The Effect of Diagnostic Misclassification on Spatial Statistics for Regional Data

Spatial epidemiological studies which assume perfect health status information can be biased if imperfect diagnostic tests have been used to obtain the health status of individuals in a population. This study investigates the effect of diagnostic misclassification on the spatial statistical methods commonly used to analyze regional health status data in spatial epidemiology. The methods considered here are: Moran's I to assess clustering in the data, a Gaussian random field model to estimate prevalence and the range and sill parameters of the semivariogram, and Kulldorff's spatial scan test to identify clusters. Various scenarios of diagnostic misclassification were simulated from a West Nile virus dead-bird surveillance program, and the results were evaluated. It was found that non-differential misclassification added random noise to the spatial pattern in observed data which created bias in the statistical results. However, when regional sample sizes were doubled, the effect from misclassification bias on the spatial statistics decreased.