Imperial College London
Alternate

ProfessorMartaBlangiardo

Faculty of Medicine, School of Public Health

Chair in Biostatistics
 
 
 
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Contact

 

m.blangiardo Website

 
 
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Location

 

528Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Blangiardo:2016:10.1016/j.sste.2016.03.001,
author = {Blangiardo, MAG and Finazzi, F and Cameletti, M},
doi = {10.1016/j.sste.2016.03.001},
journal = {Spatial and Spatio-temporal Epidemiology},
pages = {1--12},
title = {Two-stage Bayesian model to evaluate the effect of air pollution on chronic respiratory diseases using drug prescriptions},
url = {http://dx.doi.org/10.1016/j.sste.2016.03.001},
volume = {18},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Exposure to high levels of air pollutant concentration is known to be associatedwith respiratory problems which can translate into higher morbidity andmortality rates. The link between air pollution and population health hasmainly been assessed considering air quality and hospitalization or mortalitydata. However this approach limits the analysis to individuals characterizedby severe conditions. In this paper we evaluate the link between air pollutionand respiratory diseases using general practice drug prescriptions for chronicrespiratory diseases, which allow to draw conclusions based on the generalpopulation.We propose a two-stage statistical approach: in the first stage we specifya space-time model to estimate the monthly NO2 concentration integratingseveral data sources characterized by different spatio-temporal resolution;in the second stage we link the concentration to the β2-agonists prescribedmonthly by general practices in England and we model the prescription ratesthrough a small area approach.
AU  - Blangiardo,MAG
AU  - Finazzi,F
AU  - Cameletti,M
DO  - 10.1016/j.sste.2016.03.001
EP  - 12
PY  - 2016///
SN  - 1877-5853
SP  - 1
TI  - Two-stage Bayesian model to evaluate the effect of air pollution on chronic respiratory diseases using drug prescriptions
T2  - Spatial and Spatio-temporal Epidemiology
UR  - http://dx.doi.org/10.1016/j.sste.2016.03.001
UR  - http://hdl.handle.net/10044/1/30397
VL  - 18
ER  -
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