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.     

Basin analysis palaeo-landscape modelling: Testing the critical controls using experimental design constrained by a real 3D geological model,Gippsland Basin,Australia

The main controlling variables for palaeo-landscape evolution are investigated to assess their relative importance using the Gippsland Basin geological history. Palaeo-landscape reconstruction is a complicated process controlled and affected by multiple variables, including tectonic, palaeo-environment, sea-level change, rainfall, sediment erosion, transportation, deposition, etc. The Basin and Landscape Dynamics software (Badlands) software was used with an efficient experimental design (ED) to guide the selected scenarios, process the results, and generate the multi-variate equations that define and identify the important controlling variables. The ED was used to test and identify the main uncertainties and their possible ranges, based on actual field data, while at the same time ensuring that the full multi-dimensional space for those variables was covered to enable the computation of multivariate equations from the minimum number of scenario runs. A full suite of 3D forward palaeo-landscape models of the Gippsland Basin was built to reconstruct the basin history from its formation to the present (Early Cretaceous to Holocene, 137-0 Ma). The models are compared to the corresponding full 3D realistic structural and stratigraphic model of the basin that has been built in Petrel (Schlumberger software). This constrains the sedimentary, stratigraphic, burial and thermal histories to the relative subsidence rates and basin-fill for each geological sequence by using the model isopachs input to the Badlands modelling. The ED required only 22 scenarios to fit 12 identified variables and test for possible interactions with each other. The most significant variables are those that control sediment supply including non-marine erodibility, rainfall, (Rainfall × Area) exponent m, Slope and critical slope while maximum % Marine Deposition and marine dispersal are also required to fill the marine accommodation space. Sea Level and subsidence only become significant when rapid enough to outpace sediment supply. The controlling factors change over time with basin development from rift to post-rift phases and interactions are highly significant.