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Open Access Highly Accessed Research

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

Elaine Cristina de Oliveira1, Emerson Soares dos Santos2, Peter Zeilhofer2, Reinaldo Souza-Santos3 and Marina Atanaka-Santos4*

Author Affiliations

1 Epidemiological Surveillance, Health Secretary of Mato Grosso, Rua D, Political Administrative Center, Cuiabá, Mato Grosso State 78.050-970, Brazil

2 Department of Geography, Federal University of Mato Grosso, Av. Fernando Corrêa, Cuiabá, Mato Grosso State 78.060-900, Brazil

3 Department of Endemic Disease, Brazilian National School of Public Health, Oswaldo Cruz Foundation, Rua Leopoldo Bulhões, 1480, Rio de Janeiro, Rio de Janeiro State 21.041-210, Brazil

4 Institute of Public Health, Federal University of Mato Grosso, Av. Fernando Corrêa, Cuiabá, Mato Grosso State 78.060-900, Brazil

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Malaria Journal 2013, 12:420  doi:10.1186/1475-2875-12-420

Published: 15 November 2013

Abstract

Background

In 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.

Methods

A 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.

Results

Out 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.

Conclusions

Mid-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.

Keywords:
Malaria; Remote sensing; Spatial analysis; Epidemiology