Prescription Drug Coverage and Elderly Medicare Spending

The introduction of Medicare Part D has generated interest in the cost of providing drug coverage to the elderly. Of paramount importance -- often unaccounted for in budget estimates -- are the salutary effects that increased prescription drug use might have on other Medicare spending. This paper uses longitudinal data from the Medicare Current Beneficiary Survey (MCBS) to estimate how prescription drug benefits affect Medicare spending. We compare spending and service use for Medigap enrollees with and without drug coverage. Because of concerns about selection, we use variation in supply-side regulations of the individual insurance market -- including guaranteed issue and community rating -- as instruments for prescription drug coverage. We employ a discrete factor model to control for individual-level heterogeneity that might induce bias in the effects of drug coverage. Medigap prescription drug coverage increases drug spending by $170 or 22$350 or 13% (in 2000 dollars). Medigap prescription drug coverage reduces Medicare Part B spending, but the estimates are not statistically significant. Overall, a $1 increase in prescription drug spending is associated with a$2.06 reduction in Medicare spending. Furthermore, the substitution effect decreases as income rises, and thus provides support for the low-income assistance program of Medicare Part D.

The structure, energy, and electronic states of vacancies in Ge nanocrystals

The atomic structure, energy of formation, and electronic states of vacancies in H-passivated Ge nanocrystals are studied by density functional theory (DFT) methods. The competition between quantum self-purification and the free surface relaxations is investigated. The free surfaces of crystals smaller than 2 nm distort the Jahn-Teller relaxation and enhance the reconstruction bonds. This increases the energy splitting of the quantum states and reduces the energy of formation to as low as 1 eV per defect in the smallest nanocrystals. In crystals larger than 2 nm the observed symmetry of the Jahn-Teller distortion matches the symmetry expected for bulk Ge crystals. Near the nanocrystal's surface the vacancy is found to have an energy of formation no larger than 0.5 to 1.4 eV per defect, but a vacancy more than 0.7 nm inside the surface has an energy of formation that is the same as in bulk Ge. No evidence of the self-purification effect is observed; the dominant effect is the free surface relaxations, which allow for the enhanced reconstruction. From the evidence in this paper, it is predicted that for moderate sized Ge nanocrystals a vacancy inside the crystal will behave bulk-like and not interact strongly with the surface, except when it is within 0.7 nm of the surface.Comment: In Press at Phys. Rev.

Cholesterol-directed nanoparticle assemblies based on single amino acid peptide mutations activate cellular uptake and decrease tumor volume.

Peptide drugs have been difficult to translate into effective therapies due to their low in vivo stability. Here, we report a strategy to develop peptide-based therapeutic nanoparticles by screening a peptide library differing by single-site amino acid mutations of lysine-modified cholesterol. Certain cholesterol-modified peptides are found to promote and stabilize peptide α-helix formation, resulting in selectively cell-permeable peptides. One cholesterol-modified peptide self-assembles into stable nanoparticles with considerable α-helix propensity stabilized by intermolecular van der Waals interactions between inter-peptide cholesterol molecules, and shows 68.3% stability after incubation with serum for 16 h. The nanoparticles in turn interact with cell membrane cholesterols that are disproportionately present in cancer cell membranes, inducing lipid raft-mediated endocytosis and cancer cell death. Our results introduce a strategy to identify peptide nanoparticles that can effectively reduce tumor volumes when administered to in in vivo mice models. Our results also provide a simple platform for developing peptide-based anticancer drugs

Computational fluid dynamics combustion analysis evaluation

This study involves the development of numerical modelling in spray combustion. These modelling efforts are mainly motivated to improve the computational efficiency in the stochastic particle tracking method as well as to incorporate the physical submodels of turbulence, combustion, vaporization, and dense spray effects. The present mathematical formulation and numerical methodologies can be casted in any time-marching pressure correction methodologies (PCM) such as FDNS code and MAST code. A sequence of validation cases involving steady burning sprays and transient evaporating sprays will be included

Myanmar's foreign policy: shifting legitimacy, shifting strategic culture

Since 2011, while the principles of foreign policy "independent, active, and non-aligned" under the respective governments under the Union Solidarity and Development Party and the National League for Democracy have remained the same, the foreign policy approaches - including the concept of non-alignment - of the two leaders of these governments have been quite different. This article describes the survival and foreign policy of the small country of Myanmar beyond the great power lens, arguing that the impact of strategic culture on the two governments since 2011 has differed because of the different levels of legitimacy enjoyed by the two leaders. The cornerstones of Myanmar's strategic culture are (1) that it shall never tolerate foreign interference, (2) that it shall always pursue self-reliance in its diplomacy, and (3) that the very nature of Myanmar is to be independent

Similarity-Based Classification in Partially Labeled Networks

We propose a similarity-based method, using the similarity between nodes, to address the problem of classification in partially labeled networks. The basic assumption is that two nodes are more likely to be categorized into the same class if they are more similar. In this paper, we introduce ten similarity indices, including five local ones and five global ones. Empirical results on the co-purchase network of political books show that the similarity-based method can give high accurate classification even when the labeled nodes are sparse which is one of the difficulties in classification. Furthermore, we find that when the target network has many labeled nodes, the local indices can perform as good as those global indices do, while when the data is sparce the global indices perform better. Besides, the similarity-based method can to some extent overcome the unconsistency problem which is another difficulty in classification.Comment: 13 pages,3 figures,1 tabl