Imperial College London

ProfessorMartaBlangiardo

Faculty of MedicineSchool 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,
author = {Blangiardo, M and Boulieri, A and Diggle, P and Piel, F and Shaddick, G and Elliott, P},
journal = {International Journal of Epidemiology},
title = {Advances in spatio-temporal models for non-communicable disease surveillance},
url = {http://hdl.handle.net/10044/1/71630},
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Surveillance systems are commonly used to provide early warning detection or to assess an impact of an intervention/policy. Traditionally, the methodological and conceptual frameworks for surveillance have been designed for infectious diseases, but the rising burden of non-communicable diseases (NCDs) worldwide suggests a pressing need for surveillance strategies to detect unusual patterns in the data and to help unveil important risk factors in this setting. Surveillance methods need to be able to detect meaningful departures from expectation and exploit dependencies within such data to produce unbiased estimates of risk as well as future forecasts. This has led to the increasing development of a range of space-time methods specifically designed for NCD surveillance.We present an overview of recent advances in spatio-temporal disease surveillance for NCDs using hierarchically specified models. This provides a coherent framework for modelling complex data structures, dealing with data sparsity, exploiting dependencies between data sources and propagating the inherent uncertainties present in both the data and the modelling process. We then focus on three commonly used models within the Bayesian Hierarchical Model (BHM) framework and through a simulation study we compare their performance.We also discuss some challenges faced by researchers when dealing with NCD surveillance, including how to account for false detection and the modifiable areal unit problem. Finally, we consider how to use and interpret the complex models, how model selection may vary depending on the intended user group and how best to communicate results to stakeholders and the general public.
AU - Blangiardo,M
AU - Boulieri,A
AU - Diggle,P
AU - Piel,F
AU - Shaddick,G
AU - Elliott,P
SN - 1464-3685
TI - Advances in spatio-temporal models for non-communicable disease surveillance
T2 - International Journal of Epidemiology
UR - http://hdl.handle.net/10044/1/71630
ER -