Core Concepts in Pharmacoepidemiology: Multi-Database Distributed Data Networks.

Abstract

Multi-database distributed data networks for post-marketing surveillance of drug safety and effectiveness use two main approaches: common data models (CDMs) and common protocols. Networks such as the U.S. Sentinel System, the Observational Health Data Sciences and Informatics (OHDSI) network, and the Data Analysis and Real-World Interrogation Network in Europe (DARWIN-EU) use a CDM approach in which participating databases are translated into a standardized structure so that a single, common analytic program can be used. On the other hand, the common protocol approach involves applying a single common protocol to site-specific data maintained in their native format, with analytic programs tailored to each data source. Some networks, such as the Canadian Network for Observational Drug Effect Studies (CNODES) and the Asian Pharmacoepidemiology Network (AsPEN), use a variety of approaches for multi-database studies. Regardless of the approach, distributed networks support comprehensive pharmacoepidemiologic studies by leveraging large-scale health data. For example, utilization studies can uncover prescribing trends in different jurisdictions and the impact of policy changes on drug use, while safety and effectiveness studies benefit from large, diverse patient populations, leading to increased precision, representativeness, and potential early detection of safety threats. Challenges include varying coding practices and data heterogeneity, which complicate the standardization of evidence and the comparability and generalizability of findings. In this Core Concepts paper, we review the purpose and different types of distributed data networks in pharmacoepidemiology, discuss their advantages and disadvantages, and describe commonly faced challenges and opportunities in conducting research using multi-database networks.