged 1–59 Months In Sub-Saharan Africa And South Asia From 2016 To 2022: An Observational Study., Sana Mahtab, Dianna Blau, Zachary Madewell, Ikechukwu Ogbuanu, Julius Ojulong, Sandra Lako, Hailemariam Legesse, Joseph Bangura, Quique Bassat, Gunturu Revathi

Pathology, East Africa

Background; The Child Health and Mortality Prevention Surveillance (CHAMPS) Network programme undertakes post-mortem minimally invasive tissue sampling (MITS), together with collection of ante-mortem clinical information, to investigate causes of childhood deaths across multiple countries. We aimed to evaluate the overall contribution of pneumonia in the causal pathway to death and the causative pathogens of fatal pneumonia in children aged 1–59 months enrolled in the CHAMPS Network.

Methods; In this observational study we analysed deaths occurring between Dec 16, 2016, and Dec 31, 2022, in the CHAMPS Network across six countries in sub-Saharan Africa (Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, and …

Enhancing Reproducibility In Single Cell Research With Biocytometry: An Inter-Laboratory Study, Pavel Fikar, Laura Alvarez, Laura Berne, Martin Cienciala, Christopher Kan, Hynek Kasl, Mona Luo, Zuzana Novackova, Sheyla Ordonez, Zuzana Sramkova, Monika Holubova, Daniel Lysak, Lyndsay Avery, Andres A. Caro, Roslyn N. Crowder, Laura A. Diaz-Martinez, David W. Donley, Rebecca R. Giorno, Irene K. Guttilla Reed, Lori Hensley

Research, Publications & Creative Work

Biomedicine today is experiencing a shift towards decentralized data collection, which promises enhanced reproducibility and collaboration across diverse laboratory environments. This inter-laboratory study evaluates the performance of biocytometry, a method utilizing engineered bioparticles for enumerating cells based on their surface antigen patterns. In centralized and aggregated inter-lab studies, biocytometry demonstrated significant statistical power in discriminating numbers of target cells at varying concentrations as low as 1 cell per 100,000 background cells. User skill levels varied from expert to beginner capturing a range of proficiencies. Measurement was performed in a decentralized environment without any instrument cross-calibration or advanced user training outside …

Predictors Of Occupational Distress Of Catholic Priests On The Eastern Seaboard Of The United States, Michael D. Kostick, Xihe Zhu, Justin A. Haegele, Pete Baker

Human Movement Studies & Special Education Faculty Publications

With ever-increasing demands placed upon active priests in the United States, insight into protecting their mental health may help strengthen vocational resilience for individual priests. The purpose of this study was to examine the association of individual variables, workplace characteristics, and physical activity participation with occupational distress levels among Catholic priests. A 22-question survey consisting of a demographic questionnaire, the Clergy Occupational Distress Index, and the International Physical Activity Questionnaire was employed to collect individual variables, workplace characteristics, physical activity participation, and occupational distress levels of Catholic priests from the Eastern seaboard of the United States. Regression analyses showed that …

The Associations Of Physical Activity And Sedentary Behavior With Self-Rated Health In Chinese Children And Adolescents, Yahan Liang, Youzhi Ke, Yang Liu

Human Movement Studies & Special Education Faculty Publications

Objective

The study aimed to analyze the independent and joint associations of physical activity (PA) and sedentary behavior (SB) with self-rated health (SRH) among Chinese children and adolescents.

Methods

Cross-sectional data on moderate-to-vigorous physical activity (MVPA), school-based PA, extracurricular physical activity (EPA), screen time (ST), homework time, and SRH were assessed through a self-report questionnaire in the sample of 4227 Chinese children and adolescents aged 13.04 ± 2.62 years. Binary logistic regression was used to compare gender differences in PA, SB, and SRH among children and adolescents, and analyses were adjusted for age and ethnicity.

