02561nas a2200325   4500000000100000008004100001260003500042653001100077653003500088653001100123653001400134653001200148653001500160653003000175653002600205653002600231653002100257100001400278700001600292700001400308700001700322700002500339700002500364245008000389856005100469300001000520490000700530520168400537022001402221       2012                            d  c2012 FebbScielospaSao Paulo 10aBrazil10aGeographic Information Systems10aHumans10aIncidence10aleprosy10aPrevalence10aResidence Characteristics10aSocioeconomic Factors10aSpace-Time Clustering10aUrban Population1 aCury MRCO1 aPaschoal VD1 aNardi SMT1 aChierotti AP1 aRodrigues Júnior AL1 aChiaravalloti-Neto F00aSpatial analysis of leprosy incidence and associated socioeconomic factors.  uhttp://www.scielosp.org/pdf/rsp/v46n1/3087.pdf  a110-80 v463 a<p><b>OBJECTIVE: </b>To identify clusters of the major occurrences of leprosy and their associated socioeconomic and demographic factors.</p><p><b>METHODS: </b>Cases of leprosy that occurred between 1998 and 2007 in São José do Rio Preto (southeastern Brazil) were geocodified and the incidence rates were calculated by census tract. A socioeconomic classification score was obtained using principal component analysis of socioeconomic variables. Thematic maps to visualize the spatial distribution of the incidence of leprosy with respect to socioeconomic levels and demographic density were constructed using geostatistics.</p><p><b>RESULTS: </b>While the incidence rate for the entire city was 10.4 cases per 100,000 inhabitants annually between 1998 and 2007, the incidence rates of individual census tracts were heterogeneous, with values that ranged from 0 to 26.9 cases per 100,000 inhabitants per year. Areas with a high leprosy incidence were associated with lower socioeconomic levels. There were identified clusters of leprosy cases, however there was no association between disease incidence and demographic density. There was a disparity between the places where the majority of ill people lived and the location of healthcare services.</p><p><b>CONCLUSIONS: </b>The spatial analysis techniques utilized identified the poorer neighborhoods of the city as the areas with the highest risk for the disease. These data show that health departments must prioritize politico-administrative policies to minimize the effects of social inequality and improve the standards of living, hygiene, and education of the population in order to reduce the incidence of leprosy.</p>  a1518-8787