A Geostatistical Framework for Combining Spatially Referenced Disease Prevalence Data from Multiple Diagnostics

TL;DR

This paper introduces a geostatistical framework for combining spatial disease prevalence data from multiple diagnostics, addressing over-dispersion and spatial correlation to improve disease mapping accuracy.

Contribution

It develops a novel joint modeling approach for multiple diagnostics that accounts for spatial correlation and diagnostic-specific residual variation.

Findings

Joint modeling improves prevalence prediction accuracy.

Accounting for spatial correlation reduces bias in estimates.

Framework applied successfully to malaria and Loa loa case studies.

Abstract

Multiple diagnostic tests are often used due to limited resources or because they provide complementary information on the epidemiology of a disease under investigation. Existing statistical methods to combine prevalence data from multiple diagnostics ignore the potential over-dispersion induced by the spatial correlations in the data. To address this issue, we develop a geostatistical framework that allows for joint modelling of data from multiple diagnostics by considering two main classes of inferential problems: (1) to predict prevalence for a gold-standard diagnostic using low-cost and potentially biased alternative tests; (2) to carry out joint prediction of prevalence from multiple tests. We apply the proposed framework to two case studies: mapping Loa loa prevalence in Central and West Africa, using miscroscopy and a questionnaire-based test called RAPLOA; mapping Plasmodium falciparum malaria prevalence in the highlands of Western Kenya using polymerase chain reaction and a rapid diagnostic test. We also develop a Monte Carlo procedure based on the variogram in order to identify parsimonious geostatistical models that are compatible with the data. Our study highlights (i) the importance of accounting for diagnostic-specific residual spatial variation and (ii) the benefits accrued from joint geostatistical modelling so as to deliver more reliable and precise inferences on disease prevalence.