chapter  34
Sociospatial Epidemiology: Residential History Analysis
ByDavid C. Wheeler, Catherine A. Calder
Pages 22

Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 632 34.6 Bayesian Generalized Additive Model Analysis with Population

Mobility: A Simulation and Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 636 34.7 Spatial Scan Analysis with Residential Histories: An Example . . . . . . . . . . . . . . . 640 34.8 Future Directions: Cumulative Environmental Exposure Analysis . . . . . . . . . . . . 642 34.9 Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 644 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 644 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 644

In spatial analyses of chronic disease risk, the residential location at the time of diagnosis is often used as a surrogate for unknown environmental exposures, defined broadly to include household chemicals, pollutants, and radiation in environmental media, and lifestyle factors (see Chapters 2, 3, 6, 9, and 14). Residential location may be an appropriate proxy for relevant exposures in some situations; however, in other situations, it can be difficult to identify a true spatial signal in risk with only this surrogate for exposures. In particular, the combination of long disease latency and population mobility is a challenge for spatial epidemiological studies.