CKGROUND: There is considerable uncertainty in the disease rate estimation for aggregated area maps, especially for small population areas. As a consequence the delineation of local clustering is subject to substantial variation. Consider the most likely disease cluster produced by any given method, like SaTScan, for the detection and inference of spatial clusters in a map divided into areas; if this cluster is found to be statistically significant, what could be said of the external areas adjacent to the cluster? Do we have enough information to exclude them from a health program of prevention? Do all the areas inside the cluster have the same importance from a practitioner perspective?RESULTS: We propose a method to measure the plausibility of each area being part of a possible localized anomaly in the map. In this work we assess the problem of finding error bounds for the delineation of spatial clusters in maps of areas with known populations and observed number of cases. A given map with the vector of real data (the number of observed cases for each area) shall be considered as just one of the possible realizations of the random variable vector with an unknown expected number of cases. The method is tested in numerical simulations and applied for three different real data maps for sharply and diffusely delineated clusters. The intensity bounds found by the method reflect the degree of geographic focus of the detected clusters.CONCLUSIONS: Our technique is able to delineate irregularly shaped and multiple clusters, making use of simple tools like the circular scan. Intensity bounds for the delineation of spatial clusters are obtained and indicate the plausibility of each area belonging to the real cluster. This tool employs simple mathematical concepts and interpreting the intensity function is very intuitive in terms of the importance of each area in delineating the possible anomalies of the map of rates. The Monte Carlo simulation requires an effort similar to the circular scan algorithm, and therefore it is quite fast. We hope that this tool should be useful in public health decision making of which areas should be prioritized.

ckground consider uncertainti diseas rate estim aggreg area map especi small popul area consequ delin local cluster subject substanti variat consid like diseas cluster produc given method like satscan detect infer spatial cluster map divid area cluster found statist signific said extern area adjac cluster enough inform exclud health program prevent area insid cluster import practition perspectiveresult propos method measur plausibl area part possibl local anomali map work assess problem find error bound delin spatial cluster map area known popul observ number case given map vector real data number observ case area shall consid just one possibl realiz random variabl vector unknown expect number case method test numer simul appli three differ real data map sharpli diffus delin cluster intens bound found method reflect degre geograph focus detect clustersconclus techniqu abl delin irregular shape multipl cluster make use simpl tool like circular scan intens bound delin spatial cluster obtain indic plausibl area belong real cluster tool employ simpl mathemat concept interpret intens function intuit term import area delin possibl anomali map rate mont carlo simul requir effort similar circular scan algorithm therefor quit fast hope tool use public health decis make area priorit