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

Duncan Lee, Sabyasachi Mukhopadhyay, Alastair Rushworth, Sujit K. Sahu

Research output: Contribution to journalArticle

11 Citations (Scopus)
24 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)370-385
Number of pages16
JournalBiostatistics
Volume18
Issue number2
Early online date24 Dec 2016
DOIs
Publication statusPublished - 30 Apr 2017

Keywords

  • air pollution estimation
  • Bayesian spatio-temporal modeling
  • health effects analysis
  • spatio-temporal fusion model
  • disease data
  • respiratory hospitalizations

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