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ENCePP Guide on Methodological Standards in Pharmacoepidemiology

 

8.3. Challenges of different models

 

The different models described above present several challenges:

 

Related to the databases content:

  • Differences in the underlying health care systems;

  • Different mechanisms of data generation and collection;

  • Mapping of different drugs and disease dictionaries (e.g., the International Classification of Disease, 10th Revision (ICD-10), Read codes, the International Classification of Primary Care (ICPC-2));

  • Free text medical notes in different languages;

  • Differences in the validation of study variables and access to source documents for validation;

  • Differences in the type and quality of information contained within each database.

Related to the organisation of the network:

  • Different ethical and governance requirements in each country regarding processing of anonymised or pseudo-anonymised healthcare data;

  • Issues linked to intellectual property and authorship;

  • Implementing quality controls procedures at each partner and across the entire network;

  • Sustainability and funding mechanisms;

  • The networks tend to become very topic specific over time and to become isolated in ‘silos’.

Each model has strengths and weaknesses in facing the above challenges, as illustrated in Data Extraction and Management in Networks of Observational Health Care Databases for Scientific Research: A Comparison of EU-ADR, OMOP, Mini-Sentinel and MATRICE Strategies (eGEMs 2016;4(1):2). In particular, a central analysis or a CDM provide protection from problems related to variation in how protocols are implemented as individual analysts might implement protocols differently (as described in Quantifying how small variations in design elements affect risk in an incident cohort study in claims; Pharmacoepidemiol Drug Saf. 2020;29(1):84-93). Experience has shown that many of these difficulties can be overcome by full involvement and good communication between partners, and a clear governance model defining roles, responsibilities and addressing issues of intellectual property and authorship. Several of the networks have made their codes, products data models and analytics software publicly available, such as OHDSI, Sentinel, ADVANCE/VAC4EU. Timeliness or speed for running studies is important in order to meet short regulatory timelines in circumstances where prompt decision-making is needed. Solutions need therefore to be further developed and introduced to be able to run multi-database studies with shorter timelines. Independently from the model used, a critical factor that should be considered in speeding up studies relates to having tasks completed that are independent of any particular study. This includes all activities associated with governance, such as having prespecified agreements on data access, processes for protocol development and study management, and identification and characterisation of a large set of databases. This also includes some activities related to the analysis, such as creating common definitions for frequently used variables, and creating common analytical systems for the most typical and routine analyses (this latter point is made easier with the use of CDMs with standardised analytics and tools that can be re-used to support faster analysis).

 

 

 

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