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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 Magalhães1, Antonio Langa2, José Carlos Sousa-Figueiredo34, Archie CA Clements1 and Susana Vaz Nery2*

Author Affiliations

1 Infectious Disease Epidemiology Unit, School of Population Health, University of Queensland, Herston, Queensland, Australia

2 Centro de Investigação em Saúde em Angola, Caxito, Rua Direita do Caxito, Hospital Provincial do Bengo, Caxito, Angola

3 Disease Control Strategy Group, Liverpool School of Tropical Medicine, Liverpool, UK

4 Department of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK

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Malaria Journal 2012, 11:385  doi:10.1186/1475-2875-11-385

Published: 22 November 2012

Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

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.

Keywords:
Risk mapping; Malaria; Socio-economic factors; Physical environment; Disease control