Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Comparative Study
. 2011 Dec 13:10:65.
doi: 10.1186/1476-072X-10-65.

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

Affiliations
Comparative Study

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

Aurélia Stefani et al. Int J Health Geogr. .

Abstract

Background: Malaria 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.

Methods: A 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.

Results: The 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).

Conclusions: The 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.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Land-cover characterisation of the study site, with a magnification of the confluence of the Oypapock and Camopi Rivers.
Figure 2
Figure 2
Multivariate variograms of environmental variables as a function of buffer size. The envelope corresponds to the 95th and 5th quantiles of the distribution of 10000 variograms obtained after random permutations of the environmental data. Squares represent significant values, i.e. values below the 5th quantiles or above the 95th percentile. The vertical line corresponds to the distance beyond which the variogram is not interpretable, i.e. half the maximum distance between villages. The last graph shows a histogram of hamlet distances.
Figure 3
Figure 3
Mean absolute values of Pearson's correlation coefficients for all pairs of variables.
Figure 4
Figure 4
AICc values as a function of the buffer sizes and for (a) P. falciparum and (b) P. vivax. Filled circles correspond to the minimum values and numbers in brackets correspond to the AICc values for the best models. The horizontal dashed lines correspond to the AICc values for the "null" models (i.e. with only intercepts) above which the models are not valid.
Figure 5
Figure 5
Variance accounted for by the multiple regression models obtained with buffers of 100 m (P. vivax) and 400 m (P. falciparum).

Similar articles

Cited by

References

    1. Carme B, Ardillon V, Girod R, Grenier C, Joubert M, Djossou F, Ravachol F. [Update on the epidemiology of malaria in French Guiana] (in French) Med Trop. 2009;69:19–25. - PubMed
    1. Hustache S, Nacher M, Djossou F, Carme B. Malaria risk factors in Amerindian children in French Guiana. Am J Trop Med Hyg. 2007;76:619–625. - PubMed
    1. Floch H. La lutte antipaludique en Guyane française. L'anophélisme. Riv Malariol. 1955;24:57–65. - PubMed
    1. Mouchet J, Nadire-Galliot M, Poman JP, Claustre J, Bellony S. Le paludisme en Guyane. Les caractéristiques des différents foyers et la lutte antipaludique. Bull Soc Pathol Exot. 1989;82:393–405. - PubMed
    1. Pajot F-X, Le Pont F, Molez J-F, Degallier N. Agressivité d'Anopheles (Nyssorhuynchus) darlingi Root, 1926 (Diptera, Culicidae) en Guyane française. Cah ORSTOM, sér Ent méd et Parasitol. 1977;15:15–22.

Publication types

MeSH terms

LinkOut - more resources