Skip to main content

Advertisement

SpringerLink
Log in

The implementation of Bayesian structural additive regression models in multi-city time series air pollution and human health studies

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

In this study, a novel Bayesian semiparametric structural additive regression (STAR) model is introduced in multi-city time series air pollution and human health studies. This modeling approach can simultaneously take into account the fixed effects, random effects, nonlinear smoothing functions and spatial functions in an integrated model framework. This study focuses on examining the powerful functionalities of this approach in modeling air pollution and mortality data of 100 U.S. cities from 1987 to 2000. Compared with previous studies, the modeling approach used in this study yields consistent findings of nation-level and city-level PM10 (particulate matter less than 10 μm) effects on mortality. Notably, cities with significantly elevated mortality rates were concentrated in the Northeastern U.S. This modeling approach also emphasizes the important functionality of the spatial function in visualizing disease mapping. Model diagnostics were performed to confirm the availability of the STAR model. We also found consistent findings by using different hyperparameters in the sensitivity analysis. To sum up, the implementation of this modeling approach has achieved the goals of applying a spatial function and obtaining robust results in the multi-city time series air pollution and human health study.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Adebayo SB (2004) Bayesian geoadditive modelling of breastfeeding initiation in Nigeria. J Appl Econom 19(2):267–281

    Article  Google Scholar 

  • Aga E, Samoli E, Touloumi G, Anderson HR, Cadum E, Forsberg B, Goodman P, Goren A, Kotesovec F, Kriz B, Macarol-Hiti M, Medina S, Paldy A, Schindler C, Sunyer J, Tittanen P, Wojtyniak B, Zmirou D, Schwartz J, Katsouyanni K (2003) Short-term effects of ambient particles on mortality in the elderly: results from 28 cities in the APHEA2 project. Eur Respir J Suppl 40:28s–33s

    Article  CAS  Google Scholar 

  • Allshouse W, Pleil J, Rappaport S, Serre M (2009) Mass fraction spatiotemporal geostatistics and its application to map atmospheric polycyclic aromatic hydrocarbons after 9/11. Stoch Environ Res Risk Assess 23(8):1213–1223

    Article  Google Scholar 

  • Belitz C, Brezger A, Kneib T, Lang S (2009) BayesX—Software for Bayesian inference in structured additive regression models. Version 2.01. http://www.stat.uni-muenchen.de/~bayesx

  • Beyea J (1999) Geographic exposure modeling: a valuable extension of geographic information systems for use in environmental epidemiology. Environ Health Perspect 107(Suppl 1):181–190

    Article  Google Scholar 

  • Brezger A, Lang S (2006) Generalized structured additive regression based on Bayesian P-splines. Comput Stat Data Anal 50(4):967–991

    Article  Google Scholar 

  • Brunauer W, Lang S, Umlauf N (2010) Modeling house prices using multilevel structured additive regression. Working paper, Department of Economics and Statistics, University of Innsbruck, Innsbruck

  • Demissie S, LaValley MP, Horton NJ, Glynn RJ, Cupples LA (2003) Bias due to missing exposure data using complete-case analysis in the proportional hazards regression model. Stat Med 22(4):545–557

    Article  Google Scholar 

  • Dhondt S, Le Xuan Q, Van Vu H, Hens L (2011) Environmental health impacts of mobility and transport in Hai Phong, Vietnam. Stoch Environ Res Risk Assess 25(3):363–376

    Article  Google Scholar 

  • Dockery DW, Pope CA 3rd (1994) Acute respiratory effects of particulate air pollution. Annu Rev Public Health 15:107–132

    Article  CAS  Google Scholar 

  • Dominici F, Daniels M, Zeger SL, Samet JM (2002a) Air pollution and mortality: estimating regional and national dose-response relationships. J Am Stat Assoc 97(457):100–111

    Article  Google Scholar 

  • Dominici F, McDermott A, Zeger SL, Samet JM (2002b) National maps of the effects of particulate matter on mortality: exploring geographical variation. Environ Health Perspect 111(1):39–44

    Article  Google Scholar 

  • Dominici F, McDermott A, Zeger SL, Samet JM (2002c) On the use of generalized additive models in time-series studies of air pollution and health. Am J Epidemiol 156(3):193–203

