Malformações congênitas em municípios de grande utilização de agrotóxicos em Mato Grosso, BrasilCongenital defects in the cities with high use of pesticides in the state of Mato Grosso, Brazil (OR=1,66, IC95% 0,79) e quarto quartil (OR=1,88, IC95% 1,24) do perío-do pós-fecundação e no quarto quartil (OR=2,04, IC95%1,56)

BackgroundIn Brazil, 99% of the cases of malaria are concentrated in the Amazon region, with high level of transmission. The objectives of the study were to use geographic information systems (GIS) analysis and logistic regression as a tool to identify and analyse the relative likelihood and its socio-environmental determinants of malaria infection in the Vale do Amanhecer rural settlement, Brazil.MethodsA GIS database of georeferenced malaria cases, recorded in 2005, and multiple explanatory data layers was built, based on a multispectral Landsat 5 TM image, digital map of the settlement blocks and a SRTM digital elevation model. Satellite imagery was used to map the spatial patterns of land use and cover (LUC) and to derive spectral indices of vegetation density (NDVI) and soil/vegetation humidity (VSHI). An Euclidian distance operator was applied to measure proximity of domiciles to potential mosquito breeding habitats and gold mining areas. The malaria risk model was generated by multiple logistic regression, in which environmental factors were considered as independent variables and the number of cases, binarized by a threshold value was the dependent variable.ResultsOut of a total of 336 cases of malaria, 133 positive slides were from inhabitants at Road 08, which corresponds to 37.60% of the notifications. The southern region of the settlement presented 276 cases and a greater number of domiciles in which more than ten cases/home were notified. From these, 102 (30.36%) cases were caused by Plasmodium falciparum and 174 (51.79%) cases by Plasmodium vivax. Malaria risk is the highest in the south of the settlement, associated with proximity to gold mining sites, intense land use, high levels of soil/vegetation humidity and low vegetation density.ConclusionsMid-resolution, remote sensing data and GIS-derived distance measures can be successfully combined with digital maps of the housing location of (non-) infected inhabitants to predict relative likelihood of disease infection through the analysis by logistic regression. Obtained findings on the relation between malaria cases and environmental factors should be applied in the future for land use planning in rural settlements in the Southern Amazon to minimize risks of disease transmission.

BackgroundIn Brazil, 99% of malaria cases are concentrated in the Amazon, and malaria's spatial distribution is commonly associated with socio-environmental conditions on a fine landscape scale. In this study, the spatial patterns of malaria and its determinants in a rural settlement of the Brazilian agricultural reform programme called "Vale do Amanhecer" in the northern Mato Grosso state were analysed.MethodsIn a fine-scaled, exploratory ecological study, geocoded notification forms corresponding to malaria cases from 2005 were compared with spectral indices, such as the Normalized Difference Vegetation Index (NDVI) and the third component of the Tasseled Cap Transformation (TC_3) and thematic layers, derived from the visual interpretation of multispectral TM-Landsat 5 imagery and the application of GIS distance operators.ResultsOf a total of 336 malaria cases, 102 (30.36%) were caused by Plasmodium falciparum and 174 (51.79%) by Plasmodium vivax. Of all the cases, 37.6% (133 cases) were from residents of a unique road. In total, 276 cases were reported for the southern part of the settlement, where the population density is higher, with notification rates higher than 10 cases per household. The local landscape mostly consists of open areas (38.79 km²). Training forest occupied 27.34 km² and midsize vegetation 7.01 km². Most domiciles with more than five notified malaria cases were located near areas with high NDVI values. Most domiciles (41.78%) and malaria cases (44.94%) were concentrated in areas with intermediate values of the TC_3, a spectral index representing surface and vegetation humidity.ConclusionsEnvironmental factors and their alteration are associated with the occurrence and spatial distribution of malaria cases in rural settlements.

Descreveu-se a evolução temporal e espacial de malária em Mato Grosso, discriminadas em períodos de 1980-1985; 1986-1991; 1992-1997 e 1998-2003, distribuídas por microrregião homogênea. O índice parasitário anual do estado cresceu até 1992, reduzindo para 1,9 casos/mil habitantes em 2003; o coeficiente de mortalidade e a taxa de letalidade foram maiores nos anos de 1980 a 1989. Das 22 microrregiões, 13 apresentaram IPA inferior a 10 casos/1.000 habitantes em todos os períodos, ocorrendo concentração de casos nas microrregiões de Colíder, Alta Floresta, Aripuanã e Alto Guaporé. Em 2003, apenas a microrregião de Aripuanã persistia com IPA superior a 50 casos/1.000 habitantes. As microrregiões de Colíder, em 1983, 1985 a 1988 e 1990 e Alta Floresta, em 1991, apresentaram óbitos acima de 50/100.000 habitantes, sendo a maioria do sexo masculino, na faixa etária de 20 a 49 anos. A distribuição da doença por microrregiões evidenciou que a malária é predominantemente focal.

The goal of this study was to stratify priority areas for malaria control in the State of Mato Grosso, Brazil, based on spatial analysis. The variables used were: Annual Parasite Index (API), Plasmodium falciparum/Plasmodium vivax ratio, population variation, number of families settled, and percent of deforested area. The Moran's I and Local Moran Test were applied, visualized with the Box Map and Moran Map, for 1986- 1991, 1992-1997, and 1998-2003. Box Map identified areas with high, low, and intermediate priority for control, and Moran Map identified municipalities with significant autocorrelation. In the high priority area, located in the North of Mato Grosso, malaria incidence decreased drastically despite the increase in the number of municipalities from the first to the last period. Other municipalities were added to the lower priority area, from the Southeast, Southwest, and Central-South of the State. The intermediate priority area was located along the border with neighboring States and municipalities classified as high and low priority. Spatial analysis showed the importance of the neighboring phenomenon between municipalities in defining priority areas, thus highlighting the technique's advantages for use in malaria control and surveillance.