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. 2016 Nov:19:28-36.
doi: 10.1016/j.sste.2016.05.004. Epub 2016 Jun 11.

Investigating trends in asthma and COPD through multiple data sources: A small area study

Areti Boulieri  1 , Anna Hansell  2 , Marta Blangiardo  3
Affiliations

Investigating trends in asthma and COPD through multiple data sources: A small area study

Areti Boulieri et al. Spat Spatiotemporal Epidemiol. 2016 Nov.

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.

Keywords: Asthma and COPD; Detection; Space-time analysis.

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Figures

Fig. 1
Fig. 1
Spatial patterns of chronic respiratory disease across England for GP drugs (a), admissions (b), and deaths (c); heatmap showing correspondence across the three data sources (d).
Fig. 2
Fig. 2
Temporal trends in chronic respiratory disease across different data sources.
Fig. 3
Fig. 3
Temporal trend across different data sources for Isle of Wight.
Fig. 4
Fig. 4
Unusual temporal trends under HES admissions data.
See this image and copyright information in PMC

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