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Improving the power of chronic disease surveillance by incorporating residential history
Author(s) -
Manjourides Justin,
Pagano Marcello
Publication year - 2011
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4272
Subject(s) - statistics , statistic , cluster analysis , null hypothesis , scan statistic , population , econometrics , distribution (mathematics) , null distribution , demography , test statistic , computer science , geography , mathematics , medicine , statistical hypothesis testing , environmental health , mathematical analysis , sociology
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. Copyright © 2011 John Wiley & Sons, Ltd.