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AbstractClustering is a widely used unsupervised learning technique that groups data into homogeneous clusters. However, when dealing with real-world data that contain categorical values, existing algorithms can be computationally costly in high dimensions and can struggle with noisy data that has missing values. Furthermore, except for one algorithm, no others provide theoretical guarantees of clustering accuracy. In this article, we propose a general categorical data encoding method and a computationally efficient spectral-based algorithm to cluster high-dimensional noisy categorical data (nominal or ordinal). Under a statistical model for data on m attributes from n subjects in r clusters with missing probability ϵ, we show that our algorithm exactly recovers the true clusters with high probability when mn(1−ϵ)≥CMr2 log 3M, with M=max(n,m) and a fixed constant C. In addition, we show that mn(1−ϵ)2≥rδ/2 with 0<δ<1 is necessary for any algorithm to succeed with probability at least (1+δ)/2. In cases where m = n and r are fixed, the sufficient condition matches with the necessary condition up to a polylog(n) factor. In numerical studies our algorithm outperforms several existing algorithms in both clustering accuracy and computational efficiency. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Introduction: The United States Centers for Disease Control and Prevention (CDC) advises testing individuals for COVID-19 after exposure or if they display symptoms. However, a deeper understanding of demographic factors associated with testing hesitancy is necessary. Methods: A US nationwide cross-sectional survey of adults with risk factors for developing severe COVID-19 ("high-risk" individuals) was conducted from August 18–September 5, 2023. Objectives included characterizing demographics and attitudes associated with COVID-19 testing. Inverse propensity weighting was used to weight the data to accurately reflect the high-risk adult US population as reflected in IQVIA medical claims data. We describe here the weighted results modeled to characterize demographic factors driving hesitancy. Results: In the weighted sample of 5019 respondents at high risk for severe COVID-19, 58.2% were female, 37.8% were ≥ 65 years old, 77.1% were White, and 13.9% had a postgraduate degree. Overall, 67% were Non-testers (who indicated that they were unlikely or unsure of their likelihood of being tested within the next 6 months); these respondents were significantly more likely than Testers (who indicated a higher probability of testing within 6 months) to be female (60.2 vs. 54.1%; odds ratio [OR] [95% confidence interval (CI)], 1.3 [1.1‒1.4]), aged ≥ 65 years old (41.5 vs. 30.3%; OR [95% CI] compared with ages 18‒34 years, 0.6 [0.5‒0.7]), White (82.1 vs. 66.8%; OR [95% CI], 1.4 [1.1‒1.8]), and to identify as politically conservative (40.9 vs. 18.1%; OR [95% CI], 2.6 [2.3‒2.9]). In contrast, Testers were significantly more likely than Non-testers to have previous experience with COVID-19 testing, infection, or vaccination; greater knowledge regarding COVID-19 and testing; greater healthcare engagement; and concerns about COVID-19. Conclusions: Older, female, White, rural-dwelling, and politically conservative high-risk adults are the most likely individuals to experience COVID-19 testing hesitancy. Understanding these demographic factors will help guide strategies to improve US testing rates. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To operationalize and compare three models of frailty, each representing a distinct theoretical view of frailty: as deficiencies in function (Functional Domains model), as an index of health burden (Burden model), and as a biological syndrome (Biologic Syndrome model). DESIGN: Cross-sectional analysis. SETTING: 2004 wave of the Health and Retirement Study, a nationally representative, longitudinal health interview survey. PARTICIPANTS: Adults aged 65 and older (N=11,113) living in the community and in nursing homes in the United States. MEASUREMENTS: The outcome measure was the presence of frailty, as defined according to each frailty model. Covariates included chronic diseases and sociodemographic characteristics. RESULTS: Almost one-third (30.2%) of respondents were frail according to at least one model; 3.1% were frail according to all three models. The Functional Domains model showed the least overlap with the other models. In contrast, 76.1% of those classified as frail according to the Biologic Syndrome model and 72.1% of those according to the Burden model were also frail according to at least one other model. Older adults identified as frail according to the different models differed in sociodemographic and chronic disease characteristics. For example, the Biologic Syndrome model demonstrated substantial associations with older age (adjusted odds ratio (OR)=10.6, 95% confidence interval (CI)=6.1–18.5), female sex (OR=1.7, 95% CI=1.2–2.5), and African-American ethnicity (OR=2.1, % CI=1.0–4.4). CONCLUSION: Different models of frailty, based on different theoretical constructs, capture different groups of older adults. The different models may represent different frailty pathways or trajectories to adverse outcomes such as disability and death. [ABSTRACT FROM AUTHOR]

The transport layer security (TLS) protocol is widely adopted by apps as well as malware. With the geometric growth of TLS traffic, accurate and efficient detection of malicious TLS flows is becoming an imperative. However, current studies focus on either detection accuracy or detection efficiency, and few studies take into account both indicators. In this paper, we propose a two-layer detection framework composed of a filtering model (FM) and a malware family classification model (MFCM). In the first layer, a new set of TLS handshake features is presented to train the FM, which is devised to filter out a majority of benign TLS flows. For identifying malware families, both TLS handshake features and statistical features are applied to construct the MFCM in the second layer. Comprehensive experiments are conducted to substantiate the high accuracy and efficiency of the proposed two-layer framework. A total of 96.32% of benign TLS flows can be filtered out by the FM with few malicious TLS flows being discarded provided the threshold of the FM is set to 0.01. Moreover, a multiclassifier is selected to construct the MFCM to provide better performance than a set of binary classifiers under the same feature set. In addition, when the ratio of benign and malicious TLS flows is set to 10:1, the detection efficiency of the two-layer framework is 188% faster than that of the single-layer framework, while the average detection accuracy reaches 99.45%. [ABSTRACT FROM AUTHOR]

Background: Geriatric conditions, collections of symptoms common in older adults and not necessarily associated with a specific disease, increase in prevalence with advancing age. These conditions are important contributors to the complex health status of older adults. Diabetes mellitus is known to co-occur with geriatric conditions in older adults and has been implicated in the pathogenesis of some conditions.Objective: To investigate the prevalence and incidence of geriatric conditions in middle-aged and older-aged adults with diabetes.Design: Secondary analysis of nationally-representative, longitudinal health interview survey data (Health and Retirement Study waves 2004 and 2006).Participants: Respondents 51 years and older in 2004 (n=18,908).Main Measures: Diabetes mellitus. Eight geriatric conditions: cognitive impairment, falls, incontinence, low body mass index, dizziness, vision impairment, hearing impairment, pain.Key Results: Adults with diabetes, compared to those without, had increased prevalence and increased incidence of geriatric conditions across the age spectrum (p< 0.01 for each age group from 51-54 years old to 75-79 years old). Differences between adults with and without diabetes were most marked in middle-age. Diabetes was associated with the two-year cumulative incidence of acquiring new geriatric conditions (odds ratio, 95% confidence interval: 1.8, 1.6-2.0). A diabetes-age interaction was discovered: as age increased, the association of diabetes with new geriatric conditions decreased.Conclusions: Middle-aged, as well as older-aged, adults with diabetes are at increased risk for the development of geriatric conditions, which contribute substantially to their morbidity and functional impairment. Our findings suggest that adults with diabetes should be monitored for the development of these conditions beginning at a younger age than previously thought. [ABSTRACT FROM AUTHOR]