Monitoring the prescription in primary care of antibiotics for public health emergencies that may be at risk of shortages in the European Union

Study type
Protocol
Date of Approval
Study reference ID
23_003551
Lay Summary

The extended mandate of European Medicine Agency (EMA) reinforcing the role of the Agency in crisis preparedness and management of medicinal products and medical devices became applicable on 1st March 2022. The EMA is now responsible for monitoring medicine shortages that might lead to a crisis situation, as well as reporting shortages of critical medicines during public health emergencies. Such shortages would make it difficult or impossible to meet the treatment needs of individual patients or populations.

The general research question of this study is: What are the monthly prescription rates of selected medicines of importance for public health emergencies over the last 10-years? To answer it, this study aims to (1) characterise the incidence of use of 11 antibiotics used for public health emergencies that are considered at risk of shortages in order to understand trends, cycles and seasonality in the use of those medicines; and (2) to forecast short-term (6-month) prescription rates of such medicines under assumed scenarios, which could help anticipate and prevent potential shortages, or manage them.

The 'Data Analysis and Real World Interrogation Network (DARWIN EU)' is an initiative created by the EMA to generate timely evidence from healthcare data sources from across Europe. The EMA requested the DARWIN EU Coordination Centre to routinely and repeatly conduct this study in several European data bases, including CPRD, over the course of the 5-year study.

Technical Summary

AIM
To know what are the monthly prescription rates of selected medicines of importance for public health emergencies over the last 10-years and forecast the following 6-months.

OBJECTIVES 
(1)To estimate monthly incidence rates of use (prescription) of the 11 selected medicines during the last 10-years of available data, stratified by age and sex.
(2)To conduct a time series modelling by fitting an ARIMA model to data generated in objective 1 for short-term (6-month) forecasting.
The European Medicine Agency requested the DARWIN EU Coordination Centre to routinely and repeatly conduct this study in several European data bases, including CPRD, over the course of the 5-year study.

METHODS
Data: CPRD GOLD standardized to the Observational Medical Outcomes Partnership Common Data Model.
Exposure(s): Eleven antibiotics identified as potentially critical in public health emergencies.
Participant(s): incident users of antibiotics.
Study period: 10-year period from the most recent data available.
Analysis:
- Monthly incidence rates of the use of medicines of interest by calendar year, age groups (<18, 18-64, 65+ years) and sex.
- Fitting of the time series data generated in objective 1 into an Autoregressive Integrated Moving Average (ARIMA) model to then forecast prescription rates for the subsequent 6-months.

PUBLIC HEALTH BENEFITS
CPRD data on prescriptions is based on primary care prescribed drugs, thus, the observed behaviour is likely to not capture nor represent its use in hospital/specialists, but rather show the impact of shortages in primary care. The European Union pharmaceutical law applyed to the UK until January 2021, except for Northern Ireland, which continues. This project will be used to improve our understanding of drug shortages in routine health care delivery by showing trends over time as well as to forecast short-term prescription rates of such medicines under assumed scenarios, which could help anticipate and prevent potential shortages, or manage them.

Health Outcomes to be Measured

The main outcomes of this study are the incidence rates of the use of the 11 medicines of interest.
As a secondary outcome, we will forecast the prescription rates for the subsequent 6 months using an ARIMA model.

Collaborators

Daniel Prieto-Alhambra - Chief Investigator - University of Oxford
Marta Pineda Moncusi - Corresponding Applicant - University of Oxford
Antonella Delmestri - Collaborator - University of Oxford
Edward Burn - Collaborator - University of Oxford
Hezekiah Omulo - Collaborator - University of Oxford
Kim López-Güell - Collaborator - University of Oxford
Wai Yi Man - Collaborator - University of Oxford
Yuchen Guo - Collaborator - University of Oxford