ABSTRACT A crucial step in an epidemiological study of the effects of air pollution is to accurately quantify exposure of the population. In this paper, we investigate the sensitivity of the

health effects estimates associated with short-term exposure to fine particulate matter with respect to three potential metrics for daily exposure: ambient monitor data, estimated values

from a deterministic atmospheric chemistry model, and stochastic daily average human exposure simulation output. Each of these metrics has strengths and weaknesses when estimating the

association between daily changes in ambient exposure to fine particulate matter and daily emergency hospital admissions. Monitor data is readily available, but is incomplete over space and

case study of the association between fine particulate matter and emergency hospital admissions for respiratory cases for the Medicare population across three counties in New York. Of

particular interest is to quantify the impact and/or benefit to using the stochastic human exposure output to measure ambient exposure to fine particulate matter. Results indicate that the

stochastic human exposure simulation output indicates approximately the same increase in the relative risk associated with emergency admissions as using a chemistry model or monitoring data

as exposure metrics. However, the stochastic human exposure simulation output and the atmospheric chemistry model both bring additional information, which helps to reduce the uncertainly in

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Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS MONITORING VS. MODELED EXPOSURE DATA IN TIME-SERIES STUDIES OF AMBIENT AIR POLLUTION AND ACUTE HEALTH OUTCOMES Article 20 May

2022 SOURCES OF PARTICULATE-MATTER AIR POLLUTION AND ITS OXIDATIVE POTENTIAL IN EUROPE Article 18 November 2020 DISPARITIES IN AIR POLLUTION ATTRIBUTABLE MORTALITY IN THE US POPULATION BY

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Nonlinear Models: A Modern Perspective_ 2nd edn. Chapman & Hall/CRC, : Boca Raton, FL 2006. Book  Google Scholar  Download references ACKNOWLEDGEMENTS We acknowledge Howard Chang at

Emory University and Laura Boehm at North Carolina State University for their contributions in modeling methodology and data management. We also acknowledge Drs. Valeria Garcia and Halûk

Özkaynak with the US Environmental Protection Agency’s National Exposure Research Laboratory, for providing the CMAQ output data and for guidance with the application of the SHEDS-PM model

for New York City, respectively. Elizabeth Mannshardt was supported as a Post Doctoral Research Scholar through the National Science Foundation’s Collaborative Research: RNMS Statistical

Methods for Atmospheric and Oceanic Sciences under Grant No. DMS-1107046. Katarina Sucic was supported in part by Harvard University’s Statistical Methods for Population Health Research on

Chemical Mixtures, Grant No. 114346-5053742. Montserrat Fuentes was sponsored in part by the National Institutes of Health under Grant No. 2R01ES014843-04A1. Francesca Dominici was supported

in part by the National Institutes for Health (NIH), Grant No. R01ES019560, NIH/National Institute of Environmental Health Sciences, Grant No. R01ES019955, and the Environmental Protection

Agency, Grants No. RD83479801 and No. R834894. H. Christopher Frey and Wan Jiao were sponsored by the National RD 83386301. DISCLAIMER This paper has not been subject to review by the NSF,

NIH or EPA, and the authors are solely responsible for its content. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Statistics, North Carolina State University, 5109 SAS Hall,

2311 Stinson Drive, Raleigh, North Carolina, USA Elizabeth Mannshardt, Katarina Sucic, Brian Reich & Montserrat Fuentes * Department of Civil Construction and Environmental Engineering,

North Carolina, State University, Raleigh, North Carolina, USA Wan Jiao & H Christopher Frey * Harvard School of Public Health, Boston, Massachusetts, USA Francesca Dominici Authors *

Elizabeth Mannshardt View author publications You can also search for this author inPubMed Google Scholar * Katarina Sucic View author publications You can also search for this author

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Correspondence to Elizabeth Mannshardt. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no conflict of interest. RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS

ARTICLE CITE THIS ARTICLE Mannshardt, E., Sucic, K., Jiao, W. _et al._ Comparing exposure metrics for the effects of fine particulate matter on emergency hospital admissions. _J Expo Sci

Environ Epidemiol_ 23, 627–636 (2013). https://doi.org/10.1038/jes.2013.39 Download citation * Received: 19 October 2012 * Accepted: 28 April 2013 * Published: 14 August 2013 * Issue Date:

November 2013 * DOI: https://doi.org/10.1038/jes.2013.39 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a shareable

link is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative KEYWORDS * ambient monitoring data * CMAQ * SHEDS *

PM2.5.