Improving the power of chronic disease surveillance by incorporating residential history

Abstract

We present a global test for disease clustering with power to identify disturbances from the null population distribution which accounts for the lag time between the date of exposure and the date of diagnosis. Location at diagnosis is often used as a surrogate for the location of exposure; however, the causative exposure could have occurred at a previous address in a case's residential history. We incorporate models for the incubation distribution of a disease to weight each address into the residential history by the corresponding probability of the exposure occurring at that address. We then introduce a test statistic which uses these incubation-weighted addresses to test for a difference between the spatial distribution of the cases and the spatial distribution of the controls, or the background population. We follow the construction of the M statistic to evaluate the significance of these new distance distributions. Our results show that gains in detection power when residential history is accounted for are of such a degree that it might make the qualitative difference between the presence of spatial clustering being detected or not, thus making a strong argument for the inclusion of residential history in the analysis of such data.