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Events in the UK NHS have shown the need for a robust understanding of hospital mortality rates.
Surrey's research produced "a unique web-enabled pattern analysis system that is specifically designed to enable clinicians and their teams to view in detail their in-house mortality patterns in the national context" (a).
Launched on a national scale in Ireland in 2013, it has already identified `mortality outliers' and been described as a `game changer' for improving service quality at national level. The tool's impact stems from its ability to translate statistical patterns into a form readily usable by health professionals to improve care quality and sharing best practice.
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.
Research carried out by Cairns (Maxwell Institute), Blake (Cass Business School) and Dowd (Nottingham, now Durham) in 2006 produced the `CBD' model for predicting future life expectancy. The CBD model and its extensions developed in 2009 by Cairns and collaborators have had a major impact on pensions and life industry risk management practices: multinational financial institutions [text removed for publication] and other stakeholders have relied on the CBD model to risk assess, price and execute financial deals [text removed for publication] since 2010. CBD is also used by risk management consultants to advise clients, is embedded in both open-source and commercial software, and is used by the UK's Pension Protection Fund to measure and manage longevity risk.
Our research has used epidemiological insights, data and methods to enable Legal & General (L&G), a major pensions and annuity provider, to understand the drivers of long-term trends in the annual rates of improvement in mortality in older ages. Our first-ever analysis of inequalities in mortality trends by cause of death over 25 years in England, and future projections of these, has resulted in better informed pricing and risk management (capital reserving) practices at L&G. We also modelled how much of the decline in coronary heart disease, the main contributor to improving life expectancy, was due to improved healthcare versus healthier lifestyles. Projections of these, based on plausible scenarios of evolution of risk factors and disease management, helped strengthen the evidence base for L&G's assumptions of mortality improvements for the UK financial regulators.
Graduated period life tables for men and women, based on the mortality experience of the population of England and Wales, have been published by the Office for National Statistics (ONS) using data from the 2001 Census. These tables are the sixteenth in a series known as the English Life Tables which are associated with decennial population censuses, beginning with the Census of 1841. Errors in crude census data owing to the small numbers of deaths involved, particularly in childhood and at very advanced ages, can be reduced by a statistical process of smoothing. A smoothing methodology developed at Cass Business School, City University London has been used in the latest ONS Decennial Life Tables. The tables show the increasing longevity of the population of England and Wales over a long period. The impact of this research is broad as life tables are used extensively in pensions planning, demography, insurance, economics and medicine. Life tables using this statistical smoothing methodology have also been prepared for Scotland, Northern Ireland, the Republic of Ireland and Canada.
This case study describes a significant new index used to monitor death rates in hospitals. The Summary Hospital Mortality Index (SHMI) was developed as a direct result of research carried out at the School of Health and Related Research (ScHARR). This was implemented nationally in October 2011 and the SHMI is now the main mortality indicator used by the NHS. Following publication of the high profile Francis Inquiry on Mid Staffordshire in February 2013, set up to investigate excess mortality in the Trust, the Government has used the SHMI to identify and target 8 further hospitals for investigation.
A two-dimensional flood inundation model called LISFLOOD-FP, which was created by a team led by Professor Paul Bates at the University of Bristol, has served as a blueprint for the flood risk management industry in the UK and many other countries. The documentation and published research for the original model, developed in 1999, and the subsequent improvements made in over a decade of research, have been integrated into clones of LISFLOOD-FP that have been produced by numerous risk management consultancies. This has not only saved commercial code developers' time but also improved the predictive capability of models used in a multimillion pound global industry that affects tens of millions of people annually. Between 2008 and 2013, clones of LISFLOOD-FP have been used to: i) develop national flood risk products for countries around the world; ii) facilitate the pricing of flood re-insurance contracts in a number of territories worldwide; and iii) undertake numerous individual flood inundation mapping studies in the UK and overseas. In the UK alone, risk assessments from LISFLOOD-FP clones are used in the Environment Agency's Flood Map (accessed on average 300,000 times a month by 50,000 unique browsers), in every property legal search, in every planning application assessment and in the pricing of the majority of flood re-insurance contracts. This has led to more informed and, hence, better flood risk management. A shareware version of the code has been available on the University of Bristol website since December 2010. As of September 2013, the shareware had received over 312 unique downloads from 54 different countries.
A generalized additive model (GAM) explores the extent to which a single output variable of a complex system in a noisy environment can be described by a sum of smooth functions of several input variables.
Bath research has substantially improved the estimation and formulation of GAMs and hence
This improved statistical infrastructure has resulted in improved data analysis by practitioners in fields such as natural resource management, energy load prediction, environmental impact assessment, climate policy, epidemiology, finance and economics. In REF impact terms, such changes in practice by practitioners leads ultimately to direct economic and societal benefits, health benefits and policy changes. Below, these impacts are illustrated via two specific examples: (1) use of the methods by the energy company EDF for electricity load forecasting and (2) their use in environmental management. The statistical methods are implemented in R via the software package mgcv, largely written at Bath. As a `recommended' R package mgcv has also contributed to the global growth of R, which currently has an estimated 1.2M business users worldwide [A].
Ocean circulation accounts for much of the energy that drives weather and climate systems; errors in the representation of the ocean circulation in computational models affect the validity of forecasts of the dynamics of the ocean and atmosphere on daily, seasonal and decadal time scales. Research undertaken by the University of Reading investigated systematic model errors that resulted from data assimilation schemes embedded in the key processes used to predict ocean circulation. The researchers developed a new bias correction technique for use in ocean data assimilation that alleviates these errors. This has led to significant improvements in the accuracy of the forecasts of ocean dynamics. The technique has been implemented by the Met Office and by the European Centre for Medium Range Weather Forecasting (ECMWF) in their forecasting systems, resulting in major improvements to the prediction of the weather and climate from oceanic and atmospheric models. The assimilation technique is also leading to better use of expensively acquired satellite and in-situ data and improving ocean and atmosphere forecasts used by shipping and civil aviation, energy providers, insurance companies, the agriculture and fishing communities, food suppliers and the general public. The impact of the correction procedure is also important for anticipating and mitigating hazardous weather conditions and the effects of long-term climate change.
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.