Medicine/s regulators have created faster pathways for the approval of treatments for rare diseases, sometimes called orphan medicines. In order for a disease to be classified as rare, we need to know how common it is. Usual measures of this are called prevalence and incidence.
Our objective is to assess the incidence and prevalence of rare diseases in the UK, as part of an international effort to obtain similar measures from other European countries. Rare blood cancers will be studied first, followed by other rare diseases to be declared in future protocol amendments. Obtaining this information will help regulators around the world on which medicines should be prioritised or fast tracked.
We will calculate prevalence as the number of people affected by a condition during a specific period or time point, and incidence as the number of people newly diagnosed with a condition during a specific time period. We will calculate these in specific populations of interest based on age and sex, and over calendar years or months to look at trends over time.
NHS records provide a unique source of data that for estimating the incidence and prevalence of health conditions, including rare diseases. The “Data Analysis and Real World Interrogation Network (DARWIN EU)”(c) initiative [1] created by the European Medicines Agency (EMA) intends to draw upon such data to inform decision making for orphan medicine/s.
We aim to create algorithms to identify rare diseases in CPRD GOLD and CPRD AURUM data, and to estimate the incidence and/or prevalence of rare diseases listed in Orphanet (www.orpha.net). Rare blood cancers will be studied first, followed by additional rare diseases in subsequent protocol amendments. Data will be mapped to the OMOP common data model (CDM) prior to analysis. Linkage to HES APC will improve the completeness of primary care records.
Study design
Population-based cohort study
Population
All people in CPRD GOLD or AURUM with >=1 year of prior history will be eligible, with sensitivity analyses using different prior history requirements.
Variables
Rare diseases will be identified based on pre-specified diagnosis codes in OMOP-mapped data (e.g. SNOMED). Multiple myeloma, acute lymphocytic leukaemia, chronic lymphocytic leukaemia, acute myeloid leukaemia, diffuse Large B-Cell Lymphoma, and follicular lymphoma will be identified for the first analysis.
Analyses
Point and period prevalence will be estimated for all rare diseases, and partial prevalence for acute conditions that can be cured like blood cancers, where individuals will be considered as prevalent if they had a record in the prior 5 years. Similarly, incidence will be estimated for all conditions of interest, using a pre-specified 1-year washout. Analyses will be stratified by age, sex, and calendar year.
At the request of the EMA, these DARWIN EU analyses will be repeated routinely e.g. annually, using the most recently available data over the course of the 5 year study.
1.Rare blood cancers: multiple myeloma, acute lymphocytic leukaemia, chronic lymphocytic leukaemia, acute myeloid leukaemia, diffuse Large B-Cell Lymphoma, and follicular lymphoma
2.Additional rare diseases from the Orphanet inventory will be identified as of interest by DARWIN EU, and studied similarly after declaring them as outcomes of interest in subsequent amendments to this application
Daniel Prieto-Alhambra - Chief Investigator - University of Oxford
Daniel Prieto-Alhambra - Corresponding Applicant - University of Oxford
Albert Prats Uribe - Collaborator - University of Oxford
Annika Jodicke - Collaborator - University of Oxford
Daniel Dedman - Collaborator - CPRD
Edward Burn - Collaborator - University of Oxford
Junqing Xie - Collaborator - University of Oxford
Martí Català Sabaté - Collaborator - University of Oxford
Xintong Li - Collaborator - University of Oxford
Zara Cuccu - Collaborator - CPRD
HES Admitted Patient Care