Abstract
BACKGROUND: Malaria is major public health problem in Senegal. In some parts of the country, it occurs almost permanently with a seasonal increase during the rainy season. There is evidence to suggest that the prevalence of malaria in Senegal has decreased considerably during the past few years. Recent data from the Senegalese National Malaria Control Programme (NMCP) indicates that the number of malaria cases decrease from 1,500,000 in 2006 to 174,339 in 2010. With the decline of malaria morbidity in Senegal, the characterization of the new epidemiological profile of this disease is crucial for public health decision makers. METHODS: SaTScan™ software using the Kulldorf method of retrospective space-time permutation and the Bernoulli purely spatial model was used to identify malaria clusters using confirmed malaria cases in 74 villages. ArcMAp was used to map malaria hotspots. Logistic regression was used to investigate risk factors for malaria hotspots in Keur Soce health and demographic surveillance site. RESULTS: A total of 1,614 individuals in 440 randomly selected households were enrolled. The overall malaria prevalence was 12%. The malaria prevalence during the study period varied from less than 2% to more than 25% from one village to another. The results showed also that rooms located between 50 m to 100 m away from livestock holding place [adjusted O.R = 0.7, P = 0.044, 95% C.I (1.02 - 7.42)], bed net use [adjusted O.R = 1.2, P = 0.024, 95% C.I (1.02 -1.48)], are good predictors for malaria hotspots in the Keur Soce health and demographic surveillance site. The socio economic status of the household also predicted on hotspots patterns. The less poor household are 30% less likely to be classified as malaria hotspots area compared to the poorest household [adjusted O.R = 0.7, P = 0.014, 95% C.I (0.47 - 0.91)]. CONCLUSION: The study investigated risk factors for malaria hotspots in small communities in the Keur Soce site. The result showed considerable variation of malaria prevalence between villages which cannot be detected in aggregated data. The data presented in this paper are the first step to understanding malaria in the Keur Soce site from a micro-geographic perspective.