A rare disease is classified as one which affects less than one in 2,000 people. Over 7,000 conditions meet this criterion and thus, whilst rare individually, collectively they impact an estimated 3.5 million people. Most of these conditions are present from birth but others are acquired later in life. Conditions present at birth tend to manifest during childhood and often have high mortality. They place a huge burden on patients, their families and healthcare services. The government backed UK Rare Diseases Framework (UKRDF) aims to improve the lives of patients with rare diseases by optimising time to diagnosis and improving services and treatment. The RareCare Study is a collaboration of healthcare professionals, researchers, and patient advocacy groups funded by the National Institute for Healthcare Research (NIHR) to evaluate the impact that the UKRDF has on patients’ lives. The RareCare study will collect a wide range of information including that from electronic databases and patient interviews. As part of this, our study will assess how useful the Clinical Practice Research Datalink and linked Hospital Episode Statistics is in being able to select patients with the most common rare diseases. We will then assess how many people have each condition and other factors including the time it takes to be diagnosed, the age of people when diagnosed and whether early versus later diagnosis affects how often patients use healthcare services. This data will be important in assessing the impact that the UKRDF has on improving the lives of patients with rare diseases.
Whilst an individual rare disease impacts relatively few patients, there are 7,000 rare diseases collectively affecting 3.5 million patients. Many of these diseases are particularly severe but time to diagnosis and subsequent management can be sub-optimal. The UK Rare Diseases Framework (UKRDF) aims to improve the lives of patients with rare diseases by reducing diagnosis times and improving access to services and treatment. The RareCare Study will evaluate the success of the UKRDF. As part of this, we will characterise the baseline metrics of the most common rare diseases. The study will use the Clinical Practice Research Datalink (CPRD) Aurum database linked with Hospital Episode Statistics (HES) data and Office of National Statistics Index of Multiple Deprivation data. The primary objective is to establish whether each disease can be captured within the database. Secondary objectives are to characterise: i) prevalence ii) age at diagnosis, iii) presence of genetic testing iv) variation in “early” versus “late” diagnosis on resource use and outcomes v) initial symptoms vi) resource use around diagnosis vii) geographic variations. Code lists for each condition will be created and patients selected from both the CPRD Aurum and HES admitted patient care datasets. Due to the lack of coding granularity, particularly the ICD-10 classification, additional criteria such as biochemical results will be used if appropriate. The ability to capture each disease will be assessed and prevalence estimated. Age and characteristics of patients at diagnosis will be presented. Resource use at, and subsequent, to diagnosis, and clinical outcomes will be compared with age and gender matched controls. This study will assess how useful CPRD is in estimating these metrics and provide data to benchmark changes in practice following the implementation of the UKRDF to evaluate how successful it is in improving the lives and management of patients with rare diseases.
Prevalence; time to diagnosis; symptoms; utilisation of clinical genetics; primary care contacts; inpatient admissions; outpatient appointments; mortality...
Christopher Morgan - Chief Investigator - Pharmatelligence Limited t/a Human Data Sciences
Christopher Morgan - Corresponding Applicant - Pharmatelligence Limited t/a Human Data Sciences
Aron Buxton - Collaborator - Pharmatelligence Limited t/a Human Data Sciences
Craig Currie - Collaborator - Pharmatelligence Limited t/a Human Data Sciences
Vishnav Pradeep - Collaborator - Pharmatelligence Limited t/a Human Data Sciences
HES Admitted Patient Care;HES Outpatient;ONS Death Registration Data;Patient Level Index of Multiple Deprivation