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Citation
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HERO ID
2520054
Reference Type
Journal Article
Title
Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: insights into spatial variability using high-resolution satellite data
Author(s)
Alexeeff, SE; Schwartz, J; Kloog, I; Chudnovsky, A; Koutrakis, P; Coull, BA
Year
2015
Is Peer Reviewed?
1
Journal
Journal of Exposure Science & Environmental Epidemiology
ISSN:
1559-0631
EISSN:
1559-064X
Volume
25
Issue
2
Page Numbers
138-144
Language
English
PMID
24896768
DOI
10.1038/jes.2014.40
Web of Science Id
WOS:000349741600002
URL
https://www.proquest.com/scholarly-journals/consequences-kriging-land-use-regression-pm2-5/docview/1664209193/se-2
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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 R(2) 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 R(2) 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.Journal of Exposure Science and Environmental Epidemiology advance online publication, 4 June 2014; doi:10.1038/jes.2014.40.
Keywords
air pollution; kriging; land use regression; measurement error; PM2.5; spatial models
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ISA-PM (2019)
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