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The sensitivity of health effect estimates from time-series studies to fine particulate matter component sampling schedule

Journal of Exposure Science & Environmental Epidemiology volume 23, pages 481–486 (2013)Cite this article

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Abstract

The US Environmental Protection Agency air pollution monitoring data have been a valuable resource commonly used for investigating the associations between short-term exposures to PM2.5 chemical components and human health. However, the temporally sparse sampling on every third or sixth day may affect health effect estimation. We examined the impact of non-daily monitoring data on health effect estimates using daily data from the Denver Aerosol Sources and Health (DASH) study. Daily concentrations of four PM2.5 chemical components (elemental and organic carbon, sulfate, and nitrate) and hospital admission counts from 2003 through 2007 were used. Three every-third-day time series were created from the daily DASH monitoring data, imitating the US Speciation Trend Network (STN) monitoring schedule. A fourth, partly irregular, every-third-day time series was created by matching existing sampling days at a nearby STN monitor. Relative risks (RRs) of hospital admissions for PM2.5 components at lags 0–3 were estimated for each data set, adjusting for temperature, relative humidity, longer term temporal trends, and day of week using generalized additive models, and compared across different sampling schedules. The estimated RRs varied somewhat between the non-daily and daily sampling schedules and between the four non-daily schedules, and in some instances could lead to different conclusions. It was not evident which features of the data or analysis were responsible for the variation in effect estimates, although seeing similar variability in resampled data sets with relaxation of the every–third-day constraint suggests that limited power may have had a role. The use of non-daily monitoring data can influence interpretation of estimated effects of PM2.5 components on hospital admissions in time-series studies.

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References

  1. Grahame TJ, Schlesinger RB . Health effects of airborne particulate matter: do we know enough to consider regulating specific particle types or sources? Inhal Toxicol 2007; 19: 457–481.

    Article  CAS  Google Scholar 

  2. HEI panel on the health effects of traffic-related air pollution. Traffic-related air pollution: A critical review of the literature on emissions, exposure, and health effects. Health Effects Institute: Boston, MA. 2010 (HEI special report 17).

  3. US EPA. Air Quality Criteria for Particulate Matter: Volume 1. US Environmental Protection Agency: Washington, DC. 2004a (Report No. EPA 600/P-99/002aF-bF).

  4. Ostro BD, Feng WY, Broadwin R, Green S, Lipsett N . The effects of components of fine particulate air pollution on mortality in California: results from CALFINE. Environ Health Perspect 2007; 115: 13–19.

    Article  CAS  Google Scholar 

  5. Ostro BD, Feng WY, Broadwin R, Malig BJ, Green RS, Lipsett MJ . The impact of components of fine particulate matter on cardiovascular mortality in susceptible subpopulations. Occup Environ Med 2008; 65: 750–756.

    Article  CAS  Google Scholar 

  6. Ostro BD, Roth L, Malig B, Marty M . The effects of fine particle components on respiratory hospital admissions in children. Environ Health Perspect 2009; 117: 475–480.

    Article  CAS  Google Scholar 

  7. Peng RD, Bell ML, Geyh AS, McDermott A, Zeger SL, Samet JM et al. Emergency admissions of cardiovascular and respiratory diseases and the chemical composition of fine particle air pollution. Environ Health Perspect 2009; 117: 957–963.

    Article  CAS  Google Scholar 

  8. Ito K, Mathes R, Ross Z, Nádas A, Thurston G, Matte T . Fine particulate matter constituents associated with cardiovascular hospital admissions and mortality in New York City. Environ Health Perspect 2011; 119: 467–473.

    Article  CAS  Google Scholar 

  9. Lippmann M . Semi-continuous speciation analyses for ambient air particulate matter: an urgent need for health effect studies. J Exp Sci Environ Epidemiol 2009; 19: 235–247.

    Article  CAS  Google Scholar 

  10. US EPA. Integrated science assessment for particulate matter. US Environmental Protection Agency, Office of Research and Development: Research Triangle Park, NC. 2009 (Report No. EPA/600/R-08/139F).

