Spatial aspects of MRSA epidemiology: a case study using stochastic simulation, kernel estimation and SaTScan

The identification of disease clusters in space or space–time is of vital importance for public health policy and action. In the case of methicillin‐resistant Staphylococcus aureus (MRSA), it is particularly important to distinguish between community and health care‐associated infections, and to identify reservoirs of infection. 832 cases of MRSA in the West Midlands (UK) were tested for clustering and evidence of community transmission, after being geo‐located to the centroids of UK unit postcodes (postal areas roughly equivalent to Zip+4 zip code areas). An age‐stratified analysis was also carried out at the coarser spatial resolution of UK Census Output Areas. Stochastic simulation and kernel density estimation were combined to identify significant local clusters of MRSA (p<0.025), which were supported by SaTScan spatial and spatio‐temporal scan. In order to investigate local sampling effort, a spatial ‘random labelling’ approach was used, with MRSA as cases and MSSA (methicillin‐sensitive S. aureus) as controls. Heavy sampling in general was a response to MRSA outbreaks, which in turn appeared to be associated with medical care environments. The significance of clusters identified by kernel estimation was independently supported by information on the locations and client groups of nursing homes, and by preliminary molecular typing of isolates. In the absence of occupational/lifestyle data on patients, the assumption was made that an individual's location and consequent risk is adequately represented by their residential postcode. The problems of this assumption are discussed, with recommendations for future data collection.