Results

In independent associations, boys …

Teachers' Perception On Physical Activity Promotion In Kindergarten Children In China: A Qualitative Study Connecting Social Ecological Model, Yahan Liang, Fangyuan Ju, Yueran Hao, Jia Yang, Yang Liu

Human Movement Studies & Special Education Faculty Publications

Background

Globally, the majority of kindergarten-aged children face obesity issues and insufficient physical activity (PA) engagement. Regular PA participation can provide various health benefits, including obesity reduction, for kindergarten-aged children. However, limited studies have investigated the factors influencing kindergarten-aged children's PA engagement from the perspective of their teachers. This qualitative study aimed to identify factors that could help promote PA among kindergarten-aged children from teachers' perspectives, including facilitators, barriers, and teachers' recommendations.

Methods

Fifteen kindergarten teachers (age range: 28-50 years; mean age: 38.53 years) with teaching experience ranging from 2 to 31 years (mean: 16.27 years) were recruited from Shanghai …

Updates Of The Role Of B-Cells In Ischemic Stroke, Silin Wu, Sidra Tabassum, Cole T Payne, Heng Hu, Aaron M Gusdon, Huimahn A Choi, Xuefang S Ren

Faculty, Staff and Student Publications

Ischemic stroke is a major disease causing death and disability in the elderly and is one of the major diseases that seriously threaten human health and cause a great economic burden. In the early stage of ischemic stroke, neuronal structure is destroyed, resulting in death or damage, and the release of a variety of damage-associated pattern molecules induces an increase in neuroglial activation, peripheral immune response, and secretion of inflammatory mediators, which further exacerbates the damage to the blood-brain barrier, exacerbates cerebral edema, and microcirculatory impairment, triggering secondary brain injuries. After the acute phase of stroke, various immune cells initiate …

Fgf5, Evelyn A Carrion, Malcolm M Moses, Richard R Behringer

Faculty, Staff and Student Publications

FGF5 functions as a negative regulator of the hair cycle in mammals. It is expressed in the outer root sheath of hair follicles during the late anagen phase of the hair cycle. It functions as a signaling molecule, mediating the transition of the anagen growth phase to catagen regression phase of the hair cycle. Spontaneous and engineered FGF5 mutations in mammalian animal models result in long hair phenotypes. In humans, inherited FGF5 mutations result in trichomegaly (long eyelashes). Knockdown of fgf5 in zebrafish embryos results in inner ear alterations. Alterations in FGF5 expression are also associated with various human pathologies.

Management Advice For Patients With Reflux-Like Symptoms: An Evidence-Based Consensus, A Pali Hungin, Rena Yadlapati, Foteini Anastasiou, Albert J Bredenoord, Hashem El Serag, Pierluigi Fracasso, Juan M Mendive, Edoardo V Savarino, Daniel Sifrim, Mihaela Udrescu, Peter J Kahrilas

Faculty, Staff and Students Publications

Patients with reflux-like symptoms (heartburn and regurgitation) are often not well advised on implementing individualised strategies to help control their symptoms using dietary changes, lifestyle modifications, behavioural changes or fast-acting rescue therapies. One reason for this may be the lack of emphasis in management guidelines owing to 'low-quality' evidence and a paucity of interventional studies. Thus, a panel of 11 gastroenterologists and primary care doctors used the Delphi method to develop consolidated advice for patients based on expert consensus. A steering committee selected topics for literature searches using the PubMed database, and a modified Delphi process including two online meetings …

Engravings, Secrets, And Interpretability Of Neural Networks, Nathaniel Hobbs, Periklis A Papakonstantinou, Jaideep Vaidya

Faculty, Staff and Student Publications

This work proposes a definition and examines the problem of undetectably engraving special input/output information into a Neural Network (NN). Investigation of this problem is significant given the ubiquity of neural networks and society's reliance on their proper training and use. We systematically study this question and provide (1) definitions of security for secret engravings, (2) machine learning methods for the construction of an engraved network, (3) a threat model that is instantiated with state-of-the-art interpretability methods to devise distinguishers/attackers. In this work, there are two kinds of algorithms. First, the constructions of engravings through machine learning training methods. Second, …

