Spatial Modeling of Air Pollution in Studies of Its Short-Term Health Effects
Article first published online: 11 JAN 2010
© 2010, The International Biometric Society
Volume 66, Issue 4, pages 1238–1246, December 2010
How to Cite
Lee, D. and Shaddick, G. (2010), Spatial Modeling of Air Pollution in Studies of Its Short-Term Health Effects. Biometrics, 66: 1238–1246. doi: 10.1111/j.1541-0420.2009.01376.x
- Issue published online: 11 JAN 2010
- Article first published online: 11 JAN 2010
- Received March 2009. Revised October 2009. Accepted October 2009.
- Air pollution and health;
- Bayesian space–time modeling;
- Change of support problem
Summary In studies that estimate the short-term effects of air pollution on health, daily measurements of pollution concentrations are often available from a number of monitoring locations within the study area. However, the health data are typically only available in the form of daily counts for the entire area, meaning that a corresponding single daily measure of pollution is required. The standard approach is to average the observed measurements at the monitoring locations, and use this in a log-linear health model. However, as the pollution surface is spatially variable this simple summary is unlikely to be an accurate estimate of the average pollution concentration across the region, which may lead to bias in the resulting health effects. In this article, we propose an alternative approach that jointly models the pollution concentrations and their relationship with the health data using a Bayesian spatio-temporal model. We compare this approach with the simple spatial average using a simulation study, by investigating the impact of spatial variation, monitor placement, and measurement error in the pollution data. An epidemiological study from Greater London is then presented, which estimates the relationship between respiratory mortality and four different pollutants.