A rigorous statistical framework for estimating the long-term health effects of air pollution

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 each 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.

The UK government will benefit from this project, because it will contribute to the evidence base about the long-term health effects of air pollution in the UK, via our three case studies in London, Glasgow and Southampton. The conduit for this impact will be the `Committee on the Medical Effects of Air Pollution', who advise the Government on future air pollution policy and legislation. In the longer term, our research will indirectly benefit the health of the general public, who will be more aware of the health problems caused by exposure to air pollution. Any improvement in public health will also be of economic benefit to the UK, due to the reduced demand for healthcare resources. Furthermore, the impact described above could be replicated in other countries, as the outputs from this project will enable many other researchers abroad to quantify the harmful effects of air pollution in cities across the world. In addition, the outputs from the three individual streams of the project also have the potential to generate societal impact. The development of models to capture the spatio-temporal evolution of disease risk will be of interest to public health professionals, such as those working in the National Health Service (NHS) and the Association of Public Health Observatories (APHO). The development of models to capture the spatio-temporal pattern in overall air pollution levels will be of direct interest to the Department of the Environment, Food and Rural Affairs (DEFRA), as well as the air pollution regulatory group in the United States Environmental Protection Agency.

This research project will also benefit UK science as a whole, by the enhancement of scientific knowledge through the development of statistical methodology and practical tools to enable others to implement the methods developed. Potential beneficiaries include researchers working in statistics, environmental epidemiology, disease mapping and pollution modelling, as well as other areas where a spatio-temporal pattern in data needs to be quantified. The other impact of this project will be the development of two highly skilled research fellows, which will benefit the individuals concerned through their career development. This will also benefit the future of UK science, because the fellows have the potential to become future research leaders. The fellows will benefit from the project by day to day collaboration with the investigators, who are experts in their respective fields. They will also gain vital insight from the non-academic stakeholders in the project, who will provide the policy implications and substantive context for their research. Finally, the fellows will benefit from collaborating with the visiting researcher, who is a world leader in Bayesian spatio-temporal modelling and computation.