A modelling framework for developing early warning systems of COPD emergency admissions

Johnson, Olatunji and Knight, Jo and Giorgi, Emanuele (2021) A modelling framework for developing early warning systems of COPD emergency admissions. Spatial and Spatio-temporal Epidemiology, 36. ISSN 1877-5845

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Abstract

Chronic Obstructive Pulmonary Disease (COPD) is one of the leading causes of mortality worldwide and is a major contributor to the number of emergency admissions in the UK. We introduce a modelling framework for the development of early warning systems for COPD emergency admissions. We analyse the number of COPD emergency admissions using a Poisson generalised linear mixed model. We group risk factors into three main groups, namely pollution, weather and deprivation. We then carry out variable selection within each of the three domains of COPD risk. Based on a threshold of incidence rate, we then identify the model giving the highest sensitivity and specificity through the use of exceedance probabilities. The developed modelling framework provides a principled likelihood-based approach for detecting the exceedance of thresholds in COPD emergency admissions. Our results indicate that socio-economic risk factors are key to enhance the predictive power of the model.

Item Type:
Journal Article
Journal or Publication Title:
Spatial and Spatio-temporal Epidemiology
Additional Information:
This is the author’s version of a work that was accepted for publication in Spatial and Spatio-temporal Epidemiology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial and Spatio-temporal Epidemiology, 36, 2020 DOI: 10.1016/j.sste.2020.100392
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/3300/3305
Subjects:
ID Code:
149106
Deposited By:
Deposited On:
16 Nov 2020 10:45
Refereed?:
Yes
Published?:
Published
Last Modified:
24 Nov 2020 08:59