Igamt: Privacy-Preserving Electronic Health Record Synthesization With Heterogeneity And Irregularity, Wenjie Wang, Pengfei Tang, Jian Lou, Yuanming Shao, Lance Waller, Yi-An Ko, Li Xiong

Faculty, Staff and Student Publications

Utilizing electronic health records (EHR) for machine learning-driven clinical research has great potential to enhance outcome predictions and treatment personalization. Nonetheless, due to privacy and security concerns, the secondary use of EHR data is regulated, constraining researchers’ access to EHR data. Generating synthetic EHR data with deep learning methods is a viable and promising approach to mitigate privacy concerns, offering not only a supplementary resource for downstream applications but also sidestepping the privacy risks associated with real patient data. While prior efforts have concentrated on EHR data synthesis, significant challenges persist: addressing the heterogeneity of features including temporal and non-temporal …

Federated Node Classification Over Distributed Ego-Networks With Secure Contrastive Embedding Sharing, Han Xie, Li Xiong, Carl Yang

Faculty, Staff and Student Publications

Federated learning on graphs (a.k.a., federated graph learning- FGL) has recently received increasing attention due to its capacity to enable collaborative learning over distributed graph datasets without compromising local clients' data privacy. In previous works, clients of FGL typically represent institutes or organizations that possess sets of entire graphs (e.g., molecule graphs in biochemical research) or parts of a larger graph (e.g., sub-user networks of e-commerce platforms). However, another natural paradigm exists where clients act as remote devices retaining the graph structures of local neighborhoods centered around the device owners (i.e., ego-networks), which can be modeled for specific graph applications …

Bridging The Gap: Rademacher Complexity In Robust And Standard Generalization, Jiancong Xiao, Ruoyu Sun, Qi Long, Weijie J Su

Faculty, Staff and Student Publications

Training Deep Neural Networks (DNNs) with adversarial examples often results in poor generalization to test-time adversarial data. This paper investigates this issue, known as adversarially robust generalization, through the lens of Rademacher complexity. Building upon the studies by Khim and Loh (2018); Yin et al. (2019), numerous works have been dedicated to this problem, yet achieving a satisfactory bound remains an elusive goal. Existing works on DNNs either apply to a surrogate loss instead of the robust loss or yield bounds that are notably looser compared to their standard counterparts. In the latter case, the bounds have a …

Privacy-Preserving Fingerprinting Against Collusion And Correlation Threats In Genomic Data, Tianxi Ji, Erman Ayday, Emre Yilmaz, Pan Li

Faculty, Staff and Student Publications

Sharing genomic databases is critical to the collaborative research in computational biology. A shared database is more informative than specific genome-wide association studies (GWAS) statistics as it enables “do-it-yourself” calculations. Genomic databases involve intellectual efforts from the curator and sensitive information of participants, thus in the course of data sharing, the curator (database owner) should be able to prevent unauthorized redistributions and protect individuals’ genomic data privacy. As it becomes increasingly common for a single database be shared with multiple recipients, the shared genomic database should also be robust against collusion attack, where multiple malicious recipients combine their individual copies …

Fairness-Aware Estimation Of Graphical Models, Zhuoping Zhou, Davoud Ataee Tarzanagh, Bojian Hou, Qi Long, Li Shen

Faculty, Staff and Student Publications

This paper examines the issue of fairness in the estimation of graphical models (GMs), particularly Gaussian, Covariance, and Ising models. These models play a vital role in understanding complex relationships in high-dimensional data. However, standard GMs can result in biased outcomes, especially when the underlying data involves sensitive characteristics or protected groups. To address this, we introduce a comprehensive framework designed to reduce bias in the estimation of GMs related to protected attributes. Our approach involves the integration of the pairwise graph disparity error and a tailored loss function into a nonsmooth multi-objective optimization problem, striving to achieve fairness across …

Facilitating Clinical Information Extraction With Synthetic Data And Ontology Using Large Language Models, Yan Hu, Huan He, Qingyu Chen, Xiaoqian Jiang, Kirk Roberts, Hua Xu

