The online version of this article (doi:10.1186/1475-2875-11-385) contains supplementary material, which is available to authorized users.
The authors declare that they have no competing interests.
RJSM: prepared the dataset, performed data analysis and wrote the manuscript; SVN: designed, carried out the survey, prepared the dataset and contributed to the manuscript; AL: prepared the dataset and contributed to the manuscript; JCSF: contributed to study design and implementation and revised the manuscript; ACAC: helped design the survey and contributed to the manuscript. All authors read and approved the final manuscript.
Identifying and targeting hyper-endemic communities within meso-endemic areas constitutes an important challenge in malaria control in endemic countries such like Angola. Recent national and global predictive maps of malaria allow the identification and quantification of the population at risk of malaria infection in Angola, but their small-scale accuracy is surrounded by large uncertainties. To observe the need to develop higher resolution malaria endemicity maps a predictive risk map of malaria infection for the municipality of Dande (a malaria endemic area in Northern Angola) was developed and compared to existing national and global maps, the role of individual, household and environmental risk factors for malaria endemicity was quantified and the spatial variation in the number of children at-risk of malaria was estimated.
Bayesian geostatistical models were developed to predict small-scale spatial variation using data collected during a parasitological survey conducted from May to August 2010. Maps of the posterior distributions of predicted prevalence were constructed in a geographical information system.
Malaria infection was significantly associated with maternal malaria awareness, households with canvas roofing, distance to health care centre and distance to rivers. The predictive map showed remarkable spatial heterogeneity in malaria risk across the Dande municipality in contrast to previous national and global spatial risk models; large high-risk areas of malaria infection (prevalence >50%) were found in the northern and most eastern areas of the municipality, in line with the observed prevalence.
There is remarkable spatial heterogeneity of malaria burden which previous national and global spatial modelling studies failed to identify suggesting that the identification of malaria hot-spots within seemingly mesoendemic areas may require the generation of high resolution malaria maps. Individual, household and hydrological factors play an important role in the small-scale geographical variation of malaria risk in northern Angola. The results presented in this study can be used by provincial malaria control programme managers to help target the delivery of malaria control resources to priority areas in the Dande municipality.
Additional file 1: The data provided represent a description of the statistical notation used in model-based Bayesian geostatistical prediction and map of malaria endemicity for the study region derived from the global map of malaria.(DOC 396 KB)12936_2012_2534_MOESM1_ESM.doc
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- Finding malaria hot-spots in northern Angola: the role of individual, household and environmental factors within a meso-endemic area
Ricardo J Soares Magalhães
José Carlos Sousa-Figueiredo
Archie CA Clements
Susana Vaz Nery
- BioMed Central
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