  11. Vedal S, Hannigan MP, Dutton SJ, Miller SL, Milford JB, Rabinovitch N et al. The Denver Aerosol Sources and Health (DASH) study: overview and early findings. Atmos Environ 2009; 43: 1666–1673.

    Article  CAS  Google Scholar 

  12. Kim SY, Peel JL, Hannigan MP, Dutton SJ, Sheppard L, Clark ML et al. The temporal lag structure of short-term associations of fine particulate matter chemical constituents and cardiovascular and respiratory hospitalizations. Environ Health Perspect 2012; 120: 1094–1099.

    Article  Google Scholar 

  13. Dutton SJ, Schauer JJ, Vedal S, Hannigan MP . PM2.5 characterization for time series studies: pointwise uncertainty estimation and bulk speciation methods applied in Denver. Atmos Environ 2009; 43: 1136–1146.

    Article  CAS  Google Scholar 

  14. Wood SN . Some GAM theory. In:. Generalized Additive Models: An Introduction with R. Chapman & Hall/CRC, Taylor & Francis Group: Boca Raton, FL. 2006, 141–216.

    Chapter  Google Scholar 

  15. Schenker N, Gentleman JF . On judging the significance of differences by examining the overlap between confidence intervals. Am Stat 2001; 55: 182–186.

    Article  Google Scholar 

  16. Peng RD, Dominici F, Louis TA . Model choice in time series studies of air pollution and mortality. J R Stat Soc Ser A 2006; 169: 179–203.

    Article  Google Scholar 

  17. Lumley T, Sheppard L . Assessing seasonal confounding and model selection bias in air pollution epidemiology using positive and negative control analyses. Environmetrics 2000; 11: 705–717.

    Article  CAS  Google Scholar 

  18. Lumley T, Sheppard L . Time series analyses of air pollution and health: straining at gnats and swallowing camels? Epidemiology 2003; 14: 13–14.

    Article  Google Scholar 

  19. Katsouyanni K, Samet JM, Anderson HR, Atkinson R, Le Tertre A, Medina S et alHEI Health Review Committee Air Pollution and Health: A European and North American Approach (Aphena). Health Effects Institute: Boston, MA. 2009 (Res Rep Health Eff Inst).

    Google Scholar 

  20. Klemm RJ, Thomas EL, Wyzga RE . The impact of frequency and duration of air quality monitoring: Atlanta, GA, data modeling of air pollution and mortality. J Air Waste Manage Assoc 2011; 61: 1281–1291.

    Article  CAS  Google Scholar 

  21. U.S. EPA. PM2.5 Speciation Network Newsletter. Environmental Protection Agency, Office of Air Quality Planning and Standards: Research Triangle Park, NC: U.S.. 2004b, http://www.epa.gov/ttn/amtic/files/ambient/pm25/spec/spnews1.pdf).

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Acknowledgements

This work was supported by the NIEHS research grant R01 ES010197. The views expressed are those of the authors and do not necessarily reflect the views or policies of the US Environmental Protection Agency.

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Authors and Affiliations

  1. Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA

    Sun-Young Kim, Lianne Sheppard & Sverre Vedal

  2. Department of Biostatistics, University of Washington, Seattle, Washington, USA

    Lianne Sheppard

  3. Department of Mechanical Engineering, University of Colorado, Boulder, Colorado, USA

    Michael P Hannigan

  4. US Environmental Protection Agency, National Center for Environmental Assessment, Research Triangle Park, North Carolina, USA

    Steven J Dutton

  5. Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado, USA

    Jennifer L Peel & Maggie L Clark

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  1. Sun-Young Kim
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  2. Lianne Sheppard
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  5. Jennifer L Peel
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  6. Maggie L Clark
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Correspondence to Sun-Young Kim.

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Kim, SY., Sheppard, L., Hannigan, M. et al. The sensitivity of health effect estimates from time-series studies to fine particulate matter component sampling schedule. J Expo Sci Environ Epidemiol 23, 481–486 (2013). https://doi.org/10.1038/jes.2013.28

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