Estimating air pollution and its relationship with human health

The health impact of short-term exposure to air pollution has been the focus of much recent research, the majority of which is based on time-series studies. A time-series study uses health, pollution and meteorological data from an extended urban area. Aggregate level data is used to describe the health of the population living with the region, this is typically a daily count of the number of mortality or morbidity events. Air pollution data is obtained from a number of fixed site monitors located throughout the study region. These monitors measure background pollution levels at a number of time intervals throughout the day and a daily average is typically calculated for each site. A number of pollutants are measured including, carbon monoxide (CO); nitrogen dioxide (NO2); particulate matter (PM2.5 and PM10), and; sulphur dioxide (SO2). These fixed site monitors also measure a number of meteorological covariates such as temperature, humidity and solar radiation. In this thesis I have presented extensions to the current methods which are used to estimate the association between air pollution exposure and the risks to human health. The comparisons of the efficacy of my approaches to those which are adopted by the majority of researchers, highlights some of the deficiencies of the standard approaches to modelling such data. The work presented here is centered around three specific themes, all of which focus on the air pollution component of the model. The first and second theme relate to what is used as a spatially representative measure of air pollution and allowing for uncertainty in what is an inherently unknown quantity, when estimating the associated health risks, respectively. For example the majority of air pollution and health studies only consider the health effects of a single pollutant rather than that of overall air quality. In addition to this, the single pollutant estimate is taken as the average concentration level across the network of monitors. This is unlikely to be the average concentration across the study region due to the likely non random placement of the monitoring network. To address these issues I proposed two methods for estimating a spatially representative measure of pollution. Both methods are based on hierarchical Bayesian methods, as this allows for the correct propagation of uncertainty, the first of which uses geostatistical methods and the second is a simple regression model which includes a time-varying coefficient for covariates which are fixed in space. I compared the two approaches in terms of their predictive accuracy using cross validation. The third theme considers the shape of the estimated concentration-response function between air pollution and health. Currently used modelling techniques make no constraints on such a function and can therefore produce unrealistic results, such as decreasing risks to health at high concentrations. I therefore proposed a model which imposes three constraints on the concentration-response function in order to produce a more sensible shaped curve and therefore eliminate such misinterpretations. The efficacy of this approach was assessed via a simulation study. All of the methods presented in this thesis are illustrated using data from the Greater London area.EThOS - Electronic Theses Online ServiceGBUnited Kingdo