Geographic information systems and logistic regression for high-resolution malaria risk mapping in a rural settlement of the southern Brazilian Amazon

Abstract

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.

Mapping malaria in municipalities of the Coffee Triangle region of Colombia using Geographic Information Systems (GIS)

“…Until now, studies using GIS in malaria in Latin America have been published mostly from Brazil [22][23][24][25] and Venezuela [26]. There are few previous reports specifically from Colombia [11,22].…”

“…Furthermore, based on this GIS approach, other studies linking the impact of ecological and social variables should be conducted in the region, not only describing the epidemiological distribution pattern by year but also assessing the influences of such variables on disease incidence [23][24][25]. Other demographical variables, such as age and ethnic groups, in countries such as Colombia would also be useful in the geographical assessment of malaria epidemiology.…”

“…Remote sensing (RS) and GIS have proven to be an innovative and important component in studies of public health and epidemiology and have been used for monitoring, surveillance and spatial modeling of diseases, such as malaria [24,27]. These tools provide examples of how Earth observation satellites can be used in studies of ecology and the prediction of malaria, and have contributed examples for the mapping of malaria vectors using mid-resolution RS-imagery [24,27].…”

“…There are few previous reports specifically from Colombia [11,22]. In the case of Brazil, GIS studies in malaria have focused on geographically characterizing the habitat suitability of the vector Anopheles darling [25], the patterns of malaria and its determinants in rural settlements [23], as well as the use of GIS analysis and logistic regression as a tool to identify and analyze the relative likelihood and its socio-environmental determinants of malaria rural settlements [24]. In Venezuela, the approach has been to develop GIS based on official cartography with thematic overlays depicting malaria distribution, socio-economic conditions, basic environmental information and specific features associated with the natural wetlands present in one area of malaria transmission [26].…”

“…These tools provide examples of how Earth observation satellites can be used in studies of ecology and the prediction of malaria, and have contributed examples for the mapping of malaria vectors using mid-resolution RS-imagery [24,27]. With precise year-to-year malaria epidemiological maps, such as those provided here, focused interventions and planning for integrated vector management and control would be carried out in the important municipalities for better management and control of the disease [28].…”