No abstract

Electronic nicotine delivery systems (ENDS) use, including e-cigarettes, has surpassed the use of conventional tobacco products. Emerging research suggests that susceptibility to e-cigarette use is associated with actual use among adolescents. However, few studies exist involving adolescents in high-risk, rural, socioeconomically distressed environments. This study examines susceptibility to and subsequent usage in school-going adolescents in a rural distressed county in Appalachian Tennessee using data from an online survey (N = 399). Relying on bivariate analyses and logistic regression, this study finds that while 30.6% of adolescents are ever e-cigarette users, 15.5% are current users. Approximately one in three adolescents are susceptible to e-cigarettes use, and susceptibility is associated with lower odds of being a current e-cigarette user (OR = 0.03; CI: 0.01–0.12; p < 0.00). The age of tobacco use initiation was significantly associated with decreased current use of e-cigarettes (OR = 0.89; CI: 0.83–0.0.97; p < 0.01). Overall, the results of this exploratory study suggest the need for larger studies to identify unique and generalizable factors that predispose adolescents in this high-risk rural, socioeconomically disadvantaged region to ENDS use. Nevertheless, this study offers insight into e-cigarette usage among U.S adolescents in rural, socioeconomically disadvantaged environments and provides a foundation for a closer examination of this vulnerable population.

No abstract

There is a limited evaluation of an independent linguistic battery for early diagnosis of Mild Cognitive Impairment due to Alzheimer's disease (MCI-AD). We hypothesized that an independent linguistic battery comprising of only the language components or subtests of popular test batteries could give a better clinical diagnosis for MCI-AD compared to using an exhaustive battery of tests. As such, we combined multiple clinical datasets and performed Exploratory Factor Analysis (EFA) to extract the underlying linguistic constructs from a combination of the Consortium to Establish a Registry for Alzheimer's disease (CERAD), Wechsler Memory Scale (WMS) Logical Memory (LM) I and II, and the Boston Naming Test. Furthermore, we trained a machine-learning algorithm that validates the clinical relevance of the independent linguistic battery for differentiating between patients with MCI-AD and cognitive healthy control individuals. Our EFA identified ten linguistic variables with distinct underlying linguistic constructs that show Cronbach's alpha of 0.74 on the MCI-AD group and 0.87 on the healthy control group. Our machine learning evaluation showed a robust AUC of 0.97 when controlled for age, sex, race, and education, and a clinically reliable AUC of 0.88 without controlling for age, sex, race, and education. Overall, the linguistic battery showed a better diagnostic result compared to the Mini-Mental State Examination (MMSE), Clinical Dementia Rating Scale (CDR), and a combination of MMSE and CDR.

No abstract