Jonssoncarey6162

Health insurance for the low-income was significantly associated with higher odds of likely dementia for Mexican American men and women and Mexican women but not men. selleckchem Living in extended households increased the odds of likely dementia in women, but not in men for both studies. Multiple cardiovascular conditions increased the odds of likely dementia for Mexicans but not for Mexican Americans.

Our study provides evidence of the high burden of dementia among oldest-old Mexicans and Mexican Americans and its association with health and social vulnerabilities.

Our study provides evidence of the high burden of dementia among oldest-old Mexicans and Mexican Americans and its association with health and social vulnerabilities.

Numerous studies have collected Alzheimer's disease (AD) cohort data sets. To achieve reproducible, robust results in data-driven approaches, an evaluation of the present data landscape is vital.

Previous efforts relied exclusively on metadata and literature. Here, we evaluate the data landscape by directly investigating nine patient-level data sets generated in major clinical cohort studies.

The investigated cohorts differ in key characteristics, such as demographics and distributions of AD biomarkers. Analyzing the ethnoracial diversity revealed a strong bias toward White/Caucasian individuals. We described and compared the measured data modalities. Finally, the available longitudinal data for important AD biomarkers was evaluated. All results are explorable through our web application ADataViewer (https//adata.scai.fraunhofer.de).

Our evaluation exposed critical limitations in the AD data landscape that impede comparative approaches across multiple data sets. Comparison of our results to those gained by metadata-based approaches highlights that thorough investigation of real patient-level data is imperative to assess a data landscape.

Our evaluation exposed critical limitations in the AD data landscape that impede comparative approaches across multiple data sets. Comparison of our results to those gained by metadata-based approaches highlights that thorough investigation of real patient-level data is imperative to assess a data landscape.

The population of American Indians and Alaska Natives (AI/ANs) aged 65 and older is growing rapidly, raising concerns about Alzheimer's disease (AD) in their communities.

We distributed a survey incorporating the Alzheimer's Disease Knowledge Scale to 341 AI/AN community members attending cultural events. We computed average adjusted predictions and 95% confidence intervals from a linear regression model, used joint F tests to examine differences in scores according to demographic variables, calculated the percentage of correct items for each participant, and computed domain-specific averages across the sample.

The average score was 19.0 (maximum 30); the average percentage of correct responses was 63%. Higher scores were associated with education but not with age, sex, or rural versus urban residence. Low scores were observed for items on caregiving and disease risk.

Participants were moderately well informed about AD, but specific knowledge domains call for community outreach and education.

Participants were moderately well informed about AD, but specific knowledge domains call for community outreach and education.Elderly participants in Alzheimer's disease (AD) clinical trials are at high risk of morbidity and mortality with interpersonal exposure to COVID-19, a situation that is likely to continue for the foreseeable future. Yet, in-person neuropsychological assessments remain the mainstay primary outcomes for clinical trials seeking prevention and cure for AD. The Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog) is among the most commonly used cognitive assessment in AD clinical trials, and though currently lacking specific guidelines for virtual administrations, it can be used remotely with appropriate modifications and considerations. Here we propose a novel method of virtual administration of the ADAS-Cog, which considers workarounds for technological and human limitations imposed when the participant is no longer sitting across from the test administrator.Previous studies find preventative behaviors designed to reduce the number of infections during emerging disease outbreaks are associated with perceived risk of disease susceptibility. Few studies have attempted to identify underlying factors that explain differences in perceptions of risk during an infectious disease outbreak. Drawing from two early waves of American Trends Panel (n=7,441), as well as a National Science Foundation funded, Qualtrics national panel survey from the early stages of the pandemic (n=10,368), we test whether race and ethnicity, gender, and age were associated with six perceived threat and fear outcomes related to COVID-19. Results demonstrate race and ethnicity, gender, and age play a significant role in shaping threat and fear perceptions of COVID-19, but depending on the outcome, relationships vary in direction and magnitude. In some cases, historically marginalized racial and ethnic groups were more likely to report high fear and perceive coronavirus as a major threat to population and individual health, whereas, in others cases, the same marginalized racial and ethnic groups were less likely to perceive coronavirus to be a serious threat to the immune-comprised and the elderly population. We also find women were generally more likely to report high levels of threat and fear of COVID-19. Finally, we observe a clear age difference, whereby adults in older age groups report high-risk perceptions of COVID-19. Findings can inform public health programs designed to educate communities on the benefits of engaging in effective preventative practices during emerging infectious disease outbreaks.In the aftermath of a nuclear disaster, a person's radiation risk perception can harm their sociopsychological health. Although there are reports of an association between radiation risk perception and relocation, the direction of this association has not been clarified yet. In this study, we used a random-intercept and cross lagged panel model (RI-CLPM) to investigate the association and its direction between radiation risk perception and the prefectural-level relocation (i.e., inside/outside of Fukushima Prefecture). We did this by using five waves of longitudinal surveys between 2011 fiscal year and 2015 fiscal year among the people affected by the Fukushima disaster in 2011. We included 90,567 participants aged ≥15 years during the time of the disaster who responded to the questionnaire at least once. RI-CLPM was applied to examine the reciprocal relationship between radiation risk perception and locations. We used two radiation risk perception indicators (i.e., genetic effect and delayed effect) and two handling methods on missing data (i.