Space–time clustering of case–control data with residential histories: insights into empirical induction periods, age-specific susceptibility, and calendar year-specific effects
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Our research group recently developed Q-statistics for evaluating space–time clustering in case–control studies with residential histories. This technique relies on time-dependent nearest-neighbor relationships to examine clustering at any moment in the life-course of the residential histories of cases relative to that of controls. In addition, in place of the widely used null hypothesis of spatial randomness, each individual’s probability of being a case is based instead on his/her risk factors and covariates. In this paper, we extend this approach to illustrate how alternative temporal orientations (e.g., years prior to diagnosis/recruitment, participant’s age, and calendar year) influence a spatial clustering pattern. These temporal orientations are valuable for shedding light on the duration of time between clustering and subsequent disease development (known as the empirical induction period), and for revealing age-specific susceptibility windows and calendar year-specific effects. An ongoing population-based bladder cancer case–control study is used to demonstrate this approach. Data collection is currently incomplete and therefore no inferences should be drawn; we analyze these data to demonstrate these novel methods. Maps of space–time clustering of bladder cancer cases are presented using different temporal orientations while accounting for covariates and known risk factors. This systematic approach for evaluating space–time clustering has the potential to generate novel hypotheses about environmental risk factors and provides insights into empirical induction periods, age-specific susceptibility, and calendar year-specific effects.
KeywordsGIS STIS Bladder cancer Human mobility
We thank the participants for taking part in this study. We thank Dr. Jerome Nriagu of the University of Michigan for sharing the bladder cancer case–control dataset. Mr. Andy Kaufmann, Ms. Gillian Avruskin, and Dr. Pierre Goovaerts assisted with software development, database management, and construction of spatial null hypotheses. This research was funded by grants R43CA117171, R01CA096002, and R44CA092807 from the National Cancer Institute (NCI). Development of the STIS software was funded by grants R43 ES10220 from the National Institutes of Environmental Health Sciences (NIEHS) and R01 CA92669 from NCI. The views expressed in this publication are those of the researchers and do not necessarily represent those of NCI or NIEHS.
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