Guidance on the Selection of Appropriate Indicators for Quantification of Antimicrobial Usage in Humans and Animals

An increasing variety of indicators of antimicrobial usage has become available in human and veterinary medicine, with no consensus on the most appropriate indicators to be used. The objective of this review is therefore to provide guidance on the selection of indicators, intended for those aiming to quantify antimicrobial usage based on sales, deliveries or reimbursement data. Depending on the study objective, different requirements apply to antimicrobial usage quantification in terms of resolution, comprehensiveness, stability over time, ability to assess exposure and comparability. If the aim is to monitor antimicrobial usage trends, it is crucial to use a robust quantification system that allows stability over time in terms of required data and provided output; to compare usage between different species or countries, comparability must be ensured between the different populations. If data are used for benchmarking, the system comprehensiveness is particularly crucial, while data collected to study the association between usage and resistance should express the exposure level and duration as a measurement of the exerted selection pressure. Antimicrobial usage is generally described as the number of technical units consumed normalized by the population at risk of being treated in a defined period. The technical units vary from number of packages to number of individuals treated daily by adding different levels of complexity such as daily dose or weight at treatment. These technical units are then related to a description of the population at risk, based either on biomass or number of individuals. Conventions and assumptions are needed for all of these calculation steps. However, there is a clear lack of standardization, resulting in poor transparency and comparability. By combining study requirements with available approaches to quantify antimicrobial usage, we provide suggestions on the most appropriate indicators and data sources to be used for a given study objective

Impact of Sodium Layer variations on the performance of the E-ELT MCAO module

Multi-Conjugate Adaptive Optics systems based on sodium Laser Guide Stars may exploit Natural Guide Stars to solve intrinsic limitations of artificial beacons (tip-tilt indetermination and anisoplanatism) and to mitigate the impact of the sodium layer structure and variability. The sodium layer may also have transverse structures leading to differential effects among Laser Guide Stars. Starting from the analysis of the input perturbations related to the Sodium Layer variability, modeled directly on measured sodium layer profiles, we analyze, through a simplified end-to-end simulation code, the impact of the low/medium orders induced on global performance of the European Extremely Large Telescope Multi-Conjugate Adaptive Optics module MAORY.Comment: 7 pages, 5 figures, SPIE conference Proceedin

Micro-simulating child poverty in 2010 and 2020

The 2008 Pre-Budget Report (PBR) said that 'the Government will take stock of progress towards its 2010 and 2020 child poverty target in the [2009] Budget'. As background to that exercise, this paper updates our previous analysis of the prospects for child poverty in the UK in 2010-11 and 2020-21

Implementing spatial segregation measures in R.

Reliable and accurate estimation of residential segregation between population groups is important for understanding the extent of social cohesion and integration in our society. Although there have been considerable methodological advances in the measurement of segregation over the last several decades, the recently developed measures have not been widely used in the literature, in part due to their complex calculation. To address this problem, we have implemented several newly proposed segregation indices in R, an open source software environment for statistical computing and graphics, as a package called seg. Although there are already a few standalone applications and add-on packages that provide access to similar methods, our implementation has a number of advantages over the existing tools. First, our implementation is flexible in the sense that it provides detailed control over the calculation process with a wide range of input parameters. Most of the parameters have carefully chosen defaults, which perform acceptably in many situations, so less experienced users can also use the implemented functions without too much difficulty. Second, there is no need to export results to other software programs for further analysis. We provide coercion methods that enable the transformation of our output classes into general R classes, so the user can use thousands of standard and modern statistical techniques, which are already available in R, for the post-processing of the results. Third, our implementation does not require commercial software to operate, so it is accessible to a wider group of people