Combining multiple healthcare databases for postmarketing drug and vaccine safety surveillance: why and how?

Abstract

A growing number of international initiatives (e.g. EU-ADR, Sentinel, OMOP, PROTECT and VAESCO) are based on the combined use of multiple healthcare databases for the conduct of active surveillance studies in the area of drug and vaccine safety. The motivation behind combining multiple healthcare databases is the earlier detection and validation, and hence earlier management, of potential safety issues. Overall, the combination of multiple healthcare databases increases statistical sample size and heterogeneity of exposure for postmarketing drug and vaccine safety surveillance, despite posing several technical challenges. Healthcare databases generally differ by underlying healthcare systems, type of information collected, drug/vaccine and medical event coding systems and language. Therefore, harmonization of medical data extraction through homogeneous coding algorithms across highly different databases is necessary. Although no standard procedure is currently available to achieve this, several approaches have been developed in recent projects. Another main challenge involves choosing the work models for data management and analyses whilst respecting country-specific regulations in terms of data privacy and anonymization. Dedicated software (e.g. Jerboa) has been produced to deal with privacy issues by sharing only anonymized and aggregated data using a common data model. Finally, storage and safe access to the data from different databases requires the development of a proper remote research environment. The aim of this review is to provide a summary of the potential, disadvantages, methodological issues and possible solutions concerning the conduct of postmarketing multidatabase drug and vaccine safety studies, as demonstrated by several international initiatives.

Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0

“…1,2 The importance and influence of such "real world" evidence is demonstrated by commitment of governments around the world to develop infrastructure and technology to increase the capacity for use of these data in comparative effectiveness and safety research as well as health technology assessments. [3][4][5][6][7][8][9][10][11][12] Research conducted using healthcare databases currently suffers from a lack of transparency in reporting of study details. [13][14][15][16] This has led to high profile controversies over apparent discrepancies in results and reduced confidence in evidence generated from healthcare databases.…”

Converting to a Common Data Model: What is Lost in Translation?

“…In a recent review, Trifiro et al [1] discuss the international projects and initiatives, funded either on a project basis or by governments, that aim to stimulate research with multiple observational databases in order to provide rapid responses to questions regarding benefits and risks of drugs. All aim to develop methods to combine databases with different underlying data models, different types of information collected, and different coding systems.…”

“…To perform drug safety studies on multiple databases, different approaches have been developed [1]. In all projects, a distributed network design is chosen.…”

Non-steroidal anti-inflammatory drugs and risk of heart failure in four European countries: nested case-control study

“…As a result, we performed data harmonisation according to a procedure developed and assessed in the European Union (EU)-ADR (exploring and understanding adverse drug reactions by integrative mining of clinical records and biomedical knowledge) Project 18 and also implemented in other EU funded projects. 19 Specifically, the Unified Medical Language system (for clinical diagnoses and conditions) and the Anatomic Therapeutic Chemical (ATC) classification system (for drug prescriptions) were mapped into the coding systems used by the individual databases. This mapping ensured that the data extraction processes targeted the same semantic concepts across all databases, thus allowing analyses to be performed under a common data model.…”

“…This mapping ensured that the data extraction processes targeted the same semantic concepts across all databases, thus allowing analyses to be performed under a common data model. 19 Anonymised data were extracted locally and processed with Jerboa software (developed by Erasmus MC), providing individual level datasets in a common data format. These datasets were securely transferred into the SOS data warehouse, hosted by the University of Milano-Bicocca, to be analysed centrally and securely.…”