Establishing an Active Vaccine Safety Surveillance System Using Large Scale Databases in Korea: Lessons and Scalable...

Area of Science:

• Public Health
• Epidemiology
• Health Informatics

Background:

• Post-licensure surveillance is crucial for detecting rare adverse events following immunization (AEFIs).
• Existing Korean health data infrastructure includes claims databases and immunization registries but lacks a comprehensive, active surveillance framework.
• Standardized electronic health records are increasingly available in hospitals.

Purpose of the Study:

• To propose a federated, code-to-data architecture for active, database-based surveillance of AEFIs in Korea.
• To leverage existing national health data assets for enhanced vaccine safety monitoring.
• To establish a scalable system for detecting rare AEFIs and supporting evidence-based communication and compensation.

Main Methods:

• Proposing a federated architecture with data linkages between nationwide claims databases (NHIS/HIRA), immunization registries (KDCA IRIS), and hospital EHRs.
• Adopting a common data model (CDM), standardized case definitions, latency accounting, and transparent reporting.
• Addressing challenges such as administrative approvals, incomplete adult vaccination data, data heterogeneity, and privacy constraints.

Main Results:

• The proposed architecture aims to integrate disparate data sources for comprehensive AEFI surveillance.
• Key priorities include streamlining administrative processes, improving adult vaccination data capture, and enhancing data interoperability.
• Development of secure hubs for aggregated outputs is essential for practical implementation.

Conclusions:

• Establishing a scalable active surveillance system in Korea requires strategic prioritization and overcoming significant challenges.
• The proposed system will enhance the detection of rare AEFIs, promote transparent communication, and ensure equitable injury compensation.
• Successful implementation will position Korea to proactively manage vaccine safety using domestic data.