%0 Journal Article
%T Modelling age-heterogeneous Schistosoma haematobium and S. mansoni survey data via alignment factors
%A Nadine Schur
%A J¨¹rg Utzinger
%A Penelope Vounatsou
%J Parasites & Vectors
%D 2011
%I BioMed Central
%R 10.1186/1756-3305-4-142
%X We developed Bayesian geostatistical models and analysed existing schistosomiasis prevalence data by estimating alignment factors to relate surveys on individuals aged ¡Ü 20 years with surveys on individuals aged > 20 years and entire communities. Schistosomiasis prevalence data for 11 countries in the eastern African region were extracted from an open-access global database pertaining to neglected tropical diseases. We assumed that alignment factors were constant for the whole region or a specific country.Regional alignment factors indicated that the risk of a Schistosoma haematobium infection in individuals aged > 20 years and in entire communities is smaller than in individuals ¡Ü 20 years, 0.83 and 0.91, respectively. Country-specific alignment factors varied from 0.79 (Ethiopia) to 1.06 (Zambia) for community-based surveys. For S. mansoni, the regional alignment factor for entire communities was 0.96 with country-specific factors ranging from 0.84 (Burundi) to 1.13 (Uganda).The proposed approach could be used to align inherent age-heterogeneity between school-based and community-based schistosomiasis surveys to render compiled data for risk mapping and prediction more accurate.An estimated 200 million individuals are infected with Schistosoma spp. in Africa, and yet schistosomiasis is often neglected [1]. The global strategy to control schistosomiasis and several other neglected tropical diseases (NTDs) is the repeated large-scale administration of anthelminthic drugs to at-risk populations, an approach phrased 'preventive chemotherapy' [2,3]. The design, implementation, monitoring and evaluation of schistosomiasis control activities require knowledge of the geographical distribution, number of infected people and disease burden at high spatial resolution.In the absence of contemporary surveys, large-scale empirical risk mapping heavily relies on analyses of historical survey data. For example, Brooker et al. [4] compiled survey data and presented schistosomiasis
%U http://www.parasitesandvectors.com/content/4/1/142