Investigating trends in asthma and COPD through multiple data sources: a small area study
File(s)1-s2.0-S1877584515300368-main.pdf (1.08 MB)
Published version
Author(s)
Boulieri, A
Blangiardo, MB
Hansell, AH
Type
Journal Article
Abstract
This paper investigates trends in asthma and COPD by using multiple data
sources to help understanding the relationships between disease prevalence, morbidity
and mortality. GP drug prescriptions, hospital admissions, and deaths are analysed
at clinical commissioning group (CCG) level in England from August 2010 to March
2011. A Bayesian hierarchical model is used for the analysis, which takes into account
the complex space and time dependencies of asthma and COPD, while it is also
able to detect unusual areas. Main findings show important discrepancies across the
different data sources, reflecting the different groups of patients that are represented.
In addition, the detection mechanism that is provided by the model, together with
inference on the spatial, and temporal variation, provide a better picture of the
respiratory health problem.
sources to help understanding the relationships between disease prevalence, morbidity
and mortality. GP drug prescriptions, hospital admissions, and deaths are analysed
at clinical commissioning group (CCG) level in England from August 2010 to March
2011. A Bayesian hierarchical model is used for the analysis, which takes into account
the complex space and time dependencies of asthma and COPD, while it is also
able to detect unusual areas. Main findings show important discrepancies across the
different data sources, reflecting the different groups of patients that are represented.
In addition, the detection mechanism that is provided by the model, together with
inference on the spatial, and temporal variation, provide a better picture of the
respiratory health problem.
Date Issued
2016-06-11
Date Acceptance
2016-05-17
Citation
Spatial and Spatio-temporal Epidemiology, 2016, 19, pp.28-36
ISSN
1877-5853
Publisher
Elsevier
Start Page
28
End Page
36
Journal / Book Title
Spatial and Spatio-temporal Epidemiology
Volume
19
Copyright Statement
© 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Sponsor
Public Health England
Grant Number
N/A
Subjects
Asthma and COPD
Detection
Space-time analysis
1117 Public Health And Health Services
0707 Veterinary Sciences
Publication Status
Published