Faculty, Staff and Student Publications

The rapid growth of unstructured clinical text in electronic health records necessitates robust information extraction systems, yet their development is hindered by the scarcity of high-quality annotated data. This study explores the potential of large language models to generate synthetic data for clinical named entity recognition and examines its impact on model performance. We propose a novel framework that integrates self-verified synthetic data generation with domain-specific semantic mapping using SNOMED-CT. By leveraging GPT-4o-mini for synthetic data creation and refining its quality through iterative verification and anomaly detection, we systematically evaluate the influence of synthetic data quality and quantity on fine-tuning …

Narrative Feature Or Structured Feature? A Study Of Large Language Models To Identify Cancer Patients At Risk Of Heart Failure, Ziyi Chen, Mengyuan Zhang, Mustafa Mohammed Ahmed, Yi Guo, Thomas J George, Jiang Bian, Yonghui Wu

Faculty, Staff and Student Publications

Cancer treatments are known to introduce cardiotoxicity, negatively impacting outcomes and survivorship. Identifying cancer patients at risk of heart failure (HF) is critical to improving cancer treatment outcomes and safety. This study examined machine learning (ML) models to identify cancer patients at risk of HF using electronic health records (EHRs), including traditional ML, Time-Aware long short-term memory (T-LSTM), and large language models (LLMs) using novel narrative features derived from the structured medical codes. We identified a cancer cohort of 12,806 patients from the University of Florida Health, diagnosed with lung, breast, and colorectal cancers, among which 1,602 individuals developed HF …

Descriptor: Synthetic Genomic Dataset With Diverse Ancestry (Syngen6), Xinyue Wang, Sitao Min, Jaideep Vaidya

Faculty, Staff and Student Publications

Advancements in genomic analysis techniques and data-driven research are driving precision medicine. However, in many cases, these advances are not equitable and do not help all subpopulations, since many existing genomic datasets lack diversity, limiting their applicability for studying populations beyond those of European ancestry. Thus, to advance genomic analysis and to allow for a fair benchmarking of novel proposed approaches, there is a significant demand for balanced and representative datasets. To address this issue, we developed,

Impact Of P53-Associated Acute Myeloid Leukemia Hallmarks On Metabolism And The Immune Environment, Monika Chomczyk, Luca Gazzola, Shubhankar Dash, Patryk Firmanty, Binsah S George, Vakul Mohanty, Hussein A Abbas, Natalia Baran

Faculty, Staff and Student Publications

Acute myeloid leukemia (AML), an aggressive malignancy of hematopoietic stem cells, is characterized by the blockade of cell differentiation, uncontrolled proliferation, and cell expansion that impairs healthy hematopoiesis and results in pancytopenia and susceptibility to infections. Several genetic and chromosomal aberrations play a role in AML and influence patient outcomes. TP53 is a key tumor suppressor gene involved in a variety of cell features, such as cell-cycle regulation, genome stability, proliferation, differentiation, stem-cell homeostasis, apoptosis, metabolism, senescence, and the repair of DNA damage in response to cellular stress. In AML, TP53 alterations occur in 5%-12% of de novo AML …

Rural Racial Disparities And Barriers In Mammography Utilization Among Medicare Beneficiaries In Texas: A Longitudinal Study, Zhaoli Liu, Yong Shan, Yong-Fang Kuo, Sharon H Giordano

Faculty, Staff and Student Publications

This study examined rural racial/ethnic disparities in long-term mammography screening practices among Medicare beneficiaries. A retrospective longitudinal study was conducted using 100% Texas Medicare data for women aged 65-74 who enrolled in Medicare between 2010-2013. Of the 114,939 eligible women, 21.2% of Hispanics, 33.3% of non-Hispanic Blacks (NHB), and 38.4% non-Hispanic Whites (NHW) in rural areas were regular users of mammography, compared to 33.5%, 44.9%, and 45.3% of their counterparts in urban areas, respectively. Stratification analyses showed rural Hispanics and NHB were 33% (95% CI, 25% - 40%) and 22% (95% CI, 6% - 36%) less likely to be regular …