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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

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

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A modelling framework for developing early warning systems of COPD emergency admissions. / Johnson, Olatunji; Knight, Jo; Giorgi, Emanuele.

In: Spatial and Spatio-temporal Epidemiology, Vol. 36, 100392, 01.02.2021.

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@article{25429e0557a9430fa2e77ae0c8fbd063,
title = "A modelling framework for developing early warning systems of COPD emergency admissions",
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.",
keywords = "COPD, Early warning system, Exceedance probabilities, Generalised linear mixed model, Spatio-temporal models",
author = "Olatunji Johnson and Jo Knight and Emanuele Giorgi",
note = "This is the author{\textquoteright}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",
year = "2021",
month = feb,
day = "1",
doi = "10.1016/j.sste.2020.100392",
language = "English",
volume = "36",
journal = "Spatial and Spatio-temporal Epidemiology",
issn = "1877-5845",
publisher = "Elsevier Ltd",

}

RIS

TY - JOUR

T1 - A modelling framework for developing early warning systems of COPD emergency admissions

AU - Johnson, Olatunji

AU - Knight, Jo

AU - Giorgi, Emanuele

N1 - 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

PY - 2021/2/1

Y1 - 2021/2/1

N2 - 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.

AB - 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.

KW - COPD

KW - Early warning system

KW - Exceedance probabilities

KW - Generalised linear mixed model

KW - Spatio-temporal models

U2 - 10.1016/j.sste.2020.100392

DO - 10.1016/j.sste.2020.100392

M3 - Journal article

VL - 36

JO - Spatial and Spatio-temporal Epidemiology

JF - Spatial and Spatio-temporal Epidemiology

SN - 1877-5845

M1 - 100392

ER -