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    Rights statement: 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

    Accepted author manuscript, 9.84 MB, PDF document

    Embargo ends: 11/11/21

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
Article number100392
<mark>Journal publication date</mark>1/02/2021
<mark>Journal</mark>Spatial and Spatio-temporal Epidemiology
Volume36
Number of pages9
Publication StatusE-pub ahead of print
Early online date11/11/20
<mark>Original language</mark>English

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.

Bibliographic note

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