Modelling antimicrobial prescriptions in Scotland: A spatio-temporal clustering approach

In 2016 the British government acknowledged the importance of reducing antimicrobial prescriptions in order to avoid the long-term harmful effects of over-prescription. Prescription needs are highly dependent on factors that have a spatio-temporal component, such as bacterial outbreaks and urban densities. In this context, density-based clustering algorithms are flexible tools to analyse data by searching for group structures and therefore identifying peer groups of general practitioners (GPs) with similar behaviour. The case of Scotland presents an additional challenge due to the diversity of population densities under the area of study. We propose here a spatio-temporal clustering approach for modelling the behaviour of antimicrobial prescriptions in Scotland. Particularly, we consider the density-based spatial clustering of applications with noise algorithm (DBSCAN) due to its ability to include both spatial and temporal data.We extend this approach into two directions. For the temporal analysis, we use dynamic time warping to measure the dissimilarity between time series while taking into account effects such as seasonality. For the spatial component, we propose a new way of weighting spatial distances with continuous weights derived from a KDE-based process. This makes our approach suitable for cases with different local densities,which presents a well-known challenge for the original DBSCAN. We apply our approach to antibiotic prescription data in Scotland, demonstrating how the findings can be used to compare antimicrobial prescription behaviour within a group of similar peers and detect regions of extreme behaviours.