Modelling the effects of air pollution on health using Bayesian dynamic generalised linear models

Environmetrics

Volumn 19, Issue 8, 2008, Pages 785-804

  Lee, Duncan ^a   Shaddick, Gavin ^b  

^a UNIVERSITY OF GLASGOW   (United Kingdom)

^b UNIVERSITY OF BATH   (United Kingdom)

Author keywords

Air pollution; Bayesian analysis; Dynamic generalised linear model; Markov chain monte carlo simulation

Indexed keywords

ATMOSPHERIC POLLUTION; BAYESIAN ANALYSIS; ECOLOGICAL MODELING; HEALTH IMPACT; MARKOV CHAIN; MORBIDITY; MORTALITY;

EID: 56749180595     PISSN: 11804009     EISSN: 1099095X     Source Type: Journal    
DOI: 10.1002/env.894     Document Type: Article

References (30)

1. Ameen J, Harrison P. 1985. Normal discount bayesian models. Bayesian Statistics 2 2: 271-198.
2. Chatfield C. 1996. The Analysis of Time Series: An Introduction, 5th edn. Chapman and Hall: London.
3. Chiogna M, Gaetan C. 2002. Dynamic generalized linear models with applications to environmental epidemiology. Applied Statistics 51: 453-468.
4. Daniels M, Dominici F, Zeger S, Samet J. 2004. The national morbidity, mortality, and air pollution study part III: concentration-response curves and thresholds for the 20 largest US cities. HEI Project 96-97: 1-21.
5. Dominici F, Daniels M, Zeger S, Samet J. 2002. Air pollution and mortality: estimating regional and national dose-response relationships. Journal of the American Statistical Association 97: 100-111.
6. Dominici F, Samet J, Zeger S. 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 163: 263-302.
7. Fahrmeir L. 1992. Posterior mode estimation by extended Kalman filtering for multivariate dynamic generalized linear models. Journal of the American Statistical Association 87: 501-509.
8. Fahrmeir L, Hennevogl W, Klemme K. 1992. Smoothing in dynamic generalized linear models by Gibbs sampling. Advances in GLIM and Statistical Modelling. Springer: Heidelberg.
9. Fahrmeir L, Kaufmann H. 1991. On Kalman filtering, posterior mode estimation and fisher scoring in dynamic exponential family regression. Metrika 38: 37-60.
10. Fahrmeir L, Tutz G. 2001. Multivariate Statistical Modelling Based on Generalized Linear Models, 2nd edn. Springer: New York and Berlin.
11. Fahrmeir L, Wagenpfeil S. 1997. Penalized likelihood estimation and iterative Kalman smoothing for non-Gaussian dynamic regression models. Computational Statistics and Data Analysis 24: 295-320.
12. Fruhwirth-Schnatter S. 1994. Applied state space modelling of non-Gaussian time series using integration-based Kalman filtering. Statistics and Computing 4: 259-269.
13. Gamerman D. 1998. Markov chain Monte Carlo for dynamic generalized linear models. Biometrika 85: 215-227.
14. Gelman A, Carlin J, Stern H, Rubin D. 2003. Bayesian Data Analysis, 2nd edn. Chapman and Hall: London.
15. Griffin M, Neuzil K. 2002. The global implications of influenza in Hong Kong. The New England Journal Of Medicine 347: 2159-2162.
16. Katsouyanni K, Schwartz J, Spix C, Touloumi G, Zmirou D, Zanobetti A, Wojtyniak B, Vonk J, Tobias A, Ponka A, Medina S, Bacharove L, Anderson H. 1996. Short term effects of air pollution on health: a European approach using epidemiologic time series data: the APHEA protocol. Journal of Epidemiology and Community Health 50: S12-S18.
17. Kitagawa G. 1987. Non-Gaussian state-space modelling of nonstationary time series. Journal of the American Statistical Association 82: 1032-1041.
18. Kitagawa G. 1996. Monte Carlo filter and smoother for non-Gaussian nonlinear state-space models. Journal of Computational and Graphical Statistics 5: 1-25.
19. Knorr-Held L. 1999. Conditional prior proposals in dynamic models. Scandinavian Journal of Statistics 26: 129-144.
20. Peng R, Dominici F, Pastor-Barriuso R, Zeger S, Samet J. 2005. Seasonal analyses of air pollution and mortality in 100 U.S. cities. American Journal of Epidemiology 161: 585-594.
21. Pettit L. 1986. Diagnostics in Bayesian model choice. The Statistician 35: 183-190.
22. Roberts S. 2004. Biologically plausible particulate air pollution mortality concentration-response functions. Environmental Health Perspectives 112: 309-313.
23. Samet J, Zeger S, Dominici F, Curriero F, Coursac I, Dockery D, Schwartz J, Zanobetti A. 2000. The national morbidity, mortality, and air pollution study part II: morbidity and mortality, from air pollution in the United States. HEI Project 96-97: 5-47.
24. Shaddick G, Wakefield J. 2002. Modelling multiple pollutants and multiple sites. Applied Statistics 51: 351-372.
25. Shephard N, Pitt M. 1997. Likelihood analysis of non-Gaussian measurement time series. Biometrika 84: 653-667.
26. Spiegelalter D, Best N, Carlin B, Van der Linde A. 2002. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society series B 64: 583-639.
27. Vedal S, Brauer M, White R, Petkau J. 2003. Air pollution and daily mortality in a city with low levels of pollution. Environmental Health Perpespectives 111: 45-51.
28. West M, Harrison J, Migon H. 1985. Dynamic generalized linear models and bayesian forecasting. Journal of the American Statistical Association 80: 73-83.
29. Zhu L, Carlin B, Gelfand A. 2003. Hierarchical regression with misaligned spatial data: relating ambient ozone and pediatric asthma ER visits in Atlanta. Environmetrics 14: 537-557.
30. Zmirou D, Schwartz J, Saez M, Zanobetti A, Wojtyniak B, Touloumi G, Spix C, Ponce de Leon A, Le Moullec Y, Bacharova L, Schouten J, Ponka A, Katsouyanni K. 1998. Time-series analysis of air pollution and cause specific mortality. Epidemiology 9: 495-503.