Abstract
Many epidemiological studies use predicted air pollution exposures as surrogates for true air pollution levels. These predicted exposures contain exposure measurement error, yet simulation studies have typically found negligible bias in resulting health effect estimates. However, previous studies typically assumed a statistical spatial model for air pollution exposure, which may be oversimplified. We address this shortcoming by assuming a realistic, complex exposure surface derived from fine-scale (1 km × 1 km) remote-sensing satellite data. Using simulation, we evaluate the accuracy of epidemiological health effect estimates in linear and logistic regression when using spatial air pollution predictions from kriging and land use regression models. We examined chronic (long-term) and acute (short-term) exposure to air pollution. Results varied substantially across different scenarios. Exposure models with low out-of-sample R2 yielded severe biases in the health effect estimates of some models, ranging from 60% upward bias to 70% downward bias. One land use regression exposure model with >0.9 out-of-sample R2 yielded upward biases up to 13% for acute health effect estimates. Almost all models drastically underestimated the SEs. Land use regression models performed better in chronic effect simulations. These results can help researchers when interpreting health effect estimates in these types of studies.
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Acknowledgements
We greatly appreciate A. Lyapustin (NASA Goddard Space Flight Center, Baltimore, Maryland, USA) and Y. Wang (University of Maryland, Baltimore) for their work in providing the MAIAC data set for 2003. This work was supported by US EPA grant 834798 and NIH grants ES007142, ES016454, ES020871, and ES000002. This publication’s contents are solely the responsibility of the grantee and do not necessarily represent the official views of the US EPA. Further, US EPA does not endorse the purchase of any commercial products or services mentioned in the publication.
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Alexeeff, S., Schwartz, J., Kloog, I. et al. Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: insights into spatial variability using high-resolution satellite data. J Expo Sci Environ Epidemiol 25, 138–144 (2015). https://doi.org/10.1038/jes.2014.40
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DOI: https://doi.org/10.1038/jes.2014.40
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