TY - JOUR KW - Brazil KW - Endemic Diseases KW - Geographic Information Systems KW - Humans KW - leprosy KW - Risk Factors KW - Socioeconomic Factors KW - Time Factors AU - Queiroz JW AU - Dias G AU - Nobre ML AU - De Sousa Dias M AU - Araujo S AU - Barbosa JD AU - Bezerra da Trindade-Neto P AU - Blackwell J AU - Jeronimo S AB -

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 MossorĂ³, 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.

BT - The American journal of tropical medicine and hygiene C1 - http://www.ncbi.nlm.nih.gov/pubmed/20134009?dopt=Abstract DA - 2010 Feb DO - 10.4269/ajtmh.2010.08-0675 IS - 2 J2 - Am. J. Trop. Med. Hyg. LA - eng N2 -

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 MossorĂ³, 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.

PY - 2010 SP - 306 EP - 14 T2 - The American journal of tropical medicine and hygiene TI - Geographic information systems and applied spatial statistics are efficient tools to study Hansen's disease (leprosy) and to determine areas of greater risk of disease. UR - http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2813173/pdf/tropmed-82-306.pdf VL - 82 SN - 1476-1645 ER -