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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.

Semantic communication has sparked great interest, due to the rising demands of emerging applications on high communication capacity and low latency. The majority of existing semantic communication methods are task-oriented, which transmit task-related semantic information via synchronous trained deep learning-based (DL-based) encoders and decoders. However, these methods have limitations in handling multi-task communications. Moreover, the synchronous training paradigm also leads to significant communication overhead in the establishing phase. In this article, we propose an asynchronous multi-task semantic communication method. In the proposed method, the DL-based encoder is trained independently using a contrastive learning method to extract task-independent semantic knowledge. Then, the receiver trains different DL-based decoders to perform various communication tasks based on the pre-trained encoder. Our method enables the accomplishment of multiple communication tasks in a single transmission. Moreover, the asynchronous training paradigm can reduce the communication overhead during the training phase of our system. The experimental results demonstrate that the proposed method achieves state-of-the-art performance in image classification and reconstruction tasks while requiring less than 10% of the training communication time compared to existing semantic communication systems.

Model inversion attacks can reconstruct the training samples of victim deep learning models. The existing efforts heavily rely on auxiliary information of the target samples (prior target information) to achieve their adversarial goals. However, prior target information is hard to obtain in practice. In this article, we explore the effect of class information in model inversion attacks to reduce the reliance of prior target information. Our contributions on class information exploitation are two-fold. First, we propose a supervised inversion model, Supervised Model Inversion (SMI). The proposed inversion model learns pixel-level features and data-to-class features from the rounded-outputs of the victim model and labeled auxiliary dataset. Second, we leverage victim model's rounded-outputs to guide the optimization of reconstructing inversion samples after trained inversion model. Our experimental results show that inversion samples reconstructed by SMI are more visually plausible with more details, comparing to the three representative model inversion attacks. We further perform an extensive study on various auxiliary dataset settings. It is found that the class combination in the auxiliary dataset rather than the number of classes that determines the quality of inversion samples. The ground-truth labels can improve the qualities of inversion samples but not essential to inversion attacks.

To authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1532-5415.2009.02225.x Byline: Christine T. Cigolle (*[dagger]), Mary Beth Ofstedal ([double dagger]), Zhiyi Tian (s.), Caroline S. Blaum ([dagger]s.) Keywords: frailty; disability; chronic disease Abstract: 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. Author Affiliation: (*)Department of Family Medicine ([double dagger])Institute for Social Research, and (s.)Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan; and ([dagger])Geriatric Research, Education and Clinical Center, Ann Arbor Veterans Affairs Healthcare System, Ann Arbor, Michigan. Article note: Address correspondence to Christine T. Cigolle, University of Michigan 300 N. Ingalls, Room 919, Ann Arbor, MI 48109. E-mail: ccigolle@umich.edu

Background: Geriatric conditions, such as incontinence and falling, are not part of the traditional disease model of medicine and may be overlooked in the care of older adults. The prevalence of geriatric conditions and their effect on health and disability in older adults has not been investigated in population-based samples. Objective: To investigate the prevalence of geriatric conditions and their association with dependency in activities of daily living by using nationally representative data. Design: Cross-sectional analysis. Setting: Health and Retirement Study survey administered in 2000. Participants: Adults age 65 years or older (n = 11 093, representing 34.5 million older Americans) living in the community and in nursing homes. Measurements: Geriatric conditions (cognitive impairment, falls, incontinence, low body mass index, dizziness, vision impairment, hearing impairment) and dependency in activities of daily living (bathing, dressing, eating, transferring, toileting). Results: Of adults age 65 years or older, 49.9% had 1 or more geriatric conditions. Some conditions were as prevalent as common chronic diseases, such as heart disease and diabetes. The association between geriatric conditions and dependency in activities of daily living was strong and significant, even after adjustment for demographic characteristics and chronic diseases (adjusted risk ratio, 2.1 [95% CI, 1.9 to 2.4] for 1 geriatric condition, 3.6 [CI, 3.1 to 4.1] for 2 conditions, and 6.6 [CI, 5.6 to 7.6] for [greater than or equal to] 3 conditions). Limitations: The study was cross-sectional and based on self-reported data. Because measures were limited by the survey questions, important conditions, such as delirium and frailty, were not assessed. Survival biases may influence the estimates. Conclusions: Geriatric conditions are similar in prevalence to chronic diseases in older adults and in some cases are as strongly associated with disability. The findings suggest that geriatric conditions, although not a target of current models of health care, are important to the health and function of older adults and should be addressed in their care.

Some patients with diabetes may have health status characteristics that could make diabetes self-management (DSM) difficult and lead to inadequate glycemic control, or limit the benefit of some diabetes management interventions.