Making the Grade: A Scorecard for State Health Insurance Exchanges

Assesses states' progress in creating exchanges and grades established exchanges on policies regarding governance and structure, negotiating power and ability to drive value, consumer experience, and stability, including protection from adverse selection

Building a Better Health Care Marketplace

Details issues for creating insurance exchanges, including accountability and transparency, power to negotiate, innovations in cost and quality, stability, consumer-friendly design, and coordination with public programs, with a focus on small businesses

An application of the LASSO and elastic net regression to assess poverty and economic freedom on ECOWAS countries

The study of poverty has been studied from several different research approaches over the years. This analysis intended to determine which variables tell us about poverty in the Economic Community of West African State (ECOWAS) countries. Many ECOWAS countries have recorded high economic growth rates in the past few decades. However, a recent trend is that this progress is reversing, and poverty rates are increasing. In this analysis, we examined the variables describing poverty in ECOWAS. We used a statistical approach coupled with economic theory to justify the inclusion of the variables used to assess poverty. Furthermore, we include the use of the Fraser Institute's Economic Freedom index for each of the West African States. As far as we know, the economic freedom and poverty of West African states have not been presented for consideration toward African growth rates and poverty rates. With few exceptions, economic freedom research suggests that economic freedom is the foundational ingredient for increasing prosperity and reducing poverty. Then, we interpreted the empirical results and assess the validity of the model as applied to the ECOWAS countries. More specifically, we use the LASSO and elastic net regression to obtain sparse solutions to regression problems. LASSO and elastic net are computational methods that rapidly inform us about the relevant variables for the model. These computational methods' performances will in the context of the number of variables exceeds the number of observations by generating a low mean squared error (MSE)

Improving Metabolic Health Through Precision Dietetics in Mice

The incidence of diet-induced metabolic disease has soared over the last half-century, despite national efforts to improve health through universal dietary recommendations. Studies comparing dietary patterns of populations with health outcomes have historically provided the basis for healthy diet recommendations. However, evidence that population-level diet responses are reliable indicators of responses across individuals is lacking. This study investigated how genetic differences influence health responses to several popular diets in mice, which are similar to humans in genetic composition and the propensity to develop metabolic disease, but enable precise genetic and environmental control. We designed four human-comparable mouse diets that are representative of those eaten by historical human populations. Across four genetically distinct inbred mouse strains, we compared the American diet’s impact on metabolic health to three alternative diets (Mediterranean, Japanese, and Maasai/ketogenic). Furthermore, we investigated metabolomic and epigenetic alterations associated with diet response. Health effects of the diets were highly dependent on genetic background, demonstrating that individualized diet strategies improve health outcomes in mice. If similar genetic-dependent diet responses exist in humans, then a personalized, or “precision dietetics,” approach to dietary recommendations may yield better health outcomes than the traditional one-size-fits-all approach

An Observing System Experiment for Tropical Cyclone Targeting Techniques Using the Global Forecast System

Abstract In 1997, the National Oceanic and Atmospheric Administration’s National Hurricane Center and the Hurricane Research Division began operational synoptic surveillance missions with the Gulfstream IV-SP jet aircraft to improve the numerical guidance for hurricanes that threaten the continental United States, Puerto Rico, the U.S. Virgin Islands, and Hawaii. The dropwindsonde observations from these missions were processed and formatted aboard the aircraft and sent to the National Centers for Environmental Prediction and the Global Telecommunications System to be ingested into the Global Forecasting System, which serves as initial and boundary conditions for regional numerical models that also forecast tropical cyclone track and intensity. As a result of limited aircraft resources, optimal observing strategies for these missions are investigated. An Observing System Experiment in which different configurations of the dropwindsonde data based on three targeting techniques (ensemble variance, ensemble transform Kalman filter, and total energy singular vectors) are assimilated into the model system was conducted. All three techniques show some promise in obtaining maximal forecast improvements while limiting flight time and expendables. The data taken within and around the regions specified by the total energy singular vectors provide the largest forecast improvements, though the sample size is too small to make any operational recommendations. Case studies show that the impact of dropwindsonde data obtained either outside of fully sampled, or within nonfully sampled target regions is generally, though not always, small; this suggests that the techniques are able to discern in which regions extra observations will impact the particular forecast