Assessing spatial clustering of MRSA with stochastic simulations, kernel estimation and SATSCAN

Apparent spatial disease clusters may stem from a combination of factors including transmission events between individuals, heterogeneous environmental influences, population clustering, and/or chance. 832 incidences of methicillin-resistant Staphylococcus aureus (MRSA) in the West Midlands (UK) were located to postcode-centroid level to test for evidence of community transmission. In an exploratory kernel estimation analysis, clustering effects due to local population density were visualized and assessed for significance by thresholding against 'spatial nulls' (based on the 97.5th percentile of 1000 age-stratified Poisson-process realisations with no a priori assumptions of spatial autocorrelation). This approach, combined with a spatial and spatio-temporal scan, was of particular value in identifying apparent outbreaks at nursing and residential care homes. An attempt to disaggregate the approach to postcodes caused notable accuracy problems in modelling expected MRSA occurrences, biasing the apparent significance of localised occurrences. Stochastically-simulated cases were therefore aggregated to Census Output Area centroids to mitigate the effects of spatial aggregation in the real data. Isolates of methicillin-sensitive Staphylococcus aureus (MSSA) from the same region and time period were used as controls in a 'random labelling' approach to investigate possible variation in testing intensity among family doctors and primary Health Centres. We demonstrate the combination of standard spatial epidemiological tools with more novel simulation techniques in an exploratory analysis which identified community MRSA clusters. 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.