Evaluation of strategies for reducing the burden of COPD in the UK using Bayesian methods

Abstract

Chronic obstructive pulmonary disease (COPD) is responsible for 5.3% of all deaths and 1.7% of all hospital admissions in the UK. This thesis focuses on strategies to reduce COPD burden by targeting three aspects across the public healthcare system: prevention, emergency treatment, and long-term management. Analyses were performed in a Bayesian framework to exploit its flexibility in modelling uncertainty and the incorporation of prior knowledge. First, I assessed whether communication of personalised disease risk in primary care is an effective smoking cessation intervention, using cost-effectiveness and value of information analyses based on various data sources across the literature. The odds ratio for the effectiveness of communication of personalised disease risk was 1.48 (95%CrI:0.91-2.26). While I found a probability of cost-effectiveness of about 90%, further research up to a maximum of £27 million is justified to reduce the uncertainty around this estimate. Secondly, I assessed whether case ascertainment affects the detection of poorly performing hospital trusts in the treatment of acute exacerbation of COPD (AECOPD) in secondary care, using data from the National Asthma and COPD Audit Programme. Case ascertainment was associated with 30-day mortality (OR:1.74; 1.25-2.41) and adjusting for it impacted the findings, with 5 trusts becoming outliers and 2 trusts no longer classified as outliers. Finally, using general practice data from Clinical Practice Research Datalink, I assessed whether new guidelines suggesting triple therapy (long-acting beta-2 agonists, LABA + long-acting muscarinic antagonists, LAMA + inhaled corticosteroids, ICS) for the treatment of those with poorly-controlled COPD on LABA+LAMA dual therapy improves disease outcomes. Triple therapy was not associated with severe AECOPD (IRR:1.00; 0.93-1.07) or mortality (IRR:0.95; 0.86-1.06), but was associated with increased risk of pneumonia (IRR:1.19; 1.05-1.35). This thesis applied sophisticated Bayesian methods to increase understanding of how COPD burden could be reduced in different areas of the public healthcare system.Open Acces