Spatial Clustering of Disease Events Using Bayesian Methods

One of main aims of the spatial analysis of health and medical datasets is to provide additional information to the specialized medical research. These analyses can be used for disease mapping; searching for places with a higher intensity and probability of the disease event; or the influence assessment of selected natural or artificial phenomena. Suitably selected methods allow a proper analysis of these data and identification of irregularities and deviations of the phenomena in the area of interest. The structure of medical data usually needs to be standardized (over age structure of the population) before the comparison of different regions. Bayesian statistics derives the posterior probability as a consequence of a prior probability and a probability model for the data observed. Geosciences and geomedicine usually use the Bayesian theory for smoothing of data to help depict the real spatial pattern and its changeability. The Bayesian principles, together with the spatial neighbourhood and statistical models, are successfully used also for the identification of spatial and space-time clusters with significantly higher/lower risk of incidence of the disease. These procedures are denoted as methods of spatial clustering and can be used with or without utilization of properties of certain phenomena. Particularly, occurrence data of campylobacteriosis infection in four Moravian regions in period 2008 – 2012, which were provided by The National Institute of Public Health, were used for the case study.