  Diagnosis is often based on the exceedance or not of continuous health
indicators of a predefined cut-off value, so as to classify patients into
positives and negatives for the disease under investigation. In this paper, we
investigate the effects of dichotomization of spatially-referenced continuous
outcome variables on geostatistical inference. Although this issue has been
extensively studied in other fields, dichotomization is still a common practice
in epidemiological studies. Furthermore, the effects of this practice in the
context of prevalence mapping have not been fully understood. Here, we
demonstrate how spatial correlation affects the loss of information due to
dichotomization, how linear geostatistical models can be used to map disease
prevalence and thus avoid dichotomization, and finally, how dichotomization
affects our predictive inference on prevalence. To pursue these objectives, we
develop a metric, based on the composite likelihood, which can be used to
quantify the potential loss of information after dichotomization without
requiring the fitting of Binomial geostatistical models. Through a simulation
study and two applications on disease mapping in Africa, we show that, as
thresholds used for dichotomization move further away from the mean of the
underlying process, the performance of binomial geostatistical models
deteriorates substantially. We also find that dichotomization can lead to the
loss of fine scale features of disease prevalence and increased uncertainty in
the parameter estimates, especially in the presence of a large noise to signal
ratio. These findings strongly support the conclusions from previous studies
that dichotomization should be always avoided whenever feasible.
