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A dynamic structural equation approach to estimate the short‐term effects of air pollution on human health

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  • Dani Gamerman
  • Luigi Ippoliti
  • Pasquale Valentini

Abstract

Detailed knowledge on the effects of air pollutants on human health is a prerequisite for the development of effective policies to reduce the adverse impact of ambient air pollution. However, measuring the effect of exposure on health outcomes is an extremely difficult task as the health impact of air pollution is known to vary over space and over different exposure periods. In general, standard approaches aggregate the information over space or time to simplify the study but this strategy fails to recognize important regional differences and runs into the well‐known risk of confounding the effects. However, modelling directly with the original, disaggregated data requires a highly dimensional model with the curse of dimensionality making inferences unstable; in these cases, the models tend to retain many irrelevant components and most relevant effects tend to be attenuated. The situation clearly calls for an intermediate solution that does not blindly aggregate data while preserving important regional features. We propose a dimension‐reduction approach based on latent factors driven by the data. These factors naturally absorb the relevant features provided by the data and establish the link between pollutants and health outcomes, instead of forcing a necessarily high‐dimensional link at the observational level. The dynamic structural equation approach is particularly suited for this task. The latent factor approach also provides a simple solution to the spatial misalignment caused by using variables with different spatial resolutions and the state‐space representation of the model favours the application of impulse response analysis. Our approach is discussed through the analysis of the short‐term effects of air pollution on hospitalization data from Lombardia and Piemonte regions (Italy).

Suggested Citation

  • 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.
  • Handle: RePEc:bla:jorssc:v:71:y:2022:i:3:p:739-769
    DOI: 10.1111/rssc.12554
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    References listed on IDEAS

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    1. Helmut Lütkepohl, 2005. "New Introduction to Multiple Time Series Analysis," Springer Books, Springer, number 978-3-540-27752-1, June.
    2. Lopes, Hedibert Freitas & Gamerman, Dani & Salazar, Esther, 2011. "Generalized spatial dynamic factor models," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1319-1330, March.
    3. Greven, Sonja & Dominici, Francesca & Zeger, Scott, 2011. "An Approach to the Estimation of Chronic Air Pollution Effects Using Spatio-Temporal Information," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 396-406.
    4. Sudipto Banerjee & Alan E. Gelfand & Andrew O. Finley & Huiyan Sang, 2008. "Gaussian predictive process models for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 825-848, September.
    5. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
    6. Jennifer F. Bobb & Francesca Dominici & Roger D. Peng, 2013. "Reduced hierarchical models with application to estimating health effects of simultaneous exposure to multiple pollutants," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(3), pages 451-472, May.
    7. 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.
    8. L. J. Welty & R. D. Peng & S. L. Zeger & F. Dominici, 2009. "Bayesian Distributed Lag Models: Estimating Effects of Particulate Matter Air Pollution on Daily Mortality," Biometrics, The International Biometric Society, vol. 65(1), pages 282-291, March.
    9. Duncan Lee & Alastair Rushworth & Sujit K. Sahu, 2014. "A Bayesian localized conditional autoregressive model for estimating the health effects of air pollution," Biometrics, The International Biometric Society, vol. 70(2), pages 419-429, June.
    10. Duncan Lee & Gavin Shaddick, 2010. "Spatial Modeling of Air Pollution in Studies of Its Short-Term Health Effects," Biometrics, The International Biometric Society, vol. 66(4), pages 1238-1246, December.
    11. 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.
    12. George, Edward I. & Sun, Dongchu & Ni, Shawn, 2008. "Bayesian stochastic search for VAR model restrictions," Journal of Econometrics, Elsevier, vol. 142(1), pages 553-580, January.
    13. 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.
    14. W. R. Gilks & P. Wild, 1992. "Adaptive Rejection Sampling for Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 337-348, June.
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    Cited by:

    1. Angela Ferretti & L. Ippoliti & P. Valentini & R. J. Bhansali, 2023. "Long memory conditional random fields on regular lattices," Environmetrics, John Wiley & Sons, Ltd., vol. 34(5), August.

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