Epidemiological studies of traffic-related air pollution typically estimate exposures at residential locations only; however, if study subjects spend time away from home, exposure measurement error, and therefore bias, may be introduced into epidemiological analyses. For two study areas (Vancouver, British Columbia, and Southern California), we use paired residence- and mobility-based estimates of individual exposure to ambient nitrogen dioxide, and apply error theory to calculate bias for scenarios when mobility is not considered. In Vancouver, the mean bias was 0.84 (range: 0.79-0.89; SD: 0.01), indicating potential bias of an effect estimate toward the null by ∼ 16% when using residence-based exposure estimates. Bias was more strongly negative (mean: 0.70, range: 0.63-0.77, SD: 0.02) when the underlying pollution estimates had higher spatial variation (land-use regression versus monitor interpolation). In Southern California, bias was seen to become more strongly negative with increasing time and distance spent away from home (e.g., 0.99 for 0-2 h spent at least 10 km away, 0.66 for 10 ≥ h spent at least 40 km away). Our results suggest that ignoring daily mobility patterns can contribute to bias toward the null hypothesis in epidemiological studies using individual-level exposure estimates.
|Original language||English (US)|
|Number of pages||7|
|Journal||Journal of Exposure Science and Environmental Epidemiology|
|State||Published - Jan 2011|
Bibliographical noteFunding Information:
We thank Dr. Ben Armstrong for his guidance during the conceptual development of this paper. Partial funding was provided by Health Canada and by a grant from the US Environmental Protection Agency’s Science to Achieve Results (STAR) program. Although the research described in the article has been funded in part by the US Environmental Protection Agency’s STAR program through Grant RD-83362401-0, it has not been subjected to any EPA review and therefore does not necessarily reflect the views of the Agency, and no official endorsement should be inferred.
- air pollution
- measurement error
- space-time modeling.