Assuntos
Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Linfoma de Burkitt/diagnóstico , Hiperalgesia/etiologia , Protocolos de Quimioterapia Combinada Antineoplásica/administração & dosagem , Linfoma de Burkitt/tratamento farmacológico , Ciclofosfamida/administração & dosagem , Doxorrubicina/administração & dosagem , Feminino , França/etnologia , Guadalupe , Humanos , Metotrexato/administração & dosagem , Pessoa de Meia-Idade , Prednisona/administração & dosagem , Viagem , Clima Tropical , Vincristina/administração & dosagemRESUMO
Use of multispectral satellite data to predict arthropod-borne disease trouble spots is dependent on clear understandings of environmental factors that determine the presence of disease vectors. A blind test of remote sensing-based predictions for the spatial distribution of a malaria vector, Anopheles pseudopunctipennis, was conducted as a follow-up to two years of studies on vector-environmental relationships in Belize. Four of eight sites that were predicted to be high probability locations for presence of An. pseudopunctipennis were positive and all low probability sites (0 of 12) were negative. The absence of An. pseudopunctipennis at four high probability locations probably reflects the low densities that seem to characterize field populations of this species, i.e., the population densities were below the threshold of our sampling effort. Another important malaria vector, An. darlingi, was also present at all high probability sites and absent at all low probability sites. Anopheles darlingi, like An. pseudopunctipennis, is a riverine species. Prior to these collections at ecologically defined locations, this species was last detected in Belize in 1946.
Assuntos
Anopheles/fisiologia , Insetos Vetores/fisiologia , Malária/transmissão , Animais , Belize , Análise Discriminante , Feminino , Água Doce , Geografia , Processamento de Imagem Assistida por Computador , Probabilidade , Comunicações Via SatéliteRESUMO
A landscape approach using remote sensing and geographic information system (GIS) technologies was developed to discriminate between villages at high and low risk for malaria transmission, as defined by adult Anopheles albimanus abundance. Satellite data for an area in southern Chiapas, Mexico were digitally processed to generate a map of landscape elements. The GIS processes were used to determine the proportion of mapped landscape elements surrounding 40 villages where An. albimanus abundance data had been collected. The relationships between vector abundance and landscape element proportions were investigated using stepwise discriminant analysis and stepwise linear regression. Both analyses indicated that the most important landscape elements in terms of explaining vector abundance were transitional swamp and unmanaged pasture. Discriminant functions generated for these two elements were able to correctly distinguish between villages with high and low vector abundance, with an overall accuracy of 90%. Regression results found both transitional swamp and unmanaged pasture proportions to be predictive of vector abundance during the mid-to-late wet season. This approach, which integrates remotely sensed data and GIS capabilities to identify villages with high vector-human contact risk, provides a promising tool for malaria surveillance programs that depend on labor-intensive field techniques. This is particularly relevant in areas where the lack of accurate surveillance capabilities may result in no malaria control action when, in fact, directed action is necessary. In general, this landscape approach could be applied to other vector-borne diseases in areas where 1) the landscape elements critical to vector survival are known and 2) these elements can be detected at remote sensing scales.