Predictors of Death Rate during the COVID-19 Pandemic.

Coronavirus (COVID-19) is a potentially fatal viral infection. This study investigates geography, demography, socioeconomics, health conditions, hospital characteristics, and politics as potential explanatory variables for death rates at the state and county levels. Data from the Centers for Disease Control and Prevention, the Census Bureau, Centers for Medicare and Medicaid, Definitive Healthcare, and USAfacts.org were used to evaluate regression models. Yearly pneumonia and flu death rates (state level, 2014-2018) were evaluated as a function of the governors' political party using a repeated measures analysis. At the state and county level, spatial regression models were evaluated. At the county level, we discovered a statistically significant model that included geography, population density, racial and ethnic status, three health status variables along with a political factor. A state level analysis identified health status, minority status, and the interaction between governors' parties and health status as important variables. The political factor, however, did not appear in a subsequent analysis of 2014-2018 pneumonia and flu death rates. The pathogenesis of COVID-19 has a greater and disproportionate effect within racial and ethnic minority groups, and the political influence on the reporting of COVID-19 mortality was statistically relevant at the county level and as an interaction term only at the state level.

Classification of Alzheimer's Disease with and without Imagery using Gradient Boosted Machines and ResNet-50.

BACKGROUND: Alzheimer's is a disease for which there is no cure. Diagnosing Alzheimer's disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and magnetic resonance imaging (MRI) data. Early detection of AD enables family planning and may reduce costs by delaying long-term care. Accurate, non-imagery methods also reduce patient costs. The Open Access Series of Imaging Studies (OASIS-1) cross-sectional MRI data were analyzed. A gradient boosted machine (GBM) predicted the presence of AD as a function of gender, age, education, socioeconomic status (SES), and a mini-mental state exam (MMSE). A residual network with 50 layers (ResNet-50) predicted the clinical dementia rating (CDR) presence and severity from MRI's (multi-class classification). The GBM achieved a mean 91.3% prediction accuracy (10-fold stratified cross validation) for dichotomous CDR using socio-demographic and MMSE variables. MMSE was the most important feature. ResNet-50 using image generation techniques based on an 80% training set resulted in 98.99% three class prediction accuracy on 4139 images (20% validation set) at Epoch 133 and nearly perfect multi-class predication accuracy on the training set (99.34%). Machine learning methods classify AD with high accuracy. GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review.

A nurse-run walk-in clinic: cost-effective alternative to non-urgent emergency department use by the uninsured.

Non-urgent healthcare problems are responsible for more than 9 million visits to the emergency department (ED) in US hospitals each year, largely due to patients' lack of access to a primary care physician. To avoid costly and unnecessary ED usage for non-urgent health problems, a walk-in clinic run by nurses (CHEER Clinic) was developed as an extension of the services provided by an existing free clinic in a low-income neighborhood of Providence, RI, with the goal of providing uninsured patients with a convenient, no-cost means of accessing healthcare. An evaluation and cost-effectiveness analysis of the clinic's first 5 months of operation were performed. During this pilot period, 256 patients were seen. When incorporating the quality-adjusted-life-year value of preventive services rendered, an estimated $1.28 million in future healthcare costs was avoided. Dividing these cost-savings by the clinic's operational cost yielded a mean return on investment of $34 per $1 invested. Adding nurse-run walk-in hours at a free clinic significantly expanded access to healthcare for uninsured patients and was cost-effective for both the clinic and the patient. Ultimately, replication of this model in community clinics serving the uninsured could reduce ED burden by treating a substantial number of non-urgent medical concerns at a lower cost than would be incurred for treatment of the same problems in EDs.