Using Health Data Science and large-scale data to improve the lives of people with rare autoimmune rheumatic diseases and other rare diseases.

Background: A rare disease affects fewer than 1 in 2000 people. However, there are so many rare diseases that 1 in 17 people are affected by a rare disease in their lifetime.

People with rare diseases often suffer unnecessarily and die prematurely due to not being able to access appropriate healthcare or no effective treatment yet being available. Research in rare diseases has been retarded due to inability to identify large cohorts of people with a disease.

Public Health England's new National Congenital Anomaly and Rare Disease Registration Service (NCARDRS) has enabled a game-changing environment for rare diseases, by creating a national population-based register of everyone with rare diseases.

Unfortunately, NCARDRS do not yet have methods to register people with rare diseases which are not congenital anomalies nor genetic in origin.

Aim and objectives: The aim of this fellowship is to use a health data science approach to develop methods to achieve complete national registration of people with rare complex (non-genetic) diseases with NCARDRS, and use registration to improve healthcare delivery and support development of better treatments.

I will achieve this by completing the following 3 work-streams using 3 rare autoimmune rheumatic diseases as exemplars: Takayasu's arteritis, ANCA-associated vasculitis and Giant Cell arteritis.

Methods: Deliver national registration by assembling confirmed cases of each disease from data sharing with research studies, NHS networks and Blueteq, and suspected cases coded with relevant ICD-10 codes in Hospital Episode Statistics and death certificate data. I will validate the diagnosis of suspected cases by cross-reference with confirmed cases and in NHS medical notes.

I will use traditional and machine learning methods to develop algorithms to identify people in electronic health records, and then validate these methods. This will deliver prospective national registration with NCARDRS using electronic health records.

Improve healthcare delivery using the register, and linked Hospital Episode Statistics, death certificates and Bluteq data to discover information needed to plan better health services.

I will: Describe incidence and prevalence of Takayasu's arteritis stratified by age, sex and ethnicity and share this with the national commissioner to inform service planning.

I will describe important outcomes such as arterial aneurysms or stenosis, infections, cancer and death to inform patients and clinicians.

Assess geographical equity of access to high-cost drug treatments in Takayasu's arteritis and ANCA-associated vasculitis, and share the results with patients and commissioners. Support development of better treatments and their recommendation in the NHS, through evidence from real-world studies.

I will: Quantify steroid burden in Takayasu's arteritis and ANCA-associated vasculitis using linked NHS Prescription Service data, and investigate the association between cumulative steroid dose and complications/toxicity.

Measure treatment costs and frequency of outcomes in Giant Cell arteritis under current treatment regimens to inform health economic modelling of new treatments.

Anticipated impact and dissemination: This proposal aims to improve the lives of people with rare diseases by providing exemplars of how health data science can achieve national registration, and enable epidemiological studies needed to inform improvements to health service delivery and new treatments.