Combining School-Catchment Area Models with Geostatistical Models for the Analysis of School Malaria Survey Data: Inferential Benefits and Limitations

School-based sampling has been used for decades in Sub Saharan Africa (SSA) to inform targeted responses for malaria and neglected tropical diseases. Standard model-based geostatistical (MBG) methods for mapping disease prevalence use the school location to model spatial correlation, which is questionable since exposure to the disease is more likely to occur in the residential location. In this paper, we propose a modelling framework that allows us to account for the uncertainty in the location of the residence of the students. By using cost distance and cost allocation models to define spatial accessibility and in absence of any information on the travel mode of students to school, we consider three school catchment area (SCA) models that assume walking only (W), walking and bicycling (WB) and, walking and use of motorized transport (WM). We illustrate the use of this approach using two case studies of malaria in Kenya and compare it with the standard approach that uses the school locations to build geostatistical models. We argue that the proposed modelling framework presents several inferential benefits, such as the ability to combine data from multiple surveys some of which may also record the location of residence, and to deal with ecological bias when estimating the effects of malaria risk factors. However, our results show that invalid assumptions on the modes of travel to school can worsen the predictive performance of geostatistical models. We thus conclude that in order to fully exploit the advantages presented by this modelling framework, school surveys, whenever possible, should collect information on the modes of transportation to school which can then be used to better parametrize the catchment area models.

About Emanuele Giorgi

Emanuele is an Associate Professor in Biostatistics at Lancaster University and the Head of the Centre of Health Informatics, Computing and Statistics. His research interests lie in the development of novel spatial and spatio-temporal methods for disease mapping, with a special focus on tropical diseases.