Health Expenditure and Income in the United States

This paper investigates the long-run economic relationship between health care expenditure and income in the US at a State level. Using a panel of 49 US States followed over the period 1980-2004, we study the non-stationarity and cointegration between health spending and income, ultimately measuring income elasticity of health care. The tests we adopt allow us to explicitly control for cross-section dependence and unobserved heterogeneity. Specifically, in our regression equations we assume that the error is the sum of a multifactor structure and a spatial autoregressive process, which capture global shocks and local spill overs in health expenditure. Our results suggest that health care is a necessity rather than a luxury, with an elasticity much smaller than that estimated in other US studies. Further, we observe a significant spatial spill over, though with a smaller intensity than that detected in other studies on spatial concentration of US health spending. Our broad perspective of cross section dependence as well as the methods used to capture it give new insights on the debate over the relationship between health spending and income.Health expenditure; income elasticity; cross section dependence; panels

GMM estimation of spatial panels

We consider Generalized Method of Moments (GMM) estimation of a regression model with spatially correlated errors. We propose some new moment conditions, and derive the asymptotic distribution of the GMM based on them. The analysis is supported by a small Monte Carlo exercise.Generalized Method of Moments, spatial econometrics

Approximated models for aerodynamic coefficients estimation in a multidisciplinary design environment

In this paper variable fidelity analyses are investigated. Moreover different kind of approximations to be used in a wide multidisciplinary design environment for aircraft design are built. In order to obtain the surrogate models used in the main design process, a proper framework is built by different design of experiments techniques for process and variables management. Approximated models for the estimation of aerodynamic coefficients are evaluated on design spaces of different dimensions and considering different set of variables (i.e. geometric parameters and flight conditions). They are mainly based on the hybrid combination of Vortex Lattice Method (VLM) models representing the basic low fidelity analysis) and 3D finite volume Computational Fluid Dynamics models (representing the basic high fidelity analysis). Different strategies for the evaluation of the surrogate model are considered and an original methodology for the model construction is here presented

Large Panels with Common Factors and Spatial Correlations

This paper considers the statistical analysis of large panel data sets where even after condi-tioning on common observed effects the cross section units might remain dependently distrib-uted. This could arise when the cross section units are subject to unobserved common effects and/or if there are spill over effects due to spatial or other forms of local dependencies. The paper provides an overview of the literature on cross section dependence, introduces the con-cepts of time-specific weak and strong cross section dependence and shows that the commonly used spatial models are examples of weak cross section dependence. It is then established that the Common Correlated Effects (CCE) estimator of panel data model with a multifactor error structure, recently advanced by Pesaran (2006), continues to provide consistent estimates of the slope coefficient, even in the presence of spatial error processes. Small sample properties of the CCE estimator under various patterns of cross section dependence, including spatial forms, are investigated by Monte Carlo experiments. Results show that the CCE approach works well in the presence of weak and/or strong cross sectionally correlated errors. We also explore the role of certain characteristics of spatial processes in determining the performance of CCE estimators, such as the form and intensity of spatial dependence, and the sparseness of the spatial weight matrix.panels, Common Correlated Effects, strong and weak cross section dependence

Medical Technology and the Production of Health Care

This paper investigates the factors that determine differences across OECD countries inhealth outcomes, using data on life expectancy at age 65, over the period 1960 to 2007. We estimate a production function where life expectancy depends on health and social spending, lifestyle variables, and medical innovation. Our first set of regressions includes a set of observed medical technologies by country. Our second set of regressions proxy technology using a spatial process. The paper also tests whether in the long-run countries tend to achieve similar levels of health outcomes. Our results show that health spending has a significant and mild effect on health out- comes, even after controlling for medical innovation. However, its short-run adjustments do not seem to have an impact on health care productivity. Spatial spill overs in life expectancy are significant and point to the existence of interdependence across countries in technology adoption. Furthermore, nations with initial low levels of life expectancy tend to catch up with those with longer-lived populations

Weak and strong cross section dependence and estimation of large panels

This paper introduces the concepts of time-specific weak and strong cross section dependence. A double- indexed process is said to be cross sectionally weakly dependent at a given point in time, t, if its weighted average along the cross section dimension (N) converges to its expectation in quadratic mean, as N is increased without bounds for all weights that satisfy certain ‘granularity’ conditions. Relationship with the notions of weak and strong common factors is investigated and an application to the estimation of panel data models with an infinite number of weak factors and a finite number of strong factors is also considered. The paper concludes with a set of Monte Carlo experiments where the small sample properties of estimators based on principal components and CCE estimators are investigated and compared under various assumptions on the nature of the unobserved common effects. JEL Classification: C10, C31, C33Panels, Strong and Weak Cross Section Dependence, Weak and Strong Factors

Characterisation of new potential vaccine candidates against infections caused by Staphylococcus aureus

