Southeastern United States Predictors of COVID-19 in Nursing Homes.

This study's aim was to determine nursing home (NH) and county-level predictors of COVID-19 outbreaks in nursing homes (NHs) in the southeastern region of the United States across three time periods. NH-level data compiled from census data and from NH compare and NH COVID-19 infection datasets provided by the Center for Medicare and Medicaid Services cover 2951 NHs located in 836 counties in nine states. A generalized linear mixed-effect model with a random effect was applied to significant factors identified in the final stepwise regression. County-level COVID-19 estimates and NHs with more certified beds were predictors of COVID-19 outbreaks in NHs across all time periods. Predictors of NH cases varied across the time periods with fewer community and NH variables predicting COVID-19 in NH during the late period. Future research should investigate predictors of COVID-19 in NH in other regions of the US from the early periods through March 2021.

Mapping community-level determinants of COVID-19 transmission in nursing homes: A multi-scale approach.

Deaths from the COVID-19 pandemic have disproportionately affected older adults and residents in nursing homes. Although emerging research has identified place-based risk factors for the general population, little research has been conducted for nursing home populations. This GIS-based spatial modeling study aimed to determine the association between nursing home-level metrics and county-level, place-based variables with COVID-19 confirmed cases in nursing homes across the United States. A cross-sectional research design linked data from Centers for Medicare & Medicaid Services, American Community Survey, the 2010 Census, and COVID-19 cases among the general population and nursing homes. Spatial cluster analysis identified specific regions with statistically higher COVID-19 cases and deaths among residents. Multivariate analysis identified risk factors at the nursing home level including, total count of fines, total staffing levels, and LPN staffing levels. County-level or place-based factors like per-capita income, average household size, population density, and minority composition were significant predictors of COVID-19 cases in the nursing home. These results provide a framework for examining further COVID-19 cases in nursing homes and highlight the need to include other community-level variables when considering risk of COVID-19 transmission and outbreaks in nursing homes.

Sandwich analytics: A dataset comprising one year's weekly sales data correlated with crime, demographics, and weather.

Data collected from a quick-serve sandwich chain over one year provide an opportunity to study market, sociographic, meteorologic, and other factors impacting sales and sales forecasting. The weekly sales table contains over 79,000 rows which each represent summary statistics for the sales of an individual menu item in one store during one week of the year. The data were collected from the point-of-sale system of 10 stores. Secondary data regarding weather patterns, population, location, competition, and crime statistics were gathered and integrated with the original data set.

The inherent risks associated with newly traded biopharmaceutical firms.

Here, we provide a comprehensive study related to the risks of all biopharmaceutical firms going public in the USA between 1996 and 2015. We found 355 firms that met our requirements for being in the sector that focuses on creating drugs for humans. Collectively, these firms spent approximately US$86.9 billion on research and development (R&D) during this time. They also lost approximately US$69.3 billion in combined net income. We also examine the delisting of these firms from a public market, their number of collaborators at the initial public offering (IPO), and estimate the percentage ownership by other biopharmaceutical firms at the IPO.

Health Belief Model Offers Opportunities for Designing Weight Management Interventions for College Students.

OBJECTIVE: Identify weight-related beliefs of college students and test the predictive power of the Health Belief Model for body mass index (BMI). DESIGN: Cross-sectional online survey with beliefs measured on 5-point scales. SETTING: University in North Carolina. PARTICIPANTS: Undergraduates (n = 516; 91.9%), females (n = 399; 71.3%), white non-Hispanic students (n = 507; 86.2%), and 20.3% of overweight or obese status. VARIABLES MEASURED: Perceived severity, susceptibility, barriers, benefits, and internal and external cues to action. ANALYSIS: One-way ANOVA and regression. Significance was P < .05. RESULTS: Strongest beliefs concerned benefits of healthy eating and physical activity (mean, 4.1 ± 0.7); weakest beliefs concerned barriers to adopting such behaviors (mean, 2.6 ± 0.9). The regression model was statistically significant (P < .001) and explained 17% of variance in BMI (multivariate coefficient = 0.177). Perceived severity, susceptibility, external cues, barriers, and benefits predicted BMI. CONCLUSIONS AND IMPLICATIONS: Several beliefs were identified that could serve as the basis for weight-related interventions addressing specific concerns, needs, and goals of college students.

An empirical analysis of nurse retention: what keeps RNs in nursing?

OBJECTIVE: This study investigates the effects of stress, economic factors, altruism, and value congruence on intentions to leave jobs and the nursing profession. BACKGROUND: Retaining nurses will be critical for healthcare organizations as the demand for nurses increases. Regulation and cost pressures are changing the nursing work environment. METHODS: We surveyed 861 RNs in the southeastern United States. Structural equation modeling was used to analyze the survey. RESULTS: Results confirm the importance of stress and salaries and underscore the impact of both value congruence and altruism. Evidence shows a correlation between altruistic desires and intentions to leave the profession. CONCLUSIONS: Efforts to retain nurses should include matching nurse and organizational values. Initiatives need to be undertaken to increase professional autonomy and provide opportunities for the expression of altruism. Further research is indicated to investigate the unexpected result that highly altruistic nurses are leaving the profession.

Impact of CPOE usage on medication management process costs and quality outcomes.

We assess the impact of computerized physician order entry (CPOE) systems usage on cost and process quality in the medication management process. Data are compiled from 1,014 U.S. acute-care hospitals that have already implemented CPOE. Data sources include the American Hospital Association, HIMSS Analytics, and the Centers for Medicare and Medicaid Services. We examine the association of CPOE usage with nursing and pharmacy salary costs, and evidence-based medication process compliance. Empirical findings controlling for endogeneity in usage show that benefits accrue even when 100 percent usage is not achieved. We demonstrate that the relationship of CPOE usage with cost and compliance is non-linear.

Adoption of health information technology for medication safety in U.S. Hospitals, 2006.

Health information technology (IT) is regarded as an essential tool to improve patient safety, and a range of initiatives to address patient safety are under way. Using data from a comprehensive, national survey from HIMSS Analytics, we analyzed the extent of health IT adoption for medication safety in U.S. hospitals in 2006. Our findings indicate wide variation in health IT adoption by type of technology and geographic location. Hospital size, ownership, teaching status, system membership, payer mix, and accreditation status are associated with health IT adoption, although these relationships differ by type of technology. Hospitals in states with patient safety initiatives have greater adoption rates.