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Advances in spatio-temporal models for non-communicable disease surveillance

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Title: Advances in spatio-temporal models for non-communicable disease surveillance
Authors: Blangiardo, M
Boulieri, A
Diggle, P
Piel, F
Shaddick, G
Elliott, P
Item Type: Journal Article
Abstract: 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.
Issue Date: 15-Apr-2020
Date of Acceptance: 18-Jun-2019
URI: http://hdl.handle.net/10044/1/71630
DOI: 10.1093/ije/dyz181
ISSN: 1464-3685
Publisher: Oxford University Press (OUP)
Start Page: i26
End Page: i37
Journal / Book Title: International Journal of Epidemiology
Volume: 49
Issue: Supplement_1
Copyright Statement: © The Author(s) 2020. Published by Oxford University Press on behalf of the International Epidemiological Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Sponsor/Funder: Medical Research Council (MRC)
Wellcome Trust
Public Health England
Funder's Grant Number: MR/M501669/1
Keywords: Bayesian hierarchical models
non-communicable diseases
spattemporal modelling
0104 Statistics
1117 Public Health and Health Services
Publication Status: Published
Online Publication Date: 2020-04-15
Appears in Collections:School of Public Health