Circular economy : implications for the Swiss fashion retail industry

The concept of Circular Economy is much discussed among experts and in sustainably advanced business contexts such as the 2017 Sustainability Summit in London. Several multinational companies have already joined networks to accelerate the transition into futureproofed business practices. Startups, long-established companies and scientific research are devising groundbreaking solutions to work towards this new business imperative. At its core, the Circular Economy aims at replacing the traditional, linear way of extraction, production, consumption and disposal with a circular model, where waste is considered as a precious resource for new applications. However, it seems that in the Swiss business environment the concept is rather unknown or ignored, even though it is highly relevant considering the current and forecasted macro-economic and environmental developments. “Adapt or die” is one of the more recent statements in the light of environmental pollution, the tightening of resource availability together with population growth and increasing consumption on a global level. Therefore, this Bachelor’s thesis aims to analyze the present status of and to provide guidance for the Swiss fashion retail industry. By means of a multiple-case, embedded case study design, two Swiss fashion retailers are studied within their respective ecosystems. The two units of study were selected to approach a certain degree of external analytic validity, which is the reason why a large multinational and a smaller player with Swiss tradition were chosen. Qualitative and quantitative sources of primary and secondary data are adduced, whereas solely qualitative methods are applied. The assessments are then made inductively on the basis of the business model Recovery & Recycling. It is as such one of five Accenture-devised possibilities for enterprises to embark on a circular future. It was found that successfully employing the Recovery & Recycling business model embraces decoupling in two different ways: decoupling from potentially harmful resources, the environmental perspective and, decoupling from increasingly scarce resources, the economic perspective. The Swiss fashion industry turned out to be rather advanced within the environmental perspective, yet there is room for improvement when it comes to closing the material loop from an economic perspective. Smaller players with limited means are well advised to draw on the many instruments or methods already available and to imitate larger, more advanced players. Finally, some advancements depend on breakthroughs in recycling technology and material sciences. Nevertheless, much can already be improved by efficient design of products and processes in a way that facilitates reuse and recycling

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]

Evidence synthesis to inform model-based cost-effectiveness evaluations of diagnostic tests: a methodological systematic review of health technology assessments

Background: Evaluations of diagnostic tests are challenging because of the indirect nature of their impact on patient outcomes. Model-based health economic evaluations of tests allow different types of evidence from various sources to be incorporated and enable cost-effectiveness estimates to be made beyond the duration of available study data. To parameterize a health-economic model fully, all the ways a test impacts on patient health must be quantified, including but not limited to diagnostic test accuracy. Methods: We assessed all UK NIHR HTA reports published May 2009-July 2015. Reports were included if they evaluated a diagnostic test, included a model-based health economic evaluation and included a systematic review and meta-analysis of test accuracy. From each eligible report we extracted information on the following topics: 1) what evidence aside from test accuracy was searched for and synthesised, 2) which methods were used to synthesise test accuracy evidence and how did the results inform the economic model, 3) how/whether threshold effects were explored, 4) how the potential dependency between multiple tests in a pathway was accounted for, and 5) for evaluations of tests targeted at the primary care setting, how evidence from differing healthcare settings was incorporated. Results: The bivariate or HSROC model was implemented in 20/22 reports that met all inclusion criteria. Test accuracy data for health economic modelling was obtained from meta-analyses completely in four reports, partially in fourteen reports and not at all in four reports. Only 2/7 reports that used a quantitative test gave clear threshold recommendations. All 22 reports explored the effect of uncertainty in accuracy parameters but most of those that used multiple tests did not allow for dependence between test results. 7/22 tests were potentially suitable for primary care but the majority found limited evidence on test accuracy in primary care settings. Conclusions: The uptake of appropriate meta-analysis methods for synthesising evidence on diagnostic test accuracy in UK NIHR HTAs has improved in recent years. Future research should focus on other evidence requirements for cost-effectiveness assessment, threshold effects for quantitative tests and the impact of multiple diagnostic tests

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]

Disputatio Metaphysica De Angelis

DISPUTATIO METAPHYSICA DE ANGELIS Disputatio Metaphysica De Angelis ([1]) Titelseite ([1]) Widmung ([1]) Dissertation ([2]) Beitrag ([4]

"Diatribe Physiologike" De Elementis

"DIATRIBE PHYSIOLOGIKE" DE ELEMENTIS "Diatribe Physiologike" De Elementis ([1]) Titelseite ([1]) Widmung ([1]) Thesen ([2]) Corollarium ([16]

PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies

Prediction models in health care use predictors to estimate for an individual the probability that a condition or disease is already present (diagnostic model) or will occur in the future (prognostic model). Publications on prediction models have become more common in recent years, and competing prediction models frequently exist for the same outcome or target population. Health care providers, guideline developers, and policymakers are often unsure which model to use or recommend, and in which persons or settings. Hence, systematic reviews of these studies are increasingly demanded, required, and performed. A key part of a systematic review of prediction models is examination of risk of bias and applicability to the intended population and setting. To help reviewers with this process, the authors developed PROBAST (Prediction model Risk Of Bias ASsessment Tool) for studies developing, validating, or updating (for example, extending) prediction models, both diagnostic and prognostic. PROBAST was developed through a consensus process involving a group of experts in the field. It includes 20 signaling questions across 4 domains (participants, predictors, outcome, and analysis). This explanation and elaboration document describes the rationale for including each domain and signaling question and guides researchers, reviewers, readers, and guideline developers in how to use them to assess risk of bias and applicability concerns. All concepts are illustrated with published examples across different topics. The latest version of the PROBAST checklist, accompanying documents, and filled-in examples can be downloaded from www.probast.org.Drs. Moons and Reitsma received financial support from the Netherlands Organisation for Scientific Research (ZONMW 918.10.615 and 91208004). Dr. Riley is a member of the Evidence Synthesis Working Group funded by the NIHR School for Primary Care Research (project 390). Dr. Whiting (time) was supported by the NIHR Collaboration for Leadership in Applied Health Research and Care West at University Hospitals Bristol NHS Foundation Trust. Dr. Collins was supported by the NIHR Biomedical Research Centre, Oxford. Dr. Mallett is supported by NIHR Birmingham Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham

Erratum to: Methods for evaluating medical tests and biomarkers

The original MEMTAB Abstracts in Diagnostic and Prognostic Research contains the incorrect year on individual abstracts in the PDF [1].“Diagnostic and Prognostic Research 2016” under the correspondence line should therefore have been written as “Diagnostic and Prognostic Research 2017” as the journal did not launch until 2017