Model-based Geostatistical Methods Enable Efficient Design and Analysis of Prevalence Surveys for Soil-Transmitted Helminth Infection and Other Neglected Tropical Diseases.

Maps of the geographical variation in prevalence play an important role in large-scale programmes for the control of Neglected Tropical Diseases. Pre-control mapping is needed to establish the appropriate control intervention in each area of the country in question. Mapping is also needed post-intervention to measure the success of control efforts. In the absence of comprehensive disease registries, mapping efforts can be informed by two kinds of data: empirical estimates of local prevalence obtained by testing individuals from a sample of communities within the geographical region of interest; digital images of environmental factors that are predictive of local prevalence. In this paper, we focus on the design and analysis of impact surveys, i.e. prevalence surveys that are conducted post-intervention with the aim of informing decisions on what further intervention, if any, is needed to achieve elimination of the disease as a public health problem. We show that geospatial statistical methods enable prevalence surveys to be designed and analysed as efficiently as possible so as to make best use of hard-won field data. We use three case-studies based on data from soil-transmitted helminth impact surveys in Kenya, Sierra Leone and Zimbabwe to compare the predictive performance of model-based geostatistics with methods described in current World Health Organisation guidelines. In all three cases, we find that model-based geostatistics substantially outperforms the current WHO guidelines, delivering improved precision for reduced field-sampling effort. We argue from experience that similar improvements will hold for prevalence mapping of other Neglected Tropical Diseases.