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Details of Grant 

EPSRC Reference: EP/J017485/1
Title: A rigorous statistical framework for estimating the long-term health effects of air pollution
Principal Investigator: Sahu, Professor SK
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: School of Mathematics
Organisation: University of Southampton
Scheme: Standard Research
Starts: 17 January 2013 Ends: 16 January 2016 Value (£): 365,643
EPSRC Research Topic Classifications:
Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
Environment
Related Grants:
EP/J017442/1
Panel History:
Panel DatePanel NameOutcome
05 Mar 2012 Mathematics Prioritisation Panel Meeting March 2012 Announced
Summary on Grant Application Form
The adverse health effects resulting from exposure to air pollution are well known across the world, and have a substantial financial and public health impact. For example, in the UK air pollution is estimated to reduce life expectancy by 6 months, with corresponding health costs of up to £19 billion each year. Successive UK governments have acted to mediate against the harmful effects of air pollution, by introducing legislation (e.g. UK Air Quality Strategy, 2007), and setting up the Committee On the Medical Effects of Air Pollution. Numerous epidemiological studies have been conducted to assess the health impact of air pollution over the last 30 years, most of which have focused on the effects of a few days of high concentrations. Much less research has focused on the effects of long-term exposure, which can be assessed by comparing the levels of pollution and ill health in populations living in small geographical regions, such as electoral wards, over a number of years.

However, conducting such a study is a complex task, and it is important that epidemiologists have access to appropriate statistical methods to accurately quantify the health impact of pollution. In particular, numerous factors will affect the pattern in ill health over space and time, including pollution levels and socio-economic factors. However, some of the latter will be unknown or unmeasured, and their existence will induce spatio-temporal correlation into the health data. This correlation is likely to be localised in space, as the similarity of the levels of ill health in geographically adjacent areas will depend on the similarity between the populations living in those areas. To not account for these unknown factors will risk biasing the estimated pollution-health relationship, and thus one of the key challenges of this project is to develop a statistical approach to address this issue. The other key challenge will be to accurately estimate the levels of individual air pollutants for ach local population and year. This is difficult, because the spatio-temporal pattern in pollution is often driven by atmospheric processes, which themselves are influenced by meteorological processes. A further complication is that, often, these processes have non-linear effects on each other, which excludes the use of linear interpolation methods often adopted in practice. The effects of multiple pollutants and overall air quality on health are also poorly understood, as quantifying these requires multivariate pollution models which are hard to fit and analyse. This project will use state-of-the-art meteorological, climate and air quality models developed by the Met Office to produce reliable air pollution estimates.

The main aim of this project is to create and test a single integrated model for health and pollution data that addresses these issues, thus allowing the effects of overall air pollution on health to be estimated. A secondary aim is to quantity the impact (bias) that ignoring these issues has on the estimated pollution-health relationship. The health model will need to provide an accurate representation of the localised spatio-temporal correlation in small-area health data, while the pollution model will need to provide estimates and measures of uncertainty for individual pollutants and overall air quality at any spatial and temporal resolution, as required to align with the health data. Importantly, the use of a formal statistical framework allows us to make a further innovation: namely, to measure the effect of climate change on health and air pollution. This will be achieved by using output from deterministic climate models to project air pollution levels under future climate conditions, and using those projected levels in the integrated health and pollution model. Overall, this proposal outlines the most detailed linkage of health and air pollution data yet attempted, by developing and testing a set of novel statistical models.
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Organisation Website: http://www.soton.ac.uk