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Early adverse drug event signal detection within population-based health networks using sequential methods: key
Jeffrey S Brown1, Martin Kulldorff, Kenneth R Petronis
1Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA 02215, USA. jeff_brown@harvardpilgrim.org
Pharmacoepidemiology and Drug Safety
|January 17, 2009
Summary
Adjusting study parameters in health network data can speed up the detection of adverse events (AEs). Relaxing criteria for incident AEs and exposure led to earlier signal detection, improving drug safety surveillance.
Area of Science:
- Pharmacovigilance
- Health Informatics
- Public Health Surveillance
Background:
- Active surveillance using population-based health networks can enhance the timely detection of adverse events (AEs).
- Previous work established signal detection methods within these networks.
Purpose of the Study:
- To investigate the impact of alternative study specifications on signal detection performance.
- To expand upon prior research in identifying AEs within health networks.
Main Methods:
- Compared signal detection performance using historical data from nine health plans.
- Analyzed five known drug-event pairs and two negative controls.
- Assessed alternative specifications for defining incident users and incident AEs.
Main Results:
- Relaxing exclusion criteria for incident AEs (prior diagnoses) and shortening the incident exposure window (to 90 days) increased surveillance numbers by 10-20%.
- These alternative specifications led to earlier signal detection, between 10-16 months sooner.
- Increased exposure and events likely contributed to the earlier detection.
Conclusions:
- Findings offer preliminary insights into prospective safety monitoring using health plan data and sequential analytic methods.
- Supports further investigation into utilizing health plan data for active drug safety surveillance.
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