title: [Temporal-spatial risk model to identify areas at high-risk for occurrence of dengue fever].
abstract: OBJECTIVE
  To apply the temporal-spatial model to assess high-risk areas for the occurrence of dengue fever.
  
  
  METHODS
  A total of 11,989 confirmed, autochthonous dengue fever cases, geocoded by address in the city of São José do Rio Preto (Southeastern Brazil), between September of 2001 and August of 2006, were included in the study. Frequency, duration and intensity indices were used to assess the severity and magnitude of transmission. The local indicator of spatial association was adopted to identify significant spatial clusters (p-value<0.05). The values of the three indices were considered high in a spatial unit when their standard values were positive and the respective local indicator of spatial association values were significant.
  
  
  RESULTS
  Of all the geocoded dengue fever cases, 38.1% occurred in the urban spatial units, classified as highest-risk: 19.4% in 2001-2002, 13.9% in 2002-2003, 2.8% in 2003-2004, 16.7% in 2004-2005, and 21.3% in 2005-2006. The utilization of three risk measures enabled to identify higher-risk areas for the occurrence of dengue fever, concentrated in the city's northern region. Even though case notification data are subject to bias, this information is available in the health services and can lead to important conclusions, recommendations and hypotheses.
  
  
  CONCLUSIONS
  The non-complex, notification-based procedures adopted in the study could be routinely used by services that are responsible for dengue fever surveillance and control to identify high-risk areas.
authors.affiliation: Faculdade de Medicina de São José do Rio Preto, São José do Rio Preto, SP, Brasil.
authors.forename: Bruno
authors.initials: B
authors.lastname: Galli
authors.forename: Francisco
authors.initials: F
authors.lastname: Chiaravalloti Neto
mesh_terms: D000818:Animals; D001938:Brazil / Q000453:epidemiology; D003627:Data Interpretation, Statistical; D003715:Dengue / Q000453:epidemiology* / Q000517:prevention & control / Q000635:transmission; D018563:Disease Notification; D040362:Geographic Information Systems; D006801:Humans; D008962:Models, Theoretical; D011159:Population Surveillance / Q000379:methods*; D012307:Risk Factors; D012621:Seasons; D012959:Socioeconomic Factors; D013029:Space-Time Clustering