Nursing Home Quality as a Public Good

There has been much debate among economists about whether nursing home quality is a public good across Medicaid and private-pay patients within a common facility. However, there has been only limited empirical work addressing this issue. Using a unique individual level panel of residents of nursing homes from seven states, we exploit both within-facility and within-patient variation in payer source and quality to examine this issue. We also test the robustness of these results across states with different Medicaid and private-pay rate differentials. Across our various identification strategies, the results generally support the idea that quality is a public good within nursing homes. That is, within a common nursing home, there is very little evidence to suggest that Medicaid-funded residents receive consistently lower quality care relative to their private-paying counterparts.

RUNNING INJURIES: FOREFOOT VERSUS REARFOOT AND BAREFOOT VERSUS SHOD: A BIOMECHANIST’S PERSPECTIVE

In recent years, there has been a debate regarding the use of different footfall patterns to reduce injury risk and enhance performance. Humans have three footfall patterns available to them when running: rearfoot, midfoot and forefoot. These different patterns are distinguished by the portion of the foot that’s makes initial contact with the ground. Interestingly, until very recently, there has been little research to show the pros or cons of the various footfall patterns. Here we will discuss several studies that have been carried out to distinguish footfall patterns in terms of kinematics and kinetics, running economy, the effect of surface and coordination on the risk of running injury

A Bayesian method for assessing multi-scale species-habitat relationships

Context Scientists face several theoretical and methodological challenges in appropriately describing fundamental wildlife-habitat relationships in models. The spatial scales of habitat relationships are often unknown, and are expected to follow a multi-scale hierarchy. Typical frequentist or information theoretic approaches often suffer under collinearity in multiscale studies, fail to converge when models are complex or represent an intractable computational burden when candidate model sets are large. Objectives Our objective was to implement an automated, Bayesian method for inference on the spatial scales of habitat variables that best predict animal abundance. Methods We introduce Bayesian latent indicator scale selection (BLISS), a Bayesian method to select spatial scales of predictors using latent scale indicator variables that are estimated with reversible-jump Markov chain Monte Carlo sampling. BLISS does not suffer from collinearity, and substantially reduces computation time of studies. We present a simulation study to validate our method and apply our method to a case-study of land cover predictors for ring-necked pheasant (Phasianus colchicus) abundance in Nebraska, USA. Results Our method returns accurate descriptions of the explanatory power of multiple spatial scales, and unbiased and precise parameter estimates under commonly encountered data limitations including spatial scale autocorrelation, effect size, and sample size. BLISS outperforms commonly used model selection methods including stepwise and AIC, and reduces runtime by 90%. Conclusions Given the pervasiveness of scale-dependency in ecology, and the implications of mismatches between the scales of analyses and ecological processes, identifying the spatial scales over which species are integrating habitat information is an important step in understanding species-habitat relationships. BLISS is a widely applicable method for identifying important spatial scales, propagating scale uncertainty, and testing hypotheses of scaling relationships

Estimating the Use of Public Lands: Integrated Modeling of Open Populations with Convolution Likelihood Ecological Abundance Regression

We present an integrated open population model where the population dynamics are defined by a differential equation, and the related statistical model utilizes a Poisson binomial convolution likelihood. Key advantages of the proposed approach over existing open population models include the flexibility to predict related, but unobserved quantities such as total immigration or emigration over a specified time period, and more computationally efficient posterior simulation by elimination of the need to explicitly simulate latent immigration and emigration. The viability of the proposed method is shown in an in-depth analysis of outdoor recreation participation on public lands, where the surveyed populations changed rapidly and demographic population closure cannot be assumed even within a single day

A Bayesian method for assessing multi-scale species-habitat relationships

Context Scientists face several theoretical and methodological challenges in appropriately describing fundamental wildlife-habitat relationships in models. The spatial scales of habitat relationships are often unknown, and are expected to follow a multi-scale hierarchy. Typical frequentist or information theoretic approaches often suffer under collinearity in multiscale studies, fail to converge when models are complex or represent an intractable computational burden when candidate model sets are large. Objectives Our objective was to implement an automated, Bayesian method for inference on the spatial scales of habitat variables that best predict animal abundance. Methods We introduce Bayesian latent indicator scale selection (BLISS), a Bayesian method to select spatial scales of predictors using latent scale indicator variables that are estimated with reversible-jump Markov chain Monte Carlo sampling. BLISS does not suffer from collinearity, and substantially reduces computation time of studies. We present a simulation study to validate our method and apply our method to a case-study of land cover predictors for ring-necked pheasant (Phasianus colchicus) abundance in Nebraska, USA. Results Our method returns accurate descriptions of the explanatory power of multiple spatial scales, and unbiased and precise parameter estimates under commonly encountered data limitations including spatial scale autocorrelation, effect size, and sample size. BLISS outperforms commonly used model selection methods including stepwise and AIC, and reduces runtime by 90%. Conclusions Given the pervasiveness of scale-dependency in ecology, and the implications of mismatches between the scales of analyses and ecological processes, identifying the spatial scales over which species are integrating habitat information is an important step in understanding species-habitat relationships. BLISS is a widely applicable method for identifying important spatial scales, propagating scale uncertainty, and testing hypotheses of scaling relationships

Constellation Coordination System (CCS) Status

This presentation will be given at the Mission Operations Working Group meeting June 13-15, 2017 to discuss the Constellation Coordination System status

Measuring Returns to Hospital Care: Evidence from Ambulance Referral Patterns

We consider whether hospitals that receive higher payments from Medicare improve patient outcomes, using exogenous variation in ambulance company assignment among patients who live near one another. Using Medicare data from 2002–10 on assignment across ambulance companies and New York State data from 2000–6 on assignment across area boundaries, we find that patients who are brought to higher-cost hospitals achieve better outcomes. Our estimates imply that a one standard deviation increase in Medicare reimbursement leads to a 4 percentage point (or 10 percent) reduction in mortality; the implied cost per at least 1 year of life saved is approximately $80,000.National Institutes of Health (U.S.) (R01 AG41794-01