Combining data from multiple spatially referenced prevalence surveys using generalized linear geostatistical models

TL;DR

This paper introduces a multivariate geostatistical model to combine multiple prevalence surveys, accounting for heterogeneity and bias, improving inference accuracy and informing efficient hybrid sampling strategies.

Contribution

It proposes a novel multivariate generalized linear geostatistical model that handles survey heterogeneity and bias, with a Monte Carlo maximum likelihood estimation method.

Findings

More precise prevalence estimates when accounting for heterogeneity.

Simulation shows improved inference accuracy.

Application to malaria surveys in Malawi demonstrates practical utility.

Abstract

Data from multiple prevalence surveys can provide information on common parameters of interest, which can therefore be estimated more precisely in a joint analysis than by separate analyses of the data from each survey. However, fitting a single model to the combined data from multiple surveys is inadvisable without testing the implicit assumption that all of the surveys are directed at the same inferential target. In this paper we propose a multivariate generalized linear geostatistical model that accommodates two sources of heterogeneity across surveys so as to correct for spatially structured bias in non-randomised surveys and to allow for temporal variation in the underlying prevalence surface between consecutive survey-periods. We describe a Monte Carlo maximum likelihood procedure for parameter estimation, and show through simulation experiments how accounting for the different sources of heterogeneity among surveys in a joint model leads to more precise inferences. We describe an application to multiple surveys of malaria prevalence conducted in Chikhwawa District, Southern Malawi, and discuss how this approach could inform hybrid sampling strategies that combine data from randomised and non-randomised surveys so as to make the most efficient use of all available data.