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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., (© 2024. The Author(s).)

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%., Competing Interests: The authors have declared that no competing interests exist.

Patients scheduled for primary care appointments often cancel or no show. For diabetic patients, nonattendance can affect continuity of care and result in higher emergency department (ED) and hospital use. Nonattendance also impacts appointment scheduling, patient access, and clinic work load. While no show has received significant attention, little research has addressed the prevalence and impact of appointment cancellation. Data on 46,710 appointments for 7586 adult diabetic patients was used to conduct a prospective cohort study examining primary care appointment behavior. The independent variable was the status of the INDEX appointment, which was attended, cancelled, or no showed. Dependent variables included the dates of (1) the last attended appointment, (2) scheduling the NEXT appointment, (3) the next attended follow-up appointment, and (4) ED visits and hospitalizations within six months of the INDEX. Cancellation was more prevalent than no show (17.7% vs 12.2%). Of those who cancelled and scheduled a next appointment, 28.8% experienced over 30 days delay between the INDEX and NEXT appointment dates, and 59.9% delayed rescheduling until on or after the cancelled appointment date. Delay in rescheduling was associated with an 18.6% increase in days between attended appointments and a 26.0% increase in ED visits. For diabetic patients, cancellation with late rescheduling is a prevalent and unhealthy behavior. Although more work is necessary to address the health, intervention, and cost issues, this work suggests that cancellation, like no show, may be problematic for many clinics and patients.

Background: Patients who no-show to primary care appointments interrupt clinicians' efforts to provide continuity of care. Prior literature reveals no-shows among diabetic patients are common. The purpose of this study is to assess whether no-shows to primary care appointments are associated with increased risk of future emergency department (ED) visits or hospital admissions among diabetics., Methods: A prospective cohort study was conducted using data from 8,787 adult diabetic patients attending outpatient clinics associated with a medical center in Indiana. The outcomes examined were hospital admissions or ED visits in the 6 months (182 days) following the patient's last scheduled primary care appointment. The Andersen-Gill extension of the Cox proportional hazard model was used to assess risk separately for hospital admissions and ED visits. Adjustment was made for variables associated with no-show status and acute care utilization such as gender, age, race, insurance and co-morbid status. The interaction between utilization of the acute care service in the six months prior to the appointment and no-show was computed for each model., Results: The six-month rate of hospital admissions following the last scheduled primary care appointment was 0.22 (s.d. = 0.83) for no-shows and 0.14 (s.d. = 0.63) for those who attended (p < 0.0001). No-show was associated with greater risk for hospitalization only among diabetics with a hospital admission in the prior six months. Among diabetic patients with a prior hospital admission, those who no-showed were at 60% greater risk for subsequent hospital admission (HR = 1.60, CI = 1.17-2.18) than those who attended their appointment. The six-month rate of ED visits following the last scheduled primary care appointment was 0.56 (s.d. = 1.48) for no-shows and 0.38 (s.d. = 1.05) for those who attended (p < 0.0001); after adjustment for covariates, no-show status was not significantly related to subsequent ED utilization., Conclusions: No-show to a primary care appointment is associated with increased risk for hospital admission among diabetics recently hospitalized.

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.

Background: 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., Objective: To investigate how many older and middle-aged adults with diabetes have such health status characteristics., Design: Secondary data analysis of a nationally representative health interview survey, the Health and Retirement Study, and its diabetes mail-out survey., Setting/participants: Americans aged 51 and older with diabetes (n = 3506 representing 13.6 million people); aged 56 and older in diabetes survey (n = 1132, representing 9.9 million)., Measurements: Number of adults with diabetes and (a) relatively good health; (b) health status that could make DSM difficult (eg, comorbidities, impaired instrumental activities of daily living; and (c) characteristics like advanced dementia and activities of daily living dependency that could limit benefit of some diabetes management. Health and Retirement Study measures included demographics. Diabetes Survey included self-measured HbA1c., Results: Nearly 22% of adults > or =51 with diabetes (about 3 million people) have health characteristics that could make DSM difficult. Another 10% (1.4 million) may receive limited benefit from some diabetes management. Mail-out respondents with health characteristics that could make DSM difficult had significantly higher mean HbA1c compared with people with relatively good health (7.6% vs. 7.3%, P < 0.04.)., Conclusions: Some middle-aged as well as older adults with diabetes have health status characteristics that might make DSM difficult or of limited benefit. Current diabetes quality measures, including measures of glycemic control, may not reflect what is possible or optimal for all patient groups.

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.

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 > or =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.