Exploring Chronic Respiratory Disease Care using Statistical Modelling and Routine Data

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

Optimisation of laboratory methods for whole transcriptomic RNA analyses in human left ventricular biopsies and blood samples of clinical relevance

This study aimed to optimise techniques for whole transcriptome and small RNA analyses on clinical tissue samples from patients with cardiovascular disease. Clinical samples often represent a particular challenge to extracting RNA of sufficient quality for robust RNA sequencing analysis, and due to availability, it is rarely possible to optimise techniques on the samples themselves. Therefore, we have used equivalent samples from pigs undergoing cardiopulmonary bypass surgery to test different protocols for optimal RNA extraction, and then validated the protocols in human samples. Here we present an assessment of the quality and quantity of RNA obtained using a variety of commercially-available RNA extraction kits on both left ventricular biopsies and blood plasma. RNA extraction from these samples presents different difficulties; left ventricular biopsies are small and fibrous, while blood plasma has a low RNA content. We have validated our optimised extraction techniques on human clinical samples collected as part of the ARCADIA (Association of non-coding RNAs with Coronary Artery Disease and type 2 Diabetes) cohort study, resulting in successful whole transcriptome and small RNA sequencing of human left ventricular tissue

Displaced risk. Keeping mothers and babies safe: a UK ambulance service lens

Aim: The aim of this professional practice paper is to provide a critical commentary on displaced risk among perinatal and neonatal patients attended to by the ambulance service. Background: NHS services across the United Kingdom are currently facing unprecedented demand and increased scrutiny in their ability to provide safe and personalised care to patients. While current focus in the system centres around addressing social care demand, hospital bed capacity, planned care waiting times, staffing and ambulance handover delays, a less explored cohort of patients impacted by the current healthcare crisis is perinatal and neonatal populations attended to by the ambulance service. Little focus has been paid within national agendas to the care provided to women and babies outside of planned maternity and obstetric care. A case is presented to highlight the importance of considering urgent and emergency maternity care provision provided by the ambulance service, and the impact of ‘displaced risk’ due to the current pressures within healthcare systems. Conclusion: Placed in a national context, drawing upon current independent reviews into maternity services, national transformation agendas and the most recent MBRRACE-UK confidential enquiry into maternal deaths and morbidity, a case is made to commissioners and Integrated Care Systems to focus on and invest in the unplanned pre-hospital care of maternity and neonatal patients. Recognition of the ambulance service as a key provider of care to this cohort of patients is paramount, calling on services and systems to work together on realising and addressing displaced risk for perinatal populations across the United Kingdom. A system approach that acknowledges the need for high-quality care at every point of contact and equitability in access to services for pregnant, postpartum and neonatal patients is vital

Spatio-temporal modelling of referrals to outpatient respiratory clinics in the integrated care system of the Morecambe Bay area, England

Background: Promoting integrated care is a key goal of the NHS Long Term Plan to improve population respiratory health, yet there is limited data-driven evidence of its effectiveness. The Morecambe Bay Respiratory Network is an integrated care initiative operating in the North-West of England since 2017. A key target area has been reducing referrals to outpatient respiratory clinics by upskilling primary care teams. This study aims to explore space-time patterns in referrals from general practice in the Morecambe Bay area to evaluate the impact of the initiative. Methods: Data on referrals to outpatient clinics and chronic respiratory disease patient counts between 2012-2020 were obtained from the Morecambe Bay Community Data Warehouse, a large store of routinely collected healthcare data. For analysis, the data is aggregated by year and small area geography. The methodology comprises of two parts. The first explores the issues that can arise when using routinely collected primary care data for space-time analysis and applies spatio-temporal conditional autoregressive modelling to adjust for data complexities. The second part models the rate of outpatient referral via a Poisson generalised linear mixed model that adjusts for changes in demographic factors and number of respiratory disease patients. Results: The first year of the Morecambe Bay Respiratory Network was not associated with a significant difference in referral rate. However, the second and third years saw significant reductions in areas that had received intervention, with full intervention associated with a 31.8% (95% CI 17.0-43.9) and 40.5% (95% CI 27.5-50.9) decrease in referral rate in 2018 and 2019, respectively. Conclusions: Routinely collected data can be used to robustly evaluate key outcome measures of integrated care. The results demonstrate that effective integrated care has real potential to ease the burden on respiratory outpatient services by reducing the need for an onward referral. This is of great relevance given the current pressure on outpatient services globally, particularly long waiting lists following the COVID-19 pandemic and the need for more innovative models of care

Spatio-temporal modelling of referrals to outpatient respiratory clinics in the integrated care system of the Morecambe Bay area, England

Abstract Background Promoting integrated care is a key goal of the NHS Long Term Plan to improve population respiratory health, yet there is limited data-driven evidence of its effectiveness. The Morecambe Bay Respiratory Network is an integrated care initiative operating in the North-West of England since 2017. A key target area has been reducing referrals to outpatient respiratory clinics by upskilling primary care teams. This study aims to explore space-time patterns in referrals from general practice in the Morecambe Bay area to evaluate the impact of the initiative. Methods Data on referrals to outpatient clinics and chronic respiratory disease patient counts between 2012-2020 were obtained from the Morecambe Bay Community Data Warehouse, a large store of routinely collected healthcare data. For analysis, the data is aggregated by year and small area geography. The methodology comprises of two parts. The first explores the issues that can arise when using routinely collected primary care data for space-time analysis and applies spatio-temporal conditional autoregressive modelling to adjust for data complexities. The second part models the rate of outpatient referral via a Poisson generalised linear mixed model that adjusts for changes in demographic factors and number of respiratory disease patients. Results The first year of the Morecambe Bay Respiratory Network was not associated with a significant difference in referral rate. However, the second and third years saw significant reductions in areas that had received intervention, with full intervention associated with a 31.8% (95% CI 17.0-43.9) and 40.5% (95% CI 27.5-50.9) decrease in referral rate in 2018 and 2019, respectively. Conclusions Routinely collected data can be used to robustly evaluate key outcome measures of integrated care. The results demonstrate that effective integrated care has real potential to ease the burden on respiratory outpatient services by reducing the need for an onward referral. This is of great relevance given the current pressure on outpatient services globally, particularly long waiting lists following the COVID-19 pandemic and the need for more innovative models of care

Data from the InVITe RCT

Data from the InVITe randomised controlled trial. Eighteen participants aged 12-80 years old undergoing pulmonary valve replacement (PVR), PVR with Atrial Septal Defect (ASD) amenable to closure via cardiac catheter or PVR and right ventricular outlet tract (RVOT) reconstruction not requiring cardiopulmonary bypass (CPB) were eligible for the trial. The required pulmonary valve annulus had to be at least adult size (25-31mm). Patients requiring other anatomical heart corrections requiring CPB (including RVOT reconstruction) or an intra-cardiac shunt repair requiring CPB were excluded. This dataset excludes one participant who did not consent for data to be used for future research. The deposit also include the InVITe statistical analysis plan (SAP