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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.
QRisk is a statistical model / score derived from routine general practice (GP) records to calculate an individual's risk of developing cardiovascular disease (CVD). Queen Mary researchers formed the London arm of a multi-centre study and were particularly instrumental in testing the tool in general practice. QRisk targets treatment more effectively than other scores; it is also more equitable for disadvantaged and minority ethnic groups and cheaper per event prevented. QRisk is used in the NHS Health Checks programme covering 20 million people in England and is available at a keystroke in all GP computer systems in England. It has contributed to the identification of an additional 2.8 million people in England at high risk of CVD and their treatment with statins, reducing CVD deaths and events by an estimated 9,000 per year — about 50,000 to date since the NHS Checks programme started in 2009.
QRISK is a new algorithm which predicts an individual's risk of cardiovascular over 10 years. It was developed using the QResearch database and is in routine use across the NHS. It is included in national guidelines from NICE and the Department of Health and in the GP quality and outcomes framework. It is incorporated into > 90% of GP computer systems as well as pharmacy and secondary care systems. The web calculator has been used >500,000 times worldwide. ClinRisk Ltd was incorporated in 2008 to develop software to ensure the reliable widespread implementation of the QRISK algorithm into clinical practice.
Over the past ten years, the prescription of cholesterol-lowering statins has soared and they are now the most prescribed drugs in the UK and the US. However, this has raised concerns about inappropriate prescribing. University of Glasgow research has been pivotal in addressing this issue and has triggered revision of major international guidelines to stratify patients in the general population for statin therapy and guide statin use in the rheumatoid arthritis patient population. The identification of a statin-associated risk for diabetes prompted the European Medicines Agency and the US Food & Drug Administration to revise safety labelling for all classes of statins. This risk is now communicated to the 27 million patients in the UK and US who are prescribed statins.
Research at the University of Sheffield has resulted in FRAX, the first internationally-applicable fracture risk calculator that provides individualised 10-year probabilities of major osteoporotic fractures from readily available clinical risk factors. It has replaced bone mineral density (BMD) as the sole quantitative measure of fracture risk, thus increasing global access to risk assessment and improving targeting of treatment to patients at highest risk. FRAX is incorporated widely into national and international guidelines for osteoporosis management. Launched in 2008, it now provides country-specific calculations for 53 nations, in 28 languages. The online tool alone recently processed its 6.6 millionth calculation.
Onchocerciasis (river blindness) is a debilitating disease of major public health importance in the wet tropics. The African Programme for Onchocerciasis Control (APOC) seeks to control or eliminate the disease in 19 countries. Accurate mapping of Loiasis (eye-worm) was a requirement for implementation of APOC's mass-treatment prophylactic medication programme in order to mitigate against serious adverse reactions to the Onchocerciasis medication in areas also highly endemic for Loiasis. Model-based geostatistical methods developed at Lancaster were used to obtain the required maps and contributed to a change in practice of APOC in a major health programme in Africa. Our maps are used to plan the delivery of the mass-treatment programme to rural communities throughout the APOC countries, an estimated total population of 115 million.
RVC's Veterinary Epidemiology, Economics and Public Health team (VEEPH) has been at the forefront of applying and evaluating new techniques for modelling disease risk, for policy and decision makers to use in surveillance and control of animal and zoonotic infections. Application of their recommendations, including European `Commission Decision' legislation, is contributing to ensuring that Europe remains free from African swine fever (ASF). The status of FAO Reference Centre in Veterinary Epidemiology, awarded by the United Nations' Food and Agriculture Organisation in 2012, recognises the RVC as a centre of excellence in this field and reinforces its role in guiding policies relating to animal health.
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.
The Scottish Longitudinal Study (SLS) is a pioneering study, combining census, civil registration, health and education data (administrative data). It has established an approach that allows the legal and ethical use of personal, sensitive information by maintaining anonymity within the data system. This approach has become a model for the national data linkage systems that are now being established across the UK. The SLS has also enabled policy analysts to monitor key characteristics of the Scottish population in particular health inequalities (alerting policy makers to Scotland's poor position within Europe), migration (aiding economic planning) and changing tenure patterns (informing house building decisions). Finally, the study has become fully embedded in Scotland's National Statistical agency, allowing it to produce new informative statistical series.
Targeted Projection Pursuit (TPP) — developed at Northumbria University — is a novel method for interactive exploration of high-dimension data sets without loss of information. The TPP method performs better than current dimension-reduction methods since it finds projections that best approximate a target view enhanced by certain prior knowledge about the data. "Valley Care" provides a Telecare service to over 5,000 customers as part of Northumbria Healthcare NHS Foundation Trust, and delivers a core service for vulnerable and elderly people (receiving an estimated 129,000 calls per annum) that allows them to live independently and remain in their homes longer. The service informs a wider UK ageing community as part of the NHS Foundation Trust.
Applying our research enabled the managers of Valley Care to establish the volume, type and frequency of calls, identify users at high risk, and to inform the manufacturers of the equipment how to update the database software. This enabled Valley Care managers and staff to analyse the information quickly in order to plan efficiently the work of call operators and social care workers. Our study also provided knowledge about usage patterns of the technology and valuably identified clients at high risk of falls. This is the first time that mathematical and statistical analysis of data sets of this type has been done in the UK and Europe.
As a result of applying the TPP method to its Call Centre multivariate data, Valley Care has been able to transform the quality and efficiency of its service, while operating within the same budget.