Elsevier

Spatial Statistics

Volume 51, October 2022, 100679
Spatial Statistics

Combining school-catchment area models with geostatistical models for analysing school survey data from low-resource settings: Inferential benefits and limitations

https://doi.org/10.1016/j.spasta.2022.100679Get rights and content
Under a Creative Commons license
open access

Abstract

School-based sampling has been used to inform targeted responses for malaria and neglected tropical diseases. Standard geostatistical 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 to overcome the limitations of standard geostatistical methods by introducing a modelling framework that accounts 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 models that assume walking only, walking and bicycling and, walking and motorized transport. 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 residence location, 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. Future research in this area should focus on collecting information on the modes of transportation to school which can then be used to better parametrize the catchment area models.

Keywords

Catchment area models
Disease mapping
School survey
Missing locations
Model-based geostatistics
Prevalence

Availability of data and materials

Data that support the findings of this study are available at Population Health Dataverse, http://dx.doi.org/10.7910/DVN/UQLTO5 , generated gridded surfaces are located at https://doi.org/10.6084/m9.figshare.19804207.v1. The codes used in this analysis are available at https://github.com/giorgilancs/mbgmissinglocations.

Cited by (0)