Due to the rapid emergence of S. aureus strains resistant to multiple antibiotics and the therewith increased mortality rates, the development of alternative strategies to prevent and treat S. aureus infections is of great clinical and economical importance. Based on the results concerning both monovalent active and passive immunisation, it is getting obvious that only multivalent vaccine strategies might confer full protection from S. aureus related infections. Furthermore, due to their short term applicability and potential composition of immunoglobulins of different isotypes and functionalities, strategies based on passive immunisation are particularly advantageous. Using an intravenous immunoglobulin preparation (IVIG) as source of naturally occurring S. aureus specific IgGs, a significant inhibition of staphylococcal growth was observed in vitro. Thus, confirming the bacteriostatic effect on S. aureus as observed using human serum in the 1970s. Since this inhibitory effect was not observed upon treatment with IVIG depleted of S. aureus - specific IgGs (dSaIVIG), bacteriostasis is triggered solely by S. aureus specific IgGs. In order to analyse the underlying mechanism, gene expression profiling was conducted, using a S. aureus-seven genome PCR-product microarray. Comparison of IVIG to dSaIVIG treated samples led to the identification of 236 differentially expressed genes over the course of bacteriostasis. In contrast, IVIG compared to PBS treated samples as additional control resulted in 78 genes with altered expression. Only 13 genes were identified by both sets of microarrays, indicating a strong difference between the two applied controls. Moreover, the most prominent signature representing genes related to iron uptake and metabolism was only identified by comparison of IVIG to dSaIVIG samples. qPCR on iron related genes not only verified the microarray results, but also indicated that the iron signature was derived from dSaIVIG, thus not representing the mechanism underlying bacteriostasis. Due to the lack of a reliable signature the mechanism underlying bacteriostasis could not be characterised. Additionally, we aimed to enlarge the repertoire of potential candidates for a polyvalent vaccine. For this purpose a novel subtractive proteomic approach (SUPRA) on anchorless cell wall (ACW) proteins of S. aureus was developed. This method is based on immunodetection of in vivo expressed, immunogenic proteins separated by 2D gelelectrophoresis with either complete IVIG or dSaIVIG. Proteins immunoreactive with IVIG but not, or to a lesserextent using dSaIVIG were identified by MALDI-TOF analysis. SUPRA led to the identification of 37 new potential vaccine candidates among ACW proteins. Three of these, BT1, BT2 and BT3 were characterised in this study. The surface localisation of these antigens was confirmed by flow cytometry using specific antibodies enriched from IVIG. Purified IgGs for each antigen mediated opsonophagocytosis and subsequent opsonophagocytic killing by human neutrophils. However, when used for monovalent immunisation of BalbC mice only BT1 and BT3 conferred significant protection against lethal S. aureus challenge in a murine model of sepsis. Despite the protective potential upon monovalent immunisation a bivalent vaccination using BT1 and BT3 did not exhibit a synergistic protective effect, most likely due to the reduced amount of antigen used for immunisation. Among the six so far investigated vaccine candidates identified by SUPRA, three conferred protection against lethal challenge with S. aureus (hp2160, BT1 and BT3) and two led to a reduction of bacterial load in organs (eno and oxo). Therefore, SUPRA represents a valuable tool for the identification of promising vaccine candidates for subsequent use in a multicomponent vaccine against S. aureus

Social Interaction in Patients'�Hospital Choice: Evidences from Italy

In this paper we study the influence of social interaction on patients' hospital choice and its relationship with quality delivered by hospitals, using Italian data. We explore the impact on individual choices of a set of variables such as travel distance, individual- and hospital-specific characteristics, as well as a variable capturing the effect of the neighbourhood. The richness of our data allows us to disentangle contextual effects from the influence of information sharing on patients' hospital choices. We then use this framework to assess how such interaction is related to clinical hospital quality. Results show that network effect plays an important role in hospital choices, although it is less relevant for larger hospitals. Another empirical finding is the existence of a negative relationship between the degree of interaction among individuals and the quality delivered by hospitals. The absence of a source of information on the quality of hospitals accessible to all individuals, such as guidelines or star ratings, exacerbates the importance of information gathered locally in hospital choices, which may result in a lower degree of competition among hospitals and lower quality.health care, social interaction, quality

Weak and Strong Cross Section Dependence and Estimation of Large Panels

This paper introduces the concepts of time-specific weak and strong cross section dependence. A double-indexed process is said to be cross sectionally weakly dependent at a given point in time, t, if its weighted average along the cross section dimension (N) converges to its expectation in quadratic mean, as N is increased without bounds for all weights that satisfy certain ‘granularity’ conditions. Relationship with the notions of weak and strong common factors is investigated and an application to the estimation of panel data models with an infinite number of weak factors and a finite number of strong factors is also considered. The paper concludes with a set of Monte Carlo experiments where the small sample properties of estimators based on principal components and CCE estimators are investigated and compared under various assumptions on the nature of the unobserved common effects.panels, strong and weak cross section dependence, weak and strong factors