IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v9y2017i2d10.1007_s12561-016-9150-3.html
   My bibliography  Save this article

Incorporating High-Dimensional Exposure Modelling into Studies of Air Pollution and Health

Author

Listed:
  • Yi Liu

    (University of Bath)

  • Gavin Shaddick

    () (University of Bath)

  • James V. Zidek

    () (University of British Columbia)

Abstract

Performing studies on the risks of environmental hazards on human health requires accurate estimates of exposures that might be experienced by the populations at risk. Often there will be missing data and in many epidemiological studies, the locations and times of exposure measurements and health data do not match. To a large extent this will be due to the health and exposure data having arisen from completely different data sources and not as the result of a carefully designed study, leading to problems of both ‘change of support’ and ‘misaligned data’. In such cases, a direct comparison of the exposure and health outcome is often not possible without an underlying model to align the two in the spatial and temporal domains. The Bayesian approach provides the natural framework for such models; however, the large amounts of data that can arise from environmental networks means that inference using Markov Chain Monte Carlo might not be computationally feasible in this setting. Here we adapt the integrated nested Laplace approximation to implement spatio–temporal exposure models. We also propose methods for the integration of large-scale exposure models and health analyses. It is important that any model structure allows the correct propagation of uncertainty from the predictions of the exposure model through to the estimates of risk and associated confidence intervals. The methods are demonstrated using a case study of the levels of black smoke in the UK, measured over several decades, and respiratory mortality.

Suggested Citation

  • Yi Liu & Gavin Shaddick & James V. Zidek, 2017. "Incorporating High-Dimensional Exposure Modelling into Studies of Air Pollution and Health," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 559-581, December.
  • Handle: RePEc:spr:stabio:v:9:y:2017:i:2:d:10.1007_s12561-016-9150-3
    DOI: 10.1007/s12561-016-9150-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-016-9150-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. J. V. Zidek & W. Sun & N. D. Le, 2000. "Designing and integrating composite networks for monitoring multivariate gaussian pollution fields," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(1), pages 63-79.
    3. Sujit K. Sahu & Kanti V. Mardia, 2005. "A Bayesian kriged Kalman model for short‐term forecasting of air pollution levels," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 223-244, January.
    4. Kenneth K. Lopiano & Linda J. Young & Carol A. Gotway, 2014. "A pseudo-penalized quasi-likelihood approach to the spatial misalignment problem with non-normal data," Biometrics, The International Biometric Society, vol. 70(3), pages 648-660, September.
    5. Jonathan Wakefield & Ruth Salway, 2001. "A statistical framework for ecological and aggregate studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 119-137.
    6. Gavin Shaddick & Jon Wakefield, 2002. "Modelling daily multivariate pollutant data at multiple sites," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(3), pages 351-372, July.
    7. Finley, Andrew O. & Banerjee, Sudipto & Carlin, Bradley P., 2007. "spBayes: An R Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 19(i04).
    8. Stefano F. Tonellato, 2001. "A multivariate time series model for the analysis and prediction of carbon monoxide atmospheric concentrations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(2), pages 187-200.
    9. Francesca Dominici & Jonathan M. Samet & Scott L. Zeger, 2000. "Combining evidence on air pollution and daily mortality from the 20 largest US cities: a hierarchical modelling strategy," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(3), pages 263-302.
    10. Jon Wakefield, 2003. "Sensitivity Analyses for Ecological Regression," Biometrics, The International Biometric Society, vol. 59(1), pages 9-17, March.
    11. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    12. 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.
    13. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
    14. 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.
    15. Luke Bornn & Gavin Shaddick & James V. Zidek, 2012. "Modeling Nonstationary Processes Through Dimension Expansion," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 281-289, March.
    16. Montserrat Fuentes & Hae-Ryoung Song & Sujit K. Ghosh & David M. Holland & Jerry M. Davis, 2006. "Spatial Association between Speciated Fine Particles and Mortality," Biometrics, The International Biometric Society, vol. 62(3), pages 855-863, September.
    17. Birgit Schrödle & Leonhard Held, 2011. "A primer on disease mapping and ecological regression using $${\texttt{INLA}}$$," Computational Statistics, Springer, vol. 26(2), pages 241-258, June.
    18. Peter J. Diggle & Raquel Menezes & Ting‐li Su, 2010. "Geostatistical inference under preferential sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 191-232, March.
    19. Bo Li & Steve Sain & Linda Mearns & Henry Anderson & Sari Kovats & Kristie Ebi & Marni Bekkedal & Marty Kanarek & Jonathan Patz, 2012. "The impact of extreme heat on morbidity in Milwaukee, Wisconsin," Climatic Change, Springer, vol. 110(3), pages 959-976, February.
    20. Sahu, Sujit K. & Gelfand, Alan E. & Holland, David M., 2007. "High-Resolution SpaceTime Ozone Modeling for Assessing Trends," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1221-1234, December.
    Full references (including those not matched with items on IDEAS)

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:stabio:v:9:y:2017:i:2:d:10.1007_s12561-016-9150-3. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla) or (Springer Nature Abstracting and Indexing). General contact details of provider: http://www.springer.com .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.