Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution

Gavin Shaddick; Matthew L. Thomas; Amelia Green; Michael Brauer; Aaron van Donkelaar; Rick Burnett; Howard H. Chang; Aaron Cohen; Rita Van Dingenen; Carlos Dora; Sophie Gumy; Yang Liu; Randall Martin; Lance A. Waller; Jason West; James V. Zidek; Annette Prüss-Ustün

doi:10.1111/rssc.12227

Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution

Gavin Shaddick, Matthew L. Thomas, Amelia Green, Michael Brauer, Aaron van Donkelaar, Rick Burnett, Howard H. Chang, Aaron Cohen, Rita Van Dingenen, Carlos Dora, Sophie Gumy, Yang Liu, Randall Martin, Lance A. Waller, Jason West, James V. Zidek, Annette Prüss-Ustün

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	231-253
Number of pages	23
Journal	Journal of the Royal Statistical Society. Series C: Applied Statistics
Volume	67
Issue number	1
DOIs	https://doi.org/10.1111/rssc.12227
Publication status	Published - Jan 2018
Externally published	Yes

Keywords

Air pollution
Bayesian hierarchical modelling
Data fusion
Environmental health effects
Global burden of disease
Integrated nested Laplace approximations
Spatial modelling

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10.1111/rssc.12227

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Shaddick, G., Thomas, M. L., Green, A., Brauer, M., van Donkelaar, A., Burnett, R., Chang, H. H., Cohen, A., Dingenen, R. V., Dora, C., Gumy, S., Liu, Y., Martin, R., Waller, L. A., West, J., Zidek, J. V., & Prüss-Ustün, A. (2018). Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution. Journal of the Royal Statistical Society. Series C: Applied Statistics, 67(1), 231-253. https://doi.org/10.1111/rssc.12227

@article{760a466b5a034b489b6d8e36ef18dc52,

title = "Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution",

keywords = "Air pollution, Bayesian hierarchical modelling, Data fusion, Environmental health effects, Global burden of disease, Integrated nested Laplace approximations, Spatial modelling",

author = "Gavin Shaddick and Thomas, {Matthew L.} and Amelia Green and Michael Brauer and {van Donkelaar}, Aaron and Rick Burnett and Chang, {Howard H.} and Aaron Cohen and Dingenen, {Rita Van} and Carlos Dora and Sophie Gumy and Yang Liu and Randall Martin and Waller, {Lance A.} and Jason West and Zidek, {James V.} and Annette Pr{\"u}ss-Ust{\"u}n",

note = "Funding Information: The model was developed by a multidisciplinary group of experts established as part of the recommendations from the first meeting of the WHO {\textquoteleft}Global platform for air quality{\textquoteright}, Geneva, January 2014. The resulting Data Integration Task Force consists of the first, fourth–ninth and 12th–16th authors of this paper together with members of the WHO (the 10th, 11th and 17th authors). The views that are expressed in this paper are those of the authors and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated. The model was presented and reviewed at the second meeting of the {\textquoteleft}Global platform for air quality{\textquoteright} meeting, Geneva, August 2015. Matthew Lloyd Thomas is supported by a scholarship from the Engineering and Physical Sciences Research Council Centre for Doctoral Training in Statistical Applied Mathematics at Bath, under project EP/L015684/1. Amelia Green was supported for this work by WHO contracts APW 201255146 and 201255393. Publisher Copyright: {\textcopyright} 2017 World Health Organization, Journal of the Royal Statistical Society: Series C (Applied Statistics) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society.",

year = "2018",

month = jan,

doi = "10.1111/rssc.12227",

language = "English",

volume = "67",

pages = "231--253",

journal = "Journal of the Royal Statistical Society. Series C: Applied Statistics",

issn = "0035-9254",

publisher = "Wiley-Blackwell Publishing Ltd",

number = "1",

}

Shaddick, G, Thomas, ML, Green, A, Brauer, M, van Donkelaar, A, Burnett, R, Chang, HH, Cohen, A, Dingenen, RV, Dora, C, Gumy, S, Liu, Y, Martin, R, Waller, LA, West, J, Zidek, JV & Prüss-Ustün, A 2018, 'Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution', Journal of the Royal Statistical Society. Series C: Applied Statistics, vol. 67, no. 1, pp. 231-253. https://doi.org/10.1111/rssc.12227

TY - JOUR

T1 - Data integration model for air quality

T2 - a hierarchical approach to the global estimation of exposures to ambient air pollution

AU - Shaddick, Gavin

AU - Thomas, Matthew L.

AU - Green, Amelia

AU - Brauer, Michael

AU - van Donkelaar, Aaron

AU - Burnett, Rick

AU - Chang, Howard H.

AU - Cohen, Aaron

AU - Dingenen, Rita Van

AU - Dora, Carlos

AU - Gumy, Sophie

AU - Liu, Yang

AU - Martin, Randall

AU - Waller, Lance A.

AU - West, Jason

AU - Zidek, James V.

AU - Prüss-Ustün, Annette

N1 - Funding Information: The model was developed by a multidisciplinary group of experts established as part of the recommendations from the first meeting of the WHO ‘Global platform for air quality’, Geneva, January 2014. The resulting Data Integration Task Force consists of the first, fourth–ninth and 12th–16th authors of this paper together with members of the WHO (the 10th, 11th and 17th authors). The views that are expressed in this paper are those of the authors and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated. The model was presented and reviewed at the second meeting of the ‘Global platform for air quality’ meeting, Geneva, August 2015. Matthew Lloyd Thomas is supported by a scholarship from the Engineering and Physical Sciences Research Council Centre for Doctoral Training in Statistical Applied Mathematics at Bath, under project EP/L015684/1. Amelia Green was supported for this work by WHO contracts APW 201255146 and 201255393. Publisher Copyright: © 2017 World Health Organization, Journal of the Royal Statistical Society: Series C (Applied Statistics) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society.

PY - 2018/1

Y1 - 2018/1

KW - Air pollution

KW - Bayesian hierarchical modelling

KW - Data fusion

KW - Environmental health effects

KW - Global burden of disease

KW - Integrated nested Laplace approximations

KW - Spatial modelling

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U2 - 10.1111/rssc.12227

DO - 10.1111/rssc.12227

M3 - Article

AN - SCOPUS:85020394880

SN - 0035-9254

VL - 67

SP - 231

EP - 253

JO - Journal of the Royal Statistical Society. Series C: Applied Statistics

JF - Journal of the Royal Statistical Society. Series C: Applied Statistics

IS - 1

ER -

Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution

Keywords

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