A flexible class of Bayesian spatio-temporal models for cluster detection, trend estimation and forecasting of disease risk

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Maps are a common visual tool for presenting information on the spatial variability in disease rates across a city of country. Such maps are typically created for raw disease rates, and one's eye is generally drawn to areas exhibiting extremely high or low rates. Some of these extreme rates are often found in areas with small numbers of disease cases, and in such situations the rate estimates can be affected by random fluctuations and thus be highly unstable. Therefore statistical modelling of these data is often undertaken, which improves the estimation of these rates. This modelling assumes that areas which are close together have similar disease rates relative to areas which are further apart, and this assumption tends to smooth rates over adjacent areas. Taking into account this spatial autocorrelation is one of the features which makes modelling these data relatively complex, compared to other statistical analyses where the data are assumed to be independent. Separate maps could be produced for each time period, and then compared visually to assess the presence of any change in disease rates over time. Alternatively, models that identify both spatial and temporal patterns in disease rates have been developed, but these approaches currently assume that the shape of the temporal trend is the same in each area. This does not really permit the researcher to model data where the temporal trends in disease rates are different, such as increasing linearly in one area but decreasing non-linearly in another. The main contribution of this project is the development of a novel class of statistical models for estimating the spatio-temporal pattern in disease rates, which has much greater flexibility than existing methods. For example, in some areas the rates may increase, in others they may decrease first and then increase, and in others they may remain relatively constant. The methodology developed here will enable academic researchers and public health practitioners to investigate these phenomena, which are currently beyond the scope of existing methods. Widespread uptake of these methods will be achieved by the development of well-written, tested and documented software, which will use a freely available software platform and hence provide no hindrance to the use of the models. Such general-purpose software for fitting both existing and novel statistical models used in this field does not yet exist, and its development is one of the key outcomes the project will deliver. Furthermore we plan to run workshops and training events at the conclusion of the project, to demonstrate how the software can be used and how the models can be interpreted. Having developed the theory and software, we will use three example case studies to illustrate the power and flexibility of this approach. The project benefits from close collaboration with Public Health and Intelligence (PHI),part of NHS Scotland, and these links will enable the use of these models in the analysis of NHS data. The data used in these studies (vaccine uptake, GP consultations and cardiac hospitalisation and mortality) reflect important questions in public health epidemiology, where descriptive maps of raw disease rates have been used previously. The applicability of the methods and software developed in this research is not restricted to these examples however, and almost any problem involving spatio-temporal mapping of spatially aggregated data can be tackled. The project is also a timely one, as a result of a rapid expansion in the public availability of population level data at relatively small geographic areas at regular intervals such as yearly or monthly. Such data are available through the neighbourhood statistics databases, and the models developed will also interest researchers modelling spatio-temporal patterns in non-health data, such as educational attainment or house prices. Thus the successful completion of this project will yield a large impact.