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

• Published in: Spatial and spatio-temporal epidemiology Vol. 36; p. 100392
• Main Authors: Johnson, Olatunji, Gatheral, Tim, Knight, Jo, Giorgi, Emanuele
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Ltd 01.02.2021
• Subjects: COPD; Early warning system; Exceedance probabilities; Generalised linear mixed model; Hospitalization; Humans; Incidence; Likelihood Functions; Pulmonary Disease, Chronic Obstructive - diagnosis; Pulmonary Disease, Chronic Obstructive - epidemiology; Pulmonary Disease, Chronic Obstructive - etiology; Spatio-temporal models; Weather; Spatio-temporal models; Generalised linear mixed model; Exceedance probabilities; COPD; Early warning system
• ISSN: 1877-5845; 1877-5853
• DOI: 10.1016/j.sste.2020.100392

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.