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A Bayesian hierarchical distributed lag model for estimating the time course of risk of hospitalization associated with particulate matter air pollution

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  • Roger D. Peng
  • Francesca Dominici
  • Leah J. Welty

Abstract

Summary. Time series studies have provided strong evidence of an association between increased levels of ambient air pollution and increased hospitalizations, typically at a single lag of 0, 1 or 2 days after an air pollution episode. Two important scientific objectives are to understand better how the risk of hospitalization that is associated with a given day's air pollution increase is distributed over multiple days in the future and to estimate the cumulative short‐term health effect of an air pollution episode over the same multiday period. We propose a Bayesian hierarchical distributed lag model that integrates information from national health and air pollution databases with prior beliefs of the time course of risk of hospitalization after an air pollution episode. This model is applied to air pollution and health data on 6.3 million enrollees of the US Medicare system living in 94 counties covering the years 1999–2002. We obtain estimates of the distributed lag functions relating fine particulate matter pollution to hospitalizations for both ischaemic heart disease and acute exacerbation of chronic obstructive pulmonary disease, and we use our model to explore regional variation in the health risks across the USA.

Suggested Citation

  • Roger D. Peng & Francesca Dominici & Leah J. Welty, 2009. "A Bayesian hierarchical distributed lag model for estimating the time course of risk of hospitalization associated with particulate matter air pollution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(1), pages 3-24, February.
    • Handle: RePEc:bla:jorssc:v:58:y:2009:i:1:p:3-24
    DOI: 10.1111/j.1467-9876.2008.00640.x
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    References listed on IDEAS

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    1. Jones, Galin L. & Haran, Murali & Caffo, Brian S. & Neath, Ronald, 2006. "Fixed-Width Output Analysis for Markov Chain Monte Carlo," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1537-1547, December.
    2. Corradi, Corrado, 1977. "Smooth distributed lag estimators and smoothing spline functions in Hilbert spaces," Journal of Econometrics, Elsevier, vol. 5(2), pages 211-219, March.
    3. P. J. Everson & C. N. Morris, 2000. "Inference for multivariate normal hierarchical models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 399-412.
    4. Roger D. Peng & Francesca Dominici & Thomas A. Louis, 2006. "Model choice in time series studies of air pollution and mortality," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(2), pages 179-203, March.
    5. Leamer, Edward E, 1972. "A Class of Informative Priors and Distributed Lag Analysis," Econometrica, Econometric Society, vol. 40(6), pages 1059-1081, November.
    6. Shiller, Robert J, 1973. "A Distributed Lag Estimator Derived from Smoothness Priors," Econometrica, Econometric Society, vol. 41(4), pages 775-788, July.
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    1. Dani Gamerman & Luigi Ippoliti & Pasquale Valentini, 2022. "A dynamic structural equation approach to estimate the short‐term effects of air pollution on human health," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 739-769, June.

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