    Article  Google Scholar 

  • Dominici F, McDermott A, Zeger SL, Samet JM (2003) Airborne particulate matter and mortality: timescale effects in four US cities. Am J Epidemiol 157(12):1055–1065

    Article  Google Scholar 

  • Dominici F, McDermott A, Daniels M, Zeger SL, Samet JM (2005) Revised analyses of the national morbidity, mortality, and air pollution study: mortality among residents of 90 cities. J Toxicol Environ Health A 68(13–14):1071–1092

    Article  CAS  Google Scholar 

  • Eilers PHC, Marx BD (1996) Flexible smoothing with B-splines and penalties. Stat Sci 11(2):89–102

    Article  Google Scholar 

  • Fahrmeir L, Lang S (2001) Bayesian semiparametric regression analysis of multicategorical time-space data. Ann Inst Stat Math 53(1):11–30

    Article  Google Scholar 

  • Fromont A, Binquet C, Sauleau EA, Fournel I, Bellisario A, Adnet J, Weill A, Vukusic S, Confavreux C, Debouverie M, Clerc L, Bonithon-Kopp C, Moreau T (2010) Geographic variations of multiple sclerosis in France. Brain 133(Pt 7):1889–1899

    Article  Google Scholar 

  • Gamerman D (1997) Sampling from the posterior distribution in generalized linear mixed models. Stat Comput 7(1):57–68

    Article  Google Scholar 

  • Gelman A, Rubin D (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7:457–472

    Article  Google Scholar 

  • Haining R, Law J, Maheswaran R, Pearson T, Brindley P (2007) Bayesian modelling of environmental risk: example using a small area ecological study of coronary heart disease mortality in relation to modelled outdoor nitrogen oxide levels. Stoch Environ Res Risk Assess 21(5):501–509

    Article  Google Scholar 

  • Hastie T, Tibshirani R (1990) Generalized additive models. Monographs on statistics and applied probability, vol 43, 1st edn. Chapman and Hall, London

  • Kandala NB, Ji C, Stallard N, Stranges S, Cappuccio FP (2008) Morbidity from diarrhoea, cough and fever among young children in Nigeria. Ann Trop Med Parasitol 102(5):427–445

    Article  Google Scholar 

  • Kandala NB, Brodish P, Buckner B, Foster S, Madise N (2011) Millennium development goal 6 and HIV infection in Zambia: what can we learn from successive household surveys? AIDS 25(1):95–106

    Article  Google Scholar 

  • Kazembe LN (2009) A semiparametric sequential ordinal model with applications to analyse first birth intervals. Austrian J Stat 38(2):83–99

    Google Scholar 

  • Khatab K (2010) Childhood malnutrition in Egypt using geoadditive Gaussian and latent variable models. Am J Trop Med Hyg 82(4):653–663

    Article  Google Scholar 

  • Kindermann R, Snell JL (1980) Markov random fields and their applications. Contemporary mathematics, vol 1. American Mathematical Society, Providence

    Book  Google Scholar 

  • Knorr-Held L (1999) Conditional prior proposals in dynamic models. Scand J Stat 26(1):129–144

    Article  Google Scholar 

  • Lang S, Brezger A (2004) Bayesian P-splines. J Comput Graph Stat 13(1):183–212

    Article  Google Scholar 

  • Lang S, Umlauf N (2010) Applications of multilevel structured additive regression models to insurance Data. Working paper, Department of Economics and Statistics, University of Innsbruck, Innsbruck

  • Li H, Huang G, Zou Y (2008) An integrated fuzzy-stochastic modeling approach for assessing health-impact risk from air pollution. Stoch Environ Res Risk Assess 22(6):789–803

    Article  Google Scholar 

  • Musio M, Sauleau EA, Buemi A (2010) Bayesian semi-parametric ZIP models with space-time interactions: an application to cancer registry data. Math Med Biol 27(2):181–194

    Article  Google Scholar 

  • Nerich V, Monnet E, Etienne A, Louafi S, Ramee C, Rican S, Weill A, Vallier N, Vanbockstael V, Auleley GR, Allemand H, Carbonnel F (2006) Geographical variations of inflammatory bowel disease in France: a study based on national health insurance data. Inflamm Bowel Dis 12(3):218–226

