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 language | English |
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Pages (from-to) | 370-385 |
Number of pages | 16 |
Journal | Biostatistics |
Volume | 18 |
Issue number | 2 |
Early online date | 24 Dec 2016 |
DOIs | |
Publication status | Published - 30 Apr 2017 |
Keywords
- air pollution estimation
- Bayesian spatio-temporal modeling
- health effects analysis
- spatio-temporal fusion model
- disease data
- respiratory hospitalizations