In the State of Pernambuco, Brazil, leprosy has been mainly an urban disease, with an uneven geographical distribution related at least partially to the way urban space has been occupied and transformed. Spatial analysis may thus become an important tool to establish an epidemiological surveillance system for leprosy. Homogeneous micro-areas were defined in the city of Olinda through the integration of two databases, the Population Census and SINAN, and through the use of digital maps and geoprocessing techniques. Census tracts were classified according to a social deprivation index (SDI), and micro-area homogeneity was based on similar values for this indicator. Cluster analysis (K-means) was used to define cut-offs between strata. The same procedure was repeated using the income variable only. When the association was tested between the mean SDI value and the mean leprosy detection rate for the period 1991-1996, the value obtained for r2 was 66.1% in the multiplicative model, increasing to 84.3% when the income variable was used. To define different intervention strategies, census tracts were regrouped in three levels of risk: high, moderate, and low. The methodology enabled the identification (within each health district) of groups and/or areas with different risk of leprosy, hence allowing for the definition of control measures.
BT - Cadernos de saude publica C1 - http://www.ncbi.nlm.nih.gov/pubmed/11679890?dopt=Abstract DA - 2001 Sep-Oct DO - 10.1590/s0102-311x2001000500016 IS - 5 J2 - Cad Saude Publica LA - por N2 -In the State of Pernambuco, Brazil, leprosy has been mainly an urban disease, with an uneven geographical distribution related at least partially to the way urban space has been occupied and transformed. Spatial analysis may thus become an important tool to establish an epidemiological surveillance system for leprosy. Homogeneous micro-areas were defined in the city of Olinda through the integration of two databases, the Population Census and SINAN, and through the use of digital maps and geoprocessing techniques. Census tracts were classified according to a social deprivation index (SDI), and micro-area homogeneity was based on similar values for this indicator. Cluster analysis (K-means) was used to define cut-offs between strata. The same procedure was repeated using the income variable only. When the association was tested between the mean SDI value and the mean leprosy detection rate for the period 1991-1996, the value obtained for r2 was 66.1% in the multiplicative model, increasing to 84.3% when the income variable was used. To define different intervention strategies, census tracts were regrouped in three levels of risk: high, moderate, and low. The methodology enabled the identification (within each health district) of groups and/or areas with different risk of leprosy, hence allowing for the definition of control measures.
PY - 2001 SP - 1153 EP - 62 T2 - Cadernos de saude publica TI - [Leprosy surveillance in Olinda, Brazil, using spatial analysis techniques]. TT - Debate sobre o artigo de Gilberto Câmara & Antônio Miguel Vieira Monteiro UR - http://www.scielosp.org/pdf/csp/v17n5/6309g.pdf VL - 17 SN - 0102-311X ER -