This thesis presents research that has demonstrated the use of clustering algorithms in the analysis of datasets routinely collected by cancer registries. This involved a review of existing algorithms and their application in studies of spatial and temporal variations in cancer rates. As a result of continuing public and scientific concern there has been an increase in the numbers of cancer related enquiries in recent years that has helped to raise the profile of the work of cancer registries. There are no official guidelines on the approach to be taken in such studies in relation to cluster analysis. In this study, a variety of cluster algorithms were applied to leukaemia data collected by the Welsh Cancer Intelligence and Surveillance Unit in order to propose an approach that could be adopted in future investigations of cancer incidence in Wales. For example, different methodologies have been employed to determine if an excess risk occurs near hazardous sources and one of the studies in the portfolio compares the results of using three methods to determine if an increased risk of cancer occurs in the vicinity of landfill sites and electric power lines. This uses new digital products that permit a more detailed estimation of the population at risk and permit a sensitivity analysis of the results of such investigations. In the third portfolio, analysis of relative survival at small area level has been made possible using a new level of geographical resolution that has recently been released in the United Kingdom. This study shows the benefits of using this new level of geography for small area studies of cancer survival where there are generally small numbers of deaths per spatial unit. It is anticipated that together these research studies will be of wider benefit to other registries in the UK charged with investigating spatial and temporal variations in cancer rates.
|Date of Award||Sep 2008|
|Supervisor||Gary Higgs (Supervisor)|