Real-Time Vaccine Safety Surveillance for the Early Detection of Adverse Events

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Abstract

Background:

Rare but serious adverse events associated with vaccines or drugs are often nearly impossible to detect in prelicensure studies and require monitoring after introduction of the agent in large populations. Sequential testing procedures are needed to detect vaccine or drug safety problems as soon as possible after introduction.

Objective:

To develop and evaluate a new real-time surveillance system that uses dynamic data files and sequential analysis for early detection of adverse events after the introduction of new vaccines.

Research Design:

The Centers for Disease Control and Prevention (CDC)-sponsored Vaccine Safety Datalink Project developed a real-time surveillance system and initiated its use in an ongoing study of a new meningococcal vaccine for adolescents. Dynamic data files from 8 health plans were updated and aggregated for analysis every week. The analysis used maximized sequential probability ratio testing (maxSPRT), a new signal detection method that supports continuous or time-period analysis of data as they are collected.

Results:

Using the new real-time surveillance system, ongoing analyses of meningococcal conjugate vaccine (MCV) safety are being conducted on a weekly basis. Two forms of maxSPRT were implemented: an analysis using concurrent matched controls, and an analysis based on expected counts of the outcomes of interest, which were estimated based on historical data. The analysis highlights both theoretical and operational issues, including how to (1) choose appropriate outcomes and stopping rules, (2) select control groups, and (3) accommodate variation in exposed:unexposed ratios between time periods and study sites.

Conclusions:

Real-time surveillance combining dynamic data files, aggregation of data, and sequential analysis methods offers a useful and highly adaptable approach to early detection of adverse events after the introduction of new vaccines.

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