A modelling framework for developing early warning systems of COPD emergency admissions.
COPD
Early warning system
Exceedance probabilities
Generalised linear mixed model
Spatio-temporal models
Journal
Spatial and spatio-temporal epidemiology
ISSN: 1877-5853
Titre abrégé: Spat Spatiotemporal Epidemiol
Pays: Netherlands
ID NLM: 101516571
Informations de publication
Date de publication:
02 2021
02 2021
Historique:
received:
15
04
2020
revised:
22
10
2020
accepted:
06
11
2020
entrez:
29
1
2021
pubmed:
30
1
2021
medline:
26
4
2022
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 33509425
pii: S1877-5845(20)30070-8
doi: 10.1016/j.sste.2020.100392
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
100392Subventions
Organisme : Department of Health
Pays : United Kingdom
Informations de copyright
Copyright © 2020. Published by Elsevier Ltd.