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Spatial-temporal association between fine particulate matter and daily mortality


  • Choi, Jungsoon
  • Fuentes, Montserrat
  • Reich, Brian J.


Fine particulate matter (PM2.5) is a mixture of pollutants that has been linked to serious health problems, including premature mortality. Since the chemical composition of PM2.5 varies across space and time, the association between PM2.5 and mortality could also change with space and season. A statistical multi-stage Bayesian framework is developed and implemented, which provides a very broad and flexible approach to studying the spatiotemporal associations between mortality and population exposure to daily PM2.5 mass, while accounting for different sources of uncertainty. The first stage of the framework maps ambient PM2.5 air concentrations using all available monitoring data (IMPROVE and FRM) and an air quality model (CMAQ) at different spatial and temporal scales. The second stage of the framework examines the spatial temporal relationships between the health end-points and the exposures to PM2.5 by introducing a spatial-temporal generalized Poisson regression model. A method to adjust for time-varying confounders such as seasonal trends is proposed. A common seasonal trends model uses a fixed number of basis functions to account for these confounders, but the results can be sensitive to the number of basis functions. Thus, instead the number of the basis functions is treated as an unknown parameter in the Bayesian model, and a space-time stochastic search variable selection approach is used. The framework is illustrated using a data set in North Carolina for the year 2001.

Suggested Citation

  • Choi, Jungsoon & Fuentes, Montserrat & Reich, Brian J., 2009. "Spatial-temporal association between fine particulate matter and daily mortality," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2989-3000, June.
  • Handle: RePEc:eee:csdana:v:53:y:2009:i:8:p:2989-3000

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    References listed on IDEAS

    1. repec:aph:ajpbhl:1991:81:6:694-702_3 is not listed on IDEAS
    2. Smith, Michael & Kohn, Robert, 1996. "Nonparametric regression using Bayesian variable selection," Journal of Econometrics, Elsevier, vol. 75(2), pages 317-343, December.
    3. Dominici F. & Daniels M. & Zeger S. L. & Samet J. M., 2002. "Air Pollution and Mortality: Estimating Regional and National Dose-Response Relationships," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 100-111, March.
    4. Gotway C.A. & Young L.J., 2002. "Combining Incompatible Spatial Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 632-648, June.
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    Cited by:

    1. LeSage, James & Banerjee, Sudipto & Fischer, Manfred M. & Congdon, Peter, 2009. "Spatial statistics: Methods, models & computation," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2781-2785, June.
    2. Laura F. Boehm Vock & Brian J. Reich & Montserrat Fuentes & Francesca Dominici, 2015. "Spatial variable selection methods for investigating acute health effects of fine particulate matter components," Biometrics, The International Biometric Society, vol. 71(1), pages 167-177, March.

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