Modelling age-heterogeneous Schistosoma haematobium and S. mansoni survey data via alignment factors
1 Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, P.O. Box, CH-4002 Basel, Switzerland
2 University of Basel, P.O. Box, CH-4003 Basel, Switzerland
Parasites & Vectors 2011, 4:142 doi:10.1186/1756-3305-4-142Published: 20 July 2011
Reliable maps of the geographical distribution, number of infected individuals and burden estimates of schistosomiasis are essential tools to plan, monitor and evaluate control programmes. Large-scale disease mapping and prediction efforts rely on compiled historical survey data obtained from the peer-reviewed literature and unpublished reports. Schistosomiasis surveys usually focus on school-aged children, whereas some surveys include entire communities. However, data are often reported for non-standard age groups or entire study populations. Existing geostatistical models ignore either the age-dependence of the disease risk or omit surveys considered too heterogeneous.
We developed Bayesian geostatistical models and analysed existing schistosomiasis prevalence data by estimating alignment factors to relate surveys on individuals aged ≤ 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 ≤ 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.