Author ORCID Identifier

Defense Date


Document Type


Degree Name

Doctor of Philosophy



First Advisor

David C. Wheeler


In many spatial epidemiologic studies, investigators use residential location at diagnosis as a surrogate for unknown environmental exposures or as a geographic basis for assigning measured exposures. Inherently, they make assumptions about the timing and location of pertinent exposures which may prove problematic when studying long latency diseases such as cancer.

In this work we explored how the association between environmental exposures and disease risk for long-latency health outcomes like cancer is affected by residential mobility. We used simulation studies conditioned on real data to evaluate the extent to which the commonly held assumption of no residential mobility 1) affected the ability of generalized additive models to detect areas of significantly elevated historic environmental exposure and 2) increased bias in the estimates of the relationship between environmental exposures and disease in a case-control study.

While the literature suggests that some researchers have begun to develop methods to incorporate historic locations in studies of health outcomes, a number of questions remain. One reason for the knowledge gap is that residential histories have not been collected in most U.S. epidemiologic studies. In our work we evaluated the impact of using public-record database generated histories to estimate the effects of exposure in lieu of using subject-reported addresses collected during a study. Finally, we evaluated the effect of environmental exposure on cancer risk in a case-control study using an approach that combined a multiple membership conditional autoregressive (CAR) model with an environmental exposure index for temporally correlated time-varying exposure assigned based on residential histories. We used this model in a data application to explain bladder cancer risk in the New England Bladder Cancer Study. We included a temporal arsenic exposure index in the model to assess a large number of correlated arsenic exposures.


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