Maria Emília Bavia

Use of thermal and vegetation index data from earth observing satellites to evaluate the risk of schistosomiasis in Bahia, Brazil.

A geographic information system (GIS) was constructed using maps of regional agroclimatic features, vegetation indices and earth surface temperature data from environmental satellites, together with Schistosoma mansoni prevalence records from 270 municipalities including snail host distributions in Bahia, Brazil to study the spatial and temporal dynamics of infection and to identify environmental factors that influence the distribution of schistosomiasis. In an initial analysis, population density and duration (months) of the annual dry period were shown to be important determinants of disease. In cooperation with the National Institute of Spatial Research in Brazil (INPE), day and night imagery data covering the state of Bahia were selected at approximately bimonthly intervals in 1994 (six day-night pairs) from the data archives of the advanced very high resolution radiometer (AVHRR) sensor of the National Oceanic and Atmospheric Administration (NOAA)-11 satellite. A composite mosaic of these images was created to produce maps of: (1) average values between 0 and +1 of the normalized difference vegetation index (NDVI); and (2) average diurnal temperature differences (dT) on a scale of values between 0 and 15 degrees C. For each municipality, NDVI and dT were calculated for a 3x3 pixel (9 km(2) area) grid and analyzed for relationships to prevalence of schistosomiasis. Results showed a statistically significant relationship of prevalence to dT (rho=-0.218) and NDVI (rho=0.384) at the 95% level of confidence by the Spearman rank correlation coefficient. Results support use of NDVI, dT, dry period climatic stress factors and human population density for development of a GIS environmental risk assessment model for schistosomiasis in Brazil.

Estatística espacial de varredura na detecção de áreas de risco para a cisticercose bovina no estado da Bahia

In order to identify the spatial geographical distribution of the infected animals and measure the areas under risk of contracting bovine cysticercosis, spatial scan analysis was used to identify clusters of positive bovine for Cysticercus bovis recorded in the period from 2006 to 2007, from six slaughterhouses under the Federal Inspection Service. The number of slaughtered cattle was 825,951, of which (0.7%) 5,395 were diagnosed as positive for the disease, through post-mortem inspection. The spatial scan analysis through the likelihood ratio showed the spatial distribution of bovine cysticercosis concentrated in defined a geographic area less likely to have occurred by chance, with an estimated risk of 13.6. This specific area encompasses 101 municipalities that belong to Itapetinga, Litoral Sul, Medio Rio de Contas, Vitoria da Conquista and Extremo Sul Territories of Identification in Bahia State.

Análises espaciais na identificação das áreas de risco para a esquistossomose mansônica no município de Lauro de Freitas, Bahia, Brasil

The spread of schistosomiasis mansoni defies efforts by Brazils Unified National Health System, thus demonstrating the need to reassess endemic control programs in the country. The aim of this study was to demarcate geographic areas at risk of schistosomiasis in Lauro de Freitas, Bahia State, Brazil, and to establish the epidemiological and socioeconomic profile of the disease in this municipality (county). Kernel density estimator exploratory analysis was used for visual identification of areas at risk. Kulldorff & Nagarwallas spatial analysis was used to obtain statistically significant clusters and to measure risk. These technologies identified four risk areas for schistosomiasis. Clusters identified within the risk areas were characterized by lower socioeconomic conditions. Multiple correspondence analyses showed a distinct profile for positive patients in the primary cluster. The techniques employed here represent an important methodological acquisition for tracking and controlling schistosomiasis in Lauro de Freitas.

Detecção de aglomerados espaciais de casos de neoplasia mamária em cães no município de Salvador, Bahia

Spontaneous mammary tumors represent the most frequent type of cancer in canines, accounting for approximately 50% of all neoplasms. The majority of scientific papers cited in the literature are limited to non refined epidemiological data, without mentioning the trend of this disease in generating clusters in a given geographical area. In this context, this research aimed to create thematic maps of spatial distribution of mammary neoplasms in bitches and to identify disease clusters for the city of Salvador, Bahia. Trough the spatial analysis scanning, it was found that cases of breast cancer is not evenly distributed in the municipality. A significant primary cluster was detected (P>0,001) covering 67.3% of the studied cases. Considering the gap in literature available in this field, it is believed that such results will become very important, especially in leading to new studies, where intrinsic and extrinsic variables regarding the animal must be taken into consideration and analyzed for factors risk identification to formulate educational plans targeting the promotion of animal welfare.