TY - BOOK

T1 - Exploring Chronic Respiratory Disease Care using Statistical Modelling and Routine Data

AU - Mountain, Rachael

PY - 2024

Y1 - 2024

N2 - Chronic respiratory disease represents a significant burden to healthcare services and wider society. Patients benefit from early diagnosis and effective disease management, yet few patients in England are receiving the recommended levels of care. NHS services are increasingly under pressure from an ageing population, as well as disruption following the COVID-19 pandemic, raising important questions about how services can evolve to improve efficiency and standard of care. This thesis explores chronic respiratory disease care using two contrasting approaches. First, Chapters 2 and 3 utilise routinely collected health data from the Morecambe Bay area and provide insight into the impact of a local integrated care initiative. Spatio-temporal methodology is used to model referrals to outpatient respiratory clinics and a thorough data review is conducted to consider the challenge of measuring diagnostic quality. These studies exemplify different approaches to overcoming barriers encountered when using routine data for research purposes. Second, Chapters 4 and 5 apply a discrete-event microsimulation model for chronic obstructive pulmonary disease in the Canadian population to questions in the field of health economics and outcomes research. Simulated data is used to analyse the impact of interventions, both for identifying patients at an earlier stage in the disease progression and earlier initiation of more intensive pharmacotherapy to improve patient quality-of-life. The discussion points of these studies link to key NHS goals for respiratory disease. This thesis demonstrates the role of both routine and simulated data in healthcare research by providing insight into service utilisation, diagnostics, earlier detection of disease, and therapeutic management. However, neither approach is without limitations. Future research could focus on further developing methods for synthetic data, a means of using simulation to enhance the rich routine data landscape in England in order for research to be carried out in a safe and effective way.

AB - Chronic respiratory disease represents a significant burden to healthcare services and wider society. Patients benefit from early diagnosis and effective disease management, yet few patients in England are receiving the recommended levels of care. NHS services are increasingly under pressure from an ageing population, as well as disruption following the COVID-19 pandemic, raising important questions about how services can evolve to improve efficiency and standard of care. This thesis explores chronic respiratory disease care using two contrasting approaches. First, Chapters 2 and 3 utilise routinely collected health data from the Morecambe Bay area and provide insight into the impact of a local integrated care initiative. Spatio-temporal methodology is used to model referrals to outpatient respiratory clinics and a thorough data review is conducted to consider the challenge of measuring diagnostic quality. These studies exemplify different approaches to overcoming barriers encountered when using routine data for research purposes. Second, Chapters 4 and 5 apply a discrete-event microsimulation model for chronic obstructive pulmonary disease in the Canadian population to questions in the field of health economics and outcomes research. Simulated data is used to analyse the impact of interventions, both for identifying patients at an earlier stage in the disease progression and earlier initiation of more intensive pharmacotherapy to improve patient quality-of-life. The discussion points of these studies link to key NHS goals for respiratory disease. This thesis demonstrates the role of both routine and simulated data in healthcare research by providing insight into service utilisation, diagnostics, earlier detection of disease, and therapeutic management. However, neither approach is without limitations. Future research could focus on further developing methods for synthetic data, a means of using simulation to enhance the rich routine data landscape in England in order for research to be carried out in a safe and effective way.

U2 - 10.17635/lancaster/thesis/2285

DO - 10.17635/lancaster/thesis/2285

M3 - Doctoral Thesis

PB - Lancaster University

ER -