Log in
Researchers in the Epidemiology Group at the University of Warwick have an international reputation for high-quality mathematical modelling of human infectious diseases, with particular emphasis on population heterogeneity and variability. Such formulations and insights are an important component of predictive modelling performed by Public Health England (PHE), and are helping to shape national policy for a range of vaccine-preventable infections.
The Warwick group was instrumental in providing a range of real-time analyses and advice to UK authorities during the 2009 H1N1 (swine-flu) pandemic, acknowledged by the Department of Health (DoH) to be "fundamental to the construction of the UK's pandemic response" and making an important contribution to the overall programme which "led to the saving of many hundreds of millions of pounds of taxpayers money, while greatly increasing the health of the Nation". Modelling and analysis carried out at Warwick continue to provide insight into the control and containment of future pandemics and are considered "essential in determining UK pandemic policy".
Research by Professor Grassly and colleagues at Imperial College on the epidemiology of poliovirus and the efficacy of new vaccines has played a critical role in the thinking and strategy of the Global Polio Eradication Initiative (GPEI). This research has supported the introduction of new vaccines, guided the timing and location of vaccination campaigns and influenced polio `endgame' policy. This is documented in the GPEI Strategic Plan 2010-2012, where Imperial research informed 2 of the 4 `major lessons' concerning poliovirus epidemiology described in the executive summary that led to changes in the programme. The research has also informed our understanding of mucosal immunity induced by oral poliovirus vaccines, and led to two clinical trials of the potential role of inactivated vaccine to boost mucosal immunity. Results from one of these trials were used to support the recent World Health Organisations (WHO) recommendation for universal vaccination with inactivated vaccine following the switch to bivalent oral vaccine in routine programmes.
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.
Since 2008, statistical research at the University of Bristol has significantly influenced policies, practices and tools aimed at evaluating and promoting the quality of institutional and student learning in the education sector in the UK and internationally. These developments have also spread beyond the education sector and influence the inferential methods employed across government and other sectors. The underpinning research develops methodologies and a much-used suite of associated software packages that allows effective inference from complicated data structures, which are not well-modelled using traditional statistical techniques that assume homogeneity across observational units. The ability to analyse complicated data (such as pupil performance measures when measured alongside school, classroom, context and community factors) has resulted in a significant transformation of government and institutional policies and their practices in the UK, and recommendations in Organisation for Economic Co-operation and Development (OECD) policy documents. These techniques for transforming complex data into useful evidence are well-used across the UK civil service, with consequent policy shifts in areas such as higher education admissions and the REF2014 equality and diversity criteria.
Statistical analysis and methodological development carried out by Imperial College London on data from the Bristol Royal Infirmary Inquiry and the Shipman Inquiry have led to new monitoring systems in healthcare. Using routinely collected healthcare information, we have highlighted variations in performance and safety, impacting the NHS through direct interventions and/or policy change. For example: (i) findings and recommendations arising from our research for the Bristol Inquiry were reflected in the final inquiry outputs, which highlighted the importance of routinely collected hospital data to be used to undertake the monitoring of a range of healthcare outcomes, (ii) a range of monitoring recommendations have arisen as a direct result of the research on data from the Shipman Inquiry, (iii) analytical tools based on our methodological research are used by managers and clinicians in over two thirds of NHS hospital trusts, (iv) Imperial's monthly mortality alerts to the Care Quality Commission were major triggers leading to the Healthcare Commission investigation into the Mid Staffordshire NHS Trust.
This research has profoundly influenced the practice of pharmacoepidemiology in 2008-13. The self-controlled case series (SCCS) method is particularly well-suited for working with computerised databases, which are increasingly used in epidemiology. The method has been recommended by international agencies (WHO, ECDC) and is now widely used by health practitioners within national public health agencies, including the CDC (USA), Public Health England (UK) and many other national and regional public health bodies. It has influenced practice within the private sector (notably the pharmaceutical and the healthcare industries). Use of the SCCS method has impacted on health by reducing costs, improving timeliness and improving the quality of evidence upon which policy decisions are based.
Professor Hutton has applied her research on statistical models for survival analysis to cerebral palsy, a neurological disorder which afflicts around 1 in 500 of newborn children globally. The body of research has established medically-accepted norms for the life expectancy of people with cerebral palsy. Her research extends to the study of life expectancy for patients suffering from spinal cord injuries.
The impact of this work has been internationally substantial, influencing medical and legal professionals, and informing lay people with involvement in cerebral palsy. Her work is also widely cited by patient-networks and textbooks.
Hutton is regularly called by both defence and plaintiff lawyers, as an expert witness worldwide, assessing life expectancy for damages arising from negligence in obstetric or paediatric care, or from accidents. Her expertise is also used in brain and spinal cord injury cases, which also result in substantial awards. The award of appropriate damages in legal cases ensures that patients receive the best care for the rest of their lives. From Jan 2008 to July 2013 Hutton has provided expert evidence in 103 such cases around the world, which had impact on decisions about compensation totalling in the range £100M-450M.
LSHTM researchers have developed four computer models to help decision-makers make evidence-based choices about new vaccines and vaccine schedules. These models analyse the public health impact and cost-effectiveness of different options under different assumptions and scenarios on a country-by-country basis. They are used by national immunisation managers and key decision-makers, international committees and partner organisations (e.g. the Global Alliance for Vaccines and Immunisation and the Bill & Melinda Gates Foundation). LSHTM's researchers have built on this research for WHO, informing global recommendations on vaccine timing and schedules.
The WinBUGS software (and now OpenBUGS software), developed initially at Cambridge from 1989-1996 and then further at Imperial from 1996-2007, has made practical MCMC Bayesian methods readily available to applied statisticians and data analysts. The software has been instrumental in facilitating routine Bayesian analysis of a vast range of complex statistical problems covering a wide spectrum of application areas, and over 20 years after its inception, it remains the leading software tool for applied Bayesian analysis among both academic and non-academic communities internationally. WinBUGS had over 30,000 registered users as of 2009 (the software is now open-source and users are no longer required to register) and a Google search on the term `WinBUGS' returns over 205,000 hits (over 42,000 of which are since 2008) with applications as diverse as astrostatistics, solar radiation modelling, fish stock assessments, credit risk assessment, production of disease maps and atlases, drug development and healthcare provider profiling.
Professor Trevor Bailey of the University of Exeter led the methodological and computational development of new improved mathematical models to more fairly allocate resources, and particularly mental health resources, to GP practices in the UK within an interdisciplinary research team from the universities of Plymouth, Southampton and St Andrews. The mental health services component of NHS Practice based commissioning (PBC) introduced by the Department of Health (DoH) from 2007 onwards, deals with resource allocation for specialist healthcare for some 400,000 patients with severe mental illness. From 2009 to 2011, the team's mental health estimates, based upon the modelling efforts of Bailey, were used to set practice-level PBC budgets accounting for around £8 billion of NHS funding, the DoH describing this as a `step-change improvement' in how mental health needs are modelled.