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BACKGROUND: Malaria infection and disease exhibit microgeographic heterogeneity which if predictable could have implications for designing small-area intervention. Here, the space-time clustering of Plasmodium falciparum infections using data from repeat cross-sectional surveys in Gezira State, a low transmission area in northern Sudan, is investigated. METHODS: Data from cross-sectional surveys undertaken in January each year from 1999-2009 in 88 villages in the Gezira state were assembled. During each survey, about a 100 children between the ages two to ten years were sampled to examine the presence of P. falciparum parasites. In 2009, all the villages were mapped using global positioning systems. Cluster level data were analysed for spatial-only and space-time clustering using the Bernoulli model and the significance of clusters were tested using the Kulldorff scan statistic. RESULTS: Over the study period, 96,022 malaria slide examinations were undertaken and the P. falciparum prevalence was 8.6% in 1999 and by 2009 this had reduced to 1.6%. The cluster analysis showed the presence of one significant spatial-only cluster in each survey year and one significant space-time cluster over the whole study period. The primary spatial-only clusters in 10/11 years were either contained within or overlapped with the primary space-time cluster. CONCLUSION: The results of the study confirm the generally low malaria transmission in the state of Gezira and the presence of spatial and space-time clusters concentrated around a specific area in the south of the state. Improved surveillance data that allows for the analysis of seasonality, age and other risk factors need to be collected to design effective small area interventions as Gezira state targets malaria elimination.

Original publication

DOI

10.1186/1475-2875-9-172

Type

Journal article

Journal

Malar J

Publication Date

18/06/2010

Volume

9

Keywords

Child, Child, Preschool, Cross-Sectional Studies, Female, Geographic Information Systems, Geography, Humans, Malaria, Falciparum, Male, Microscopy, Models, Statistical, Plasmodium falciparum, Population Surveillance, Prevalence, Residence Characteristics, Risk Assessment, Risk Factors, Seasons, Space-Time Clustering, Sudan