DARWIN EU® Monitoring prescription of medicines for public health emergencies at risk of shortages

Study type

• Study topic:
  Human medicinal product
• Study type:
  Non-interventional study
• Scope of the study:
  Drug utilisation
• Data collection methods:
  Secondary use of data

Non-interventional study

• Non-interventional study design:
  Cohort

Study drug and medical condition

• Study drug International non-proprietary name (INN) or common name:
  AMOXICILLIN

  AZITHROMYCIN

  BENZYLPENICILLIN

  CEFTRIAXONE

  CLAVULANIC ACID

  MEROPENEM TRIHYDRATE

  PHENOXYMETHYLPENICILLIN POTASSIUM

  TAZOBACTAM SODIUM
• Anatomical Therapeutic Chemical (ATC) code:
  (J01CA04) amoxicillin

  amoxicillin

  (J01CE01) benzylpenicillin

  benzylpenicillin

  (J01CE02) phenoxymethylpenicillin

  phenoxymethylpenicillin

  (J01CR02) amoxicillin and beta-lactamase inhibitor

  amoxicillin and beta-lactamase inhibitor

  (J01CR05) piperacillin and beta-lactamase inhibitor

  piperacillin and beta-lactamase inhibitor

  (J01DC02) cefuroxime

  cefuroxime

  (J01DD01) cefotaxime

  cefotaxime

  (J01DD04) ceftriaxone

  ceftriaxone

  (J01DH02) meropenem

  meropenem

  (J01FA09) clarithromycin

  clarithromycin

  (J01FA10) azithromycin

  azithromycin

Population studied

• Short description of the study population:
  All individuals present in the database in the last 10 years of available data were included in the analysis.
• Age groups:
  • Preterm newborn infants (0 – 27 days)
  • Term newborn infants (0 – 27 days)
  • Infants and toddlers (28 days – 23 months)
  • Children (2 to < 12 years)
  • Adolescents (12 to < 18 years)
  • Adults (18 to < 46 years)
  • Adults (46 to < 65 years)
  • Adults (65 to < 75 years)
  • Adults (75 to < 85 years)
  • Adults (85 years and over)
• Estimated number of subjects:
  59.800.000

Study design details

• Study design:
  • Population-level cohort (Objective 1, Population-level drug utilisation study) ; • Time series modelling (Objective 2, Time series modelling based on Objective 1 findings)
• Main study objective:
  To estimate monthly incidence rates of use prescription or dispensation of the 11 selected medicines during the last 10 years of available data, stratified by age and sex, in each of the databases.
• Setting:
  Inpatient and outpatient setting
• Data analysis plan:
  Analyses will be conducted separately for each database. Before study initiation, test runs of the analytics are performed on a subset of the data sources or on a simulated set of patients and quality control checks are performed. Once all the tests are passed the final package is released in the version controlled Study Repository for execution against all the participating data sources. The data partners locally execute the analytics against the OMOP CDM in R Studio and review and approve the by default aggregated results before returning them to the Coordination Centre. Sometimes multiple execution iterations are performed, and additional fine tuning of the code base is needed. A service desk will be available during the study execution for support. The study results of all data sources are checked after which they are made available to the team in the Digital Research Environment and the Dissemination Phase can start. All results are locked and timestamped for reproducibility and transparency.
• Summary results:
  CONCLUSION ; sARIMA(X) models fitted to time series data on prescription rates enabled forecasting future incidence rates. The models produced more accurate predictions when adding an indicator term to account for the impact of the COVID-19 pandemic between January 2020 and December 2021 (i.e., sARIMAX models). Since the selected databases are representative for the study population in the respective countries, the fitted models can be used to anticipate the incidence rates of antibiotic use in the four European countries of the study. The observation of drug usage beyond the sARIMAX-based forecast could be useful for the management of potential shortages. The forecasted values for IMASIS, IQVIA Belgium and IQVIA Germany databases need to be interpreted with caution, as the forecasted usage may be slightly overestimated.