Abstract
Malaria is still the major parasitic disease in the world, with approximately 438,000 deaths in 2015. Environmental risk factors (ERF) have been widely studied, however, there are discrepancies in the results about their influence on malaria transmission. Recently, papers have been published about geospatial analysis of ERF of malaria to explain why malaria varies from place to place. Our primary objective was to identify the environmental variables most used in the geospatial analysis of malaria transmission. The secondary objective was to identify the geo-analytic methods and techniques, as well as geo-analytic statistics commonly related to ERF and malaria. We conducted a systematized review of articles published from January 2004 to March 2015, within Web of Science, Pubmed and LILACS databases. Initially 676 articles were found, after inclusion and exclusion criteria, 29 manuscripts were selected. Temperature, land use and land cover, surface moisture and vector breeding site were the most frequent included variables. As for geo-analytic methods, geostatistical models with Bayesian framework were the most applied. Kriging interpolations, Geographical Weighted Regression as well as Kulldorff’s spatial scan were the techniques more widely used. The main objective of many of these studies was to use these methods and techniques to create malaria risk maps. Spatial analysis performed with satellite images and georeferenced data are increasing in relevance due to the use of remote sensing and Geographic Information System. The combination of these new technologies identifies ERF more accurately, and the use of Bayesian geostatistical models allows a wide diffusion of malaria risk maps. It is known that temperature, humidity vegetation and vector breeding site play a critical role in malaria transmission; however, other environmental risk factors have also been identified. Risk maps have a tremendous potential to enhance the effectiveness of malaria-control programs.
Highlights
Despite substantial progress having made, malaria was still a major global health problem in 2015 and the cause of about 438,000 deaths, mostly in the World Health Organization (WHO) African Region (90%) [1]
Since malaria varies from place to place and understanding certain ecological conditions may lead to the presence of malaria, in the recent years, there has been an increasing amount of literature on spatial analysis of Environmental risk factors (ERF) of malaria transmission in order to explain this variation
ERF were checked in order to extract only those that were statistically significant since the authors usually started with a large number of ERF and after applied some statistical analysis, to check for collinearity and for statistical significance, a reduced number of variables least were left in the final spatial model
Summary
Despite substantial progress having made, malaria was still a major global health problem in 2015 and the cause of about 438,000 deaths, mostly in the World Health Organization (WHO) African Region (90%) [1]. Environmental risk factors (ERF) of malaria have been widely studied, there are discrepancies on the results about their influence on malaria transmission, especially at the local level [2]. There has been little discussion about the geo-analytic methods and techniques used to model the ERF. Notwithstanding, papers about the importance of the malaria ERF have lately been published to claim that little attention has been giving to environmental variables selection [2] [3]. Advances in technology specially in Geographic Information System (GIS) and remote sensing facilitate researchers mapping the results of their models and help in the control, planning, implementation, and evaluation of malaria risk, at geographic scales ranging from local to global as well as to identify where most vulnerable populations are [3]
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