Detecting nested clusters of human alveolar echinococcosis.

Recent changes in the epidemiology of alveolar echinococcosis (AE) in Eurasia have led to increasing concerns about the risk of human AE and the need for a thorough evaluation of the epidemiological situation. The aim of this study was to explore the use of a National Register to detect complex distribution patterns on several scales. The data were human AE cases from the FrancEchino register, diagnosed in France from 1982 to 2011. We used the Kulldorff spatial scan analysis to detect non-random locations of cases. We proposed an exploratory method that was based on the successive detection of nested clusters inside each of the statistically significant larger clusters. This method revealed at least 4 levels of disease clusters during the study period. The spatial variations of cluster location over time were also shown. We conclude that National Human AE registers, although not exempted from epidemiological biases, are currently the best way to achieve an accurate representation of human AE distribution on various scales. Finally, we confirm the multi-scale clustered distribution of human AE, and we hypothesize that our study may be a reasonable starting point from which to conduct additional research and explore the processes that underlie such distributions.