Suchergebnisse

Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Authors -- Chapter 1 Environmental Compliance and the Corporate Structure -- 1.1 Defining the Compliance Profile -- 1.2 Alignment with the Mission of the Company -- 1.3 Organizational Structures That Enable Compliance -- 1.3.1 Functional Organization Structure -- 1.3.2 Divisional Organization Structure -- 1.3.3 Matrixed Organization Structure -- 1.3.4 Flat Organization Structure -- 1.4 Applied Learning -- 1.4.1 Case Study -- References -- Chapter 2 Corporate Culture -- 2.1 Introduction -- 2.2 Top-Down Leadership: Demonstrated Commitment -- 2.2.1 Leadership Commitment -- 2.3 Employee Buy-In and Engagement -- 2.4 Cultural Impacts on Compliance -- 2.4.1 The Belief Attribute -- 2.4.2 The Behavior Attribute -- 2.4.3 The Practices Attribute -- 2.4.4 The Values Attribute -- 2.5 Evaluating Culture for Effectiveness -- 2.6 Applied Learning -- 2.6.1 Scenario 1 -- 2.6.2 Scenario 2 -- References -- Chapter 3 Identifying Applicable Requirements -- 3.1 Environmental Regulations Overview -- 3.2 Regulatory Requirements -- 3.3 Federal Laws -- 3.3.1 The Clean Water Act -- 3.3.2 The Clean Water Act Today -- 3.3.2.1 Wastewater Management -- 3.3.2.2 Pretreatment Program -- 3.3.2.3 Storm Water Pollution -- 3.3.2.4 Construction Storm Water -- 3.3.2.5 Industrial Storm Water -- 3.3.2.6 Municipal Storm Water -- 3.3.2.7 Concentrated Animal Feeding Operations -- 3.3.2.8 Spills of Oils and Hazardous Substances (SPCC) -- 3.3.2.9 Tier I Facility -- 3.3.2.10 Tier II Facility -- 3.3.2.11 Facility Response Plan -- 3.3.2.12 Wetlands Protection -- 3.3.3 The Clean Air Act -- 3.3.4 The Clean Air Act Today -- 3.3.4.1 Title I -- 3.3.4.2 Title II -- 3.3.4.3 Title III -- 3.3.4.4 Title IV -- 3.3.4.5 Title V -- 3.3.4.6 Title VI -- 3.3.5 Resource Conservation and Recovery Act -- 3.3.5.1 RCRA Today.

ABSTRACTObjectivesThe main objective of this poster is to present a pilot project in determining emerging population health themes and identifying key research-enabling datasets ahead of time.
At present, large-scale databanks, such as the Secure Anonymised Information Linkage (SAIL) Databank at Swansea University Medical School, already manage large quantities of health and administrative linked datasets. While these datasets are valuable for research purposes, complementary datasets may be required by collaborating researchers to answer detailed population health research questions. Dataset acquisition can take several years, which is a serious delay to a project with time-limited funding.
The ability to pre-emptively acquire datasets so that these are ready for use before a researcher requests them would obviously be beneficial. However, a recent study conducted by the Farr Cipher team at Swansea University identified over 800 health and administrative datasets in Wales alone.
With limited resources such as available funding and time, which of these datasets is worth its effort in acquiring?
ApproachBibliometrics has long been a means of measuring the impact of papers on the wider academic community. Lately, the focus of analyses has been extended to include the topics, authorship and citations of the publications. Existing bibliometric data mining techniques suggest that it is possible to identify emerging topic trends and through this assist in prioritising dataset identification and acquisition.
The project explored mining available literature through bibliometric analysis in order to predict emerging trends and through these identify potentially relevant and valuable datasets for acquisition on behalf of the Dementias Platform UK (DPUK). Literature searches were conducted for papers published on the topic of "dementia" over the last 20 years. Additional keywords and topics were extracted to identify emerging areas of research and clinical interest. These were then compared against an existing list of over 800 Welsh datasets currently not held in SAIL.
ResultsResults focus on:

Using bibliometric methods in the context of DPUK cohort publications
Identifying emerging trends in the field of dementia research.
Identifying and prioritising datasets which might be useful for the SAIL Databank to acquire

ABSTRACTObjectivesData safe havens can bring together and combine a rich array of anonymised person-based data for research and policy evaluation within a secure setting. To date, the majority of available datasets have been structured micro-data derived from routine health-related records. Possibilities are opening up for the greater reuse of genomic data such as Genome Wide Association studies (GWAS) and Whole Exome/Genome Sequencing (WES or WGS). However, there are considerable challenges to be addressed if the benefits of using these data in combination with health-related data are to be realized safely.
ApproachWe explore the benefits and challenges of using genomic datasets with health-related data, and using the Secure Anonymised Information Linkage (SAIL) system as a case study, the implications and way forward for Data Safe Havens in seeking to incorporate genomic data for use with health-related data.
ResultsThe benefits of using GWAS, WES and WGS data in conjunction with health-related data include the potential to explore genetics at a population level and open up novel research areas. These include the ability to increasingly stratify and personalize how medical indications are detected and treated through precision medicine by understanding rare conditions and adding socioeconomic and environmental context to genomic data. Among the challenges are: data availability, computing capacity, technical solutions, legal and regulatory frameworks, public perceptions, individual privacy and organizational risk. Many of the challenges within these areas are common to person-based data in general, and often Data Safe Havens have been designed to address these. But there are also aspects of these challenges, and other challenges, specific to genomic data. These include issues due to the unknown clinical significance of genomic information now or in the future, with corresponding risks for privacy and impact on individuals.
ConclusionGenomic data sets contain vast amounts of valuable information, some of which is currently undefined, but which may have direct bearing on individual health at some point. The use of these data in combination with health-related data has the potential to bring great benefits, better clinical trial stratification, epidemiology project design and clinical improvements. It is, therefore, essential that such data are surrounded by a properly-designed, robust governance framework including technical and procedural access controls that enable the data to be used safely.