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Comparing exposure metrics for the effects of fine particulate matter on emergency hospital admissions

Journal of Exposure Science & Environmental Epidemiology volume 23, pages 627–636 (2013)Cite this article

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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 time. The atmospheric chemistry model output is spatially and temporally complete but may be less accurate than monitor data. The stochastic human exposure estimates account for human activity patterns and variability in pollutant concentration across microenvironments, but requires extensive input information and computation time. To compare these metrics, we consider a 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 our estimated risk.

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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.

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Authors and Affiliations

  1. 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

  2. Department of Civil Construction and Environmental Engineering, North Carolina, State University, Raleigh, North Carolina, USA

    Wan Jiao & H Christopher Frey

  3. Harvard School of Public Health, Boston, Massachusetts, USA

    Francesca Dominici

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  1. Elizabeth Mannshardt
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Correspondence to Elizabeth Mannshardt.

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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

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