Diseases and Geographic Variation: Are we missing the whole picture? IJPDS (2017) Issue 1, Vol 1:156, Proceedings of the IPDLN Conference (August 2016) - John, Gareth (International Journal of Population Data Science, 2017)
ABSTRACT Background Epidemiology is the study and analysis of the patterns, causes, and effects of health and disease conditions in defined populations. However, where an epidemiological analysis relies solely on data from large-scale anonymous administrative data sources, the available geographic information for the individuals concerned relates in general to just one single point in time; for example the places of residence of individual on the day that they were registered with a particular disease, or their places of residence on the day that they were admitted to hospital. This information may not be sufficient however, especially when considering diseases where there may be a long period of time between an exposure to a particular hazard and the subsequent onset of disease. Method A solution to this problem possibly lies within administrative sources of data such as the Welsh Demographic Service (WDS), which is accessible to NHS Wales analysts and users of the SAIL databank in a pseudonymised format. The WDS contains the details of all Welsh residents who have been registered with a GP Practice since 1992, including a full history of changes to their addresses and GP practices. This data can be used to easily ascertain an individual’s address at any point in time, for example on a particular census date, or within a time period that is relative to a particular event, e.g. 10 years prior to disease registration. However, this research will look at how to incorporate all of an individual’s available address information into an epidemiological analysis. Results Two main approaches will be demonstrated; the first using a “Person Years at Risk” approach which attempts to apportion numerator and denominator according to the number of previous residences, and the second using a Case Control approach, comparing the geographic spread of addresses in the diseased group of patients versus the non-diseased (control) group, with age and gender matched controls also drawn from the WDS.