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 real-world healthcare data sources from across Europe. One area of research relates to estimating how often a specific drug of interest is prescribed and how common a specific condition relating to the drug is among the general population. For regulators such as the EMA, it is important to have access to this kind information as it helps to understand the uptake of specific drugs over time and distribution of potential health outcomes.
The objective of this study is to estimate the new users (“incidence”) and existing users (“prevalence”) of a medicine. Secondly, to estimate those affected by (“prevalence”) or newly diagnosed (“incidence”) with a condition during a specific period of time. All these will be calculated in specific populations of interest based on age and sex groups, and over calendar years to look at possible changes over time. In addition, we will look at characteristics of newly diagnosed people such as age and sex, comorbidities, and prescriptions of other medications.
The EMA will request several studies of the same design to assess how often the population takes a certain drug and how common specific conditions are in the population. This will guide regulators in establishing preventative measures to reduce the health condition. The first example will focus on the use of a medicine called “paracetamol” and examine how often people overdose on the medication.
Primary care records provide a unique source of data for estimating the incidence and prevalence of medication use in the community and related health conditions, as most medicines are prescribed by general practitioners. The DARWIN EU initiative created by the European Medicines Agency (EMA) intends to draw upon such data to inform regulatory decision-making. To understand the uptake of specific drugs of interest and potential health outcomes related to specific drugs of interest for assessing disease burden in the population.
Study design: Cohort study
Population: All people in CPRD GOLD with >=1 year of prior history comprise the source population. Among those, people with the pre-specified condition of regulatory interest will be selected for characterisation. 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: Drug exposure based on prescription data as available within CPRD GOLD. Drugs will be selected by means of the respective RxNorm codes. Conditions will be identified based on SNOMED codes in the mapped data (CPRD and/or HES). Date of death will be retrieved from CPRD.
Drug of interest as expressed by EMA:
• Paracetamol
• Acitretin
Conditions of regulatory interest:
• Paracetamol overdose
• Purpura and related conditions
Additional medicines and conditions of regulatory interest will be declared in future protocol amendments upon request by EMA to the DARWIN Coordination Centre.
Analyses:
(1) Incidence/prevalence of medications of regulatory interest
(2) Incidence/prevalence of conditions of regulatory interest
(3) Summary of characteristics of people with specific conditions of regulatory interest
(4) Utilisation of pre-specified medicines in terms of duration of use, dose or treatment sequence.
All analyses will be stratified by age, sex, and calendar year where relevant.
Incidence and/or prevalence of drug of regulatory interest, incidence and/or prevalence of condition of regulatory interest, summary characteristics (demographics, comorbidities, comedication, incidence of short-term complications and mortality) of people diagnosed with the condition of regulatory interest. Drug utilisation
Condition of regulatory interest:
• Paracetamol overdose
• Purpura and related conditions
Daniel Prieto-Alhambra - Chief Investigator - University of Oxford
Wanning Wang - Corresponding Applicant - University of Oxford
Albert Prats Uribe - Collaborator - University of Oxford
Annika Jodicke - Collaborator - University of Oxford
Antonella Delmestri - Collaborator - University of Oxford
Eng Hooi Tan - Collaborator - University of Oxford
Hezekiah Omulo - Collaborator - University of Oxford
Mandickel Kamtengeni - Collaborator - University of Oxford
Martí Català Sabaté - Collaborator - University of Oxford
Mike Du - Collaborator - University of Oxford
Wai Yi Man - Collaborator - University of Oxford
Xihang Chen - Collaborator - University of Oxford
Xintong Li - Collaborator - University of Oxford
Yuchen Guo - Collaborator - University of Oxford
HES Admitted Patient Care