Spatial distribution of Leishmania seropositive dogs in the Angelim neighborhood, Teresina, Piauí, Brazil: appraisal of three spatial clustering methods

Abstract

The detection of spatial clusters has been suggested as a potential tool for guiding the delivery of interventions against visceral leishmaniasis; however, little attention has been given to the consistency of results by using different spatial clustering methods. The present study aimed to assess the performance of three different techniques for identifying patterns in the spatial distribution of canine leishmaniasis in the city of Teresina, Brazil. This cross-sectional study was based on a serological survey for canine leishmaniasis in which each dog domicile was georeferenced to the exact location of each animal in space. The spatial analysis was performed using three methods: the Cuzick-Edwards statistic, the Hierarchical Nearest Neighbour analysis, and the Kulldorff Scan statistic. All techniques were able to identify clusters of high prevalences of canine leishmaniasis, but results were not consistent among techniques. The feasibility of the identification and location of clusters of cases in a restricted number of villages in neighborhoods might contribute to the optimization of control measures against visceral leishmaniasis. However, given the relative inconsistency of the results provided by the different methods, protocols for assessing clusters of diseases should always include more than one method of evaluation.