@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 = "Oxford University Press",
number = "1",
}