Project Overview

A brief history of air pollution epidemiology

 

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. One of the most famous high-pollution episodes was the London Smog of December 1952, which resulted in around 4000 excess deaths (http://www.metoffice.gov.uk/education/teens/case-studies/great-smog). The graph below is taken from http://www.air-quality.org.uk/03, and shows the impact of the smog, and was one of the first times that high concentrations of air pollution were observed to have negative impacts on health. It shows the smoke levels and the numbers of daily deaths for the first two weeks in December 1952, and as the smike levels rise so do the numbers of deaths.

 

 

Today, pollution concentrations in the UK are substantially lower, but air pollution is still 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' (http://www.comeap.org.uk/). 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 (chronic) exposure, which can be assessed using either cohort or small area ecological studies. Cohort studies, as the name suggests, follow the health of a cohort of people living in different cities over an extended time period, and relate their health status to their estimated pollution exposures. However, such studies are costly to set up, so alternatively small-area ecological studies can be conducted. These studies compare the levels of pollution and ill health in populations living in small geographical regions, such as electoral wards, and an example is given here.

 

 

 

Background to the project

 

Conducting  a small-area ecological study is a complex task, and numerous factors will affect the pattern in ill health over space and time, including pollution levels and socio-economic and demographic 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 small geographical region. The pollution data come in two types, a spatially sparse set of concentrations measured at point locations and managed by the Department for the Environment, Food and Rural Affairs (DEFRA), and a spatially dense set of modelled estimates provided by the Met Office. The challenge is thus to combine these data sources together, and use them to predict the concentrations of individual pollutants at the small area scale of the health data. Once these estimates have been created they are subject to uncertainty, and the model linking them to health should acknowledge this uncertainty.

 

 

Aims and objectives of the project

The aims of the projects are:

1. To develop a statistical model that can accurately estimate the long-term effects of air pollution on health, whilst accounting for localised spatio-temporal correlation and uncertainty in the pollution exposure estimates.
2. To develop a statistical model that can fuse modelled and monitored pollution data together and predict concentrations of individual pollutants at the small-area geographical scales requiem to align to the health data.
3. To apply the model to real and simulated data sets, to quantify the improvement in the estimation of the pollution-health relationship from the our models compared with those currently used.
4. To study the effect of future climate on health and air pollution, by using UK specific regional climate model projections to 2050.
5. To develop a user-friendly software package enabling others to implement the methods developed in this project.
6. To disseminate the results of this project to academic and public policy audiences as well as the general public.