A rigorous statistical framework for spatio-temporal pollution prediction and estimation of its long-term impact on health

Lee, Duncan and Mukhopadhyay, Sabyasachi and Rushworth, Alastair and Sahu, Sujit K. (2017) A rigorous statistical framework for spatio-temporal pollution prediction and estimation of its long-term impact on health. Biostatistics, 18 (2). pp. 370-385. ISSN 1465-4644 (https://doi.org/10.1093/biostatistics/kxw048)

[thumbnail of Lee-etal-Biostatistics2016-rigorous-statistical-framework-for-spatio-temporal-pollution]
Preview
 Text. Filename: Lee_etal_Biostatistics2016_rigorous_statistical_framework_for_spatio_temporal_pollution.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (578kB)| Preview

Abstract

In the United Kingdom, air pollution is linked to around 40000 premature deaths each year, but estimating its health effects is challenging in a spatio-temporal study. The challenges include spatial misalignment between the pollution and disease data; uncertainty in the estimated pollution surface; and complex residual spatio-temporal autocorrelation in the disease data. This article develops a two-stage model that addresses these issues. The first stage is a spatio-temporal fusion model linking modeled and measured pollution data, while the second stage links these predictions to the disease data. The methodology is motivated by a new five-year study investigating the effects of multiple pollutants on respiratory hospitalizations in England between 2007 and 2011, using pollution and disease data relating to local and unitary authorities on a monthly time scale.

ORCID iDs

Lee, Duncan, Mukhopadhyay, Sabyasachi, Rushworth, Alastair ORCID logoORCID: https://orcid.org/0000-0002-1092-0463 and Sahu, Sujit K.;
CORE (COnnecting REpositories)