The 'Data Analysis and Real World Interrogation Network (DARWIN EU)' is an initiative created by the European Medicines Agency (EMA) to generate timely evidence from healthcare data sources from across Europe.
One area of research relates to estimating how common a specific condition is among the general population. We will calculate how many people are affected by a condition during a specific period or time point ("prevalence"), and how many people were newly diagnosed with a condition during a specific time period ("incidence"). We will calculate these in subgroups of the population based on age and/or sex, and over calendar years or months to look at trends over time.
In addition, we will look at characteristics of newly diagnosed people such as age and sex, and look at how many people die due to the condition of interest.
The EMA will request several studies of the same design to assess how common a specific conditions are in the population and how many people die from these conditions. This will help the regulators to inform preventative measures that could be introduced to reduce disease spread or disease burden, and subsequently reduce risk for mortality for affected people wherever possible. The first example will focus on a specific respiratory virus “Respiratory Syncytial Virus” in combination with other respiratory diseases such as COVID-19 or the flu.
Primary care records provide a unique source of data for estimating the population-level incidence and prevalence of specific diseases, and mortality rates in people affected by these diseases. The “Data Analysis and Real World Interrogation Network (DARWIN EU)” initiative created by the European Medicines Agency (EMA) intends to draw upon such data for regulatory decision making: such studies could help to assess disease burden in the population, understand the impact of measures to reduce mortality in affected patients and, for rare diseases, provide the possibility of faster approval of new, innovative treatments through a different regulatory pathway for orphan medicines. EMA will therefore request several studies assessing the natural history of diseases.
Study design: Cohort study
Population: All people in CPRD GOLD and CPRD AURUM with >=1 year of prior history comprise the source population. Among those, people with the pre-specified disease of regulatory interest will be selected for characterisation and survival analyses. Where the condition is typically requiring hospitalisation, we will use CPRD linked to HES. Data sources will be mapped to the OMOP common data model (CDM) prior to analysis.
Variables: Conditions will be identified based on SNOMED codes in the mapped data (CPRD and/or HES). Date of death will be retrieved from CPRD.
Diseases of regulatory interest:
- Respiratory Syncytial Virus in co-infection with other respiratory viruses
- Interstitial Lung Disease
Additional diseases of regulatory interest will be declared in future protocol amendments upon request by EMA.
Analyses:
(1) Prevalence of the disease of regulatory interest
(2) Incidence of the respective disease
(3) Summary characteristics of people diagnosed with respective disease
(4) All-cause mortality rates among people with respective disease
(5) Rates of death due to the respective disease among people with that disease
All analyses will be stratified by age, sex, and calendar year where relevant.
Incidence and/or prevalence of disease of regulatory interest, summary characteristics (demographics, comorbidities, comedication) of people diagnosed with the disease of regulatory interest, mortality rates (all-cause or disease-specific).
Diseases of regulatory interest:
- Respiratory Syncytial Virus in co-infection with other respiratory viruses
- Intestinal Lung Disease incl. common subtypes (alveolitis/pneumonitis and lung fibrosis)
Daniel Prieto-Alhambra - Chief Investigator - University of Oxford
Annika Jodicke - Corresponding Applicant - University of Oxford
Albert Prats Uribe - Collaborator - University of Oxford
Antonella Delmestri - Collaborator - University of Oxford
Edward Burn - Collaborator - University of Oxford
Eng Hooi Tan - Collaborator - University of Oxford
Hezekiah Omulo - Collaborator - University of Oxford
Kim López-Güell - Collaborator - University of Oxford
Mandickel Kamtengeni - Collaborator - University of Oxford
Marta Pineda Moncusi - Collaborator - University of Oxford
Martí Català Sabaté - Collaborator - University of Oxford
Mike Du - Collaborator - University of Oxford
Wai Yi Man - Collaborator - University of Oxford
Xintong Li - Collaborator - University of Oxford
Yuchen Guo - Collaborator - University of Oxford
HES Admitted Patient Care