A Comparative Pharmacokinetic Estimate of Mercury in U.S. Infants Following Yearly Exposures to Inactivated Influenza Vaccines Containing Thimerosal

The use of thimerosal preservative in childhood vaccines has been largely eliminated over the past decade in the United States because vaccines have been reformulated in single‐dose vials that do not require preservative. An exception is the inactivated influenza vaccines, which are formulated in both multidose vials requiring preservative and preservative‐free single‐dose vials. As part of an ongoing evaluation by USFDA of the safety of biologics throughout their lifecycle, the infant body burden of mercury following scheduled exposures to thimerosal preservative in inactivated influenza vaccines in the United States was estimated and compared to the infant body burden of mercury following daily exposures to dietary methylmercury at the reference dose established by the USEPA. Body burdens were estimated using kinetic parameters derived from experiments conducted in infant monkeys that were exposed episodically to thimerosal or MeHg at identical doses. We found that the body burden of mercury (AUC) in infants (including low birth weight) over the first 4.5 years of life following yearly exposures to thimerosal was two orders of magnitude lower than that estimated for exposures to the lowest regulatory threshold for MeHg over the same time period. In addition, peak body burdens of mercury following episodic exposures to thimerosal in this worst‐case analysis did not exceed the corresponding safe body burden of mercury from methylmercury at any time, even for low‐birth‐weight infants. Our pharmacokinetic analysis supports the acknowledged safety of thimerosal when used as a preservative at current levels in certain multidose infant vaccines in the United States.     

Leveraging the common and outsourcing the distinct: institutional difference and multinational company identity in emerging economies

ABSTRACT

This paper explores the challenges of outsidership faced by multinational companies (MNCs) accessing emerging economies. The paper departs from popular conceptualisations of culture drawing instead on social identity theory as part of an institutions-based view, to explain how employing a singularities approach can allow the identification of commonalities of Indianness and Australianness that can be leveraged to ameliorate negative socially embedded business norms that pose a challenge to MNC success in emerging economies. This study shows that despite recognised formal (regulatory) and informal (cultural) differences, managers of Australian MNCs operating in India were able to innovatively identify and exploit cultural commonalties/singularities to ease cultural adjustment and mitigate intergroup conflict, allowing them to achieve an innovative institutional fit. The paper discusses how, using this approach, sole-venture MNCs can access many of the benefits provided by collaborative entry modes while preserving MNC identity. We further propose that the singularities approach can provide a valuable starting point to develop culturally appropriate competence training initiatives.     

Relocation stress syndrome in older adults transitioning from home to a long-term care facility: myth or reality?

Relocation stress syndrome is a nursing diagnosis characterized by symptoms such as anxiety, confusion, hopelessness, and loneliness. It usually occurs in older adults shortly after moving from a private residence to a nursing home or assisted-living facility. The primary purpose of this study was to validate the symptoms of relocation stress syndrome. Eight nursing home residents and 8 assisted-living facility residents were interviewed 2 to 10 weeks after admission, when symptoms of relocation stress syndrome are most likely to appear. Results of this study indicate that the incidence of relocation stress syndrome may be overestimated. More accurate diagnosis and treatment of depression in older adults is needed.     

Decision settings analysis – a tool for analysis and design of human activity systems

The paper describes a methodology to be used for analysis and design of human activity systems. The methodology is based on an analysis of the decision settings whereas most other decision analysis methodologies are analysing the process. The decision concept is analysed and discussed. A distinction between programmed and programmable as well as non-programmed and non-programmable decisions is proposed. A classification of different information types for decision making is presented. A methodology based on a systemic and systematic analysis of the information requirements of an organization is proposed. This methodology also indicates organizational discrepancies and information imbalances. The methodology focuses the settings of the decisions on all levels of organizations. The methodology can be regarded as a dynamic, learning system. The author proposes further research on the individuals decision making abilities.     

Differential equation models for statistical functions

Differential equations have been used in statistics to define functions such as probability densities. But the idea of using differential equation formulations of stochastic models has a much wider scope. The author gives several examples, including simultaneous estimation of a regression model and residual density, monotone smoothing, specification of a link function, differential equation models of data, and smoothing over complicated multidimensional domains. This paper aims to stimulate interest in this approach to functional estimation problems, rather than provide carefully worked out methods.     

A semi-parametric method for estimating the beta coefficients of the hidden two-sided asset return jumps

ABSTRACT

We introduce a new methodology for estimating the parameters of a two-sided jump model, which aims at decomposing the daily stock return evolution into (unobservable) positive and negative jumps as well as Brownian noise. The parameters of interest are the jump beta coefficients which measure the influence of the market jumps on the stock returns, and are latent components. For this purpose, at first we use the Variance Gamma (VG) distribution which is frequently used in modeling financial time series and leads to the revelation of the hidden market jumps' distributions. Then, our method is based on the central moments of the stock returns for estimating the parameters of the model. It is proved that the proposed method provides always a solution in terms of the jump beta coefficients. We thus achieve a semi-parametric fit to the empirical data. The methodology itself serves as a criterion to test the fit of any sets of parameters to the empirical returns. The analysis is applied to NASDAQ and Google returns during the 2006–2008 period.     

Extending Bayesian back-calculation to estimate age and time specific HIV incidence

Lifetime Data Analysis - CD4-based multi-state back-calculation methods are key for monitoring the HIV epidemic, providing estimates of HIV incidence and diagnosis rates by disentangling their...