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{Pirani:2020:10.1002/bimj.201900241,
author = {Pirani, M and Mason, A and Hansell, A and Richardson, S and Blangiardo, M},
doi = {10.1002/bimj.201900241},
journal = {Biometrical Journal: journal of mathematical methods in biosciences},
pages = {1650--1669},
title = {A flexible hierarchical framework for improving inference in area-referenced environmental health studies},
url = {http://dx.doi.org/10.1002/bimj.201900241},
volume = {62},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Study designs where data have been aggregated by geographical areas are popular in environmental epi-demiology.   These  studies  are  commonly  based  on  administrative  databases  and,  providing  a  completespatial coverage, are particularly appealing to make inference on the entire population.  However, the re-sulting estimates are often biased and difficult to interpret due to unmeasured confounders, which typicallyare not available from routinely collected data. We propose a framework to improve inference drawn fromsuch studies exploiting information derived from individual-level survey data. The latter are summarized inan area-level scalar score by mimicking at ecological-level the well-known propensity score methodology.The literature on propensity score for confounding adjustment is mainly based on individual-level studiesand assumes a binary exposure variable.   Here we generalize its use to cope with area-referenced stud-ies characterized by a continuous exposure.  Our approach is based upon Bayesian hierarchical structuresspecified into a two-stage design: (i) geolocated individual-level data from survey samples are up-scaled atecological-level, then the latter are used to estimate a generalizedecological propensity score(EPS) in thein-sample areas; (ii) the generalized EPS is imputed in the out-of-sample areas under different assumptionsabout the missingness mechanisms, then it is included into the ecological regression, linking the exposureof interest to the health outcome. This delivers area-level risk estimates which allow a fuller adjustment forconfounding than traditional areal studies.  The methodology is illustrated by using simulations and a casestudy investigating the risk of lung cancer mortality associated with nitrogen dioxide in England (UK).
AU  - Pirani,M
AU  - Mason,A
AU  - Hansell,A
AU  - Richardson,S
AU  - Blangiardo,M
DO  - 10.1002/bimj.201900241
EP  - 1669
PY  - 2020///
SN  - 0323-3847
SP  - 1650
TI  - A flexible hierarchical framework for improving inference in area-referenced environmental health studies
T2  - Biometrical Journal: journal of mathematical methods in biosciences
UR  - http://dx.doi.org/10.1002/bimj.201900241
UR  - https://onlinelibrary.wiley.com/doi/10.1002/bimj.201900241
UR  - http://hdl.handle.net/10044/1/79189
VL  - 62
ER  -
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