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

type="main" xml:id="rssa12069-abs-0001"> 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. We propose a multivariate generalized linear geostatistical model that accommodates two sources of heterogeneity across surveys to correct for spatially structured bias in non-randomized 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 the prevalence of malaria conducted in Chikhwawa District, Southern Malawi, and discuss how this approach could inform hybrid sampling strategies that combine data from randomized and non-randomized surveys to make the most efficient use of all available data.