OBJECTIVE: To analyze the environmental and socioeconomic risk factors of malaria transmission at municipality level, from 2010 to 2015, in the Brazilian Amazon. METHODS: The municipalities were stratified into high, moderate, and low transmission based on the annual parasite incidence. A multinomial logistic regression that compared low with medium transmission and low with high transmission was performed. For each category, three models were analyzed: one only with socioeconomic risk factors (Gini index, illiteracy, number of mines and indigenous areas); a second with the environmental factors (forest coverage and length of the wet season); and a third with all covariates (full model). RESULTS: The full model showed the best performance. The most important risks factors for high transmission were Gini index, length of the wet season and illiteracy, OR 2.06 (95%CI 1.19–3.56), 1.73 (95%CI 1.19–2.51) and 1.10 (95%CI 1.03–1.17), respectively. The medium transmission showed a weaker influence of the risk factors, being illiteracy, forest coverage and indigenous areas statistically significant but with marginal influence. CONCLUSIONS: As a disease of poverty, the reduction in wealth inequalities and, therefore, health inequalities, could reduce the transmission considerably. Besides, environmental risk factors as length of the wet season should be considered in the planning, prevention and control. Municipality-level and fine-scale analysis should be done together to improve the knowledge of the local dynamics of transmission.

Introduction:Malaria still is a public health problem in the Americas. In 2015, Brazil accounted for 37% of all cases in the Americas, and of these cases, 99.5% were located in the Brazilian Amazon. Despite the mobilization of resources from the Brazilian National Plan for Malaria Control, too many municipalities have high transmission levels. The objective of this study is to evaluate the local epidemiological profile of malaria and its trend between 2010 and 2015 in the Brazilian Amazon. This study also aims to recognize the epidemiological differences in the local temporo-spatial dynamics of malaria.Methods:Malaria data were stratified by the annual parasite incidence (API) over the six-year period and by municipality. We used the method of seasonal decomposition by Loess smoothing to capture trend, seasonal and irregular components. A generalized linear model was applied to quantify trends, and the Kruskal-Wallis Rank Sum was applied to test for seasonality significance.Results:The malaria API declined by 61% from 2010 to 2015, and there was a 40% reduction of municipalities with high transmission (determined as an API higher than 50). In 2015, 9.4% of municipalities had high transmission and included 62.8% of the total cases. The time-series analyses showed different incidence patterns by region after 2012; several states have minimized the effect of the seasonality in their incidence rates, thus achieving low rates of incidence. There were 13 municipalities with sustained high transmission that have become the principal focus of malaria control; these municipalities contained 40% of the cases between 2013 and 2015.Discussion:Brazil has achieved advances, but more sustained efforts are necessary to contain malaria resurgence. The use of malaria stratification has been demonstrated as a relevant tool to plan malaria programs more efficiently, and spatiotemporal analysis corroborates the idea that implementing any intervention in malaria should be stratified by time to interpret tendencies and by space to understand the local dynamics of the disease.

We recently developed a superhydrophobic cone-based method for the collection of mosquito excreta/feces (E/F) for the molecular xenomonitoring of vector-borne parasites showing higher throughput compared to the traditional approach. To test its field applicability, we used this platform to detect the presence of filarial and malaria parasites in two villages of Ghana and compared results to those for detection in mosquito carcasses and human blood. We compared the molecular detection of three parasites (Wuchereria bancrofti, Plasmodium falciparum and Mansonella perstans) in mosquito E/F, mosquito carcasses and human blood collected from the same households in two villages in the Savannah Region of the country. We successfully detected the parasite DNA in mosquito E/F from indoor resting mosquitoes, including W. bancrofti which had a very low community prevalence (2.5-3.8%). Detection in the E/F samples was concordant with detection in insect whole carcasses and human blood, and a parasite not vectored by mosquitoes was detected as well.Our approach to collect and test mosquito E/F successfully detected a variety of parasites at varying prevalence in the human population under field conditions, including a pathogen (M. perstans) which is not transmitted by mosquitoes. The method shows promise for further development and applicability for the early detection and surveillance of a variety of pathogens carried in human blood.

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.