Geostatistical methods for modelling spatially aggregated data

Johnson, Olatunji and Giorgi, Emanuele and Knight, Joanne (2020) Geostatistical methods for modelling spatially aggregated data. PhD thesis, Lancaster University.

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Abstract

Spatially aggregated epidemiological data is nowadays increasingly common because of ethical concern of data use as well as preservation of patient confidentiality. They are typically presented either as the count of disease cases or as an average measurement from districts partitioning a study region. In most cases, the partitioning is based on administrative convenience rather than information about the aetiology of any disease or public health problem. While inference for spatially aggregated data commonly make use of model that assumes a spatially discrete variation, we argue that a spatially continuous model should be considered when there is a scientific justification for its use, especially when the underlying generating process of the disease outcome is hypothesised to behave in a spatially continuous manner. In this thesis, we consider geostatistical methods as a framework that can be used to analyse spatially aggregated data. This thesis is a series of papers, two methodological and one public health application. In the first methodological paper, we developed a computationally efficient discrete approximation to log-Gaussian Cox process (LGCP) models for the analysis of spatially aggregated disease count data. We compare the predictive performance of our modelling approach with LGCP through a simulation study and an application to primary biliary cirrhosis incidence data in Newcastle-Upon-Tyne, UK. Our results suggest that when disease risk is assumed to be a spatially continuous process, the proposed approximation to LGCP provides reliable estimates of disease risk both on spatially continuous and aggregated scales. In the second methodological paper, We developed a model-based geostatistical approach that allows us to model the relationship between the Life expectancy at birth (LEB) and the index of multiple deprivation (IMD), when these are available over different partitions of the study region. We found that the effect of IMD on LEB is higher for males than for females. We show that our proposed model-based geostatistical approach does not only provide solution to any form of misalignment problem but also allows for spatially continuous inferences. In the third application paper, we developed a spatio-temporal model for monthly Chronic Obstructive Pulmonary Disease (COPD) emergency admissions data in South Cumbria and North Lancashire, UK, 2012-2018. We assess the relative contribution of socio-economic and environmental variables for forecasting COPD emergency admissions. In addition, we develop an early warning system that triggers an alarm whenever COPD emergency admissions exceeds a predefined incidence thresholds. The result of our analysis can potentially help NHS Morecambe Bay Clinical Commissioning Group stakeholders to define areas to target early intervention as well as inform resource allocation for healthcare system so that its limited resources can be used to maximum effect.