'Expanding Horizons': investigating the Glasgow 2014 legacy for young people in the East End of Glasgow

The recent staging of Glasgow 2014 drew universal praise as the ‘Best Games Ever’. Yet the substantial undertaking of hosting the Commonwealth Games (CWG) was sold to the nation as more than just eleven days of sporting spectacle and cultural entertainment. Indeed, the primary strategic justification offered by policymakers and city leaders was the delivery of a bundle of positive and enduring benefits, so-called ‘legacy’. This ubiquitous and amorphous concept has evolved over time to become the central focus of contemporary hosting bids, reflecting a general public policy shift towards using major sporting mega events as a catalyst to generate benefits across economic, environmental and social dimensions, on a scale intended to be truly transformative. At the same time, the academy has drawn attention to the absence of evidence in support of the prevailing legacy rhetoric and raised a number of sociological concerns, not least the socially unequitable distribution of purported benefits. This study investigated how young people living in the core hosting zone related to, and were impacted upon, by the CWG and its associated developments and activities with reference to their socio-spatial horizons, the primary outcome of interest. An ‘ideal world’ Logic Model hypothesised that four mechanisms, identified from official legacy documents and social theories, would alter young people’s subjective readings of the world by virtue of broadening their social networks, extending their spatial boundaries and altering their mind sets. A qualitative methodology facilitated the gathering of situated and contextualised accounts of young people’s attitudes, perceptions, beliefs and behaviours relating to Glasgow 2014. In-depth interviews and focus groups were conducted before and after the Games with 26 young people, aged 14-16 years, at two schools in the East End. This approach was instrumental in privileging the interests of people ‘on the ground’ over those of city-wide and national stakeholders. The findings showed that young people perceived the dominant legacy benefit to be an improved reputation and image for Glasgow and the East End. Primary beneficiaries were identified by them as those with vested business interests e.g. retailers, restaurateurs, and hoteliers, as well as national and local government, with low expectations of personal dividends or ‘trickle down’ benefits. Support for Glasgow 2014 did not necessarily translate into individual engagement with the various cultural and sporting activities leading up to the CWG, including the event itself. The study found that young people who engaged most were those who had the ability to ‘read’ the opportunities available to them and who had the social, cultural and economic capital necessary to grasp them, with the corollary that those who might have gained most were the least likely to have engaged with the CWG. Doubts articulated by research participants about the social sustainability of Glasgow 2014 underscored inherent tensions between the short-lived thrill of the spectacle and the anticipated longevity of its impacts. The headline message is that hosting sporting mega events might not be an effective means of delivering social change. Aspirant host cities should consider more socially equitable alternatives to sporting mega events prior to bidding; and future host cities should endeavour to engage more purposefully with more young people over longer time frames.

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.