    Article  Google Scholar 

  • Nugroho S, Fujiwara A, Zhang J (2011) An empirical analysis of the impact of a bus rapid transit system on the concentration of secondary pollutants in the roadside areas of the TransJakarta corridors. Stoch Environ Res Risk Assess 25(5):655–669

    Article  Google Scholar 

  • Peng RD, Dominici F, Pastor-Barriuso R, Zeger SL, Samet JM (2005) Seasonal analyses of air pollution and mortality in 100 US cities. Am J Epidemiol 161(6):585–594

    Article  Google Scholar 

  • Pope CA 3rd, Burnett RT, Thun MJ, Calle EE, Krewski D, Ito K, Thurston GD (2002) Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA 287(9):1132–1141

    Article  CAS  Google Scholar 

  • Samet JM, Zeger SL, Dominici F, Curriero F, Coursac I, Dockery DW, Schwartz J, Zanobetti A (2000a) The national morbidity, mortality, and air pollution study, part II: morbidity and mortality from air pollution in the United States. Health Effects Institute, Cambridge

    Google Scholar 

  • Samet JM, Dominici F, Curriero FC, Coursac I, Zeger SL (2000b) Fine particulate air pollution and mortality in 20 U.S. cities, 1987–1994. N Engl J Med 343(24):1742–1749

    Article  CAS  Google Scholar 

  • Serio CD, Claudia L (2009) Investigating determinants of multiple sclerosis in longitudinal studies: a Bayesian approach. J Probab Stat 2009:24

    Google Scholar 

  • Ugarte MD, Goicoa T, Militino AF (2010) Spatio-temporal modeling of mortality risks using penalized splines. Environmetrics 21(3–4):270–289

    Google Scholar 

  • van der Heijden GJMG, Donders ART, Stijnen T, Moons KGM (2006) Imputation of missing values is superior to complete case analysis and the missing-indicator method in multivariable diagnostic research: a clinical example. J Clin Epidemiol 59(10):1102–1109

    Article  Google Scholar 

  • Wang Q, Yuan X, Ma C, Zhang Z, Zuo J (2012) Research on the impact assessment of urbanization on air environment with urban environmental entropy model: a case study. Stoch Environ Res Risk Assess (in press)

  • Welty LJ, Zeger SL (2005) Are the acute effects of particulate matter on mortality in the national morbidity, mortality, and air pollution study the result of inadequate control for weather and season? A sensitivity analysis using flexible distributed lag models. Am J Epidemiol 162(1):80–88

    Article  Google Scholar 

  • Yu HL, Kolovos A, Christakos G, Chen JC, Warmerdam S, Dev B (2007) Interactive spatiotemporal modelling of health systems: the SEKS–GUI framework. Stoch Environ Res Risk Assess 21(5):555–572

    Article  Google Scholar 

  • Yu HL, Chen JC, Christakos G, Jerrett M (2009) BME estimation of residential exposure to ambient PM10 and ozone at multiple time scales. Environ Health Perspect 117(4):537–544

    CAS  Google Scholar 

  • Zeger SL, Dominici F, McDermott A, Samet JM (2008) Mortality in the Medicare population and chronic exposure to fine particulate air pollution in urban centers (2000–2005). Environ Health Perspect 116(12):1614–1619

    Article  Google Scholar 

Download references

Acknowledgments

We appreciate Dr. Thomas Kneib, who provided methodological and technical advice of our model. We also thank Mary Trottier for editing the English in this article.

Conflict of interest

No potential conflicts of interest were disclosed.

Author information

Authors and Affiliations

  1. Department of Internal Medicine, Division of Health Behavior Research, Washington University School of Medicine, 4444 Forest Park Ave, Suite 6700, St. Louis, MO, 63108, USA

    Lung-Chang Chien

  2. Department of Biostatistics, University of North Carolina at Chapel Hill, 137 E. Franklin St., Suite 203, Chapel Hill, NC, 27514, USA

    Shrikant I. Bangdiwala

Authors
  1. Lung-Chang Chien
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shrikant I. Bangdiwala
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lung-Chang Chien.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chien, LC., Bangdiwala, S.I. The implementation of Bayesian structural additive regression models in multi-city time series air pollution and human health studies. Stoch Environ Res Risk Assess 26, 1041–1051 (2012). https://doi.org/10.1007/s00477-012-0562-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-012-0562-4

Keywords

Search

Navigation