DARWIN EU® - Evaluating and investigating modelling approaches for the prediction of use of drugs for public health emergencies and/or at risk of shortages in the DARWIN EU® Network

Study type

• Study topic:
  Human medicinal product
• Study type:
  Non-interventional study
• Scope of the study:
  Disease epidemiology

  Method development or testing
• Data collection methods:
  Secondary use of data

Non-interventional study

• Non-interventional study design:
  Cohort

  Other
• Non-interventional study design, other:
  Methodological study

Study drug and medical condition

No information provided.

Population studied

• Short description of the study population:
  All objectives ; Inclusion criteria ; • Under observation at any time during the study period (1 January 2022 to 31 December 2025). ; • At least 365 days of prior history for all individuals aged 1 year or older at the index date. No prior history is required for individuals under the age of 1. ; Exclusion criteria ; • Missing information on age or sex. ; Additional eligibility criteria for Objective 1 and 3 ; Exclusion criteria ; • Diagnosis of the index condition at any time before the index date (-inf, -1). ; Additionally, for Objective 1, a population of sex- and age-matched individuals without the condition of interest and with the same index date will be defined.

Study design details

• Main study objective:
  1. To temporally and geographically evaluate the predictive performance of a selected subset of previously developed forecasting models for predicting the use of drugs for public health emergencies at risk of shortages.; 2. To refine and improve the forecasting methods (ARIMA/ARIMAX) for prospective predictions in the selected subset of models through a set of alternative modelling assumptions, parameter specifications, handling of censored values, seasonality structures, and external covariates.; 3. To apply the analysis and learnings from Objective 1 and Objective 2 on a new set of drugs of interest selected based on their use in public health emergencies and their risk of shortages.; Explorative objective; 4. To explore the feasibility and usability of advanced forecasting methods, including alternative statistical forecasting methods and machine learning/deep learning (ML/DL) methods.;