Studying relationships between environment and malaria incidence in Camopi (French Guiana) through the objective selection of buffer-based landscape characterisations

Abstract

BackgroundMalaria remains a major health problem in French Guiana, with a mean of 3800 cases each year. A previous study in Camopi, an Amerindian village on the Oyapock River, highlighted the major contribution of environmental features to the incidence of malaria attacks. We propose a method for the objective selection of the best multivariate peridomestic landscape characterisation that maximises the chances of identifying relationships between environmental features and malaria incidence, statistically significant and meaningful from an epidemiological point of view.MethodsA land-cover map, the hydrological network and the geolocalised inhabited houses were used to characterise the peridomestic landscape in eleven discoid buffers with radii of 50, 100, 200, 300, 400, 500, 600, 700, 800, 900 and 1000 metres. Buffer-based landscape characterisations were first compared in terms of their capacity to discriminate between sites within the geographic space and of their effective multidimensionality in variable space. The Akaike information criterion (AIC) was then used to select the landscape model best explaining the incidences of P. vivax and P. falciparum malaria. Finally, we calculated Pearson correlation coefficients for the relationships between environmental variables and malaria incidence, by species, for the more relevant buffers.ResultsThe optimal buffers for environmental characterisation had radii of 100 m around houses for P. vivax and 400 m around houses for P. falciparum. The incidence of P. falciparum malaria seemed to be more strongly linked to environmental features than that of P. vivax malaria, within these buffers. The incidence of P. falciparum malaria in children was strongly correlated with proportions of bare soil (r = -0.69), land under high vegetation (r = 0.68) and primary forest (r = 0.54), landscape division (r = 0.48) and the number of inhabited houses (r = -0.60). The incidence of P. vivax malaria was associated only with landscape division (r = 0.49).ConclusionsThe proposed methodology provides a simple and general framework for objective characterisation of the landscape to account for field observations. The use of this method enabled us to identify different optimal observation horizons around houses, depending on the Plasmodium species considered, and to demonstrate significant correlations between environmental features and the incidence of malaria.

Mapping a Knowledge-Based Malaria Hazard Index Related to Landscape Using Remote Sensing: Application to the Cross-Border Area between French Guiana and Brazil

“…As in Stefani et al [46], the assumption is also made that the transmission is domiciliary, making it possible to link incidence rates to the dwellings and the environment features surrounding them; due to the small magnitude of the study area (15 km 2 ), the spatial disparity of the incidence rates cannot be explained by climatic and/or major environmental disparities in space; finally, the children in the cohort share similar immunity levels and genetic susceptibility to malaria transmission [10]. However, according to Stefani et al [10], the number of inhabitants in the home, the use of tropical repellents and domestic insecticides, the interventions of the county mosquito control services, and going to bed before 7:00 pm are protective factors against malaria transmission and can partially explain incidence rate disparities.…”

“…While a discoidal window intuitively seems the most appropriate, the choice of the disk radius is not trivial. Stefani et al used a data-driven selection for the optimal radius of the discoïdal window to explain the malaria incidence rates in the village of Camopi, and concluded that the optimal radius was 400 m for the P. falciparum and 100 m for the P.vivax incidence rates [46]. However, it was shown that P. vivax incidence was poorly correlated to the landscape features in comparison with P. falciparum incidence, suggesting that the P. vivax relapses were not correctly identified [46].…”

“…Stefani et al used a data-driven selection for the optimal radius of the discoïdal window to explain the malaria incidence rates in the village of Camopi, and concluded that the optimal radius was 400 m for the P. falciparum and 100 m for the P.vivax incidence rates [46]. However, it was shown that P. vivax incidence was poorly correlated to the landscape features in comparison with P. falciparum incidence, suggesting that the P. vivax relapses were not correctly identified [46]. Therefore, only the P. falciparum incidence rates were used in the present study and the computation window was defined as a moving discoidal window with a 400 m radius.…”

“…All households in the village of Camopi were geolocalized with the global positioning system (GPS)-Magellan eXplorist™ 600-or by digitalization from the aerial photographs of BD-ORTHO ® [10,46]. For each NLHI candidate, the values were extracted for the locations of those households with at least one child less than seven year old included in the cohort, and then averaged at the hamlet level to explore the correlation between NLHI and P. falciparum incidence rates.…”

Housing Surroundings and Self-reported Cases of Malaria in two Communities in Central Region of Ghana

“…Plasmodium falciparum, is known to be the plasmodium species that causes almost all the cases of malaria in Ghana (3) . The incidence of Plasmodium falciparum seems to be linked to environmental factors (4) . Sites for mosquitoes (the vectors for malaria parasites) to breed include ditches, vegetation, and rainfall (5,6) which abounds in Ghana.…”