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Small area estimation (SAE) describes the use of Bayesian modelling of survey and administrative data in order to provide estimates of survey responses at a much finer level than is possible from the survey alone. Over the recent past, academic publications have mostly targeted the development of the methodology for SAE using small-scale examples. Only predictions on the basis of realistically sized samples have the potential to impact on governance and our contribution is to fill a niche by delivering such SAEs on a national scale through the use of a scaling method. The impact case study concerns the use of these small area predictions to develop disease-level predictions for some 8,000 GPs in England and so to produce a funding formula for use in primary care that has informed the allocation of billions of pounds of NHS money. The value of the model has been recognised in NHS guidelines. The methodology has begun to have impact in other areas, including the BIS `Skills for Life' survey.
A team at the University of Liverpool has undertaken research that has informed practice and policy worldwide in the management of patients presenting with newly diagnosed epilepsy, which has achieved international impact on health. Seizures are common and 3-5% of the population will be given a diagnosis of epilepsy during their lifetime. Decisions about when to start treatment, and if so with which drug are crucial and can have a significant effect on outcomes for the individual and have significant economic consequences for society. The research includes the undertaking and analysis of data from randomised controlled trials. The data analysis is based on the statistical research initiated by Dr Paula Williamson while in the Department of Mathematical Sciences at the University of Liverpool between 1996 and 2000. The research identified the most appropriate first line treatments for patients with newly diagnosed epilepsy, addressing both clinical and cost effectiveness. This work has underpinned national policy and triggered the most recent update of the NICE (National Institute for Clinical Excellence) epilepsy guidelines in 2012.
The School of Mathematics at Cardiff University has developed important statistical and mathematical models for forecasting consumer buying behaviour. Enhancements to classical models, inspired by extensively studying their statistical properties, have allowed us to exploit their vast potential to benefit the sales and marketing strategies of manufacturing and retail organisations. The research has been endorsed and applied by Nielsen, the #1 global market research organisation that provides services to clients in 100 countries. Nielsen has utilised the models to augment profits and retain their globally leading corporate position. This has led to a US$30 million investment and been used to benefit major consumer goods manufacturers such as Pepsi, Kraft, Unilever, Nestlé and Procter & Gamble. Therefore the impact claimed is financial. Moreover, impact is also measurable in terms of public engagement since the work has been disseminated at a wide range of national and international corporate events and conferences. Beneficiaries include Tesco, Sainsbury's, GlaxoSmithKline and Mindshare WW.
In a series of papers from 2003, Gibson (Maxwell Institute) and collaborators developed Bayesian computational methods for fitting stochastic models for epidemic dynamics. These were subsequently applied to the design of control programmes for pathogens of humans and plants. A first application concerns the bacterial infection Clostridium difficile in hospital wards. A stochastic model was developed which was instrumental in designing control measures, rolled out in 2008 across NHS Lothian region, and subsequently adopted across NHS Scotland. Incidence in Lothian reduced by around 65%, saving an estimated £3.5M per annum in treatment and other costs, reducing mortality and improving patient outcomes, with similar impacts elsewhere in Scotland. A second application concerns the spread of epidemics of plant disease in agricultural, horticultural and natural environments. Models developed in collaboration with plant scientists from Cambridge have been exploited by the Department for Environment, Food and Rural Affairs (Defra) and the Forestry Commission under a £25M scheme, initiated in 2009, to control sudden oak death in the UK, and by the United States Department of Agriculture to control sudden oak death in the USA.
The Cambridge-led Emerging Risk Factors Collaboration (ERFC) is a global consortium involving individual-participant data on 2.5 million participants from 130 cohort studies. The ERFC has helped optimise approaches to cardiovascular disease (CVD) risk assessment by: 1) quantifying the incremental predictive value provided by assessment of risk factors 2) evaluating the independence of associations between risk factors and CVD and 3) addressing uncertainties related to the implementation of screening. ERFC publications on lipids, lipoproteins, and inflammation biomarkers have been cited by 9 guidelines published since 2010, including those of the European Society of Cardiology and the American Heart Association.
Research at the University of Manchester (UoM) has developed new approaches, methods and algorithms to improve the statistical confidentiality practices of data stewardship organisations (DSOs), such as the UK's Office for National Statistics. The research and its products have had significant impacts on data dissemination practice, both in the UK and internationally, and have been adopted by national statistical agencies, government departments and private companies. The primary beneficiaries of this work are DSOs, who are able to both disseminate useful data products, and protect respondent confidentiality more effectively. Secondary beneficiaries are respondents, whose confidentiality is better protected, and the research community, as without `gold standard' disclosure risk analysis, data holders can be overcautious.
This impact case study is based on a Knowledge Transfer Partnership (KTP) between the School of Mathematics, Statistics and Actuarial Science, University of Kent and KROHNE Ltd, a world leading manufacturer of industrial measuring instruments. These precision instruments (typically flow meters and density meters) need to be calibrated accurately before being used and this is an expensive and time-consuming process.
The purpose of the KTP was to use Bayesian methodology developed by Kent statisticians to establish a novel calibration procedure that improves on the existing procedure by incorporating historical records from calibration of previous instruments of the same type. This reduces substantially the number of test runs needed to calibrate a new instrument and will increase capacity by up to 50%.
The impact of the KTP, which was graded as `Outstanding', has been to change the knowledge and capability of the Company, so that they can improve the performance of their manufacturing process by implementing this novel calibration method. This has been achieved by adapting the underpinning Kent research to the specific context of the calibration problem, by running many calibrations to demonstrate the effectiveness of the method in practice, and by supporting the implementation of the new calibration method within the Company's core software.
Moreover, the project has changed the Company's thinking on fundamental science, particularly industrial mathematics. The value of historical data, and the usefulness of Bayesian methods, is now widely appreciated and training for staff in Bayesian Statistics is being introduced. Thus the project has not only changed the protocols of the Company, it has also changed their practice.
The Galatean Risk and Safety Tool (GRiST) is a clinical decision support system (CDSS) conceived and developed by computer scientists at Aston University from 2000 onwards, where it is being delivered as a cloud-computing service. It is used every day by mental-health practitioners in the NHS, charities, and private hospitals to assess and manage risks associated with mental-health problems. Between 1/1/2011 and 31/7/2013, clinicians provided 285,426 completed patient risk assessments using GRiST. It has changed organisational and clinical processes by its systematic collection of risk information, explicitly linking data to clinical risk judgements, and showing how those judgments are derived. Increasing international awareness has come through presentations to mental-health practitioners in Europe, America, and Australia.
This case study concerns the development and subsequent uptake of the Feature Selective Validation (FSV) method for data comparisons. The method has been adopted as the core of IEEE Standard 1597.1: a `first of its kind' standard on validation of computational electromagnetics and is seeing increasingly wide adoption in industry practice where comparison of data is needed, indicating the reach and significance of this work. The technique was developed by, and under the guidance of, Dr Alistair Duffy, who has remained the world-leading researcher in the field. The first paper on the subject was published in 1997 with key papers being published in 2006.
Advanced technologies for data visualisation and data mining, developed in the Unit in collaboration with national and international teams, are widely applied for development of medical services. In particular, a system for canine lymphoma diagnosis and monitoring developed with [text removed for publication] has now been successfully tested using clinical data from several veterinary clinics. The risk maps produced by our technology provide early diagnosis of lymphoma several weeks before the clinical symptoms develop. [text removed for publication] has estimated the treatment test, named [text removed for publication], developed with the Unit to add [text removed for publication] to the value of their business. Institute Curie (Paris), applies this data mapping technique and the software that has been developed jointly with Leicester in clinical projects.