Spatial Scan Statistics to Assess Sampling Strategy of Antimicrobial Resistance Monitoring Program

Abstract

The collection and analysis of data on antimicrobial resistance in human and animal populations are important for establishing a baseline of the occurrence of resistance and for determining trends over time. In animals, targeted monitoring with a stratified sampling plan is normally used. However, to our knowledge it has not previously been analyzed whether animals have a random chance of being sampled by these programs, regardless of their spatial distribution. In this study, we used spatial scan statistics, based on a Poisson model, as a tool to evaluate the geographical distribution of animals sampled by the Danish Integrated Antimicrobial Resistance Monitoring and Research Programme (DANMAP), by identifying spatial clusters of samples and detecting areas with significantly high or low sampling rates. These analyses were performed for each year and for the total 5-year study period for all collected and susceptibility tested pig samples in Denmark between 2002 and 2006. For the yearly analysis, both high and low sampling rates areas were significant, with two clusters in 2002 (relative risk [RR]: 2.91, p < 0.01 and RR: 0.06, p < 0.01) and one in 2005 (RR: < 0.01, p < 0.01). For the 5-year analysis, one high sampling rate cluster was detected (RR: 2.56, p = 0.01). These findings allowed subsequent investigation to clarify the source of the sampling clusters. Overall, the detected clusters presented different spatial locations over the years and we can conclude that they were more associated to temporary sampling problems than to a failure in the sampling strategy adopted by the monitoring program. Spatial scan statistics proved to be a useful tool for assessment of the randomness of the sampling distribution, which is important when evaluating the validity of the results obtained by an antimicrobial monitoring program.