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 pathophysiology of CADASIL: studies in a Scottish cohort

Since identification that mutations in NOTCH3 are responsible for cerebral autosomal dominant arteriopathy with subcortical infarcts and leucoencephalopathy (CADASIL) in the early 1990s, there has been extensive characterisation of the clinical and radiological features of the disease. However therapeutic interventions remain elusive, partly due to a limited understanding of the vascular pathophysiology and how it leads to the development of strokes, cognitive decline and disability. The apparent rarity and heterogenous natural history of CADASIL potentially make conducting any longitudinal or therapeutic trials difficult. The role of disease biomarkers is therefore of some interest. This thesis focuses on vascular function in CADASIL and how it may relate to clinical and radiological markers of disease. Establishing the prevalence of CADASIL in the West of Scotland was important to assess the impact of the disease, and how feasible a trial would be. A mutation prevalence of 10.7 per 100,000 was demonstrated, suggesting significant under diagnosis of the disease across much of Scotland. Cerebral hypoperfusion is thought to be important in CADASIL, and it has been shown that vascular abnormalities precede the development of brain pathology in mouse models. Investigation of vascular function in patients, both in the brain and systemically, requires less invasive measures. Arterial spin labelling magnetic resonance imaging (MRI) and transcranial Doppler ultrasound (TCD) can both be used to obtain non-invasive and quantifiable indices of vascular function. Monitoring patients with MRI whilst they receive different concentrations of inspired oxygen and carbon dioxide can provide information on brain function, and I reviewed the practicalities of this technique in order to guide the design of the studies in this thesis. 22 CADASIL patients were recruited to a longitudinal study. Testing included peripheral vascular assessment, assessment of disability, neurological dysfunction, mood and cognition. A CO2 reactivity challenge during both TCD and arterial spin labelling MRI, and detailed MRI sequences were obtained. I was able to demonstrate that vasoreactivity was associated with the number of lacunes and brain atrophy, as were carotid intima-media thickness, vessel stiffness, and age. Patients with greater disability, higher depressive symptoms and poorer processing speed showed a tendency to worse cerebral vasoreactivity but numbers were small. This observation suggests vasoreactivity may have potential as a therapeutic target, or a biomarker. I then wished to establish if arterial spin labelling MRI was useful for assessing change in cerebral blood flow in CADASIL patients. Cortical grey matter showed the highest blood flow, mean (SD), 55 (10) ml/100g/min and blood flow was significantly lower within hyperintensities (19 (4) ml/100g/min; p <0.001). Over one year, blood flow in both grey matter (mean -7 (10) %; p = 0.028) and deep white matter (-8 (13) %; p = 0.036) declined significantly. Cerebrovascular reactivity did not change over one year. I then investigated whether baseline vascular markers were able to predict change in radiological or neuropsychological measures of disease. Changes in brain volume, lacunes, microbleeds and normalised subcortical hyperintensity volume (increase of 0.8%) were shown over one year. Baseline vascular parameters were not able to predict these changes, or those in neuropsychological testing. NOTCH3 is found throughout the body and a systemic vasculopathy has been seen particularly affecting resistance vessels. Gluteal biopsies were obtained from 20 CADASIL patients, and ex vivo myography investigated the response to vasoactive agents. Evidence of impairment in both vasodilation and vasoconstriction was shown. The addition of antioxidants improved endothelium-dependent relaxation, indicating a role for oxidative stress in CADASIL pathology. Myography measures were not related to in vivo measures in the sub-group of patients who had taken part in both studies. The small vessels affected in CADASIL are unable to be imaged by conventional MR imaging so I aimed to establish which vessels might be responsible for lacunes with use of a microangiographic template overlaid onto brain images registered to a standard brain template. This showed most lacunes are small and associated with tertiary arterioles. On the basis of this thesis, it is concluded that vascular dysfunction plays an important role in the pathophysiology of CADASIL, and further assessment of vascular measures in longitudinal studies is needed. Arterial spin labelling MRI should be used as it is a reliable, non-invasive modality that can measure change over one year. Furthermore conventional cardiovascular risk factor prevention should be undertaken in CADASIL patients to delay the deleterious effects of the disease.