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Supervisors:
Gwyneth Davies and Ofran Almossawi
Project Description:
Background
Cystic fibrosis (CF) is a life shortening autosomal recessive condition affecting over 11,000 people in the UK(1). It is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene and the commonest genetic variant is F508del. Over the past decade, new highly effective therapies that correct the underlying molecular defect in CFTR have become available in routine clinical care for people with at least one copy of F508del. Around 90% of the UK’s CF population have access to these treatments, called CFTR modulators, which are rapidly transforming clinical outcomes. However for the remaining 10%, the clinical picture is often one of significant morbidity and early mortality. These people are disproportionately from minority ethnic groups(2).
Clinical outcomes such as lung function have been found to be associated with socioeconomic status in CF but the literature pre-dates the introduction of CFTR modulators into routine care(3). It is unknown whether health inequalities have now narrowed or remain. Intersectional considerations include race and ethnicity, and CFTR genetics. A further key consideration relates to how measures of lung function are considered. Lung function measured by spirometry is recorded using percent predicted values, which summarise status according to expected values in health. In the UK these are currently based on equations which consider race and ethnicity, however new race-neutral equations are now preferred and endorsed globally(4). The extent to which these influence the magnitude of any health inequalities requires investigation.
This project will be conducted using UK CF Registry data. It collects detailed genetic, demographic, lung function and treatment data from >99% of the UK CF population. Appropriate statistical methodology for data analytics and innovative methods of presenting findings which are accessible to lay and professional stakeholder groups will be undertaken to quantify health gaps.
Aims/Objectives
1. Investigate the impact of CFTR modulators on clinical outcomes according to socioeconomic status, taking account of intersectional considerations (CFTR genetics and race/ethnicity).
2. Investigate the impact of GLI-Global (race neutral) lung function equations(5) on quantifying any health inequalities
3. Consider how population data should be summarised to appropriately reflect the increasingly heterogeneous CF population.
Methods
This project will use data from the UK CF Registry. A literature review will be undertaken to inform the study protocol and data request. Skills and training will be in data science including optimal statistical approaches for analysis, tailored to the requirements of the successful candidate. Importantly, this project will also involve people from the UK CF community to ensure that research questions addressed will have the greatest impact for under-represented groups, working with key stakeholders including the UK CF Trust to disseminate results. The supervisory team has relevant clinical and statistical expertise, and the student will also join the Child Health Informatics Group.
Timeline
Months 1-6: Literature review, protocol development and CF Registry data request
Months 6-12: Protocol refinement, skills development and training
Months 9-30: Data analysis, training, engagement with CF community members
Months 24-33: Dissemination to lay and professional communities
Months 30-36: Thesis write up
References
1. UK Cystic Fibrosis Registry 2022 annual data report, published September 2023. Available from https://www.cysticfibrosis.org.uk/about-us/uk-cf-registry/reporting-and-....
2. Desai M et al. Respir Med. 2022 Aug:199:106878. doi: 10.1016/j.rmed.2022.106878
3. Taylor-Robinson DC et al. Lancet Respir Med. 2013 Apr;1(2):121-8.
4. Bhakta NR et al. Am J Respir Crit Care Med 2023 Apr 15;207(8):978-995
5. Bowerman C et al. Am J Respir Crit Care Med 2023;207:768-774
Contact Information:
Gwyneth Davies