Quantification of air quality in space and time and its effects on health

The long-term adverse effects on health associated with air pollution exposure can be estimated using either cohort or spatio-temporal ecological designs. In a cohort study, the health status of a cohort of people are assessed periodically over a number of years, and then related to estimated ambient pollution concentrations in the cities in which they live. However, such cohort studies are expensive and time consuming to implement, due to the long-term follow up required for the cohort. Therefore, spatio-temporal ecological studies are also being used to estimate the long-term health effects of air pollution as they are easy to implement due to the routine availability of the required data. Spatio-temporal ecological studies estimate the health impact of air pollution by utilising geographical and temporal contrasts in air pollution and disease risk across $n$ contiguous small-areas, such as census tracts or electoral wards, for multiple time periods. The disease data are counts of the numbers of disease cases occurring in each areal unit and time period, and thus Poisson log-linear models are typically used for the analysis. The linear predictor includes pollutant concentrations and known confounders such as socio-economic deprivation. However, as the disease data typically contain residual spatial or spatio-temporal autocorrelation after the covariate effects have been accounted for, these known covariates are augmented by a set of random effects. One key problem in these studies is estimating spatially representative pollution concentrations in each areal which are typically estimated by applying Kriging to data from a sparse monitoring network, or by computing averages over modelled concentrations (grid level) from an atmospheric dispersion model. The aim of this thesis is to investigate the health effects of long-term exposure to Nitrogen Dioxide (NO2) and Particular matter (PM10) in mainland Scotland, UK. In order to have an initial impression about the air pollution health effects in mainland Scotland, chapter 3 presents a standard epidemiological study using a benchmark method. The remaining main chapters (4, 5, 6) cover the main methodological focus in this thesis which has been threefold: (i) how to better estimate pollution by developing a multivariate spatio-temporal fusion model that relates monitored and modelled pollution data over space, time and pollutant; (ii) how to simultaneously estimate the joint effects of multiple pollutants; and (iii) how to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. Specifically, chapters 4 and 5 are developed to achieve (i), while chapter 6 focuses on (ii) and (iii). In chapter 4, I propose an integrated model for estimating the long-term health effects of NO2, that fuses modelled and measured pollution data to provide improved predictions of areal level pollution concentrations and hence health effects. The air pollution fusion model proposed is a Bayesian space-time linear regression model for relating the measured concentrations to the modelled concentrations for a single pollutant, whilst allowing for additional covariate information such as site type (e.g. roadside, rural, etc) and temperature. However, it is known that some pollutants might be correlated because they may be generated by common processes or be driven by similar factors such as meteorology. The correlation between pollutants can help to predict one pollutant by borrowing strength from the others. Therefore, in chapter 5, I propose a multi-pollutant model which is a multivariate spatio-temporal fusion model that extends the single pollutant model in chapter 4, which relates monitored and modelled pollution data over space, time and pollutant to predict pollution across mainland Scotland. Considering that we are exposed to multiple pollutants simultaneously because the air we breathe contains a complex mixture of particle and gas phase pollutants, the health effects of exposure to multiple pollutants have been investigated in chapter 6. Therefore, this is a natural extension to the single pollutant health effects in chapter 4. Given NO2 and PM10 are highly correlated (multicollinearity issue) in my data, I first propose a temporally-varying linear model to regress one pollutant (e.g. NO2) against another (e.g. PM10) and then use the residuals in the disease model as well as PM10, thus investigating the health effects of exposure to both pollutants simultaneously. Another issue considered in chapter 6 is to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. There are in total four approaches being developed to adjust the exposure uncertainty. Finally, chapter 7 summarises the work contained within this thesis and discusses the implications for future research.

The impact of social housing on health in Glasgow and Baltimore, 1930-1980

This dissertation seeks to discern the impact of social housing on public health in the cities of Glasgow, Scotland and Baltimore, Maryland in the twentieth century. Additionally, this dissertation seeks to compare the impact of social housing policy implementation in both cities, to determine the efficacy of social housing as a tool of public health betterment. This is accomplished through the exposition and evaluation of the housing and health trends of both cities over the course of the latter half of the twentieth century. Both the cities of Glasgow and Baltimore had long struggled with both overcrowded slum districts and relatively unhealthy populations. Early commentators had noticed the connection between insanitary housing and poor health, and sought a solution to both of these problems. Beginning in the 1940s, housing reform advocates (self-dubbed ‘housers') pressed for the development of social housing, or municipally-controlled housing for low-income persons, to alleviate the problems of overcrowded slum dwellings in both cities. The impetus for social housing was twofold: to provide affordable housing to low-income persons and to provide housing that would facilitate healthy lives for tenants. Whether social housing achieved these goals is the crux of this dissertation. In the immediate years following the Second World War, social housing was built en masse in both cities. Social housing provided a reprieve from slum housing for both working-class Glaswegians and Baltimoreans. In Baltimore specifically, social housing provided accommodation for the city’s Black residents, who found it difficult to occupy housing in White neighbourhoods. As the years progressed, social housing developments in both cities faced unexpected problems. In Glasgow, stable tenant flight (including both middle class and skilled artisan workers)+ resulted in a concentration of poverty in the city’s housing schemes, and in Baltimore, a flight of White tenants of all income levels created a new kind of state subsidized segregated housing stock. The implementation of high-rise tower blocks in both cities, once heralded as a symbol of housing modernity, also faced increased scrutiny in the 1960s and 1970s. During the period of 1940-1980, before policy makers in the United States began to eschew social housing for subsidized private housing vouchers and community based housing associations had truly taken off in Britain, public health professionals conducted academic studies of the impact of social housing tenancy on health. Their findings provide the evidence used to assess the second objective of social housing provision, as outlined above. Put simply, while social housing units were undoubtedly better equipped than slum dwellings in both cities, the public health investigations into the impact of rehousing slum dwellers into social housing revealed that social housing was not a panacea for each city’s social and public health problems.