01993nas a2200337   4500000000100000008004100001260001300042653001100055653002100066653003500087653001100122653001200133653001700145653002600162653001700188100001500205700001100220700001300231700002000244700001300264700001500277700003100292700001600323700001500339245017300354856007900527300001100606490000700617520101700624022001401641       2010                            d  c2010 Feb10aBrazil10aEndemic Diseases10aGeographic Information Systems10aHumans10aleprosy10aRisk Factors10aSocioeconomic Factors10aTime Factors1 aQueiroz JW1 aDias G1 aNobre ML1 aDe Sousa Dias M1 aAraujo S1 aBarbosa JD1 aBezerra da Trindade-Neto P1 aBlackwell J1 aJeronimo S00aGeographic information systems and applied spatial statistics are efficient tools to study Hansen's disease (leprosy) and to determine areas of greater risk of disease.  uhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2813173/pdf/tropmed-82-306.pdf  a306-140 v823 a<p>Applied Spatial Statistics used in conjunction with geographic information systems (GIS) provide an efficient tool for the surveillance of diseases. Here, using these tools we analyzed the spatial distribution of Hansen's disease in an endemic area in Brazil. A sample of 808 selected from a universe of 1,293 cases was geocoded in Mossoró, Rio Grande do Norte, Brazil. Hansen's disease cases were not distributed randomly within the neighborhoods, with higher detection rates found in more populated districts. Cluster analysis identified two areas of high risk, one with a relative risk of 5.9 (P = 0.001) and the other 6.5 (P = 0.001). A significant relationship between the geographic distribution of disease and the social economic variables indicative of poverty was observed. Our study shows that the combination of GIS and spatial analysis can identify clustering of transmissible disease, such as Hansen's disease, pointing to areas where intervention efforts can be targeted to control disease.</p>  a1476-1645